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Safeguarding International Student Mental Health

Safeguarding International Student Mental Health

UK universities often pride themselves on having a diverse student population, with international students from all over the world travelling to study here. Like any students, international students are at risk of suffering from poor mental health, however, studies have found that factors unique to international students may increase this risk further. Familial pressures, culture shock, travel costs and stigma are just a few factors that are more likely to be experienced by international students and have a negative impact on their mental health. This is reflected in the rise in use of wellbeing services by international students in the past few years.

Currently, many universities direct international students through the same channels put in place for students resident in the UK, which works….during term time. But what happens when these students travel to their home countries during holidays or breaks from teaching? Traditional wellbeing services cannot accommodate these students as they rely on face to face therapy which, obviously, cannot continue if it’s not possible to physically attend a session.

In best case scenarios, therapy is put on hold until the student returns to the UK and can resume their course. However, some services have in place, policies that mean that if a student cannot attend an appointment in a specified period of time, their therapy allocation is terminated and should they wish to continue receiving help, they must rejoin the increasingly long waiting lists.

It’s important that we recognise this flaw in the system that’s disadvantaging international students, and instead, utilise a system that means therapy can be accessed anywhere, anytime in any time zone across the world. The Dr Julian platform does exactly that. Students can begin their therapy in whichever country they happen to be in at the time of referral and continue with the same therapist even if they have to travel to another country during the duration of their therapy.

In addition to this, therapy can be received in a range of languages. This means that students whose first language is not English can receive therapy in their native tongue and need not worry about language barriers during such a difficult time, allowing them to speak freely and comfortably.

With innovative platforms like this available, universities have no excuse for not providing equitable mental healthcare for all students. By utilising modern technologies and ideas, we can help improve mental healthcare for everyone and work towards a better future of care.

 

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Better Patient Recovery With Dr Julian

Better Patient Recovery With Dr Julian

The true sign of a successful mental healthcare system lies, not in financial gains or widespread usage, but in recovery rates and patient satisfaction. As a mental health platform, we’re extremely aware that many of our patients comes to us during the most vulnerable periods of their lives, trusting that we will help them to get back on their feet and support them throughout their journey to recovery. While for us, Dr Julian is daily life, for many it’s a new experience that will bring up a mixture of emotions. The platform design aims to give control back to the patient when things, in general, can seem out of control. This, combined with high quality therapy, means that patient experience is more positive.

 

One of the main reasons that psychological therapies fail to result in recovery is patient dropouts. There are a multitude of reasons why a patient may not complete their course of therapy, but in order for a full recovery to be made, it’s crucial that they do. A recent study (KSSAHSN, 2020) found that the Dr Julian platform reduces patient dropout rates by 49.8%. It also reduces DNA (do not attend) rates by 50.9% (DNA is when a patient fails to attend any of their sessions, leading to the same issues as those who dropout along the way). It’s unclear from the study data exactly how the platform achieves these results, but as it was designed to eliminate factors known to lead to dropouts, such as travel distance/time or availability, it shows us that what we’re doing is working.

 

 

This was further validated when Dr Julian recovery rates were found to be 9.3% higher than those of current NHS IAPT services, with reliable improvement rates being 17.3% higher. Not only are we achieving better attendance to therapy sessions, but we are also delivering the recoveries that many of our patients are hoping for.

These figures have highlighted the fact that the platform is making quality mental healthcare more accessible and that, in turn, results in happier patients with better recovery outcomes.

 

At the end of the day, mental healthcare is about simply that. Caring for mental health. This report produced some wonderful statistics that give us motivation to keep expanding our services to reach more people, but it’s these statistics that give us the most joy. Dr Julian is a service that works, and that is all we ever wanted it to do.

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Health Economic Report and Independent Evaluation by KSS AHSN

 

Table of Contents

Executive Summary. 4

  1. Dr Julian. 4
  2. Objectives of the report 4
  3. Key Findings. 4

1)   Dr Julian live site results. 4

2)   Health Economic Results. 5

3)   Insights: 6

  1. Limitations. 7
  2. Suggestions. 7

Introduction. 11

  1. Project Overview.. 11
  2. Current Problem.. 11

1)   Demand. 11

2)   Equity of access. 13

3)   Resource pressures. 14

4)   Service Equity. 15

  1. Local Context / Health Context 20
  2. Current Pathway. 21
  3. Dr Julian. 23
  4. Dr Julian results. 24

Health Economic Analysis Methodology/Health Economic Models. 28

  1. Perspective. 28
  2. General Approach and Sources. 29
  3. Choice of Analysis and Methodology. 30

1)   Cost-Benefit Analysis. 30

2)   Approach and structuring of outcomes. 31

3)   Optimism Bias. 32

4)   Year Weight Percentage. 34

5)   NHS and Gross Benefit 34

6)   Benefit-cost Ratio. 34

  1. Sensitivity Analysis. 35

1)   Step One: Allocation of ranges. 35

2)   Step Two: Allocation of a distribution shape. 35

3)   Step Three: Random selection of values within the range. 36

4)   Step Four: Repetition. 36

Key inputs and outcomes. 38

  1. Scenario Analysis. 38

1)   Scenario 1: 38

2)   Scenario 2: 39

3)   Scenario 3: 39

  1. Population. 40
  2. Uptake/Service Composition Assumption. 41

1)   High Intensity (HI) treatment 41

2)   Suitability of Online Therapy. 42

3)   Phased implementation. 44

  1. Outcome Streams. 45

1)   Approach and structuring of outcomes. 45

2)   Monetisation of outcomes. 48

3)   Selection of Benefit Streams. 53

4)   Cost Streams: 57

5)   Sensitivity distribution. 59

Cost -Benefit Model Findings. 60

  1. Summary of Results. 60

1)   NHS non-cash releasing. 60

2)   Social QALY. 60

3)   Non-Social QALY. 60

  1. Scenario Results. 62

Discussion, Limitations and Suggestions. 70

  1. Insights. 70

1)   Social benefits. 70

2)   Broader Patient Population/ Key Population Group. 70

3)   Qualitative impact 71

  1. Limitations and Caveats. 71

1)   Data collection/ Limited data around pathway assumptions. 71

2)   Population/ Quantification of existing population. 72

3)   Inclusion of Implementation Costs. 72

4)   Conservative approach and optimism bias. 72

5)   Broader limitations. 73

1)   Patients experience. 73

2)   Implementation requirements. 73

  1. Suggestions. 74

1)   Evaluation strategy. 74

2)   Business model 77

3)   Commercial approach. 77

Conclusion / Concluding remarks. 79

Appendix. 80

Appendix A. 80

References. 84

 

 

1.

Executive Summary

1.   Dr Julian

Dr Julian is an innovative mental healthcare platform that increases accessibility of mental healthcare. It connects patients almost immediately to mental healthcare therapists by secure video/audio/text appointments using a calendar appointment booking system. Matching a patient to the correct therapist for them using filters via language/ issue and therapy type and offer every organisation we work with their own unique customised white-labelled version of the platform. Patients are offered choice of who they want to see a time convenient to them 24/7 in the medium of their choice (video/audio or instant message) and from the location of their choice such as the comfort of their own home.

2.   Objectives of the report

To understand the potential value of Dr Julian, KSS AHSN have undertaken a health economic analysis to understand, from the healthcare and societal perspective, the return on investment for the NHS, relating to the costs and benefits.

The objective of this report is to set out the current context, explain how the Dr Julian intervention could address the current issues with the current NHS IAPT service, such as an increasing demand on the service, equity of access, workplace pressures and unwarranted variation, and to assess the extent to which Dr Julian could impact economically the healthcare system and wider society.

3.   Key Findings

1)    Dr Julian live site results

Below outline some key service findings from four Dr Julian live sites compared to that of the current IAPT service of the NHS in 2019:

  • The patient drop-out rate was 49.8% lower
  • Reliable recovery rates were 9.3% higher
  • Reliable Improvement rates were 17.3% higher
  • The DNA (do not attend) rate was 50.9% lower

Further details on the above measures and how it has been collected can be found in the Results section of this report.

2)    Health Economic Results

Based on the assumptions and calculations outlined in this report, Table 1 below highlights the key results of the Dr Julian Cost Benefit health economic model. Scenario 1a and 1b are from a provider perspective, scenario 2 looks at a Sustainability and transformation partnership (STP)/ Integrated care system (ICS) to show the impact across a wider system, and scenario 3 looks at the potential impact across the NHS. All results include social benefits:

Table 1: Dr Julian Key results

Dr Julian 5-Year NPV
In Year
Figures in (£m)20202021202220232024(2020-2024)
 
Dr Julian – scenario 1a – Oxleas  
Net Benefit£0.29£0.61£0.97£1.20£1.47£4.31
Cost:Benefit Ratio 1 : 3.361 : 3.361 : 3.361 : 3.361 : 3.361 : 3.54
Dr Julian – scenario 1b – Lancashire  
Net Benefit £1.26£2.67£4.20£5.22£6.40£18.77
Cost:Benefit Ratio 1 : 3.321 : 3.321 : 3.321 : 3.321 : 3.321 : 3.50
 
Dr Julian – scenario 2 – STP 
Net Benefit £1.47£3.11£6.54£10.44£14.92£34.60
Cost:Benefit Ratio 1 : 3.271 : 3.271 : 3.271 : 3.271 : 3.271 : 3.46
 
Dr Julian – scenario 3 – NHS 
Net Benefit £15.40£32.61£68.60£127.92£195.88£421.13
Cost:Benefit Ratio 1 : 2.661 : 2.661 : 2.661 : 2.661 : 2.661 : 2.83

For full results please see the Cost -Benefit Model Findings section of this report.  Please see section Year Weight Percentage for more details on applied discount for Net Present Value (NPV) calculations.

3)    Insights:

Benefits

In each scenario, NHS and social benefits are split evenly, with the reduction in do not attend (DNA), and an reduction in the cost of an appointments accounting for NHS benefits, with improvements in Quality of life, and a reduction in loss of earnings contributing to the social benefits.  It is unsurprising that Social benefits are so high since mental health interventions have a broad impact on wider society. In cases where the current NHS IAPT service can provide above the average of 7.1 amount of appointments per treatment patient outcomes tend to be positive compared to the NHS average and so already have good social outcomes in the main. However, where current patient service outcomes are relatively poor, social outcomes are also low. Poor patient outcomes tend to derive from a low number of appointments per treatment. In both cases, the impact of Dr Julian remains positive, as cost of treatment tends to be lower and social outcomes improved.  The ratio of social to non-social benefits will vary depending on the service provisions and treatment of the current IAPT provider.

This being the case, it would be more meaningful to view the impact of Dr Julian across a wider care system, such as an STP or ICS, drawing the benefits to a system across multiple care provision. In the scenario 2 (STP), returns are positive from both a social and non-social perspective.

Broader Patient Population/ Key Population Group

IAPT services which are currently offering high volumes of appointments per course of treatment, with positive patient outcomes appear to already have enough resources to service the population. In this instance, there may be limitation to the social impact that Dr Julian could have, however there may be financial gains seen through the reduced cost of a full course of treatment through Dr Julian, assuming there are patients who are happy to choose online therapy over face to face. In contrast, given a current IAPT service, which only has enough resources for a small amount of appointments per course of treatment, the financial cost of using the Dr Julian would most likely increase due to the higher amount of appointments patients will go through, however from a social perspective, the Dr Julian impact on patient outcomes could be greater due to an increase in recovery rates.

4.   Limitations

A prudent approach has been taken to ensure that the results and findings derived from the Health economic analysis are in line with the HMRC Green book: Central government guidance on appraisal and evaluation (HM Treasury, 2018) and have been based on professional literature review, published studies and data.

Where data has been unavailable, literature review has been conducted and assumptions made to calculate the impact of the Dr Julian platform on the current NHS IAPT service.  The total number of potential patients and uptake of suitable patients has been based on previous IAPT data and statistics gathered from Dr Julian, and external studies, which only provide an estimate to the potential future volume of patients using the service. Details on the findings in this model are based on data available at a given time and may differ as more information becomes available.  Results should be considered with the context of the study and assumptions used.

5.   Suggestions

Improving Data Collection on baseline

The current baseline data source from NHS digital provides enough information to create a detailed analysis of the current state of the IAPT service however it is limited in that patient data is aggregated.  There are also many gaps for which data from literature review and external studies have had to be incorporated into the model.

Obtaining more up to date figures will help improve the validity of the Dr Julian study and strengthen the impact results.

Quality adjusted life year (QALY) outcomes in the analysis represents a small proportion of the overall benefit seen, however in theory it should have a far greater influence on the overall outcomes.  The NHS use PHQ9 and other Anxiety Disorder Specific Measures (ADSM) scores to measure patient outcomes, however there is a gap in previous studies linking these measures to QALYs. This has made it difficult to quantify the true social impact Dr Julian could have. It is strongly advised that this outcome measure is investigated further, and revised assumptions are made.

Gathering data on uptake and accessIbility of DR Julian

Assumptions made within this analysis have been based on the data available, and many of the external studies used are not recent enough to build any strong conclusions around.  To assess the true impact of Dr Julian it is important to grasp a better understanding on the potential population suited for the service.  The three key areas for which more data is required are:

  • Patient accessibility, such as internet connectivity
  • Clinical suitability, assessing whether a patient is clinically suitable to receive therapy remotely.
  • Patient choice will provide insight as to what patients would truly prefer given the choice of multiple modes of therapy.

It is worth noting that when approaching providers and commissioners on setting up resource to gather this data, it is in their interest to understand the potential uptake and engagement of their patients using Dr Julian, and assess and understand the reasons.

 

Applying quantitative judgement to new evaluations sites

The data collected for this study has been solely quantitative and provided a good means to assess the impact of the Dr Julian platform from an economic perspective. An additional validation method that should be considered is around collecting data from surveys and other qualitative means to further contextualise the results and figures highlighted in this report.  Qualitative research can also provide further insight into the acceptance, usability and accessibility of not only the current service, but that of Dr Julian.  Gaining feedback from patients, staff and other stakeholders provides a greater evidence base for the potential impact the platform could impose.

Qualitative judgement can help contextualise the results of qualitative analysis and answer the questions that the current datasets cannot.

 

COmMercial Approach

Further data may help provide an insight into the acceptance and demand for Dr Julian and help assess the potential future demand for the service. The COVID-19 pandemic has presented an opportunity to accelerate the adoption of digital technologies, such as Dr Julian into the NHS. A consideration could be to use existing NHS therapists on the platform. Additionally, due to the range and expertise of the therapists that Dr Julian have there is scope to provide specific services such as maternity and perinatal support or support tailored to the vulnerable and isolated groups that may be worse off due to COVID-19. Although the IAPT service in the NHS services most of the patients in England, there are other commercial opportunities through private organisations, such as universities, and large firms, even private care homes, for which Dr Julian could be suitable.

Capacity of the Dr Julian service is based on the number of therapists available, and so understanding demand will play a critical role is assessing how many therapists may be required in the future to offer the service to all applicable patients. The key being, to be able to offer the same consistent quality of service to all patients no matter how many use the Dr Julian platform.

Patient demand and costing data can be used to assist in creating not just health economic models but also assist in business modelling. The analysis of financial forecasting and planning for the business can be assisted by the data collected for this study and could be used to support further investment cases in the future.

This study goes a long way to showing the impact that Dr Julian could have on the NHS, it is still worth noting that it is only one answer, at one moment in time, and so it is recommended that the Dr Julian service is monitored continuously, as the service grows to help further assess the economic, financial and social impact.

 

 

Introduction

1.   Project Overview

Dr Julian aims to create the best online Mental Healthcare and wellbeing service in the world.  A range of highly experienced therapists/psychologists offer psychological treatment through on-line video sessions, audio chat and instant messaging via the Dr Julian platform.  As there is no subscription service, patients can choose who they wish to see and when and where they require an appointment, with no pressure to book again and again. Dr Julian claims that video online therapy is as effective as traditional face to face therapy and can deliver a better patient experience.

Dr Julian’s current customers are those who choose to use the service direct (private), company corporate services, which are white labelled under a company benefit package, and through NHS provider companies.  NHS mental trust foundations that are currently using the platform include Lancashire, Oxleas Trust, Milton Keynes University Hospital, and Basildon and Thurrock.

Dr Julian offers vetted therapists/psychologists and/or the patients’ chosen therapists, without taking them away from their NHS work. Because the service enables therapists to work from home, they can work additional hours of their choosing to earn additional income. This often provides an out of hours service, to meet the increasing need for patient choice.

As Dr Julian is essentially a platform for patients to access a personalised mental healthcare and wellbeing service, patient outcomes are based on the experience they have with the therapists themselves. As a result, NICE guidelines potentially do not require Tier 3a/b evidencing standards for digital health technologies (DHTs).  The platform itself offers simple monitoring, communication and information for patients, and therefore is only considered a Tier 2 DHT.

2.   Current Problem

1)    Demand

As of 2014, an estimated one adult in six has symptoms of a common mental disorder (such as depression, anxiety or panic) according to the Adult Psychiatric Morbidity Study in England. Additionally, the “proportion of people with severe symptoms of common mental disorders in the past week”, defined as those scoring in “the highest category for overall neurotic symptoms”, has increased from 6.9% in 1993 to 9.3% in 2014 (McManus, et al., 2014). Figure 1 demonstrates that these results are not outliers and that there is a consistent upward trend during that period across adult psychiatric morbidity surveys.

Figure 1 – The proportion of people with severe symptoms of common mental disorders in the past week, 1993 – 2014, as recorded by Adult Psychiatric Morbidity studies. Source: NHS Digital data; McManus, et al. (2014)

The demands on mental health services today create a perfect storm of exceptionally high prevalence, which services are struggling to meet due to historically poor access and infrastructure, and recently, a fast increase in severe and more complex presentations.

With this context, NHS England (NHSE) has set a clear mandate for the development of IAPT services, first through the Five Year Forward View (FYFV) for Mental Health (NHSE Mental Health Taskforce, 2016) and built on shortly after by the NHS Long Term Plan (LTP) (NHS England, 2019). The most significant commitment is to expand access to IAPT services to cover 1.9 million adults per year by 2024, while simultaneously maintaining performance targets such as (but not limited to):

  • Recovery rates above 50%
  • Patients starting treatment within 6 weeks
  • 95% of people starting treatment within 18 weeks(NHS England, 2019).

Currently, for England in 2018/19, IAPT services receive just over 1.6 million adult referrals per year. This means that an extra 300,000 individuals must be referred every year within 5 years to meet the access-to-services commitment.

Additionally, the 1.9 million target is estimated to cover only 25% of overall demand (NHSE Mental Health Taskforce, 2016), meaning that mental health services and IAPT will almost certainly continue to grow rapidly beyond 2024. This requirement for ever-expanding services, while maintaining performance standards, demands IAPT services are as efficient as possible in order to remain sustainable in the future.

2)    Equity of access

The IAPT manual (The National Collaborating Centre for Mental Health, 2020) identifies eleven groups that “tend to be under-represented in IAPT services” from national data. Many of the groups that have been identified also experience inequitable access across the NHS as a whole, and due consideration should be given to ensure that new interventions or innovation are not harming their access to services further and widening health inequalities.

The groups identified may be under-represented for different reasons. Men, for example, are less than half of IAPT patients but this group is so large and diverse that it is unlikely that the service itself is systematically excluding them. Rather, men appear to be less likely to seek help (Mental Health Foundation, 2016), driven by societal pressures and stigmas around mental health. It is difficult for the service itself to tackle these societal issues driving inequalities.

Some of the other under-represented groups, however, relate directly to the service and how it might engage. Older people and those living in deprived areas are two groups that are more likely to suffer from a ‘digital gap’, either from a lack of access to technology or the internet, making it a real challenge for these individuals to access digitally delivered healthcare.

The above groups are identified from national IAPT data. The data presented in IAPT datasets excludes whether the patient lives more rurally or in more urban areas. Any service that requires travel creates a barrier to access treatment, especially in rural communities, and this will exacerbate the inequalities already faced by the previously-mentioned groups such as those in deprived areas or older people without their own transport. Digital technology, such as Dr Julian, may be valuable to bridge this specific gap.

Access to treatment is also directly related to capacity. Long wait times for treatment combined with a poor quality of service, symptoms of a service operating above capacity, build barriers to accessing treatment. The strain that IAPT services are under is highlighted in the next sub-section.

3)    Resource pressures

The drive to greatly expand access while maintaining (or, for many providers, improving) performance creates a challenge to maximise efficient use of resources. Mental health has historically received a much lower proportion of funding compared to activity, a situation which has improved since the introduction of the Mental Health Investment Standard and increased ringfenced funding in the NHS LTP (NHS England, 2019), but funding (relative to activity) parity is still to be achieved between physical and mental health (Gilburt, 2018). Mental health services, including IAPT, face an enormous challenge to grow rapidly while facing these funding constraints.

Another barrier to growth is the mental health workforce. This has included issues with the diversity and accessibility of training (Centre for Outcomes Research and Effectiveness, UCL, 2019) but predominately there is an issue of staff numbers caused by training, recruitment and retention. As of 2017, there were approximately 20,000 vacancies out of 214,000 posts across the NHS dedicated to mental health, with these gaps being filled by expensive bank and agency staff (Health Education England, 2017). In addition to issues with recruitment, turnover, or leaver, rates amongst IAPT staff was approximately 22% for the low-intensity (LI) working group and 12% for high intensity (HI) therapists offering CBT (NHSE & HEE, 2015). Training and qualifications are a further issue highlighted by the most recent IAPT workforce census, which found that a large number of staff providing low intensity therapy were not fully qualified as a psychological wellbeing practitioner (PWP) (NHSE & HEE, 2015).

The workforce constraints have led to a focus on digital technologies to help maximise the use of therapist time, a priority shared by the NHS LTP (NHS England, 2019). It is hoped that digital technologies such as Dr Julian will maximise the amount of time that therapists spend treating patients, thus relieving some of the pressure to expand the workforce in the process.

4)    Service Equity

IAPT services vary in performance across England. While some of this variation is due to the local population, through factors such as age or population density, there is certainly also some degree of variability in service provision.

For example, for 2018/19, there was a marked distinction in recovery rates between Birmingham and Solihull CCG (50%) and Stoke on Trent CCG (64%), with this likely to be at least partly driven by Stoke on Trent CCG providing 2.5 more appointments on average for a course of treatment (NHS Digital, 2019). Some of this variation is likely to be driven by the different types of therapies being offered to patients in each service (Perfect, et al., 2016), at least proportionally, with a course of highly-effective but expensive CBT requiring more appointments than a less intensive option such as guided self-help. The previously mentioned service and resource pressures likely causes most of this variation in treatments offered.

1)   Referrals

Looking at the STP level, demand in the form of referrals varies when adjusted for population size (Figure 2). There is a spike in demand in the North-West of England, centred around Manchester, which peaks at 122.6 referrals per 1,000 population and compares to a minimum of just 5.3 referrals per 1,000 population. It is important to note that referrals are not measured at a patient level, meaning that two patients being referred once each is indistinguishable from one patient being referred twice. This could mean that areas struggling to cope with high demand will be highlighted even further, as a high number of patients failing to recover may repeatedly be referred back into the service.. It is interesting to note the marked difference between two neighbouring STPs with similar populations, Surrey Heartlands STP and Sussex and East Surrey STP, which suggests that the data may also be reflecting variations in referral pathways or inconsistencies with diagnosis.

Figure 2 – Referrals per 1,000 population by STP in England. Source: IAPT Annual Report data 18/19 (NHS Digital, 2019)

2)   Waiting times

IAPT currently only has waiting time targets for the time from referral to the first treatment that the patient receives, with 75% expected to start therapy within 6 weeks and 95% within 18 weeks (The National Collaborating Centre for Mental Health, 2020). Waiting times between first and second treatments are also measured and reported but does not have a target to reach. As a result, it may be the case that some IAPT services will prioritise resources to ensure patients receive their first treatment but then allow the first to second treatment waiting time to lengthen. No data is currently reported by IAPT services for the average wait between all appointments over the entire course of treatment.

Figure 3 – Mean wait time for first treatment by STP in England. Source: IAPT Annual Report data 18/19 (NHS Digital, 2019)

 

Wait times for the first treatment (Figure 3) range from 7 to 38 days, representing the vast inequality that patients face in access to treatment. The worst performance for this metric is typically found in the South East, East Midlands and North of England, with Cornwall also representing a poor-performing outlier.  These statistics are based on both low and high intensity treatment, and so it is hard to draw conclusions on how long patients suitable for high intensity treatment, which is expected to be far longer.

The contrast between first treatment waiting time and first to second treatment waiting time is stark, as shown in Figure 4. Many of the best performers on the first treatment measure are amongst the worst performers in the first to second treatment measure and vice-versa. Gloucestershire is a clear example of this variation, being the top ranked STP for first treatment waiting time but last out of all STPs for the first-to-second treatment wait. Mid-performing STPs on the first treatment measure also tend to be somewhere in the middle again for the first-to-second wait time. A sharp contrast is also visible between the far North East and North West areas of the country, as Humber, Coast and Vale performs much worse in getting patients into treatment than Lancashire and the areas around Manchester but this difference flips for the first to second treatment wait time. This effect demonstrates variations in the referral pathway, with some STPs preferring to focus on getting patients into their first appointment but making them wait for further treatment while others take longer to get patients into treatment but then offer a much smoother course of treatment from then onwards.

Figure 4 – Comparison of waiting time measures by STP in England. Source: IAPT Annual Report data 18/19 (NHS Digital, 2019)

 

 

3)   Recovery rates

The variation in service provision culminates in significant health inequalities for patients, as shown by reliable recovery rates in Figure 5 with worse outcomes depicted in darker blue. Interestingly, areas surrounding large cities (such as the STPs surrounding London and Birmingham) tend to perform much better while inner cities and remote areas see poorer outcomes for patients. A possible explanation could be that densely populated areas struggle to deliver their service due to demand pressures while sparsely populated areas struggle to reach people and improve access, leaving a ‘sweet spot’ in the middle of services that are able to both reach their population and keep up with demand.  Further exploration to the data may uncover answers.

The map also shows that, while recovery rates are above target for England as a whole, there is poor performance in reliable recovery; indicating that services are much more effective at treating patients with mild symptoms but are struggling to help patients with more severe symptoms to recover, most likely at Step 3 care.

Figure 5 – Reliable recovery rates (%) by STP in England. Source: IAPT Annual Report data 18/19 (NHS Digital, 2019)

The above analysis has focused on service provision at the STP level for ease of explanation and demonstration. There is, of course, even more variation beneath this level of aggregation amongst individual providers. The definition of ‘Reliable recovery’ is explained in detail in the Recovery rates section of this document.

3.   Local Context / Health Context

Mental health conditions can have a material impact on the quality of life of a patient. It not only affects the life of a patient in terms of their health, but also their perspective on the world and their wider life, which can affect wellbeing and happiness to a great, and possibly serious, extent.

Individuals suffering from mental health conditions often face detrimental impacts to their physical lifestyle. The relationship is likely to be mutually causational, but it’s certainly the case that a patient suffering from a more severe mental health condition is likely to experience a deterioration in their physical health as their eating, sleeping and exercise habits are affected. Moreover, the impact of mental health on motivation can be key as patients may become less likely to maintain their health or to seek care when needed. A combination of these effects is shown by the cost of care for a patient with a long term physical condition increasing by an average of 45% when that patient also develops a mental health condition (NHSE Mental Health Taskforce, 2016).

Untreated mental health conditions additionally cause spill over effects that impact on everyday lives. The individual’s hobbies, relationships and interests can be affected by their mental health, caused by the many demotivating aspects of conditions such as depression, and affecting wellbeing. A person may also struggle to maintain a job, or at least see their career prospects diminish, causing a loss of earnings and structure that can impact their mental health even further (Modini, et al., 2016). There is a 65% employment gap between those who are supported by secondary care mental health services and the general population (NHSE Mental Health Taskforce, 2016). These effects demonstrate the need to consider the wider social impact of mental health, rather than focusing purely on the healthcare system.

4.   Current Pathway

IAPT services utilise a NICE-recommended stepped-care model for most mental health conditions, escalating care based on the severity of symptoms or responsiveness to treatment. The initial step is assessment and watchful waiting, before escalating the intensity of treatment through each of the following steps as appropriate to the patient. The model is shown in Figure 6.

Figure 6 – Stepped care model for mental health. Source: Mental Health Matters (2020)

Patients are generally referred for treatment at a particular step depending on the severity of their symptoms, based on the standard Patient Health Questionnaire 9 (PHQ9) for Depression or some Anxiety Disorder Specific measure (ADSM), usually the Generalised Anxiety Disorder 7 (GAD7), for anxiety. These scores are taken at the assessment conducted in Step 1 and are usually taken regularly throughout a course of treatment to monitor how the patient responds to treatment.

The PHQ9 and GAD7 scores are collected by asking a standardised questionnaire of questions such as, “Over the last two weeks, how often have you been feeling down, depressed or hopeless?”, with a score from 0-3 allocated for responses ranging from “Not at all” to “Nearly every day”. The higher the score, the more severe the symptoms. A case is considered to meet the threshold for clinical diagnosis if the patient receives a total score of 10 or higher on the PHQ9 test for depression, or a score of 8 or higher on the GAD7 questionnaire for anxiety. The questionnaires are clinically validated, but this does not mean they are without issues. For example, the questionnaires are not able to accurately provide information on the longevity of the symptoms, beyond the two week scope of the questions, such that a patient with severe symptoms for two weeks may be conflated with a patient who has had severe symptoms for years. This is why clinicians tend to collect further information on the background of a patient and seek to understand the wider context rather than relying solely on the questionnaires.

Once patients have been assessed, they may initially be referred for treatment at a particular step, but then moved up or down depending on how they are responding to the treatment and whether the treatment is appropriate for the individual. It is not uncommon for a patient to start an initial course of treatment and then immediately be referred onto an alternative, especially if the patient feels that the treatment is unsuitable to them.

The most common route for referral onto an IAPT service is via the patient’s GP. The GP will typically conduct early assessment, including collecting symptom scores through the questionnaires mentioned previously, and then may refer the patient for a specific course of treatment or offer a degree of choice depending on what the local IAPT service offers. Other sources of referral include other parts of the healthcare system, such as secondary care, and even, in cases where it is offered by the local IAPT service, self-referral.

IAPT provides care for Steps 2 and 3. Step 2 involves lower-intensity treatment such as guided self-help or computerised CBT (cCBT) and is usually for patients with mild to moderate symptoms. These treatments are considered effective at treating patients with mild symptoms but quickly become ineffective as symptoms worsen. The interventions at this stage are also generally cheaper, mainly because they require a lower salary-band Psychological Wellbeing Practitioner (PWP) and less staff time dedicated to the patient.

Step 3 involves higher-intensity interventions such as face-to-face CBT, counselling, and interpersonal therapy. Patients are usually referred into Step 3 if they have moderate to moderately severe symptoms or if they had mild to moderate symptoms that did not respond to Step 2 treatment.

Step 4 is usually where the patient is treated in secondary or tertiary care with highly severe symptoms or a complex condition. It is the primary goal of Steps 2 and 3 to prevent patients reaching this level of deterioration and experiencing the poor outcomes associated.

5.   Dr Julian

Dr Julian offers an alternative means for delivering the standard therapies, such as CBT, typically offered at Step 3 of IAPT. It is an online digital platform for patients to receive their IAPT treatment through video conferencing, audio chats or instant messaging with their therapists. Patients can sign-in to the service online from any location, providing they have a reasonable internet connection, and connect to their therapist when the appointment starts.

Patient choice is core to the Dr Julian model. Patients have the freedom to choose where to have their treatment, such as in a safe space within their own home, which therapist they use, and receive much more flexibility on the time of day to have an appointment. This patient-centred experience should allow patients to find a much better fit for them in terms of the treatment, therapist, and the logistics of the appointment – likely leading to better attendance/reduced DNAs, improved patient satisfaction and, ultimately, better clinical outcomes. The improved efficiency and flexibility should also enable a reduction in waiting times for the patient.

Therapists themselves also gain much more control of their schedule, being able to choose their working hours and their offering while also cutting out commuting or travel time and potentially seeing a reduction in the burden of DNAs.  The system itself provides a platform for therapists to monitor and manage their patients, as well as share any information between therapists if the patient decides to switch.

For providers, the delivery model could help alleviate local workforce pressures because the geographical location of the therapist does not matter. Therapists from Cornwall could feasibly consult with patients in the North East of England with zero change in the quality of treatment. Additionally, Dr Julian can also provide their own therapists who can be used to supplement the existing workforce in each service.

6.   Dr Julian results

To assess the impact of the Dr Julian, data was extracted from the platform for sites that are using the service.  This data was analysed and used in the comparison against the current IAPT service. Data was collected from four provider sites:

  • Lancashire care NHS foundation Trust
  • Oxleas NHS foundation trust
  • Basildon and Thurrock University Hospitals
  • Milton Keynes University Hospital NHS Foundation Trust

Non identifiable data was supplied to KSS AHSN to assess patient outcomes and the quality of the current service Dr Julian providers.  Following government measures of social distancing put in place from 23 March 2020 following the Coronavirus (COVID-19) outbreak (UK Government, 2020), it was agreed only patient data up to this date was used. The rationale behind this decision was based on changing patterns of current patients and the effect social distancing may have on the wider population and their mental health. The result on the NHS also saw most IAPT services stop face to face appointments to concentrate on teletherapy and video therapy (NHSE & NHSI, 2020).

The data from the four sites was collected, merged, and analysed to produce the following high-level results:

Population and engagement Figures:

  • Referred onto the Dr Julian Platform (n=287)
  1. Non-engagement (n=84)
  2. Waiting to book first appointment: excluded from patient outcome results and engagement statistics (n=43)
  • Ongoing treatment: excluded from patient outcomes (n=72)
  1. Drop-outs (n=19)
  2. Finished treatment (n=69).
  • Of 244 patients included in the engagement population, 65.5% started treatment (n=160).

Patient outcomes

PHQ9 and GAD7 (as the ADSM measure) have been used to calculate patient outcomes.

Out of the 69 patients who finished treatment (n=61) began or ended treatment in caseness, and so included in the following patient outcome results:

  1. Reliable Recovery: (n=35) 54.1%
  2. Reliable improvement (n=49) 80.3%
  • Reliable improvement (excluding Reliable recovery patients) (n=16) 26.2%
  1. Reliable No Change (n=7) 11.5%
  2. Reliable Deterioration (n=5) 8.2%

More details on the above definitions of patient outcome measures can be found under the Recovery rates section of this document.

Appointments attended per patient

  • By finished treatment patients and those who began or ended in caseness (n=61) the mean number of appointments attended was 10.3
  • By drop-out patients (n=19) the mean number appointments attended was 4.6
  • Of patients who dropped out (n=19), there was recovery data on (n=18) for which 5.6% of patients had recovered (n=1) and 94.4% (n=17) had not recovered when dropped out of treatment.

Dna rate

Assessing patients who have booked appointments, and so patients who have ongoing treatment, finished treatment, or dropped out (n=160)

  • A total of 1468 appointments have been booked of which:
  1. (n=704) from patients who have finished treatment,
  2. (n=87) of patients who have dropped out
  • (n=677) of patients who are ongoing treatment.
  • A total of 79 appointments that were booked have been recorded as DNAs, of which:
  1. (n=35) from patients who have finished treatment giving a DNA rate of 5.0%,
  2. (n=9) of patients who have dropped out, giving a DNA rate of 10.3%
  • (n=35) of patients who are ongoing treatment, giving a DNA rate of 5.2%
  • The total DNA rate for all Dr Julian patients above (weighted average) was (79/1468) 5.4%

Other data

Despite the following statistics not being used within this analysis, they may be useful when assessing future effectiveness of treatment considering the time (in weeks) from referral to starting treatment:

  • Ongoing treatment (n=72) the mean waiting time was 2.8 weeks
  • Drop-outs (n=19) the mean waiting time was 3.4 weeks
  • Finished treatment (n=69) the mean waiting time was 3.9 weeks
  • All patients (n=160) the mean waiting time was 3.3 weeks

Waiting times for Dr Julian represent the time taken from referral to start of treatment and so do not consider when the patient booked appointments. Realistically, patients would not need to wait this amount of time for therapy and the time taken for them to book the appointment heavily contributes to this statistic.

The data collected for this analysis has key indicators that can be directly comparable to the current IAPT service data.  This has helped shape the assessment measures to help illustrate the impact the Dr Julian platform could have.

Health Economic Analysis Methodology/Health Economic Models

1.   Perspective

A health economic model can provide answers to multiple stakeholders as described in Table 2.

Table 2: Perspectives of key stakeholders towards a health economic and qualitative evaluation

Relevant StakeholderPurpose of the Health Economic Model
Dr Julian<   Can assist in business decisions and modelling.

<   Gives a broader understanding to the size of the target patient population.

<   Provides an understanding of the current differences in patient outcomes across different IAPT services in the NHS.

<   Helps provide a business case by quantifying economic and social outcomes.

Commissioners<   Shows wider social and economic benefit, rather than just cost savings.

<   Can be used to show current resources and costs required for the service.

<   Can provide some guidance for future commissioning and tariff structure.

Providers<   Provides an understanding of relevance and fit between the product and the site of implementation.

<   Current costs and benefits of providing a Mental health service.

<   Helps predict future demand for mental health service and the cost of such.

<   Can provide tangible evidence as to where the intervention could save costs and improve patient outcomes.

NHS Workforce<   Can identify suitability of types of therapy delivered, and potentially the difference in patient uptake.

<   Capacity of workforce, and the effectiveness of therapy.

Patients / Citizens<   Social and economic effect on recovering patients, dependent on recovery rate.

<   Qualitative analysis could highlight the extent to which the new. technology is accepted and provide useful insight as to what area of the service is most valued.

2.   General Approach and Sources

The approach taken has been highlighted below which helps understand the potential impact Dr Julian could have relative to the current IAPT pathway based on preliminary assumptions:

  • Building the health economic model using a tried and tested approach; for each outcome stream identified, data is needed to determine inputs for the model.
  • Data collection from existing literature and live sites.
  • Discussing findings and confirming preliminary assumptions around the impact. across different scenarios and regional scales, based on the cost-benefit analysis.
  • Identifying data that needs to be collected to create a more robust health economic model with validated assumptions.

 

This study produces a to-date current and an ex-ante appraisal of the prospective impact of Dr Julian estimated using:

  • Data from live sites using the Dr Julian model
  • Emerging academic research and industry reports
  • Statistics from relevant public-sector bodies

In addition to the framework described above, HM Government has sought to enable quicker and more efficient delivery of cost-benefit appraisals, particularly by local government. This has been achieved through the funding and development of two sets of standardised unit cost databases, from which data will be sought as standard. These are:

  • PSSRU’s ‘Unit Costs of Health and Social Care 2010 – 2018’(PSSRU, 2019)
  • New Economy ‘Unit Cost Database’ (2015), which divides costs into financial costs and economic costs. These terms broadly equate to ‘public sector delivery costs’ and ‘all other socio-economic costs’ (GMCA Research Team, 2019)

These sources present an efficient but effective mechanism for identifying values for many costs and outcome benefits. They are broadly consistent with one another but where they are not, the original source data has been sought where possible to identify the most relevant data.

 

3.   Choice of Analysis and Methodology

1)    Cost-Benefit Analysis

The aim of a cost-benefit analysis, which follows a similar approach to a cost-effectiveness analysis, lies in determining if the economic value of an intervention can justify its cost by comparing the cost of two or more alternatives and reviewing the return on investment. Savings are estimated from the healthcare system’s perspective and the effects of an intervention on all costs should be considered (i.e. direct cost, effect on health expenditures, social and health outcomes to the patient). Costs and benefits ought to be discounted to reflect the lower economic value of an expense, accounting for the time value of money, as well as the higher value of a benefit that is realised earlier (HERC, 2020).

The calculation in figure 7 is applied to all benefit streams realised by the programme and summarised to show the full benefit potential from a financial and economic perspective.

Figure 7: valuation of benefit stream calculation

2)    Approach and structuring of outcomes

To turn outcomes into a financial benefit, each stream had to be monetised. There are two broad benefit categories relevant to the cost-benefit analysis: NHS cash and non-cash releasing benefits.

How these benefits are realised depends on the cash ability of the saving. Cash ability refers to the way a change in an outcome will result in a reduction of fiscal expenditure. The ability to cash depends on the type of benefit, scale, timing and the leadership in place to realise the savings. This report takes a prudent approach to identifying benefits and separates the fiscal savings into the following benefit streams:

  • NHS related cash releasing benefits: These benefits produce immediate cashable savings to the provider; an example of this benefit would be a direct reduction in procurement costs such as, in the case of a manufactured product, lower material costs.
  • NHS related non-cash releasing benefits: These benefits are important to reducing demand and strain on services, but a fiscal value cannot be realised without decommissioning of services. Benefits which can be described as non-cash releasing include the generation of time savings for staff that allows staff to either improve the quality of their activity or carry out alternative activities.
  • Social benefits: The overall benefit to the public, including, but not limited to, employment related benefits, such as fewer sick days and improved health and wellbeing. A key element of understanding these benefits is the approach the model takes in calculating quality of life changes. Quality of life related benefits use a Quality Adjusted Life Year (QALY) calculation. The basic construction of a QALY valuation for a particular health state is the number of years of life spent in that state multiplied by a health state utility-based weighting (cf.Wiliams, 1985). So, for example, a health state which lasts 10 years and is valued at 0.9 in terms of health state utility would give 9 QALYs. The QALY provides a single index allowing a measurement of the effects of health interventions on mortality and morbidity.

This QALY is then given a financial value using the willingness to pay threshold value used by NICE on behalf of the NHS. NICE methods refer to a threshold of £20,000 -£30,000 per QALY. A sensitivity range is used to reflect the range within which this threshold is applied, with the lower value (£20,000) taken as the modal value.

3)    Optimism Bias

When the data and evidence upon which the cost effectiveness model is based are uneven, old or incomplete, a certain factor needs to be applied to correct for these. Therefore, the model applies optimism bias correction factors in response to the level of uncertainty in the data or assumptions used. The optimism bias approach used is based on the confidence grade definitions shown in Table 3.

 

 

Table 3: Optimism bias correction grading

Confidence gradeColour coding in modelData SourceAge of dataKnown data errorOptimism bias correction
1Formal service delivery contract costs1-2 years old+/- 5%5%
Figures derived from local stats / RCT trials
2Practitioner monitored costs2-3 years old+/- 10%10%
Figures based on national analysis in similar areas
3Costs developed from ready reckoners3-4 years old+/- 1515%
Figures based on generic national analysis
4Costs from similar interventions elsewhere4-5 years old+/-20%25%
Figures based on international analysis
5Cost from uncorroborated expert judgement>5 years old+-25%40%
Benefit from uncorroborated expert judgement

 

The confidence grade which the cost benefit analysis model applies to the data is determined by the lowest assessment in any of the descriptive columns. The optimism bias correction factor for the data is then determined, based on the lowest confidence grade found in relation to each individual outcome and costs are increased by the corresponding percentage factor (shown in Table 3).

 

This calculation is applied to all benefit streams realised by the programme and summarised to show the full benefit potential from a financial and economic perspective. Costs are inflated and benefits deflated using this correction to reduce the overall impact of the intervention.

4)    Year Weight Percentage

Financial and economic weightings are applied to benefits to show how inflationary and economic pressures effect the value of benefit streams over time. For the in-year calculations, only inflationary pressures are applied to show effects in nominal terms, however for the NPV, a discount rate of 3.5% is applied to deflate the benefit to real terms to reflect the changing value of healthcare within GDP (See the Green book for more details (HM Treasury, 2018)).  For Social outcome streams linked to QALYs, the discount rate applied is 1.5%, as this excludes the change in value from an economic perspective and only considers social differences.

5)    NHS and Gross Benefit

The NHS monetary difference represents the difference between the monetary cash and non-cash benefits and the costs incurred from the intervention. Total Gross benefit represents the full economic impact and therefore includes social benefits.

 

 

6)    Benefit-cost Ratio

The benefit-cost ratio is a measure of benefits against costs and shows the return on investment. This can indicate the scale of investment and return based on the intervention’s impact. This figure shows a measure of efficiency and good investment based on the overall return; £X return for every £1 invested. The calculation can be applied for both NHS benefits and total gross benefits to show the wider economic impact the intervention may have.

 

4.   Sensitivity Analysis

Monte Carlo analysis is a modelling technique which simulates the impact of the expected variance in key variables on the output of interest, in this case the net present value. The approach is best described using an example.

 

1)    Step One: Allocation of ranges

Variables of interest are given base-case values (or mean estimates), and an expected range. In the example below we look at quality of life adjustment factor and life expectancy:

Table 4: Example of sensitivity range allocation

VariableLower range estimateBase-case / mean estimateUpper range estimate
Quality of life adjustment factor0.4200.5650.710
Life expectancy (years)4.736.307.88

 

2)    Step Two: Allocation of a distribution shape

All data has a shape to its distribution. If there is equal likelihood of any value within a range being drawn, then a rectangular distribution can be used (so called because a graph of the probability of any specific value being drawn would appear to be a rectangle). If there is a lower likelihood of a value at the extreme ends of the range being drawn, then a triangular distribution could be used.

 

If there is reason to believe the distribution meets the statistical qualities required to be defined as normal, Poisson, etc, then these can be applied. In this study, we have generally applied triangular distributions as this best reflects the ranges used and diminishing probabilities of more extreme ends. Where a different distribution has been used, it is expressly noted in the text.

 

3)    Step Three: Random selection of values within the range

The model selects at random a value for each variable from within the range between the upper and lower estimate and calculates the outcome from each draw, considering the distribution shape selected and therefore the probability of any value being drawn.

 

4)    Step Four: Repetition

Table 5: Example of random variation within Monte Carlo simulation

VariableDraw 1Draw 2Draw 3Draw 4Draw 5
Quality of life adjustment factor0.450.500.550.600.75
Life expectancy (years)4.55.05.56.07.5
Quality of Life Year monetary value£20,000£20,000£20,000£20,000£20,000
Benefit (lives saved x value of lives saved)£40,500£50,000£60,500£72,000£112,500

 

Five draws are given above, using a rectangular distribution. These deliver estimates lying between £40,500 and £112,500. The draw is repeated thousands of times. In this evaluation we use 10,000 runs as standard.

Creating 10,000 estimates allows the creation of a distribution of possible outcomes from the draws made. From this distribution we can then compute the range within which we expect 90% of the observations from the draws to fall. This is called the 90% confidence interval, illustrated in Figure 7.

Figure 7: Illustration of sensitivity analysis

 

 

Key inputs and outcomes

In order to build an economic model, such as a cost-benefit analysis, a certain number of inputs are required for calculation purposes and to compute the desired outputs. Various inputs are listed below in a structured approach, as used in the model.

1.   Scenario Analysis

1)    Scenario 1:

In order to assess the impact of Dr Julian, it is important to first run the model through a scenario, with a single provider using the platform within their Mental health service.  Dr Julian is currently being used across four provider sites, for which the model will be run for Oxleas and Lancashire.

  1. Oxleas NHS Foundation Trust (scenario 1a): Dr Julian has been implemented at Oxleas and is currently being used in addition to its IAPT service. Patients referred onto the IAPT service are assessed for suitability and then offered the platform appropriately. Patients requiring anything other than step 3 IAPT treatment will not be suitable for the Dr Julian service and so are offered alternatives.
  2. Lancashire Care NHS Foundation Trust (scenario 1b): Due to the current capacity issues within the IAPT service, there is a large back log of patients who require treatment. Although Dr Julian is only suitable for step 3 patients, it is believed that most patients referred into the IAPT service, even if there are more suitable for step 2 or 4 are offered the platform. Although this could skew the results at the detriment to Dr Julian (as fewer patients recover), this is probably a realistic use of the platform within an already stretched IAPT service.

The scenario has been run from 2020 for five years to show the short term in-year financial impact and a medium 5-year NPV wider economic impact.

One of the limitations to looking at the model from a provider perspective, is that some of the benefits realised will not be seen at the same level as the costs, i.e. at the provider level.  It is therefore important to assess the impact of Dr Julian from a wider system perspective.

2)    Scenario 2:

When assessing the impact of any mental health intervention it is vital to look at costs and benefits from a more social economical perspective. The broader impact of mental health patients affects both the healthcare and social care systems, which is why it is important to assess the impact of Dr Julian on an STP (Sustainability and Transformation Partnership) or ICS (Integrated Care System) (NHS England, 2020).

It has been agreed that the Our Healthier South East London ICS (formally known as South East London STP) will be used in scenario 2 to represent a systemwide analysis.  It should also be noted that the Dr Julian platform is currently offered in the Oxleas NHS Foundation Trust IAPT service, which sits within this ICS.

As not all providers within an STP/ICS will take up the Dr Julian service at the same time, there has been several uptake assumptions applied, which are outlined in section 3.Uptake/Service Composition Assumption below.

The scenario has been run from 2020 for five years to show the short term in-year financial impact and a medium 5-year NPV wider economic impact.

3)    Scenario 3:

For a perspective of the wider impact on all NHS patients, the model will be run across all IAPT providers. In reality it is unlikely for all providers to take up the Dr Julian intervention, and it would take a considerable amount of time to spread and implement across the whole NHS, which is why a maximum of 80% of the patient population will ever be used in this scenario.

The uptake assumptions applied have been outlined in section 3.Uptake/Service Composition Assumption below.

The scenario has been run from 2020 for five years to show the short term in-year financial impact and a medium 5-year NPV wider economic impact.

 

2.   Population

For each scenario, the IAPT data set (NHS Digital, 2019) provides enough detail to assess patients who have gone through the service or have been referred.  For the purposes of the target population, the volumes of patients who have been referred to the service in 2019 have been pulled through for each of the scenarios. This set of patients have not necessarily been treated or gone through the system in 2019, however represent those who have a demand for the service this year. The data point named ‘EndReferral’ represent patients who may not have been referred that year to the service, however, have ended treatment, or dropped out during this period.  These set of patients are used to assess the outcomes of the service.

The volume of referrals for 2014-2019 have been used as a basis for projecting the population from 2020-2024. The average growth rate for the total NHS referrals across the past five-year period is used to project the next five years growth. These growth rates have then been applied to the 2019 referral figures of patients for scenarios 1 and 2 in order to predict the growing demand for the service. The below Table 6 shows the total NHS referral volumes and rates applied from 2014 – 2024.

Table 6: Actual and predicted IAPT service referrals and growth rate

YearVolume of referrals into IAPT serviceGrowth rate
20151,267,19313.2%Actual
20161,399,08810.4%
20171,385,664-1.0%
20181,439,9573.9%
20191,603,34311.3%
20201,725,0637.6%Predicted
20211,836,5206.5%
20221,940,6805.7%
20232,076,4857.0%
20242,234,5847.6%

 

The population of patients for scenario 1a and 1b based on the referrals received into the IAPT service in 2019.

Scenario 1a: Oxleas NHS Foundation trust had 7,180 referrals in 2019, and so 7,725 referrals are predicted for 2020 (based on predicted growth rate in table 6).

Scenario 1b: Lancashire NHS Foundation trust had 43,865 referrals in 2019, and so 47,195 referrals are predicted for 2020 (based on predicted growth rate in table 6).

Scenario 2: Our Healthier South East London ICS had 53,030 referrals in 2019, and so 57,056 referrals are predicted for 2020 (based on predicted growth rate in table 6).

Scenario 3: NHS figures in table 6 above are used for 2020 – 2024.

Although the true population of people across England could be far greater, not all people would consider seeking help. Equally, those who are referred may not always be suitable for the Dr Julian service, or choose to use it, which is why we must consider the uptake of the current service and the Dr Julian service to better predict how many patients will actually be affected by the intervention.

The estimated IAPT population increase of 5-7% year on year is based on previous years, which suggests that more people are referred to the IAPT service relative to the rate of general population growth of around 0.9% each year (ONS, 2020).  It may be that over time referral rates slow relative to population growth, resulting in less estimated referrals compared to what has been used in this study, although further research is suggested.

3.   Uptake/Service Composition Assumption

1)    High Intensity (HI) treatment

Uptake is used to estimate the proportion of the total population above that will end up going through the intervention pathway.  The Dr Julian service model has been deemed only appropriate for high intensity (HI) treatment which is why it is important to first exclude any patients who would normally go through low intensity (LI) treatment.  The IAPT dataset (NHS Digital, 2019) includes volumes of patients who have gone through HI and LI treatment in 2019, broken down by provider and STP (ICS).  It is assumed that the proportion of LI to HI patients remains constant by each Provider/STP (ICS) for future years.  Table 7 shows the proportion of HI patients for each scenario:

Table 7: HI Referral volumes by scenario

Scenario2019 HI treatment given2020 referrals2020 Patients suitable for HI*
1a: Oxleas60.1%7,7254,642
1b: Lancashire43.6%47,19520,554
2: SE London ICS57.3%57,05632,691
3: NHS54.2%1,725,385935,409

*Note due to rounding some figures may not match

These figures will be the populations for each scenario that are fed through the model.

2)    Suitability of Online Therapy

The number of patients who are currently referred to an IAPT service will not necessarily represent those who would go through the Dr Julian service, which is why we must consider the following to filter out patients who will continue to use the current IAPT service (and so will not affect the model outcomes):

  • Connectivity: Dr Julian service is mostly delivered through online video consultations, and so it has been assumed that patients without high speed internet connectivity will be unable to use the service. According to the Office of national statistics (ONS, 2019), 93% of the English population will have access.
  • Clinical Suitability: According to Dr Julian, clinically most HI patients who would be suitable for the normal IAPT service will be suitable for Online therapy, however in their professional opinion the figure is more around 99% of patients.
  • Patient Choice: Of those who are offered online therapy it is assumed that around half will choose to still have face to face appointments (47.2%), leaving 52.8% of patients choosing Dr Julian for their therapy. This is based on a 2013 study from the Netherlands, Kenter, et al. (2013).
  • Start treatment: Many patients in the IAPT service who are referred to treatment will never actually start treatment, of which most drop out due to long waiting lists or believe they no longer require the treatment. Dr Julian patients are similar, however reasons for never starting treatment may differ and could be something to investigate in the future. Using data obtained from the four pilot sites, of those referred to Dr Julian, 65.6% began treatment.

With this step process in mind, the total engagement rate of referred patients suitable for Dr Julian would be 31.9%. Due to the basis of evidence however, we would look to be prudent and use a large sensitivity factor (Optimism bias) to account for the quality of data and studies used.  Expert opinion and any research over 5 years old will result in a 40% reduction in the overall figure. Consequently, out of all patients referred to the service (suitable for HI Step 3 treatment), 19.13% will use Dr Julian.  This is broadly in line with an Australian study (Batterham & Calear, 2017) which suggests that 16.7% of patients seeking help are suitable and happy to begin treatment using online therapy only.

Figure 8 is a graphical representation of how patients have been filtered out by suitability and engagement of Dr Julian, based on a sample of 100 Patients.

Figure 8: Dr Julian Patient engagement rate for Dr Julian per 100 patients

The model uses these assumptions so that for every 100 patients who are referred to an IAPT service and suitable for HI treatment, 19.1 will end up starting treatment through Dr Julian. This figure will be known as the Dr Julian Engagement rate.

3)    Phased implementation

Across all scenarios, it will be unlikely that all patients suitable and engaged will be using the Dr Julian service from inception, which is why a phased uptake % assumption per year is applied to the population. This represents time for planning and implementation of integrating Dr Julian into the current IAPT service model, acceptance of the new technology and ensuring that all appropriate patients are reached. Table 8 shows different uptake % assumptions applied for each scenario.

Table 8: uptake spread assumptions for years 2020-24 by scenario

1a & b Provider2 STP (ICS)3 NHS
Year 1 202020%10%5%
Year 2202140%20%10%
Year 3202260%40%20%
Year 4202370%60%35%
Year 5202480%80%50%

At Provider and STP (ICS) level (scenarios 1 and 2), the Dr Julian service would reach a maximum of 80% of all suitable patients by year 5, with a provider taking up the service at a faster rate than a wider STP (ICS).  IAPT services across the NHS cover vast geographies and patient volumes, so the uptake has been modelled at a far slower rate for scenario 3.

4.   Outcome Streams

1)    Approach and structuring of outcomes

To assess the impact of Dr Julian, the key cost elements of an IAPT service have been identified and monetised. The below is a list of key measures required to monetise and calculate elements of an IAPT service that could be influenced using Dr Julian:

Treatment costs

Treatment costs represent the total cost of all appointments a patient attends until they complete the treatment or drop out. As IAPT data is only available at an aggregated level, the average number of appointments for each scenario has been used. This is multiplied by the average cost per appointment to calculate the average total cost of treatment per patient. Treatment costs are considered a Non-cash releasing benefit stream.

DNAs (do not attends)

DNAs are assumed to be a situation where a patient has booked an appointment however does not show up or cancels within 24 hours. As that appointment could have been used for another patient, the cost of this is then still incurred. DNAs are considered a Non-cash releasing benefit stream as the cost of treatment would be incurred in any instance; however the value of the treatment is only gained given the patient attends.

Recovery rates

For the purposes of the analysis, patient outcomes are broken down into four categories. For each level, patients are assumed to be at caseness prior to starting treatment. Caseness represents a patient with a PHQ9 score of above 9 and/or a GAD7 score of above 7 (NHS Digital, 2019). Appendix D of the ‘Improving Access to Psychological Therapies Manual Appendices and helpful resources’ (NHSE, 2019) document has been used as a guideline to measure clinical cut-offs and reliable change below.

  • Reliable recovery: A patient has a PHQ9 and/or GAD7 score that has started in caseness and has since dropped below the level of caseness and is therefore assumed recovered. To fall into this category, they must also be considered to have ‘‘Reliable improvement’, explained below. Another measure recorded is ‘Recovery’, which is when the patients PHQ9 or GAD7 score drops below that of caseness, however does not meet the criteria of ‘Reliable improvement’.
  • Reliable Improvement: A patient’s PHQ9 score has fallen by at least 6 and/or their GAD7 score by at least 4 since the start of treatment. In conjunction, neither PHQ9 or GAD7 scores could have increased by 6 or more or 4 or more, respectively (threshold). A patient could have reliably improved, however still be at a level of caseness for the PHQ9 or GAD7 measures. The measure of ‘Improvement’ is recorded the same way as ‘Reliable Improvement’.
  • Reliable No change: Represents no reliable improvement or reliable deterioration of PHQ9 or GAD7 scores. This is where there is no change of 6 or more in PHQ9 score, nor any changes of 4 or more in GAD7 score. The measure of ‘No Change’ is recorded the same way as ‘Reliable No change’.
  • Reliable Deterioration: Although uncommon, a patient may deteriorate in health, which is a situation where a PHQ9 score increased by 6 or more, or a GAD7 score has increased by 4 or more. In conjunction, neither PHQ9 or GAD7 scores could have decreased by 6 or more, or 4 or more, respectively. Deterioration could show that the treatment is at a detriment to the patient, or external factors are worsening the condition of the patient throughout their treatment. The measure of ‘Deterioration’ is recorded the same way as ‘Reliable Deterioration’.

Recovery rates can impact the system from an economic and social perspective. A recovered patient is assumed to no longer require further HI treatment and so would have less of an impact on the NHS system, which is considered a non-cash releasing benefit. There is also the social element, which can look at both the effects on the quality of life of a recovered patient and the economic impact on the patient, such as employment and education.

The PHQ9 and GAD7 scores for patients to assess recovery rates are recorded in the same way by both the IAPT service and the Dr Julian platform, which makes the comparison like for like.  The full description of how these recovery rates are calculated can be found in Appendix A.

QALY Associated to recovery

QALY (Quality adjusted life years) are an important measure to assess the social impact of the recovery outcomes of patients. As outlined in the methodology section NICE guidelines allow a financial value of £20,000 to be given to one QALY.

NHS (system) cost of untreated patient: Anxiety and depression.

Any patients untreated or not recovering or improving are considered to continue to have a cost on the NHS system. A study from the Kingsfund (McCrone, et al., 2008) assesses the NHS service cost of patients with moderate to severe depression and anxiety.  This assumes that an untreated patient is likely to cost the NHS more in not just IAPT and psychiatric treatment but also across other areas of primary, secondary and community care such as through increases in GP appointments, SSD (social services department) and non-psychiatric inpatient. This is considered a non-cash releasing element.

Social cost of untreated patient: Anxiety and Depression.

Looking at the same study from the Kingsfund (McCrone, et al., 2008), a social element is considered, where patients suffering with moderate to severe anxiety or depression are more likely to have loss of earnings and education due to their condition.

Mortality rates during treatment have not been considered during this analysis. Although it is possible that people die whilst receiving a course of treatment, the proportion will be relatively insignificant and so has not been included as an outcome measure. Mortality rates may be considered when approaching an analysis on more severe patients, such as those in IAPT step 4.

2)    Monetisation of outcomes

Each element listed in, ‘Approach and structuring of outcomes’, has been monetised to value each benefit stream and assess the full impact of Dr Julian.

 Treatment costs

As discussed, treatment costs are calculated as the average amount of appointments a patient attends during their course of treatment, multiplied by the average cost of an appointment. For this section, the average number of appointments have been broken down by patient outcome following treatment.

Baseline IAPT treatment

IAPT data shows the average number of appointments a patient attended, broken down by whether they reliably recovered, improved, did not change, or deteriorated. These are then multiplied by the respective rates of outcomes to give the weighted mean average of appointments attended per treatment completed.

One issue with the IAPT data set is that it does not split out the average appointments for HI and LI treatment based on outcomes, however the overall average of only HI is provided, which gives an indication as to, on average, how many more appointments HI treatment incur.  For example, given the mean amount of appointments attended of 8.6, and a mean number of appointments attended for HI of 9.3, HI-only treatment has had an additional 8% more appointments. This is then applied to the number of appointments broken down by outcome as a ‘HI Factor’ to bring the overall number of appointments per patient outcome, more in line with if they were all through HI Treatment. There are cases where patients receive both HI and LI during one course of treatment, however for the purposes of the model, the average appointments for these patients have not been considered.

The cost of current IAPT appointments has been sourced from the PSSRU: Unit Costs of Health and Social Care (PSSRU, 2019), which has been calculated as £96, per appointment. For scenarios 1a and 1b: Provider level, a market force factor (MMF) has been considered due to economical differences across differed geographies in the NHS (NHS Digital, 2019) This is a common practice often used for commissioning data to more accurately calculate costs within certain regions. Scenarios 2 and 3 look across much wider areas and so an MFF has not been applied.

Applying each of the elements above, it is possible to calculate the average cost of treatment for patients specific to the scenario selected. The below shows an example calculation used for Oxleas NHS Foundation trust:

  • Average Number of HI Appointments per patient: 5
  • Cost per Appointment: £96.00
  • Underlying MFF: 109%

Average completed treatment cost for patients who go through the Oxleas NHS Foundation trust IAPT service:  9.3* £96.00*109% = £973.15.

It is worth noting that the average number of HI appointments per patient varies considerably across Providers, ranging from 2.0 – 13.6 which translates to the cost of treatment across IAPT providers also differing considerably, and in most cases the service of care delivered.

The average number of HI appointments across the NHS is 7.1, which translates to total treatment cost for patients of 7.1 * £96.00 = £681.60

Dr Julian Treatment

The cost of treatment to the NHS for Dr Julian therapy will be calculated in the same way as IAPT services, however the cost per appointment is £60.00. The number of appointments will also change based on the results from the Pilot sites. For example, across the four sites, the average number of appointments per patient completing treatment was 10.27.  The total cost of treatment for a Dr Julian patient is therefore assumed as £615.98. The actual number of appointments required per patient may differ per site, we have therefore applied an optimism bias to this figure explained in section 3 Selection of Benefit / Cost Streams.

As Dr Julian offers a fixed cost to the NHS, and is not influenced by market force factors, the MFF has not been applied to this figure based on the provider location.

patients Dropping out

Both the current IAPT service and Dr Julian will have patients who begin treatment (based on at least one treatment, excluding initial assessment appointment) however end up not completing the full course and drop out. In this instance, these patients are still considered to have a cost to the system, as they attended appointments. The cost of this treatment is calculated in the same way as completed treatment; however, it considers the average number of appointments attended before dropping out. Unfortunately, current public data sources do not provide enough data to calculate the current amount of appointments attended by drop out patients, and so data from the Dr Julian Study of 4.6 has been applied to both Dr Julian patients and the current IAPT service.  As this data only covers a fraction of NHS patients, and optimism bias has been applied to both baseline and intervention figures.

In all cases of treatment costs, it is essential we apply an inflation rate to account for the increase in prices for the service over time. Due to the healthcare nature of the treatment a HSHC inflation rate has been applied to the 2019 figures for years 2020 – 2024 to give a more accurate estimate of prices in the future.

HCHS Inflation rate up to 2016/17 then the new health inflation rate (New Health Services Index using CPI), after 2017/18 the forecast is generated based on average of preceding period (HM Treasury, 2019).

DNAs

DNA rates have been widely recorded by provider and analysed by Public Health England (2020).

The rates shown represent the proportion of all appointments booked that end in a DNA. Where this figure is unavailable the national average of 10.45% has been used. The average number of DNAs per person is therefore assumed as the total average appointments per treatment multiplied by the DNA rate. Although data on DNA rates for drop out patients may be considerably higher, this data is unavailable and so a standard DNA rate has been applied for all patients.

The cost of a DNA for the baseline IAPT service is calculated as the cost of the appointment, calculated as £96.00 multiplied by the MFF. The cost of a DNA for Dr Julian is calculated as £60.00.

Recovery rates

Recovery rates are an important measure of the effectiveness of a course of treatment. In this study we have used the Reliable recovery, Reliable improvement, Reliable No Change and Reliable Deterioration measures for patient outcomes. Patients who recover or improve will, on average, have less impact on the NHS system and an increased quality of life. To monetise recovery rates, NHS and Social measures have been calculated.

QALY Associated to recovery

Mental health plays a large part to quality of life and therefore it is important to monetise the impact Dr Julian could have from a Social perspective. Despite mental health becoming a long-term priority for the NHS, there is a distinctive lack of research on linking mental health conditions to quality of life, and more appropriately for health economics, QALY values.

For the purposes of the model, a study from NICE has been used (NICE, 2010). This study suggests the impact of CBT on patients to those with no treatment saw a QALY difference of 0.09.  It is therefore assumed that the social gain for patients who improve or recover because of treatment is £20,000 * 0.09 = £1,800 per year. Conversely, a patient who deteriorates in health is assumed to see a reduction in the quality of life and so this figure is applied negatively to this patient outcome. No change in patient outcomes following treatment will result in no change to quality of life.

As it is not possible to account for what happens to a patient after treatment, the social benefit of quality of life is only applied for one year. It is however possible that a patient will have an improved quality of life for the remainder that they live, and so the true social gain of successful treatment far surpasses the values used in this analysis.

It is recommended that further investigation is undertaken to gain a better understanding of the link between mental health and QALYs. Due to the poor quality of evidence in this instance, a large optimism bias has been applied to the QALY gain from improvement and recovery.

NHS (system) cost of untreated patient: Anxiety and depression.

The service cost for patients with Anxiety and Depression has been calculated by the Study (McCrone, et al., 2008). This is commonly used to assess the cost of mental healthcare across the NHS and the wider economic impact. For the purposes of this model, figures relating to the Service Cost (£) per patient have been pulled from the report. In 2007 it was calculated that the cost to the NHS for patients with depression or anxiety was £1,354.84 and £543.86 respectively.  Further investigation into these figures, show additional costs such as residential and medication applied, and so removing these, 2007 values of £1,138.06 and £499.82 respectively have been used. Using a GDP inflation rate, these figures have been increased up to 2019 which give NHS service costs for untreated depression and anxiety per patient of £1,410.08 and £619.27.

Using the baseline IAPT data, the proportion of anxiety and depression patients can be calculated and applied to the above figures to give an average system cost to the NHS. This can be calculated by provider for scenario 1, STP (ICS) for scenario 2 and NHS for scenario 3. For example, the 2019 total NHS rate of depression for patients is 30.36% and rate for anxiety is 69.64% and so the weighted NHS system cost is £859.34 per untreated patient. An appropriate rate of inflation has then been applied for figures for years 2020 – 2024.

Social cost of untreated patient: Anxiety and Depression.

Similarly, to the above methodology, the same study and calculations have been applied, however using figures for loss of earnings for patients, which is considered a non QALY social element. The study has calculated 2007 loss of earnings for patients with anxiety or depression to be £4,693.55 and £3,377.19 respectively giving a 2019 weighted cost of earnings for an untreated patient of £4,679.51. An appropriate rate of inflation has then been applied for figures for years 2020 – 2024.

 

 

3)    Selection of Benefit Streams

The below benefit streams are those used within the model to assess the positive impact Dr Julian could have from a health economic perspective, run for each of the scenarios:

1.1 Reduced Face to Face appointments

For the purposes of the model it is assumed all patients that go through the Dr Julian service, would have normally gone through the baseline IAPT service of Face to Face appointments and so the cost of face to face appointments in the IAPT service are no longer required for these patients, representing a non-cash releasing benefit.

In some instances, however, the Dr Julian platform could be used to treat a large backlog of patients sitting on waiting lists. In this instance, patients who would normally go through the current treatment probably still will and additional patients will go through the Dr Julian service or remain on the waiting list. This means that there will be no cash-releasing savings for face to face appointments, and only the additional cost of Dr Julian patients. This has not been considered in the model.

Both the cost of patients who finish treatment and the cost of those who drop out have been considered. The calculation for this benefit stream is the following:

Total scenario population * Dr Julian engagement (%) * Scenario uptake year X (%) *

[(Baseline finish treatment rate * Baseline average appointments for finished treatment * baseline cost per appointment (£) * MFF) +

(Baseline dropout treatment rate * Baseline average appointments for dropout patients * baseline cost per appointment (£) * MFF)].

An optimism bias of grade 1(+5%) has been applied to the finished treatment rate of baseline IAPT patients due to the reliability of the dataset. For The cost of treatment for Dr Julian is calculated in the subsequent cost section.

1.2 Reduced DNAs

As the Dr Julian platform puts the control into the hands of the patient, it is expected that there will be a reduction in DNAs compared to the current baseline IAPT service. To calculate the benefit that Dr Julian has on DNAs the average cost of DNAs for the baseline and for Dr Julian patients is calculated and the difference being the benefit. The calculation for this benefit stream is the following:

Total scenario population * Dr Julian engagement (%) * Scenario uptake year X (%) *

[Average baseline DNA rate (%) * Baseline average appointments* baseline cost per appointment (£) * MFF)] –

[Average Dr Julian DNA rate (%) * Dr Julian average appointments * Dr Julian cost per appointment)]

An optimism bias of grade 1 (-5%) has been applied to the DNA rate of baseline IAPT service and grade 2 (+10%) to the DNA rate of the Dr Julian service due to the reliability of the data sources.

1.3 Improved Patient Outcomes:
a) Improved Recovery Rate (Social)

To assess the benefit of patient recovery, the QALY gained per patient who goes through the Dr Julian service has been applied based on the difference in patient outcome (recovery, improvement, no change and deterioration) rates of Dr Julian and the Baseline IAPT service.

The calculation is as follows:

Total scenario population * Dr Julian engagement (%) * Scenario uptake year X (%) *

(Sum product of [difference between Dr Julian and Baseline patient outcomes] and [QALY gained per outcome]) *

Value of QALY (£20,000)

In this instance it is assumed that the social QALY outcomes of patients who drop out is the same as those who finish treatment due to a lack of data to split out the four outcome measures. An optimism bias of grade 2 (-10%) has been applied to the difference between the Dr Julian patient outcomes and baseline patient outcomes.

QALY values have been reduced by a grade 5 (-40%) to account for the unreliability of the study used.

b) Improved Recovery Rate (NHS)

Calculating the difference between the baseline and Dr Julian patients who do not recover or improve is a means to measure the NHS benefit of recovered patients. The reduction in cost of untreated patients is the benefit in this case. Of patients who finish treatment, both the baseline and Dr Julian No change and deteriorated rates are used, with the difference being the total patients who do not recover and therefore continue to have a service cost on the NHS. That service cost has been calculated using the proportion of anxiety and depression cases multiplied by the service cost of each (as discussed in section 5 NHS (system) cost of untreated patient: anxiety and depression. It has been assumed that the proportion of anxiety to depression patients for the Dr Julian patients matches that of the baseline as it is comparing the same scenario population. The MFF has been applied to account for healthcare cost differences in services across the NHS.

The calculation for this benefit stream is as follows:

Total scenario population * Dr Julian engagement (%) * Scenario uptake year X (%) *

[Baseline finished treatment rate*(Baseline patients who finished treatment with no recovery rate% + deterioration rate%)] –

[Dr Julian finish treatment rate* (Dr Julian patients who finished treatment with no recovery rate% + deterioration rate%)] *

[Baseline depression rate * NHS cost of untreated depression + Baseline anxiety rate * NHS cost of untreated anxiety)) * MFF]

An optimism bias of grade 2 (+/- 10%) has been applied to Dr Julian figures and grade 5 (-40%) to the NHS costs of untreated patients.

1.4 Reduction in Treatment Dropout rates

Patients may drop out of treatment due to various reasons, however it is important to assess whether those who do have recovered or are still suffering from a mental illness.  As there is a lack of data for baseline patients, on whether they dropped out as recovered or not, it is assumed that the proportion of drop out patients who are not recovered for the baseline is the same as Dr Julian patients.  Due to the quality of the evidence base, a grade 2 optimism bias of 10% has been applied to the figure of 94.4% from the Dr Julian study of patients who dropped out and have not recovered. It has been applied so there is a 10% difference between the Baseline and Dr Julian figures resulting in a baseline rate of 89.44% compared to a Dr Julian rate of 99.44%. It is recommended that further studies are conducted to obtain more reliable data on the baseline IAPT patients.

The social and NHS system benefit that Dr Julian could have is through the reduction in overall patients who drop out of treatment and do not recover. This number is then calculated by the social cost of an untreated patient to show the loss of earnings patients endure through not recovering from a mental health illness. The same number of patients are then multiplied by the NHS system cost of patients who have not recovered to show the continued cost on the NHS.

The total additional number of patients who drop out and do not recover of the baseline compared to Dr Julian is calculated as follows:

Total Scenario Population * Dr Julian engagement (%) * Scenario uptake year X (%)*

(Baseline dropout rate * Baseline dropout rate who do not recover) –

(Dr Julian dropout rate * Dr Julian dropout rate who do not recover)

Social Benefit

The benefit here is the reduction in patients who drop out and do not recover, resulting in less patients who would suffer loss of earnings as a result of continuing to live with a mental health illness. The earnings lost per untreated patient have been calculated as the following.

[(Baseline depression rate * Loss of earning of untreated depression) + (Baseline anxiety rate * Loss of earning of untreated anxiety) * MFF]

NHS System Benefit

Like benefit stream 1.3b, the NHS system benefit is assuming a reduction in patients who drop out and do not recover, therefore resulting in less NHS system cost in the future. This is calculated as:

[(Baseline depression rate * NHS cost of untreated depression) + (Baseline anxiety rate * NHS cost of untreated anxiety) * MFF]

An optimism bias of grade 2 (+/- 10%) has been applied to Dr Julian figures and grade 5 (-40%) to the NHS and social costs of untreated patients.

4)    Cost Streams:

The only recognised cost stream of Dr Julian is the cost for patient treatment, calculated as number of appointments required for treatment multiplied by the cost per appointment. This is because, in some cases, the cost of a full treatment of Dr Julian can sometimes surpass the cost of the current baseline treatment. All other benefit streams are considered to always remain positive.

Both the cost of patients who finish treatment and the cost of those who drop out have been considered. The calculation for this benefit stream is the following:

Total Scenario Population * Dr Julian engagement (%) * Scenario uptake year X (%)*

[(Dr Julian finish treatment rate * Dr Julian Average appointments for finished treatment * Cost per Dr Julian Appointment (£)) +

(Dr Julian dropout treatment rate * Dr Julian Average appointments for dropout patients * Cost per Dr Julian Appointment (£)]

An optimism bias of grade 2 (+/-10%) has been applied to the Finished treatment rate, dropout rates and average number of appointments for treatment of Dr Julian patients due to the reliability of the dataset.

The figures used to assess the effectiveness of Dr Julian will not differ by scenario as the likelihood of differences has been considered using an optimism bias. The baseline IAPT service data does provide differences in the current service for each of the considered scenarios. The Table 9 outlines the key metrices and assumptions used for each scenario which are the key comparative elements against Dr Julian.

Table 9: Dr Julian Key metrics for Modelling scenarios

Elements Scenario 1a Oxleas Scenario 1b

Lancashire

Scenario 2

STP

Scenario 3 NHS
PopulationProvider (IAPT/MHT)Provider (IAPT/MHT)STPNHS
2020 population considered for HI Treatment4,64220,55432,691869,407
MFF (%)109.1%95.5%100%100%
Engagement rate (%) (Referred and started treatment) 72%73.1%70.1%68.4%
Completion of Treatment (%)44.8%42.1%52.5%57.0%
Drop out (%)55.2%57.9%47.5%43.0%
Outcomes:

Reliable recovery:

53%49%47%49.5%
Improvement/Reliable improvement:

 

20%14%20%19.0%
No Change22%29%27%25.7%
Deterioration:

 

5%8%6%5.8%
Number of Completed HI Treatment Appointments9.37.18.07.1
Number of Dropout treatment appointments 4.64.64.64.6
DNA Rate (%)10.5%14.1%11.00%11.00%
Anxiety to Depression Ratio74.5 : 25.572.2: 27.868.5 : 31.569.6 : 30.4

In addition to the individual assumption optimism bias confidence grades applied a further grade – 3 (15%) rate has been applied to the final nominal benefits and cost streams before inflation and discount rates applied.  For scenario 3: NHS, this optimism bias has been applied twice due to the uncertainty around outcomes on such a wide scale.

5)    Sensitivity distribution

For the purposes of the sensitivity analysis a triangular distribution has been applied with the lower and upper ranges estimated at +/- 25%.  In cases where the assumption is a percentage, and where the upper range could exceed 100%, the range is capped, and the same proportion applied to the lower range to maintain an even triangular distribution.

 

 

Cost -Benefit Model Findings

1.   Summary of Results

The following section provides an overview of the findings of our modelling, based on the assumptions, cost and benefit streams outlined in the previous section.

Results have been categorised into the following types of benefits with the benefit stream for each listed below:

1)    NHS non-cash releasing

Reduced Face to Face Appointment Costs

Reduced DNAs

Improved Recovery Rate (NHS)

Reduction in Treatment Dropout rates (NHS)

2)    Social QALY

Improved Recovery Rate (Social)

3)    Non-Social QALY

Reduction in Treatment Dropout rates (Social)

Table of results

The below Table 10 summarise the results of the Dr Julian scenario analysis for the Oxleas and Lancashire providers, Our Healthier South East London ICS (STP) and the wider NHS.  All results include social outcomes.

Table 10: Dr Julian Key results

Dr Julian 5-Year NPV
In Year
Figures in (£m)20202021202220232024(2020-2024)
 
Dr Julian – scenario 1a – Oxleas  
Net Benefit£0.29£0.61£0.97£1.20£1.47£4.31
Cost:Benefit Ratio 1 : 3.361 : 3.361 : 3.361 : 3.361 : 3.361 : 3.54
Dr Julian – scenario 1b – Lancashire  
Net Benefit £1.26£2.67£4.20£5.22£6.40£18.77
Cost:Benefit Ratio 1 : 3.321 : 3.321 : 3.321 : 3.321 : 3.321 : 3.50
 
Dr Julian – scenario 2 – STP 
Net Benefit £1.47£3.11£6.54£10.44£14.92£34.60
Cost:Benefit Ratio 1 : 3.271 : 3.271 : 3.271 : 3.271 : 3.271 : 3.46
 
Dr Julian – scenario 3 – NHS 
Net Benefit £15.40£32.61£68.60£127.92£195.88£421.13
Cost:Benefit Ratio 1 : 2.661 : 2.661 : 2.661 : 2.661 : 2.661 : 2.83

 

 

In all scenario cases the net in-year benefit and 5-year NPV are positive. Note that the NHS Cost:Benefit ratio is the lowest of each scenario due to a more stringent approach taken when analysing the outcome measures.  This is due to the wider uncertainty of the scenario compared to scenarios 1 and 2.

It is worth noting that the in-Year Cost: Benefit ratio remains the same as benefits and costs rise in line with the same inflationary measures.  NPV appears higher as the discount rate applied to economic costs are greater than those applied to social outcomes, as patients are expected to value social benefits with regards to quality of life, greater than economical ones.

 

 

 

2.        Scenario Results

Scenario 1a – Oxleas NHS Foundation Trust

Taking the costs and benefits specified into account, the following return on overall investment can be seen for Oxleas NHS Foundation trust.

Table 11: Scenario 1a results

Dr Julian – scenario 1 – ProviderOXLEAS NHS FOUNDATION TRUST5-Year NPV
 In Year
Figures in (£m)20202021202220232024(2020-2024)
 
Benefits
NHS non-cash releasing£0.22£0.47£0.74£0.91£1.12£3.21
Social non QALY£0.20£0.42£0.66£0.83£1.01£2.90
Social QALY£0.01£0.01£0.02£0.02£0.03£0.08
Total Benefits£0.42£0.90£1.42£1.76£2.16£6.18
 
Costs £0.12£0.26£0.41£0.51£0.62£1.70
 
Net Benefit (Ex Social)£0.10£0.21£0.33£0.40£0.50£1.51
Net Benefit£0.30£0.64£1.01£1.25£1.53£4.49
 
Cost : Benefit Ratio (Ex Social)1 : 1.801 : 1.801 : 1.801 : 1.801 : 1.801 : 1.89
Cost : Benefit Ratio 1 : 3.461 : 3.461 : 3.461 : 3.461 : 3.461 : 3.64

 

Most of the benefits are NHS Non-cash releasing.  To put into context, the current IAPT service at Oxleas has a 53% recovery rate (above the 49.5% NHS Average) and 20% improvement rate with patients on average receiving 9.3 number of appointments per course of treatment (7.1 NHS Average).  In addition, this provider has an MFF of 109% due to its location.  The cost of Dr Julian treatment is therefore lower, providing a NHS non-cash releasing saving, with social non-QALY also positive as patient outcomes improve slightly after applying an optimism bias to the figures.   Social QALY outcomes remain around the same due to similar proportion of patients deteriorating and a similar drop-out rates.

The total NPV Cost: Benefit ratio shows for every £1 spent, a return on investment (ROI) of £1.89 when excluding social benefits.  Due to the nature of Mental Health, it is expected that a large proportion of benefits arise from social aspects, which is why the overall 5-year ROI of £3.64 is more applicable.

 Sensitivity analysis 1a – Oxleas NHS Foundation Trust

A sensitivity analysis to investigate the NPV over 5 years has been processed. This shows that overall NPV including social benefits could vary between £3.57m and £5.52m at the 90% confidence level. Even towards the lower end of the results, the NPV remains positive, suggesting that, with the evidence given, it is still likely that in this scenario, Dr Julian will have a positive economic impact. The benefits presented in the table above, however, are the most likely scenario.

Figure 9: Dr Julian scenario 1a Sensitivity analysis

Scenario 1b – LANCASHIRE CARE NHS FOUNDATION TRUST

Taking the costs and benefits specified into account, the following return on overall investment can be seen for Lancashire care NHS Foundation trust.

Table 12: Scenario 1b results

Dr Julian – scenario 1 – ProviderLANCASHIRE CARE NHS FOUNDATION TRUST5-Year NPV
In Year
Figures in (£m)20202021202220232024(2020-2024)
 
Benefits
NHS non-cash releasing£0.78£1.66£2.61£3.24£3.97£11.39
Social non QALY£0.96£2.03£3.21£4.00£4.90£14.03
Social QALY£0.12£0.24£0.38£0.47£0.56£1.68
Total Benefits£1.86£3.93£6.20£7.71£9.44£27.10
 
Costs £0.54£1.15£1.81£2.25£2.76£7.52
 
Net Benefit (Ex Social)£0.24£0.51£0.80£0.99£1.22£3.88
Net Benefit£1.32£2.78£4.39£5.45£6.68£19.58
 
Cost : Benefit Ratio (Ex Social)1 : 1.441 : 1.441 : 1.441 : 1.441 : 1.441 : 1.52
Cost : Benefit Ratio 1 : 3.431 : 3.421 : 3.421 : 3.421 : 3.421 : 3.61

 

Most of the benefits are Social.  To put into context, the current IAPT service at Lancashire has a 49% recovery rate (below the 49.5% NHS Average), and a 14% improvement rate, with patients on average receiving around the same number of appointments per course of treatment as the NHS (7.1).  The MFF within this area is 95% which also contributes to the overall lower total treatment cost of the current.  Despite this the cost of Dr Julian treatment remains lower than the average in this case, however does not contribute as much as scenario 1a to the total net benefit.  It is also worth noting that the DNA rate for the current service is substantially higher than the NHS average at 14.1%, which, in turn translates to a greater non-cash releasing saving when using Dr Julian.  Social non-QALY and QALY outcomes, on the other hand, provide most of the benefit realised due to the potential positive impact Dr Julian could have on the poor patient outcomes seen in the current IAPT service.

The total NPV Cost: Benefit ratio shows for every £1 spent, a return of £1.52 when excluding social benefits.  Social benefits account for the majority of the return for this scenario, which is why the overall 5-year ROI of £3.61 brings the total returns almost equal to scenario 1a Oxleas.

 

 

Sensitivity analysis 1b – LANCASHIRE CARE NHS FOUNDATION TRUST

The sensitivity analysis to investigate the net present value (NPV) over 5 years shows that overall NPV including social benefits could vary between £15.91m and £24.24m at the 90% confidence level. Even towards the lower end of the results, the NPV remains positive, suggesting that, with the evidence given, it is still very likely that in this scenario, Dr Julian will have a positive economic impact. The benefits presented in the table above, however, are the most likely scenario.

Figure 10: Dr Julian scenario 1b Sensitivity analysis

 

Scenario 2 – Our Healthier South East London ICS

Taking the costs and benefits specified into account, the following return on overall investment can be seen for the Out Healthier South East London ICS.

Table 13: Scenario 2 results

Dr Julian – scenario 2 – STPOur Healthier South East London ICS5-Year NPV
In Year
Figures in (£m)20202021202220232024(2020-2024)
Benefits
NHS non-cash releasing£0.98£2.09£4.39£7.02£10.02£22.66
Social non QALY£1.10£2.33£4.90£7.84£11.22£25.32
Social QALY£0.09£0.18£0.38£0.59£0.84£1.96
Total Benefits£2.17£4.60£9.67£15.45£22.07£49.94
 
Costs £0.65£1.37£2.88£4.60£6.58£14.06
 
Net Benefit (Ex Social)£0.34£0.72£1.51£2.41£3.44£8.61
Net Benefit£1.52£3.23£6.79£10.84£15.50£35.89
 
Cost : Benefit Ratio (Ex Social)1 : 1.521 : 1.521 : 1.521 : 1.521 : 1.521 : 1.61
Cost : Benefit Ratio 1 : 3.361 : 3.351 : 3.351 : 3.351 : 3.361 : 3.55

As scenario 2 considers a wider patch, which covers multiple providers, the net benefit is higher than that in scenarios 1a and 1b.  Non-cash releasing and Social benefits are broadly similar, however significantly increasing year-on-year due to the spread assumptions used.

The total NPV Cost: Benefit ratio shows for every £1 spent, a return of £1.61 when excluding social benefits.  This is somewhere in the middle of both scenario 1a and 1b, as it represents a larger sample of patients over a larger geographical patch, accounting for variabilities of services within the area.   The overall, 5-year ROI of £3.55 is also very similar.

 

Sensitivity analysis 2 – Our Healthier South East London ICS

The sensitivity analysis to investigate the net present value (NPV) over 5 years shows that overall NPV including social benefits could vary between £28.26m and 44.93 at the 90% confidence level.

Figure 11: Dr Julian scenario 2 Sensitivity analysis

 

Scenario 3 – NHS

Taking the costs and benefits specified into account, the following return on overall investment can be seen for the NHS.

Table 14: Scenario 3 results

Dr Julian – scenario 3 – NHS 5-Year NPV
In Year
Figures in (£m)20202021202220232024(2020-2024)
 
Benefits
NHS non-cash releasing£11.57£24.54£51.60£96.18£147.22£305.82
Social non QALY£12.19£25.83£54.40£101.56£155.68£322.95
Social QALY£0.89£1.85£3.83£7.05£10.63£22.84
Total Benefits£24.65£52.22£109.84£204.79£313.54£651.61
 
Costs £9.25£19.61£41.24£76.86£117.66£230.48
 
Net Benefit (Ex Social)£2.32£4.93£10.36£19.32£29.57£75.34
Net Benefit£15.40£32.61£68.60£127.92£195.88£421.13
 
Cost : Benefit Ratio (Ex Social)1 : 1.251 : 1.251 : 1.251 : 1.251 : 1.251 : 1.33
Cost : Benefit Ratio 1 : 2.661 : 2.661 : 2.661 : 2.661 : 2.661 : 2.83

Scenario 3 assess the impact Dr Julian could have across the wider NHS. Despite stringent spread assumptions, and an additional optimism bias applied, the first in-year net benefits (ex-social) are almost £2.3m.  Looking at the overall treatment cost of the NHS compared to Dr Julian, the different per patient is not significant.  On average the total treatment cost of the NHS is around £680 (based on 7.1 appointments), with Dr Julian £618 (based on 10.3 appointment). And so, without applying any optimism bias the total treatment saving per patient is £62.  The true non-cash releasing saving occur firstly, in dropout rates, where the NHS sees more patients dropping out, not recovering, and therefore costing the system, and secondly, the reduction in DNAs.

The average DNA rate for the NHS is 11.0% compared to Dr Julian which is less than half this at 5.4%.  This benefit stream provides greater justification to using the Dr Julian service as DNAs cost less and the rate is lower.

An additional prudency has been applied in scenario 3, due to the uncertainty of the data which has resulted in a reduction of the cost benefit ratio.  Despite more stringent measures, the 5-year ROI remains positive at £1.33, more than doubling to £2.83 when considering social benefits.

Sensitivity analysis 3

The sensitivity analysis to investigate the net present value (NPV) over 5 years shows that overall NPV including social benefits could vary between £326.19m and £523.24 at the 90% confidence level.

Figure 12: Dr Julian scenario 3 Sensitivity analysis

 

 

 

 

Discussion, Limitations and Suggestions

1.   Insights

1)    Social benefits

In each scenario, social benefits account for around half of the total benefits received, which is as expected due to the nature of mental health interventions having a large impact on social gain as well as wider economics gains. The main social benefits are, improvement in quality of life, through increased recovery rates, represented as QALYs, and a reduction in loss of earnings.  As expected, the full benefits for scenario 1a -Oxleas are mostly non-cash releasing, as the current service provides relatively positive social outcomes for patients due to a greater number of appointments per treatment. Scenario 2b – Lancashire however, where current patient service outcomes are relatively poor, most of the total benefit of Dr Julian will be social. This is also since the current number of appointments per treatment relatively low, resulting in the relative cost of Dr Julian being higher than scenario 1a. These results are inline the expected NHS and Social impact of Dr Julian and how they will vary depending on the current service provisions, treatment duration and patient outcomes.

This being the case, it would be more meaningful to view the impact of Dr Julian across a health and social care system, such as an STP or ICS, rather than just a stand alone IAPT provider. The model itself has been created, so that the system benefits can be switched off at Provider level to show costs and benefits directly linked to the IAPT service, rather than including the additional wider NHS system benefits. This feature could be useful from a budgeting perspective at IAPT Provider level.

2)    Broader Patient Population/ Key Population Group

Dr Julian require no fixed costs implementation or spread across the NHS, which means costs are based on volumes of patients who use the service through appointment costs. This variable cost means that the impact it could have is based on the state of the current service.

IAPT services which are currently offering high volumes of appointments per course of treatment, with relatively good patient outcomes appear to already have enough resources to service the population. In this instance there may be limitation to the social impact that Dr Julian could have, however there may be financial gains be seen through the reduced cost  of a full course of treatment through Dr Julian, assuming there are patients who are happy to choose online therapy over Face to Face. On the other hand, given a current IAPT service which only has enough resources for a small amount of appointments per course of treatment, the financial cost of using the Dr Julian would most likely increase due to the higher amount of appointments patients will go through, however from a social perspective, patient outcomes would probably be far greater due to an increase in recovery rates.

3)    Qualitative impact

In this study, there was no scope for qualitative feedback from patients or professional/clinical staff to provide further context to the acceptance and impact of the Dr Julian platform on the existing IAPT service. Future qualitative research and study could provide greater insight and real-world validation the enhance the analysis going forward.

2.        Limitations and Caveats

1)    Data collection/ Limited data around pathway assumptions

The current Dr Julian results are based on a study of n=244 patients across four mental health providers which provides a reasonable sample size and geographical spread, however the volume of patients who finished treatment with the platform was only n=69, with  n=61 of these at caseness before or after treatment, and so used in the outcomes measure.  The time period for which the data was collected was from October 2018, however most patients at caseness (n=27) began treatment from August 2019. Ideally the data will show patients treatment evenly distributed throughout the year to account for any seasonal difference in patient outcomes.

The IAPT data set provided us mostly with like-for-like data to the Dr Julian study, however in some instances such as the recovery rates of drop out patients and the true figure of number of appointments for patients who have received any type of HI treatment, the data was manipulated and assumptions were made. Future studies should collect baseline data directly from the IAPT service for a more robust comparison.

2)    Population/ Quantification of existing population

Patient population figures have been based on the previous number of patients who have been referred to the current IAPT service. The true population of people across the England who suffer from a mental health illness and require step 3 IAPT treatment could be substantially higher.  Barriers to entry into the service such that effect minority groups, or those who feel stigma around mental health, may choose to not access the service. Dr Julian may help address these barriers, which means the target population used in this model may be far greater if patients felt the service was more accessible to them. This would require further investigation before any real conclusions can be made.

It was decided that baseline data up to 23 March 2020 was used due to the government measure to implement social distancing as a result of the COVID-19 pandemic. These measures could have a great impact, not only on the way patients access the current IAPT service, but also the number of patients who may require HI treatment. The future target population used in this model has been calculated using previous year referrals, however it is likely that the actual figure in the future would be far greater.

3)    Inclusion of Implementation Costs

Although it has been stated that the costs of Dr Julian would only be the cost per appointment, a time cost to get the systems and staff of current IAPT service up and running could be included in the analysis.

4)    Conservative approach and optimism bias

The IAPT data sets used provide reliable figures of past patient outcomes and experiences for comparison, however historic data cannot always predict the future accurately and a degree of error will always be necessary to take into consideration. For the purposes of this model a Grade-1 confidence level has been applied to all data driven from the NHS digital IAPT dataset which. This improves all economic and social assumptions by 5% before comparing them to the Dr Julian figures.

Dr Julian data has had a grade-2 confidence level (+/-10%) applied due to the reliability of the studies. This has resulted in a reduction of 10% to all social and economic outcomes of the Dr Julian pathway. A grade 1 optimism bias level (+/- 5%) would require an RCT study or equivalent across a broader time period with a greater number of patients.

Where there has been gaps in the analysis such as the general uptake of online consultations, and the QALY value of improved mental health, a grade 5 confidence rating has been applied due to the un-reliability of the data. This reduces impact measures by 40%, which brings some impact measures and patient uptake percentage of Dr Julian down considerably. If more reliable studies could be sourced or conducted, then this confidence grading can be improved, and the results of the model be made more accurate.

5)    Broader limitations

1)    Patients experience

Although not every Provider or STP (ICS) scenario has been modelled in this analysis, in some instances, the total cost of treatment of Dr Julian is greater than that of the current baseline IAPT service, which results in a negative benefit to cost ratio when excluding social benefits. This is the reality in certain cases, as some services look to push through patients with a relatively low volume of appointments in order to receive the commissioning required per patient for the treatment. Although this results in a relatively lower cost of treatment per patient, the wider system cost is greater as in most cases the recovery and effectiveness of the treatment is reduced as a result of less appointments. Due to variations in services, the patient experience should be considered to contextualise each IAPT service and assess the impact of Dr Julian on a case by case basis.

Qualitative studies can provide an insight into patient experience and assess reasons why a service may not be performing as well as expected. It may also give insight into how the service performs for certain population groups. Equally, it could also provide valuable data as to how a current service may be treating patients more successfully without the requirement for high volumes of appointments per course of treatment.

2)    Implementation requirements

In this study the assumption is that Dr Julian will be suitable for a maximum of 80% of all IAPT providers. This is, however loosely based on the assumption that the majority of providers will have the necessary technology, staff and acceptance of such a platform.  It is however worth noting that many providers may already have in place an alternative online consultation platform, such as the way in which GP consultations are conducted. Equally there has been no data or feedback collected on the way in which Dr Julian has been implemented at the current four sites which could provide insight to how it may be spread across other IAPT providers in the future. It has been assumed that there will be no cost to the NHS with regards to implementation, such as software, training and maintenance costs, however it may take up staff time, or require additional staff to administer during use, which could provider additional indirect cost to the NHS.  This is an area that should be explored further to help understand the potential of spread across the wider NHS.

2.   Suggestions

Below outline some suggestions based on the Dr Julian study that can help shape the next steps for the intervention model and improve data collection and the assumptions made in this report.

1)    Evaluation strategy

Improving Data Collection on baseline

The social QALY outcome in the analysis represents a very small proportion of the overall benefit seen, however in theory it should have a far greater influence on the overall outcomes.  Unfortunately, there are not enough reliable studies available to assess the link between QALY utility values and mental health issues. It is strongly advised that this outcome measure is investigated further, and revised assumptions are made.

The current baseline data source from NHS digital provides enough information to create a detailed analysis of the current state of the IAPT service however it is limited in that patient data is aggregated.  There are also many gaps for which data from literature review and external studies have had to be incorporated into the model.  The below table 15 highlights where assumptions have been made due to the unavailability of exact figures:

Table 15: Assumption breakdown

MetricsBreakdownCurrent assumption
Suitability and engagement of patients Patients eligible for High intensity (HI) treatment54.2% (Proportion of patients who have ended referral on HI Treatment – Assumed suitable)
Engagement rate (of HI Patients)68.4% (Proportion of referrals who have had at least 1 treatment) – Currently includes HI and LI
Finish Treatment Rate (Of HI patients)57% (of patients who are engaged, proportion of those who finish course of treatment) – Currently includes HI and LI
% of patients who finish treatment HI Treatment patients’ outcomes: Reliable recoveryAll patients (HI and LI): 49.5% (Total NHS)
HI Treatment patients’ outcomes: ImprovementAll patients (HI and LI): 19% (Total NHS)
HI Treatment patients’ outcomes: No changeAll patients (HI and LI): 25.7% (Total NHS)
HI Treatment patients’ outcomes: DeteriorationAll patients (HI and LI): 5.8% (Total NHS)
Mean Total Appointments required to finish treatment HI Treatment patients’ outcomes: Reliable recoveryNHS mean HI treatments are 118% than mean total and so this factor is multiplied by the mean breakdown of appointments per patient outcome:  7.9
HI Treatment patients’ outcomes: Improvement7.7
HI Treatment patients’ outcomes: No change5.8
HI Treatment patients’ outcomes: Deterioration6.2
Drop-Outs% of patients who drop out after starting treatment (have had 1st appointment) – HI patient breakdown.43%
Average number of appointments attended before dropping outno data – Used Dr Julian study assumptions
% of drop out patients who have not recoveredno data – Used Dr Julian study assumptions
DNA/Cancellations per patientNumber of DNAs per HI patient treated10.98% – Based on all patients, not split by HI.
Number of DNAs per HI patient drop out10.98% – Based on all patients, not split by HI or whether patients have dropped out.

The majority of the above is data around pulling out data, just on HI patients as this is expected to differ from that of LI patients. Obtaining more up to date figures will help improve the validity of the Dr Julian study and strengthen the impact results.

Although the social and economic impact of a reduction in waiting times was not included as a metric in the cost-benefit model, it may be worth gathering more baseline figures on waiting times from high intensity treatment within the current IAPT service.  The data set available currently does not distinguish between low and high intensity waiting times.  Although waiting times may be used as a measure to show capacity and flow for a service, rather than as a measure for economic and social benefit.

Gathering data on uptake and accessability of DR Julian

Assumptions made within this analysis have been based on the data available, and many of the external studies used are not recent enough to build any strong conclusions around.  Because of this, large confidence bias ratings have been used to minimise the benefits realised in the modelling.  To assess the true impact of the Dr Julian platform it is important to grasp a better understanding on the potential population suited for the service. The three key areas for which more data is required are:

  • Connectivity: Currently 93%, based on the proportion of the whole of the UK who have a high-speed broadband connection. It is suggested that more data focused on the proportion of the population have suitable technology to access an online consultation platform such as Dr Julian.
  • Clinical Suitability: the figure of 99% has been taken from clinical judgement from health professionals who work for the Dr Julian service. No data has been collected outlining the key features that will affect the clinical suitability, which would enable a profile analysis to be conducted to assess the true suitability of online consultations for patients.
  • Patient Choice: The current measure of 52.8% is based on a study over 7 years old, which is why a grade 5 (-40%) optimism bias has been applied. It may be possible to obtain this data from the current Dr Julian pilot sites. Where patients are given the choice, providers should be able to record whether patients would prefer to use usual face to face means of treatment or are happy to be referred to the Dr Julian online platform.

 

2)    Business model

Further data may help provide an insight into the acceptance and demand of DR Julian and help assess the potential future demand for the service. Data collected from the current sites have helped shape the model from a patient outcomes perspective, however gathering data on revenues for the business itself, and how much commission is charged by each therapist, could help the way in which pricing and costs and structured in the future.

Analysing the potential future demand can also help account for the number of therapists are required so patients are still able to receive the same therapy and go through the same experience as the current Dr Julian service. Further analysis on the demographics of an IAPT service may also help profile patients from a cultural and ethnicity perspective, for which appropriate therapists can then be offered, to help break down any cultural barriers. This would be particularly important for areas with high volumes of non-English speaking patients who may struggle to use the current service.

It could be worth exploring patient groups and demographics who may be more susceptible to having a mental health issue following self-isolation, such as students or adults who live alone.  This may present an opportunity outside the NHS, for use within Universities, private care homes and private firms, who wish to offer welfare to their employees.

3)    Commercial approach

As a result of the Covid-19 outbreak, there has been an acceleration in the adoption of digital therapies to reduce face to face appointments across primary care, which is now being pushed across secondary care also.  The Dr Julian platform could offer a solution to this change, as well as assist in clearing any backlog of patients who are waiting for treatment. As Dr Julian is a platform, it could be suitable for existing NHS therapists, which could help manage the ever-increasing demand on the service by providing flexibility for IAPT providers to deliver care in multiple ways. The range of expertise of existing therapists on the Dr Julian platform could provide scope to offer tailored services such as maternity and perinatal support, ensuring some of the most vulnerable groups of the patient population have access to the treatment they may need.

In cases where the current IAPT service is already stretched and only offering a limited amount of appointments for patients, it may be that the Dr Julian service is implemented however capped at a certain amount of appointments to keep the overall cost of treatment down. A generic number of appointments an IAPT service would offer, tends to be capped at 8, and so given that this translates to the same amount of Dr Julian appointments, the average cost of patient treatment would be £60 * 8 = £480. Given the results of the data from the current four sites, the average number of appointments is currently 10.3, which means limiting treatment to 8 appointments may influence the expected Dr Julian recovery rates.

Patient demand and costing data can be used to assist in creating not just health economic models but also assist in business modelling. Data that has been collected during this study could support the analysis of financial forecasting and planning.

 

 

 

Conclusion / Concluding remarks

Across all four scenarios, the use of Dr Julian returns a positive ROI.  At prover level, the two scenarios returned strong positive returns, it is recommended that the impact Dr Julian could have on other providers, is considered on a case by case basis. This is mostly down to the difference in current service provisions and capacity across providers, which could influence the impact Dr Julian could have.  Services with poor patient outcomes could see mostly social gains, where those who are able to offer a relatively high number of appointments per treatment, could see more NHS Non-cash releasing savings, and cheaper overall treatment costs due to the fixed cost per appointment with Dr Julian.

The benefits of using the Dr Julian platform within an IAPT service in this analysis is seen at patient level, for which most of the benefit is social. Unfortunately, further analysis and research is required to assess the true extent to which Dr Julian could improve patients from a social perspective, using utility measures such as QALYs.  Obtaining data to where there are current gaps in the assumptions could strengthen the model and improve the validity of the results. A more detailed population health study could present greater insight to patient groups who are more vulnerable, for which Dr Julian could have a greater impact on, such as young adults and students. From a commercial perspective, there is value in exploring further outside the NHS for which the platform currently operates, however the situation following the COVID-19 pandemic presents an opportunity that cannot be ignored. The flexibility of the platform and range of expertise of therapists can be utilised to reach vulnerable patient groups and improve the capacity of an existing IAPT service. As a commercial offering, the platform could be used within a private setting, such as Universities, private firms and private care homes.

It is important to remember that this analysis only gives one answer to how Dr Julian can impact the NHS and so it important to consider further costs and benefits for future study. Qualitative research will contextualise findings in this analysis, as well as provide further answers to the question of ‘where could Dr Julian best fit in the NHS?’.

 

 

Appendix

Appendix A

Improving Access to Psychological Therapies (IAPT): Metadata for 2018-19 annual publication.

Extract from Table 1: Metadata for measures contained in psych-ther-ann-rep-csv-2018-19-main-csv:

CSV field nameDescription of measureTechnical constructionAdditional notes
CountImprovementCount of referrals that finished a course of treatment (end date in the year and having a minimum of two attended treatment appointments during the referral)  where the service user showed reliable improvement.Count of distinct IC_PATHWAY_ID
where
ENDDATE is within the period
and IC_COMPLETED_TREATMENT_FLAG = “Y”
and IC_RELIABLE_IMPROV_FLAG = ‘Y’
Equivalent of Improvement in IAPT Monthly Activity Data File CSV.

Published as measure “Reliably improved” in Table 7a of the 2016-17 annual IAPT report.

This measure is the numerator for reliable improvement rates (see measure PercentageImprovement).

CountNoReliableChangeCount of referrals that finished a course of treatment (end date in the year and a minimum of two attended treatment appointments during the referral) that show no reliable change.Count of distinct IC_PATHWAY_ID
where
ENDDATE is within the period
and IC_COMPLETED_TREATMENT_FLAG = “Y”
and IC_NO_CHANGE_FLAG = ‘Y’
Equivalent of NoReliableChange in IAPT Monthly Activity Data File CSV.

Published as measure “No reliable change” in Table 7a of the 2016-17 annual IAPT report.

CountDeteriorationCount of referrals that finished a course of treatment (end date in the year and a minimum of two attended treatment appointments during the referral) that show reliable deterioration.Count of distinct IC_PATHWAY_ID
where
ENDDATE is within the period
and IC_COMPLETED_TREATMENT_FLAG = “Y”
and IC_RELIABLE_DETER_FLAG = ‘Y’
Equivalent of Deterioration in IAPT Monthly Activity Data File CSV.

Published as measure “Reliably deteriorated” in Table 7a of the 2016-17 annual IAPT report.

CountRecoveryCount of referrals that finished a course of treatment (end date in the year and a minimum of two attended treatment appointments during the referral) where the service user has moved to recovery.Count of distinct IC_PATHWAY_ID
where
ENDDATE is within the period
and IC_COMPLETED_TREATMENT_FLAG = “Y”
and IC_RECOVERY_FLAG = ‘Y’
Equivalent of Recovered in IAPT Monthly Activity Data File CSV.

Published as measure “Moved to recovery” in Table 7a of the 2016-17 annual IAPT report.

This measure is the numerator for recovery rates (see measure PercentageRecovery).

For a full explanation of clinical caseness, see the ‘Guide to IAPT data and publications’ available from http://www.digital.nhs.uk/iaptreports.

CountReliableRecoveryCount of referrals that finished a course of treatment (end date in the year and a minimum of two attended treatment appointments during the referral) where the service user showed reliable recovery (has moved to recovery and showed reliable improvement).Count of distinct IC_PATHWAY_ID
where
ENDDATE is within the period
and IC_COMPLETED_TREATMENT_FLAG = “Y”
and IC_LAST_PHQ9 is not null and IC_LAST_ADSM_SCORE is not null
and (    (IC_FIRST_PHQ9 – IC_LAST_PHQ9 > reliable improvement threshold)
or (IC_FIRST_ADSM_SCORE – IC_LAST_ADSM_SCORE > reliable improvement threshold)
)
and (    (IC_FIRST_PHQ9 – IC_LAST_PHQ9 not < reliable deterioration threshold)
and (IC_FIRST_ADSM_SCORE – IC_LAST_ADSM_SCORE not < reliable deterioration threshold)
)
and (IC_FIRST_PHQ9 at caseness or IC_FIRST_ADSM_SCORE at caseness)
and IC_LAST_PHQ9 and IC_LAST_ADSM_SCORE not at caseness
Equivalent of ReliableRecovery in IAPT Monthly Activity Data File CSV.

Published as measure “Reliably recovered” in Table 7a of the 2016-17 annual IAPT report.

This measure is the numerator for reliable recovery rates (see measure PercentReliableRecovery).

For a full explanation of clinical caseness, see the ‘Guide to IAPT data and publications’ available from http://www.digital.nhs.uk/iaptreports.

Extract from Table 5: Metadata for derived data fields used in annual reporting:

NameField DescriptionCalculationFormat
IC_RECOVERY_FLAGFlag to indicate whether the referral has recovered.Flag as ‘Y’ to indicate where a referral has met the below criteria (and therefore has moved to recovery), otherwise NULL, where:
– The referral has ended in the annual reporting period and
– The referral has completed a course of treatment (IC_COMPLETED_TREATMENT_FLAG = ‘Y’) and
– The referral has paired PHQ9 measures (IC_LAST_PHQ9 is not NULL) and the referral has paired ADSM measures (IC_LAST_ADSM_SCORE is not NULL) and
– The first recorded PHQ9 score is greater than or equal to the caseness threshold (IC_FIRST_PHQ9 >=10) OR the first recorded ADSM is greater than or equal to the caseness threshold (based on IC_FIRST_ADSM) and
– The last recorded PHQ9 score is below the caseness threshold (IC_LAST_PHQ9 <10) AND the last recorded ADSM is below the caseness threshold (based on IC_LAST_ADSM)* Note that the caseness threshold for AMI has changed since derivations were built, affecting 4 records with a problem descriptor of Agoraphobia. See Methodological Change Note – Feb 2018 published at http://www.digital.nhs.uk/iaptreports for full details.
an1
IC_RELIABLE_DETER_FLAGFlag to indicate whether the referral has reliably deteriorated.

A finished referral with paired scores on both PHQ9 and ADSM measures can either reliably improve, reliably deteriorate or have no reliable change. These are mutually exclusive.

Flag as ‘Y’ to indicate where a referral has met the below criteria (and therefore has shown reliable deterioration), otherwise NULL, where:
– The referral has ended in the annual reporting period and
– The referral has completed a course of treatment (IC_COMPLETED_TREATMENT_FLAG = ‘Y’) and
– The referral has paired PHQ9 measures (IC_LAST_PHQ9 is not NULL) and the referral has paired ADSM measures (IC_LAST_ADSM_SCORE is not NULL) and
– The last PHQ9/Anxiety measure score is greater than the first score, and the magnitude of the change is greater than or equal to the reliable change threshold in either: both scores OR one score with the other showing no change or change less than the reliable change threshold
an1
IC_RELIABLE_IMPROV_FLAGFlag to indicate whether the referral has reliably improved.

A finished referral with paired scores on both PHQ9 and ADSM measures can either reliably improve, reliably deteriorate or have no reliable change. These are mutually exclusive.

Flag as ‘Y’ to indicate where a referral has met the below criteria (and therefore has shown reliable improvement), otherwise NULL, where:
– The referral has ended in the annual reporting period and
– The referral has completed a course of treatment (IC_COMPLETED_TREATMENT_FLAG = ‘Y’) and
– The referral has paired PHQ9 measures (IC_LAST_PHQ9 is not NULL) and the referral has paired ADSM measures (IC_LAST_ADSM_SCORE is not NULL) and
– The last PHQ9/Anxiety measure score is smaller than the first score, and the magnitude of the change is greater than or equal to the reliable change threshold in either: both scores OR one score with the other showing no change or change less than the reliable change threshold
an1

 

 

 

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Mental Health Matters. (2020). Stepped Care. Retrieved May 2020, from https://www.mhm.org.uk/Pages/FAQs/Category/stepped-care

Modini, M., Joyce, S., Mykletun, A., Christensen, H., Bryant, R. A., Mitchell, P. B., & Harvey, S. B. (2016). The mental health benefits of employment: Results of a systematic meta-review. Australasian Psychiatry, 24(4), 331-336.

NHS Digital. (2019). Psychological Therapies, Annual report on the use of IAPT services 2018-19. Retrieved 2020, from https://digital.nhs.uk/data-and-information/publications/statistical/psychological-therapies-annual-reports-on-the-use-of-iapt-services/annual-report-2018-19

NHS England. (2019). NHS Mental Health Implementation Plan 2019/20 – 2023/24. London: NHS England.

NHS England. (2019). The NHS Long Term Plan. London: NHS England.

NHS England. (2020). Frequently Asked Questions – STPs. Retrieved May 2020, from https://www.england.nhs.uk/integratedcare/stps/faqs/

NHSE & HEE. (2015). 2015 Adult IAPT Workforce Census Report. London: NHS England.

NHSE & NHSI. (2020). IAPT guide for delivering treatment remotely during the coronavirus pandemic. London: NHS England.

NHSE Mental Health Taskforce. (2016). Five Year Forward View for Mental Health. London: NHS England.

NICE. (2010). Depression: The NICE guideline on the treatment and management of depression in adults; NICE Guidance: CG90. London: The British Psychological Society and the Royal College of Psychiatrists.

ONS. (2019). Internet access – households and individuals, Great Britain: 2019. London: ONS.

ONS. (2020). Dataset: Estimates of the population for the UK, England and Wales, Scotland and Northern Ireland. London: ONS.

Perfect, D., Jackson, C., Pybis, J., & Hill, A. (2016). Choice of therapies in IAPT: An overview of the availability and client profile of step 3 therapies. Lutterworth: British Association for Counselling & Psychotherapy.

PSSRU. (2019). Unit Costs of Health and Social Care. Canterbury: Personal Social Services Research Unit, University of Kent. Retrieved from https://www.pssru.ac.uk/project-pages/unit-costs/unit-costs-2019/

Public Health England. (2020). PHE Fingertips database: Common Mental Health Disorders. London: Public Health England.

The National Collaborating Centre for Mental Health. (2020). The Improving Access to Psychological Therapies Manual. London: NHS England.

UK Government. (2020, March 23). Staying at home and away from others (social distancing). Retrieved May 2020, from https://www.gov.uk/government/publications/full-guidance-on-staying-at-home-and-away-from-others

 

 

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Quality Mental Healthcare AND Saving the NHS?

A recent independent evaluation found that the Dr Julian platform not only provides quality mental healthcare, but is also more cost effective than traditional mental healthcare services.

Throughout the COVID-19 pandemic, many families and organisations have donated money to NHS charities to help support our health service. It’s widely known that the NHS has struggled heroically to continue to provide free healthcare, whilst also facing harsh cuts to funding and increased demand for much needed services. It’s therefore, important to us to do our part in safeguarding the NHS, by ensuring our services are as cost effective as possible.

Kent Surrey Sussex AHSN recently reviewed the Dr Julian platform and found that per referral, the NHS would have a net financial benefit of £179. With IAPT series receiving just over 1.6 million adult referrals per year, that could result in a staggering £286.4 million saved for the NHS in both cash and non-cash savings,  when compared to traditional models of mental healthcare. The online nature of the platform means that we can provide these financial incentives, while also maintaining high quality therapy with the excellent patient satisfaction that we pride ourselves on.

Dr Julian not only provides a more economical approach to mental healthcare in terms of the NHS alone but the Kent Surrey Sussex AHSN survey also found that social benefits made up 60% of all benefits of using the platform. Social benefits describe the overall benefit to the public including, but not limited to, fewer sick days needed, employee related benefits and improved health and wellbeing. Use of the Dr Julian platform was found to save £275 per referral in social benefits. When multiplied by the average number of referrals per year (1.6 million), the net social benefit would be around £440 million.

These figures are phenomenal and we, as a team, are pleased to be able to share them. With demand for mental health services increasing every year, it’s vital that high quality, yet cost effective solutions are found to ensure services can continue to grow with the ever increasing referrals.

For more information on how to introduce Dr Julian in your NHS trust, get in touch using the contact section on our website.

 

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You CAN do it!

As we enter into another weekend in lockdown, we want to say a massive congratulations to everyone who has managed to stick to the social distancing rules. We know it’s not easy when you’re struggling with your mental health to remain inside and can make things seem even harder. But even when it feels like things are tough and you’re not managing, you’re still doing incredibly well! Please remember that you’re not alone in this and if you’re struggling, please don’t hesitate to reach out and ask for help. Over the next few weeks we’ll be posting some more factual content about how our behaviours and mindsets change in a crisis and how understanding how we’ll functioning differently, could help us cope better in such a stressful situation, so make sure to check our page to find out more about that.

 

We’d also like to extend a huge thank you to all our therapists who are continuing to work during the pandemic (while obeying lockdown rules). Without your dedication, Dr Julian would not be able to help nearly as many people. We are truly grateful to have such an amazing, hard-working team!

 

We hope clients and staff alike have a mindful weekend and feel free to send us some ways you’re smashing life in lockdown so we can share it with other members of the Dr Julian community!

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COVID-19: The final straw for mental health

COVID-19: The final straw for mental health

Introduction

In light of recent updates regarding the COVID-19 epidemic, the world has descended into a state of panic, with shops being stripped of their stock of toilet rolls, hand sanitiser and soap and public events being cancelled to prevent further spread of disease. However, while emphasis has been put on the detrimental physical effects of the illness, has society truly considered the psychological effects self-isolation, the current guidance for individuals who think they may have the illness, might have?  

Social Interaction 

Studies have shown that social isolation is associated with a higher incidence of depression, anxiety and self-harm (Bennett, 2018). Humans are naturally social beings whose biological instincts crave social interaction and relationships. In the same way that modern taste buds were developed through natural selection in order to favour individuals who showed preference to fats, sugars and salts, all of which are high in calories and therefore, beneficial for survival, the need for social interaction was developed over many millennia in response to the “safety in numbers” mentality. While in the modern era, social isolation is not as much of a threat to physical safety in the same way it would have been when these traits were developed, the innate biological alarm bells will still ring and induce feelings of loneliness and depression in the isolated individual. 

Inability to attend mental healthcare sessions while self-isolatingTraditional systems would, in situations like these, call for patients to seek referral from GPs before being offered face-to-face appointments for services like counselling, Cognitive Behavioural Therapy (CBT) and other talking therapies. However, with individuals being advised to stay at home, this is not feasible and is inevitably going to 

home, this is not feasible and is inevitably going to lead to individuals feeling stuck, with nowhere to go for help (NHS, 2020). 

To further escalate this issue, individuals with pre-existing mental health conditions, for which they receive support, who are self-isolating will be unable to attend their sessions, not only putting their progress at risk but also potentially risking losing their allocated slot and as a result being put to the back of the waiting lists. 

Mass panic

It has been evident over the preceding months, that public fear surrounding the COVID-19 virus has been on the rise. I general, humans react poorly to changes in the environment, with many people experiencing feelings of unease, anxiety and lack of safety as a result of even minor changes to the immediate environment (Shigemura, 2019). However, with daily media reporting on the current situation from a variety of sources of varying credibility, the public have descended into a state of unknown and mistrust. It is, therefore, understandable that individuals may be struggling with their mental health during a period of such uncertainty. 

The solution

While COVID-19 has caused large scale panic and chaos in a wide variety of areas, in terms of mental health, the solution is simple. Digital mental healthcare has been in development for years and has recently been introduced to a select few NHS trusts with great success. The Dr Julian team are proud to have been at the forefront of this movement to modernise mental healthcare.A review of over 200 randomised controlled trials of tele-therapy/psychiatry appointments delivered via video link showed equivalent clinical outcomes 

to in room therapy with better patient satisfaction (Chakrabarti, 2015). Since going live in December 2018, all patients referred for online therapy with Dr Julian have been offered online appointments within 24 hours of first logging on to the platform, with early results showing full recovery in 70% of patients and reliable improvement in 90% of patients. 

The Dr Julian platform reduces the rate of “Did not attend” (DNA) patients by cutting out the need for travel, which is particularly important in the context of COVID-19. Patients can attend sessions from the comfort of their own homes, without the risk of infecting/infection from others. They do not need to be concerned if usual travel routes are disrupted by public safety measures and they can continue to receive quality mental health care no matter what their infection status or their concern about contracting the virus. 

References 

Bennett, K., Gualtieri, T., Kazmierczyk, B., 2018. Undoing solitary urban design: A review of risk factors and mental health outcomes associated with living in social isolation. [online] Available at: https://pennstate.pure.elsevier.com/en/publications/undoing-solitary-urban-design-a-review-of-risk-factors-and-mental-2 [Accessed 13 Mar 2020] 

Chakrabarati, S., 2015. Usefulness of tele psychiatry: A critical evaluation of videoconferencing-based approaches. World Journal of Psychiatry 22:5(3), 286-304. [online] Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4582305/pdf/WJP-5-286.pdf [Accessed 13 Mar 2020]

NHS, 2020. Corona Virus (COVID-19) – Overview. NHS Online. [Online] Available at: https://www.nhs.uk/conditions/coronavirus-covid-19/ [Accessed 13 Mar 2020]

Shigemura J, Ursano RJ, Morganstein JC, Kurosawa M, Benedek DM. Public responses to the novel 2019 coronavirus (2019-nCoV) in Japan: mental health consequences and target populations. Psychiatry and Clinical Neurosciences. [online] Available at: https://onlinelibrary.wiley.com/doi/full/10.1111/pcn.12988

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What is a healthy Breakfast?

What is a healthy Breakfast?

You probably have heard that breakfast is the most important meal of the day that shouldn’t be skipped. While this might be true, it doesn’t mean that you should eat just about anything. You need to eat healthily. Generally, a healthy breakfast consists of carbohydrates, fiber, healthy fats, and proteins.

It’s not only meant to make you fuller but to also fuel your body for the day. Unhealthy breakfast, on the other hand, can only make you sluggish and contributes to weight issues and cardiovascular complications. So, choose to eat healthy at breakfast and start enjoying a healthy life. Here’s an elaborate guide to use.

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Foods Considered Healthy for Breakfast

In general, these foods are considered healthy for breakfast, thus highly recommended:

1. Eggs

Although eggs can be taken at any time of the day, they are more effective during breakfast. One study shows that eggs can regulate blood sugar, especially after a sharp rise. They also contain antioxidants that promote better cognitive functions, eye health, and heart fitness. The protein content is enough to make you fuller.

2. Homemade Smoothies

Homemade smoothies are a quick option for concentrated nutrients. They aid in weight loss since they make you fuller quicker and are effective in reducing cravings. The advantage of making smoothies at home is that you have absolute control of your nutrients intake.

3. Greek Yogurt

Studies show that Greek yogurt is packed with conjugated linoleic acid (CLA), known to aid in fat shedding and offers protection against breast cancer. It also makes you fuller quickly and improves your metabolism. There are also a few Greek yogurts that come with live cultures (probiotics), known to boost gut health.

4. Fresh Fruits

Fruits are generally delicious for breakfast. The interesting part is that they are also nutrient-packed, thus a healthy choice. Top options for breakfast include:

  • Citrus fruits – High in fiber and Vitamin C and are great in hydrating the body.
  • Berries – Rich in antioxidants and fibers, known to promote good heart health.
  • Grapefruits – Low in calories but high in fiber to aid in weight management.

5. Whole-Wheat Bread

You need carbohydrates for breakfast and there’s no budget-friendlier healthier option than whole-wheat bread. It’s packed with dietary fiber that’s essentially meant to make you fuller quickly. More importantly, fiber promotes better digestion and heart health.

6. Coffee

Coffee is a popular beverage at home and workplaces. It contains caffeine which promotes mental alertness, better mood, and brain vitality. Research shows that caffeine can also boost metabolism and may aid in weight loss. So, if you are the workout type, coffee can help a great deal. It fuels your muscles to help you train more effectively.

7. Oatmeal

If you are a cereal fan, then you should go for oatmeal. It originates from oat, which means it’s packed with enough fiber to help reduce cholesterol levels and antioxidants to fight hypertension and Heart Disease. Moreover, oatmeal makes you fuller quickly and so it’s great for promoting better weight management.

8. Herbal Tea

Herbal tea is one of the healthiest and most nutritious beverages on earth. It contains enough caffeine, known to improve mood and mental sharpness. It also has beneficial antioxidants like EGCG that’s good for your heart and brain. A cup of herbal tea a day may go a long way in fighting chronic conditions.

Foods Considered Unhealthy for Breakfast

The following foods are considered the worst for breakfast and thus should be avoided:

1 Toaster Pastries

Though toaster pastries are a common option for breakfast in most places, they are not healthy. Recent studies show that they are low in protein and this makes you hungrier during the day. As a result, you may end up eating more leading to fast weight gain.

2. Waffles

Waffles have one major issue – they are made from highly refined flour. This implies they are low in fiber and may contribute to weight gain as they are slow in making you fuller. Most waffle options are also topped with syrup, which means more sugar in your diet.

3. Pancakes

Pancakes have the same issues as waffles. They are made from refined flour and can be topped with artificial sweeteners. So, they don’t offer protection against obesity or diabetes. The secret is to just avoid them.

4. Fat-Free Flavored Yoghurt

Even though whole milk Greek yogurt is good for breakfast, fat-free flavored yogurt is not. For one, flavored fat-free yogurt comes sweetened with sugars and this is a risk for diabetes. The absence of fat also means that it takes time to get fuller when taking fat-free flavored yogurt.

5. Fruit Juices

Unlike fresh fruits, fruit juices are more of high-fructose syrup than natural fruit. So, they are a riskfor diabetes and obesity. This makes them a poor choice for breakfast. What’s more, they make you hungrier throughout the day.

Key Steps to a Healthy Breakfast

To stick to a healthy breakfast plan, you need these key steps:

  • Make it a routine – Your family needs to value the importance of a healthy breakfast. So, it should be a daily thing. Kids, especially, need to be trained to appreciate healthy breakfast when they are still young. It makes it easier for them to continue the routine when they grow up.
  • Consider budget options – There are so many healthier breakfast options you can go for. For example, cheese only cost $2-$3 per containers but is low in fat and high in protein. Cereals, on the other hand, only cost $4 per box and comes fortified with valuable minerals. You should also note that fresh fruits are cheaper than processed fruit juices.
  • Look for nutrients fortification – If you are shopping for your kids, look at food products that are fortified with essential nutrients like Vitamin D, iron, calcium, and omega 3. Such nutrients are great for boosting physical and mental development in kids.
  • Include wholegrain – Whole-grain meals are a perfect substitute for processed grains. They contain enough fiber to offer protection against cancer, diabetes, and heart issues.
  • Don’t forget to hydrate – You should not forget to take fluids in the morning. Including a healthy drink like coffee, herbal tea or water can help tremendously in preventing issues like headaches, grumpiness, overeating, and tiredness.

It’s undeniable that we need breakfast to start our day, so, skipping it is not an option. The only sensible option is to consider healthy choices. Besides, not all healthy breakfast food options are costly. Others are budget-friendly but very beneficial for your health. For this reason, you have no excuse to eat unhealthily during breakfast.

Thanks to Dr. Amanda Dee from Healing Hands Chiropractic for sending us this blog post. 

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World Mental Health Day 2019

World Mental Health Day 2019

Today is World Mental Health Day. On this day every year, people all over the world aim to highlight important issues surround mental health and attempt to find ways to combat them. While mental health is an issue that needs to be addressed EVERY day, this day unites all those who are fighting for all things mental health related and also shows those who are suffering that they are #notalone .

 

While mental illness is preventable to some degree, just like any illness it has an element of chance. ANYONE can struggle with their mental health. This is why we at Dr Julian believe that EVERYONE should have access to high quality, reliable mental health care if and when they need it. The Mental Health Foundation agrees, listing easy access to effective mental health care as one of the top protective factors against suicide. Too many lives are lost as a result of mental illness, and we want to do what we can to help care for those battling suicidal thoughts before it’s too late. 

 

At Dr Julian, we eliminate many of the factors that prevent access to mental health care such as travel time/cost, limited availability, long waiting lists and even allows you to receive therapy from YOUR safe place, making therapy more comfortable and talking through your problems easier.

 

“After a very difficult couple of years during which I suffered from depression and anxiety due to illness and bereavement, I finally found I could talk to someone.
Even the thought of walking into a doctor’s surgery and speaking to a receptionist would prevent me from seeking help. At last I could talk to someone from the security and comfort of my own home. No feeling uncomfortable and embarrassed in a cold, formal office – just someone I could talk to who understands and will help me work through my feelings.
I confess that I didn’t think that talking to a therapist could help – how wrong was I!
I am so glad that I found the Dr Julian app.”

-Patient Review

 

We’re passionate about what we do, because we can see how much our service helps people. That’s why we continue to find ways to improve and expand to reach and help as many people as we can. Having partnered with several NHS trusts this year, we’ve received excellent feedback and we hope to continue to collaborate with other trusts to help provide better mental healthcare to those who need it.

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