1. Introduction

2. Background

3. Methodology

4. Results

5. Overarching Themes

6. Discussion

7. Conclusions & Recommendations

8. References

9. Appendices

The key policy group for this work is the NHS Evidence task and Finish Group, part of the Primary care, community health services and personalised care team at NHS England. This group sought to commission a piece of work to address the gaps (Appendix 1). The commissioners of the review have requested the team look specifically at three key condition areas (diabetes, MSK and COPD), with a focus on literature in three areas:  strong evidence base, a key role in improving health, and impact to current priorities.

There have been previous reviews of the impact of Personalised Care, which have been used to inform our approach. Three key examples are:

• Personalised Care Group Evidence Review 2019/20. Undertaken by the NHS Data, Analysis and Intelligence Service (DAIS).

This paper reviewed the evidence for each of the components of PC in turn. For shared decision-making (SDM), there was substantial evidence that there is potential for improvements in developing a supportive health care system and enabling NHS workforce. The main impacts are on patient experience, but there is no new evidence of impacts on the system. For SSM, the evidence again was strong for improved health and wellbeing outcomes, with implications for system design. PCSPs were found to aid mental and physical recovery. Community-based approaches showed limited and mixed support.

• National Voices: Supporting self-management, summarising the evidence from systematic reviews [Linked]

The best support in the evidence was found for group-based self-management, disease-specific self-management education, self-monitoring, online education, and telehealth. The outcomes for these interventions focussed, again, on patient health and wellbeing rather than system impact.

• Nesta: At the Heart of Health: realising the value of people and communities [Linked]

Mental and physical health and well-being: Person- and community-centred approaches have been shown to increase people’s self-efficacy and confidence to manage their health and care, improve health outcomes and experience, reduce social isolation and loneliness, and build community capacity and resilience.

NHS sustainability: These approaches can impact how people use health and care services – and in particular can lead to reduced demand on services, particularly emergency admissions and A&E visits.

Wider social outcomes: Person- and community-centred approaches can lead to a wide range of social outcomes, from improving employment prospects and school attendance to increasing volunteering. They also can potentially contribute to reducing health inequalities for individuals and communities.

At this point, we can outline the aim and objectives for this project.

Given constraints on time and resource, there is no possibility to undertake a full systematic review. In addition, the literature in this area is broad and highly complex. For these reasons, we have chosen to focus on previously undertake systematic reviews and meta-analyses, rather than primary research (Smith et al, 2011).

This evidence synthesis takes the form of a Rapid Evidence Assessment (REA) (Watt et al, 2008). An REA is a tool in the systematic review methods family and is based on comprehensive electronic searches of appropriate databases, internet sources and follow-up of cited references. To complete REAs in a short timeframe, researchers make some concessions in comparison with a full systematic review. Exhaustive hand searching of journals and textbooks is not undertaken, and searching of ‘grey’ literature is necessarily curtailed. This shortened timeframe is essential for policymakers to meet deadlines, but increases the risk of publication bias. In this case, the review process has been carefully developed and planned to reduce biases and eliminate irrelevant studies.

In order to undertake such a review, we deployed a validated and tested tools to aid us. The chosen tool aids identification of the key components of the research question and is endorsed by the Cochrane Collaboration (Higgins & Green, 2013).

The tool is called PICO, and this breaks the research question into four elements: Patient or Population, Interventions, Comparison and Outcomes of interest. Using these elements, it is a far more straightforward task to build a search strategy and draft a strong research question.

Using PICO as the structure, we formed the research question below, as refined post engagement with key stakeholders:

‘In those with MSK, diabetes or respiratory disease (P), what has been the effect of “Personalised Care” [as defined by the universal model of personalised care] (I) on health utilisation, clinical outcomes, wellbeing outcomes, patient experience & safety (O)?’

In accordance with an accepted extension, we added S to PICO to make PICOS, where the S refers to the study design. This allowed us to further refine our search strategy (Methley et al, 2014).

In order to i) maintain the focus of the review and ii) reduce the number of studies to a manageable level, we used some specific exclusion criteria:

1. Papers more than ten years old. Personalised Care and its components were less likely to have been studied outside this timeframe, and research in many cases has been superseded as theory and evidence move on.
2. Papers which aren’t a systematic review. Due to time constraints, we believed this study design provided the most value for our research.
3. Findings lacking relevance to the research question. With many papers to review and a limited resource, it is important to focus on those studies directly relevant to the research question.
4. Studies lacking probative value. As an effectiveness review (with a clear mandate from the PCG), we excluded papers that did not tend to offer evidence as to the effectiveness of interventions, in whichever direction.
5. Papers of low quality. We excluded papers where the methodology or findings were weak or lacked evidential basis.

Our search strategy included alternative terminology and subject headings (/), combined with Boolean operators (AND, OR, ADJN), truncations (*), wildcards (?) and the Explode or Focus function (exp), allowing us to expand the scope of our search.

Terms were informed by the NHS Long Term Plan, discussions with subject matter experts and the synonym function within the databases. We refined our use of subject headings, truncations, and terminology as part of an iterative search process to obtain a manageable number of studies for review.

A full table of search terms is available at Appendix 1.

Searches were performed in Embase, Medline and The Cochrane Library. These three were chosen to maximise the coverage we would achieve, and would include journals focussing on social care, not just health.

This was supplemented by a review of relevant NICE Guidance.

This search strategy left us with the following literature:

We used the AMSTAR 2 (A MeaSurement Tool to Assess systematic Reviews; Shea et al, 2017) tool to rate the quality of the included systematic review (This tool provides a robust, systematic framework for the assessment of systematic reviews. The tool gives four levels of confidence rating:

• High: No or one non-critical weakness: the systematic review provides an accurate and comprehensive summary of the results of the available studies that address the question of interest
• Moderate: More than one non-critical weakness*: the systematic review has more than one weakness but no critical flaws. It may provide an accurate summary of the results of the available studies that were included in the review
• Low: One critical flaw with or without non-critical weaknesses: the review has a critical flaw and may not provide an accurate and comprehensive summary of the available studies that address the question of interest
• Critically low: More than one critical flaw with or without non-critical weaknesses: the review has more than one critical flaw and should not be relied on to provide an accurate and comprehensive summary of the available studies

The AMSTAR 2 assessment tool has sixteen items, however 7 of these are classed as critical to review quality:

• Protocol registered before commencement of the review (item 2)
• Adequacy of the literature search (item 4)
• Justification for excluding individual studies (item 7)
• Risk of bias from individual studies being included in the review (item 9)
• Appropriateness of meta-analytical methods (item 11)
• Consideration of risk of bias when interpreting the results of the review (item 13)
• Assessment of presence and likely impact of publication bias (item 15)

Each paper within the full text review was read and notes were taken to capture:

• Age
• Study Design
• Intervention
• Population
• Outcomes
• Participants (n)
• Studies (n)
• Results
• Mediating Factors
• Setting
• Key Takeaways
• Lessons for Implementation
• Appraisal
• AMSTAR Score

The process for development of Clinical Guidelines (NICE, 2022) is seen as the gold standard for systematic reviewing, and we therefore undertook an additional review of relevant Guidelines.

The following Guidelines were identified as relevant:

• Relevant Guidelines for diabetes:
  • T1 in adults
  • T2 management in adults
  • T2 prevention 
• Relevant Guidelines for MSK:
  • Osteoarthritis >16
  • Chronic pain >16 factors
  • Chronic pain >16 communication
  • Chronic pain >16 pain management programmes
  • Chronic pain >16 social interventions
• Relevant Guidelines for COPD:
  • COPD >16

The reference list of evidence included for each guideline was captured, and the abstracts searched for any relevant keywords. Papers were excluded if they were >10 years old, not UK based, not relevant to Personalised Care, or not probative for our research question.

In total, 110 papers included the keywords, and 14 passed the exclusion criteria. Two were already within our screened list, and seven more passed the final screening.

This review took a narrative effectiveness synthesis approach. As part of the synthesise process, key information in the studies were identified, extracted, and coded as either a clinical, wellbeing, patient experience, patient safety, or healthcare utilisation outcomes.

During the data extraction and synthesis process, we also assigned additional tags to key information within the literature, covering areas such as intervention details, the topic/theme discussed, and the levels of effect.

This would go on to inform our synthesise table, an excel based database which provided us with added functionality to analyse the impacts of personalised care. Once all data was extracted, we manipulated the data in the synthesis table to identify key themes, findings, and mediating factors within the body of research.

The AMSTAR 2 (A Measurement Tool to Assess Systematic Reviews) checklist is a tool designed to assess the methodological quality of systematic reviews. It consists of 16 items that evaluate various aspects of a systematic review, including the research questions and inclusion criteria, establishment of review methods, study selection and data extraction procedures, risk of bias assessment, data synthesis, and publication bias investigation, among others. The checklist is intended to help researchers and reviewers evaluate the quality of a systematic review and identify any potential methodological weaknesses or biases that may affect the validity and reliability of its findings.

As all our included studies are either Systematic Reviews or Meta-Analysis, this influenced our decision to use the AMSTAR2 Checklist as our quality assessment tool. To provide a comparison, we allocated a weighting scores to each of the AMSTAR 2 outcomes. Weighted scores were added and divided by the number of papers to give a mean.

If we were to compare the quality of studies, COPD has the highest scoring literature when assessing against the AMSTAR2 checklist. However, COPD also had the fewest number of studies included. The largest evidence base was around diabetes and personalised care interventions, providing us enough data to group the studies by broad intervention types.

Our research identified three broad categories of personalized diabetic care intervention: telehealth, education-based, and peer support.

Telehealth interventions include self-management through remote communication (Hanlon et al, 2017), web-based programs (Hadjiconstantinou et al, 2016), interactive digital interventions (McLean et al, 2016), digital tools for children with type 1 diabetes (Knox et al, 2019), smartphone technologies for type 2 diabetes (Wu et al, 2018), wearables for monitoring (Mattison et al, 2022), nurse-led telerehabilitation (Lee et al, 2022), and computer-based self-management (Pal et al, 2013).

Education-based interventions aim to enhance diabetes management with tailored support, such as via the teach-back method (Dinh et al, 2016), culturally tailored diabetes education (Nam et al, 2012; Ricci-Cabello et al, 2014), individual face-to-face patient education for diabetes management (Duke et al, 2019), and as part of self-management for patients with severe mental illness (Mcbain et al, 2016).

Peer support programs were studied by Li Qi et al. (2017) and Elnaggar et al. (2018), focusing on structured education and social media-based interventions. Empowerment-based approaches, such as those by Chen et al. (2021) and Mogueo et al. (2016) targeted lifestyle modifications, self-management support, psychological interventions, and equipping patients with diabetes management skills.

Telehealth: Mobile phone interventions reduced HbA1c values by a mean of 0.5% over 6 months (Hanlon et al., 2017). Web-based interventions decreased feelings of depression and distress (Hadjiconstantinou et al., 2016). Interactive digital interventions significantly reduced systolic blood pressure (SBP) with a weighted mean difference (WMD) of -3.74mmHg (95% CI: -2.19 to -2.58; McLean et al., 2016). Smartphone technologies significantly reduced HbA1c levels with a pooled weighted mean difference of -0.51% (95% CI: -0.71% to -0.30%; p < 0.001; Wu et al., 2018). Nurse-led telehealth interventions improved systolic blood pressure, quality of life, self-efficacy, and overall depression levels (Wong et al., 2022; Lee et al., 2022). A wearable insole intervention reduced diabetic foot ulcer occurrence by 86% at 18-month follow-up (Mattison et al., 2022).

Education-based: Patient education interventions were found to increase medical adherence by 20% and knowledge scores in diabetes (Negarandeh, 2011; Swavely 2013). Culturally tailored diabetes educational interventions improved glycaemic control with a pooled effect size (ES) of -0.29 (95% CI: -0.46 to -0.13; Nam et al.). Ricci-Cabelo et al. reported that 59% of studies observed significant improvements in HbA1c and 57% of studies observed improvements in fasting blood sugar levels.

Peer Support: Pharmacist-led medication therapy resulted in a 52.1% reduction in hospital admissions (Mogueo et al., 2020). Empowerment-based interventions, when compared to routine care, were associated with reduced glycated haemoglobin levels, increased diabetes empowerment scores, and increased diabetes knowledge scores (Chen et al., 2021) with peer support interventions resulting in a pooled mean difference of -0.57% for HbA1c.

(Note: Summary table detailing the more significant impacts of personalised care can be found in Appendix 4).

The following factors have been identified as impacting the effectiveness of their respective interventions.

Self-monitoring: In McLean, G. et al. (2016), it was found that self-monitoring increased the effectiveness of telehealth/decision support interventions (-4.02, 95% CI -2.93 to -5.12) compared to no self-monitoring (-0.88, 95% CI 0.05 to -1.80). In Pal, K. et al. (2013), it was also found that prompt self-monitoring of behavioural outcomes and providing feedback on performance were associated with more positive outcomes.

Duration of the intervention: In Pal, K. et al. (2013), they found that shorter studies with short-term follow-up appeared to have a larger effect size for HbA1c. In Hadjiconstantinou, M. et al. (2016), they discovered that the interventions that ran between 2 and 6 months showed significant improvement in distress or depression.

Age and duration of diagnosis: Wu, I.X. et al. (2018) found that a subgroup of T2DM patients (disease duration <8.5 years) appeared to benefit more from Smartphone Technology interventions. In Wu, I.X. et al. (2018), it was also found that Smartphone Technology interventions may provide a similar benefit to both younger and older T2DM patients.

Intervention delivery: In Hadjiconstantinou, M. et al. (2016), the majority of studies that provided professional support showed more promising results than those providing non-professional support. Whilst Pal, K. et al. (2013), found that interventions delivered via mobile phones performed better than interventions delivered at home.

Baseline HbA1c levels: Qi, L. et al. (2015) found that the reduction in HbA1c levels was greater among patients with a baseline HbA1c level ≥ 8.5% and between 7.5 ~ 8.5%, compared to patients with a baseline HbA1c level < 7.5%. This suggests that peer support interventions may be more effective for patients with poor glycaemic control than for those with better glycaemic control.

Intervention structure: The same study reported that patients provided with individual intervention responded by a greater reduction in HbA1c level than patients provided with group session education or a combination of group and individual education. This suggests that individual peer support interventions may be more effective in reducing HbA1c levels.

Frequency of contact: Qi, L. et al. (2015) found that programs with a high or moderate frequency of contact reported a significant reduction in HbA1c levels compared with usual care, while programs with a low frequency of contact showed no significant reduction in HbA1c levels. This suggests that the frequency of contact is a key feature of the effectiveness of peer support interventions, and that only moderate or intensive interventions should be implemented.

Mode of delivery: There was no significant difference between the impact of community health workers or peer coaches as peer supporters. This suggests that both modes of delivery could be potential peer supporters based on different settings and populations.

Health Literacy: People with low health literacy generally achieved greater disease-specific knowledge gains than those with high health literacy, indicating that the level of health literacy is a mediating factor in patient education effectiveness (Dinh et al., 2016).

Setting: Clinic or hospital-based diabetes educational centre settings were found to be more effective in reducing HbA1c levels compared to community-based settings, though this difference was not statistically significant (Nam, S. et al. 2012).

Baseline HbA1c level: This was found to affect the effectiveness of culturally tailored interventions, with the intervention being more effective for those with baseline HbA1c level equal to or less than 8.5% (Nam, S. et al. 2012).

Face-to-face patient education: This was found to be more effective in improving glycaemic control than telecommunication-based formats, although telecommunication programs have the potential to improve attrition rates (Ricci-Cabello et al, 2014).

Peer providers: Their involvement produced better results, as they provide a more credible source of information, empower those involved, and reinforce learning through ongoing contact (Ricci-Cabello et al, 2014).

Intervention Complexity: Less complex interventions led to greater improvements in glycaemic control (Ricci-Cabello et al, 2014).

Backed by theory: Theory-based interventions were found to produce more effects than non-theory-based interventions (Chen, Y. et al. 2021).

• Multi-study papers
• Telehealth (including = positive impacts on patient self-management expertise and physical health/ activity levels as well as level of pain
• SSM interventions broadly positive outcomes

Positive outcomes across two areas: pain (clinical) and wellbeing (such as physical/ mental health, satisfaction)

• Little support for assertions with regard to healthcare utilisation
• More research is needed, particularly of high quality
• Booster sessions generally showed no effect

Shared decision-making: Coylewright et al. (2014) focused on using Decision Aids to facilitate shared decision-making between clinicians and patients.

Self-management interventions: Carnes et al. (2012), Donnelly et al. (2020), and Kroon et al. (2014) examined various self-management interventions for chronic musculoskeletal pain, rheumatoid arthritis, and osteoarthritis, respectively.

eHealth and mobile health technologies: Butler et al. (2021) and Mattison et al. (2022) assessed the effectiveness of eHealth, mobile health, and wearable interventions in supporting specific patient populations.

Multidisciplinary approaches and structured programs: Buzasi et al. (2021) and Safari et al. (2019) investigated the use of single modality and multidisciplinary approaches for pain management, combining various treatments and theories.

In Coylewright, M (2014), decision support tools were found to improve patients’ general knowledge, as participants who received care with decision aids had greater gains in knowledge compared to those who received usual care (62% vs 45%, p<0.0001). Patients who used decision aids also had a better understanding of their personalized risk (50% vs 20%, p<0.0001), and experienced lower decisional conflict (13 points vs 18 points). Clinicians also encouraged patient empowerment significantly more often when using decision support (39 vs 21).

Telehealth interventions were also found to be effective in reducing school absenteeism (43% to 14%) in one study (Butler et al, 2021), and in reducing pain intensity (1.73-point reduction on 1-10 scale) in another study (Butler et al, 2021). A telehealth intervention using FitBit (Mattison, G.,et al, 2022) showed an increase of 183.1 min/week in walking time.

Self-management education programs, including booster sessions (Buzasi, E. et al, 2021) were found to be effective in reducing pain in patients with chronic pain conditions. In (Buzasi, E. et al, 2021), there was a significant reduction in pain catastrophizing (SMD 20.42; 95% CI), while in Kroon, F et al (2019), the SMD between groups was ‐0.26 (95% CI ‐0.44 to ‐0.09), with a mean reduction of 0.8 points on the VAS Scale.

Finally, a digital-based structured self-management program was found to be effective in reducing pain (5.7% reduction) and improving physical function (5.07% improvement) in Safari, R.et al 2019. The same study showed that digital-based structured self-management programs were more effective than health education conditions (SMD 0.26; 95% CI).

(Note: Summary table detailing the more significant impacts of personalised care can be found in Appendix 4).

Reporting frequency: A systematic review and meta-analysis of eHealth and mobile health interventions supporting children and young people living with Juvenile Idiopathic Arthritis found that adherence rates were higher when pain was reported once a week compared with when pain was reported once a day or twice a day or as and when pain was experienced. However, there were no significant differences in pain interference scores because of reporting frequency, and the children and young people reported that they preferred once a day or as and when reporting schedules (Butler et al., 2021).

Seasonal fluctuations: A study found a seasonal intervention effect when comparing a winter intervention group to a summer intervention group for physical activity promotion. For the winter group, a 24-minute reduction in rest was recorded using an accelerometer, and this result was significant (Butler et al., 2021).

Intervention components: There may be no one-size-fits-all intervention, or there may be a need for a combination of interventions. For example, a pain self-management application combined two interventions (real-time pain monitoring and self-management) and then compared this combination to standalone pain monitoring. This combination demonstrated a greater decrease in pain intensity scores, and although this finding was not significant, the inclusion of self-management programs could be clinically beneficial. Our research found evidence in the benefit of having a psychological component across most outcomes in the short and medium-term. In the short term, self-management was favoured for most outcomes irrespective of the presence of a pain education component, but in the medium-term, there was more evidence in favour (Carnes et al., 2012).

Setting: A review of effective delivery styles and content for self-management interventions for chronic musculoskeletal pain found that there was evidence of benefit for courses held in medical settings for all outcomes in the short term. Courses in community settings showed statistically significant beneficial effects for self-efficacy across all time points. Occupational settings were too rarely reported to make inferences about their effect (Carnes et al., 2012).

Mode of delivery: The number of disciplines involved in intervention delivery and the method of delivery (remote vs face to face) influenced the effect of self-management booster sessions. The different intensities of the main intervention did not influence physical function and pain-related disability, and treatment type and method of delivery of boosters did not affect physical function.

Long et al., 2019 examined the impact of an intervention on hospital admissions, reporting a significant reduction in hospital admissions (OR = 0.46; 95% CI: 0.31, 0.69; p = 0.0001), based on five studies. Song et al., 2021 evaluated a blended self-management intervention and found a reduction in exacerbation frequency with a relative risk of 0.38 (95% CI 0.26-0.56), as well as a significant reduction in BMI with a mean difference of 0.81 (95% CI 0.25-1.34) and an improvement in quality of life with a standardized mean difference of 0.81.

Collins et al (2012) investigated the impact of nutritional support and found significantly greater increases in mean total protein and energy intakes, with a mean difference of 1.94 ± 0.26 kg (p<0.001). Schrijver, J et al (2022) evaluated self-management interventions and found a 2.86-point lower score on the St. George’s Respiratory Questionnaire adjusted total score, indicating significantly better health-related quality of life (95% CI 4.87 lower vs 0.85 lower). The same study also reported a lower risk of emergency department visits with a hazard ratio of -0.52 (95% CI), as well as an improvement in exercise capability with a mean difference of 45.14 meters in walking (95% CI 9.16 to 81.13). Additionally, self-management interventions that included action plans and smoking cessation programs were found to contribute to significant improvements in health-related quality of life, with a mean difference from usual care of -2.69 points (95% CI -4.49 to -0.90).

Lenferink et al., (2017) evaluated the impact of self-management education on activity levels, reporting significant improvements in exercise capacity with a pooled mean difference of 45.14 meters in the six-minute walking test (95% CI). Additionally, self-management interventions, including exacerbation action plans with smoking cessation programs, were found to significantly improve health-related quality of life, with a mean difference from usual care of -2.69 points (95% CI -4.49 to -0.90).

In summary, these studies provide evidence supporting the effectiveness of various interventions, including decision support, telehealth, self-management education, and nutritional support, for improving outcomes such as hospital admissions, exacerbation frequency, BMI, quality of life, and exercise capacity.

Frequency and difficulty: For health coaching interventions (Long et al., 2019), the frequency and number of coaching sessions, the difficulty of goals, and baseline HRQoL scores can impact effectiveness.

Intervention components: For self-management interventions (Schrijver, J, 2022), factors such as home-based exercise, COPD action plans, smoking cessation, and coping with breathlessness can impact effectiveness. Smoking cessation and digital components might not significantly affect HRQoL, while medium-term interventions show better results in HRQoL improvement.

Mode of delivery: Smart technology interventions proved more effective than face-to-face or written support, improving symptoms and impacts (McCabe, C 2017)). The frequency of assessment also plays a role in the intervention’s effectiveness, with significant HRQoL improvements observed at four weeks, four months, and six months.

Given the breadth of Personalised Care and the populations that studies have focussed on, we undertook a brief thematic extraction across all findings.

1. Quality of the reviews assessed was variable
2. Most studies focus on SSM, across all conditions, with little on the other areas.
3. impact of PC interventions can be see in 5 areas:
   1. patient expertise in managing their condition. This includes understanding of their condition, greater efficacy in management, and empowerment
   1. Clinical. Measured using standard clinical outcomes. Evidence varies in strength.
   1. General health, both physical and mental. The review of NICE Guidelines strongly supported this theme.
   1. System. Clear evidence that PC interventions reduce hospital admissions, at least for diabetes and COPD.
   1. Quality of life. Factors beyond health that improve the patient’s quality of life

The NHS has made a commitment to Personalised Care. There is some evidence to support the increase in deployment of PC interventions, and this review adds to that, for these three specific condition areas.

The original brief for this review asked us to explore, for PC in diabetes, MSK and COPD has:

1. A strong evidence base

The evidence in each condition area varies. For MSK, our research returned no evidence with regard to system impact via lower admissions, healthcare usage and the like. The impacts are secondary, via lower levels of pain and higher levels of patient expertise in managing their condition. We might speculate and extrapolate that this will lead ultimately to a reduction in system pressures, but this will manifest beyond the timescale of most studies. For diabetes, there is some of evidence to support an improvement in clinical outcomes, and indeed for a reduction in system pressures mostly through lower levels of hospitalisation. The former can be implied to impact system utilisation indirectly, through less interaction with primary care, although this has not been explicitly examined. For COPD, the evidence for impact on healthcare utilisation is well founded.

• A key role in improving health and care

What is clear from this review is that PC interventions impact patients’ lives in many positive ways. They can help them to better understand and manage their condition, leading to a profound improvement in their lived experience. It can help them to feel more supported and empowered, better able to cope with what might be a debilitating long-term condition. It can help them to feel more in control of their condition, and therefore more able to participate in society and the economy. These indirect impacts are laudable, but the current financial pressures require initiatives to justify their existence over and above care as usual, through measurable system impacts. Only two of the condition areas show evidence for this.

• Aligned to a high priority area for the NHS

Alignment of research evidence with areas of high priority for policy-makers can be uneven. Research will take place where funding is available, and funding will follow research priorities, which may or may not align with policy priorities. In this case, there is good evidence that SSM and to a lesser extent SDM and SP are effective in impacting healthcare utilisation. There is an argument for a deep dive into the variation in activity around SSM and in fact all PC activity to reduce variation and level up areas with a deficit. In addition, there may be value in exploring the relationship between deprivation and other wider determinants of health, in areas where PC may have a higher impact, due to the higher prevalence of social deficits.

• Can demonstrate high impact to current priorities

The primary theme of this set of literature is that SSM works to reduce hospital admissions in diabetes and COPD. The evidence is not there for MSK, partly perhaps because this is a condition area that is less likely to lead to hospitalisation when exacerbated, and partly because the studies have not been carried out. More broadly, the quality literature has focussed almost exclusively on SSM, with very little looking at the other PC interventions. More research is required to explore the impact of PCSPs and PHBs, as well as social prescribing. Another notable gap in the literature is around healthcare utilisation. Most of the evidence is focussed on secondary care. While this is laudable, there may be significant undetected impacts in primary care, which is where most management of long-term conditions takes place. If PC can prevent a GP visit, then the impact across the system could be substantial.

In many cases, the implementation of PC interventions will be iterative, over the course of a patient’s illness, cumulative, or possible in series. Current research does not capture the impact for these patients.

Increasingly, patients are presenting to care with more than one condition or illness, often these being long-term conditions. As our population ages, this issue will become more challenging. This review, necessarily, focussed on the three condition areas as described, diabetes, MSK and COPD, and therefore did not specifically seek research that used comorbid patients as the study populations.

None of the research considered within this review looked at the impact of PC interventions in Primary Care. The evidence shows, via a variety of effects, that there is clear impact on patients’ health and wellbeing, both physical and mental. Better managed health would arguably lead to less interaction with primary care, where the majority of management for patients with long-term conditions takes place. Given the pressures currently affecting primary care, this is an important gap in our knowledge.

The literature has yet to consider the impact on staff of personalised care interventions. There is an argument that personalised care may bring benefit to staff working with those patients; in the current environment of staffing pressures in the NHS, anything that may improve morale should be explored (Dave Pearson’s story, page 36, https://www.england.nhs.uk/wp-content/uploads/2019/01/universal-personalised-care.pdf)

Finally, this review included only the three prescribed condition areas. Further reviews could look at other long-term conditions.

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Personalised Care is becoming ‘business as usual’ in the NHS, and is part of the NHS Long Term Plan – and indeed is more vital than ever in the context of the recovery of elective care (and indeed other demands) in the NHS in the wake of Covid-19. 

In this context, the Personalised Care Group are seeking to commission a literature review of personalised care interventions in key, high impact, clinical specialist areas in order to give confidence to the system of the impact and efficacy of these interventions and encourage their further uptake.  

Establish 3 key high impact areas where felt personalised care has

• A strong evidence base
• A key role in improving health and care
• Aligned to a high priority area for the NHS
• Can demonstrate high impact to current priorities

Conduct and produce literature review of the evidence in these key areas.

Develop resource/products for each high impact area that can be used to influence decision making where personalised care makes its greatest impact.            

These areas are to be Diabetes, MSK and acute respiratory disease (COPD).

To de defined further throughout commission. Key overarching area includes:

What is the evidence based impact of personalised care in MSK, diabetes and respiratory care? Particularly looking at evidence aligned to

• Volume
• Needs
• Inequity
• And where personalised care interventions are mostly taking place

Personalised care is defined in the criteria below using the NHS England universal model for personalised care.

Our work hasn’t included evidence that isn’t in English (check with them)

Exclude anything over 10 years old – but please highlight if most evidence is over 10 years old which would leave us with too little to analyse

• Electronic databases
• Medline
• Embase
• Cochrane

We have defined clinical outcomes for personalised care interventions on diabetes to include:

• Glycaemic control, as measured by changes in HbA1c levels
• Blood pressure
• Lipid profile
• Body weight
• Complications of diabetes, such as neuropathy, nephropathy, retinopathy, and cardiovascular disease.

We have defined wellbeing outcomes for personalised care interventions on diabetes to include:

• Quality of life: This includes measures of physical, emotional, and social wellbeing.
• Mental health: This includes measures of anxiety, depression, and stress.
• Self-efficacy: This includes the patient’s belief in their ability to manage their diabetes and the extent to which they feel in control of their condition.
• Health-related behaviours: This includes measures of healthy eating, physical activity, and other lifestyle changes that can help manage diabetes.

we have defined healthcare utilisations for personalised care interventions on diabetes to include:

• Hospitalization rates: The number of hospitalizations due to diabetes-related complications, such as cardiovascular disease, kidney disease, or foot ulcers.
• Emergency department visits: The number of emergency department visits due to diabetes-related complications, such as hypoglycemia or hyperglycemia.
• Outpatient visits: The number of outpatient visits for diabetes-related care, such as check-ups, diabetes education, or medication management.
• Medication adherence: The proportion of patients who take their diabetes medication as prescribed by their healthcare provider.
• Cost-savings: Refers to reducing expenses and improving efficiency without sacrificing the quality of care.

We have defined patient experience outcomes to include:

• Empowerment: This refers to a patient’s sense of control over their diabetes management and can be assessed through surveys or questionnaires.
• Patient knowledge:
• Perceived support: This refers to a patient’s perception of the support they receive from healthcare providers, family members, and peers, and can be assessed through surveys or questionnaires.
• Patient satisfaction: This includes measures of patient satisfaction with the care they receive and their overall experience of the intervention.

We have defined patient safety for personalised care interventions on diabetes to include:

• Hypoglycaemia: This refers to abnormally low blood sugar levels and can be a serious and potentially life-threatening complication of diabetes management. The frequency and severity of hypoglycaemic events may be monitored.
• Diabetic ketoacidosis (DKA): This is a serious complication that can occur when there is a shortage of insulin in the body. The frequency and severity of DKA episodes may be monitored.
• Adverse drug reactions: Diabetes management often involves the use of medications such as insulin and oral hypoglycaemic agents, which can have side effects. Adverse drug reactions may be monitored to ensure patient safety.
• Foot ulcers: Diabetes can cause nerve damage and poor circulation, which can increase the risk of foot ulcers. Monitoring the frequency and severity of foot ulcers can help to prevent complications.

The tables below lists the most impactful personalised care outcomes across the research studied. If the numerical data was unavailable within a systematic review, we searched the primary paper they cited for this information. In such cases, the primary authors have been referenced in the ‘Outcome’ column. More caution should be given to these outcomes/impacts, as their results may not have been validated as part of a wider meta-analysis.