Leadership for integrated care: A case study

Purpose. Integration of health services involves multiple interdependent leaders acting at several levels of their organisation and across organisations. This paper explores the complexities of leadership in an integrated care project and aims to understand what leadership arrangements are needed to enable service transformation. Design/Methodology/Approach. This case study analysed system and organisational leadership on a project aiming to integrate primary and specialist care. To explore the former, the national policy documents and guidelines were reviewed. To explore the latter, the official documents from the transformation team meetings and interview data from seventeen healthcare professionals and commissioners were analysed using thematic analysis with the coding framework derived from the comprehensive and multilevel framework for change. Findings. Although integration was supported in the narratives of the system and organisational leaders, there were multiple challenges: 1) insufficient support by the system level leadership for the local leadership, 2) insufficient organisational support for (clinical) leadership within the transformation team, and 3) insufficient leadership within the transformation team due to disruptions caused by personnel changes, roles ambiguity, conflicting priorities, and insufficient resources. Practical implications. This study provides insights into the interdependencies of leadership across multiple levels and proposes steps to maximise the success of complex transformational projects. Value. This study’s practical findings are useful for those involved in the bottom-up integrated projects, especially the transformation teams’ members. The case study highlights the need for a toolkit enabling local leaders to operate effectively within the system and organisational leadership contexts.

Local enhanced services: Is a lack of outcome data affecting diabetes care?

The Local Enhanced Service (LES) is a popular commissioning tool to boost the quality of health care. Primary care providers are invited to participate in the LES and offered financial incentives. There are many LESs for Diabetes across the UK, creating opportunities for comparing their variation in content and determining best practice. However, little is known about the individual LESs and their outcomes. Calling on mostly unpublished Clinical Commissioning Groups’ documents, this paper discusses the current practice in Diabetes LESs and highlights a need for their systematic evaluation

Population health management in diabetes care: combining clinical audit, risk stratification, and multidisciplinary virtual clinics in a community setting to improve diabetes care in a geographically defined population. An integrated diabetes care pilot in the North East Locality, Oxfordshire, UK

Background: Disparities in diabetes care are prevalent, with significant inequalities observed in access to, and outcomes of, healthcare. A population health approach offers a solution to improve the quality of care for all with systematic ways of assessing whole population requirements and treating and monitoring sub-groups in need of additional attention. Description of the care practice: Collaborative working between primary, secondary and community care was introduced in seven primary care practices in one locality in England, UK, caring for 3560 patients with diabetes and sharing the same community and secondary specialist diabetes care providers. Three elements of the intervention included 1) clinical audit, 2) risk stratification, and 3) the multi-disciplinary virtual clinics in the community. Methods: This paper evaluates the acceptability, feasibility and short-term impact on primary care of implementing a population approach intervention using direct observations of the clinics and surveys of participating clinicians. Results and discussion: Eighteen virtual clinics across seven teams took place over six months between March and July 2017 with organisation, resources, policies, education and approximately 150 individuals discussed. The feedback from primary care was positive with growing knowledge and confidence managing people with complex diabetes in primary care. Conclusion: Taking a population health approach helped to identify groups of people in need of additional diabetes care and deliver a collaborative health intervention across traditional organisational boundaries

Glucocorticoid‐induced hyperglycaemia in hospitalised adults: A matched cohort study (2013–2023)

Aims: To compare the risk of new‐onset hyperglycaemia between inpatients treated versus non‐treated with systemic glucocorticoids and identify factors associated with glucocorticoid‐induced hyperglycaemia (GIH). Materials and methods: We conducted a cohort study using electronic healthcare records of adults admitted to the Oxford University Hospitals between 2013 and 2023. We excluded patients with diabetes or prescribed systemic glucocorticoids before admission. The outcome was new‐onset hyperglycaemia defined as a new glucose‐lowering therapy, coded diagnosis of diabetes or random blood glucose ≥11.1 mmol/L. We used Poisson regression to estimate the incidence rate ratio (IRR) of new‐onset hyperglycaemia during periods of exposure versus non‐exposure to systemic glucocorticoids, adjusting for confounders. We used Poisson regression models to identify potential risk factors for GIH. Results: Of 451 606 included patients, 17 258 (3.8%) received systemic glucocorticoids during admission. Totally 316 (1.8%) of patients exposed to systemic glucocorticoids developed new‐onset hyperglycaemia versus 3430 (0.8%) non‐exposed to systemic glucocorticoids. The multivariable‐adjusted IRR (95% CI) for new‐onset hyperglycaemia among exposed versus non‐exposed was 2.15 (1.18–3.12). Covariates associated with GIH were: age (relative risk, 95% CI) 1.02 (1.01–1.03) per year, ethnicity (1.72 [1.04–2.86] Asian vs. White, 1.26 [1.05–2.70] other vs. White), weight 1.01 (1.01–1.03) per kg, indication (2.15 [1.21–3.52] autoimmune/inflammatory/infection vs. malignant, 2.11 [1.18–4.20] other vs. malignant) and cumulative glucocorticoid dose (1.23 [1.04–1.42], for 51–205 mg vs. >0–50 mg and 2.53 [1.89–3.40] for > 205 mg vs. >0–50 mg). Conclusions: Treatment with systemic glucocorticoids versus no glucocorticoid treatment during hospitalisation more than doubles the risk of new‐onset hyperglycaemia. Higher age, weight, cumulative glucocorticoid dose, non‐White ethnicity and autoimmune/inflammatory conditions were independently associated with a higher risk of GIH

The potential for utilising in-hospital glucose measurements to detect individuals at high risk of previously undiagnosed diabetes: retrospective cohort study

Background Many people with undiagnosed diabetes have hyperglycaemia when admitted to hospital. Inpatient hyperglycaemia can be an indication of diabetes mellitus but can also indicate a stress response. This study reports the extent to which an in-hospital maximum observed random glucose measurement is an indicator of the need for in-hospital (or subsequent) HbA1c measurement to look for undiagnosed diabetes. Methods Blood glucose, HbA1c, age and sex were collected for all adults following admission to a UK NHS trust hospital from 1 January 2019 to 31 December 2020. We restricted the analysis to those participants who were registered with a GP practice that uses the trust laboratory and who had at least some tests requested by those practices since 2008. We stratified individuals according to their maximum in-hospital glucose measurement and report the number of these with HbA1c measurement ≥48 mmol/mol (6.5%) prior to the index admission, and during and after admission. We calculated an estimated proportion of individuals in each blood glucose stratum without a follow-up HbA1c who could have undiagnosed diabetes. Results In toal, 764,241 glucose measurements were recorded for 81,763 individuals who were admitted to the Oxford University Hospitals Trust. The median (Q1, Q3) age was 70 (56, 81) years, and 53% were males. Of the population, 70.7% of individuals declared themselves to be of White ethnicity, 3.1% of Asian background, and 1.1% of Black background, with 23.1% unstated. Of those individuals, 22,375 (27.4%) had no previous HbA1c measurement recorded. A total of 1689 individuals had a diabetes-range HbA1c during or after their hospital admission (2.5%) while we estimate an additional 1496 (2.2%) may have undiagnosed diabetes, with the greatest proportion of these having an in-hospital glucose of ≥15 mmol/L. We estimate that the number needed to detect a possible new case of diabetes falls from 16 (in-hospital glucose 8 mmol/L to <9 mmol/L) to 4 (14 mmol/L to <15 mmol/L). Conclusion The number of people who need to be tested to identify an individual who may have diabetes decreases as a testing threshold based on maximum in-hospital glucose concentration increases. Among those with hyperglycaemia and no previous HbA1c measurement in the diabetes range, there appears to be a lack of subsequent HbA1c measurement. This work identifies the potential for integrating the testing and follow-up of people, with apparently unrecognised hospital hyperglycaemia across primary and secondary care

Increase in hypoglycaemia and hyperglycaemia in people with diabetes admitted to hospital during COVID-19 pandemic

BACKGROUND: We used detailed information on patients with diabetes admitted to hospital to determine differences in clinical outcomes before and during the COVID-19 pandemic in the UK. METHODS: The study used electronic patient record data from Imperial College Healthcare NHS Trust. Hospital admission data for patients coded for diabetes was analysed over three time periods: pre-pandemic (31st January 2019-31st January 2020), Wave 1 (1st February 2020-30th June 2020), and Wave 2 (1st September 2020-30th April 2021). We compared clinical outcomes including glycaemia and length of stay. RESULTS: We analysed data obtained from 12,878, 4008 and 7189 hospital admissions during the three pre-specified time periods. The incidence of Level 1 and Level 2 hypoglycaemia was significantly higher during Waves 1 and 2 compared to the pre-pandemic period (25 % and 25.1 % vs. 22.9 % for Level 1 and 11.7 % and 11.5 % vs. 10.3 % for Level 2). The incidence of hyperglycaemia was also significantly higher during the two waves. The median hospital length of stay increased significantly (4.1[1.6, 9.8] and 4.0[1.4, 9.4] vs. 3.5[1.2, 9.2] days). CONCLUSIONS: During the COVID-19 pandemic in the UK, hospital in-patients with diabetes had a greater number of hypoglycaemic/hyperglycaemic episodes and an increased length of stay when compared to the pre-pandemic period. This highlights the necessity for a focus on improved diabetes care during further significant disruptions to healthcare systems and ensuring minimisation of the impact on in-patient diabetes services. SUMMARY: Diabetes is associated with poorer outcomes from COVID-19. However the glycaemic control of inpatients before and during the COVID-19 pandemic is unknown. We found the incidence of hypoglycaemia and hyperglycaemia was significantly higher during the pandemic highlighting the necessity for a focus on improved diabetes care during further pandemics

Analysis of continuous glucose tracking data in people with type 1 diabetes after COVID-19 vaccination reveals unexpected link between immune and metabolic response, augmented by adjunctive oral medication

Introduction: The COVID-19 vaccination programme is under way worldwide. Anecdotal evidence is increasing that some people with type 1 diabetes mellitus (T1DM) experience temporary instability of blood glucose (BG) levels post-vaccination which normally settles within 2-3 days. We report an analysis of BG profiles of 20 individuals before/after vaccination. Methods: We examined the BG profile of 20 consecutive adults (18 years of age or more) with T1DM using the FreeStyle Libre flash glucose monitor in the period immediately before and after COVID-19 vaccination. The primary outcome measure was percentage (%) BG readings in the designated target range 3.9-10 mmmol/L as reported on the LibreView portal for 7 days prior to the vaccination (week −1) and the 7 days after the vaccination (week +1). Results: There was a significant decrease in the %BG on target following the COVID-vaccination for the 7 days following vaccination (mean 45.2% ± SE 4.2%) vs pre-COVID-19 vaccination (mean 52.6% ± SE 4.5%). This was mirrored by an increase in the proportion of readings in other BG categories 10.1%-13.9%/≥14%. There was no significant change in BG variability in the 7days post-COVID-19 vaccination. This change in BG proportion on target in the week following vaccination was most pronounced for people taking Metformin/Dapagliflozin+basal-bolus insulin (−23%) vs no oral hypoglycaemic agents (−4%), and median age <53 vs ≥53 years (greater reduction in %BG in target for older individuals (−18% vs −9%)). Conclusion: In T1DM, we have shown that COVID-19 vaccination can cause temporary perturbation of BG, with this effect more pronounced in patients talking oral hypoglycaemic medication plus insulin, and in older individuals. This may also have consequences for patients with T2DM who are currently not supported by flash glucose monitoring

Implementing a text message-based intervention to support type 2 diabetes medication adherence in primary care: a qualitative study with general practice staff

Background: The Support through Mobile Messaging and digital health Technology for Diabetes (SuMMiT-D) project has developed, and is evaluating, a mobile phone-based intervention delivering brief messages targeting identified behaviour change techniques promoting medication use to people with type 2 diabetes in general practice. The present study aimed to inform refinement and future implementation of the SuMMiT-D intervention by investigating general practice staff perceptions of how a text message-based intervention to support medication adherence should be implemented within current and future diabetes care. Methods: Seven focus groups and five interviews were conducted with 46 general practice staff (including GPs, nurses, healthcare assistants, receptionists and linked pharmacists) with a potential role in the implementation of a text message-based intervention for people with type 2 diabetes. Interviews and focus groups were audio-recorded, transcribed and analysed using an inductive thematic analysis approach. Results: Five themes were developed. One theme ‘The potential of technology as a patient ally’ described a need for diabetes support and the potential of technology to support medication use. Two themes outlined challenges to implementation, ‘Limited resources and assigning responsibility’ and ‘Treating the patient; more than diabetes medication adherence’. The final two themes described recommendations to support implementation, ‘Selling the intervention: what do general practice staff need to see?’ and ‘Fitting the mould; complementing current service delivery’. Conclusions: Staff see the potential for a text message-based support intervention to address unmet needs and to enhance care for people with diabetes. Digital interventions, such as SuMMiT-D, need to be compatible with existing systems, demonstrate measurable benefits, be incentivised and be quick and easy for staff to engage with. Interventions also need to be perceived to address general practice priorities, such as taking a holistic approach to care and having multi-cultural reach and relevance. Findings from this study are being combined with parallel work with people with type 2 diabetes to ensure stakeholder views inform further refinement and implementation of the SuMMiT-D intervention

Developing and exploring the validity of a patient reported experience measure for adult inpatient diabetes care

AIM: To develop and explore the validity of a Patient Reported Experience Measure (PREM) for adult inpatient diabetes care.METHOD: 27 in-depth interviews were conducted to inform the development of the 42-item PREM which was cognitively tested with 10 people. A refined 38-item PREM was piloted with 228 respondents completing a paper (n = 198) or online (n = 30) version. The performance of the PREM was evaluated by exploring (i) uptake/number of responses and (ii) survey validity by investigating whether the PREM data were of adequate quality and delivered useful information.RESULTS: The PREM had low drop-out or missing data rates suggesting it was appropriately constructed. Analysis of item frequencies and variances, and problem score calculations concluded that questions provided sufficient score differentiation.CONCLUSIONS: This new PREM allows for experiences of inpatient diabetes care to be measured, understood and reported on to help identify priority areas for improving care quality.</p