Re-admission patterns in England and the Netherlands: a comparison based on administrative data of all hospitals.

BACKGROUND: Examining variation in patterns of re-admissions between countries can be valuable for mutual learning in order to reduce unnecessary re-admissions. The aim of this study was to compare re-admission rates and reasons for re-admissions between England and the Netherlands. METHODS: We used data from 85 Dutch hospitals (1 355 947 admissions) and 451 English hospitals (5 260 227 admissions) in 2014 (96% of all Dutch hospitals and 100% of all English NHS hospitals). Re-admission data from England and the Netherlands were compared for all hospital patients and for specific diagnosis groups: pneumonia, urinary tract infection, chronic obstructive pulmonary disease, coronary atherosclerosis, biliary tract disease, hip fracture and acute myocardial infarction. Re-admissions were categorized using a classification system developed on administrative data. The classification distinguishes between potentially preventable re-admissions and other reasons for re-admission. RESULTS: England had a higher 30-day re-admission rate (adjusted for age and gender) compared to the Netherlands: 11.17% (95% CI 11.14-11.20%) vs. 9.83% (95% CI 9.77-9.88%). The main differences appeared to be in re-admissions for the elderly (England 17.2% vs. the Netherlands 10.0%) and in emergency re-admissions (England 85.3% of all 30-day re-admissions vs. the Netherlands 66.8%). In the Netherlands, however, more emergency re-admissions were classified as potentially preventable compared to England (33.8% vs. 28.8%). CONCLUSIONS: The differences found between England and the Netherlands indicate opportunities to reduce unnecessary re-admissions. For England this concerns more expanded palliative care, integrated social care and reduction of waiting times. In the Netherlands, the use of treatment plans for daily life could be increased.

Factors associated with variation in hospital use at the end of life in England.

OBJECTIVE: To identify the relative importance of factors influencing hospital use at the end of life. DESIGN: Retrospective cohort study of person and health system effects on hospital use in the past 12 months modelling differences in admissions, bed days and whether a person died in hospital. SETTING: Residents in England for the period 2009/2010 to 2011/2012 using Hospital Episodes Statistics (HES) data from all acute care hospitals in England funded by the National Health Service (NHS). PARTICIPANTS: 1 223 859 people registered with a GP in England who died (decedents) in England (April 2009-March 2012) with a record of NHS hospital care. MAIN OUTCOME MEASURES: Hospital admissions, and hospital bed days and place of death (in or out of hospital) in the past 12 months of life. RESULTS: The mean number of admissions in the past 12 months of life averaged 2.28 occupying 30.05 bed days-excluding 9.8% of patients with no hospital history. A total of 50.8% of people died in hospital. Difference in hospital use was associated with a range of patient descriptors (age, gender and ethnicity). The variables with the greatest 'explanatory power' were those that described the diagnoses and causes of death. So, for example, 65% of the variability in the model of hospital admissions was explained by diagnoses. Only moderate levels of variation were explained by the hospital provider variables for admissions and deaths in hospital, though the impacts on total bed days was large. CONCLUSIONS: Comparative analyses of hospital utilisation should standardise for a range of patient specific variables. Though the models indicated some degree of variability associated with individual providers, the scale of this was not great for admissions and death in hospital but the variability associated with length of stay differences suggests that attempts to optimise hospital use should look at differences in lengths of stay and bed use. This study adds important new information about variability in admissions by diagnostic group, and variability in bed days by diagnostic group and eventual cause of death.

Development and validation of a Hospital Frailty Risk Score focusing on older people in acute care settings using electronic hospital records: an observational study.

BACKGROUND: Older people are increasing users of health care globally. We aimed to establish whether older people with characteristics of frailty and who are at risk of adverse health-care outcomes could be identified using routinely collected data. METHODS: A three-step approach was used to develop and validate a Hospital Frailty Risk Score from International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnostic codes. First, we carried out a cluster analysis to identify a group of older people (≥75 years) admitted to hospital who had high resource use and diagnoses associated with frailty. Second, we created a Hospital Frailty Risk Score based on ICD-10 codes that characterised this group. Third, in separate cohorts, we tested how well the score predicted adverse outcomes and whether it identified similar groups as other frailty tools. FINDINGS: In the development cohort (n=22 139), older people with frailty diagnoses formed a distinct group and had higher non-elective hospital use (33·6 bed-days over 2 years compared with 23·0 bed-days for the group with the next highest number of bed-days). In the national validation cohort (n=1 013 590), compared with the 429 762 (42·4%) patients with the lowest risk scores, the 202 718 (20·0%) patients with the highest Hospital Frailty Risk Scores had increased odds of 30-day mortality (odds ratio 1·71, 95% CI 1·68-1·75), long hospital stay (6·03, 5·92-6·10), and 30-day readmission (1·48, 1·46-1·50). The c statistics (ie, model discrimination) between individuals for these three outcomes were 0·60, 0·68, and 0·56, respectively. The Hospital Frailty Risk Score showed fair overlap with dichotomised Fried and Rockwood scales (kappa scores 0·22, 95% CI 0·15-0·30 and 0·30, 0·22-0·38, respectively) and moderate agreement with the Rockwood Frailty Index (Pearson's correlation coefficient 0·41, 95% CI 0·38-0·47). INTERPRETATION: The Hospital Frailty Risk Score provides hospitals and health systems with a low-cost, systematic way to screen for frailty and identify a group of patients who are at greater risk of adverse outcomes and for whom a frailty-attuned approach might be useful. FUNDING: National Institute for Health Research.

The impact of alcohol care teams on emergency secondary care use following a diagnosis of alcoholic liver disease - a national cohort study.

BACKGROUND: The increasing mortality rates from alcohol-related liver disease (ARLD) are a public health concern. To address this, alcohol care teams (ACT) case-find and lead management of alcohol issues for these patients. Local assessments of ACTs have shown reductions in emergency admissions and emergency department attendances. We examine the impact of ACTs on emergency hospital activity following a diagnosis of ARLD. METHODS: Administrative Hospital Episode Statistics (HES) data were extracted. Information on ACT provision at English NHS hospital trusts and sites in 2009/10 was taken from a survey by Public Health England. We undertook a difference-in-difference analysis to compare emergency hospital activity for a cohort of individuals diagnosed with ARLD who presented to hospitals either with or without an ACT in the one year before and after a first ARLD diagnosis during 2009/10. RESULTS: Over the study period, 9,165 individuals eligible for inclusion in our study had a first diagnosis of ARLD. 4,768 presented to one of 41 hospital trusts with an ACT (59 sites) and 4,397 presented to one of 50 non-ACT hospital trusts (65 sites). Whilst age and sex demographics were similar between the two cohorts, the ACT hospital cohort had a higher proportion of individuals in the most deprived quintile (41.6 % v 28.5 % p < .0001). In the difference-in-difference analysis, the presence of an ACT at a hospital trust was not associated with a change in all-cause emergency admissions (0.020 (95 % CI -0.070, 0.111), p = 0.656), alcohol-related emergency admissions (-0.025 (95 % CI -0.104, 0.054), p = 0.536) or all-cause emergency department attendances (0.042 (95 % CI -0.087, 0.171), p = 0.521). Sensitivity analyses by sex and hospital site did not affect the study findings. CONCLUSIONS: In this study, the presence of an ACT at the NHS hospital trust where individuals have their first recorded diagnosis of ARLD does not appear to be associated with subsequent emergency hospital activity within these populations. Further analysis focussing on the components and specific effects of ACT interventions on individuals and systems both pre- and post-diagnosis of ARLD may reveal important avenues to improve care.

Effect of telehealth on hospital utilisation and mortality in routine clinical practice: a matched control cohort study in an early adopter site.

OBJECTIVES: To assess the effects of a home-based telehealth intervention on the use of secondary healthcare and mortality. DESIGN: Observational study of a mainstream telehealth service, using person-level administrative data. Time to event analysis (Cox regression) was performed comparing telehealth patients with controls who were matched using a machine-learning algorithm. SETTING: A predominantly rural region of England (North Yorkshire). PARTICIPANTS: 716 telehealth patients were recruited from community, general practice and specialist acute care, between June 2010 and March 2013. Patients had chronic obstructive pulmonary disease, congestive heart failure or diabetes, and a history of associated inpatient admission. Patients were matched 1:1 to control patients, also selected from North Yorkshire, with respect to demographics, diagnoses of health conditions, previous hospital use and predictive risk score. INTERVENTIONS: Telehealth involved the remote exchange of medical data between patients and healthcare professionals as part of the ongoing management of the patient's health condition. Monitoring centre staff alerted healthcare professionals if the telemonitored data exceeded preset thresholds. Control patients received usual care, without telehealth. PRIMARY AND SECONDARY OUTCOME MEASURES: Time to the first emergency (unplanned) hospital admission or death. Secondary metrics included time to death and time to first admission, outpatient attendance and emergency department visit. RESULTS: Matched controls and telehealth patients were similar at baseline. Following enrolment, telehealth patients were more likely than matched controls to experience emergency admission or death (adjusted HR 1.34, 95% CI 1.16 to 1.56, p<0.001). They were also more likely to have outpatient attendances (adjusted HR=1.25, 1.11 to 1.40, p<0.001), but mortality rates were similar between groups. Sensitivity analyses showed that we were unlikely to have missed reductions in the likelihood of an emergency admission or death because of unobserved baseline differences between patient groups. CONCLUSIONS: Telehealth was not associated with a reduction in secondary care utilisation.

Continuous monitoring of emergency admissions of older care home residents to hospital.

BACKGROUND: evidence from inspection programmes suggest that the quality of care provided by individual care homes for older people is very variable. Aside from periodic inspection, there is limited information that is routinely collected and can be used to monitor quality. OBJECTIVES: to describe a method for using routine hospital data on admissions of older people as means for monitoring quality of care within a care home. To explore how this might be applied and used. METHODS: we linked hospital admissions to care homes using postcode matching and analysed hospital admission data as a time series, using the Cumulative Sum (CUSUM) technique to detect unusually high rates of admission. RESULTS: if we develop the CUSUM so that the number of times it falsely signals a high rate of admissions is limited to a rate of 0.1% per year, the chances of successfully detecting a doubling of the admission rate within 2 years will range from 48% for the smaller homes to 96% for the larger homes. CONCLUSION: monitoring tools using data on admissions to hospital are both possible and feasible, particularly for the larger homes. However, due to data limitations, users need to be careful about how they interpret triggers and thus ensure follow-up is appropriate. Some of the problems caused by using routine national data can be overcome if care homes used their own information for local monitoring.

An Approach to Assess Generalizability in Comparative Effectiveness Research: A Case Study of the Whole Systems Demonstrator Cluster Randomized Trial Comparing Telehealth with Usual Care for Patients with Chronic Health Conditions.

BACKGROUND: Policy makers require estimates of comparative effectiveness that apply to the population of interest, but there has been little research on quantitative approaches to assess and extend the generalizability of randomized controlled trial (RCT)-based evaluations. We illustrate an approach using observational data. METHODS: Our example is the Whole Systems Demonstrator (WSD) trial, in which 3230 adults with chronic conditions were assigned to receive telehealth or usual care. First, we used novel placebo tests to assess whether outcomes were similar between the RCT control group and a matched subset of nonparticipants who received usual care. We matched on 65 baseline variables obtained from the electronic medical record. Second, we conducted sensitivity analysis to consider whether the estimates of treatment effectiveness were robust to alternative assumptions about whether "usual care" is defined by the RCT control group or nonparticipants. Thus, we provided alternative estimates of comparative effectiveness by contrasting the outcomes of the RCT telehealth group and matched nonparticipants. RESULTS: For some endpoints, such as the number of outpatient attendances, the placebo tests passed, and the effectiveness estimates were robust to the choice of comparison group. However, for other endpoints, such as emergency admissions, the placebo tests failed and the estimates of treatment effect differed markedly according to whether telehealth patients were compared with RCT controls or matched nonparticipants. CONCLUSIONS: The proposed placebo tests indicate those cases when estimates from RCTs do not generalize to routine clinical practice and motivate complementary estimates of comparative effectiveness that use observational data. Future RCTs are recommended to incorporate these placebo tests and the accompanying sensitivity analyses to enhance their relevance to policy making.

Pre-dialysis hospital use and late referrals in incident dialysis patients in England: a retrospective cohort study.

BACKGROUND: Timely referral to specialist kidney care can improve outcomes for patients and delay the onset of dialysis, yet late referral (LR) remains a problem in many countries. We aimed to estimate the proportion of LRs that could potentially have been detected earlier because of increases in patients' general hospital activity. METHODS: A cohort of patients starting dialysis in the English NHS (National Health Service) during 2010/11 was approximated using hospital administrative data. The time between first recorded contact with a consultant nephrologist and starting dialysis was used to categorize the timeliness of referral. Monthly rates of inpatient activity prior to starting dialysis for both referral types were compared with the national average. RESULTS: A cohort of 3928 patients was detected. One-third (34%) of the cohort started dialysis <90 days after their first referral to a nephrologist. Rates were higher for patients starting haemodialysis than peritoneal dialysis. The proportion of patients receiving their first dialysis as an emergency rises from 27% for those referred before 3 months to 67% for those referred on or after the day of starting dialysis. Half of the late referred patients (49%) have hospital activity rates more than double the national average (adjusted for age and sex) at 90 days before they start dialysis. CONCLUSIONS: A substantial proportion of patients (49%) referred late for specialist kidney care have had regular contact with other hospital services. This could represent a missed opportunity to improve outcomes by timely management of their kidney disease.

Classifying emergency 30-day readmissions in England using routine hospital data 2004-2010: what is the scope for reduction?

BACKGROUND: Many health systems across the globe have introduced arrangements to deny payment for patients readmitted to hospital as an emergency. The purpose of this study was to develop an exploratory categorisation based on likely causes of readmission, and then to assess the prevalence of these different types. METHODS: Retrospective analysis of 82 million routinely collected National Health Service hospital records in England (2004-2010) was undertaken using anonymised linkage of records at person-level. Numbers of 30-day readmissions were calculated. Exploratory categorisation of readmissions was applied using simple rules relating to International Classification of Diseases (ICD) diagnostic codes for both admission and readmission. RESULTS: There were 5 804 472 emergency 30-day readmissions over a 6-year period, equivalent to 7.0% of hospital discharges. Readmissions were grouped into hierarchically exclusive categories: potentially preventable readmission (1 739 519 (30.0% of readmissions)); anticipated but unpredictable readmission (patients with chronic disease or likely to need long-term care; 1 141 987 (19.7%)); preference-related readmission (53 718 (0.9%)); artefact of data collection (16 062 (0.3%)); readmission as a result of accident, coincidence or related to a different body system (1 101 818 (19.0%)); broadly related readmission (readmission related to the same body system (1 751 368 (30.2%)). CONCLUSIONS: In this exploratory categorisation, a large minority of emergency readmissions (eg, those that are potentially preventable or due to data artefacts) fell into groups potentially amenable to immediate reduction. For other categories, a hospital's ability to reduce emergency readmission is less clear. Reduction strategies and payment incentives must be carefully tailored to achieve stated aims.

Effect of a telephonic alert system (Healthy outlook) for patients with chronic obstructive pulmonary disease: a cohort study with matched controls.

BACKGROUND: Healthy Outlook was a telephonic alert system for patients with chronic obstructive pulmonary disease (COPD) in the UK. It used routine meteorological and communicable disease reports to identify times of increased risk to health. We tested its effect on hospital use and mortality. METHODS: Enrolees with a history of hospital admissions were linked to hospital administrative data. They were compared with control patients from local general practices, matched for demographic characteristics, health conditions, previous hospital use and predictive risk scores. We compared unplanned hospital admissions, admissions for COPD, outpatient attendances, planned admissions and mortality, over 12 months following enrolment. RESULTS: Intervention and matched control groups appeared similar at baseline (n = 1413 in each group). Over the 12 months following enrolment, Healthy Outlook enrolees experienced more COPD admissions than matched controls (adjusted rate ratio 1.26, 95% confidence interval (CI), 1.05-1.52) and more outpatient attendances (adjusted rate ratio 1.08, 95% CI, 1.03-1.12). Enrolees also had lower mortality rates over 12 months (adjusted odds ratio 0.61, 95% CI, 0.45-0.84). CONCLUSION: Healthy Outlook did not reduce admission rates, though mortality rates were lower. Findings for hospital utilization were unlikely to have been affected by confounding.

Effect of telehealth on glycaemic control: analysis of patients with type 2 diabetes in the Whole Systems Demonstrator cluster randomised trial.

BACKGROUND: The Whole Systems Demonstrator was a large, pragmatic, cluster randomised trial that compared telehealth with usual care among 3,230 patients with long-term conditions in three areas of England. Telehealth involved the regular transmission of physiological information such as blood glucose to health professionals working remotely. We examined whether telehealth led to changes in glycosylated haemoglobin (HbA1c) among the subset of patients with type 2 diabetes. METHODS: The general practice electronic medical record was used as the source of information on HbA1c. Effects on HbA1c were assessed using a repeated measures model that included all HbA1c readings recorded during the 12-month trial period, and adjusted for differences in HbA1c readings recorded before recruitment. Secondary analysis averaged multiple HbA1c readings recorded for each individual during the trial period. RESULTS: 513 of the 3,230 participants were identified as having type 2 diabetes and thus were included in the study. Telehealth was associated with lower HbA1c than usual care during the trial period (difference 0.21% or 2.3 mmol/mol, 95% CI, 0.04% to 0.38%, p = 0.013). Among the 457 patients in the secondary analysis, mean HbA1c showed little change for controls following recruitment, but fell for intervention patients from 8.38% to 8.15% (68 to 66 mmol/mol). A higher proportion of intervention patients than controls had HbA1c below the 7.5% (58 mmol/mol) threshold that was targeted by general practices (30.4% vs. 38.0%). This difference, however, did not quite reach statistical significance (adjusted odds ratio 1.63, 95% CI, 0.99 to 2.68, p = 0.053). CONCLUSIONS: Telehealth modestly improved glycaemic control in patients with type 2 diabetes over 12 months. The scale of the improvements is consistent with previous meta-analyses, but was relatively modest and seems unlikely to produce significant patient benefit. TRIAL REGISTRATION NUMBER: International Standard Randomized Controlled Trial Number Register ISRCTN43002091.

Experiences of front-line health professionals in the delivery of telehealth: a qualitative study.

BACKGROUND: Telehealth is an emerging field of clinical practice but current UK health policy has not taken account of the perceptions of front-line healthcare professionals expected to implement it. AIM: To investigate telehealth care for people with long-term conditions from the perspective of the front-line health professional. DESIGN AND SETTING: A qualitative study in three sites within the UK (Kent, Cornwall, and the London Borough of Newham) and embedded in the Whole Systems Demonstrator evaluation, a large cluster randomised controlled trial of telehealth and telecare for patients with long-term and complex conditions. METHOD: Semi-structured qualitative interviews with 32 front-line health professionals (13 community matrons, 10 telehealth monitoring nurses and 9 GPs) involved in the delivery of telehealth. Data were analysed using a modified grounded theory approach. RESULTS: Mixed views were expressed by front-line professionals, which seem to reflect their levels of engagement. It was broadly welcomed by nursing staff as long as it supplemented rather than substituted their role in traditional patient care. GPs held mixed views; some gave a cautious welcome but most saw telehealth as increasing their work burden and potentially undermining their professional autonomy. CONCLUSION: Health care professionals will need to develop a shared understanding of patient self-management through telehealth. This may require a renegotiation of their roles and responsibilities.

Cost-effectiveness of telecare for people with social care needs: the Whole Systems Demonstrator cluster randomised trial.

PURPOSE OF THE STUDY: to examine the costs and cost-effectiveness of 'second-generation' telecare, in addition to standard support and care that could include 'first-generation' forms of telecare, compared with standard support and care that could include 'first-generation' forms of telecare. DESIGN AND METHODS: a pragmatic cluster-randomised controlled trial with nested economic evaluation. A total of 2,600 people with social care needs participated in a trial of community-based telecare in three English local authority areas. In the Whole Systems Demonstrator Telecare Questionnaire Study, 550 participants were randomised to intervention and 639 to control. Participants who were offered the telecare intervention received a package of equipment and monitoring services for 12 months, additional to their standard health and social care services. The control group received usual health and social care. PRIMARY OUTCOME MEASURE: incremental cost per quality-adjusted life year (QALY) gained. The analyses took a health and social care perspective. RESULTS: cost per additional QALY was £297,000. Cost-effectiveness acceptability curves indicated that the probability of cost-effectiveness at a willingness-to-pay of £30,000 per QALY gained was only 16%. Sensitivity analyses combining variations in equipment price and support cost parameters yielded a cost-effectiveness ratio of £161,000 per QALY. IMPLICATIONS: while QALY gain in the intervention group was similar to that for controls, social and health services costs were higher. Second-generation telecare did not appear to be a cost-effective addition to usual care, assuming a commonly accepted willingness to pay for QALYs. TRIAL REGISTRATION NUMBER: ISRCTN 43002091.

The effect of telecare on the quality of life and psychological well-being of elderly recipients of social care over a 12-month period: the Whole Systems Demonstrator cluster randomised trial.

BACKGROUND: home-based telecare (TC) is utilised to manage risks of independent living and provide prompt emergency responses. This study examined the effect of TC on health-related quality of life (HRQoL), anxiety and depressive symptoms over 12 months in patients receiving social care. DESIGN: a study of participant-reported outcomes [the Whole Systems Demonstrator (WSD) Telecare Questionnaire Study; baseline n = 1,189] was nested in a pragmatic cluster-randomised trial of TC (the WSD Telecare trial), held across three English Local Authorities. General practice (GP) was the unit of randomisation and TC was compared with usual care (UC). METHODS: participant-reported outcome measures were collected at baseline, short-term (4 months) and long-term (12 months) follow-up, assessing generic HRQoL, anxiety and depressive symptoms. Primary intention-to-treat analyses tested treatment effectiveness and were conducted using multilevel models to control for GP clustering and covariates for participants who completed questionnaire measures at baseline assessment plus at least one other assessment (n = 873). RESULTS: analyses found significant differences between TC and UC on Short Form-12 mental component scores (P < 0.05), with parameter estimates indicating being a member of the TC trial-arm increases mental component scores (UC-adjusted mean = 40.52; TC-adjusted mean = 43.69). Additional significant analyses revealed, time effects on EQ5D (decreasing over time) and depressive symptoms (increasing over time). CONCLUSIONS: TC potentially contributes to the amelioration in the decline in users' mental HRQoL over a 12-month period. TC may not transform the lives of its users, but it may afford small relative benefits on some psychological and HRQOL outcomes relative to users who only receive UC. International Standard Randomised Controlled Trial Number Register: ISRCTN 43002091.

Reflections from the NHS in England.

This paper describes the approach taken to "high-risk patients" in England. It provides an overview of the use of risk stratification to identify those at high risk and policy innovations designed to provide preventive care, including the community matron and virtual ward initiatives. We review how such initiatives have been evaluated and discuss the findings of those evaluations. We conclude with a discussion of next steps for the use of risk stratification, such as honing the focus to identify those individuals most likely to respond to preventive care, and we sound a cautionary note in favour of realistic expectations regarding the use of risk stratification and the emergence of anticipated benefits.

Integrating care for high-risk patients in England using the virtual ward model: lessons in the process of care integration from three case sites.

BACKGROUND: Patients at high risk of emergency hospitalisation are particularly likely to experience fragmentation in care. The virtual ward model attempts to integrate health and social care by offering multidisciplinary case management to people at high predicted risk of unplanned hospitalisation. OBJECTIVE: To describe the care practice in three virtual ward sites in England and to explore how well each site had achieved meaningful integration. METHOD: Case studies conducted in Croydon, Devon and Wandsworth during 2011-2012, consisting of semi-structured interviews, workshops, and site visits. RESULTS: Different versions of the virtual wards intervention had been implemented in each site. In Croydon, multidisciplinary care had reverted back to one-to-one case management. CONCLUSIONS: To integrate successfully, virtual ward projects should safeguard the multidisciplinary nature of the intervention, ensure the active involvement of General Practitioners, and establish feedback processes to monitor performance such as the number of professions represented at each team meeting.

Impact of telehealth on general practice contacts: findings from the whole systems demonstrator cluster randomised trial.

BACKGROUND: Telehealth is increasingly used in the care of people with long term conditions. Whilst many studies look at the impacts of the technology on hospital use, few look at how it changes contacts with primary care professionals. The aim of this paper was to assess the impacts of home-based telehealth interventions on general practice contacts. METHOD: Secondary analysis of data from a Department of Health funded cluster-randomised trial with 179 general practices in three areas of England randomly assigned to offer telehealth or usual care to eligible patients. Telehealth included remote exchange of vitals signs and symptoms data between patients and healthcare professionals as part of the continuing management of patients. Usual care reflected the range of services otherwise available in the sites, excluding telehealth. Anonymised data from GP systems were used to construct person level histories for control and intervention patients. We tested for differences in numbers of general practitioner and practice nurse contacts over twelve months and in the number of clinical readings recorded on general practice systems over twelve months. RESULTS: 3,230 people with diabetes, chronic obstructive pulmonary disease or heart failure were recruited in 2008 and 2009. 1219 intervention and 1098 control cases were available for analysis. No statistically significant differences were detected in the numbers of general practitioner or practice nurse contacts between intervention and control groups during the trial, or in the numbers of clinical readings recorded on the general practice systems. CONCLUSIONS: Telehealth did not appear associated with different levels of contact with general practitioners and practice nurses. We note that the way that telehealth impacts on primary care roles may be influenced by a number of other features in the health system. The challenge is to ensure that these systems lead to better integration of care than fragmentation. TRIAL REGISTRATION NUMBER: International Standard Randomised Controlled Trial Number Register ISRCTN43002091.

Integrated care pilot in north-west London: a mixed methods evaluation.

INTRODUCTION: This paper provides the results of a year-long evaluation of a large-scale integrated care pilot in north-west London. The pilot aimed to integrate care across primary, acute, community, mental health and social care for people with diabetes and/or those aged 75+ through care planning, multidisciplinary case reviews, information sharing and project management support. METHODS: The evaluation team conducted qualitative studies of change at organisational, clinician and patient levels (using interviews, focus groups and a survey); and quantitative analysis of change in service use and patient-level clinical outcomes (using patient-level datasets and a matched control study). RESULTS: The pilot had successfully engaged provider organisations, created a shared strategic vision and established governance structures. However, the engagement of clinicians was variable and there was no evidence to date of significant reductions in emergency admissions. There was some evidence of changes in care processes. CONCLUSION: Although the pilot has demonstrated the beginnings of large-scale change, it remains in the early stages and faces significant challenges as it seeks to become sustainable for the longer term. It is critical that National Health Service managers and clinicians have realistic expectations of what can be achieved in a relatively short period of time.