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.

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.

Effect of telephone health coaching (Birmingham OwnHealth) on hospital use and associated costs: cohort study with matched controls.

OBJECTIVES: To test the effect of a telephone health coaching service (Birmingham OwnHealth) on hospital use and associated costs. DESIGN: Analysis of person level administrative data. Difference-in-difference analysis was done relative to matched controls. SETTING: Community based intervention operating in a large English city with industry. PARTICIPANTS: 2698 patients recruited from local general practices before 2009 with heart failure, coronary heart disease, diabetes, or chronic obstructive pulmonary disease; and a history of inpatient or outpatient hospital use. These individuals were matched on a 1:1 basis to control patients from similar areas of England with respect to demographics, diagnoses of health conditions, previous hospital use, and a predictive risk score. INTERVENTION: Telephone health coaching involved a personalised care plan and a series of outbound calls usually scheduled monthly. Median length of time enrolled on the service was 25.5 months. Control participants received usual healthcare in their areas, which did not include telephone health coaching. MAIN OUTCOME MEASURES: Number of emergency hospital admissions per head over 12 months after enrolment. Secondary metrics calculated over 12 months were: hospital bed days, elective hospital admissions, outpatient attendances, and secondary care costs. RESULTS: In relation to diagnoses of health conditions and other baseline variables, matched controls and intervention patients were similar before the date of enrolment. After this point, emergency admissions increased more quickly among intervention participants than matched controls (difference 0.05 admissions per head, 95% confidence interval 0.00 to 0.09, P=0.046). Outpatient attendances also increased more quickly in the intervention group (difference 0.37 attendances per head, 0.16 to 0.58, P<0.001), as did secondary care costs (difference £175 per head, £22 to £328, P=0.025). Checks showed that we were unlikely to have missed reductions in emergency admissions because of unobserved differences between intervention and matched control groups. CONCLUSIONS: The Birmingham OwnHealth telephone health coaching intervention did not lead to the expected reductions in hospital admissions or secondary care costs over 12 months, and could have led to increases.

Choosing a model to predict hospital admission: an observational study of new variants of predictive models for case finding.

OBJECTIVES: To test the performance of new variants of models to identify people at risk of an emergency hospital admission. We compared (1) the impact of using alternative data sources (hospital inpatient, A&E, outpatient and general practitioner (GP) electronic medical records) (2) the effects of local calibration on the performance of the models and (3) the choice of population denominators. DESIGN: Multivariate logistic regressions using person-level data adding each data set sequentially to test value of additional variables and denominators. SETTING: 5 Primary Care Trusts within England. PARTICIPANTS: 1 836 099 people aged 18-95 registered with GPs on 31 July 2009. MAIN OUTCOME MEASURES: Models to predict hospital admission and readmission were compared in terms of the positive predictive value and sensitivity for various risk strata and with the receiver operating curve C statistic. RESULTS: The addition of each data set showed moderate improvement in the number of patients identified with little or no loss of positive predictive value. However, even with inclusion of GP electronic medical record information, the algorithms identified only a small number of patients with no emergency hospital admissions in the previous 2 years. The model pooled across all sites performed almost as well as the models calibrated to local data from just one site. Using population denominators from GP registers led to better case finding. CONCLUSIONS: These models provide a basis for wider application in the National Health Service. Each of the models examined produces reasonably robust performance and offers some predictive value. The addition of more complex data adds some value, but we were unable to conclude that pooled models performed less well than those in individual sites. Choices about model should be linked to the intervention design. Characteristics of patients identified by the algorithms provide useful information in the design/costing of intervention strategies to improve care coordination/outcomes for these patients.

Evaluation of complex integrated care programmes: the approach in North West London.

BACKGROUND: Several local attempts to introduce integrated care in the English National Health Service have been tried, with limited success. The Northwest London Integrated Care Pilot attempts to improve the quality of care of the elderly and people with diabetes by providing a novel integration process across primary, secondary and social care organisations. It involves predictive risk modelling, care planning, multidisciplinary management of complex cases and an information technology tool to support information sharing. This paper sets out the evaluation approach adopted to measure its effect. STUDY DESIGN: We present a mixed methods evaluation methodology. It includes a quantitative approach measuring changes in service utilization, costs, clinical outcomes and quality of care using routine primary and secondary data sources. It also contains a qualitative component, involving observations, interviews and focus groups with patients and professionals, to understand participant experiences and to understand the pilot within the national policy context. THEORY AND DISCUSSION: This study considers the complexity of evaluating a large, multi-organisational intervention in a changing healthcare economy. We locate the evaluation within the theory of evaluation of complex interventions. We present the specific challenges faced by evaluating an intervention of this sort, and the responses made to mitigate against them. CONCLUSIONS: We hope this broad, dynamic and responsive evaluation will allow us to clarify the contribution of the pilot, and provide a potential model for evaluation of other similar interventions. Because of the priority given to the integrated agenda by governments internationally, the need to develop and improve strong evaluation methodologies remains strikingly important.

Is secondary preventive care improving? Observational study of 10-year trends in emergency admissions for conditions amenable to ambulatory care.

OBJECTIVE: To identify trends in emergency admissions for patients with clinical conditions classed as 'ambulatory care sensitive' (ACS) and assess if reductions might be due to improvements in preventive care. DESIGN: Observational study of routinely collected hospital admission data from March 2001 to April 2011. Admission rates were calculated at the population level using national population estimates for area of residence. PARTICIPANTS: All emergency admissions to National Health Service (NHS) hospitals in England from April 2001 to March 2011 for people residents in England. MAIN OUTCOME MEASURES: Age-standardised emergency admissions rates for each of 27 specific ACS conditions (ICD-10 codes recorded as primary or secondary diagnoses). RESULTS: Between April 2001 and March 2011 the number of admissions for ACS conditions increased by 40%. When ACS conditions were defined solely on primary diagnosis, the increase was less at 35% and similar to the increase in emergency admissions for non-ACS conditions. Age-standardised rates of emergency admission for ACS conditions had increased by 25%, and there were notable variations by age group and by individual condition. Overall, the greatest increases were for urinary tract infection, pyelonephritis, pneumonia, gastroenteritis and chronic obstructive pulmonary disease. There were significant reductions in emergency admission rates for angina, perforated ulcers and pelvic inflammatory diseases but the scale of these successes was relatively small. CONCLUSIONS: Increases in rates of emergency admissions suggest that efforts to improve the preventive management of certain clinical conditions have failed to reduce the demand for emergency care. Tackling the demand for hospital care needs more radical approaches than those adopted hitherto if reductions in emergency admission rates for ACS conditions overall are to be seen as a positive outcome of for NHS.

Development of a predictive model to identify inpatients at risk of re-admission within 30 days of discharge (PARR-30).

OBJECTIVES: To develop an algorithm for identifying inpatients at high risk of re-admission to a National Health Service (NHS) hospital in England within 30 days of discharge using information that can either be obtained from hospital information systems or from the patient and their notes. DESIGN: Multivariate statistical analysis of routinely collected hospital episode statistics (HES) data using logistic regression to build the predictive model. The model's performance was calculated using bootstrapping. SETTING: HES data covering all NHS hospital admissions in England. PARTICIPANTS: The NHS patients were admitted to hospital between April 2008 and March 2009 (10% sample of all admissions, n=576 868). MAIN OUTCOME MEASURES: Area under the receiver operating characteristic curve for the algorithm, together with its positive predictive value and sensitivity for a range of risk score thresholds. RESULTS: The algorithm produces a 'risk score' ranging (0-1) for each admitted patient, and the percentage of patients with a re-admission within 30 days and the mean re-admission costs of all patients are provided for 20 risk bands. At a risk score threshold of 0.5, the positive predictive value (ie, percentage of inpatients identified as high risk who were subsequently re-admitted within 30 days) was 59.2% (95% CI 58.0% to 60.5%); representing 5.4% (95% CI 5.2% to 5.6%) of all inpatients who would be re-admitted within 30 days (sensitivity). The area under the receiver operating characteristic curve was 0.70 (95% CI 0.69 to 0.70). CONCLUSIONS: We have developed a method of identifying inpatients at high risk of unplanned re-admission to NHS hospitals within 30 days of discharge. Though the models had a low sensitivity, we show how to identify subgroups of patients that contain a high proportion of patients who will be re-admitted within 30 days. Additional work is necessary to validate the model in practice.