Faculty Bibliography

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.

OBJECTIVES: To develop a formula for allocating resources for commissioning hospital care to all general practices in England based on the health needs of the people registered in each practice DESIGN: Multivariate prospective statistical models were developed in which routinely collected electronic information from 2005-6 and 2006-7 on individuals and the areas in which they lived was used to predict their costs of hospital care in the next year, 2007-8. Data on individuals included all diagnoses recorded at any inpatient admission. Models were developed on a random sample of 5 million people and validated on a second random sample of 5 million people and a third sample of 5 million people drawn from a random sample of practices. SETTING: All general practices in England as of 1 April 2007. All NHS inpatient admissions and outpatient attendances for individuals registered with a general practice on that date. SUBJECTS: All individuals registered with a general practice in England at 1 April 2007. MAIN OUTCOME MEASURES: Power of the statistical models to predict the costs of the individual patient or each practice's registered population for 2007-8 tested with a range of metrics (R(2) reported here). Comparisons of predicted costs in 2007-8 with actual costs incurred in the same year were calculated by individual and by practice. RESULTS: Models including person level information (age, sex, and ICD-10 codes diagnostic recorded) and a range of area level information (such as socioeconomic deprivation and supply of health facilities) were most predictive of costs. After accounting for person level variables, area level variables added little explanatory power. The best models for resource allocation could predict upwards of 77% of the variation in costs at practice level, and about 12% at the person level. With these models, the predicted costs of about a third of practices would exceed or undershoot the actual costs by 10% or more. Smaller practices were more likely to be in these groups. CONCLUSIONS: A model was developed that performed well by international standards, and could be used for allocations to practices for commissioning. The best formulas, however, could predict only about 12% of the variation in next year's costs of most inpatient and outpatient NHS care for each individual. Person-based diagnostic data significantly added to the predictive power of the models.

BACKGROUND: This retrospective study will assess the extent to which multidisciplinary case management in the form of virtual wards (VWs) leads to changes in the use of health care and social care by patients at high risk of future unplanned hospital admission. VWs use the staffing, systems and daily routines of a hospital ward to deliver coordinated care to patients in their own homes. Admission to a VW is offered to patients identified by a predictive risk model as being at high risk of unplanned hospital admission in the coming 12 months. STUDY DESIGN AND DATA COLLECTION METHODS: We will compare the health care and social care use of VW patients to that of matched controls. Controls will be drawn from (a) national, and (b) local, individual-level pseudonymous routine data. The costs of setting up and running a VW will be determined from the perspectives of both health and social care organizations using a combination of administrative data, interviews and diaries. METHODS OF ANALYSIS: Using propensity score matching and prognostic matching, we will create matched comparator groups to estimate the effect size of virtual wards in reducing unplanned hospital admissions. CONCLUSIONS: THIS STUDY WILL ALLOW US TO DETERMINE RELATIVE TO MATCHED COMPARATOR GROUPS: whether VWs reduce the use of emergency hospital care; the impact, if any, of VWs on the uptake of primary care, community health services and council-funded social care; and the potential costs and savings of VWs from the perspectives of the national health service (NHS) and local authorities.

BACKGROUND: the costs of delivering health and social care services are rising as the population ages and more people live with chronic diseases. OBJECTIVES: to determine whether predictive risk models can be built that use routine health and social care data to predict which older people will begin receiving intensive social care. DESIGN: analysis of pseudonymous, person-level, data extracted from the administrative data systems of local health and social care organisations. SETTING: five primary care trust areas in England and their associated councils with social services responsibilities. SUBJECTS: people aged 75 or older registered continuously with a general practitioner in five selected areas of England (n = 155,905). METHODS: multivariate statistical analysis using a split sample of data. RESULTS: it was possible to construct models that predicted which people would begin receiving intensive social care in the coming 12 months. The performance of the models was improved by selecting a dependent variable based on a lower cost threshold as one of the definitions of commencing intensive social care. CONCLUSIONS: predictive models can be constructed that use linked, routine health and social care data for case finding in social care settings.

This paper offers some reflections on the grant-making process from a former foundation executive. Some of the opportunities, challenges, and pitfalls inherent in the foundation world are described, and one approach to grant making, the "call for proposals," is examined as an example of the need for greater attention to and investment in the science of grant making itself, to maximize the potential return from philanthropy.

Increased policy attention is being focused on the management of high-cost cases in Medicaid. In this paper we present an algorithm that identifies patients at high risk of future hospitalizations and offer a business-case analysis with a range of assumptions about the rate of reduction in future hospitalization and the cost of the intervention. The characteristics of the patients identified by the algorithm are described, and the implications of these findings for policymakers, payers, and providers interested in responding more effectively to the needs of these patients are discussed, including the challenges likely to be encountered in implementing an intervention initiative.