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Context: Predictive models can be used to identify people at high risk of unplanned hospitalization, although some of the high-risk patients they identify may not be amenable to preventive care. This study describes the development of "impactibility models," which aim to identify the subset of at-risk patients for whom preventive care is expected to be successful. Methods: This research used semistructured interviews with representatives of thirty American organizations that build, use, or appraise predictive models for health care. Findings: Impactibility models may refine the output of predictive models by (1) giving priority to patients with diseases that are particularly amenable to preventive care; (2) excluding patients who are least likely to respond to preventive care; or (3) identifying the form of preventive care best matched to each patient's characteristics. Conclusions: Impactibility models could improve the efficiency of hospital-avoidance programs, but they have important implications for equity and access. © 2010 Milbank Memorial Fund. Published by Wiley Periodicals Inc.

Blumenthal D.,Harvard University | Dixon J.,Nuffield Trust
The Lancet | Year: 2012

Two landmark and controversial bills reforming health care in the USA and England were recently passed. Despite the different history and context to health care in both countries, there is much room for mutual learning. This paper identifies three areas relating to financing, organisation, and information technology. For example, new payment mechanisms to encourage higher quality and efficiency are being developed and tested, particularly bundled payments, pay for performance, and value-based purchasing. In the USA, new national bodies to scrutinise payments in health care and to test promising new interventions to improve quality and efficiency will have lessons for the NHS. The faster adoption of electronic health records and their use in England to assess quality is a useful lesson for the USA. The new accountable care organisations and clinical commissioning groups have much to learn from each other as they develop.

Billings J.,New York University | Georghiou T.,Nuffield Trust | Blunt I.,Nuffield Trust | Bardsley M.,Nuffield Trust
BMJ Open | Year: 2013

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.

Bardsley M.,Nuffield Trust | Steventon A.,Nuffield Trust | Doll H.,University of East Anglia
BMC Health Services Research | Year: 2013

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. © 2013 Bardsley et al.; licensee BioMed Central Ltd.

Steventon A.,Nuffield Trust | Roberts A.,Nuffield Trust
BMC Health Services Research | Year: 2012

Background: Information about how long people stay in care homes is needed to plan services, as length of stay is a determinant of future demand for care. As length of stay is proportional to cost, estimates are also needed to inform analysis of the long-term cost effectiveness of interventions aimed at preventing admissions to care homes. But estimates are rarely available due to the cost of repeatedly surveying individuals. Methods. We used administrative data from three local authorities in England to estimate the length of publicly-funded care homes stays beginning in 2005 and 2006. Stays were classified into nursing home, permanent residential and temporary residential. We aggregated successive placements in different care home providers and, by linking to health data, across periods in hospital. Results: The largest group of stays (38.9%) were those intended to be temporary, such as for rehabilitation, and typically lasted 4 weeks. For people admitted to permanent residential care, median length of stay was 17.9 months. Women stayed longer than men, while stays were shorter if preceded by other forms of social care. There was significant variation in length of stay between the three local authorities. The typical person admitted to a permanent residential care home will cost a local authority over £38,000, less payments due from individuals under the means test. Conclusions: These figures are not apparent from existing data sets. The large cost of care home placements suggests significant scope for preventive approaches. The administrative data revealed complexity in patterns of service use, which should be further explored as it may challenge the assumptions that are often made. © 2012 Steventon and Roberts.; licensee BioMed Central Ltd.

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