John Billings

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This DataWatch examines the potential impact of socioeconomic differences on rates of hospitalization, based on patterns of hospital use in New York City in 1988. The research suggests that lack of timely and effective outpatient care may lead to higher hospitalization rates in low-income areas. For certain conditions identified as ambulatory care(More)
OBJECTIVE To examine whether the higher hospital admission rates for chronic medical conditions such as asthma, hypertension, congestive heart failure, chronic obstructive pulmonary disease, and diabetes in low-income communities resulted from community differences in access to care, prevalence of the diseases, propensity to seek care, or physician(More)
OBJECTIVE To assess the effect of home based telehealth interventions on the use of secondary healthcare and mortality. DESIGN Pragmatic, multisite, cluster randomised trial comparing telehealth with usual care, using data from routine administrative datasets. General practice was the unit of randomisation. We allocated practices using a minimisation(More)
OBJECTIVE To develop a method of identifying patients at high risk of readmission to hospital in the next 12 months for practical use by primary care trusts and general practices in the NHS in England. DATA SOURCES Data from hospital episode statistics showing all admissions in NHS trusts in England over five years, 1999-2000 to 2003-4; data from the 2001(More)
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)(More)
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(More)
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(More)
Patients with frequent hospitalizations generate a disproportionate share of hospital visits and costs. Accurate determination of patients who might benefit from interventions is challenging: most patients with frequent admissions in 1 year would not continue to have them in the next. Our objective was to employ a validated regression algorithm to case-find(More)
This study seeks to understand the perspective of Black and Hispanic/Latino residents of the South Bronx, New York, on the causes of persistent racial and ethnic disparities in health outcomes. In particular, it focuses on how people who live in this community perceive and interact with the health care system. Findings from 9 focus groups with 110(More)
Existing ML-like languages guarantee type-safety, ensuring memory safety and protecting the invariants of abstract types, but only within single executions of single programs. Distributed programming is becoming ever more important, and should benefit even more from such guarantees. In previous work on theoretical calculi and the Acute prototype language we(More)