Biomedical subjects

David Wennberg

Publications and source records attributed to David Wennberg.

4 recordsLinked to original sources

PubMed2006

Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients.

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 census for England. Population All residents in England admitted to hospital in the previous four years with a subset of "reference" conditions for which improved management may help to prevent future admissions. DESIGN: Multivariate statistical analysis of routinely collected data to develop an algorithm to predict patients at highest risk of readmission in the next 12 months. The algorithm was developed by using a 10% sample of hospital episode statistics data for all of England for the period indicated. The coefficients for 21 most powerful (and statistically significant) variables were then applied against a second 10% test sample to validate the findings of the algorithm from the first sample. RESULTS: The key factors predicting subsequent admission included age, sex, ethnicity, number of previous admissions, and clinical condition. The algorithm produces a risk score (from 0 to 100) for each patient admitted with a reference condition. At a risk score threshold of 50, the algorithm identified 54.3% of patients admitted with a reference condition who would have an admission in the next 12 months; 34.7% of patients were "flagged" incorrectly (they would not have a subsequent admission). At risk score threshold levels of 70 and 80, the rate of incorrectly "flagged" patients dropped to 22.6% and 15.7%, but the algorithm found a lower percentage of patients who would be readmitted. The algorithm is made freely available to primary care trusts via a website. CONCLUSIONS: A method of predicting individual patients at highest risk of readmission to hospital in the next 12 months has been developed, which has a reasonable level of sensitivity and specificity. Using various assumptions a "business case" has been modelled to demonstrate to primary care trusts and practices the potential costs and impact of an intervention using the algorithm to reduce hospital admissions.

Aged↗

PubMed2006

Use of medical insurance claims data for occupational health research.

OBJECTIVE: The objective of this study was to demonstrate that health claims data, widely available due to the unique nature of the U.S. healthcare system, can be linked to other relevant databases such as personnel files and exposure data maintained by large employers. These data offer great potential for occupational health research. METHODS: In this article, we describe the process for linking claims data to industrial hygiene exposure data and personnel files of a single large employer to conduct epidemiologic research. RESULTS: Our results demonstrate the ability to replicate previously published findings using commonly maintained data sets and illustrate methodological issues that may arise as newer hypotheses are tested in this way. CONCLUSIONS: Health claims files offer potential for epidemiologic research in the United States, although the full extent and guidelines for successful application await further clarification through empiric research.

Adult↗

PubMed2004

Estimating the return on investment in disease management programs using a pre-post analysis.

Disease management programs have become increasingly popular over the past 5-10 years. Recent increases in overall medical costs have precipitated new concerns about the cost-effectiveness of medical management programs that have extended to the program directors for these programs. Initial success of the disease management movement is being challenged on the grounds that reported results have been the result of the application of faulty, if intuitive, methodologies. This paper discusses the use of "pre-post" methodology approaches in the analysis of disease management programs, and areas where application of this approach can result in spurious results and incorrect financial outcome assessments. The paper includes a checklist of these items for use by operational staff working with the programs, and a comprehensive bibliography that addresses many of the issues discussed.

Cost-Benefit Analysis↗

PubMed2004

Principles for assessing disease management outcomes.

Disease management (DM) is rapidly becoming an important force in the late 20th and early 21st century as a strategy for managing the chronic illness of large populations. Given the increasing visibility of DM programs, the clinical, economic and financial impact of this support are vital to DM program accountability and its acceptance as a solution to the twin challenges of achieving affordable, quality health care. Measuring and reporting outcomes in DM is difficult. DM programs must adapt to local market conditions and customer desires, which in turn limits generalizability, and still account for the overlapping/interlocking/multifaceted nature of the interventions included in any DM program. The Disease Management Association of America convened a Steering Committee to suggest a preferred approach, not a mandated or standardized approach for DM program evaluation. This paper presents the Steering Committee's "Consensus Statement" and "Guiding Principles" for robust evaluation.

Disease Management↗