Benchmarking hospital lengths of stay using histograms.

The authors demonstrated that length of stay histograms can provide considerably more benchmark information concerning hospital lengths of stay than numerical benchmarks. Examples of histograms described the complete distribution of hospital stays, as well as levels of outliers, rather than simple numerical averages. Gathering such data led to a clearer understanding of the significant LOS impact of certain DRG outliers in the two different hospitals in Syracuse, NY. Given that the other two communities represented, Seattle, Washington and San Diego, California, were more influenced by extensive managed care penetration, variations in histogram data were less in evidence there. Histograms were designed with bars to show LOS distributions at the 50th, 75th, and 90th percentiles for each of the above DRGs. The greatest variations could be shown when comparing the 1997 LOS data on the various DRGs at the two Syracuse hospitals. At both hospitals the presence of a large contingent of outliers (for different types of mostly medical patients) could be seen as the major factor in driving up their overall LOS.

Combined benchmarking of hospital outcomes and utilization.

A strong case is made for developing quantitative benchmarks for hospital outcomes as well as utilization that includes both acute care re-admissions and lengths of stay. A number of hospitals in two distinctly different geographic health care environments [CA and NY] are studied as to the differences in outcomes and utilization for three of the most common high-cost DRGs. Unscheduled hospital readmissions (within 30 days of initial discharge) were employed as outcome indicators because they reflect both the quality of acute care and the need for case management in the post-discharge period. Benchmark targets were established for patients with a diagnosis of congestive heart failure, acute MI treated medically, or COPD using scattergrams that showed each hospital's mean acute LOS on the x axis and the re-admission rates on the y axis. "Benchmarks were identified as those points with the lowest values for both indicators, as demonstrated by points that were closest to the intersection of the two axes."

Using data in the case management process.

This article describes the role of the case manager in improving hospital utilization and outcomes. It suggests a number of indicators for measurement of quality and resource use based on extensive experience with the case management process. It also provides guidance concerning the development and use of these indicators within acute care organizations. It provides information concerning specific situations encountered by case managers.

Length of stay reduction: two innovative hospital approaches.

As staffing limitations and reimbursement constraints challenge hospitals to provide effective care, the development of greater clinical efficiency has become imperative. One promising alternative in this area is the reduction in hospital lengths of stay. This study describes two different efforts at length of stay reduction implemented by large general hospitals in Syracuse, New York. One of these programs, implemented by Crouse Hospital, focused on the use of continuum of care guidelines implemented by case managers and nursing staff on a daily basis. The other approach, employed by St. Joseph's Hospital Health Center, focused on the use of physician education and the periodic distribution of individual length of stay profiles. Each of these approaches has generated notable successes in the reduction of inpatient acute care lengths of stay.

Using health care data to improve utilization and outcomes. Introduction.

This article suggests that the information age has generated extensive amounts of additional health care data, but has not provided the tools to access this information. It identifies principles for development and use of health care data including clarity of sources, validity and consistency, importance and relevance, accessibility, and the human element.

Reducing hospital inpatient lengths of stay.

This article describes the importance of hospital length of stay as an indicator of health care efficiency and provides guidance concerning the development of data for length of stay reduction. It identifies variables involved in length of stay evaluation including the mean stay, median stay, and length of stay standard deviation. It describes how consistent length of stay data can be generated and analyzed for local populations and benchmark communities.

Hospital readmission: predicting the risk.

This approach focused on identifying specific variables that predict the likelihood of readmission. It involved clinical, utilization, and demographic variables that are generally available on hospital computer abstract databases. The approach included a process for identifying and comparing individual variables with the highest risk of readmission. It also contained a procedure for assembling risk populations including combinations of variables. The approach demonstrated the potential for using risk analysis to maximize the focus of clinical management on patient outcomes while reducing the amount of resources required for this process.

Hospital readmissions at the communitywide level: implications for case management.

This study describes the development of information concerning the distribution of hospital readmissions by diagnosis in seven different United States metropolitan areas. The data demonstrated that circulatory disorders were associated with the largest number of communitywide readmissions in all of the communities. It also showed that circulatory, respiratory, and digestive disorders accounted for a majority of readmissions in all of the areas. This information suggested that case management efforts to reduce readmissions can focus on a limited range of clinical diagnoses. This approach should enable the process to function effectively within resource constraints.

Analyzing hospital readmissions using statewide discharge databases.

Hospital readmissions are an important indicator of the outcomes of care as well as a source of unnecessary health care expenditures. This study focused on development of a uniform algorithm for identification of hospital readmission data. It involved development of a uniform definition of readmissions which could be applied to multiple statewide computer databases. Through this approach, comparable readmit data were generated for use in benchmarking and quality improvement activities.

Using data to reduce hospital readmissions.

This article describes the importance and the development of data concerning hospital readmissions as an outcomes indicator. It emphasizes the need for consistent definition of readmissions according to time intervals and diagnostic categories. It describes the development of readmission information using computer abstract databases to ensure consistency of indicators. It also provides examples of data developed through this approach.