American Journal of Medical Quality

The interest in quality management in long-term care has been rapidly escalating. This movement to assess and improve quality parallels the effort carried out by hospital management in the past 10 years. The methodological concerns of the 2 areas are similar. This essay identifies 10 issues to which quality management leaders should pay attention as they begin to expand the capability of addressing quality in long-term care: client-centered performance versus whole-organization performance; standardization of methods and instruments; reliability; and validity, multimethod thinking, the meaning of data, comparability of data across organizations, cost barriers, feedback mechanisms, management use of quality data, and public control of data.

A system to predict which patients will suffer medical complications or poor financial outcomes during a hospitalization would be very useful to providers of medical care. To develop such a system, we applied two previously developed indices that predict in-hospital complications to all 321,558 adult patients discharged from our hospital network. The indices identified 26,377 patients (8.2%) who experienced one or more medical complications. For these patients, high-risk admitting diagnoses were identified. We tabulated 4235 admitting diagnoses and focused on 26 (0.6%) diagnoses that were high-risk and high-volume for complications. We found that 25% of patients with these admitting diagnoses experienced complications during hospitalization. Prevention of these complications could have saved 1241 hospital days, 11 lives, and $10.5 million. Administrative data available at the time of admission can be useful in identifying the small subset of patients who are likely to experience adverse clinical outcomes during a hospitalization and those who are likely to generate adverse financial outcomes for the hospital.

Prior studies of resource use for coronary artery bypass graft (CABG) surgery have either focused on a limited number of hospitals or have used charges instead of costs. We used a large statewide database (n = 6791) to study predictors of cost and length of stay (LOS) for CABG surgery. We used linear regression to sequentially model (a) specific procedures performed, (b) preoperative patient characteristics, and (c) postoperative events to determine the relative impact of these 3 factors on resource use. We then used the resulting models to calculate adjusted mean hospital costs and LOS. These 3 factors were all significantly associated with resource use. Postoperative events were the greatest determinant of costs, while preoperative characteristics were the greatest determinant of LOS. Despite risk adjustment for these factors, resource use differed significantly across 12 hospitals (mean cost range, $22,200 to $41,900; mean LOS range, 11 to 18 days), suggesting that some institutions may need to reduce their resource use.

The effectiveness of risk adjustment in improving mortality as a performance measure for hospitals remains uncertain. New techniques of risk adjustment should be empirically tested, and health care professionals, using the data derived from such measures, should be queried before final acceptance of these technologies of measurement is warranted. The Risk Adjusted Clinical Outcomes Methodology-Quality Measures (RACOM-QM), a relatively new risk-adjustment methodology developed by the QuadraMed Corporation, was used by Maryland hospitals for risk adjustment for the first time in 1997. A research study was undertaken by the Maryland Hospital Association to determine the impact of RACOM-QM on mortality rates, its empirical validity, and its acceptance in the field. The relationship between RACOM-QM mean risk scores and mortality rates was examined using inpatient hospital mortality data for Maryland in 1996. Using these same data, the empirical relationship between risk-adjusted and unadjusted mortality by diagnosis-related group (DRG) was also investigated. Case studies were undertaken to glean information about the use and acceptability of this new methodology in 2 hospital settings in Maryland. There was a strong relationship between mean mortality risk scores and mortality rates. The analysis of the empirical relationship between risk-adjusted and unadjusted mortality by DRG yielded support for the impact of RA-COM-QM in adjusting inpatient mortality rates. The case studies supported the utility of this method of risk adjustment in increasing the interpretation of mortality data and in helping to identify areas in which to investigate quality in more depth in 2 hospital settings. This study provides overall support for the usefulness of risk adjustment and, specifically, the RACOM-QM, in increasing the interpretation of inpatient mortality rates in Maryland's acute care hospitals. This study also suggests that use of the RACOM-QM improved comparative analysis of inpatient mortality rates among Maryland hospitals. Finally, the results of the case study analysis suggest that improved internal review of mortality rates and increased clinician acceptance of these rates as indicators of performance were enhanced by the use of a risk adjustment methodology.

In an effort to provide high quality care in a more cost-effective manner, health care providers have found it necessary to implement a series of decision support strategies designed to improve outcomes of care. While each of these strategies has measurable benefits, each comes along with additional costs. As more and more technology becomes available and more labor resources are devoted to these efforts, it becomes crucial to be able to assess the costs and benefits of these programs. A return-on-investment methodology is used to assess the financial impact of service-related operating expenses compared to revenue gains from service delivery. However, unlike traditional return-on-investment models, in health care, benefits are frequently gained from cost avoidance rather than from revenue enhancement activities. This article will describe a methodology for measuring the direct and indirect costs and qualitative and quantitative benefits of decision support activities.

The objective of this study was to develop and validate a method for identifying Medicare beneficiaries with diabetes by using Medicare claims data. We used self-reports of diabetes status from participants in the Medicare Current Beneficiary Survey to determine disease status, and then we examined these participants' Medicare claims. Using self-reported diabetes status as the "gold standard," we determined the sensitivity, specificity, and reliability of claims data in identifying beneficiaries with diabetes. We found that to construct a method that is adequately sensitive (-70%1/), highly specific (?97.5%), and reliable (kappa - 0.80), researchers must combine information from different types of Medicare claims files, use 2 years of data to identify cases, and require at least 2 diagnoses of diabetes among claims involving ambulatory care. Since these criteria are met by more than one method, the choice of method should be governed by the goals of the research as well as more practical concerns.