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Hermann, R.C., Regner, J.L., Erickson, P., and Yang, D. (2000, November). "Developing a quality management system for behavioral health care: The Cambridge Health Alliance experience." (AHRQ grant HS10303). Harvard Review of Psychiatry 8, pp. 251-260.

These authors use a case study of one organization's experience to demonstrate the implementation of a quality management program in a behavioral health care delivery system. The case study emphasizes how theoretical frameworks were operationalized and how organizational structure and process were shaped to address challenges well known in quality management, such as authority, accountability, and follow-through. The study revealed that continuous quality improvement (CQI) activities had some impact in some areas. For example, CQI reduced high readmission rates for patients who were admitted for both a mental disorder and active substance abuse and who had previously left the program by changing off-unit privileges for patients undergoing detoxification. Adolescent inpatient services used interventions such as staff training in de-escalation techniques to reduce the high rates of physical restraint of adolescents.

Monheit, A.C., and Selden, T.M. (2000). "Cross-subsidization in the market for employment-related health insurance." Health Economics 9, pp. 699-714.

Most non-elderly individuals in the United States receive their health insurance through their employer. These researchers used data from the 1987 National Medical Expenditure Survey to examine the nature of equilibrium in the market for employment-related health insurance. They examined coverage generosity, premiums, and insurance benefits net of expenditures on premiums. Despite a degree of market segmentation, there was a substantial amount of pooling of heterogeneous risks in 1987 among households with employment-related coverage. These results were largely invariant to firm size and whether or not employers offered a choice among health plans. The findings suggest the need for caution concerning the incremental reforms that would weaken the link between employment and insurance without substituting alternative institutions for the pooling of risks.

Reprints (AHRQ Publication No. 01-R034) are available from the AHRQ Publications Clearinghouse.

Peterson, E.D., DeLong, E.R., Muhlbaier, L.H., and others (2000). "Challenges in comparing risk-adjusted bypass surgery mortality results." (AHRQ grant HS09940). Journal of the American College of Cardiology 36(7), pp. 2174-2184.

Cardiovascular "report cards" often compare surgical outcomes after adjustment for patient risk factors. However, it is unclear to what extent the risk-adjustment process itself may affect these metrics. As part of the Cooperative Cardiovascular Project's Pilot Revascularization Study, these authors compared the accuracy of four coronary artery bypass graft (CABG) surgery clinical risk models to predict mortality among 3,654 Medicare patients undergoing CABG surgery at 28 hospitals in Alabama and Iowa. Although the four risk models had similar discriminatory abilities, certain models tended to overpredict mortality in higher risk patients. There was a high correlation between a hospital's risk-adjusted mortality rates regardless of which model was used, supporting their use as a means of provider performance feedback. In contrast, there was limited agreement on identification of hospitals as "performance outliers" (with either superior or inferior performance compared with other hospitals), depending on which risk-adjustment model was used. For example, one model identified 10 significantly superior hospitals but no hospitals as inferior performers. Another model identified only one significantly superior hospital and four inferior hospitals.

Richardson, D.K., Corcoran, J.D., Escobar, G.J., and others (2001). "SNAP-II and SNAPPE-II: Simplified newborn illness severity and mortality risk scores." (AHRQ grant HS07015). Journal of Pediatrics 138, pp. 92-100.

These authors developed and validated simplified neonatal illness severity and mortality risk scores based on data on newborns treated at 30 neonatal intensive care units in the mid-1990s. The first-generation newborn illness severity score, Score for Neonatal Acute Physiology (SNAP), was cumbersome to use because of the number and complexity of items. These researchers started with the 34 data items of the SNAP to derive the most simple logistic model for in-hospital mortality using 10,819 randomly selected Canadian cases. SNAP-II includes six physiologic items. To this are added points for birthweight, low Apgar score, and small for gestational age to create a nine-item SNAP-Perinatal Extension-II (SNAPPE-II). The authors validated SNAPPE-II on the remaining 14,610 cases. For all birthweights, SNAPPE-II had excellent discrimination and goodness of fit. The researchers conclude that SNAP-II and SNAPPE-II are empirically validated illness severity and mortality risk scores for newborn intensive care. They are simple, accurate, and robust across populations.

Schneeweiss, S., Maclure, M., Walker, A.M., and others (2001, February). "On the evaluation of drug benefits policy changes with longitudinal claims data: The policy maker's versus the clinician's perspective." (AHRQ grant HS10881). Health Policy 55, pp. 97-109.

These authors explore evaluation of drug benefits policy changes using longitudinal claims data, using the example of differential cost sharing (DCS). In DCS, patients must pay a prescription copayment, which is higher for more expensive medications. In a policy model, estimates represent summary effects of benefits and harms, separately identifiable in those complying with the intended policy and those not complying. Results from a policy model apply only to a specific policy implementation and tend to underestimate effects when non-compliance is high. However, clinical decisionmakers and patients are interested in the consequences of patients' actual compliance with the policy. A clinical model assesses the effects of DCS depending on the actual treatment in contrast to the treatment intended by the policy. The authors conclude that both policy and clinical models should be tested with a clear understanding of these different perspectives to evaluate the effects of drug cost-containment policies.

Selden, T.M., and Moeller, J.F. (2000). "Estimates of the tax subsidy for employment-related health insurance." National Tax Journal 53(4), pp. 877-887.

These AHRQ researchers employed the MEDSIM microsimulation model to compute new tax revenue estimates associated with the preferential tax treatment of employment-related coverage. They examine a range of methods for constructing estimates, exploring the impact of alternative methods for aging premiums, and comparing effective versus statutory Social Security tax rates. Their results, based on statutory Social Security rates, coincide closely with Congressional Budget Office estimates. However, those estimates overstate the tax expenditures in that they ignore the effect of higher taxable wages on future Social Security benefits. Switching to effective Social Security tax rates reduced their estimates of the tax expenditure by 17 percent. When they allowed for increased medical expense itemization and for reductions in duplicative coverage, their estimates of tax expenditures were reduced even more.

Reprints (AHRQ Publication No. 01-R030) are available the AHRQ Publications Clearinghouse.

Shortell, S.M., Bazzoli, G.J., Dubbs, N.L., and Kralovec, P. (2000, fall). "Classifying health networks and systems: Managerial and policy implications." (AHRQ grant HS09524). Health Care Management Review 25(4), pp. 9-17.

About 72 percent of U.S. hospitals now belong to a health network or system. These health networks and systems reflect complex relationships among hospitals, physician groups, and insurance plans that cannot be adequately captured by traditional hospital descriptors of ownership, size, teaching status, and/or location. New measures are needed to more appropriately reflect the restructuring of the U.S. health care system. These authors classified networks and systems into five categories: centralized health networks/systems, centralized physician/insurance health systems, moderately centralized health networks and systems, decentralized health networks/systems, and independent health networks/systems. They used three different health care systems to illustrate how this new approach to classification can be used to evaluate the readiness of health care organizations to accept risk. The classification system also can be used to assist executive and physician leaders in making decisions involving the centralization of services, the number of different services to offer, and whether or not to enter into various strategic alliances. The system can be updated to help track the evolution of the U.S. health care system over time.

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Current as of March 2001
AHRQ Publication No. 01-0024

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