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Biomedical subjects

M Pine

Publications and source records attributed to M Pine.

At least 19 recordsLinked to original sources

Risk-adjusted measurement of primary cesarean sections: reliable assessment of the quality of obstetrical services.

A two-hospital system reported widely disparate Cesarean section rates in its component institutions. Statistical analysis determined that the apparent discrepancy was due primarily to patient-related factors. When risk-adjusted, both hospitals' rates were indistinguishable from expected rates. Reporting Cesarean section rates without appropriate risk adjustment yields potentially misleading results. Since reliable risk adjustment currently exists only for primary Cesarean sections, primary rates should be reported separately from "raw" rates for other procedures.

Cesarean Section↗

Laboratory values improve predictions of hospital mortality.

OBJECTIVE: To compare the precision of risk adjustment in the measurement of mortality rates using: (i) data in hospitals' electronic discharge abstracts, including data elements that distinguish between comorbidities and complications; (ii) these data plus laboratory values; and (iii) these data plus laboratory values and other clinical data abstracted from medical records. DESIGN: Retrospective cohort study. SETTING: Twenty-two acute care hospitals in St Louis, Missouri, USA. STUDY PARTICIPANTS: Patients hospitalized in 1995 with acute myocardial infarction, congestive heart failure, or pneumonia (n = 5966). MAIN OUTCOME MEASURES: Each patient's probability of death calculated using: administrative data that designated all secondary diagnoses present on admission (administrative models); administrative data and laboratory values (laboratory models); and administrative data, laboratory values, and abstracted clinical information (clinical models). All data were abstracted from medical records. RESULTS: Administrative models (average area under receiver operating characteristic curve=0.834) did not predict death as well as did clinical models (average area under receiver operating characteristic curve=0.875). Adding laboratory values to administrative data improved predictions of death (average area under receiver operating characteristic curve=0.860). Adding laboratory data to administrative data improved its average correlation of patient-level predicted values with those of the clinical model from r=0.86 to r=0.95 and improved the average correlation of hospital-level predicted values with those of the clinical model from r=0.94 for the administrative model to r=0.98 for the laboratory model. CONCLUSIONS: In the conditions studied, predictions of inpatient mortality improved noticeably when laboratory values (sometimes available electronically) were combined with administrative data that included only those secondary diagnoses present on admission (i.e. comorbidities). Additional clinical data contribute little more to predictive power.

Clinical Laboratory Information Systems↗

Anthem Blue Cross and Blue Shield's coronary services network: a managed care organization's approach to improving the quality of cardiac care for its members.

OBJECTIVE: To describe a managed care organization's efforts to improve value for its members by forming a coronary services network (CSN). DESIGN: To identify high-quality facilities for its CSN, Anthem Blue Cross and Blue Shield reviewed claims data and clinical data from hospitals that met its general quality standards. An external firm measured and risk-adjusted applicant hospitals' mortality rates. Hospitals that demonstrated superior performance were eligible to join the CSN. In 1996, 2 years after the CSN was formed, clinical outcomes of participants and new applicants were analyzed again by the same external firm. PATIENTS AND METHODS: Data on more than 10,000 consecutive (all-payer) inpatients discharged after coronary bypass surgery in 1993 were collected from 16 applicant hospitals using a uniform format and data definitions. This analysis was expanded to 23 participating and applicant hospitals that discharged more than 13,000 patients who underwent either bypass surgery or coronary revascularization in 1995. We compared risk-adjusted routine length of stay (a measure of efficiency), mortality rates, and adverse outcome rates between CSN and non-CSN facilities. RESULTS: From 1993 to 1995, overall length of stay in the network decreased by 20%, from 12.3 to 9.8 days (P < or = 0.01) and severity-adjusted mortality rates decreased by 7.3%, from 2.9% to 2.7%. Initially, facilities outside the network had comparable efficiency but much higher mortality. However, they improved so much in both measures that their severity-adjusted mortality rate for bypass surgery in 1995 was no more than 10% higher than that of CSN hospitals. CONCLUSION: The creation of a statewide CSN that emphasized and improved the level of performance among providers ultimately benefited the carrier's managed care members. The desirability of participation was evidenced by an increase in the number of applicant hospitals over the 2 years. This may have stimulated quality improvement among competing providers in the region and among CSN facilities themselves.

Blue Cross Blue Shield Insurance Plans↗

Predictions of hospital mortality rates: a comparison of data sources.

BACKGROUND: Comparing hospital mortality rates requires accurate adjustment for patients' intrinsic differences. Commercial severity systems require either administrative data that omit vital clinical facts about patients' conditions at hospital admission or costly, time-consuming abstraction of medical records. The validity of supplementing administrative data with laboratory data has not been assessed. OBJECTIVE: To compare risk-adjusted mortality predictions using administrative data alone; administrative data plus laboratory values; and the combination of administrative, laboratory, and clinical data. DESIGN: Retrospective cohort study. SETTING: 30 acute care hospitals. PATIENTS: 46,769 patients hospitalized with acute myocardial infarction, cerebrovascular accident, congestive heart failure, or pneumonia. MEASUREMENTS: Each patient's probability of dying was estimated by using administrative data only (unrestricted administrative models), administrative data restricted to secondary diagnoses that are unlikely to be hospital-acquired complications (restricted administrative models), restricted administrative data plus laboratory data (laboratory models), and restricted administrative data plus laboratory and abstracted clinical data (clinical models). RESULTS: The unrestricted administrative models predicted death better than the restricted administrative models (average areas under the receiver-operating characteristic [ROC] curves, 0.87 and 0.75, respectively) and as well as the laboratory models and the clinical models (average areas under the ROC curves, 0.86 and 0.87, respectively). The good mortality predictions obtained by using the unrestricted administrative models result from inclusion of hospital-acquired complications that commonly precede death. The laboratory models ranked 93% of patients and 95% of hospitals in a manner similar to the clinical models; in comparison, rankings provided by the laboratory models were similar to those provided for 75% of patients and 69% of hospitals by the unrestricted administrative models and for 72% of patients and 77% of hospitals by the restricted administrative models. CONCLUSIONS: Adding laboratory data (often available electronically) to restricted administrative data sets can provide accurate predictions of inpatient death from acute myocardial infarction, cerebrovascular accident, congestive heart failure, or pneumonia. This alternative avoids the cost of data abstraction and the serious errors associated with using administrative data alone.

Adult↗

Care of patients with upper gastrointestinal hemorrhage in academic medical centers: a community-based comparison.

BACKGROUND & AIMS: A common perception among purchasers is that academic medical centers are inefficient and overutilize technology; however, little empirical information exists. The aim of this study was to compare treatment and outcomes of patients with upper gastrointestinal hemorrhage admitted to major teaching hospitals and other hospitals in a large metropolitan area. METHODS: Data on 3801 consecutive eligible patients admitted to five major teaching hospitals and 25 other hospitals from 1991 to 1993 were obtained by review of medical records. Admission severity of illness was measured using validated multivariable models. RESULTS: Rates of upper endoscopy were somewhat lower among the 1004 patients discharged from fellowship hospitals, compared with the other 2797 patients (82.9% vs. 85.6%; P < 0.05), and the use of other procedures was similar. Although patients admitted to fellowship hospitals tended to have a higher severity of illness, both unadjusted (6.3 +/- 9.0 vs. 7.1 +/- 7.5 days; P < 0.01) and risk-adjusted length of stay were somewhat shorter. Mortality rates were similar between hospitals, and patients admitted to fellowship hospitals were somewhat less likely to be transfused. CONCLUSIONS: In patients with upper gastrointestinal hemorrhage, teaching hospitals do not appear to provide inefficient care or overutilize expensive treatments when compared with community facilities. These findings are noteworthy at a time when viability of academic centers and fellowship training is threatened.

Academic Medical Centers↗

Forming a coronary services network on the basis of quality of care.

An Ohio insurance company's initiative to emphasize risk-adjusted clinical outcomes as criteria for selecting and reimbursing members of a network is stimulating a new emphasis on quality of care throughout the market area. Hospitals inside the network are cooperating to improve their collective results, while providers on the outside have launched major quality improvement programs in the effort to become measurably competitive with these centers of excellence. This case study in network selection demonstrates a new role for fiscal intermediaries in health care.

Cardiology Service, Hospital↗

Important considerations in using indicators to profile providers.

The demand is accelerating for information about the clinical performance of providers. In the more competitive and value-sensitive marketplace that is already developing, purchasers (consumers, employers, and insurers) of health care services will require more information to better assess the relative value of providers' (professional and hospital) services. The cornerstone of a wise, value-based strategy in selecting health care services is careful assessment of each provider's performance based on detailed, quantitative data in the form of clinical indicators. The use of indicators to profile the comparative performances of providers allows purchasers to compare as well as to influence provider performance.

Health Services Research↗

Standardization of terms and analytic methods for performance evaluation: achievable goal or impossible dream?

Communication about health care management is severely limited by the usage of words borrowed from the diverse professional groups that have contributed to the field. This article suggests development of a glossary of terms to eliminate much of the current confusion in discussions of outcomes, reduce redundancy, and form the basis for interdisciplinary understanding. As a starting point, some major concepts are discussed that warrant further refinement and definition. Also highlighted is the need for standards in describing and evaluating systems designed to measure outcomes of health care, with the goal of enabling consumers to compare these systems meaningfully.

Health Services Research↗

Twelve questions to ask about your outcomes monitoring system--Part I.

Outcomes monitoring is an integral part of any decision maker's information resources--the cornerstone of a provider's commitment to quality improvement or of a purchaser's strategy for seeking value. In their eagerness to obtain useful information about provider performance, purchasers and consumers naively may accept flawed evaluations and thereby create perverse incentives for providers that undermine the very qualities they wish to foster. Inaccurate or misleading information about provider performance will lead managers to reward the wrong behavior and so induce more of it. Inaccurate information also can discourage better providers whose performances are not recognized and can lead all providers to distrust and denounce clinical monitoring in general. When these things happen, the great value of outcomes monitoring systems as a tool for quality improvement is lost.

Data Collection↗

Twelve questions to ask about your outcomes monitoring system--Part II.

Commercial and customized outcomes monitoring systems designed to assess the results of care, whether clinical outcomes or resource use, are not all of equal value or equally appropriate for every use. In creating each system, its developers had to make critical decisions about such matters as definitions of outcomes for study, selection of patients, selection of data elements, methods and timing of data collection, and method of analysis and reporting. Each system represents a unique set of choices that were made. This series of two articles presents answers to 12 questions that will help users understand the basic workings of an outcomes monitoring system--to be able to distinguish good systems from the mediocre and the bad, and to make wise use of a system already in operation. In addition to the six questions presented in the April 1994 issue of Physician Executive, the following six questions are of critical importance in determining a system's value to you and your organization.

Data Collection↗

Designing and using case mix indices.

Any assessment of clinical care in which provider performances will be compared to norms requires adjusting for differences among patient populations. Basic issues that must be addressed include: (1) specification of the population that will be adjusted for case mix; (2) selection, definition, and weighting of factors that will be used to determine case mix; (3) validation of the proposed case mix indices; and (4) application of indices to samples of interest. The authors consider each of these issues, using illustrations from the Greater Cleveland Health Quality Choice Project and other outcomes monitoring projects.

Data Collection↗

The quality encounter between purchasers and providers: what to ask and how to answer.

It is not easy to lay prejudices aside, to find and face the facts, and to let the chips fall where they may. There is no need to wait for improvements in technology before beginning to use outcomes monitoring to guide the business exchange between purchasers and providers. Although future technological improvements will greatly improve the capacity to use objective data to differentiate among providers and to identify strengths and weaknesses within a health care organization, reasonable methods are available today to undertake these important tasks. All that is needed is the courage and willingness to embrace them. The future belongs to purchaser and provider organizations that are willing to seek enlightenment rather than blame.

Managed Care Programs↗

Data about mortality.

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Data Interpretation, Statistical↗

Using clinical variables to estimate the risk of patient mortality.

The Health Care Financing Administration (HCFA) uses information from hospital bills, such as age, sex, and diagnoses, to estimate statistical models for the probability, or risk, of death during and after hospital stays. The average risk estimates (expected death rates) are compared with the actual death rates to identify potentially poor quality of care. However, the methods have been criticized as inadequate and an often cited reason is the failure to incorporate risk factors for mortality that are known from clinical research. This hypothesis was tested using a stratified, random sample of 41,963 Medicare patients in 84 hospitals. Many clinical measurements were abstracted for testing as possible risk factors, and a few (26) were identified as useful predictors of death using logistic regression. The estimated regressions accounted for 39% of the variation in mortality, a standard severity classification accounted for 29%, and a relatively simple classification of patients into 17 groups, based on diagnoses, accounted for 17%. The logistic regressions yielded more accurate estimated mortality rates than the severity classification, which in turn was superior to the estimation methods used by HCFA. The HCFA methods were found to be biased in identifying outlier hospitals and this bias can be removed or ameliorated by using clinical risk factors to predict mortality. It is possible to estimate the risk of death more accurately using clinical risk factors and to measure the quality of care.

Centers for Medicare and Medicaid Services, U.S.↗

Clinical relevance of verapamil plasma levels in stable angina pectoris.

Plasma levels of verapamil and norverapamil were evaluated in 77 patients who received oral verapamiL for treatment of angina pectoris. There was a sixfold interpatient variation in verapamil plasma concentrations, but plasma concentrations were linearly related to doses of the drug (240 to 480 mg/day) in the same patient (r = 0.81). Plasma concentrations of norverapamil, the major active metabolite of verapamil, were similar to those of verapamil. Although verapamil produced a significant improvement in exercise tolerance in most patients, the increase in exercise time was not related to plasma levels of the drug; however, most patients with improvement had plasma levels exceeding 100 ng/ml. An increase in the dose of verapamil from 320 to 480 mg daily produced a substantial (71%) increase in plasma levels of the drug but no significant increase in exercise tolerance (4%). The average increase in the P-R interval with 480 mg of verapamil in these 77 patients was small (8%) (161 +/- 18 to 174 +/- 22 ms); on.y six patients had first-degree heart block. These minor effects on atrioventricular conduction were noted despite plasma verapamil concentrations in 76 patients that exceeded 100 ng/ml, a level that successfully converts supraventricular tachycardias after intravenous drug administration. We differences between the effects of oral and intravenous verapamil on atrioventricular conduction may be the result of stereoselective hepatic inactivation of verapamil's I-isomer. Determination of plasma levels of verapamil is of limited value in the management of patients with angina pectoris, but may be useful in the identification of nonresponders with plasma levels less than 100 ng/ml who may benefit from a further increment in drug dose.

Administration, Oral↗

Mechanical circulatory support in postoperative cardiogenic shock.

A 38-year-old white woman had cardiogenic shock after elective mitral valve replacement and was unresponsive to pressor drugs and intra-aortic balloon counterpulsation. A left ventricular assist device (left ventricle to ascending aorta) was implanted 16 hours after the initial operation and provided circulatory support for 8 days. Improvement in the patient's own cardiac performance was documented, and there were no complications attributable to the assist device. However, intercurrent medical problems resulted in clinical deterioration on the fifth day after operation, and the patient died 8 days after operation. The findings in this patient suggest a potential role for this left ventricular assist device in future cases of acute, intractable, but potentially reversible myocardial failure.

Acute Kidney Injury↗

The use of drug concentration measurements in studies of the therapeutic response to propranolol.

The dose of propranolol which produces the optimal therapeutic effect in patients with angina pectoris has been found to vary widely among patients. Because the plasma concentration of propranolol also differs markedly due to interpatient variability in absorption it seemed possible that this might be the reason for the wide range of effective doses in angina and that plasma propranolol might provide a useful guide to therapeutic response. To examine this possibility we selected ten patients with coronary artery disease complicated by angina and studied them at varying propranolol doses to a maximum of 320 mg/day. Exercise capacity was tested on a treadmill and plasma propranolol concentration was measured by gas liquid chromatography. In seven normal subjects beta-blockade was quantified precisely as inhibition of exercise tachycardia and was related to plasma propranolol levels at various doses. Maximal beta-blockade occurred at 100 ng/ml of plasma propranolol, but the dose response curve of blockade was relatively flat and the ED50 of plasma propranolol was 8+/-1 ng/ml. In the patients maximal therapeutic benefit from propranolol occurred at 30+/-7 ng/ml and at a dose of 144 mg/day. This resulted in an increase in exercise capacity from an estimated 12-7+/-0-8 ml/kg/min of oxygen consumption during control to 17-2+/-1-1 ml/kg/min on the drug. Thus, there was a wide variation of both dose and concentration among these patients at the maximum therapeutic response. However, when plasma propranolol was related to pharmacologic activity, the maximum therapeutic response was observed between 64 and 98% of total blockade. These studies indicated the extent of beta-blockade necessary to produce an effective therapeutic response in angina, but demonstrate that plasma drug levels provide no practical guide to therapy in patients with angina pectoris. A further study was conducted measuring plasma propranolol in twenty hypertensive patients to investigate the fall in blood pressure in relations to the change in plasma renin activity and the inhibition of cardiac adrenergic receptors. The inhibition of plasma renin closely resemble the response seen in heart rate inhibition in that the maximum response is seen at 100 ng/ml and the ED50 was 11 ng/ml. In contrast propranolol is shown only to begin to have a significant effect on blood pressure at a plasma level of 30 ng/ml and the effect becomes progressively greater as the plasma level increases. This suggests that the hypotensive effect of propranolol may be dissociated from the beta-blocking effects of cardiac and renin releasing receptors.

Angina Pectoris↗