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D J Brailer

Publications and source records attributed to D J Brailer.

9 recordsLinked to original sources
PubMed1996

Improving performance with clinical decision support.

BACKGROUND: CADU/CIS (Clinical and Administrative Decision-support Utility and Clinical Information System) is a clinical decision-support workstation that allows large volumes of clinical information systems data to be analyzed in a timely and user-friendly fashion. CARE PROCESS MEASUREMENT: For any given disease, subgroups of patients are identified, and automated, customized "clinical pathways" are generated. For each subgroup, the best practice norms for use of test and therapies are identified. Practice style variations are then compared to outcomes to focus inquiry on decisions that significantly affect outcomes. CASE STUDY: INTESTINAL OBSTRUCTION: Graduate Health Systems, a multisite integrated provider in the Philadelphia area, has used CADU/CIS to improve quality problems, reduce treatment-intensity variations, and improve clinical participation in care process evaluation and decision making. A task force selected intestinal obstruction without hernia as its first study because of the related high-volume and high-morbidity complications. Use of a ten-step method for clinical performance improvement showed that the intravenous administration of unnecessary fluids to 104 patients with intestinal obstruction induced congestive heart failure (CHF) in 5 patients. Task force members and other practicing physicians are now developing guidelines and other interventions aimed at fluid use. Indeed, the task force used CADU/CIS to identify an additional 250 patients in one year whose conditions were complicated by CHF. CONCLUSION: A clinical decision support tool can be instrumental in detecting problems with important clinical and economic implications, identifying their important underlying causes, tracking the associated tests and therapies, and monitoring interventions.

Aged↗
PubMed1996

Comorbidity-adjusted complication risk: a new outcome quality measure.

The measurement of inpatient complications his received substantial attention in recent years because mortality rates and other outcome measures often appear unable to discriminate superior from inferior hospital care. Complication measurement holds out the promise of being more sensitive to variations in patient care because complications occur more frequently than do mortalities, and because complications are more direct consequences of the process of care. The authors developed a new measure of complications that seeks to give insight into the patient care given by different hospitals or physicians by using commonly available data. Specifically, this measure is based on a decision-theoretic model that estimates the probability of a complication for combinations of admitting and secondary International Classification of Diseases, 9th Revision, Clinical Modification diagnoses. The measure can be evaluated at the patient level, or aggregated and risk-adjusted for the population of a given care provider (eg, physician or hospital). When applied to a set of patient-level UB- 82/92 data, this measure estimates the risk of complication for any member of a population, controlling for comorbidity, and hence is designated comorbidity-adjusted complication risk (CACR). The authors describe the development of CACR and its testing and validation using data acquired from the states of Pennsylvania, California, and Florida, as well as facility data obtained directly from hospitals. The data set includes 480,000 patients from 50 Pennsylvania hospitals, 300,000 patients from 33 Florida hospitals, 370,000 patients from 35 California hospitals, and 37,000 patients from six validation hospitals. Comorbidity-adjusted complication risk is constructed from widely available data common to most patient cases. Comorbidity-adjusted complication risk can be adjusted for its case mix, but such risk adjustment has much less effect on CACR than on other adverse outcomes such as mortality and morbidity. Comorbidity-adjusted complication risk varies widely across the hospitals in this sample, yet it is stable across time and is correlated with other known quality outcomes, including such accepted "gold standards" as hospital-documented adverse event rates and chart review determinations of complications.

Aged↗
PubMed1996

Measuring hospital outcomes from a buyer's perspective.

This study reports lessons learned from a project to develop a flexible, generalizable, and valid method for corporate buyers of hospital care that would permit them to use available secondary data to rate the outcomes quality of all hospitals in a local market area. As hospitalization insurance has moved from coverage that applied equally to all licensed hospitals to arrangements which selected a certain preferred hospital or hospitals and rejected others, the need to determine the quality of different hospitals (as well as what they would cost the insurer or buyer) has become apparent. The product of this project was the development and demonstration of a set of rating methods that build on the strengths available in large hospital discharge data bases, such as (but by no means limited to) that of the Pennsylvania Health Care Cost Containment Council (PHC4). These measures, or others developed using these methods, deal with uncertainty in the data--its diagnosis and treatment--in a conceptually valid and practically useful way, illustrate a process that might be used in the general development of quality measures, and provide a useful critique of some other measures.

Commerce↗
PubMed1996

The impact of nonclinical factors on practice variations: the case of hysterectomies.

OBJECTIVE: This study investigates the role of nonclinical factors (physician characteristics) in explaining variations in hysterectomy practice patterns. DATA SOURCES AND STUDY SETTING: Patient discharge data are obtained from the Arizona state discharge database for the years 1989-1991. Physician data are obtained from the Arizona State Medical Association. The analyses are based on 36,104 cases performed by 339 physicians in 43 hospitals. STUDY DESIGN: This article measures the impact of physician factors on the decision to perform a hysterectomy, controlling for a host of patient and hospital characteristics. Physician factors include background characteristics and training, medical experience, and physician's practice style. Physician effects are evaluated in terms of their overall contribution to the explanatory power of regression models, as well as in terms of specific hypotheses to be tested. DATA COLLECTION: The sources of data were linked to produce one record per patient. PRINCIPAL FINDINGS: As a set, physician factors account for a statistically significant increase in the explanatory power of the model after addition of patient and hospital effects. Parameter estimates provide further support for the hypothesized effects of physicians' background, experience, and practice characteristics. CONCLUSIONS: Overall, the results confirm that nonclinical (physician) factors play a statistically significant role in the hysterectomy decision. Substantively, however, these factors play a smaller, secondary role compared to that of clinical and patient factors in explaining practice variations in hysterectomies. The results suggest that efforts to reduce unnecessary hysterectomies should be directed at identifying the appropriate clinical indications for hysterectomy and disseminating this information to physicians and patients. This may require such intervention strategies as continuing clinical education, promulgation of explicit practice guidelines, peer review, public education, and greater understanding and inclusion of patient preference in the decision process.

Adult↗
PubMedDate not supplied

The impact of computer-assisted test interpretation on physician decision making: the case of electrocardiograms.

This research investigated the effect of computer-assisted test interpretation (CATI) on physicians' readings of electrocardiograms (ECGs). The authors used an experimental method based on direct observations of 22 cardiologists, each reading 80 ECGs, for a total of 1,760 (of which 1,745 were used in the study). There were 40 sets of clinically-matched pairs of ECGs, one with CATI and one without. Reading time was observed and interpretation accuracy was measured by criterion-referenced aggregate scoring. To control for potential biases, the findings were subjected to multivariate analyses using ordinary least-squares regressions. The impact of CATI on cardiologists' readings of ECGs is demonstrably beneficial: the main empirical conclusion of this study is that, compared with conventional interpretation, the use of computer-assisted interpretation of ECGs cuts physician time by an average of 28% and significantly improves the concordance of the physician's interpretation with the expert benchmark, without increasing the false-positive rate. Moreover, CATI is the most accurate and saves the most time when the ECGs have many unambiguous diagnoses. Given that computers alone cannot perform the task of cardiovascular diagnosis, and that cardiologists' ECG interpretations are greatly enhanced by ubiquitous CATI technology, it appears that the best approach is one that combines person and machine.

Adult↗