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The goal of this study was to determine the accuracy and the impact of 5 different claims-based pneumonia definitions. Three International Classification of Diseases, Version 9, (ICD-9), and 2 diagnosis-related group (DRG)-based case identification algorithms were compared against an independent, clinical pneumonia reference standard. Among 10748 patients, 272 (2.5%) had pneumonia verified by the reference standard. The sensitivity of claims-based algorithms ranged from 47.8% to 66.2%. The positive predictive values ranged from 72.6% to 80.8%. Patient-related variables were not significantly different from the reference standard among the 3 ICD-9-based algorithms. DRG-based algorithms had significantly lower hospital admission rates (57% and 65% vs 73.2%), lower 30-day mortality (5.0% and 5.8% vs 10.7%), shorter length of stay (3.9 and 4.1 days vs 5.6 days), and lower costs (USD $4543 and USD $5159 vs USD $8585). Claims-based identification algorithms for defining pneumonia in administrative databases are imprecise. ICD-9-based algorithms did not influence patient variables in our population. Identifying pneumonia patients with DRG codes is significantly less precise. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Do-not-resuscitate (DNR) orders and advance directives are increasingly prevalent and may affect medical interventions and outcomes. Simple, automated techniques to identify patients with DNR orders do not currently exist but could help avoid costly and time-consuming chart review. This study hypothesized that a decision to withhold cardiopulmonary resuscitation would be included in a patient's dictated reports. The authors developed and validated a simple computerized search method, which screens dictated reports to detect patients with DNR status. METHODS: A list of concepts related to DNR order documentation was developed using emergency department, hospital admission, consult, and hospital discharge reports of 665 consecutive, hospitalized pneumonia patients during a four-year period (1995-1999). The list was validated in an independent group of 190 consecutive inpatients with pneumonia during a five-month period (1999-2000). The reference standard for the presence of DNR orders was manual chart review of all study patients. Sensitivity, specificity, predictive values, and nonerror rates were calculated for individual and combined concepts. RESULTS: The list of concepts included: DNR, Do Not Attempt to Resuscitate (DNAR), DNI, NCR, advanced directive, living will, power of attorney, Cardiopulmonary Resuscitation (CPR), defibrillation, arrest, resuscitate, code, and comfort care. As determined by manual chart review, a DNR order was written for 32.6% of patients in the derivation and for 31.6% in the validation group. Dictated reports included DNR order-related information for 74.5% of patients in the derivation and 73% in the validation group. If mentioned in the dictated report, the combined keyword search had a sensitivity of 74.2% in the derivation group (70.0% in the validation group), a specificity of 91.5% (81.5%), a positive predictive value of 80.9% (63.6%), a negative predictive value of 88.0% (85.5%), and a nonerror rate of 85.9% (77.9%). DNR and resuscitate were the most frequently used and power of attorney and advanced directives the least frequently used terms. CONCLUSION: Dictated hospital reports frequently contained DNR order-related information for patients with a written DNR order. Using an uncomplicated keyword search, electronic screening of dictated reports yielded good accuracy for identifying patients with DNR order information. [ABSTRACT FROM AUTHOR]

Planning the clinical evaluation of a computerized decision support system requires a strategy that encompasses the different aspects of the clinical problem, the technical difficulties of software and hardware integration and implementation, the behavioral aspects of the targeted users, and the discipline of study design. Although clinical information systems are becoming more widely available, only a few decision support systems have been formally evaluated in clinical environments. Published accounts of difficulties associated with the clinical evaluation of decision support systems remain scarce. The authors report on a variety of behavioral, logistical, technical, clinical, cost, and work flow issues that they had to address when choosing a study design for a clinical trial for the evaluation of an integrated, real-time decision support system for the automatic identification of patients likely to have pneumonia in an emergency department. In the absence of a true gold standard, they show how they created a credible, clinically acceptable, and economical reference standard for the diagnosis of pneumonia, to determine the overall accuracy of the system. For the creation of a reference standard, they describe the importance of recognizing verification bias and avoiding it. Finally, advantages and disadvantages of different study designs are explored with respect to the targeted users and the clinical setting. [ABSTRACT FROM AUTHOR]

Radiology Information Systems (RIS) are designed to capture and manage the data associated with ordering, executing, reporting, and billing x-ray procedures. The HELP Hospital Information System contains a radiology subsystem that supports these functions. In an effort to enhance quality assurance initiatives, we have created a supplemental data base. This data base contains not only the data traditionally generated by RISs but also data from the hospital system that is relevant to quality assurance. One of the goals associated with this data base is to use techniques from the discipline of Continuous Quality Improvement (CQI) in the radiology department. A focus of our initial efforts has been the time necessary to provide x-ray reports to ordering physicians once the imaging examination has been performed. Efforts to manage the portion of this time interval caused by transcription have resulted in a substantial decrease in the time required for this function. A second goal of this project is to evaluate the quality of x-ray ordering. This objective requires a computerized record of the outcome of the x-ray procedure. Initial analysis of data derived from this data base indicates significant differences in the ordering behavior for computed tomography (CT) examinations among a test group of physicians. A third goal is to do quality assurance on x-ray reports. Experience with pilot systems has shown promising results using a mathematical model of report quality. We hope to leverage these techniques and this quality assurance data base to define a CQI process for medical reports in general and for x-ray reports in particular. (C) 1997 by W.B. Saunders Company [ABSTRACT FROM AUTHOR]