Parisa Gholami | PubMed Health

PubMed2004

Translating research into practice: organizational issues in implementing automated decision support for hypertension in three medical centers.

Information technology can support the implementation of clinical research findings in practice settings. Technology can address the quality gap in health care by providing automated decision support to clinicians that integrates guideline knowledge with electronic patient data to present real-time, patient-specific recommendations. However, technical success in implementing decision support systems may not translate directly into system use by clinicians. Successful technology integration into clinical work settings requires explicit attention to the organizational context. We describe the application of a "sociotechnical" approach to integration of ATHENA DSS, a decision support system for the treatment of hypertension, into geographically dispersed primary care clinics. We applied an iterative technical design in response to organizational input and obtained ongoing endorsements of the project by the organization's administrative and clinical leadership. Conscious attention to organizational context at the time of development, deployment, and maintenance of the system was associated with extensive clinician use of the system.

Academic Medical Centers↗

PubMed2002

Accuracy of computerized outpatient diagnoses in a Veterans Affairs general medicine clinic.

BACKGROUND: Electronically available data, both administrative, such as outpatient encounter diagnostic data, and clinical, such as problem lists, are being used increasingly for outcome and quality assessment, risk adjustment, and clinical reminder systems. OBJECTIVE: To determine the accuracy of outpatient primary care diagnostic information recorded in administrative and clinical files in a Veterans Affairs VISTA (Veterans Health Information Systems and Technology Architecture) database compared with medical chart notes. STUDY DESIGN: Cross-sectional medical chart review of 148 patients attending a general medicine clinic at a university-affiliated Veterans Affairs hospital for 9 diagnoses relevant to the choice of drug therapy for hypertension. PATIENTS AND METHODS: An administrative file of encounter diagnoses, for a 2-year period, and a clinical file of the problem list maintained by the clinician were the sources of electronic diagnoses. We compared these sources with diagnoses abstracted by medical chart review. We estimated the sensitivity and specificity of each electronic data source for detecting medical chart note diagnoses. RESULTS: The sensitivity for 8 of the 9 study diagnoses was greater than 80% in the administrative file and 49% in the clinical problem list. The specificity was good for the administrative file (91% to 100%) and even better for the clinical file (98% to 100%). CONCLUSIONS: Outpatient encounter diagnoses relevant to hypertension recorded as electronic data had high specificity, and some codes had high sensitivity when collected over multiple visits. The administrative file was more sensitive but less specific than the clinical file. Administrative vs clinical files can be selected to minimize either the false-negative or the false-positive designations, respectively, as dictated by the needs of the quality assessment review.

California↗