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Biomedical subjects

Harvey Murff

Publications and source records attributed to Harvey Murff.

4 recordsLinked to original sources
PubMed2003

Primary care clinician attitudes towards electronic clinical reminders and clinical practice guidelines.

Compliance with outpatient practice guidelines is low and clinical reminders have had variable success in improving adherence rates. We surveyed primary care physicians (PCPs) regarding practice guidelines and the perceived utility of electronic reminders for both routine health maintenance (HM) items and chronic disease management. Most PCPs preferred receiving reminders in an electronic format rather than a paper format. Electronic reminders were felt to be more useful for HM items than for diabetes management. The majority of clinicians felt that electronic reminders significantly improved overall health care quality.

Attitude of Health Personnel↗
PubMedDate not supplied

Detecting adverse events using information technology.

CONTEXT: Although patient safety is a major problem, most health care organizations rely on spontaneous reporting, which detects only a small minority of adverse events. As a result, problems with safety have remained hidden. Chart review can detect adverse events in research settings, but it is too expensive for routine use. Information technology techniques can detect some adverse events in a timely and cost-effective way, in some cases early enough to prevent patient harm. OBJECTIVE: To review methodologies of detecting adverse events using information technology, reports of studies that used these techniques to detect adverse events, and study results for specific types of adverse events. DESIGN: Structured review. METHODOLOGY: English-language studies that reported using information technology to detect adverse events were identified using standard techniques. Only studies that contained original data were included. MAIN OUTCOME MEASURES: Adverse events, with specific focus on nosocomial infections, adverse drug events, and injurious falls. RESULTS: Tools such as event monitoring and natural language processing can inexpensively detect certain types of adverse events in clinical databases. These approaches already work well for some types of adverse events, including adverse drug events and nosocomial infections, and are in routine use in a few hospitals. In addition, it appears likely that these techniques will be adaptable in ways that allow detection of a broad array of adverse events, especially as more medical information becomes computerized. CONCLUSION: Computerized detection of adverse events will soon be practical on a widespread basis.

Accidental Falls↗