Article

Reporting trends in a regional medication error data-sharing system

Department of Sociology, Purdue University, West Lafayette, IN 47907-2059, USA.
Health Care Management Science (Impact Factor: 1.05). 03/2010; 13(1):74-83. DOI: 10.1007/s10729-009-9111-1
Source: PubMed

ABSTRACT

Inter-organizational systems for sharing data about medication errors have emerged as an important strategy for improving patient safety and are expected to encourage not only voluntary error reporting but also learning from errors. Yet, few studies have examined the hypothesized benefits of inter-organizational data sharing. The current study examined the developmental trends in information reported by hospitals participating in a regional reporting system for medication errors. A coalition of hospitals in southwestern Pennsylvania, under the auspices of the Pittsburgh Regional Healthcare Initiative (PRHI), implemented a voluntary system for quarterly sharing of information about medication errors. Over a 12-month period, 25 hospitals shared information about 17,000 medication errors. Using latent growth curve analysis, we examined longitudinal trends in the quarterly number of errors and associated corrective actions reported by each hospital. Controlling for size, teaching status, and JCAHO accreditation score, for the hospitals as a group, error reporting increased at a statistically significant rate over the four quarters. Moreover, despite significant baseline differences among hospitals, error reporting increased at similar rates across hospitals over subsequent quarters. In contrast, the reporting of corrective actions remained unchanged. However, the baseline levels of corrective actions reporting were significantly different across hospitals. Although data sharing systems promote error reporting, it is unclear whether they encourage corrective actions. If data sharing is intended to promote not just error reporting but also root-cause-analysis and process improvement, then the design of the reporting system should emphasize data about these processes as well as errors.

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