Context To improve patient safety, healthcare facilities are focussing on reducing patient harm. Automated harm-detection methods using information technology show promise for efficiently measuring harm. However, there have been few systematic reviews of their effectiveness.
Objective To perform a systematic literature review to identify, describe and evaluate effectiveness of automated inpatient harm-detection methods.
Methods Data sources included MEDLINE and CINAHL databases indexed through August 2008, extended by bibliographic review and search of citing articles. The authors included articles reporting effectiveness of automated inpatient harm-detection methods, as compared with other detection methods. Two independent reviewers used a standardised abstraction sheet to extract data about automated and comparison harm-detection methods, patient samples and events identified. Differences were resolved by discussion.
Results From 176 articles, 43 articles met inclusion criteria: 39 describing field-defined methods, two using natural language processing and two using both methods. Twenty-one studies used automated methods to detect adverse drug events, 10 detected general adverse events, eight detected nosocomial infections, and four detected other specific adverse events. Compared with gold standard chart review, sensitivity and specificity of automated harm-detection methods ranged from 0.10 to 0.94 and 0.23 to 0.98, respectively. Studies used heterogeneous methods that often were flawed.
Conclusion Automated methods of harm detection are feasible and some can potentially detect patient harm efficiently. However, effectiveness varied widely, and most studies had methodological weaknesses. More work is needed to develop and assess these tools before they can yield accurate estimates of harm that can be reliably interpreted and compared.
- Patient safety
- medical error
- adverse event
- information technology
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Funding Funding for the literature review was provided by the Institute for Healthcare Improvement (IHI) to MG and ADVC. Subsequent data analysis and interpretation, as well as conceptualisation, preparation, and review of the manuscript were not financially supported.
Competing interests JK-C was employed by Premier Inc. from 31 March 2007 to 2 July 2008. Premier has developed an automated event detection product, SafetySurveillor. This study does not reference or endorse this product. No other authors disclosed any potential conflicts of interest.
Provenance and peer review Not commissioned; externally peer reviewed.
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