Automated detection of harm in healthcare with information technology: a systematic review

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.

To perform a systematic literature review to identify, describe and evaluate effectiveness of automated inpatient harm-detection 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.

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.

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.