Extracting information from the text of electronic medical records to improve case detection: a systematic review.

BACKGROUND: Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text into case-detection algorithms can improve research quality. METHODS: A systematic search returned 9659 papers, 67 of which reported on the extraction of information from free text of EMRs with the stated purpose of detecting cases of a named clinical condition. Methods for extracting information from text and the technical accuracy of case-detection algorithms were reviewed. RESULTS: Studies mainly used US hospital-based EMRs, and extracted information from text for 41 conditions using keyword searches, rule-based algorithms, and machine learning methods. There was no clear difference in case-detection algorithm accuracy between rule-based and machine learning methods of extraction. Inclusion of information from text resulted in a significant improvement in algorithm sensitivity and area under the receiver operating characteristic in comparison to codes alone (median sensitivity 78% (codes + text) vs 62% (codes), P = .03; median area under the receiver operating characteristic 95% (codes + text) vs 88% (codes), P = .025). CONCLUSIONS: Text in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with codes. More harmonization of reporting within EMR studies is needed, particularly standardized reporting of algorithm accuracy metrics like positive predictive value (precision) and sensitivity (recall).

Data-to-text summarisation of patient records: using computer-generated summaries to access patient histories.

OBJECTIVE: We assess the efficacy and utility of automatically generated textual summaries of patients' medical histories at the point of care. METHOD: Twenty-one clinicians were presented with information about two cancer patients and asked to answer key questions. For each clinician, the information on one of the patients comprised their official hospital records, and for the other patient it comprised summaries that were computer-generated by a natural language generation system from data extracted from the official records. We measured the accuracy of the clinicians' responses to the questions, the time they took to complete them, and recorded their attitude to the computer-generated summaries. RESULTS: Results showed no significant difference in the accuracy of responses to the computer-generated records over the official records, but a significant difference in the time taken to assess the patients' condition from the computer-generated records. Clinicians expressed a positive attitude towards the computer-generated records. CONCLUSION: AI-based computer-generated textual summaries of patient histories can be as accurate as, and more efficient than, human-produced patient records for clinicians seeking to accurately identify key information about a patients overall history. PRACTICE IMPLICATIONS: Computer-generated textual summaries of patient histories can contribute to the management of patients at the point-of-care.

Calculating alcohol risk in a visualization tool for promoting healthy behavior.

OBJECTIVE: To investigate effective methods for communicating the personalized risks of alcohol consumption, particularly to young people. METHODS: An interactive computerized blood alcohol content calculator was implemented in Flash based on literature findings for effectively communicating risk. Young people were consulted on attitudes to the animation features and visualization techniques used to display personalized risk based on disclosed alcohol consumption. RESULTS: Preliminary findings reveal the calculator is relatively enjoyable to use for its genre. However, the primary aims of the visualization tool to effectively communicate personalized risk were undermined for some users by technical language. Transparency of risk calculations might further enhance the tool for others. Worryingly, user feedback revealed a tension between accurate presentation of risk and its consequent lack of sensationalism in terms of personal risk to the individual. CONCLUSION: Initial findings suggest the tool may provide a relatively engaging vehicle for exploring the link between action choices and risk outcomes. Suggestions for enhancing risk communication include using intelligent techniques for selecting data presentation formats and for demonstrating the effects of sustained risky behavior. PRACTICE IMPLICATIONS: Effective communication of risk contributes only partially to effecting behavior change; the role of the tool in influencing contributing attitudinal factors is also discussed.