Methodological Review: The Technology Acceptance Model: Its past and its future in health care

Information technology can improve patient safety by preventing errors and facilitating rapid response to adverse events. Computerized prescribing by physicians reduces the rate of medication-related errors. Systems that automatically page clinicians about serious laboratory abnormalities and remote monitoring of patients in intensive care units also appear promising.

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Improving safety with information technology.

The adoption of electronic health records will provide a rich resource for biomedical researchers. This Review discusses the potential for their use in informed decision making in the clinic, for a finer understanding of genotype–phenotype relationships and for selection of research cohorts, along with the current challenges for their mining and use. Clinical data describing the phenotypes and treatment of patients represents an underused data source that has much greater research potential than is currently realized. Mining of electronic health records (EHRs) has the potential for establishing new patient-stratification principles and for revealing unknown disease correlations. Integrating EHR data with genetic data will also give a finer understanding of genotype–phenotype relationships. However, a broad range of ethical, legal and technical reasons currently hinder the systematic deposition of these data in EHRs and their mining. Here, we consider the potential for furthering medical research and clinical care using EHR data and the challenges that must be overcome before this is a reality.

Mining electronic health records: towards better research applications and clinical care

https://www.cs.tufts.edu/comp/150AIH/pdf/NadkarniOC11.pdf

Natural language processing: an introduction.

Objectives: We examine recent published research on the extraction of information from textual documents in the Electronic Health Record (EHR). Methods: Literature review of the research published after 1995, based on PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers already included. Results: 174 publications were selected and are discussed in this review in terms of methods used, pre-processing of textual documents, contextual features detection and analysis, extraction of information in general, extraction of codes and of information for decision-support and enrichment of the EHR, information extraction for surveillance, research, automated terminology management, and data mining, and de-identification of clinical text. Conclusions: Performance of information extraction systems with clinical text has improved since the last systematic review in 1995, but they are still rarely applied outside of the laboratory they have been developed in. Competitive challenges for information extraction from clinical text, along with the availability of annotated clinical text corpora, and further improvements in system performance are important factors to stimulate advances in this field and to increase the acceptance and usage of these systems in concrete clinical and biomedical research contexts. Cli

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Extracting information from textual documents in the electronic health record: a review of recent research.

Detecting adverse events using information technology.

http://www.acgroup.org/images/2009_White_Paper_-_EHR_based_on_structured_narrative.pdf

An electronic health record based on structured narrative.

Quantifying clinical narrative redundancy in an electronic health record.

Background Nurses alter their monitoring behavior as a patient’s clinical condition deteriorates, often detecting and documenting subtle changes before physiological trends are apparent. It was hypothesized that a nurse’s behavior of recording optional documentation (beyond what is required) reflects concern about a patient’s status and that mining data from patients’ electronic health records for the presence of these features could help predict patients’ mortality. Methods Data-mining methods were used to analyze electronic nursing documentation from a 15-month period at a large, urban academic medical center. Mortality rates and the frequency of vital sign measurements (beyond required) and optional nursing comment documentation were analyzed for a random set of patients and patients who experienced a cardiac arrest during their hospitalization. Patients were stratified by age-adjusted Charlson comorbidity index. Results A total of 15 000 acute care patients and 145 cardiac arrest patients were studied. Patients who died had a mean of 0.9 to 1.5 more optional comments and 6.1 to 10 more vital signs documented within 48 hours than did patients who survived. A higher frequency of comment and vital sign documentation was also associated with a higher likelihood of cardiac arrest. Of patients who had a cardiac arrest, those with more documented comments were more likely to die. Conclusions For the first time, nursing documentation patterns have been linked to patients’ mortality. Findings were consistent with the hypothesis that some features of nursing documentation within electronic health records can be used to predict mortality. With future work, these associations could be used in real time to establish a threshold of concern indicating a risk for deterioration in a patient’s condition. (American Journal of Critical Care. 2013;22:306-313)

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Peter D. Stetson

Papers

Detecting adverse events using information technology.

An electronic health record based on structured narrative.

Quantifying clinical narrative redundancy in an electronic health record.

Relationship Between Nursing Documentation and Patients’ Mortality

Perceived Information Needs and Communication Difficulties of Inpatient Physicians and Nurses