Computerized history-taking improves data quality for clinical decision-making-Comparison of EHR and computer-acquired history data in patients with chest pain

PLoS One. 2021 Sep 27;16(9):e0257677. doi: 10.1371/journal.pone.0257677. eCollection 2021.

Abstract

Patients' medical histories are the salient dataset for diagnosis. Prior work shows consistently, however, that medical history-taking by physicians generally is incomplete and not accurate. Such findings suggest that methods to improve the completeness and accuracy of medical history data could have clinical value. We address this issue with expert system software to enable automated history-taking by computers interacting directly with patients, i.e. computerized history-taking (CHT). Here we compare the completeness and accuracy of medical history data collected and recorded by physicians in electronic health records (EHR) with data collected by CHT for patients presenting to an emergency room with acute chest pain. Physician history-taking and CHT occurred at the same ED visit for all patients. CHT almost always preceded examination by a physician. Data fields analyzed were relevant to the differential diagnosis of chest pain and comprised information obtainable only by interviewing patients. Measures of data quality were completeness and consistency of negative and positive findings in EHR as compared with CHT datasets. Data significant for the differential of chest pain was missing randomly in all EHRs across all data items analyzed so that the dimensionality of EHR data was limited. CHT files were near complete for all data elements reviewed. Separate from the incompleteness of EHR data, there were frequent factual inconsistencies between EHR and CHT data across all data elements. EHR data did not contain representations of symptoms that were consistent with those reported by patients during CHT. Trial registration: This study is registered at https://www.clinicaltrials.gov (unique identifier: NCT03439449).

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Chest Pain / diagnosis*
  • Chest Pain / drug therapy
  • Clinical Decision-Making*
  • Datasets as Topic
  • Decision Making, Computer-Assisted
  • Electronic Health Records / standards*
  • Emergency Medical Services / methods
  • Expert Systems
  • Female
  • Humans
  • Male
  • Medical History Taking / methods*
  • Middle Aged
  • Nitroglycerin / therapeutic use
  • Software
  • Time Factors
  • Vasodilator Agents / therapeutic use
  • Young Adult

Substances

  • Vasodilator Agents
  • Nitroglycerin

Associated data

  • ClinicalTrials.gov/NCT03439449

Grants and funding

This work was funded by the Robert Bosch Stiftung (https://www.bosch-stiftung.de/de, Stuttgart, Germany), grant number 11.5.1000.0258.0 to DZ. Region Stockholm (ALF project; https://www.vr.se/english/about-us/organisation/advisory-groups-and-administrative-offices/office-for-alf.html, Stockholm, Sweden), grant number 20190593 to TK. Karolinska Institutet Research Foundation (https://staff.ki.se/ki-research-foundation-grants-2020-2021, Stockholm, Sweden) and Stiftelsen Hjärtat (http://www.stiftelsenhjartat.se, Stockholm, Sweden) to TK. Funders had no role or influence on the design and conduct of the research, including software development, and were not involved in data analysis, conclusions drawn from the data, and drafting or editing the manuscript.