Computerized history-taking improves data quality for clinical decision-making-Comparison of EHR and computer-acquired history data in patients with chest pain.
David ZakimHelge BrandbergSami El AmraniAndreas HultgrenNatalia StathakarouSokratis NifakosThomas KahanJonas SpaakSabine KochCarl Johan SundbergPublished in: PloS one (2021)
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).
Keyphrases
- electronic health record
- end stage renal disease
- clinical decision support
- big data
- newly diagnosed
- ejection fraction
- prognostic factors
- chronic kidney disease
- emergency department
- healthcare
- primary care
- peritoneal dialysis
- randomized controlled trial
- data analysis
- decision making
- machine learning
- mass spectrometry
- deep learning
- depressive symptoms
- working memory
- clinical trial
- single cell
- case report
- patient reported
- drug induced
- mechanical ventilation
- rna seq
- open label