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

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).

Incidence of Diagnostic Errors Among Unexpectedly Hospitalized Patients Using an Automated Medical History–Taking System With a Differential Diagnosis Generator: Retrospective Observational Study

“…The quality of clinical history documented by AI Monshin may be a key component of the results. There may be high discrepancies in clinical history between patient reports and physician documentation [ 45 ]; in addition, the automated medical history–taking system, as compared to physicians, may have the potential to take clinical histories that are more diagnostically useful and of higher quality [ 19 , 20 ]. Therefore, routine use of automated history-taking systems may improve diagnostic accuracy by establishing a high-quality base of clinical history for the correct diagnosis.…”

“…From this perspective, newly developed technology, such as computerized automated history-taking systems and diagnostic decision support systems, can be leveraged to address this issue; these systems have a long history, since they were introduced in the 1960s and 1970s [ 16 - 18 ]. Computerized automated history-taking systems perform better in clinical documentation tasks for taking patient histories than do physicians [ 19 , 20 ]. The use of a diagnostic support system (ie, differential diagnosis generator) before collecting information by physicians showed a significant impact on the improvement of diagnostic accuracy in terms of clinical reasoning and differential diagnosis [ 21 - 23 ].…”

Clinical Decision Support Systems: Contributions from 2021

“…Oluoch et al, [3] Stephens et al, [4] Solberg et al, [5] Rizk et al, [6] Orenstein et al, [7] Zakim et al, [8] Andruchow et al, [9] Seol et al, [10] Orenstein et al, [11] Dente et al, [12] Dutta et al, [13] Austrian et al, [14] Saegerman et al, [15] Title interventions. In the second paper, Dutta et al, [13] described the implementation of a CDS alert in an electronic health record to warn providers about a previously documented tetanus vaccine, when they are ordering a new one.…”

ICT in Healthcare: the role of IoT and the SECANT solution

“…The SECANT plafofrm will be validated through four different Pilot Use Cases that are aiming (i) to validate SECANT's ability to improve transportation safety and ensure zero-error delivery of patients transported using timeconstraint Emergency Medical Services (Protecting the connected ambulance of the future), (ii) to validate SECANT's efficiency to deal with cascading effects of cyber threats and with propagated vulnerabilities in connected healthcare infrastructures, as well as in remote healthcare settings (Cyber security for connected medical devices and mobile applications [23], [24]), (iii) To validate the capabilities of SECANT to ensure privacy, data protection and accountability when operational data is being exchanged within the healthcare supply chain (Health data protection in the healthcare supply chain) and (iv) to validate the capabilities of SECANT's cyber security training modules and critical infrastructure cyber range (Cyber Security training). These Pilot Use Cases will be further discussed on separate papers that are under preparation.…”