Abstract
Physicians are required to work with rapidly growing amounts of medical data. Approximately 62% of time per patient is devoted to reviewing electronic health records (EHRs), with clinical data review being the most time-consuming portion. To determine whether an artificial intelligence (AI) system developed to organize and display new patient referral records would improve a clinician's ability to extract patient information compared with the current standard of care. In this prognostic study, an AI system was created to organize patient records and improve data retrieval. To evaluate the system on time and accuracy, a nonblinded, prospective study was conducted at a single academic medical center. Recruitment emails were sent to all physicians in the gastroenterology division, and 12 clinicians agreed to participate. Each of the clinicians participating in the study received 2 referral records: 1 AI-optimized patient record and 1 standard (non-AI-optimized) patient record. For each record, clinicians were asked 22 questions requiring them to search the assigned record for clinically relevant information. Clinicians reviewed records from June 1 to August 30, 2020. The time required to answer each question, along with accuracy, was measured for both records, with and without AI optimization. Participants were asked to assess overall satisfaction with the AI system, their preferred review method (AI-optimized vs standard), and other topics to assess clinical utility. Twelve gastroenterology physicians/fellows completed the study. Compared with standard (non-AI-optimized) patient record review, the AI system saved first-time physician users 18% of the time used to answer the clinical questions (10.5 [95% CI, 8.5-12.6] vs 12.8 [95% CI, 9.4-16.2] minutes; P = .02). There was no significant decrease in accuracy when physicians retrieved important patient information (83.7% [95% CI, 79.3%-88.2%] with the AI-optimized vs 86.0% [95% CI, 81.8%-90.2%] without the AI-optimized record; P = .81). Survey responses from physicians were generally positive across all questions. Eleven of 12 physicians (92%) preferred the AI-optimized record review to standard review. Despite a learning curve pointed out by respondents, 11 of 12 physicians believed that the technology would save them time to assess new patient records and were interested in using this technology in their clinic. In this prognostic study, an AI system helped physicians extract relevant patient information in a shorter time while maintaining high accuracy. This finding is particularly germane to the ever-increasing amounts of medical data and increased stressors on clinicians. Increased user familiarity with the AI system, along with further enhancements in the system itself, hold promise to further improve physician data extraction from large quantities of patient health records.
Highlights
It has been estimated that more than one-half of a general clinician’s workday can be spent interacting with electronic health records (EHRs).[1,2,3,4,5] A survey of more than 500 primary care physicians[6] reported that 62% of time devoted to each patient visit is spent referring to EHRs
A large study of approximately 100 million patient encounters across multiple medical specialties concluded that medical record review accounted for the largest segment of time spent in the EHR, a trend seen in inpatient medicine.[7,8,9]
This study reported on items in the Standards for Reporting of Diagnostic Accuracy (STARD) and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) reporting guidelines
Summary
It has been estimated that more than one-half of a general clinician’s workday can be spent interacting with electronic health records (EHRs).[1,2,3,4,5] A survey of more than 500 primary care physicians[6] reported that 62% of time devoted to each patient visit is spent referring to EHRs. Recently, a large study of approximately 100 million patient encounters across multiple medical specialties concluded that medical record review (ie, reviewing clinical results, patient data, and notes) accounted for the largest segment of time spent in the EHR, a trend seen in inpatient medicine.[7,8,9] Along with the increased adoption of EHRs, there has been a concomitant increase in the amount of data stored in these systems.[10] a theme of challenges experienced by EHR users is information overload, because much of the EHR data (including new patient referral information) is often not pertinent to the particular patient encounter.[11,12,13,14] Many factors— including overwhelming amounts of data—associated with EHR use have been contributors to physician dissatisfaction and burnout.[15,16,17,18]
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