Researchers at the Massachusetts Institute of Technology (MIT) Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Beth Israel Deaconess Medical Center have teamed up to improve electronic health records (EHRs) with artificial intelligence (AI) machine learning and published their findings in a recent study.
The automation of patient health records gives hope to benefits to clinicians, patients, and stakeholders such as increase speed of data transfer, lower costs in maintaining paper records, increase efficiency, improve outcomes by avoiding or reducing clinical errors. However, electronic health records have yet to achieve many of these positive benefits and is a leading cause of burnout and stress among physicians according to the researchers. Clinicians are spending time on using the electronic health records instead of talking with patients.
The worldwide electronic health records market was USD 26.8 billion in 2020 with North America having the highest revenue share of 45 percent according to Grand View Research. The global market for EHR is expected to grow at a compound annual growth rate (CAGR) of 3.7 percent during 2021-2028 per the same report. The major companies in the EHR market include Epic Systems Corporation, NextGen McKesson Corporation, MEDITECH, Allscripts, and Cerner.
Patient health records were largely in paper form until the electronic health records (EHRs) first emerged in the 1960s. Electronic health records, also called electronic medical records (EMRs), are used in medical settings to electronically store patient information such as medical history, prescriptions, lab test results, radiology images, demographics, immunization status, billing history, and more data.
Fast forward to modern-times and electronic health records have nearly replaced paper records altogether. Among American office-based physicians, 85.9 percent use an electronic medical records system according to figures from the U.S. Centers for Disease Control and Prevention (CDC) Nation Center for Health Statistics.
According to the researchers, the process of clinical documentations remains a “tedious, time-consuming, and error-prone process.” The scientists cite how this is especially the case in emergency rooms, where clinicians may see as many as thirty-five patients during a shift, requiring them to rapidly absorb the content of medical histories of patients from “multi-faceted requirements and fragmented interfaces for information exploration and documentation” that are often new to them before creating an informed diagnosis and targeted plan of care.
Although EHRs offer vast improvements in speeding up the access and retrieval of patient records, the documentation systems can be time-consuming and burdensome for clinicians to use.
“To better support this information synthesis, clinical documentation tools must enable rapid contextual access to the patient's medical record,” wrote the researchers.
To address these pain-points of electronic health records, the MIT CSAIL researchers created an AI machine learning system called MedKnowts and implemented it at the Beth Israel Deaconess Medical Center in Boston, Massachusetts. The AI-backed EHR system integrates the information retrieval system with a note-taking editor so that the search is efficient. The system enables clinicians to use natural language and automates the intake of structured data. Documentation is streamlined with features such as auto-population of text, proactive information retrieval, and easy parsing of long notes.
According to the study, the average system usability scale rating by the scribes was 83.75 out of a possible 100. The researchers report that the scribes found their AI-based system easy to learn and use, and that they would use it frequently.
With this new proof-of-concept, the researchers plan to further enhance the AI machine learning to identify the portion of a patient’s health record that is most relevant for the clinician to focus on reading. The researchers plan to incorporate clinician contributions in the future such as medical terminology so that the system adapts over time. As next steps, the team is looking into the possibility of bringing to market the AI machine learning technology in the future.
Copyright © 2021 Cami Rosso All rights reserved.
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