The Hidden Toll of Medico-Legal Record Review on Physicians: How AI Can Help
Michael D. Lesh, SM MD FACC
Professor of Medicine, UCSF | Founder & CEO, Quench
Introduction
Physician burnout is a well-documented issue, often attributed to the increasing demands of electronic health records (EHRs) at the point of care.¹ Doctors spend countless hours navigating complex systems like Epic, leading to frustration and fatigue. However, there’s a less visible but equally taxing form of burden affecting over 100,000 physicians annually: the exhaustive process of reviewing medical records for medico-legal purposes.
The Crucial Societal Role of Medical Experts
Physicians serving as medical experts in medico-legal contexts fulfill a vital societal function.² They contribute their specialized knowledge to legal proceedings, ensuring that medical facts are accurately interpreted and justice is served.³ Whether acting as expert witnesses in malpractice cases, evaluating claims in mass tort litigation, or assessing injuries in workers’ compensation appeals,⁴ these physicians bridge the gap between medicine and law.
This role is fundamentally different from the point-of-care responsibilities most people associate with physicians. While clinical practice focuses on diagnosing and treating individual patients, medico-legal work involves analyzing medical records to provide objective opinions that can influence legal outcomes. The integrity and expertise physicians bring to this process are essential for fair and informed decision-making in the legal system.
Medico-legal Record Review is Time-Consuming
Despite its importance, the medical-legal role places additional demands on physicians. They are often required to sift through thousands of pages of patient records, typically delivered as unwieldy PDF files rather than modern electronic formats. This task is time-consuming and mentally draining.
Pain Points in the Current Process
Physicians engaged in medical-legal work face numerous challenges when reviewing medical records as they strive to produce an accurate and defensible opinion:
1. Large Volume of Medical and Legal Records: Physicians frequently receive thousands of pages of medical records in PDF format, making the process of creating medical summaries and chronologies, and extracting key facts, lengthy and overwhelming.
2. Poor Organization and Redundancy: The lack of standardization in how records are organized leads to duplicated or irrelevant information.
3. Handwriting and Poor Scan Quality: Complicates the review process.
4. Needle-in-the-Haystack Searches: Finding critical pieces of information among thousands of pages increases the time burden and heightens the risk of missing key details.
5. Identifying Missing Documents: It’s often difficult to identify gaps in documentation, especially when records are disorganized or incomplete.
6. Report Writing: Physicians must synthesize their findings into concise, legally sound reports. Ensuring that these reports include all necessary legal and medical information is a meticulous and time-consuming process.
7. High Accuracy Requirements: Physicians must deliver highly accurate analyses, as their findings could be scrutinized in legal settings where errors or omissions could be fatal to a case.
These pain points, combined with the sheer mental fatigue of reviewing such vast quantities of disorganized information, exacerbate physician burnout in medico-legal roles.
AI-Assisted Medical Record Review
Advancements in artificial intelligence, like medical chronology software and document processing tools, can potentially alleviate these burdens⁵:
Automated Data Extraction: AI algorithms can scan PDFs to identify and extract relevant medical information, highlighting key data points for quick review. With advanced AI engineering, even information from handwriting, multi-column documents, and tables can be extracted
Natural Language Processing (NLP): NLP can interpret and summarize complex medical narratives, providing concise overviews.
Intelligent Search Functions: AI-powered search tools can help physicians rapidly locate specific terms or events within large documents. These searches are based on the meaning in the text, not simply keyword searching as is the case for off-the-shelf document viewers such as Adobe Acrobat.
Identifying Missing Documents: AI can also analyze the context of a case and alert physicians when critical documents or results are missing. For example, if the records show a test was ordered, but no results are present, AI can flag this omission.
By incorporating AI solutions like Quench SmartChart into the review process, physicians can dramatically reduce the time and effort spent on manual tasks, allowing them to focus on applying their expertise where it truly matters. These technologies can also help ensure consistency, reducing errors caused by human fatigue or oversight.
AI-driven tools can help physicians work at the top of their license, focusing on high-level decision-making rather than getting bogged down in manual tasks. In this way, AI acts as an assistant, allowing medical experts to leverage their skills and knowledge more effectively while reducing the likelihood of burnout.
Conclusion
Physician burnout is a multifaceted issue that extends beyond the walls of clinical practice. Medical record review for legal purposes can be very time-consuming. Yet, the role physicians play in medical-legal contexts is crucial for upholding justice and ensuring accurate legal outcomes. Embracing AI technologies can transform this process, enabling physicians to work more efficiently and sustainably.
Ultimately, this not only benefits the physicians themselves but also enhances the integrity of the legal system and the well-being of society as a whole.
Article Sources:
Physician Burnout and EHRs:
Mayo Clinic Proceedings. (2019). Physician Burnout and the Impact of Electronic Health Records (EHRs). Retrieved from: https://www.mayoclinicproceedings.org/article/S0025-6196(19)30836-5/fulltext
The Crucial Societal Role of Medical Experts:
Freckelton, I. (2020). Expert evidence in medical litigation: Rules, challenges, and recent developments. Medical Law Review, 28(2), 308–333. https://doi.org/10.1093/medlaw/fwaa002
Jourdan, C., Matusevich, D., & Picard, E. (2017). The role of the medical expert in the legal system. Frontiers in Psychiatry, 8, 59. https://doi.org/10.3389/fpsyt.2017.00059
California Department of Industrial Relations. (2021). QME Regulations in Title 8, California Code of Regulations. https://www.dir.ca.gov/dwc/DWCPropRegs/QME-Regulations/QME-Regulations.htm
AI-Assisted Medical Record Review:
Shi, X., Hu, Y., Xu, J., Wang, Z., & Chen, J. (2023). A Survey on Artificial Intelligence Techniques for Electronic Health Records. IEEE Access, 11, 24590–24614. https://doi.org/10.1109/ACCESS.2023.3551935
