Loreen Straub1, Joshua J Gagne2, Judith C Maro3, Michael D Nguyen4, Nicolas Beaulieu3, Jeffrey S Brown3, Adee Kennedy3, Margaret Johnson3, Adam Wright5, Li Zhou5, Shirley V Wang2. 1. Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, 02120, USA. lstraub@bwh.harvard.edu. 2. Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, 02120, USA. 3. Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA. 4. Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA. 5. Division of General Internal Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Abstract
INTRODUCTION: While medical chart review remains the gold standard to validate health conditions or events identified in administrative claims and electronic health record databases, it is time consuming, expensive and can involve subjective decisions. AIM: The aim of this study was to describe the landscape of technology-enhanced approaches that could be used to facilitate medical chart review within and across distributed data networks. METHOD: We conducted a semi-structured survey regarding processes for medical chart review with organizations that either routinely do medical chart review or use technologies that could facilitate chart review. RESULTS: Fifteen out of 17 interviewed organizations used optical character recognition (OCR) or natural language processing (NLP) in their chart review process. None used handwriting recognition software. While these organizations found OCR and NLP to be useful for expediting extraction of useful information from medical charts, they also mentioned several challenges. Quality of medical scans can be variable, interfering with the accuracy of OCR. Additionally, linguistic complexity in medical notes and heterogeneity in reporting templates used by different healthcare systems can reduce the transportability of NLP-based algorithms to diverse healthcare settings. CONCLUSION: New technologies including OCR and NLP are currently in use by various organizations involved in medical chart review. While technology-enhanced approaches could scale up capacity to validate key variables and make information about important clinical variables from medical records more generally available for research purposes, they often require considerable customization when employed in a distributed data environment with multiple, diverse healthcare settings.
INTRODUCTION: While medical chart review remains the gold standard to validate health conditions or events identified in administrative claims and electronic health record databases, it is time consuming, expensive and can involve subjective decisions. AIM: The aim of this study was to describe the landscape of technology-enhanced approaches that could be used to facilitate medical chart review within and across distributed data networks. METHOD: We conducted a semi-structured survey regarding processes for medical chart review with organizations that either routinely do medical chart review or use technologies that could facilitate chart review. RESULTS: Fifteen out of 17 interviewed organizations used optical character recognition (OCR) or natural language processing (NLP) in their chart review process. None used handwriting recognition software. While these organizations found OCR and NLP to be useful for expediting extraction of useful information from medical charts, they also mentioned several challenges. Quality of medical scans can be variable, interfering with the accuracy of OCR. Additionally, linguistic complexity in medical notes and heterogeneity in reporting templates used by different healthcare systems can reduce the transportability of NLP-based algorithms to diverse healthcare settings. CONCLUSION: New technologies including OCR and NLP are currently in use by various organizations involved in medical chart review. While technology-enhanced approaches could scale up capacity to validate key variables and make information about important clinical variables from medical records more generally available for research purposes, they often require considerable customization when employed in a distributed data environment with multiple, diverse healthcare settings.
Authors: Pedro L Teixeira; Wei-Qi Wei; Robert M Cronin; Huan Mo; Jacob P VanHouten; Robert J Carroll; Eric LaRose; Lisa A Bastarache; S Trent Rosenbloom; Todd L Edwards; Dan M Roden; Thomas A Lasko; Richard A Dart; Anne M Nikolai; Peggy L Peissig; Joshua C Denny Journal: J Am Med Inform Assoc Date: 2016-08-07 Impact factor: 4.497
Authors: Sarah L Cutrona; Sengwee Toh; Aarthi Iyer; Sarah Foy; Gregory W Daniel; Vinit P Nair; Daniel Ng; Melissa G Butler; Denise Boudreau; Susan Forrow; Robert Goldberg; Joel Gore; David McManus; Judith A Racoosin; Jerry H Gurwitz Journal: Pharmacoepidemiol Drug Saf Date: 2012-06-29 Impact factor: 2.890
Authors: David S Carrell; Robert E Schoen; Daniel A Leffler; Michele Morris; Sherri Rose; Andrew Baer; Seth D Crockett; Rebecca A Gourevitch; Katie M Dean; Ateev Mehrotra Journal: J Am Med Inform Assoc Date: 2017-09-01 Impact factor: 4.497