A search algorithm for identifying likely users and non-users of marijuana from the free text of the electronic medical record

• Published in: PloS one 2018-03, Vol.13 (3), p.e0193706
• Main Authors: Keyhani, Salomeh, Vali, Marzieh, Cohen, Beth, Woodbridge, Alexandra, Arenson, Melanie, Eilkhani, Elnaz, Aivadyan, Christina, Hasin, Deborah
• Format: Article
• Language: English
• Subjects: Aged; Algorithms; Biology and Life Sciences; Cancer; Cannabis; Cardiology; Cardiovascular disease; Cohort Studies; Coronary artery; Coronary artery disease; Coronary Artery Disease - complications; Coronary Artery Disease - diagnosis; Coronary Artery Disease - drug therapy; Coronary Artery Disease - epidemiology; Coronary vessels; Diagnosis, Computer-Assisted - methods; Electronic Health Records; Electronic medical records; Epidemiology; Health; Heart diseases; Humans; Identification methods; Informatics; Interviews as Topic; Marijuana; Marijuana Abuse - complications; Marijuana Abuse - diagnosis; Marijuana Abuse - epidemiology; Marijuana Smoking - epidemiology; Medical electronics; Medical Marijuana - adverse effects; Medical Marijuana - therapeutic use; Medical records; Medical research; Medicine; Medicine and Health Sciences; Methods; Patient Selection; Patients; Population; Questionnaires; Recruitment; Search algorithms; Search Engine - methods; Self Report; Social Sciences; Strings; Systematic review; Tobacco; Veterans
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0193706

The harmful effects of marijuana on health and in particular cardiovascular health are understudied. To develop such knowledge, an efficient method of developing an informative cohort of marijuana users and non-users is needed. We identified patients with a diagnosis of coronary artery disease using ICD-9 codes who were seen in the San Francisco VA in 2015. We imported these patients' medical record notes into an informatics platform that facilitated text searches. We categorized patients into those with evidence of marijuana use in the past 12 months and patients with no such evidence, using the following text strings: "marijuana", "mjx", and "cannabis". We randomly selected 51 users and 51 non-users based on this preliminary classification, and sent a recruitment letter to 97 of these patients who had contact information available. Patients were interviewed on marijuana use and domains related to cardiovascular health. Data on marijuana use collected from the medical record was compared to data collected as part of the interview. The interview completion rate was 71%. Among the 35 patients identified by text strings as having used marijuana in the previous year, 15 had used marijuana in the past 30 days (positive predictive value = 42.9%). The probability of use in the past month increased from 8.8% to 42.9% in people who have these keywords in their medical record compared to those who did not have these terms in their medical record. Methods that combine text search strategies for participant recruitment with health interviews provide an efficient approach to developing prospective cohorts that can be used to study the health effects of marijuana.