Toward Electronic Medical Records That Improve Care
Authors: William Tierney, J. M. Overhage, C. J. McDonald
Journal: Annals of Internal Medicine (1995)

Editorials1 May 1995Toward Electronic Medical Records That Improve CareWilliam M. Tierney, MD, J. Marc Overhage, MD, and Clement J. McDonald, MDWilliam M. Tierney, MDRegenstrief Institute for Health Care, Indianapolis, IN 46202, J. Marc Overhage, MDRegenstrief Institute for Health Care, Indianapolis, IN 46202, and Clement J. McDonald, MDRegenstrief Institute for Health Care, Indianapolis, IN 46202Author, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-122-9-199505010-00011 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Computers and other machinery of the Information Age have been touted as bringing a revolution to medical care that would improve its quality and lower its costs [1]. However, accomplishing these tasks requires electronic medical record systems that are not merely electronic renditions of paper charts. For maximum effect, electronic medical record systems should actively participate in improving patient outcomes.The first attempts to improve care with electronic medical records began more than 20 years ago with the computerizing of guidelines for simple preventive care and for identifying abnormal test results and potential drug interactions [2, 3]. Over the ensuing ...References1. Dick RB, Steen EB. Institute of Medicine. Committee on Improving the Medical Record. The Computer-based Patient Record: An Essential Technology for Health Care. Washington, D.C.: National Acad Pr; 1991. Google Scholar2. McDonald CJ. Protocol-based computer reminders, the quality of care and the non-perfectibility of man. N Engl J Med. 1976; 292:1351-5. Google Scholar3. Weed LL. Medical records that guide and teach. N Engl J Med. 1968; 278:593-600. Google Scholar4. Wennberg J, Gittelsohn A. Small area variations in health care delivery. Science. 1973; 182:1102-8. Google Scholar5. Woolf SH. Practice guidelines: a new reality in medicine. I. Recent developments. Arch Intern Med. 1990; 150:1811-8. Google Scholar6. McDonald CJ, Murray R, Jeris D, Bhargava B, Seeger J, Blevins L. A computer-based record and clinical monitoring system for ambulatory care. Am J Public Health. 1977; 67:240-5. Google Scholar7. McDonald CJ, Hui SL, Smith DM, Tierney WM, Cohen SJ, Weinberger M, et al. Reminders to physicians from an introspective computer medical record. A two-year randomized trial. Ann Intern Med. 1984; 100:130-8. Google Scholar8. Barnett GO, Winickoff RN, Morgan MM, Zielstorff RD. A computer-based monitoring system for follow-up of elevated blood pressure. Med Care. 1983; 21:400-9. Google Scholar9. Rind DM, Safran C, Phillips RS, Wang Q, Calkins DR, Delbanco TL, et al. Effect of computer-based alerts on the treatment and outcomes of hospitalized patients. Arch Intern Med. 1994; 154:1511-7. Google Scholar10. McDonald CJ, Hui SL, Tierney WM. Effects of computer reminders for influenza vaccination on morbidity during influenza epidemics. MD Comput. 1992; 9:304-12. Google Scholar11. McDonald CJ, Hammond WE. Standard formats for electronic transfer of clinical data. Ann Intern Med. 1989; 110:333-5. Google Scholar12. United States Health Care Financing Administration. The International Classification of Diseases: 9th Revision, Clinical Modification. Washington, D.C.: U.S. Department of Health and Human Services; 1980. Google Scholar13. Pryor TA, Hripcsak G. The Arden syntax for medical logic modules. Int J Clin Monit Comput. 1993; 10:215-24. Google Scholar14. Hripcsak G, Friedman C, Alderson PO, DuMouchel W, Johnson SB, Clayton PD. Unlocking clinical data from narrative reports: a study of natural language processing. Ann Intern Med. 1995; 122:681-8. Google Scholar15. Hripcsak G, Clayton PE, Cimino JJ, Johnson SB, Friedman C. Medical decision support at Columbia-Presbyterian Medical Center. In: Timmers T, Blum BI, eds. Software Engineering in Medical Informatics. Amsterdam: North-Holland; 1991:471-9. Google Scholar16. Evans DA, Cimino JJ, Hersh WR, Huff SM, Bell DS. Toward a medical-concept representation language. J Am Med Informatics Assoc. 1994; 1:207-17. Google Scholar17. Hendrickson G, Anderson RK, Clayton PD, Cimino J, Hripcsak GM, Johnson SB, et al. The integrated academic information management system at Columbia-Presbyterian Medical Center. MD Comput. 1992; 9:35-42. Google Scholar18. Litzelman DK, Dittus RS, Miller ME, Tierney WM. Requiring physicians to respond to computerized reminders improves their compliance with preventive care protocols. J Gen Intern Med. 1993; 8:311-7. Google Scholar19. Clark CM Jr, Kinney ED. The potential role of diabetes guidelines in the reduction of medical injury and malpractice claims involving diabetes. Diabetes Care. 1994; 17:155-9. Google Scholar Author, Article, and Disclosure InformationAffiliations: Regenstrief Institute for Health Care, Indianapolis, IN 46202Corresponding Author: William M. Tierney, MD, Regenstrief Institute for Health Care, RHC, Fifth Floor, 1001 West Tenth Street, Indianapolis, IN 46202Grant Support: In part by HS07632, HS07763, and HS07719 from the Agency for Health Care Policy and Research; PHB93-S1 from the Indiana State Department of Health; and contract N01-LM-4-3510 from the National Library of Medicine. The opinions expressed are solely those of the authors. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoUnlocking Clinical Data from Narrative Reports: A Study of Natural Language Processing George Hripcsak , Carol Friedman , Philip O. Alderson , William DuMouchel , Stephen B. Johnson , and Paul D. Clayton Metrics Cited byA machine-learning model to predict postoperative delirium following knee arthroplasty using electronic health recordsA web-based machine-learning algorithm predicting postoperative acute kidney injury after total knee arthroplastyNatural Language Processing of Serum Protein Electrophoresis Reports in the Veterans Affairs Health Care SystemCluster Analysis on Utilization Patterns of Patients with Chronic Diseases Based on Flattened Electronic Medical RecordsClinical Decision Support at Intermountain HealthcareDeveloping a natural language processing application for measuring the quality of colonoscopy proceduresRounds reportsHypercholesterolemia in Youth: Opportunities and Obstacles to Prevent Premature Atherosclerotic Cardiovascular DiseaseA comprehensive appropriateness of prescribing questionnaire was validated by nominal consensus groupModeling Medical Treatment Using Markov Decision ProcessesTowards a PropeR combination of patient records and protocolsUse of Natural Language Processing to Translate Clinical Information from a Database of 889,921 Chest Radiographic ReportsHOW SHOULD WE MAKE THE ADMISSION DECISION IN COMMUNITY-ACQUIRED PNEUMONIA?Assessing the Quality of Clinical Data in a Computer-based Record for Calculating the Pneumonia Severity IndexThe Use of an Electronic Patient Record System to Identify Advanced Cancer Patients and Antidepressant Drug UsePositive predictive value of the diagnosis of acute myocardial infarction in an administrative databaseThe Use of Clinical Practice Guidelines in CardiologyHospital-Based Decision SupportA review of methodologies for assessing drug effectiveness and a new proposal: randomized database studiesCRITICAL CARE IN THE UNITED STATESClinical Decision Support at Intermountain Healthcare 1 May 1995Volume 122, Issue 9Page: 725-726KeywordsComputersElectronic medical recordsHealth careInformation storage and retrievalNatural language processingResearch designSpecificityTechniciansThoraxTreatment guidelines ePublished: 15 August 2000 Issue Published: 1 May 1995 Copyright & PermissionsCopyright © 1995 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...