Measuring Patient Safety in Real Time: An Essential Method for Effectively Improving the Safety of Care
Authors: David C. Classen, Frances A. Griffin, Donald M. Berwick
Journal: Annals of Internal Medicine (2017)

Ideas and Opinions19 December 2017Measuring Patient Safety in Real Time: An Essential Method for Effectively Improving the Safety of CareDavid C. Classen, MD, MS, Frances A. Griffin, RRT, MPA, and Donald M. Berwick, MD, MPPDavid C. Classen, MD, MSUniversity of Utah, Salt Lake City, Utah (D.C.C.), Frances A. Griffin, RRT, MPAInstitute for Healthcare Improvement, Cambridge, Massachusetts (F.A.G., D.M.B.), and Donald M. Berwick, MD, MPPInstitute for Healthcare Improvement, Cambridge, Massachusetts (F.A.G., D.M.B.)Author, Article, and Disclosure Informationhttps://doi.org/10.7326/M17-2202 Annals Author Insight Video - Donald Berwick, MD In this video, Donald Berwick, MD, offers additional insight into the article, "Measuring Patient Safety in Real Time: An Essential Method for Effectively Improving the Safety of Care." SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail The continuing evidence of preventable deaths due to medical error has led to recent calls to improve measurement of safety in hospitals. This need can be adequately addressed by harnessing health information technology. Electronic health records (EHRs), which have been broadly adopted, offer the opportunity for measurement of all-cause harm in hospitalized and ambulatory patients and real-time mitigation to reduce it. Yet, the recent Institute of Medicine (IOM) report, "Health IT and Patient Safety," which recommends this method, notes that this opportunity has generally not been taken (1).Widespread preventable injury to hospitalized patients persists more than 15 years after ...References1. Committee on Patient Safety and Health Information Technology; Board on Health Care Services; Institute of Medicine. Health IT and Patient Safety: Building Safer Systems for Better Care. Washington, DC: National Academies Pr; 2012. Google Scholar2. Makary MA, Daniel M. Medical error-the third leading cause of death in the US. BMJ. 2016;353:i2139. [PMID: 27143499] doi:10.1136/bmj.i2139 CrossrefMedlineGoogle Scholar3. Leape LL, Brennan TA, Laird N, Lawthers AG, Localio AR, Barnes BA, et al. The nature of adverse events in hospitalized patients. Results of the Harvard Medical Practice Study II. N Engl J Med. 1991;324:377-84. [PMID: 1824793] CrossrefMedlineGoogle Scholar4. Classen DC, Resar R, Griffin F, Federico F, Frankel T, Kimmel N, et al. 'Global Trigger Tool' shows that adverse events in hospitals may be ten times greater than previously measured. Health Aff (Millwood). 2011;30:581-9. [PMID: 21471476] doi:10.1377/hlthaff.2011.0190 CrossrefMedlineGoogle Scholar5. Levinson DR; Office of Inspector General. Adverse Events in Hospitals: Methods for Identifying Events. OEI-06-08-00221. Washington, DC: U.S. Department of Health and Human Services; March 2010. Accessed at https://oig.hhs.gov/oei/reports/oei-06-08-00221.pdf on 10 August 2017. Google Scholar6. Levinson DR; Office of Inspector General. Adverse Events in Hospitals: National Incidence Among Medicare Beneficiaries. OEI-06-09-00090. Washington, DC: U.S. Department of Health and Human Services; November 2010. Accessed at https://oig.hhs.gov/oei/reports/oei-06-09-00090.pdf on 10 August 2017. Google Scholar7. Office of the National Coordinator for Health Information Technology. Non-federal Acute Care Hospital Electronic Health Record Adoption. Washington, DC: Office of the National Coordinator for Health Information Technology; May 2016. Accessed at https://dashboard.healthit.gov/quickstats/pages/FIG-Hospital-EHR-Adoption.php on 23 October 2017. Google Scholar8. Snow D. Kaiser Permanente Experience with Automating the IHI Global Trigger Tool (Text Version). Presented at AHRQ 2010 Annual Conference, Bethesda, Maryland, 26–29 September 2010. Accessed at https://archive.ahrq.gov/news/events/conference/2010/snow/index.html on 23 October 2017. Google Scholar9. Sammer C, Miller S, Jones C, Nelson A, Garrett P, Classen D, et al. Developing and evaluating an automated all-cause harm trigger system. Jt Comm J Qual Patient Saf. 2017;43:155-65. [PMID: 28325203] doi:10.1016/j.jcjq.2017.01.004 CrossrefMedlineGoogle Scholar10. Centers for Medicare & Medicaid Services. Technical Expert Panel Composition (Membership) List. 2017. Accessed at www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/MMS/Downloads/Hospital-Harm-Performance-Measure-Composition-List.pdf on 4 October 2017. Google Scholar Author, Article, and Disclosure InformationAffiliations: University of Utah, Salt Lake City, Utah (D.C.C.)Institute for Healthcare Improvement, Cambridge, Massachusetts (F.A.G., D.M.B.)Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M17-2202.Corresponding Author: Frances A. Griffin, RRT, MPA, Fran Griffin & Associates, LLC, 318 Sea Spray Lane, Neptune, NJ 07753; e-mail, [email protected]com.Current Author Addresses: Dr. Classen: Professor of Medicine, University of Utah, 30 North 1900 East, Salt Lake City, UT 84132.Ms. Griffin: Fran Griffin & Associates, LLC, 318 Sea Spray Lane, Neptune, NJ 07753.Dr. Berwick: Senior Fellow, Institute for Healthcare Improvement, 20 University Road, 7th Floor, Cambridge, MA 02138.Author Contributions: Conception and design: D.C. Classen, F.A. Griffin, D.M. Berwick.Analysis and interpretation of the data: D.M. Berwick.Drafting of the article: D.C. Classen, F.A. Griffin, D.M. Berwick.Critical revision of the article for important intellectual content: D.C. Classen, F.A. Griffin, D.M. Berwick.Final approval of the article: D.C. Classen, F.A. Griffin, D.M. Berwick.Administrative, technical, or logistic support: D.C. Classen, F.A. Griffin.This article was published at Annals.org on 21 November 2017. PreviousarticleNextarticle Advertisement Annals Author Insight Video - Donald Berwick, MD In this video, Donald Berwick, MD, offers additional insight into the article, "Measuring Patient Safety in Real Time: An Essential Method for Effectively Improving the Safety of Care." FiguresReferencesRelatedDetails Metrics Cited byBending the patient safety curve: how much can AI help?Constancy of Purpose for Improving Patient Safety — Missing in ActionEngaging Patients in the Use of Real-Time Electronic Clinical Data to Improve the Safety and Reliability of Their Own CareDevelopment and Usability Testing of a System to Detect Adverse Events and Medical MistakesValidating a Trigger Tool for Detecting Adverse Drug Events in Elderly Patients With Multimorbidity (TRIGGER-CHRON)Utility of a trigger tool (TRIGGER-CHRON) to detect adverse events associated with high-alert medications in patients with multimorbidityThe Emergency Department Trigger Tool: Validation and Testing to Optimize YieldPortable Automated Surveillance of Surgical Site Infections Using Natural Language ProcessingDesign of a safety dashboard for patientsAn Electronic Health Record–Based Real-Time Analytics Program For Patient Safety Surveillance And Improvement 19 December 2017Volume 167, Issue 12Page: 882-883KeywordsAdverse eventsBacteremiaChild healthDisclosureElectronic medical recordsGlobal healthHealth careMedicareRetrospective studiesSafety ePublished: 21 November 2017 Issue Published: 19 December 2017 Copyright & PermissionsCopyright © 2017 by American College of Physicians. 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