The effects and preventability of 2627 patient safety incidents related to health information technology failures: a retrospective analysis of 10 years of incident reporting in England and Wales

被引:26
作者
Martin, Guy [1 ]
Ghafur, Saira [2 ]
Cingolani, Isabella [3 ]
Symons, Joshua [3 ]
King, Dominic [1 ,4 ]
Arora, Sonal [1 ]
Darzi, Ara [1 ]
机构
[1] Imperial Coll London, Natl Inst Hlth Res Patient Safety Translat Res Ct, St Marys Hosp, London W2 1NY, England
[2] Imperial Coll London, Ctr Hlth Policy, Inst Global Hlth Innovat, London, England
[3] Imperial Coll London, Big Data & Analyt Unit, Inst Global Hlth Innovat, London, England
[4] DeepMind Hlth, London, England
基金
美国国家卫生研究院;
关键词
ADVERSE EVENTS; RECORD USABILITY; ORDER ENTRY; SYSTEM; HOSPITALS; CARE; CLASSIFICATION; CHALLENGES; FRAMEWORK; DOWNTIME;
D O I
10.1016/S2589-7500(19)30057-3
中图分类号
R-058 [];
学科分类号
摘要
Background The use of health information technology (IT) is rapidly increasing to support improvements in the delivery of care. Although health IT is delivering huge benefits, new technology can also introduce unique risks. Despite these risks, evidence on the preventability and effects of health IT failures on patients is scarce. In our study we therefore sought to evaluate the preventability and effects of health IT failures by examining patient safety incidents in England and Wales. Met hods We designed our study as a retrospective analysis of 10 years of incident reporting in England and Wales. We used text mining with the words "computer", "systettii", "workstation", and "network" to explore free -text incident descriptors to identify incidents related to health IT failures following a previously described approach. We then applied an n -gram model of searching to identify contiguous sequences of words and provide spatial context. We examined incident details, recorded harm, and preventability. Standard descriptive statistics were applied. Degree of harm was identified according to standardised definitions and preventability was assessed by two independent reviewers. Findings We identified 2627 incidents related to health IT failures. 2557 (97%) of 2627 incidents were assessed for halm (70 incidents were excluded). 2106 (82%) of 2557 health IT failures caused no harm to patients, 331 (13%) caused low harm, 102 (4%) caused moderate harm, 14 (1%) caused severe ham', and four (<1%) contributed to the death of a patient. 1964 (75%) of 2627 incidents were deemed to be preventable. Interpretation Health IT is fundamental to the delivery of high-quality care, yet there is a poor understanding of the effects of IT failures on patient safety and whether they can be prevented. Failures are complex and involve interlinked aspects of technology, people, and the environment. Health IT failures are undoubtedly a potential source of substantial harm, but they are likely to be under-reported. Worryingly, three-quarters of IT failures are potentially preventable. There is a need to see health IT as a fundamental tenet of patient safety, develop better methods for capturing the effects of IT failures on patients, and adopt simple measures to reduce their probability and mitigate their risk. Copyright (C) 2019 The Author(s). Published by Elsevier Ltd.
引用
收藏
页码:E127 / E135
页数:9
相关论文
共 46 条
[1]   Understanding and preventing wrong-patient electronic orders: a randomized controlled trial [J].
Adelman, Jason S. ;
Kalkut, Gary E. ;
Schechter, Clyde B. ;
Weiss, Jeffrey M. ;
Berger, Matthew A. ;
Reissman, Stan H. ;
Cohen, Hillel W. ;
Lorenzen, Stephen J. ;
Burack, Daniel A. ;
Southern, William N. .
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2013, 20 (02) :305-310
[2]  
[Anonymous], 1983, MODERN INFORM RETRIE
[3]   Text mining for the Vaccine Adverse Event Reporting System: medical text classification using informative feature selection [J].
Botsis, Taxiarchis ;
Nguyen, Michael D. ;
Woo, Emily Jane ;
Markatou, Marianthi ;
Ball, Robert .
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2011, 18 (05) :631-638
[4]   INCIDENCE OF ADVERSE EVENTS AND NEGLIGENCE IN HOSPITALIZED-PATIENTS - RESULTS OF THE HARVARD MEDICAL-PRACTICE STUDY-I [J].
BRENNAN, TA ;
LEAPE, LL ;
LAIRD, NM ;
HEBERT, L ;
LOCALIO, AR ;
LAWTHERS, AG ;
NEWHOUSE, JP ;
WEILER, PC ;
HIATT, HH .
NEW ENGLAND JOURNAL OF MEDICINE, 1991, 324 (06) :370-376
[5]   Downtime in Digital Hospitals: An Analysis of Patterns and Causes Over 33 Months [J].
Chen, Jessica ;
Wang, Ying ;
Magrabi, Farah .
INTEGRATING AND CONNECTING CARE, 2017, 239 :14-20
[6]   'Global Trigger Tool' Shows That Adverse Events In Hospitals May Be Ten Times Greater Than Previously Measured [J].
Classen, David C. ;
Resar, Roger ;
Griffin, Frances ;
Federico, Frank ;
Frankel, Terri ;
Kimmel, Nancy ;
Whittington, John C. ;
Frankel, Allan ;
Seger, Andrew ;
James, Brent C. .
HEALTH AFFAIRS, 2011, 30 (04) :581-589
[7]   Classifying disease outbreak reports using n-grams and semantic features [J].
Conway, Mike ;
Doan, Son ;
Kawazoe, Ai ;
Collier, Nigel .
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, 2009, 78 (12) :E47-E58
[8]  
Donaldson L., 2000, J ROYAL COLL PHYS, V2, P452
[9]   Identifying health information technology related safety event reports from patient safety event report databases [J].
Fong, Allan ;
Adams, Katharine T. ;
Gaunt, Michael J. ;
Howe, Jessica L. ;
Kellogg, Kathryn M. ;
Ratwani, Raj M. .
JOURNAL OF BIOMEDICAL INFORMATICS, 2018, 86 :135-142
[10]  
Food and Drug Administration, 2018, MAN US FAC DEV EXP