Appl Clin Inform 2019; 10(01): 158-167
DOI: 10.1055/s-0039-1678693
Research Article
Georg Thieme Verlag KG Stuttgart · New York

Understanding Health Information Technology Induced Medication Safety Events by Two Conceptual Frameworks

Ju Wang
1   School of Biomedical Informatics, University of Texas Health Science Center, Houston, Texas, United States
,
Hongyuan Liang
2   Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
,
Hong Kang
1   School of Biomedical Informatics, University of Texas Health Science Center, Houston, Texas, United States
,
Yang Gong
1   School of Biomedical Informatics, University of Texas Health Science Center, Houston, Texas, United States
› Institutsangaben
Funding This project is supported by the Agency for Healthcare Research and Quality (grant number R01HS022895) and the UTHealth Innovation for Cancer Prevention Research Training Program Postdoctoral Fellowship (Cancer Prevention and Research Institute of Texas; grant #RP160015). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality.
Weitere Informationen

Publikationsverlauf

21. September 2018

07. Januar 2019

Publikationsdatum:
06. März 2019 (online)

Abstract

Background While health information technology (health IT) is able to prevent medication errors in many ways, it may also potentially introduce new paths to errors. To understand the impact of health IT induced medication errors, this study aims to conduct a retrospective analysis of medication safety reports.

Methods From the U.S. Food and Drug Administration (FDA) Manufacturer and User Facility Device Experience database, we identified reports in which health IT is a contributing factor to medication errors. We applied two conceptual frameworks, Sittig and Singh's sociotechnical model and Coiera's information value chain, to examine the identified reports.

Results We identified 152 unique reports on health IT induced medication errors as the final report set for review. The majority (65.13%) of the reports involved multiple contributing factors according to the sociotechnical model. Three dimensions, that is, clinical content, human–computer interface, and people, were involved in more reports than the others. The transition of the effects of health IT on medication practice was summarized using information value chain. Health IT related contributing factors may lead to receiving wrong information, missing information, receiving partial information and delayed information, and receiving wrong information and missing information tend to cause the commission errors in decision-making.

Conclusion The two frameworks provide an opportunity to understand a comprehensive context of safety event and the impact of health IT induced errors on medication safety. The sociotechnical model helps identify the aspects causing medication safety issues. The information value chain helps uncover the effect of the health IT induced medication errors on health care process and patient outcomes.

Protection of Human and Animal Subjects

No human/animal subjects were involved in the project.


 
  • References

  • 1 Medication without Harm-Global Patient Safety Challenge on Medication Safety. Geneva: World Health Organization; 2017
  • 2 Medicine Io. Preventing Medication Errors. Washington, DC: The National Academies Press; 2007
  • 3 Sittig DF, Singh H. Defining health information technology-related errors: new developments since to err is human. Arch Intern Med 2011; 171 (14) 1281-1284
  • 4 Bates DW, Gawande AA. Improving safety with information technology. N Engl J Med 2003; 348 (25) 2526-2534
  • 5 Radley DC, Wasserman MR, Olsho LEW, Shoemaker SJ, Spranca MD, Bradshaw B. Reduction in medication errors in hospitals due to adoption of computerized provider order entry systems. J Am Med Inform Assoc 2013; 20 (03) 470-476
  • 6 Poon EG, Keohane CA, Yoon CS. , et al. Effect of bar-code technology on the safety of medication administration. N Engl J Med 2010; 362 (18) 1698-1707
  • 7 Khammarnia M, Kassani A, Eslahi M. The efficacy of patients' wristband bar-code on prevention of medical errors: A meta-analysis study. Appl Clin Inform 2015; 6 (04) 716-727
  • 8 Rothschild JM, Lee TH, Bae T, Bates DW. Clinician use of a palmtop drug reference guide. J Am Med Inform Assoc 2002; 9 (03) 223-229
  • 9 Ateya MB, Aiyagari R, Moran C, Singer K. Insulin bolus calculator in a pediatric hospital. Safety and user perceptions. Appl Clin Inform 2017; 8 (02) 529-540
  • 10 Koppel R, Metlay JP, Cohen A. , et al. Role of computerized physician order entry systems in facilitating medication errors. JAMA 2005; 293 (10) 1197-1203
  • 11 Castro GM, Buczkowski L, Hafner JM. the contribution of sociotechnical factors to health information technology-related sentinel events. Jt Comm J Qual Patient Saf 2016; 42 (02) 70-76
  • 12 Adler-Milstein J. Falling between the Cracks in the Software. Rockville, MD: AHRQ PSNet; 2016. Available at: https://psnet.ahrq.gov/webmm/case/382/falling-between-the-cracks-in-the-software . Accessed January 15, 2018
  • 13 Adams KT, Howe JL, Fong A. , et al. An analysis of patient safety incident reports associated with electronic health record interoperability. Appl Clin Inform 2017; 8 (02) 593-602
  • 14 Abramson E, Kaushal R. Situational (Un) Awareness. Rockville, MD: AHRQ PSNet; 2011. Available at: https://psnet.ahrq.gov/webmm/case/249/situational-unawareness . Accessed January 22, 2018
  • 15 Amato MG, Schiff GD. Slow Down: Right Drug, Wrong Formulation. Rockville, MD: AHRQ PSNet; 2018. Available at: https://psnet.ahrq.gov/webmm/case/432/slow-down-right-drug-wrong-formulation . Accessed February 08, 2018
  • 16 Ashton EW. E-Prescribing: E for Error? Rockville, MD: AHRQ PSNet; 2012. Available at: https://psnet.ahrq.gov/webmm/case/260/e-prescribing-e-for-error . Accessed February 05, 2018
  • 17 Wong A, Wright A, Seger DL, Amato MG, Fiskio JM, Bates D. Comparison of overridden medication-related clinical decision support in the intensive care unit between a commercial system and a legacy system. Appl Clin Inform 2017; 8 (03) 866-879
  • 18 Rehr CA, Wong A, Seger DL, Bates DW. Determining inappropriate medication alerts from “inaccurate warning” overrides in the intensive care unit. Appl Clin Inform 2018; 9 (02) 268-274
  • 19 Gong Y, Kang H, Wu X, Hua L. Enhancing patient safety event reporting: a systematic review of system design features. Appl Clin Inform 2017; 8 (03) 893-909
  • 20 Magrabi F, Ong M-S, Runciman W, Coiera E. An analysis of computer-related patient safety incidents to inform the development of a classification. J Am Med Inform Assoc 2010; 17 (06) 663-670
  • 21 Magrabi F, Ong M-S, Runciman W, Coiera E. Using FDA reports to inform a classification for health information technology safety problems. J Am Med Inform Assoc 2012; 19 (01) 45-53
  • 22 Cheung K-C, van der Veen W, Bouvy ML, Wensing M, van den Bemt PMLA, de Smet PAGM. Classification of medication incidents associated with information technology. J Am Med Inform Assoc 2014; 21 (e1): e63-e70
  • 23 Medicine Io. Health IT and Patient Safety: Building Safer Systems for Better Care. Washington, DC: The National Academies Press; 2012
  • 24 Sittig DF, Singh H. A new sociotechnical model for studying health information technology in complex adaptive healthcare systems. Qual Saf Health Care 2010; 19 (Suppl. 03) i68-i74
  • 25 Meeks DW, Smith MW, Taylor L, Sittig DF, Scott JM, Singh H. An analysis of electronic health record-related patient safety concerns. J Am Med Inform Assoc 2014; 21 (06) 1053-1059
  • 26 Coiera E. A new informatics geography. Yearb Med Inform 2016; (01) 251-255
  • 27 Kim MO, Coiera E, Magrabi F. Problems with health information technology and their effects on care delivery and patient outcomes: a systematic review. J Am Med Inform Assoc 2017; 24 (02) 246-250
  • 28 Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. J Am Med Inform Assoc 2004; 11 (02) 104-112
  • 29 Kang H, Wang J, Yao B, Zhou S, Gong Y. Toward safer health care: a review strategy of FDA medical device adverse event database to identify and categorize health information technology related events. JAMIA Open 2018 Doi: 10.1093/jamiaopen/ooy042
  • 30 Coiera E, Westbrook J, Wyatt J. The safety and quality of decision support systems. Yearb Med Inform 2006; 1: 20-25
  • 31 Kobus DA, Amundson D, Moses JD, Rascona D, Gubler KD. A computerized medical incident reporting system for errors in the intensive care unit: initial evaluation of interrater agreement. Mil Med 2001; 166 (04) 350-353
  • 32 Fernald DH, Pace WD, Harris DM, West DR, Main DS, Westfall JM. Event reporting to a primary care patient safety reporting system: a report from the ASIPS collaborative. Ann Fam Med 2004; 2 (04) 327-332
  • 33 Kohn LT, Corrigan JM, Donaldson MS. To Err Is Human: Building a Safer Health System. Washington, DC: U.S. Institute of Medicine; 1999
  • 34 Denecke K. Automatic analysis of critical incident reports: requirements and use cases. Stud Health Technol Inform 2016; 223: 85-92