Subscribe to RSS
Assessing the Safety of Custom Web-Based Clinical Decision Support Systems in Electronic Health Records: A Case StudyFunding Dr. Fiks is a coinventor of the “Care Assistant” software that was evaluated in this study. He holds no patent on the software and has earned no money from this invention. No licensing agreement exists. Dr. Fiks has also received an Independent Research Grant from Pfizer from which he personally drew no support. Dr. Grundmeier is a coinventor of the “Care Assistant” software that was evaluated in this study. He holds no patent on the software and has earned no money from this invention. No licensing agreement exists. Mr. Miller is a coinventor of “Project SHARE” which is a Care Assistant module to support the care of children with attention deficit disorder. He holds no patent on the software and has earned no money from this invention. No licensing agreement exists. The other authors have indicated they have no financial relationships relevant to this article to disclose.
29 November 2018
13 February 2019
03 April 2019 (online)
Background With the widespread adoption of vendor-supplied electronic health record (EHR) systems, clinical decision support (CDS) customization efforts beyond those anticipated by the vendor may require the use of technologies external to the EHR such as web services. Pursuing such customizations, however, is not without risk. Validating the expected behavior of a customized CDS system in the high-volume, complex environment of the live EHR is a challenging problem.
Objective This article identifies technology failures that impacted clinical care related to web service-based advanced custom CDS systems embedded in the complex sociotechnical context of a production EHR.
Methods In an academic health system’s primary care network, we performed an inventory of incidents between January 1, 2008 and December 31, 2016 related to a customized CDS system and performed a targeted review of changes in the CDS source code. Additional feedback on the root cause of individual incidents was obtained through interviews with members of the CDS project teams.
Results We identified five CDS malfunctions that impaired clinical workflow. The mechanisms for these failures are mapped to four characteristics of well-behaved applications: (1) system integrity; (2) data integrity; (3) reliability; and (4) scalability. Over the 9-year period, two malfunctions of the customized CDS significantly impaired clinical workflow for a total of 5 hours. Lesser impacts—loss of individual features with straightforward workarounds—arose from three malfunctions, which affected users on 53 days.
Discussion Advanced customization of EHRs for the purpose of CDS can present significant risks to clinical workflow.
Conclusion This case study highlights that advanced customization of CDS within a commercial EHR may support care for complex patient populations, but ongoing monitoring and support is required to ensure its safe use.
Keywordsclinical decision support - electronic health records and systems - clinical information systems - safety - error management
Protection of Human and Animal Subjects
This study did not involve human or animal subjects research.
- 1 Sim I, Gorman P, Greenes RA. , et al. Clinical decision support systems for the practice of evidence-based medicine. J Am Med Inform Assoc 2001; 8 (06) 527-534
- 2 Johnston ME, Langton KB, Haynes RB, Mathieu A. Effects of computer-based clinical decision support systems on clinician performance and patient outcome: a critical appraisal of research. Ann Intern Med 1994; 120 (02) 135
- 3 Kaplan B. Evaluating informatics applications--clinical decision support systems literature review. Int J Med Inform 2001; 64 (01) 15-37
- 4 Balas EA, Weingarten S, Garb CT, Blumenthal D, Boren SA, Brown GD. Improving preventive care by prompting physicians. Arch Intern Med 2000; 160 (03) 301-308
- 5 Gardner RM, Lundsgaarde HP. Evaluation of user acceptance of a clinical expert system. J Am Med Inform Assoc 1994; 1 (06) 428-438
- 6 Eligible Professional Meaningful Use Core Measures Measure 6 of 17, Stage 2, Clinical Decision Support Rule. Available at: https://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/downloads/Stage2_EPCore_6_ClinicalDecisionSupport.pdf . Accessed July 12, 2017
- 7 Definition of Terms Eligible Professional Meaningful Use Core Measures Measure 10 of 13. Available at: https://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/downloads/11_Clinical_Decision_Support_Rule.pdf . Accessed July 12, 2017
- 8 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
- 9 Wright A, Hickman T-TT, McEvoy D. , et al. Analysis of clinical decision support system malfunctions: a case series and survey. J Am Med Inform Assoc 2016; 23 (06) 1068-1076
- 10 Campbell EM, Sittig DF, Ash JS, Guappone KP, Dykstra RH. Types of unintended consequences related to computerized provider order entry. J Am Med Inform Assoc 2006; 13 (05) 547-556
- 11 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
- 12 Ash JS, Sittig DF, Poon EG, Guappone K, Campbell E, Dykstra RH. The extent and importance of unintended consequences related to computerized provider order entry. J Am Med Inform Assoc 2007; 14 (04) 415-423
- 13 Wright A, Ai A, Ash J. , et al. Clinical decision support alert malfunctions: analysis and empirically derived taxonomy. J Am Med Inform Assoc 2018; 25 (05) 496-506
- 14 Kassakian SZ, Yackel TR, Gorman PN, Dorr DA. Clinical decisions support malfunctions in a commercial electronic health record. Appl Clin Inform 2017; 8 (03) 910-923
- 15 Mandl KD, Mandel JC, Murphy SN. , et al. The SMART Platform: early experience enabling substitutable applications for electronic health records. J Am Med Inform Assoc 2012; 19 (04) 597-603
- 16 Mandel JC, Kreda DA, Mandl KD, Kohane IS, Ramoni RB. SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. J Am Med Inform Assoc 2016; 23 (05) 899-908
- 17 Mandl KD, Kohane IS. No small change for the health information economy. N Engl J Med 2009; 360 (13) 1278-1281
- 18 App Orchard Program Details. 2017 . Available at: http://www.epic.com/ . Accessed October 9, 2017
- 19 Gorton I. Software quality attributes. In: Essential Software Architecture. Berlin, Heidelberg: Springer Berlin Heidelberg; 2011: 23-38 . Available at: http://link.springer.com/10.1007/978-3-642-19176-3_3 . Accessed October 9, 2017
- 20 Fiks AG, Grundmeier RW, Margolis B. , et al. Comparative effectiveness research using the electronic medical record: an emerging area of investigation in pediatric primary care. J Pediatr 2012; 160 (05) 719-724
- 21 Overview - FHIR v3.0.1. Available at: https://www.hl7.org/fhir/overview.html . Accessed July 13, 2017
- 22 Encounter - FHIR v3.0.1. Available at: https://www.hl7.org/fhir/encounter.html . Accessed August 29, 2017
- 23 Gordon WJ, Baronas J, Lane WJ. A FHIR human leukocyte antigen (HLA) interface for platelet transfusion support. Appl Clin Inform 2017; 8 (02) 603-611
- 24 Bell LM, Grundmeier R, Localio R. , et al. Electronic health record-based decision support to improve asthma care: a cluster-randomized trial. Pediatrics 2010; 125 (04) e770-e777
- 25 Utidjian LH, Hogan A, Michel J. , et al. Clinical decision support and palivizumab: a means to protect from respiratory syncytial virus. Appl Clin Inform 2015; 6 (04) 769-784
- 26 Fiks AG, Mayne S, Hughes CC. , et al. Development of an instrument to measure parents' preferences and goals for the treatment of attention deficit-hyperactivity disorder. Acad Pediatr 2012; 12 (05) 445-455
- 27 Fiks AG, Mayne SL, Karavite DJ. , et al. Parent-reported outcomes of a shared decision-making portal in asthma: a practice-based RCT. Pediatrics 2015; 135 (04) e965-e973
- 28 Fiks AG, Mayne S, Karavite DJ, DeBartolo E, Grundmeier RW. A shared e-decision support portal for pediatric asthma. J Ambul Care Manage 2014; 37 (02) 120-126
- 29 Fiks AG, Grundmeier RW, Biggs LM, Localio AR, Alessandrini EA. Impact of clinical alerts within an electronic health record on routine childhood immunization in an urban pediatric population. Pediatrics 2007; 120 (04) 707-714
- 30 Fiks AG, Grundmeier RW, Mayne S. , et al. Effectiveness of decision support for families, clinicians, or both on HPV vaccine receipt. Pediatrics 2013; 131 (06) 1114-1124
- 31 Forrest CB, Fiks AG, Bailey LC. , et al. Improving adherence to otitis media guidelines with clinical decision support and physician feedback. Pediatrics 2013; 131 (04) e1071-e1081
- 32 Lipman TH, Cousounis P, Grundmeier RW. , et al. Electronic health record mid-parental height auto-calculator for growth assessment in primary care. Clin Pediatr (Phila) 2016; 55 (12) 1100-1106
- 33 Asthma Clinical Pathway — Inpatient | Children's Hospital of Philadelphia. Available at: http://www.chop.edu/clinical-pathway/asthma-inpatient-care-clinical-pathway . Accessed July 12, 2017
- 34 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
- 35 Wickens CD, Hollands JG, Banbury S, Parasuraman R. Engineering Psychology and Human Performance. Vol. 4, Engineering psychology and human performance. Pearson; 2013: 394-395
- 36 Breznitz S. Cry-wolf: The Psychology of False Alarms. Hillsdale, NJ: Lawrence Erlbaum Associates; 1984: 9-14
- 37 Sorkin RD. Why are people turning off our alarms?. J Acoust Soc Am 1988; 84 (03) 1107-1108
- 38 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
- 39 Drews FA. The frequency and impact of task interruptions in the ICU. Proc Hum Factors Ergon Soc Annu Meet 2007; 51 (11) 683-686
- 40 Wiegmann DA, ElBardissi AW, Dearani JA, Daly RC, Sundt III TM. Disruptions in surgical flow and their relationship to surgical errors: an exploratory investigation. Surgery 2007; 142 (05) 658-665
- 41 Sevdalis N, Forrest D, Undre S, Darzi A, Vincent C. Annoyances, disruptions, and interruptions in surgery: the Disruptions in Surgery Index (DiSI). World J Surg 2008; 32 (08) 1643-1650
- 42 Horsky J, Phansalkar S, Desai A, Bell D, Middleton B. Design of decision support interventions for medication prescribing. Int J Med Inform 2013; 82 (06) 492-503
- 43 Gaba DM, Howard SK, Small SD. Situation awareness in anesthesiology. Hum Factors 1995; 37 (01) 20-31
- 44 Horsky J, Schiff GD, Johnston D, Mercincavage L, Bell D, Middleton B. Interface design principles for usable decision support: a targeted review of best practices for clinical prescribing interventions. J Biomed Inform 2012; 45 (06) 1202-1216
- 45 Walker JM, Carayon P, Leveson N. , et al. EHR safety: the way forward to safe and effective systems. J Am Med Inform Assoc 2008; 15 (03) 272-277
- 46 Sittig DF, Classen DC. Safe electronic health record use requires a comprehensive monitoring and evaluation framework. JAMA 2010; 303 (05) 450-451
- 47 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
- 48 Zheng K, Haftel HM, Hirschl RB, O'Reilly M, Hanauer DA. Quantifying the impact of health IT implementations on clinical workflow: a new methodological perspective. J Am Med Inform Assoc 2010; 17 (04) 454-461