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Appl Clin Inform 2020; 11(05): 692-698
DOI: 10.1055/s-0040-1716537
Case Report

Application of Human Factors Methods to Understand Missed Follow-up of Abnormal Test Results

Deevakar Rogith
1   The University of Texas Health Science Center at Houston School of Biomedical Informatics, Houston, Texas, United States
,
Tyler Satterly
2   Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veteran Affairs Medical Center, Houston, Texas, United States
3   Department of Medicine, Section of Health Services Research, Baylor College of Medicine, Houston, Texas, United States
,
Hardeep Singh
2   Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veteran Affairs Medical Center, Houston, Texas, United States
3   Department of Medicine, Section of Health Services Research, Baylor College of Medicine, Houston, Texas, United States
,
Dean F. Sittig
1   The University of Texas Health Science Center at Houston School of Biomedical Informatics, Houston, Texas, United States
4   UT-Memorial Hermann Center for Healthcare Quality and Safety, Houston, Texas, United States
,
Elise Russo
5   Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
,
Michael W. Smith
6   Department of Industrial and Mechanical Engineering, Universidad de las Americas Puebla, Cholula, Mexico
,
Don Roosan
7   Department of Pharmacy Practice and Administration, College of Pharmacy Western University of Health Sciences, Pomona, California, United States
,
Viraj Bhise
8   Department of Medicine, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States
,
Daniel R. Murphy
2   Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veteran Affairs Medical Center, Houston, Texas, United States
3   Department of Medicine, Section of Health Services Research, Baylor College of Medicine, Houston, Texas, United States
› Institutsangaben Funding This project is funded by the Agency for Health Care Research and Quality (R01HS022087) and partially funded by the Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety (CIN 13–413). D.R.M. is additionally funded by an Agency for Healthcare Research and Quality Mentored Career Development Award (K08-HS022901) and H.S. is additionally supported by the VA Health Services Research and Development Service (CRE 12–033; Presidential Early Career Award for Scientists and Engineers USA 14–274), the VA National Center for Patient Safety, and the Agency for Health Care Research and Quality (R01HS022087). These funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. There are no conflicts of interest for any authors.
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Abstract

Objective This study demonstrates application of human factors methods for understanding causes for lack of timely follow-up of abnormal test results (“missed results”) in outpatient settings.

Methods We identified 30 cases of missed test results by querying electronic health record data, developed a critical decision method (CDM)-based interview guide to understand decision-making processes, and interviewed physicians who ordered these tests. We analyzed transcribed responses using a contextual inquiry (CI)-based methodology to identify contextual factors contributing to missed results. We then developed a CI-based flow model and conducted a fault tree analysis (FTA) to identify hierarchical relationships between factors that delayed action.

Results The flow model highlighted barriers in information flow and decision making, and the hierarchical model identified relationships between contributing factors for delayed action. Key findings including underdeveloped methods to track follow-up, as well as mismatches, in communication channels, timeframes, and expectations between patients and physicians.

Conclusion This case report illustrates how human factors–based approaches can enable analysis of contributing factors that lead to missed results, thus informing development of preventive strategies to address them.

Keywords

contextual inquiry - fault tree analysis - abnormal laboratory follow-up - human factors - patient safety

Protection of Human and Animal Subjects

The study was approved by Baylor College of Medicine Institutional Review board. Informed consent was obtained from the physicians prior to the interview.


Supplementary Material



Publikationsverlauf

Eingereicht: 04. März 2020

Angenommen: 01. August 2020

Artikel online veröffentlicht:
21. Oktober 2020

Georg Thieme Verlag KG
Stuttgart · New York

 

  • References

  • 1 Wynia MK, Classen DC. Improving ambulatory patient safety: learning from the last decade, moving ahead in the next. JAMA 2011; 306 (22) 2504-2505
    CrossrefPubMedGoogle Scholar
  • 2 Callen JL, Westbrook JI, Georgiou A, Li J. Failure to follow-up test results for ambulatory patients: a systematic review. J Gen Intern Med 2012; 27 (10) 1334-1348
    CrossrefPubMedGoogle Scholar
  • 3 Callen J, Georgiou A, Li J, Westbrook JI. The safety implications of missed test results for hospitalised patients: a systematic review. BMJ Qual Saf 2011; 20 (02) 194-199
    CrossrefPubMedGoogle Scholar
  • 4 Singh H, Thomas EJ, Sittig DF. et al. Notification of abnormal lab test results in an electronic medical record: do any safety concerns remain?. Am J Med 2010; 123 (03) 238-244
    CrossrefPubMedGoogle Scholar
  • 5 Singh H, Thomas EJ, Mani S. et al. Timely follow-up of abnormal diagnostic imaging test results in an outpatient setting: are electronic medical records achieving their potential?. Arch Intern Med 2009; 169 (17) 1578-1586
    CrossrefPubMedGoogle Scholar
  • 6 Hysong SJ, Sawhney MK, Wilson L. et al. Understanding the management of electronic test result notifications in the outpatient setting. BMC Med Inform Decis Mak 2011; 11 (01) 22
    CrossrefPubMedGoogle Scholar
  • 7 Singh H, Vij MS. Eight recommendations for policies for communicating abnormal test results. Jt Comm J Qual Patient Saf 2010; 36 (05) 226-232
    PubMedGoogle Scholar
  • 8 Hysong SJ, Sawhney MK, Wilson L. et al. Provider management strategies of abnormal test result alerts: a cognitive task analysis. J Am Med Inform Assoc 2010; 17 (01) 71-77
    CrossrefPubMedGoogle Scholar
  • 9 Powell L, Sittig DF, Chrouser K, Singh H. Assessment of health information technology-related outpatient diagnostic delays in the US veterans affairs health care system: a qualitative study of aggregated root cause analysis data. JAMA Netw Open 2020; 3 (06) e206752
    CrossrefPubMedGoogle Scholar
  • 10 Lacson R, Cochon L, Ip I. et al. Classifying safety events related to diagnostic imaging from a safety reporting system using a human factors framework. J Am Coll Radiol 2019; 16 (03) 282-288
    CrossrefPubMedGoogle Scholar
  • 11 Hoffman RR, Crandall B, Shadbolt N. Use of the critical decision method to elicit expert knowledge: a case study in the methodology of cognitive task analysis. Hum Factors 1998; 40 (02) 254-276
    CrossrefPubMedGoogle Scholar
  • 12 Pascual R, Henderson S. Evidence of naturalistic decision making in military command and control. Naturalistic Decision Making 1997; 217-26
    PubMedGoogle Scholar
  • 13 Crandall B, Getchell-Reiter K. Critical decision method: a technique for eliciting concrete assessment indicators from the intuition of NICU nurses. ANS Adv Nurs Sci 1993; 16 (01) 42-51
    CrossrefPubMedGoogle Scholar
  • 14 Patterson MD, Militello LG, Bunger A. et al. Leveraging the critical decision method to develop simulation-based training for early recognition of sepsis. J Cogn Eng Decis Mak 2016; 10 (01) 36-56
    CrossrefPubMedGoogle Scholar
  • 15 Power N, Baldwin J, Plummer NR, Laha S. Critical care decision making: a pilot study to explore and compare the decision making processes used by critical care and non-critical care doctors when referring patients for admission. 2017 :255. Presented at 13th International Conference on Naturalistic Decision; Bath, UK
    PubMedGoogle Scholar
  • 16 Schnittker R, Marshall SD, Horberry T, Young K. Decision-centred design in healthcare: The process of identifying a decision support tool for airway management. Appl Ergon 2019; 77: 70-82
    CrossrefPubMedGoogle Scholar
  • 17 Klein GA, Calderwood R, Macgregor D. Critical decision method for eliciting knowledge. IEEE Trans Syst Man Cybern 1989; 19 (03) 462-472
    CrossrefPubMedGoogle Scholar
  • 18 Coble J, Maffitt J, Orland M, Kahn M. Contextual inquiry: discovering physicians' true needs. Proc Annu Symp Comput Appl Med Care 1995; 469-473
    PubMedGoogle Scholar
  • 19 Ho J, Aridor O, Glinski DW. et al. Needs and workflow assessment prior to implementation of a digital pathology infrastructure for the US Air Force Medical Service. J Pathol Inform 2013; 4: 32
    CrossrefPubMedGoogle Scholar
  • 20 Liang J, Yan B, Yang C, Yang H. The design of hemiplegia rehabilitation equipment based on contextual inquiry. IOP Conf. Ser.: Mater. Sci. Eng 2019; 520: 012020 . Doi: 10.1088/1757-899X/520/1/012020
    CrossrefPubMedGoogle Scholar
  • 21 Vesely WE, Roberts N. Fault Tree Handbook. Washington, D.C.: Systems and Reliability Research, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission; 1981
    Google Scholar
  • 22 Hessian R, Salter BB, Goodwin EF. Fault-tree analysis for system design, development, modification, and verification. IEEE Trans Reliab 1990; 39 (01) 87-91
    CrossrefPubMedGoogle Scholar
  • 23 Rao KD, Gopika V, Rao VS, Kushwaha H, Verma AK, Srividya A. Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment. Reliab Eng Syst Saf 2009; 94 (04) 872-883
    CrossrefPubMedGoogle Scholar
  • 24 Hauptmanns U. Semi-quantitative fault tree analysis for process plant safety using frequency and probability ranges. J Loss Prev Process Ind 2004; 17 (05) 339-345
    CrossrefPubMedGoogle Scholar
  • 25 Lee W-S, Grosh D, Tillman FA, Lie CH. Fault tree analysis, methods, and applications: a review. reliability. IEEE Transactions on. 1985; 34 (03) 194-203
    PubMedGoogle Scholar
  • 26 Rogith D, Iyengar MS, Singh H. Using fault trees to advance understanding of diagnostic errors. Jt Comm J Qual Patient Saf 2017; 43 (11) 598-605
    PubMedGoogle Scholar
  • 27 Jonas JA, Devon EP, Ronan JC. et al. Determining preventability of pediatric readmissions using fault tree analysis. J Hosp Med 2016; 11 (05) 329-335
    CrossrefPubMedGoogle Scholar
  • 28 McElroy LM, Khorzad R, Rowe TA. et al. Fault tree analysis: assessing the adequacy of reporting efforts to reduce postoperative bloodstream infection. Am J Med Qual 2017; 32 (01) 80-86
    CrossrefPubMedGoogle Scholar
  • 29 Li X-Z, Gao J-M, Zhao Y-Q, Chen Y-J. Infusion pump system safety analysis based on fault tree and Markov analysis. In: Wei J. , ed. Mechanical Engineering and Control Systems: Proceedings of the 2016 International Conference on Mechanical Engineering and Control System (MECS2016). New Jersey, NJ: World Scientific Publishing Co. Pte. Ltd.; 2017
    Google Scholar
  • 30 Sittig DF, Singh H. A new socio-technical model for studying health information technology in complex adaptive healthcare systems. Qual Saf Health Care 2010; 19 (Suppl. 03) i68-i74
    CrossrefPubMedGoogle Scholar