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

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.

[1]  B Crandall,et al.  Critical decision method: A technique for eliciting concrete assessment indicators from the intuition of NICU nurses , 1993, ANS. Advances in nursing science.

[2]  Nigel Shadbolt,et al.  Use of the Critical Decision Method to Elicit Expert Knowledge: A Case Study in the Methodology of Cognitive Task Analysis , 1998, Hum. Factors.

[3]  Dean F. Sittig,et al.  Assessment of Health Information Technology–Related Outpatient Diagnostic Delays in the US Veterans Affairs Health Care System , 2020, JAMA network open.

[4]  Mariëlle Stoelinga,et al.  Fault tree analysis: A survey of the state-of-the-art in modeling, analysis and tools , 2014, Comput. Sci. Rev..

[5]  Ulrich Hauptmanns.  Semi-quantitative fault tree analysis for process plant safety using frequency and probability ranges , 2004 .

[6]  Ronilda C. Lacson,et al.  Classifying Safety Events Related to Diagnostic Imaging from a Safety Reporting System using a Human Factors Framework , 2018, Journal of the American College of Radiology : JACR.

[7]  Andrew Georgiou,et al.  The safety implications of missed test results for hospitalised patients: a systematic review , 2011, Quality and Safety in Health Care.

[8]  Erin Pete Devon,et al.  Determining preventability of pediatric readmissions using fault tree analysis. , 2016, Journal of hospital medicine.

[9]  Hardeep Singh,et al.  Eight recommendations for policies for communicating abnormal test results. , 2010, Joint Commission journal on quality and patient safety.

[10]  Simran Singh,et al.  Understanding the management of electronic test result notifications in the outpatient setting , 2011, BMC Medical Informatics Decis. Mak..

[11]  M G Kahn,et al.  Contextual inquiry: discovering physicians' true needs. , 1995, Proceedings. Symposium on Computer Applications in Medical Care.

[12]  D. Classen,et al.  Improving ambulatory patient safety: learning from the last decade, moving ahead in the next. , 2011, JAMA.

[13]  Zachary A Abecassis,et al.  Fault Tree Analysis , 2017, American journal of medical quality : the official journal of the American College of Medical Quality.

[14]  Dean F Sittig,et al.  A new sociotechnical model for studying health information technology in complex adaptive healthcare systems , 2010, Quality and Safety in Health Care.

[15]  Andrew Georgiou,et al.  Failure to Follow-Up Test Results for Ambulatory Patients: A Systematic Review , 2011, Journal of General Internal Medicine.

[16]  Gary Klein,et al.  Leveraging the Critical Decision Method to Develop Simulation-Based Training for Early Recognition of Sepsis , 2016 .

[17]  Dean F Sittig,et al.  Notification of abnormal lab test results in an electronic medical record: do any safety concerns remain? , 2010, The American journal of medicine.

[18]  Liron Pantanowitz,et al.  Needs and workflow assessment prior to implementation of a digital pathology infrastructure for the US Air Force Medical Service , 2013, Journal of pathology informatics.

[19]  Huaiguang Yang,et al.  The Design of Hemiplegia Rehabilitation Equipment Based on Contextual Inquiry , 2019, IOP Conference Series: Materials Science and Engineering.

[20]  T. Horberry,et al.  Decision-centred design in healthcare: The process of identifying a decision support tool for airway management. , 2019, Applied ergonomics.

[21]  Hardeep Singh,et al.  Research paper: Provider management strategies of abnormal test result alerts: a cognitive task analysis , 2010, J. Am. Medical Informatics Assoc..

[22]  Deevakar Rogith,et al.  Using Fault Trees to Advance Understanding of Diagnostic Errors. , 2017, Joint Commission journal on quality and patient safety.

[23]  R. T. Hessian,et al.  Fault-tree analysis for system design, development, modification, and verification , 1990 .

[24]  Roberta Calderwood,et al.  Critical decision method for eliciting knowledge , 1989, IEEE Trans. Syst. Man Cybern..

[25]  Dean F. Sittig,et al.  Timely follow-up of abnormal diagnostic imaging test results in an outpatient setting: are electronic medical records achieving their potential? , 2009, Archives of internal medicine.

[26]  Ajit Srividya,et al.  Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment , 2009, Reliab. Eng. Syst. Saf..