Cognitive task analysis of clinicians' drug-drug interaction management during patient care and implications for alert design.

BACKGROUND: Drug-drug interactions (DDIs) are common and can result in patient harm. Electronic health records warn clinicians about DDIs via alerts, but the clinical decision support they provide is inadequate. Little is known about clinicians' real-world DDI decision-making process to inform more effective alerts. OBJECTIVE: Apply cognitive task analysis techniques to determine informational cues used by clinicians to manage DDIs and identify opportunities to improve alerts. DESIGN: Clinicians submitted incident forms involving DDIs, which were eligible for inclusion if there was potential for serious patient harm. For selected incidents, we met with the clinician for a 60 min interview. Each interview transcript was analysed to identify decision requirements and delineate clinicians' decision-making process. We then performed an inductive, qualitative analysis across incidents. SETTING: Inpatient and outpatient care at a major, tertiary Veterans Affairs medical centre. PARTICIPANTS: Physicians, pharmacists and nurse practitioners. OUTCOMES: Themes to identify informational cues that clinicians used to manage DDIs. RESULTS: We conducted qualitative analyses of 20 incidents. Data informed a descriptive model of clinicians' decision-making process, consisting of four main steps: (1) detect a potential DDI; (2) DDI problem-solving, sensemaking and planning; (3) prescribing decision and (4) resolving actions. Within steps (1) and (2), we identified 19 information cues that clinicians used to manage DDIs for patients. These cues informed their subsequent decisions in steps (3) and (4). Our findings inform DDI alert recommendations to improve clinicians' decision-making efficiency, confidence and effectiveness. CONCLUSIONS: Our study provides three key contributions. Our study is the first to present an illustrative model of clinicians' real-world decision making for managing DDIs. Second, our findings add to scientific knowledge by identifying 19 cognitive cues that clinicians rely on for DDI management in clinical practice. Third, our results provide essential, foundational knowledge to inform more robust DDI clinical decision support in the future.

Medication decision-making for patients with renal insufficiency in inpatient and outpatient care at a US Veterans Affairs Medical Centre: a qualitative, cognitive task analysis.

BACKGROUND: Many studies identify factors that contribute to renal prescribing errors, but few examine how healthcare professionals (HCPs) detect and recover from an error or potential patient safety concern. Knowledge of this information could inform advanced error detection systems and decision support tools that help prevent prescribing errors. OBJECTIVE: To examine the cognitive strategies that HCPs used to recognise and manage medication-related problems for patients with renal insufficiency. DESIGN: HCPs submitted documentation about medication-related incidents. We then conducted cognitive task analysis interviews. Qualitative data were analysed inductively. SETTING: Inpatient and outpatient facilities at a major US Veterans Affairs Medical Centre. PARTICIPANTS: Physicians, nurses and pharmacists who took action to prevent or resolve a renal-drug problem in patients with renal insufficiency. OUTCOMES: Emergent themes from interviews, as related to recognition of renal-drug problems and decision-making processes. RESULTS: We interviewed 20 HCPs. Results yielded a descriptive model of the decision-making process, comprised of three main stages: detect, gather information and act. These stages often followed a cyclical path due largely to the gradual decline of patients' renal function. Most HCPs relied on being vigilant to detect patients' renal-drug problems rather than relying on systems to detect unanticipated cues. At each stage, HCPs relied on different cognitive cues depending on medication type: for renally eliminated medications, HCPs focused on gathering renal dosing guidelines, while for nephrotoxic medications, HCPs investigated the need for particular medication therapy, and if warranted, safer alternatives. CONCLUSIONS: Our model is useful for trainees so they can gain familiarity with managing renal-drug problems. Based on findings, improvements are warranted for three aspects of healthcare systems: (1) supporting the cyclical nature of renal-drug problem management via longitudinal tracking mechanisms, (2) providing tools to alleviate HCPs' heavy reliance on vigilance and (3) supporting HCPs' different decision-making needs for renally eliminated versus nephrotoxic medications.

Evaluating a Modular Decision Support Application For Colorectal Cancer Screening.

BACKGROUND: There is a need for health information technology evaluation that goes beyond randomized controlled trials to include consideration of usability, cognition, feedback from representative users, and impact on efficiency, data quality, and clinical workflow. This article presents an evaluation illustrating one approach to this need using the Decision-Centered Design framework. OBJECTIVE: To evaluate, through a Decision-Centered Design framework, the ability of the Screening and Surveillance App to support primary care clinicians in tracking and managing colorectal cancer testing. METHODS: We leveraged two evaluation formats, online and in-person, to obtain feedback from a range primary care clinicians and obtain comparative data. Both the online and in-person evaluations used mock patient data to simulate challenging patient scenarios. Primary care clinicians responded to a series of colorectal cancer-related questions about each patient and made recommendations for screening. We collected data on performance, perceived workload, and usability. Key elements of Decision-Centered Design include evaluation in the context of realistic, challenging scenarios and measures designed to explore impact on cognitive performance. RESULTS: Comparison of means revealed increases in accuracy, efficiency, and usability and decreases in perceived mental effort and workload when using the Screening and Surveillance App. CONCLUSION: The results speak to the benefits of using the Decision-Centered Design approach in the analysis, design, and evaluation of Health Information Technology. Furthermore, the Screening and Surveillance App shows promise for filling decision support gaps in current electronic health records.