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.

Study protocol for a type III hybrid effectiveness-implementation trial to evaluate scaling interoperable clinical decision support for patient-centered chronic pain management in primary care.

BACKGROUND: The US continues to face public health crises related to both chronic pain and opioid overdoses. Thirty percent of Americans suffer from chronic noncancer pain at an estimated yearly cost of over $600 billion. Most patients with chronic pain turn to primary care clinicians who must choose from myriad treatment options based on relative risks and benefits, patient history, available resources, symptoms, and goals. Recently, with attention to opioid-related risks, prescribing has declined. However, clinical experts have countered with concerns that some patients for whom opioid-related benefits outweigh risks may be inappropriately discontinued from opioids. Unfortunately, primary care clinicians lack usable tools to help them partner with their patients in choosing pain treatment options that best balance risks and benefits in the context of patient history, resources, symptoms, and goals. Thus, primary care clinicians and patients would benefit from patient-centered clinical decision support (CDS) for this shared decision-making process. METHODS: The objective of this 3-year project is to study the adaptation and implementation of an existing interoperable CDS tool for pain treatment shared decision making, with tailored implementation support, in new clinical settings in the OneFlorida Clinical Research Consortium. Our central hypothesis is that tailored implementation support will increase CDS adoption and shared decision making. We further hypothesize that increases in shared decision making will lead to improved patient outcomes, specifically pain and physical function. The CDS implementation will be guided by the Exploration, Preparation, Implementation, Sustainment (EPIS) framework. The evaluation will be organized by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. We will adapt and tailor PainManager, an open source interoperable CDS tool, for implementation in primary care clinics affiliated with the OneFlorida Clinical Research Consortium. We will evaluate the effect of tailored implementation support on PainManager's adoption for pain treatment shared decision making. This evaluation will establish the feasibility and obtain preliminary data in preparation for a multi-site pragmatic trial targeting the effectiveness of PainManager and tailored implementation support on shared decision making and patient-reported pain and physical function. DISCUSSION: This research will generate evidence on strategies for implementing interoperable CDS in new clinical settings across different types of electronic health records (EHRs). The study will also inform tailored implementation strategies to be further tested in a subsequent hybrid effectiveness-implementation trial. Together, these efforts will lead to important new technology and evidence that patients, clinicians, and health systems can use to improve care for millions of Americans who suffer from pain and other chronic conditions. TRIAL REGISTRATION: ClinicalTrials.gov, NCT05256394 , Registered 25 February 2022.

Evaluating a Prototype Clinical Decision Support Tool for Chronic Pain Treatment in Primary Care.

OBJECTIVES: The Chronic Pain Treatment Tracker (Tx Tracker) is a prototype decision support tool to aid primary care clinicians when caring for patients with chronic noncancer pain. This study evaluated clinicians' perceived utility of Tx Tracker in meeting information needs and identifying treatment options, and preferences for visual design. METHODS: We conducted 12 semi-structured interviews with primary care clinicians from four health systems in Indiana. The interviews were conducted in two waves, with prototype and interview guide revisions after the first six interviews. The interviews included exploration of Tx Tracker using a think-aloud approach and a clinical scenario. Clinicians were presented with a patient scenario and asked to use Tx Tracker to make a treatment recommendation. Last, participants answered several evaluation questions. Detailed field notes were collected, coded, and thematically analyzed by four analysts. RESULTS: We identified several themes: the need for clinicians to be presented with a comprehensive patient history, the usefulness of Tx Tracker in patient discussions about treatment planning, potential usefulness of Tx Tracker for patients with high uncertainty or risk, potential usefulness of Tx Tracker in aggregating scattered information, variability in expectations about workflows, skepticism about underlying electronic health record data quality, interest in using Tx Tracker to annotate or update information, interest in using Tx Tracker to translate information to clinical action, desire for interface with visual cues for risks, warnings, or treatment options, and desire for interactive functionality. CONCLUSION: Tools like Tx Tracker, by aggregating key information about past, current, and potential future treatments, may help clinicians collaborate with their patients in choosing the best pain treatments. Still, the use and usefulness of Tx Tracker likely relies on continued improvement of its functionality, accurate and complete underlying data, and tailored integration with varying workflows, care team roles, and user preferences.

A user-centered evaluation of medication therapy management alerts for community pharmacists: Recommendations to improve usability and usefulness.

BACKGROUND: Community pharmacists provide comprehensive medication reviews (CMRs) through pharmacy contracts with medication therapy management (MTM) vendors. These CMRs are documented in the vendors' web-based MTM software platforms, which often integrate alerts to assist pharmacists in the detection of medication therapy problems. Understanding pharmacists' experiences with MTM alerts is critical to optimizing alert design for patient care. OBJECTIVES: The objectives of this study were to 1) assess the usability and usefulness of MTM alerts for MTM vendor-contracted community pharmacists and 2) generate recommendations for improving MTM alerts for use by community pharmacists. METHODS: This was a convergent, parallel mixed-methods evaluation of data collected from 3 sources, with individual pharmacists contributing data to one or more sources: 1) community pharmacists' submissions of observational data about MTM alerts encountered during routine MTM provision, 2) videos of naturalistic usability testing of MTM alerts, and 3) semi-structured interviews to elicit pharmacists' perspectives on MTM alert usefulness and usability. MTM alert data submitted by pharmacists were summarized with descriptive statistics. Usability testing videos were analyzed to determine pharmacists' time spent on MTM alerts and to identify negative usability incidents. Interview transcripts were analyzed using a hybrid approach of deductive and inductive codes to identify emergent themes. Triangulation of data (i.e., determination of convergence/divergence in findings across all data sources) occurred through investigator discussion and identified overarching findings pertaining to key MTM alert challenges. These resulted in actionable recommendations to improve MTM alerts for use by community pharmacists. RESULTS: Collectively, two and four overarching key challenges pertaining to MTM alert usability and usefulness, respectively, were identified, resulting in 15 actionable recommendations for improving the design of MTM alerts from a user-centered perspective. CONCLUSIONS: Recommendations are expected to inform enhanced MTM alert designs that can improve pharmacist efficiency, patient and prescriber satisfaction with MTM, and patient outcomes.

Primary Care Clinicians' Beliefs and Strategies for Managing Chronic Pain in an Era of a National Opioid Epidemic.

BACKGROUND: Little is known about how primary care clinicians (PCCs) approach chronic pain management in the current climate of rapidly changing guidelines and the growing body of research about risks and benefits of opioid therapy. OBJECTIVE: To better understand PCCs' approaches to managing patients with chronic pain and explore implications for technological and administrative interventions. DESIGN: We conducted adapted critical decision method interviews with 20 PCCs. Each PCC participated in 1-5 interviews. PARTICIPANTS: PCCs interviewed had a mean of 14 years of experience. They were sampled from 13 different clinics in rural, suburban, and urban health settings across the state of Indiana. APPROACH: Interviews included discussion of participants' general approach to managing chronic pain, as well as in-depth discussion of specific patients with chronic pain. Interviews were audio recorded. Transcripts were analyzed thematically. KEY RESULTS: PCCs reflected on strategies they use to encourage and motivate patients. We identified four associated strategic themes: (1) developing trust, (2) eliciting information from the patient, (3) diverting attention from pain to function, and (4) articulating realistic goals for the patient. In discussion of chronic pain management, PCCs often explained their beliefs about opioid therapy. Three themes emerged: (1) Opioid use tends to reduce function, (2) Opioids are often not effective for long-term pain treatment, and (3) Response to pain and opioids is highly variable. CONCLUSIONS: PCC beliefs about opioid therapy generally align with the clinical evidence, but may have some important gaps. These findings suggest the potential value of interventions that include improved access to research findings; organizational changes to support PCCs in spending time with patients to develop rapport and trust, elicit information about pain, and manage patient expectations; and the need for innovative clinical cognitive support.

Factors That Influence Changes to Existing Chronic Pain Management Plans.

BACKGROUND: The objective of this qualitative study is to better understand primary care clinician decision making for managing chronic pain. Specifically, we focus on the factors that influence changes to existing chronic pain management plans. Limitations in guidelines and training leave clinicians to use their own judgment and experience in managing the complexities associated with treating patients with chronic pain. This study provides insight into those judgments based on clinicians' first-person accounts. Insights gleaned from this study could inspire innovations aimed at supporting primary care clinicians (PCCs) in managing chronic pain. METHODS: We conducted 89 interviews with PCCs to obtain their first-person perspective of the factors that influenced changes in treatment plans for their patients. Interview transcripts were analyzed thematically by a multidisciplinary team of clinicians, cognitive scientists, and public health researchers. RESULTS: Seven themes emerged through our analysis of factors that influenced a change in chronic pain management: 1) change in patient condition; 2) outcomes related to treatment; 3) nonadherent patient behavior; 4) insurance constraints; 5) change in guidelines, laws, or policies; 6) approaches to new patients; and 7) specialist recommendations. CONCLUSIONS: Our analysis sheds light on the factors that lead PCCs to change treatment plans for patients with chronic pain. An understanding of these factors can inform the types of innovations needed to support PCCs in providing chronic pain care. We highlight key insights from our analysis and offer ideas for potential practice innovations.

An Analysis of Primary Care Clinician Communication About Risk, Benefits, and Goals Related to Chronic Opioid Therapy.

Background. Safe opioid prescribing and effective pain care are particularly important issues in the United States, where decades of widespread opioid prescribing have contributed to high rates of opioid use disorder. Because of the importance of clinician-patient communication in effective pain care and recent initiatives to curb rising opioid overdose deaths, this study sought to understand how clinicians and patients communicate about the risks, benefits, and goals of opioid therapy during primary care visits. Methods. We recruited clinicians and patients from six primary care clinics across three health systems in the Midwest United States. We audio-recorded 30 unique patients currently receiving opioids for chronic noncancer pain from 12 clinicians. We systematically analyzed transcribed, clinic visits to identify emergent themes. Results. Twenty of the 30 patient participants were females. Several patients had multiple pain diagnoses, with the most common diagnoses being osteoarthritis (n = 10), spondylosis (n = 6), and low back pain (n = 5). We identified five themes: 1) communication about individual-level and population-level risks, 2) communication about policies or clinical guidelines related to opioids, 3) communication about the limited effectiveness of opioids for chronic pain conditions, 4) communication about nonopioid therapies for chronic pain, and 5) communication about the goal of the opioid tapering. Conclusions. Clinicians discuss opioid-related risks in varying ways during patient visits, which may differentially affect patient experiences. Our findings may inform the development and use of more standardized approaches to discussing opioids during primary care visits.

Decision-Centered Design of Patient Information Visualizations to Support Chronic Pain Care.

BACKGROUND: For complex patients with chronic conditions, electronic health records (EHRs) contain large amounts of relevant historical patient data. To use this information effectively, clinicians may benefit from visual information displays that organize and help them make sense of information on past and current treatments, outcomes, and new treatment options. Unfortunately, few clinical decision support tools are designed to support clinical sensemaking. OBJECTIVE: The objective of this study was to describe a decision-centered design process, and resultant interactive patient information displays, to support key clinical decision requirements in chronic noncancer pain care. METHODS: To identify key clinical decision requirements, we conducted critical decision method interviews with 10 adult primary care clinicians. Next, to identify key information needs and decision support design seeds, we conducted a half-day multidisciplinary design workshop. Finally, we designed an interactive prototype to support the key clinical decision requirements and information needs uncovered during the previous research activities. RESULTS: The resulting Chronic Pain Treatment Tracker prototype summarizes the current treatment plan, past treatment history, potential future treatments, and treatment options to be cautious about. Clinicians can access additional details about each treatment, current or past, through modal views. Additional decision support for potential future treatments and treatments to be cautious about is also provided through modal views. CONCLUSION: This study designed the Chronic Pain Treatment Tracker, a novel approach to decision support that presents clinicians with the information they need in a structure that promotes quick uptake, understanding, and action.

Cognitive requirements for primary care providers during the referral process: Information needed from and interactions with an electronic health record system.

OBJECTIVES: This study sought to identify and describe the cognitive requirements and associated information needs of referring primary care providers (PCPs) during the referral process as well as characterize referring PCPs' experiences with current health information technology. MATERIALS AND METHODS: We interviewed 62 referring PCPs. Our four-member analysis team used hierarchical task analysis to construct a goal-directed hierarchy. We utilized extensions of the task analysis to describe PCPs' common experiences with health information technologies throughout the referral process. RESULTS: The resultant goal hierarchy includes one main goal (Referral for Additional Care), two sub-goals (Assess Patient's Condition and Manage Referrals), and four major tasks with respective decisions (What consultation is warranted; What information should I provide; What additional action is needed; and How to integrate specialists' findings). Approximately 22 information needs were commonly identified and PCPs described their use of various sources - other PCPs, electronic health records, chat software, and paper- to satisfy those information needs. CONCLUSION: Cognitive demand for referring PCPs is high throughout the referral process. They have to search, identify, compose, track, and integrate information across multiple screens, systems, and people. Existing interfaces do not adequately support the communication, information exchange, or care coordination related to the referral process. Results from this study provide an important foundation for developing patient-centered displays that support PCPs' decision-making process and reduce cognitive challenges.

Alerts for community pharmacist-provided medication therapy management: recommendations from a heuristic evaluation.

BACKGROUND: Medication therapy management (MTM) is a service, most commonly provided by pharmacists, intended to identify and resolve medication therapy problems (MTPs) to enhance patient care. MTM is typically documented by the community pharmacist in an MTM vendor's web-based platform. These platforms often include integrated alerts to assist the pharmacist with assessing MTPs. In order to maximize the usability and usefulness of alerts to the end users (e.g., community pharmacists), MTM alert design should follow principles from human factors science. Therefore, the objectives of this study were to 1) evaluate the extent to which alerts for community pharmacist-delivered MTM align with established human factors principles, and 2) identify areas of opportunity and recommendations to improve MTM alert design. METHODS: Five categories of MTM alerts submitted by community pharmacists were evaluated: 1) indication, 2) effectiveness; 3) safety; 4) adherence; and 5) cost-containment. This heuristic evaluation was guided by the Instrument for Evaluating Human-Factors Principles in Medication-Related Decision Support Alerts (I-MeDeSA) which we adapted and contained 32 heuristics. For each MTM alert, four analysts' individual ratings were summed and a mean score on the modified I-MeDeSA computed. For each heuristic, we also computed the percent of analyst ratings indicating alignment with the heuristic. We did this for all alerts evaluated to produce an "overall" summary of analysts' ratings for a given heuristic, and we also computed this separately for each alert category. Our results focus on heuristics where ≤50% of analysts' ratings indicated the alerts aligned with the heuristic. RESULTS: I-MeDeSA scores across the five alert categories were similar. Heuristics pertaining to visibility and color were generally met. Opportunities for improvement across all MTM alert categories pertained to the principles of alert prioritization; text-based information; alarm philosophy; and corrective actions. CONCLUSIONS: MTM alerts have several opportunities for improvement related to human factors principles, resulting in MTM alert design recommendations. Enhancements to MTM alert design may increase the effectiveness of MTM delivery by community pharmacists and result in improved patient outcomes.

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.

Information Needs and Requirements for Decision Support in Primary Care: An Analysis of Chronic Pain Care.

Decision support system designs often do not align with the information environments in which clinicians work. These work environments may increase Clinicians' cognitive workload and harm their decision making. The objective of this study was to identify information needs and decision support requirements for assessing, diagnosing, and treating chronic noncancer pain in primary care. We conducted a qualitative study involving 30 interviews with 10 primary care clinicians and a subsequent multidisciplinary systems design workshop. Our analysis identified four key decision requirements, eight clinical information needs, and four decision support design seeds. Our findings indicate that clinicians caring for chronic pain need decision support that aggregates many disparate information elements and helps them navigate and contextualize that information. By attending to the needs identified in this study, decision support designers may improve Clinicians' efficiency, reduce mental workload, and positively affect patient care quality and outcomes.

UNDERSTANDING HOW PRIMARY CARE CLINICIANS MAKE SENSE OF CHRONIC PAIN.

Chronic pain leads to reduced quality of life for patients, and strains health systems worldwide. In the U.S. and some other countries, the complexities of caring for chronic pain are exacerbated by individual and public health risks associated with commonly used opioid analgesics. To help understand and improve pain care, this article uses the data-frame theory of sensemaking to explore how primary care clinicians in the U.S. manage their patients with chronic noncancer pain. We conducted Critical Decision Method interviews with 10 primary care clinicians about 30 individual patients with chronic pain. In these interviews, we identified several patient, social/environmental, and clinician factors that influence the frames clinicians use to assess their patients and determine a pain management plan. Findings suggest significant ambiguity and uncertainty in clinical pain management decision making. Therefore, interventions to improve pain care might focus on supporting sensemaking in the context of clinical evidence rather than attempting to provide clinicians with decontextualized and/or algorithm-based decision rules. Interventions might focus on delivering convenient and easily interpreted patient and social/environmental information in the context of clinical practice guidelines.

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.