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 clinicians9 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.

To highlight the often overlooked role of user interface (UI) design in mitigating bias in artificial intelligence (AI)-based clinical decision support (CDS). This perspective paper discusses the interdependency between AI-based algorithm development and UI design and proposes strategies for increasing the safety and efficacy of CDS. The role of design in biasing user behavior is well documented in behavioral economics and other disciplines. We offer an example of how UI designs play a role in how bias manifests in our machine learning-based CDS development. Much discussion on bias in AI revolves around data quality and algorithm design; less attention is given to how UI design can exacerbate or mitigate limitations of AI-based applications. This work highlights important considerations including the role of UI design in reinforcing/mitigating bias, human factors methods for identifying issues before an application is released, and risk communication strategies.

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. 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. 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. 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.

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.