Association of physician burnout with perceived EHR work stress and potentially actionable factors

The contextual interview: a cross-cutting patient-interviewing approach for social context

Patient interviewing pedagogy in medical education has not evolved to comprehensively capture the biopsychosocial model of healthcare delivery. While gathering a patient's social history targets important aspects of social context it does not adequately capture and account for the real-time reassessment required to understand evolving factors that influence exposure to drivers of health inequities, social determinants of health, and access to supports that promote health. The authors offer a patient interviewing approach called the Contextual Interview (CI) that specifically targets dynamic and ever-changing social context information. To substantiate the use of the CI in medical education, the authors conducted a qualitative review of the Accreditation Council for Graduate Medical Education Milestones for primary care specialties (Family Medicine, Internal Medicine, and Pediatrics). Milestones were coded to the extent to which they reflected the learner's need to acknowledge, assess, synthesize and/or apply patient contextual data in real-time patient encounters. Approximately 1 in 5 milestones met the context-related and patient-facing criteria. This milestone review further highlights the need for more intentional training in eliciting meaningful social context data during patient interviewing. The CI as a cross-cutting, practical, time-conscious, and semi-structured patient interviewing approach that deliberately elicits information to improve the clinician's sense and understanding of a patient's social context. The authors reviewed future directions in researching adapted versions of the CI for undergraduate and graduate medical education.

Resident-Driven Clinical Decision Support Governance to Improve the Utility of Clinical Decision Support

OBJECTIVES

 This resident-driven quality improvement project aimed to better understand the known problem of a misaligned clinical decision support (CDS) strategy and improve CDS utilization.

METHODS

 An internal survey was sent to all internal medicine (IM) residents to identify the most bothersome CDS alerts. Survey results were supported by electronic health record (EHR) data of CDS firing rates and response rates which were collected for each of the three most bothersome CDS tools. Changes to firing criteria were created to increase utilization and to better align with the five rights of CDS. Findings and proposed changes were presented to our institution's CDS Governance Committee. Changes were approved and implemented. Postintervention firing rates were then collected for 1 week.

RESULTS

 Twenty nine residents participated in the CDS survey and identified sepsis alerts, lipid profile reminders, and telemetry renewals to be the most bothersome alerts. EHR data showed action rates for these CDS as low as 1%. We implemented changes to focus emergency department (ED)-based sepsis alerts to the right provider, better address the right information for lipid profile reminders, and select the right time in workflow for telemetry renewals to be most effective. With these changes we successfully eliminated ED-based sepsis CDS reminders for IM providers, saw a 97% reduction in firing rates for the lipid profile CDS, and noted a 55% reduction in firing rates for telemetry CDS.

CONCLUSION

 This project highlighted that alert improvements spearheaded by resident teams can be completed successfully using robust CDS governance strategies and can effectively optimize interruptive alerts.

Navigating the doctor-patient-AI relationship - a mixed-methods study of physician attitudes toward artificial intelligence in primary care

BACKGROUND

Artificial intelligence (AI) is a rapidly advancing field that is beginning to enter the practice of medicine. Primary care is a cornerstone of medicine and deals with challenges such as physician shortage and burnout which impact patient care. AI and its application via digital health is increasingly presented as a possible solution. However, there is a scarcity of research focusing on primary care physician (PCP) attitudes toward AI. This study examines PCP views on AI in primary care. We explore its potential impact on topics pertinent to primary care such as the doctor-patient relationship and clinical workflow. By doing so, we aim to inform primary care stakeholders to encourage successful, equitable uptake of future AI tools. Our study is the first to our knowledge to explore PCP attitudes using specific primary care AI use cases rather than discussing AI in medicine in general terms.

METHODS

From June to August 2023, we conducted a survey among 47 primary care physicians affiliated with a large academic health system in Southern California. The survey quantified attitudes toward AI in general as well as concerning two specific AI use cases. Additionally, we conducted interviews with 15 survey respondents.

RESULTS

Our findings suggest that PCPs have largely positive views of AI. However, attitudes often hinged on the context of adoption. While some concerns reported by PCPs regarding AI in primary care focused on technology (accuracy, safety, bias), many focused on people-and-process factors (workflow, equity, reimbursement, doctor-patient relationship).

CONCLUSION

Our study offers nuanced insights into PCP attitudes towards AI in primary care and highlights the need for primary care stakeholder alignment on key issues raised by PCPs. AI initiatives that fail to address both the technological and people-and-process concerns raised by PCPs may struggle to make an impact.

Streamlining Prior Authorization to Improve Care

This Viewpoint addresses the challenges of prior authorization: decreased access, delayed care, decreased patient satisfaction and outcomes, and increased clinician burnout.