A qualitative study of clinician perspectives and desired characteristics of a clinical decision support system for early sepsis recognition (Preprint)

Abstract

Sepsis is a major burden for healthcare systems in the United States, with over 750,000 cases annually and total costs nearing $20 billion. The hallmark of sepsis treatment is early and appropriate initiation of antibiotic therapy. Although sepsis clinical decision support (CDS) systems can provide clinicians with early predictions of suspected sepsis or imminent clinical decline, such systems have not reliably demonstrated improvements in clinical outcomes or care processes. Growing evidence suggests that challenges of integrating sepsis CDS systems into clinical workflows, gaining the trust of clinicians, and making sepsis CDS systems clinically relevant at the bedside are all obstacles to successful deployment. However, significant knowledge gaps exist about how to achieve these implementation and deployment goals.

We sought to (1) to identify perceptions of predictive information in sepsis CDS systems based on clinicians’ past experiences, and (2) to explore clinicians’ perceptions of a hypothetical sepsis CDS system, and (3) to identify characteristics of a CDS system that would be helpful to promote timely recognition and management of suspected sepsis in a multidisciplinary, team-based clinical setting.

We conducted semi-structured interviews with practicing beside nurses, advanced practice providers (APPs), and physicians at a large, academic medical center between September 2020 and March 2021. We used modified human factors methods (contextual interview and cognitive walkthrough performed over video calls due to the COVID-19 pandemic) and conducted a thematic analysis utilizing an abductive approach for coding to identify important patterns and concepts in the interview transcripts.

We interviewed 6 bedside nurses and 9 clinicians responsible for ordering antibiotics (APPs or physicians), who had a median of 4 years (interquartile range 4 to 6.5) of experience working in an inpatient setting. We then synthesized critical content from thematic analysis of the data into four domains: (1) clinician perceptions of prediction models and alerts; (2) previous experiences of clinician encounters with predictive information and risk scores; (3) desired characteristics of a CDS system build, including predictions, supporting information, and delivery methods for a potential alert; and (4) the clinical relevance and potential utility of a CDS system. These four domains were strongly linked to clinicians’ perceptions of likelihood of adoption and impact on clinical workflows when diagnosing and managing patients with suspected sepsis. Ultimately, clinicians desired a trusted and actionable CDS system to improve sepsis care.

Building a trusted and actionable sepsis CDS alert is paramount to achieving acceptability and usage among clinicians. These findings can inform development, implementation, and deployment strategies for CDS systems that support the early detection and treatment of sepsis. This study also highlights several key opportunities when eliciting clinician input prior to prediction model development and deployment.