"How did you get to this number?" Stakeholder needs for implementing predictive analytics: a pre-implementation qualitative study. (6th March 2020)

• Record Type: Journal Article
• Authors: Benda, Natalie C; Das, Lala Tanmoy; Abramson, Erika L; Blackburn, Katherine; Thoman, Amy; Kaushal, Rainu; Zhang, Yongkang; Ancker, Jessica S
• Is Part Of: Journal of the American Medical Informatics Association. Volume 27:Number 5(2020)
• Page Start: 709
• Page End: 716
• Publication Date: 2020-03-06
• Subjects: predictive analytics -- implementation -- user-centered design -- quality -- healthcare utilization; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocaa021
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: Predictive analytics are potentially powerful tools, but to improve healthcare delivery, they must be carefully integrated into healthcare organizations. Our objective was to identify facilitators, challenges, and recommendations for implementing a novel predictive algorithm which aims to prospectively identify patients with high preventable utilization to proactively involve them in preventative interventions. Materials and Methods: In preparation for implementing the predictive algorithm in 3 organizations, we interviewed 3 stakeholder groups: health systems operations (eg, chief medical officers, department chairs), informatics personnel, and potential end users (eg, physicians, nurses, social workers). We applied thematic analysis to derive key themes and categorize them into the dimensions of Sittig and Singh's original sociotechnical model for studying health information technology in complex adaptive healthcare systems. Recruiting and analysis were conducted iteratively until thematic saturation was achieved. Results: Forty-nine interviews were conducted in 3 healthcare organizations. Technical components of the implementation (hardware and software) raised fewer concerns than alignment with sociotechnical factors. Stakeholders wanted decision support based on the algorithm to be clear and actionable and incorporated into current workflows. However, how to make this disease-independent classification tool actionable was perceived as a challenge, and appropriate patient interventions informed by the algorithm appeared likely to require substantial external and institutional resources. Stakeholders also described the criticality of trust, credibility, and interpretability of the predictive algorithm. Conclusions: Although predictive analytics can classify patients with high accuracy, they cannot advance healthcare processes and outcomes without careful implementation that takes into account the sociotechnical system. Key stakeholders have strong perceptions about facilitators and challenges to shape successful implementation.