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Updated: Dec 26, 2025

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"How did you get to this number?" Stakeholder needs for implementing predictive analytics: a pre-implementation

Natalie C Benda1, Lala Tanmoy Das2, Erika L Abramson1,3

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Journal of the American Medical Informatics Association : JAMIA
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PubMed
Summary

Implementing predictive analytics in healthcare requires careful attention to sociotechnical factors, not just technology. Successful integration hinges on stakeholder trust, clear workflows, and actionable insights for patient interventions.

Keywords:
healthcare utilizationimplementationpredictive analyticsqualityuser-centered design

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Area of Science:

  • Health Informatics
  • Healthcare Management
  • Predictive Analytics in Healthcare

Background:

  • Predictive analytics offer potential for improving healthcare delivery.
  • Effective integration into healthcare organizations is crucial for realizing benefits.
  • A novel predictive algorithm aims to identify patients with high preventable utilization for proactive interventions.

Purpose of the Study:

  • To identify facilitators, challenges, and recommendations for implementing a novel predictive algorithm.
  • To understand stakeholder perspectives on integrating predictive analytics into healthcare.
  • To inform best practices for deploying predictive tools in complex healthcare systems.

Main Methods:

  • Conducted 49 interviews across 3 healthcare organizations with diverse stakeholder groups.
  • Applied thematic analysis to interview data, categorized using Sittig and Singh's sociotechnical model.
  • Iterative recruitment and analysis ensured thematic saturation for robust findings.

Main Results:

  • Sociotechnical factors presented greater implementation challenges than technical components.
  • Stakeholders emphasized the need for clear, actionable decision support integrated into existing workflows.
  • Trust, credibility, and interpretability of the predictive algorithm were critical concerns.
  • Patient interventions informed by the algorithm likely require significant institutional resources.

Conclusions:

  • Predictive analytics require careful sociotechnical implementation to improve healthcare processes and outcomes.
  • Stakeholder perceptions are vital for shaping successful predictive analytics deployment.
  • Addressing trust, workflow integration, and resource allocation is key for effective utilization.