ArticleJournal of the American Medical Informatics Association : JAMIA2020
"How did you get to this number?" Stakeholder needs for implementing predictive analytics: a pre-implementation qualitative study.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence.Journal of medical Internet research · 2023Pooled it
- Article
- A qualitative interview study investigating patient, health professional, and developer perspectives on real-world implementation of patient-centered AI systems.NPJ digital medicine · 2026Article
- A qualitative Interview Study Investigating Patient, Health Professional, and Developer Perspectives on Real-World Implementation of Patient-Centered AI Systems.Research square · 2025Article
- Incorporating end-user perspectives into the development of a machine learning algorithm for first time perinatal depression prediction.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Exploring Stakeholder Perceptions about Using Artificial Intelligence for the Diagnosis of Rare and Atypical Infections.Applied clinical informatics · 2025Article
- Exploring home healthcare clinicians' needs for using clinical decision support systems for early risk warning.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Patient Perspectives on AI for Mental Health Care: Cross-Sectional Survey Study.JMIR mental health · 2024Article
- Nurses' perceptions of the design, implementation, and adoption of machine learning clinical decision support: A descriptive qualitative study.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2024Article
- Visualizing machine learning-based predictions of postpartum depression risk for lay audiences.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Clinician Perspectives on a Predictive Model for Recommending Opioid Use Disorder Treatment.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- An inaugural forum on epidemiological modeling for public health stakeholders in Arizona.Frontiers in public health · 2024Article
- Inpatient nurses' preferences and decisions with risk information visualization.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Article
- Women's perspectives on the use of artificial intelligence (AI)-based technologies in mental healthcare.JAMIA open · 2023Article
- Behavioral Health Decision Support Systems and User Interface Design in the Emergency Department.Applied clinical informatics · 2023Article
- Intensive Care Unit Physicians' Perspectives on Artificial Intelligence-Based Clinical Decision Support Tools: Preimplementation Survey Study.JMIR human factors · 2023Article
- Guidelines for Artificial Intelligence in Medicine: Literature Review and Content Analysis of Frameworks.Journal of medical Internet research · 2022Review
- Predictive Analytics in Clinical Practice: Advantages and Disadvantages.Acta neurochirurgica. Supplement · 2022Article
- A Graphical Toolkit for Longitudinal Dataset Maintenance and Predictive Model Training in Health Care.Applied clinical informatics · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivePredictive 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
methodsIn 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.
resultsForty-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.
conclusionsAlthough 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.