Article Text

Download PDFPDF
018 A conceptual framework for predictive algorithm impact studies with focus on shared decision making
  1. Carol Bennett1,
  2. Krishnpriya Singh1,
  3. Sarah Beach1,
  4. Douglas G Manuel1,2,
  5. Personalized Health at End-of-Life team
  1. 1Ottawa Hospital Research Institute, Ottawa, ON, Canada
  2. 2School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada


Introduction There is a proliferation of predictive algorithm development studies but a need for real- world effectiveness evaluation. Current predictive algorithm reporting guidelines and frameworks focus on technical performance. We propose the development of a Predictive algorithm Impact Evaluation (PIE) framework to address the impact of predictive algorithms in the clinical setting while prioritizing patient-centred outcomes and shared-decision making.

Methods We used a flexible approach to synthesize knowledge related to the evaluation of predictive algorithms and enable exploration and mapping of key evaluation criteria. The following steps were taken: 1) defining key concepts, 2) conducting a preliminary exploration of the literature, and 3) extracting and synthesising the data into themes. The framework was developed iteratively by a multi- disciplinary group of researchers (expertise in artificial intelligence in healthcare, implementation science, learning health systems, and patient-oriented research) and our Patient and Family Advisory Committee.

Results There are limited real-world evaluative studies, but a wide range of scope, approaches, and outcomes. Preliminary domains include: uses (e.g., risk stratification, risk communication); target population (e.g., patient-centred, health systems, clinical or clinician-centred); development stage (pilot, scaling); and, cross-cutting themes (e.g., equity, data privacy, bioethics, transparency).

Discussion Concepts of implementation science, learning health systems, patient-centred uses are not well-established in impact or real-world evaluative studies of predictive algorithms. The iterative, consensus driven nature of the process and involvement of diverse stakeholders, including patients and families, will help ensure a comprehensive and inclusive evaluation framework.

Conclusion The imperative for evaluating predictive algorithms in clinical practice is rooted in commitment to providing safe, effective, and patient-centered healthcare. A comprehensive evaluation framework can help ensure that these algorithms contribute positively to patient and clinical decision- making while upholding ethical standards and promoting equity in healthcare.

Statistics from

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.