Developing an integrated multilevel model of uncertainty in health care: a qualitative systematic review and thematic synthesis

Abstract

<jats:sec><jats:title>Introduction</jats:title><jats:p>Uncertainty is an inevitable part of healthcare and a source of confusion and challenge to decision-making. Several taxonomies of uncertainty have been developed, but mainly focus on decisions in clinical settings. Our goal was to develop a holistic model of uncertainty that can be applied to both clinical as well as public and global health scenarios.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We searched Medline, Embase, CINAHL, Scopus and Google scholar in March 2021 for literature reviews, qualitative studies and case studies related to classifications or models of uncertainty in healthcare. Empirical articles were assessed for study limitations using the Critical Appraisal Skills Programme (CASP) checklist. We synthesised the literature using a thematic analysis and developed a dynamic multilevel model of uncertainty. We sought patient input to assess relatability of the model and applied it to two case examples.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>We screened 4125 studies and included 15 empirical studies, 13 literature reviews and 5 case studies. We identified 77 codes and organised these into 26 descriptive and 11 analytical themes of uncertainty. The themes identified are global, public health, healthcare system, clinical, ethical, relational, personal, knowledge exchange, epistemic, aleatoric and parameter uncertainty. The themes were included in a model, which captures the macro, meso and microlevels and the inter-relatedness of uncertainty. We successfully piloted the model on one public health example and an environmental topic. The main limitations are that the research input into our model predominantly came from North America and Europe, and that we have not yet tested the model in a real-life setting.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>We developed a model that can comprehensively capture uncertainty in public and global health scenarios. It builds on models that focus solely on clinical settings by including social and political contexts and emphasising the dynamic interplay between different areas of uncertainty.</jats:p></jats:sec>