Solutions to reduce healthcare costs and improve outcomes in diagnostics: safety nets, optimized navigation, and decision support

Abstract

Abstract Objectives Diagnostic error is common, harmful, and costly, yet most health systems lack active interventions to improve diagnostic safety. This study aimed to identify scalable care models that advance diagnostic excellence while reducing costs for healthcare systems across diverse delivery and reimbursement environments. Methods We employed a multi-method care model development framework that integrated: (1) a literature review of 1,632 sources, (2) 19 semi-structured expert interviews, (3) in-depth analysis of four exemplar programs, and (4) four iterative refinement cycles with a 12-member cross-institutional expert panel. Candidate interventions were evaluated by diagnostic failure points, potential net cost savings, operational feasibility, stakeholder value alignment, and payment-model fit. Results We identified three highest value care model archetypes. (1) Diagnostic safety nets identify patients with abnormal findings lacking appropriate follow-up and re-engage them before harm escalates. (2) Optimized navigation routes patients to the right level of care through risk stratification and selective specialist input. (3) Decision support strengthens diagnostic reasoning at the point of care through evidence-based diagnostic tools. These archetypes differed in infrastructure requirements and financial attractiveness across payment models, with diagnostic safety nets most broadly attractive across both fee-for-service and risk-bearing environments. Conclusions This framework introduces three diagnostic improvement archetypes that health systems can use to select and sequence interventions based on local failure points, operational feasibility, and reimbursement context. By linking intervention choice to real-world implementation conditions, the framework offers a pragmatic approach for advancing diagnostic excellence across diverse care delivery settings.