The five papers reviewed this week address public involvement in low-value care, patient-engagement de-implementation trials, cost reporting in de-implementation research, predictive analytics governance, and value-based care implementation. Considered individually, each paper offers a discrete literature review. Collectively, they reveal a consistent system-wide pattern: the clinical and technical rationale for improved care frequently lags behind the financial, governance, and measurement infrastructure required for large-scale implementation. The reviews from Monday and Tuesday demonstrate the effectiveness of patient-facing tools. Wednesday highlights the frequent absence of cost data. Thursday reveals that even well-resourced systems often lack the governance necessary to operationalize predictive tools. Friday synthesizes these findings within the context of value-based care, illustrating how infrastructure gaps influence organizational success.
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Monday: Patients Are Already the Front Line - Policy Hasn’t Caught Up
Health systems devote significant resources to debates regarding the governance of low-value care at policy and utilization-management levels. However, evidence indicates that the most effective mechanism for reducing low-value care is already present within the clinical encounter. Sypes et al.'s (2020) scoping review of 218 studies found that public involvement in reducing low-value care is concentrated almost exclusively within the patient-clinician interaction, rather than within the research or policy structures that typically receive strategic focus from executives.
6,736 - citations screened | 72% - studies at point of care | 91% - SDM studies, positive results | 15% - studies on policy involvement
What’s Working: Shared Decision-Making
Shared decision-making (SDM) and patient-targeted educational materials are prevalent in the literature due to their demonstrated effectiveness. Ninety-one percent of SDM studies in the review reported positive outcomes, such as reduced utilization of low-value tests and treatments and improved patient satisfaction. For clinical leaders, this indicates that SDM has progressed beyond the pilot stage and should be considered an established, evidence-based operational tool suitable for integration into standard workflow design.
What’s Missing: A Policy-Level Analog
In contrast, public involvement in policy decisions regarding low-value care remains limited. Only 15% of the included studies addressed this area, and the review found that both administrators and the public view policy-level engagement with skepticism. Recurring concerns included expertise, feasibility, and the extent to which public input influences decision-making. This results in a notable asymmetry: while effective mechanisms exist at the level of individual patient encounters, there is no comparably validated approach for population-level cost and utilization management.
“Efforts examining public involvement in low-value care concentrate within the patient-clinician interaction… tools to promote inclusion of the public in low-value care policy decisions are less well-developed.” (Sypes et al., 2020, Implementation Science)
Strategic Takeaways
For clinical operations leaders: SDM tools are supported by sufficient evidence to warrant deployment within electronic health record (EHR) systems, extending beyond clinician training. The primary barrier is workflow integration rather than proof of concept.
For payers and policymakers, population-level engagement strategies, such as member advisory panels and benefit-design input, remain underdeveloped and unvalidated. These approaches should be considered pilot-stage investments until more robust evidence is available.
For electronic medical record (EMR) developers: the current evidence gap presents an opportunity for development. Decision-support tools that facilitate SDM at the point of care possess a stronger evidence base for design than most other de-implementation strategies.
Citation: Sypes, E. E., de Grood, C., Clement, F. M., Parsons Leigh, J., Whalen-Browne, L., Stelfox, H. T., & Niven, D. J. (2020). Understanding the public’s role in reducing low-value care: A scoping review. Implementation Science, 15(20). https://doi.org/10.1186/s13012-020-00986-0
Audience: Clinicians, Health System Leaders, Payers / Policy
🧠 SDM works and is ready to scale • 🏛️ Policy-level engagement in SDM is effective and ready for broader implementation. Policy-level engagement remains unproven, while educational tools continue to be the most validated intervention.Now it's a Scaling
Problem
While the previous review identified where public involvement occurs, the companion paper by Sypes et al. (2020) addresses whether such involvement reduces low-value care. Their systematic review and meta-analysis pooled 22 studies-9 randomized controlled trials (RCTs) and 13 quasi-experimental designs-evaluating patient-engagement interventions targeting low-value tests, treatments, and procedures. The pooled effect sizes provide compelling evidence supporting the integration of these tools into standard care delivery.
RR 0.74 - RCTs (95% CI 0.66–0.84) | RR 0.61 - quasi-exp. (95% CI 0.43–0.87) | 86% of studies showed significant reduction | 82% used educational materials
Consistency Across Settings and Targets
The distinguishing feature of this evidence is its consistency across subgroups defined by both the type of low-value practice and the specific engagement strategy employed. The observed benefits were not confined to a single condition, setting, or intervention design. Interventions targeted low-value medications (77% of studies, primarily antibiotics), unnecessary imaging, cardiac stress testing, and elective procedures, with reductions observed across primary care (45%), inpatient wards, and emergency departments.
The Real Constraint Isn’t Evidence - It’s Delivery Design
For health system executives, the strategic question is no longer whether patient engagement reduces low-value care, but rather why these interventions are not yet embedded in all relevant workflows. Media campaigns alone (18% of studies) demonstrated effectiveness, though combined approaches yielded stronger results. The findings suggest that educational materials and SDM tools are cost-effective, well-tolerated, and supported by robust evidence, yet remain underutilized relative to their demonstrated impact. This represents an uncommon scenario in health care delivery where the gap between evidence and adoption is not attributable to uncertain efficacy.
“De-implementation interventions that engage patients within the patient-clinician interaction led to a significant reduction in low-value care… Intervention effects were consistent across subgroups defined by low-value practice and patient-engagement strategy.” (Sypes et al., 2020, BMC Medicine)
Strategic Takeaways
For clinical leaders: prioritize the expansion of existing SDM and educational material programs over initiating new pilot projects, as the evidence base for scaling is already well established.
For financial and operations executives: reducing low-value care through patient engagement represents a comparatively low-cost intervention category. The primary constraint is implementation capacity, rather than concerns regarding unproven mechanisms.
For payers: benefit designs that support or incentivize SDM tool deployment are supported by consistent randomized controlled trial (RCT) evidence, rather than preliminary or exploratory data.
Citation: Sypes, E. E., de Grood, C., Whalen-Browne, L., Clement, F. M., Parsons Leigh, J., Niven, D. J., & Stelfox, H. T. (2020). Engaging patients in de-implementation interventions to reduce low-value clinical care: A systematic review and meta-analysis. BMC Medicine, 18(116). https://doi.org/10.1186/s12916-020-01567-0
Audience: Clinicians, Health System Leaders, Payers / Policy
Strong and consistent effect sizes were observed across 22 studies. SDM interventions reduced the use of antibiotics, imaging, and elective procedures, and were effective across primary care, inpatient, and emergency department settings.
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Wednesday: We Know De-Implementation Works. We Rarely Know What It Costs.
The evidence from Monday and Tuesday demonstrates that patient-engagement strategies reliably reduce low-value care. However, the paper reviewed on Wednesday highlights a persistent barrier to adoption by health systems and payers: the lack of cost data. Falkenbach et al.'s (2023) scoping review screened 10,733 records and included 227 de-implementation randomized controlled trials (RCTs), finding that direct intervention costs and downstream cost impacts are rarely reported.
92% - omit direct intervention costs | 81% - omit downstream cost impact | $32.3K - median total cost (of the few reporting) | 22% - reported exact cost figures
A Missing Variable in Every Business Case
Among the 227 trials reviewed, only 18 (8%) reported the direct cost of the de-implementation strategy, and only 43 (19%) reported changes in health care costs resulting from the intervention. Of those reporting cost impact, 63% indicated decreased health care costs, 5% reported increases, and 33% showed no change. However, the small and inconsistently measured sample limits the ability to construct a robust system-level financial argument. No study separated costs by development, execution, or maintenance phase, and costing methodologies were not disclosed.
Why This Blocks Adoption, Not Just Research
This represents the most significant finding for non-clinical stakeholders: health system chief financial officers (CFOs) and payer medical economics teams cannot construct defensible return-on-investment (ROI) models without cost data. Clinical effectiveness alone is insufficient to secure budget, staffing, or electronic health record (EHR) integration resources; such decisions require comprehensive cost information, which current de-implementation research typically lacks.
“De-implementation randomized controlled trials typically did not report direct costs of the de-implementation strategies (92%) or the impacts of de-implementation on health care costs (81%)… A lack of cost information may limit the value of de-implementation trials to decision-makers.” (Falkenbach et al., 2023, Implementation Science)
Strategic Takeaways
For health system financial leaders: consider published de-implementation effect sizes as clinically credible but financially unvalidated until internal cost tracking is implemented alongside any pilot programs.
For researchers and grant funders: standardized cost-reporting requirements, including development, execution, and maintenance phases as well as disclosure of costing methodology, should be prerequisites for future de-implementation trial funding.
For policymakers and stakeholders involved with the Center for Medicare and Medicaid Innovation (CMMI): the absence of cost data constitutes a policy-relevant finding, as it limits the evidence base available for designing value-based payment models.
Citation: Falkenbach, P., Raudasoja, A. J., Vernooij, R. W. M., Mustonen, J. M. J., Agarwal, A., Aoki, Y., Blanker, M. H., Cartwright, R., Garcia-Perdomo, H. A., Kilpeläinen, T. P., Lainiala, O., Lamberg, T., Nevalainen, O. P. O., Raittio, E., Richard, P. O., Violette, P. D., Tikkinen, K. A. O., Sipilä, R., Turpeinen, M., & Komulainen, J. (2023). Reporting of costs and economic impacts in randomized trials of de-implementation interventions for low-value care: A systematic scoping review. Implementation Science, 18(36). https://doi.org/10.1186/s13012-023-01290-3
Audience: Health System Leaders, Payers / Policy, Researchers
Ninety-two percent of trials omit direct cost reporting. Downstream costs often decrease, but available data are limited. The absence of cost data restricts the real-world policy relevance of these findings.
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Thursday: Big Systems, Small Teams - Predictive Analytics Outruns Its Own Governance
Attention now turns from de-implementation to a second infrastructure gap: predictive analytics. Rojas et al.'s (2022) national survey of large U.S. health systems-all members of the Scottsdale Institute, with 25 of 42 systems responding-found that even organizations with substantial scale (most operating 10 or more hospitals and over 2,000 beds) maintain predictive analytics programs that are small, inconsistently governed, and frequently lack dedicated funding relative to their scope.
64% - have accountable team/individual | 24% - have a dedicated budget | ≤9 - typical team size | 56% - cite clinician acceptance as top threat
Scale Doesn’t Guarantee Structure
Only 64% of responding systems reported having a team or individual formally accountable for the clinical application of predictive algorithms, indicating that over one-third of large, well-resourced systems lack clear ownership structures for clinical use. Only 24% maintained a dedicated budget. Responsibilities included identifying, developing, validating, deploying, and monitoring algorithms, but systems varied in which steps were managed internally versus outsourced. Algorithms most commonly targeted sepsis, readmissions, clinical deterioration, and utilization prediction.
The Threats Are Human, Not Technical
When identifying the primary threats to predictive analytics programs, leaders most frequently cited clinical team acceptance (56%) and integration into existing care workflows (52%), ranking these concerns above liability, data sharing, or model generalizability. This finding is significant for investment strategy: the primary constraint on predictive analytics value is not model accuracy or data availability, but rather clinician trust in and adoption of the technology, as well as its fit within existing care delivery processes.
“Only 64% reported having a team or individual accountable for the clinical application of predictive algorithms… acceptance by clinical teams and the technology needed to integrate algorithms into care were viewed as the most significant threats.” (Rojas et al., 2022, Journal of General Internal Medicine)
Strategic Takeaways
For health system executives: investment in predictive analytics should be accompanied by formal governance structures and dedicated budget lines from the outset. Retrofitting accountability after deployment has been identified as a recurrent failure pattern.
For clinical informatics and electronic medical record (EMR) leaders: investment in workflow integration and clinician trust-building should be prioritized at least as highly as model development.
For boards and finance committees: the lack of standardized governance frameworks, even among advanced systems, indicates an industry-wide maturity gap rather than an organization-specific execution failure. This context is valuable for benchmarking internal programs.
Citation: Rojas, J. C., Rohweder, G., Guptill, J., Arora, V. M., & Umscheid, C. A. (2022). Predictive analytics programs at large healthcare systems in the USA: A national survey. Journal of General Internal Medicine, 37(15), 4015–4017. https://doi.org/10.1007/s11606-022-07517-1
Audience: Health System Leaders, EMR / IT Leaders, Clinicians
Predictive analytics programs are present, but governance structures are frequently lacking. Teams are typically small and budgets limited. Clinician trust, rather than technology, is the primary challenge.
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Friday: The Pattern Across the Week - Evidence and Technology Are Ahead of Infrastructure
Khalil et al.'s (2025) scoping review of 145 studies on value-based health care (VBHC) implementation provides a comprehensive synthesis, as it addresses the convergence of the gaps identified earlier in the week. VBHC seeks to maximize health outcomes relative to the cost of care delivered, a goal that depends on three elements highlighted as underdeveloped in this week's literature: validated patient-engagement mechanisms at scale, reliable cost data, and robust digital infrastructure governance.
65% - VBHC studies from the U.S. | 38% - use value-based purchasing | 54% - care still delivered in-hospital | 145 - studies included in review
Financial Models Are Transitional, Not Settled
Value-Based Purchasing is the predominant funding model (38%), followed by Time-Driven Activity-Based Costing (16%), while fee-for-service remains present in 15% of the studied models. The continued prevalence of fee-for-service within VBHC-focused literature underscores the persistent gap between financial infrastructure and the clinical rationale for value-based delivery. In-hospital care continues to account for 54% of delivery settings, despite the identification of outpatient, telehealth, and mobile models as VBHC-aligned innovations.
Measurement Heterogeneity Is the Common Thread
Outcome measures across the 145 studies included patient-reported outcome measures (PROMs), quality-of-life metrics, and cost savings calculated variably for patients, providers, and systems. This heterogeneity reflects earlier findings: de-implementation trials lack standardized costing methods, and predictive analytics programs lack standardized governance. Facilitators identified in the review-such as strong leadership, multidisciplinary collaboration, data transparency, aligned incentives, and digital tools-represent infrastructure investments rather than clinical interventions. Advancing these facilitators requires organizational commitment to develop the necessary infrastructure, rather than additional clinical evidence.
“Value-based health care aims to maximize health outcomes relative to the cost of care delivered… significant barriers remain in adapting care models, engaging stakeholders, and measuring outcomes.” (Khalil et al., 2025, Frontiers in Public Health)
This Week, in One Sentence
Patient-engagement tools for reducing low-value care are supported by strong evidence (Monday, Tuesday), yet their cost savings remain unquantified (Wednesday). Predictive analytics platforms are being implemented more rapidly than the governance structures required for their safe operation (Thursday). Value-based care, intended to align clinical outcomes with financial sustainability, is similarly constrained by gaps in measurement and incentive alignment (Friday). Across these literatures, the clinical and technical rationale for improved care continues to outpace the development of delivery infrastructure, including funding models, cost accounting, governance structures, and standardized measurement.
Strategic Takeaways
For health system executives: align investment in infrastructure-such as governance, cost accounting, and standardized measurement-with the pace of clinical program expansion, rather than deferring it.
For payers and policymakers: the continued presence of fee-for-service within value-based health care (VBHC) models indicates that payment reform is lagging behind delivery reform. This gap should be monitored in contract design.
For physical therapy (PT), musculoskeletal (MSK), and clinical innovation leaders: the literature reviewed this week directly highlights the field’s core challenge-clinical evidence has advanced more rapidly than the delivery, financial, and governance infrastructure required for large-scale implementation.
Citation: Khalil, H., Ameen, M., Davies, C., & Liu, C. (2025). Implementing value-based healthcare: A scoping review of key elements, outcomes, and challenges for sustainable healthcare systems. Frontiers in Public Health, 13, 1514098. https://doi.org/10.3389/fpubh.2025.1514098
Audience: Health System Leaders, Payers / Policy, Clinicians, EMR / IT Leaders
Evidence for value-based health care (VBHC) is predominantly from the United States and remains in development. Fee-for-service persists within value-based models. The primary finding of the week is that infrastructure, rather than evidence, constitutes the main bottleneck.
