Medical Decision Making

Importance: The 2026 public charge rule could discourage immigrants from accessing health coverage programs, creating a chilling effect that potentially leads to negative health outcomes; However, its long-term health impact is poorly understood. Objective: To model potential impacts of the 2026 public charge rule on primary care and premature mortality among immigrants in New York City (NYC). Design, Setting, and Participants: The simulation used a deterministic compartmental model with Ordinary Differential Equations (ODEs) using 2023 NYC Vital statistics data and American Community Survey, and estimates obtained from 2 previous studies about effects of healthcare access on primary care and Medicaid expansion on premature mortality. Main Outcomes and Measures: Rates of primary care outcomes (access, doctor's visits) in 5 years, and premature mortality in 5 and 20 years, projected by the model under conservative, moderate, and aggressive scenarios of avoidance/disenrollment due to the public charge rule, known as the 'chilling effect'. Effects of the avoidance/disenrollment on primary care outcomes and premature mortality were obtained from 2 previous studies. Projected rates of the outcomes under each scenario were compared with counterfactuals to estimate the health impacts of the chilling effect. Results: Implementation of the public charge rule was projected to decrease the primary care access rate by 4.1% (conservative) to 9.9% (aggressive) over 5 years, relative to the counterfactual scenario without the rule. The rate of doctors' visits was projected to decrease over 5 years by 5.1% (conservative) to 12.2% (aggressive). Premature mortality was projected to increase by 4.4% (conservative) to 10.6% (aggressive) in 5 years and 7.4% (conservative) to 17.4% (aggressive) in 20 years. Legal noncitizens and Black immigrant New Yorkers were predicted to experience higher burdens of premature mortality attributed to the chilling effect, compared with other immigrant groups and racial/ethnic groups, respectively. Conclusions and Relevance: This study demonstrates adverse health consequences of federal public charge rule changes among immigrants in NYC. The model projected a decrease in primary care visits and increase in premature mortality across various scenarios. These findings suggest urgent reconsideration of a regulatory change that disproportionately increases risk of premature mortality among immigrants in NYC.

Multi-Cancer Detection (MCD) tests can detect more than 50 different types of cancer using a blood test. Recently passed law in the U.S. guarantees that Medicare will pay for these tests when they are FDA approved and show evidence for clinical benefit. This manuscript provides estimates of the cost of MCD tests to Medicare under different assumptions of cost per test, eligibility, and screening uptake in the eligible population. This manuscript additionally estimates the cost of follow-up testing resulting from false positive results, which are considered avoidable costs caused by the screening test.

Introduction: Primary care clinicians (PCC) commonly make decisions in the context of time delay and uncertainty. Delay discounting (DD) and probability discounting (PD) are cognitive biases related to delay and uncertainty that are minimally explored in PCC. We assessed DD and PD in PCC and evaluated their association with low-value care (LVC) decision-making. Methods: We administered a survey to PCC in a Southeastern U.S health system and within the American Academy of Family Physicians networks. The survey comprised standardized psychometric assessments of DD and PD and four LVC clinical vignettes. Outcomes included DD and PD discounting rates for two monetary rewards ($100 and $10,000) and ratings of LVC likelihood (0-100). We used regression analysis with model selection to evaluate the relationship between variables. Results: 225 PCC (89% physicians, 11% advanced practice providers) participated. Heterogeneity in DD and PD rates was observed. For the $10,000 reward, ln k(DD)= -6.80, IQR:-7.60--6.10) and ln h(PD)= 1.75, IQR:1.75-2.36). The reward amount impacted DD and PD in opposing directions (i.e., lower DD/higher PD rates for $10,000 vs. $100). LVC likelihood was highest for low-value antibiotics and lowest for low-value cervical cancer screening (median 20, IQR:10-40 and 0, IQR:0-10, respectively). Model selection revealed demographic associations with LVC likelihood, but no association with DD or PD. Conclusions: Consistent with effects previously reported in non-clinicians, PCC exhibited a range of DD and PD, which ranged by reward magnitude. Neither DD nor PD predicted vignette-based LVC likelihood. Further research should investigate actual clinical practice patterns and other LVC scenarios.

FeePredict is a three-stage random forest machine learning framework to simul-taneously predict whether Medicare reimbursement rates for specific procedures will change, in which direction they will change, and by how much. FeePredict was ap-plied to the four major Medicare fee schedules: the Clinical Laboratory Fee Schedule (CLFS), the Physician Fee Schedule (PFS), the Ambulance Fee Schedule (AFS), and the Durable Medical Equipment, Prosthetics, Orthotics, and Supplies (DMEPOS) fee schedule. Each of these fee schedules contains publicly available data from the Centers for Medicare & Medicaid Services (CMS) for the years 2024, 2025, and 2026, with the number of procedures represented in the data ranging from 3,264 to 2,952,842 observations.FeePredict utilizes lag-1 feature engineering and train-only preprocessing steps to ensure that there is no data leakage into the model. Chronological out-of-time valida-tion was performed on three of the four fee schedules to determine the generalizability of the model over time. FeePredict significantly outperformed the assumption that there would be no changes to Medicare reimbursement rates for procedures (p < 0.001), achieving concordance indices between 0.815 and 0.998, and reducing the mean abso-lute error for predicting changes to reimbursement rates by 29% to 85%. Permutation testing of the model with shuffled reimbursement rate labels indi-cates that there is no evidence of data leakage (AUC values: 0.467-0.515). The model achieved concordance indices of 0.854 and 0.972 for the CLFS and DMEPOS fee sched-ules, respectively, outside of its training period, but performed less well outside of its training period for the PFS, indicating that it generalizes less well to changes to the Medicare policy regime that existed after its training period. Overall, though, these re-sults indicate that it is possible to accurately predict whether Medicare reimbursement rates for medical procedures will change using only data from the historical versions of those fee schedules.

Clinical recommendations are often expressed in narrative form, which limits their direct execution, auditability, and patient-specific interpretation. This paper presents a hybrid decision-support framework that combines Decision Model and Notation (DMN), survey-weighted rule-ensemble learning, and counterfactual sensitivity analysis. The framework is evaluated using an NHANES-derived fasting cohort for classification of documented diabetes status. The full fasting analysis cohort contained 2,582 participants, and a non-diagnostic laboratory subgroup, Gate0, contained 2,111 participants. On untouched test data, the rule-ensemble model achieved ROC-AUC and PR-AUC values of 0.959 and 0.873 in the full fasting cohort and 0.861 and 0.499 in Gate0. Four clinically interpretable candidate rules were selected using validation data only. A nonnegative survey-weighted logistic model removed one redundant rule and converted the remaining three binary activations into an auditable DMN score and model-estimated probability. The final DMN achieved ROC-AUC 0.769, PR-AUC 0.153, and Brier score 0.029 in the untouched Gate0 test set. In small rule-defined test subgroups, hypothetical five-unit BMI reductions lowered mean model-estimated probability by 2.40 to 5.89 percentage points when one or more BMI thresholds were crossed. These findings characterize policy sensitivity rather than causal effects and require external validation.

Community-level estimates of healthcare utilization are essential for identifying inequities, allocating resources, and evaluating place-based interventions. However, in the United States, no single data source adequately captures healthcare utilization within geographically defined populations. Population-based surveys often lack sufficient geographic resolution, insurance claims represent only covered populations, and electronic health records are limited to care delivered within participating health systems. Increasingly, researchers combine these fragmented data sources, yet limited guidance exists for conducting valid population-based descriptive analyses using incomplete and overlapping data. We review the strengths and limitations of major data sources used to characterize community healthcare utilization and propose an approach for conducting population-based descriptive analyses using fragmented data. Rather than focusing on the limitations of individual data sources, our approach begins by explicitly defining the target population and the ideal observational study that would answer the research question. Available data sources are then conceptualized as incomplete or imperfect realizations of that ideal, providing a structured approach to (a) identifying sources of selection bias, missingness, and measurement error, (b) articulating required assumptions, and (c) selecting appropriate analytic strategies. We illustrate our approach using colorectal cancer screening utilization among adults residing in Brooklyn, New York during 2022. By shifting attention from individual data sources to the target community and the assumptions required for valid inference, this approach provides a practical approach for strengthening descriptive analyses of community healthcare utilization and informing place-based public health research, policy, and practice.

Elective surgery late cancellations and ``did not attend'' (LCDNA) events waste theatre capacity, lengthen waiting lists, and impose avoidable costs on NHS Trusts. We present a decision-support approach that ranks upcoming elective procedures by expected cancellation cost and supports capacity-constrained outreach by selecting the highest-risk Top-K cases for intervention. Using cost-sensitive learning and a clinically grounded cost model, the policy reduces expected cost from approximately 103 GBP per case under business-as-usual to 77.08 GBP per case in a hospital-holdout (cross-site) evaluation designed to mimic deployment to a new hospital. In a complementary time-forward evaluation, representing prospective use within the same service environment, expected cost falls further to 70.97 GBP per case. The 6.11 GBP per-case difference between the two regimes highlights the added uncertainty introduced by cross-site operational shift and supports a conservative roll-out with local calibration and monitoring. Explainability analyses suggest that booking-to-procedure lead time, specialty or service line, calendar effects, and prior cancellation history are the strongest drivers of prediction, helping to inform tiered intervention workflows that prioritise near-term bookings and use model--pathway mismatches as an audit signal. Overall, the framework turns predictive performance into practical, capacity-aware policy guidance for reducing avoidable cancellations while supporting safe and equitable implementation.

Background: Descriptive mapping of intensive care unit (ICU) and high care unit (HCU) capacity across Japan's secondary medical areas (SMAs) characterizes where beds exist, but medical planning also requires answers to prospective questions: how likely is a capacity shortfall under demand surge, which assumptions drive that risk, and how much protection do inter-zone transfer arrangements provide. No openly available tool addresses these questions at the SMA level, the geographic unit at which Japanese medical plans are written. Methods: We developed MeshScope-Scenario, a probabilistic capacity-demand framework operating on the MeshScope-Region platform. For a selected SMA, observed inputs (notified ICU/HCU beds from the Hospital Bed Function Reports; resident population) are combined with four explicitly flagged assumption parameters - effective staffed-bed rate, concurrent severe-care demand per 100,000 population, surge multiplier, and net cross-boundary inflow - each with a user-specified distribution. A seeded Monte Carlo engine (deterministic reproduction under a fixed seed) estimates the distribution of bed shortfall; interventions are compared under common random numbers. Parameter dependence is introduced by a Gaussian copula with automatic positive-semidefinite correction; global sensitivity is quantified by Sobol first-order and total-order indices (Saltelli sampling, Jansen estimators) alongside a deterministic one-at-a-time tornado analysis. A two-zone extension transfers unmet demand to the nearest ICU-holding SMA using road-network travel times measured in MeshScope-Region, with a transfer time limit and an acceptance cap; because both zones share the same systemic draws, correlated exhaustion of donor capacity under surge ("shared-fate" risk) is represented structurally. A seasonal layer applies twelve monthly surge multipliers and reports the distribution of annual maximum shortfall and month-specific shortfall probabilities. The engine is a dependency-free pure-function module verified by 40 statistical tests. Results: The framework reproduces identical output under identical seed and input; a flat seasonal profile reproduces the non-seasonal model exactly; copula factorization error is below 1e-9; and 10,000 iterations across three intervention variants complete in approximately 50 ms in a standard browser, permitting fully interactive use. Applied to three archetypal SMAs from the observed FY2024 supply map (seed 42, 20,000 iterations, demand prior 5 per 100,000), an ICU-zero zone with a transfer partner 48 minutes away has shortfall probability 66.0% (P50 4.8, P90 20.7 beds); a median metropolitan zone, 32.7% (P90 5.9), with Sobol indices ranking demand density and surge dominant; a high-supply zone shows zero shortfall up to approximately 2.5x surge. For the ICU-zero zone, independent-donor reasoning credits the transfer arrangement with a 2.27-bed reduction in expected shortfall, of which shared-fate correlation removes 93%; the probability of severe shortfall under the arrangement equals that with no arrangement at all, while a committed pool of five donor beds (6% of donor effective supply) restores a 13-point reduction and more than halves the arrangement's correlation exposure. Conclusions: MeshScope-Scenario extends SMA-level capacity mapping from description to prospective risk assessment. All demand-side inputs are declared assumptions with adjustable distributions rather than estimates presented as fact; the framework's value is to make the consequences of those assumptions, and their interaction with observed supply, explicit, reproducible, and inspectable for planning deliberation.

Improving primary care is a long-standing strategy to constrain health care spending. Yet, evaluations of primary care models focused on payment reform have shown minimal effects on total cost of care. We report the results from a large-scale, real-world evaluation of an advanced primary care model that restructures access through same-day and next-day appointments, on-demand video visits, asynchronous clinician messaging, and extended hours. Using a stacked-cohort difference-in-differences design with entropy balancing and inverse probability of censoring weighting, we analyzed multi-payer claims covering April 2022 through March 2025. Advanced primary care use was associated with an 8.6% reduction in total cost of care (-$729 per patient per year; P = 0.004), driven by lower specialist cost (-$939/year; P < 0.001) and, to a lesser degree, by reductions in inpatient (-$134/year; P < 0.001), urgent care (-$70/year; P < 0.001), and emergency department cost (-$16/year; P = 0.02), partially offset by higher primary care cost (+$350/year; P < 0.001). The specialist reduction was concentrated in knowledge-based consultative encounters (-$663/year; P < 0.001), while procedural specialist cost was largely unchanged (-$276/year; P = 0.09). Cost differences emerged in the first post-index month. These findings suggest that advanced primary care may reduce total health care spending, with observed savings driven primarily by lower spending on consultative specialty care.

Across 30 US states and the District of Columbia, eliminating the AIDS Drug Assistance Program is projected to save $6.45 billion in direct costs while generating $14.89 billion in downstream HIV care costs attributable to excess incident infections from 2026-2035. Costs are projected to surpass savings within six years.

Inappropriate antibiotic use presents a major global health challenge, particularly in low-resource settings where access to quality care is limited but antibiotics remain relatively unrestricted. This study estimates the causal effect of frontline primary care quality on inappropriate community antibiotic use, combining detailed community-based data from approximately 100 rural villages in rural China with an instrumental variable (IV) approach embedded within a double/debiased machine learning (DML) framework. We linked objective measures of village doctor clinical practice quality, measured through unannounced standardized patient visits, to household-level antibiotic use data collected from the same villages. To identify the causal effect, we constructed multiple candidate instruments from extensive provider characteristics and used an ensemble of machine learning algorithms within a flexible DML-IV framework to approximate an optimal instrument, addressing a many-weak-instruments problem. We found that improving village provider clinical practice quality reduced both antibiotic receipt during healthcare encounters for common diseases and household antibiotic storage for future self-medication. Our findings suggest that strengthening frontline primary care quality can meaningfully reduce inappropriate community antibiotic use without restricting access to essential treatment. More broadly, this study illustrates how causal machine learning can strengthen conventional causal estimation in complex observational settings in global health economics research.

The US spends more on health care than any other nation, yet tens of millions of Americans are uninsured or underinsured, and coverage retractions enacted in 2025 are widening these gaps. The misalignment between the for-profit insurance architecture and optimal patient care, together with the inefficiencies of a fragmented system, contributes to both unnecessary costs and preventable mortality. We update our previous analyses with the most recent data to project the economic benefits and the number of lives saved that would be achieved by single-payer universal coverage, as proposed in the Medicare for All Act. We estimate that such a system would reduce national health expenditure by $1,041 billion annually. Sources of savings include reductions in administrative overhead, pharmaceutical prices, fraudulent billing, and avoidable emergency care. Combined with the reversal of recent retractions, universal coverage would save over 114,000 lives annually.

Vaccination can be a useful intervention for reducing infectious disease burden. Estimating numbers of vaccine-prevented health outcomes is one approach to quantifying the benefits of vaccination. Here we improve a method described by Foppa et al. (1) that assumes vaccination has only direct effects, that is, it cannot prevent infection or onward transmission of the disease. We rederive this method and derive an improved method that increases estimation accuracy with minimal additional analytical complexity. To evaluate the improved method, we simulated disease outbreaks and compared the accuracy of the two methods for estimating prevented disease outcomes. In 84% of simulations performed over a wide parameter space, the improved method had an equal or smaller estimation error compared to the original Foppa method, with 7.9-fold smaller mean error and 44-fold smaller standard deviation of errors. Our study improves a method for estimating prevented burden when assuming vaccination has only direct effects.

Introduction: Research has documented that obesity and morbidity are associated. Black persons in the United States (U.S.) incur higher financial costs of obesity-related multimorbidity (ORM). However, lifetime healthcare costs (LHCs) remain underexamined for these populations. Objective: We quantified racial differences in 1) LHCs and 2) lifetime healthcare cost differential (LCD) associated with ORM for ages > 40 years. Methods: We used the 2008- 2012 Medical Expenditure Panel Survey Household Component to examine unique obesity-related diseases (ORDs): high blood sugar, hypertension, coronary heart disease, and stroke. We used a prior published Markov model to simulate a person's life history of ORDs and compute LHCs among ages > 40 years. We computed LCD-associated ORM as the difference in LHC for those with ORM and LHC for members without ORDs. We quantified differences in race as the difference between LHC or LCD among White and Black men and women. Results: Our analytic sample included 53,035 Black and White persons representing 97,229,611 (S.E., 2,104,365), 12.4% as Black and 87.6% as White persons. ORM was more prevalent in the Black (21.2%) than the White group (13.4%). LHCs by race (Black/White) for women/men with ORM and LCDs associated with ORM (2012$) were $3 1,035/43,595 and $11,350/26,948 for age 40-49, $2 1,567/25,6 115 and $3,846/9,808 for 50-59, $9,863/18,515 and -$2,566/7,426 for 60-69, -$8,220/16,285 and -$11,524/3,865 for 70-79. Conclusions: Racial Differences in LHCs and LCDs related to ORM persist and vary across subpopulations. Future interventions designed to prevent/manage ORM are crucial for prioritizing populations with high LHCs and advancing health equity.

Ambient artificial intelligence scribes are being increasingly used in healthcare to improve efficiency and reduce provider clinical documentation burden, yet their performance across linguistically diverse patient populations is not well characterized. We conducted a retrospective analysis of 54,160 outpatient encounters within a U.S. safety net health system to evaluate the performance of an artificial intelligence documentation tool in English and non-English clinical encounters, and in encounters where an interpreter or bilingual provider was present. Documentation performance was measured by the percentage of words in the final note that were generated by the ambient AI documentation tool and not edited by the provider. Associations between language factors and documentation performance were measured using Generalized Estimating Equations with exchangeable correlation structures to account for clustering of multiple encounters within unique patients. Univariable models were fitted to estimate the odds of adequate performance by language and interpreter modality, and a multivariable interaction model was used to evaluate within-language differences between bilingual providers and interpreter-mediated encounters. Non-English encounters were 21% to 25% less likely than English encounters to achieve the same performance threshold. There was no significant difference in generative documentation performance between interpreter-mediated and bilingual provider encounters. These findings underscore the importance of equity-focused evaluation and multilingual model refinement to ensure that artificial intelligence documentation benefits are distributed fairly across diverse patient populations.

Background During the COVID-19 pandemic, public health agencies used near real-time observational data to answer questions regarding vaccine effectiveness. However, traditional observational methods do not allow conclusions regarding counterfactual scenarios to be drawn from clinical data. Counterfactuals, which are outcomes that would have occurred under alternative interventions, can be used to formally assess the causal effects of public health interventions on health outcomes while accounting for the effects of confounding. Ideally individual patient data is used for the development of counterfactuals. Low-fidelity synthetic data may be useful for advancing methodological development where governance and privacy constraints prohibit access to sensitive personal data. Methods We simulated synthetic datasets based on the EAVE-II COVID-19 platform which has been limited to use for surveillance purposes. EAVE-II includes almost all resident people in Scotland registered with qualified general medical practitioners. Patient characteristics were simulated to reflect the known distribution of the Scottish population, accounting for dependencies between variables. Each synthetic dataset was encoded to different realistic scenarios for EAVEII 'ground truth' vaccine rollout and effectiveness results, explicitly stating the causal and confounding mechanisms, using a statistically sound method based on marginal structural models. Synthetic datasets of 100,000 individuals were then generated across five confounding scenarios and five severe outcome types. Results In scenarios with weak confounding, both unweighted and inverse probability of treatment weighted (IPTW) logistic regression recovered the true causal parameters. As confounding strength increased, only weighted models recovered the true mechanism. Conclusions Low-fidelity synthetic datasets simulated from EAVE-II data analysts to build and test causal inference pipelines, develop novel analysis pipelines, and train new researchers while awaiting access to real data. We showed how to generate synthetic datasets from a marginal structural model under different confounding scenarios.

Controlled human infection models (CHIMs) are increasingly used in infectious disease research to study pathogen dynamics and evaluate interventions under controlled conditions. However, these studies are resource-intensive and involve ethical and safety constraints, making efficient study design critical. Dose-finding is a key early component in CHIMs, where the aim is to identify a challenge dose that achieves a target infection probability. Traditional rule-based designs are commonly used but can be inefficient, motivating the use of model-based adaptive approaches such as the Bayesian Continual Reassessment Method (CRM). Although CRM has been extensively studied and widely adopted in Phase I oncology trials for identifying the maximum tolerated dose of therapeutics, its application in CHIM settings remains limited, particularly when the endpoint of interest is infection. This tutorial provides step-by-step guidance for implementing a Bayesian CRM in dose-finding CHIMs, using an oropharyngeal Neisseria gonorrhoeae challenge as a motivating case study. The framework outlines key design components, including dose-grid specification, dose-response model, prior elicitation, Bayesian updating, decision rules, and stopping criteria, with particular emphasis on a clinically interpretable parameterisation. Trial operating characteristics are evaluated through simulation studies under multiple dose-response scenarios and prior-predictive analyses, and compared with a commonly used '3+3' type rule-based design. This work highlights the advantages of Bayesian model-based designs for dose-finding in CHIMs over classic rule-based designs and provides a structured, reproducible framework for implementing CRM, supporting their application in future CHIM studies.

Background: Digital reminders reduce outpatient no-shows, but the optimal timing and frequency of messages remain unclear, particularly in Latin American public health systems. We emulated a target trial to evaluate the comparative effectiveness of four WhatsApp reminder strategies on appointment absenteeism and patient-initiated cancellations. Methods: We analyzed administrative and electronic health-record data from the public health system of the Autonomous City of Buenos Aires, Argentina (June 2023-May 2024). Eligible individuals had scheduled an in-person outpatient appointment in one of 15 prioritized specialties at least 75 hours in advance and had a mobile phone on record. We compared four strategies: (1) dual reminders at ~72 and ~24 hours before the appointment; (2) a single reminder at ~72 hours; (3) a single reminder at ~24 hours; and (4) no reminders. The primary outcome was the proportion of no-shows by the end of follow-up. Secondary outcomes were the cumulative incidence of patient-initiated cancellations overall, within 12 hours of the appointment, and followed by rebooking. We emulated the target trial using a cloning-censoring-weighting approach to estimate per-protocol controlled direct effects, with inverse-probability weights to address time-varying confounding and selection bias. Cumulative incidence of secondary outcomes was estimated using weighted Kaplan-Meier curves. Three pre-specified sensitivity analyses and standardized mean differences assessed robustness and covariate balance. Results: A total of 475,214 first eligible person-appointments were included; baseline no-show risk in the control arm was 34.6%. All three active strategies reduced no-shows compared with no reminders. The single 24-hour reminder produced the largest reduction (Risk Ratio [RR] 0.76, 95% CI 0.72, 0.81; Risk Difference [RD] -8.21 percentage points [pp], 95% CI -9.68, -6.54), followed by the dual-reminder strategy (RR 0.80, 95% CI 0.79,0.81; RD -7.05 pp, 95% CI -7.41, -6.71) and the single 72-hour reminder (RR 0.91, 95% CI 0.84,0.99; RD -3.16 pp, 95% CI -5.69, -0.49). All active strategies increased patient-initiated cancellations relative to control, with the dual-reminder strategy producing the largest increase. Sensitivity analyses preserved the qualitative ranking of strategies across all specifications. Conclusions: In this large target trial emulation, a single just-in-time WhatsApp reminder sent ~24 hours before the appointment was as effective as a dual-reminder schedule in preventing no-shows and superior to a distal 72-hour reminder alone. Adding a second, distal reminder provided no measurable benefit for attendance but substantially increased patient-initiated cancellations, which may be operationally valuable when active slot reallocation is a goal. These findings support timing, rather than frequency, as the primary lever of digital-reminder effectiveness, and favor the deployment of a single proximal reminder as the default strategy in resource-constrained outpatient settings.

Introduction Patients with nephrolithiasis face challenges in making a high-quality, preference sensitive decision. Our prior work established feasibility and patient acceptance of a software-based decision aid (DA). The objectives for this study were to identify implementation strategies for the DA in routine care and determine whether DA implementation enhances decisional quality for patients. Methods New nephrolithiasis patients were recruited from the institution Medical Center from June 2018 to April 2024 to receive a software-based pre-visit DA that measured care preferences and used decision analysis to rank treatments. The RE-AIM framework and Plan-Do-Study-Act (PDSA) cycles were used to improve implementation outcomes. Patients completed survey instruments evaluating decisional conflict, shared decision-making, care satisfaction, and treatment choice following their provider visit. These metrics were compared in the DA cohort (n=81) to those in a usual care cohort (n=78) with Wilcoxon rank-sum and Chi-square (or Fishers exact) tests. Results Implementation data revealed sustained reach and progressive improvement in fidelity. The DA cohort reported higher decisional quality relative to controls (p=0.003) and reported greater support/advice to make a choice (p=0.005). The DA cohort more often discussed options with their doctor (87.5% vs 69.2%, p=0.005) and were more likely to be promoters of their provider (p<0.001) and health system (p=0.029). The DA cohort was less likely to have switched their treatment preference post-consultation (32.1% vs 71.8%, p<0.001) suggesting greater consistency in decision-making. Conclusions Software-based DAs in nephrolithiasis can mitigate decisional conflict, improve SDM, and improve patient satisfaction. Further work should explore broader implementation and long-term clinical outcomes.

This study aims to map significant cold spots of postacute rehabilitation therapy delivery rates for Original Medicare beneficiaries in the U.S. and determine the prevalence of those cold spots in rural areas. Statistical spatial clustering of postacute therapy delivery rates was conducted in ArcGIS Pro using hot and cold spot analyses (Getis-Ord Gi*). County-level therapy delivery volume was defined as the total minutes of physical, occupational, and speech therapy provided by skilled nursing facilities (SNFs), home health agencies (HHAs), and inpatient rehabilitation facilities (IRFs). Therapy delivery rates were then computed as minutes per Original Medicare beneficiary at the county level and adjusted using a county-level Hierarchical Condition Category risk score. Spatial clustering identified cold spots (statistically significant clusters of low rates) and hot spots (clusters of high rates). We also computed the proportion of cold spots in rural counties and the relative percentage difference compared to the national rural county baseline, using two rural classification systems. Identified coldspots varied by provider type. For SNFs, they were notably identified in the Mountain and West North Central US divisions. For HHAs, cold spots appeared across more U.S. Census Divisions, including areas (e.g., Kentucky, Indiana, southern Illinois) where SNFs showed hot spots. Cold spots were more prevalent in rural--and especially in small rural--counties across all provider types. In rural counties, cold spot rates were 42.8% to 71.5% higher than the rural county baseline. In small rural counties, differences were larger, at 69.1% to 95.5% higher. Concluding, cold spots of postacute therapy delivery varied across the continental U.S. by provider type but were more prevalent in rural and especially in rural counties with smaller population size -- across provider types. Identifying these cold-spot locations may support geographically targeted policy responses and the development of alternative service?delivery models in underserved areas.