Medical Decision Making[JOURNAL]
Sun
200 papers
Hospital Adoption of Diversity, Equity, and Inclusion (DEI) Disaggregated Data for Organizational Decision Making.
Doan TT, Iott BE
Med Decis Making · 2025 Oct · PMID 40553449 · Publisher ↗
IntroductionHospitals are interested in improving the quality of data disaggregation and collection to advance diversity, equity, and inclusion (DEI) goals. We evaluated the extent to which hospitals are adopting DEI dis...
IntroductionHospitals are interested in improving the quality of data disaggregation and collection to advance diversity, equity, and inclusion (DEI) goals. We evaluated the extent to which hospitals are adopting DEI disaggregated data to inform organizational decisions and the characteristics associated with this adoption.MethodsWe analyzed data from the 2022 American Hospital Association Annual Survey, which included the final iteration of a new survey item about hospital DEI disaggregated data adoption for decision making. Descriptive statistics, logistic regression, and negative binomial regression were used to evaluate this survey item.ResultsAmong hospitals adopting DEI disaggregated data ( = 2,596, 41.9%), two-thirds used these data to inform decisions about patient outcomes, half about training or professional development, and one-third about supply chain or procurement. Larger, tax-exempt, Veteran Affairs, or metropolitan hospitals are significantly more likely to adopt DEI disaggregated data for decision making.LimitationsOur work is limited by the reporting of 1-y cross-sectional results.ConclusionsMost hospitals adopt DEI disaggregated data to inform decisions about patient outcomes. Future research should explore whether hospital decisions or disaggregated data adoption have advanced DEI and health equity for underserved communities.ImplicationsAnalysis of disaggregated data adoption could reveal how hospitals make decisions and funding allocations to advance DEI goals and health equity.HighlightsThere is a limited understanding of the extent to which hospitals adopt diversity, equity, and inclusion (DEI) disaggregated data to inform organizational decision making, highlighting a knowledge gap at the intersection of data equity and health care management.Among hospitals that adopt DEI disaggregated data, two-thirds use them to inform organizational decisions about patient outcomes, and half about professional development.Larger, tax-exempt, Veteran Affairs, or metropolitan hospitals are more likely to adopt DEI disaggregated data for organizational decision making.Future research is needed to explore whether hospital adoption of DEI disaggregated data has advanced DEI organizational goals and health equity for underserved populations.
Postpartum Sterilization after a Preterm Delivery Is Not Associated with Decision Regret.
Toscano M, Betstadt SJ, Spielman S … +2 more , Guru Murthy G, Levandowski BA
Med Decis Making · 2025 Aug · PMID 40542624 · Full text
BackgroundAlthough sterilization is one of the most effective methods of birth control, some physicians may hesitate to perform postpartum sterilizations on patients after preterm birth, as preterm labor and delivery may...
BackgroundAlthough sterilization is one of the most effective methods of birth control, some physicians may hesitate to perform postpartum sterilizations on patients after preterm birth, as preterm labor and delivery may preclude adequate counseling.MethodsThis is a cross-sectional study conducted at a single, tertiary care, academic institution of adult pregnant patients who experienced a spontaneous or iatrogenic preterm delivery between March 15, 2011, and May 10, 2014 and underwent postpartum female surgical sterilization within 12 wk of delivery. A validated Decision Regret Scale was administered 7 to 11 y later. Univariate and bivariate analyses were conducted. Unadjusted and multivariate logistic regression analyses identified factors associated with moderate to severe decision regret.ResultsMost participants (75.5%) with a preterm delivery reported no or mild regret associated with their sterilization. Circumstances surrounding the sterilization decision were positive, as 85.7% reported having enough information, 81.6% reported enough emotional support, and 75.5% reported adequate decision time. Adjusting for maternal and gestational age at delivery plus other covariates, only those reporting they had adequate time to make their sterilization decision remained significantly associated with no or mild regret (odds ratio: 0.002, 95% confidence interval: <0.001-0.61).DiscussionStudy results indicated high confidence in the sterilization decision, which was not affected by maternal age at delivery or the fact that the individual had a preterm delivery, emphasizing the importance of individualized counseling and support for patients during the decision-making process.ConclusionProviding adequate time for patients to decide on postpartum surgical sterilization was the most important factor for decreased sterilization regret.ImplicationsThe decision for sterilization should be made using a patient-centered, shared decision-making framework.HighlightsAmong patients with a preterm delivery who underwent postpartum surgical sterilization, maternal age at delivery was not associated with increased decision regret.Providing adequate time for patients to decide on postpartum surgical sterilization was the most important factor for decreased sterilization regret among patients with a preterm delivery.We must trust the patient knows they are making the right decision for themselves in that moment, even if this is at the time of a preterm delivery.
Relative Survival Modeling for Appraising the Cost-Effectiveness of Life-Extending Treatments: An Application to Tafamidis for the Treatment of Transthyretin Amyloidosis with Cardiomyopathy.
Young R, Said J, Large S
Med Decis Making · 2025 Aug · PMID 40528187 · Full text
BackgroundEconomic evaluations for life-extending treatments frequently require clinical trial data to be extrapolated beyond the trial duration to estimate changes in life expectancy. Conventional survival models often...
BackgroundEconomic evaluations for life-extending treatments frequently require clinical trial data to be extrapolated beyond the trial duration to estimate changes in life expectancy. Conventional survival models often display hazard profiles that do not rise as expected in an aging population and require the incorporation of external data to ensure plausibility. Relative survival (RS) models can enable the incorporation of external data at model fitting. A comparison was performed between RS and "standard" all-cause survival (ACS) in modeling outcomes from the tafamidis for the treatment of transthyretin amyloid cardiomyopathy (ATTR-ACT) trial.MethodsPatient-level data from the 30-mo ATTR-ACT trial were used to develop survival models based on parametric ACS and RS models. The latter was composed of an expected hazard and an independent excess hazard. Models were selected according to statistical goodness of fit and clinical plausibility, with extrapolation up to 72 mo validated against ATTR-ACT long-term extension (LTE) data.ResultsInformation criteria were too similar to discriminate between RS or ACS models. Several ACS models were affected by capping with general population mortality rates and considered implausible. Selected RS models matched the empirical hazard function, could not fall below general population hazards, and predicted well compared with the LTE data. The preferred RS model predicted the restricted mean survival (RMST) to 72 mo of 51.0 mo (95% confidence interval [CI]: 46.1, 55.3); this compared favorably to the LTE RMST of 50.9 mo (95% CI: 47.7, 53.9).DiscussionRS models can improve the accuracy for modeling populations with high background mortality rates (e.g., the ATTR-CM trial). RS modeling enforces a plausible long-term hazard profile, enables flexibility in medium-term hazard profiles, and increases the robustness of medical decision making.HighlightsTo inform survival extrapolations for health technology assessment, a relative survival model incorporating external data per the recommendations of the National Institute for Health and Care Excellence (NICE) Decision Support Unit was used in support of the NICE evaluation of tafamidis for treatment of transthyretin amyloid cardiomyopathy (ATTR-CM).Relative survival modeling allowed selection of a broader range of hazard profiles compared with all-cause survival modeling by ensuring plausible long-term predictions.Predictions from plausible relative survival models of overall survival in patients with ATTR-CM, extrapolated from the ATTR-ACT trial, validated very well to outcomes after a doubling of follow-up and demonstrated improved precision and accuracy versus parametric all-cause survival models.
Mapping and Linking between the EQ-5D-5L and the PROMIS Domains in the United States.
Tang X, Hays RD, Cella D … +5 more , Acaster S, Schalet BD, Sikora Kessler A, Vera Llonch M, Hanmer J
Med Decis Making · 2025 Aug · PMID 40509799 · Full text
ObjectivesThe EQ-5D-5L and Patient-Reported Outcomes Measurement Information System (PROMIS®) preference score (PROPr) are preference-based measures. This study compares mapping and linking approaches to align the PROPr...
ObjectivesThe EQ-5D-5L and Patient-Reported Outcomes Measurement Information System (PROMIS®) preference score (PROPr) are preference-based measures. This study compares mapping and linking approaches to align the PROPr and the PROMIS domains included in PROPr plus Anxiety with EQ-5D-5L item responses and preference scores.MethodsA general population sample of 983 adults completed the online survey. Regression-based mapping methods and item response theory (IRT) linking methods were used to align scores. Mapping was used to predict EQ-5D-5L item responses or preference scores using PROMIS domain scores. Equating strategies were applied to address regression to the mean. The linking approach estimated item parameters of EQ-5D-5L based on the PROMIS score metric and generated bidirectional crosswalks between EQ-5D-5L item responses and relevant PROMIS domain scores.ResultsEQ-5D-5L item responses were significantly accounted for by PROMIS domains of Anxiety, Depression, Fatigue, Pain Interference, Physical Function, Social Roles, and Sleep Disturbance. EQ-5D-5L preference scores were accounted for by the same PROMIS domains, excluding Anxiety and Fatigue, and by the PROPr preference scores. IRT-linking crosswalks were generated between EQ-5D-5L item responses and PROMIS domains of Physical Function, Pain, and Depression. Small differences were found between observed and predicted scores for all 3 methods. The direct mapping approach (directly predicting EQ-5D-5L scores) with the equipercentile equating strategy proved superior to the linking method due to improved prediction accuracy and comparable score range coverage.ConclusionsThe PROPr and the PROMIS domains included in the PROMIS-29+2 predict EQ-5D-5L preference scores or item responses. Both methods can generate acceptably precise EQ-5D-5L preference scores, with the direct mapping approach using the equating strategy offering better precision. We summarized recommended score conversion tables based on available and desired scores.HighlightsThis study compares mapping (score prediction) and IRT-based linking approaches to align the PROPr and the PROMIS domains with EQ-5D-5L item responses and preference scores.Researchers, clinicians, and stakeholders can use this study's regression formulas and score crosswalks to convert scores between PROMIS and EQ-5D-5L.Mapping can generate more precise scores, while linking offers greater flexibility in score estimation when fewer PROMIS domain scores are collected.
Decision Frameworks for Assessing Cost-Effectiveness Given Previous Nonoptimal Decisions.
Coyle D, Glynn D, Goldhaber-Fiebert JD … +1 more , Wilson ECF
Med Decis Making · 2025 Aug · PMID 40503819 · Full text
IntroductionEconomic evaluations identify the best course of action by a decision maker with respect to the level of health within the overall population. Traditionally, they identify 1 optimal treatment choice. In many...
IntroductionEconomic evaluations identify the best course of action by a decision maker with respect to the level of health within the overall population. Traditionally, they identify 1 optimal treatment choice. In many jurisdictions, multiple technologies can be covered for the same heterogeneous patient population, which limits the applicability of this framework for directly determining whether a new technology should be covered. This article explores the impact of different decision frameworks within this context.MethodsThree alternate decision frameworks were considered: the traditional normative framework in which only the optimal technology will be covered (normative); a commonly adopted framework in which the new technology is recommended for reimbursement only if it is optimal, with coverage of other technologies remaining as before (current); and a framework that assesses specifically whether coverage of the new technology is optimal, incorporating previous reimbursement decisions and the market share of current technologies (positivist). The implications of the frameworks were assessed using a simulated probabilistic Markov model for a chronic progressive condition.ResultsResults illustrate how the different frameworks can lead to different reimbursement recommendations. This in turn produces differences in population health effects and the resultant price reductions required for covering the new technology.ConclusionBy covering only the optimal treatment option, decision makers can maximize the level of health across a population. If decision makers are unwilling to defund technologies, however, the second best option of adopting the positivist framework has the greatest relevance with respect to deciding whether a new technology should be covered.HighlightsTraditionally, economic evaluations focus on identifying the optimal treatment choice.This paper considers three alternative decision frameworks, within the context of multiple technologies being covered for the same heterogeneous patient population.This paper highlight that if decision makers are unwilling to defund therapies, current approaches to assessing cost effectiveness may be non-optimal.
Comparing Potential Contributors of Health-Related Quality of Life and Mortality Among US Older Adults.
Jia H, Lubetkin EI
Med Decis Making · 2025 Aug · PMID 40503817 · Publisher ↗
BackgroundMany contributing factors can influence individuals' health, and these factors may not affect health outcomes equally. This study compared the importance of 38 predictors of health-related quality of life (HRQO...
BackgroundMany contributing factors can influence individuals' health, and these factors may not affect health outcomes equally. This study compared the importance of 38 predictors of health-related quality of life (HRQOL) and 2-y mortality for US older adults.MethodsData were from the Medicare Health Outcome Survey Cohort 23 (baseline 2020, follow-up 2022). This study included participants ≥65 y ( = 142,551). HRQOL measures included physically unhealthy days (PUD), mentally unhealthy days (MUD), and activity limitation days (ALD) from the Healthy Days questions and 3 measures from the Veterans RAND 12-Item Health Survey (VR-12). A variable's importance was measured as the average gain in after adding the variable in all submodels.ResultsFor physical health (PUD), pain interfered with daily activities was the most important predictor with an importance score (I) of 8.4, indicating that this variable contributed 8.4% variance of PUD. Other leading predictors included pain interfered with socializing (I = 7.3) and pain rating (I = 6.7). For mental health (MUD), depression (I = 11.6) was far more important than any of the other predictors, contributing 38% of the total importance. For perceived disability (ALD), pain interfered with socializing was the most important predictor (I = 8.3), followed by difficulty doing errands (I = 6.1) and pain interfered with activities (I = 6.0). Of note, this general pattern was consistent for VR-12 HRQOL measures. Variables' importance scores for 2-y morality were very different from that for HRQOL. Age (I = 2.8) and difficulty doing errands (I = 2.6) were the most important variables.ConclusionsThis study demonstrated a large discrepancy in the variables' importance for HRQOL and 2-y mortality. Functional limitations/disabilities and geriatric syndromes were more important for the prediction of HRQOL than were chronic conditions and other factors combined.HighlightsFor older adults, large differences were found in variable importance for explaining health-related quality of life (HRQOL) and 2-y mortality among 38 explanatory variables, including functional limitations, geriatric syndromes, chronic conditions, and other factors.Pain and pain interference, difficulty doing errands, difficulty concentrating, memory problems, problems with walking/balance, and depression were the most important predictors of HRQOL.Age, marital status, education, difficulty doing errands, congestive heart failure, chronic obstructive pulmonary disease, and any cancer were more important for 2-y mortality than HRQOL.Health care providers and policy makers should focus on the impact of multimorbidity and the interaction between often multifactorial conditions, as opposed to focusing only on individual diseases.
So You've Got a High AUC, Now What? An Overview of Important Considerations when Bringing Machine-Learning Models from Computer to Bedside.
Deng J, Elghobashy ME, Zang K … +3 more , Patel SK, Guo E, Heybati K
Med Decis Making · 2025 Aug · PMID 40439482 · Full text
Machine-learning (ML) models have the potential to transform health care by enabling more personalized and data-driven clinical decision making. However, their successful implementation in clinical practice requires care...
Machine-learning (ML) models have the potential to transform health care by enabling more personalized and data-driven clinical decision making. However, their successful implementation in clinical practice requires careful consideration of factors beyond predictive accuracy. We provide an overview of essential considerations for developing clinically applicable ML models, including methods for assessing and improving calibration, selecting appropriate decision thresholds, enhancing model explainability, identifying and mitigating bias, as well as methods for robust validation. We also discuss strategies for improving accessibility to ML models and performing real-world testing.HighlightsThis tutorial provides clinicians with a comprehensive guide to implementing machine-learning classification models in clinical practice.Key areas covered include model calibration, threshold selection, explainability, bias mitigation, validation, and real-world testing, all of which are essential for the clinical deployment of machine-learning models.Following these guidance can help clinicians bridge the gap between machine-learning model development and real-world application and enhance patient care outcomes.
Modeling Disability-Adjusted Life-Years for Policy and Decision Analysis.
Leech AA, Zhu J, Peterson H … +4 more , Martin MH, Ratcliff G, Garbett S, Graves JA
Med Decis Making · 2025 Jul · PMID 40437834 · Full text
This study outlines methods for modeling disability-adjusted life-years (DALYs) in common decision-modeling frameworks. Recognizing the wide spectrum of experience and programming comfort level among practitioners, we ou...
This study outlines methods for modeling disability-adjusted life-years (DALYs) in common decision-modeling frameworks. Recognizing the wide spectrum of experience and programming comfort level among practitioners, we outline 2 approaches for modeling DALYs in its constituent parts: years of life lost to disease (YLL) and years of life lived with disability (YLD). Our beginner approach draws on the Markov trace, while the intermediate approach facilitates more efficient estimation by incorporating non-Markovian tracking elements into the transition probability matrix. Drawing on an existing disease progression discrete time Markov cohort model, we demonstrate the equivalence of DALY estimates and cost-effectiveness analysis results across our methods and show that other commonly used "shortcuts" for estimating DALYs will not, in general, yield accurate estimates of DALY levels nor incremental cost-effectiveness ratios in a modeled population.HighlightsThis study introduces 2 DALY estimation methods-beginner and intermediate approaches-that produce similar results, expanding the toolkit available to decision modelers.These methods can be adapted to estimate other outcomes (e.g., QALYs, life-years) and applied to other common decision-modeling frameworks, including microsimulation models with patient-level attributes and discrete event simulations that estimate YLDs and YLLs based on time to death and disease duration.Our findings further reveal that commonly used shortcut methods for DALY calculations may lead to differing results, particularly for DALY levels and incremental cost-effectiveness ratios.
Clinical Notes Contain Limited Documentation of Shared Decision Making for Colorectal Cancer Screening Decisions.
Mancini B, Siar J, Valentine KD … +3 more , Simmons L, Leavitt L, Sepucha K
Med Decis Making · 2025 Aug · PMID 40437831 · Publisher ↗
BackgroundEffective shared decision making (SDM) in health care involves thorough discussions of options, pros, cons, and patient preferences. While SDM is recommended for engaging adults aged 76 to 85 y in colorectal ca...
BackgroundEffective shared decision making (SDM) in health care involves thorough discussions of options, pros, cons, and patient preferences. While SDM is recommended for engaging adults aged 76 to 85 y in colorectal cancer (CRC) screening decisions, the extent of SDM documentation in clinical notes remains unclear.ObjectiveThis study aimed to evaluate the current state of SDM documentation in clinical notes regarding CRC screening discussions for adults aged 76 to 85 y. It also sought to assess the impact of an SDM training intervention on documentation quality and compare documented SDM elements with physician- and patient-reported SDM.MethodsData from 465 patient participants and 58 primary care physicians in a multisite cluster randomized trial were analyzed. Physicians in the intervention arm underwent a 2-h SDM skills training and received support tools, including an electronic health record SmartPhrase. Coders analyzed clinical notes using content analysis to identify SDM elements. Linear multilevel models and multilevel partial correlations were used for analysis.ResultsOverall, SDM Note scores were low ( = 0.80, = 0.99). The intervention arm exhibited higher SDM Note scores than the comparator arm did (adjusted mean 1.02 v. 0.66; = 0.006), with more frequent documentation of stool-based tests (52% v. 33%; = 0.02) and colonoscopy cons (28% v. 8%; = 0.001). No significant differences were observed in patient preference documentation. SDM Note scores correlated moderately with patient- and physician-reported SDM.ConclusionDocumentation of CRC screening discussions with older adults lacks comprehensive SDM elements. The intervention improved SDM documentation, particularly regarding alternative screening options and potential cons. Given the limited documentation of SDM even after a training intervention, attention to more robust SDM documentation, including patient preferences and discussion of stopping CRC screening, is needed.HighlightsShared decision-making (SDM) documentation in clinical notes is limited for discussions on colon cancer screening among older adults.SDM training improves SDM documentation of screening options for colorectal cancer, specifically documentation of stool-based testing and the downsides of screening options.SDM documentation in clinical notes is related to patient and provider reports of SDM.
Machine Learning-Based Prediction to Support ICU Admission Decision Making among Very Old Patients with Respiratory Infections: A Proof of Concept on a Nationwide Population-Based Cohort Study.
Tchatat Wangueu L, Kassa-Sombo A, Ilango G … +4 more , Gaborit C, Si-Tahar M, Grammatico-Guillon L, Guillon A
Med Decis Making · 2025 Jul · PMID 40377186 · Publisher ↗
BackgroundIntensive care unit (ICU) hospitalizations of very old patients with acute respiratory infection have risen. The decision-making process for ICU admission is multifaceted, and the prediction of long-term surviv...
BackgroundIntensive care unit (ICU) hospitalizations of very old patients with acute respiratory infection have risen. The decision-making process for ICU admission is multifaceted, and the prediction of long-term survival outcome is an important component. We hypothesized that data-driven algorithms could build long-term prediction by examining massive real-life data. Our objective was to assess machine learning (ML) algorithms to predict the 1-y survival of very old patients with severe respiratory infections.MethodsA national 2011-2020 study of ICU patients ≥80 y with respiratory infection was carried out, using French hospital discharge databases. Data for the training cohort were collected from 2013 to 2016 to build the models, and the data of patients extracted in 2017 were used for external validation. Our proposed models were developed using random forest, logistic regression (LR), and XGBoost. The optimal model was selected based on its accuracy, sensitivity, specificity, Matthews coefficient correlation (MCC), receiver-operating characteristic curve (AUROC), and decision curve analysis (DCA). The local interpretable model-agnostic explanation (LIME) algorithm was used to analyze the contribution of individual features.ResultsA total of 24,270 very old patients were hospitalized in the ICU for respiratory infection (2013-2017) with a known vital status at 1 y. The 1-y survival rate was 41.3% (median survival: 3 mo [2.7-3.3]). Of the 3 ML models tested, LR exhibited promising performance with an accuracy, sensitivity, specificity, MCC, and AUROC (95% confidence interval) of 0.65, 0.76, 0.60, 0.27, and 0.70 (0.69-0.72), respectively. LR achieved an AUROC of 0.70 (0.68-0.71) in external validation by temporal splitting. LR demonstrated higher net benefits across a range of threshold probability values in DCA. The LIME algorithm identified the 10 most influential features at an individual scale.ConclusionsWe demonstrated that a ML model has the potential to predict long-term outcomes for very old patients with acute respiratory infections. As a proof of concept, we proposed a program that acts as an "explainer" for the ML model. This work represents a step forward in translating ML models into practical, transparent, and reliable clinical tools to support medical decision making.HighlightsThe decision to admit a very old patient to the ICU is one of the most complex challenges faced by intensivists, often relying on subjective judgment.In this study, we evaluated the efficacy of machine learning algorithms in predicting the 1-y survival rate of critically ill very old patients (≥80 y) with severe respiratory infections, using data available prior to the admission decision.Our findings demonstrate that machine learning can effectively predict long-term outcomes in very old patients. We used an innovative approach that aims to support medical decision making about admission in ICU.
Exploring Values Clarification and Health-Literate Design in Patient Decision Aids: A Qualitative Interview Study.
Ayre J, Jenkins H, Kumarage R … +3 more , McCaffery KJ, Maher CG, Hancock MJ
Med Decis Making · 2025 Jul · PMID 40365685 · Full text
BackgroundThis study explores patient and clinician perceptions of a patient decision aid, focusing on 2 features that are often absent: a health-literate approach (e.g., using plain language, encouraging question asking...
BackgroundThis study explores patient and clinician perceptions of a patient decision aid, focusing on 2 features that are often absent: a health-literate approach (e.g., using plain language, encouraging question asking) and a tool that explicitly shows how treatment options align with patient values. The aim was to gather qualitative feedback from patients and clinicians to better understand how such features might be useful in guiding future decision aid development.MethodsWe present a secondary analysis of data collected during the development of a decision aid for patients considering surgery for sciatica (20 patients with sciatica or low-back pain; 20 clinicians). Patient and clinician feedback on the design was collected via semi-structured interviews with a think-aloud protocol. Transcripts were analyzed using framework analysis.ResultsTheme 1 explored designs that reinforced key messages about personal autonomy, including an interactive values-clarification tool. Theme 2 explored how participants valued encouragement and scaffolding to ask questions. Theme 3 described how patients preferred information they felt was complete, balanced, and understandable.LimitationsFurther experimental and observational research is needed to quantitatively evaluate these decision aid features including evaluation among patients with and without low health literacy.ConclusionsA health-literate approach to decision aid design and embedding an interactive values-clarification tool may be useful strategies for increasing patient capacity to engage in key aspects of shared decision making. These features may support patients in developing an understanding of personal autonomy in the choice at hand and confidence to ask questions.ImplicationsFindings presented here were specific to the clinical context but provide generalizable practical insights for decision aid developers. This study provides insight into potential future areas of research for decision aid design.HighlightsThis qualitative study explored clinician and patient perceptions of health literacy features and an interactive values-clarification task within a decision aid for patients considering surgery for sciatica.The first theme described how patients and clinicians appreciated sections of the decision aid that reinforced the importance of personal choice. Patients and clinicians thought the interactive values-clarification task would help patients reflect on their values and support shared decision-making discussions.The second theme described how patients and clinicians appreciated strategies to encourage patients to ask questions of the surgeon.The third theme described patients' preference for information that they felt was complete, balanced, and understandable.
Evaluating Semi-Markov Processes and Other Epidemiological Time-to-Event Models by Computing Disease Sojourn Density as Partial Differential Equations.
Worthington J, Feletto E, He E … +6 more , Wade S, de Graaff B, Nguyen ALT, George J, Canfell K, Caruana M
Med Decis Making · 2025 Jul · PMID 40340615 · Full text
IntroductionEpidemiological models benefit from incorporating detailed time-to-event data to understand how disease risk evolves. For example, decompensation risk in liver cirrhosis depends on sojourn time spent with cir...
IntroductionEpidemiological models benefit from incorporating detailed time-to-event data to understand how disease risk evolves. For example, decompensation risk in liver cirrhosis depends on sojourn time spent with cirrhosis. Semi-Markov and related models capture these details by modeling time-to-event distributions based on published survival data. However, implementations of semi-Markov processes rely on Monte Carlo sampling methods, which increase computational requirements and introduce stochastic variability. Explicitly calculating the evolving transition likelihood can avoid these issues and provide fast, reliable estimates.MethodsWe present the sojourn time density framework for computing semi-Markov and related models by calculating the evolving sojourn time probability density as a system of partial differential equations. The framework is parametrized by commonly used hazard and models the distribution of current disease state and sojourn time. We describe the mathematical background, a numerical method for computation, and an example model of liver disease.ResultsModels developed with the sojourn time density framework can directly incorporate time-to-event data and serial events in a deterministic system. This increases the level of potential model detail over Markov-type models, improves parameter identifiability, and reduces computational burden and stochastic uncertainty compared with Monte Carlo methods. The example model of liver disease was able to accurately reproduce targets without extensive calibration or fitting and required minimal computational burden.ConclusionsExplicitly modeling sojourn time distribution allows us to represent semi-Markov systems using detailed survival data from epidemiological studies without requiring sampling, avoiding the need for calibration, reducing computational time, and allowing for more robust probabilistic sensitivity analyses.HighlightsTime-inhomogeneous semi-Markov models and other time-to-event-based modeling approaches can capture risks that evolve over time spent with a disease.We describe an approach to computing these models that represents them as partial differential equations representing the evolution of the sojourn time probability density.This sojourn time density framework incorporates complex data sources on competing risks and serial events while minimizing computational complexity.
Motivated Interpretations of Survival Rates in Icon Arrays: An Issue of Frequency Format?
Strueder JD, Park I, McDonnell SM … +2 more , Basir MA, Windschitl PD
Med Decis Making · 2025 Jul · PMID 40302242 · Publisher ↗
BackgroundIcon arrays, which visually depict frequencies, are commonly recommended for communicating risk information such as survival rates. However, they have been found to be ineffective at buffering against motivated...
BackgroundIcon arrays, which visually depict frequencies, are commonly recommended for communicating risk information such as survival rates. However, they have been found to be ineffective at buffering against motivated reasoning that can lead to undue optimism. To determine whether the impersonal frequency format of icon arrays (reporting a number affected out of a reference class) makes them vulnerable to motivated reasoning, a novel intervention is tested as a means for reducing undue optimism.MethodsFemale US participants from Amazon's MTurk ( = 399) imagined a scenario in which their infant would be born extremely preterm. They were presented with icon array information about the survival chances (15-in-100 or 45-in-100) of prematurely born infants with intensive care. For the key intervention, some participants were asked a reflection question immediately after seeing the icon array, which prompted them to indicate what the information meant for their own infant's percent-chance of survival (i.e., they converted a frequency about a reference class to a probability value about the personal outcome of interest). For other participants, the reflection question merely asked about frequency. The main dependent measure came next and assessed gut-level optimism.ResultsPeople's gut-level beliefs about their infant's chances of survival were optimistically biased; the intervention did not reduce this. These gut-level beliefs, rather than the objective survival rate information conveyed through icon arrays, were predictive of subsequent treatment choices.ConclusionsThe results suggest that the inability of icon arrays to buffer against motivated reasoning is not due to their frequency format. Moreover, the findings highlight the usefulness of measuring gut-level interpretations of likelihood, which can reveal significant insights into the psychological mechanisms driving patient-treatment choices.HighlightsIcon arrays, which visually depict frequencies, are commonly recommended as best-practice for communicating risk information in health contexts.However, recent work has found that they are ineffective at reducing the extent to which people engage in motivated reasoning when processing likelihood information.We find that the frequency format of icon arrays-depicting a rate for outcomes in a group of people rather than a case-specific probability-is not a primary reason why they are ineffective at reducing optimism biasWe also find that measures of gut-level beliefs of likelihood are particularly well suited for detecting optimism bias, yet also predict subsequent treatment decisions.
Cancer Patients' Experiences of Burden when Involved in Treatment Decision Making.
Huijgens FL, Hillen MA, Huisinga MJ … +5 more , Vis AN, Tillier CN, Oldenburg HSA, Diepenhorst GMP, Henselmans I
Med Decis Making · 2025 Jul · PMID 40302226 · Full text
PurposePatients are increasingly involved in decision making by their clinicians. Yet, there are concerns that involvement in decision making may cause emotional distress in patients. Little research has examined the nat...
PurposePatients are increasingly involved in decision making by their clinicians. Yet, there are concerns that involvement in decision making may cause emotional distress in patients. Little research has examined the nature of the burden experienced by patients confronted with a life-changing treatment decision. Therefore, we explored the nature and manifestations of burden experienced by patients with early-stage breast and prostate cancer regarding their involvement in decision making. We further aimed to identify patient-perceived causes and potential solutions for their experienced burden.MethodsWe used semi-structured interviews to explore the perspectives of patients with early-stage breast and prostate cancer. Patients ( = 24) were eligible if they were diagnosed in the past 6 mo and reported some degree of burden regarding their involvement in decision making. Two researchers independently inductively coded the interviews using thematic analysis.ResultsPatients described being burdened by the decision in various ways and at various moments in the decision-making process. Patients attributed their decision-related burden mainly to uncertainty, fear of making the wrong decision, insufficient guidance by their clinician, and feeling an overwhelming sense of responsibility for their treatment decision. Patients indicated various factors that mitigated their burden or facilitated decision making, including having sufficient time, the opportunity to discuss the choice with experts and/or family, and receiving advice or confirmation from family or the clinician.ConclusionThese findings suggest that decision-related burden could be caused by the uncertainty and anxiety patients experience and by a nonpreferred division of roles within the decision-making process.ImplicationsAccordingly, acknowledging patients' feelings by discussing the presence of uncertainty and distress might normalize the burden for patients. Moreover, clinicians could explore and adjust to patients' role preference in decision making and discuss what would facilitate the decision process for patients.HighlightsPatients experience emotional, cognitive, and physical burden from their involvement in decision making.Some of the burden appears to result from the way clinicians involve patients in decision making.In addition to information about options, benefits, and harms, patients require active support in their decision-making process.Clinicians could aim to avoid overfocus on patient autonomy and instead establish authentic, shared decisions, with a role for some clinician control if needed.
Stress-Testing US Colorectal Cancer Screening Guidelines: Decennial Colonoscopy from Age 45 is Robust to Natural History Uncertainty and Colonoscopy Sensitivity Assumptions.
Nascimento de Lima P, Maerzluft C, Ozik J … +2 more , Collier N, Rutter CM
Med Decis Making · 2025 Jul · PMID 40302197 · Full text
PurposeThe 2023 American College of Physicians (ACP) guidelines for colorectal cancer (CRC) screening are at odds with the United States Preventive Task Force (USPSTF) guidelines, with the former recommending screening s...
PurposeThe 2023 American College of Physicians (ACP) guidelines for colorectal cancer (CRC) screening are at odds with the United States Preventive Task Force (USPSTF) guidelines, with the former recommending screening starting at age 50 y and the latter at age 45 y. This article "stress tests" CRC colonoscopy screening strategies to investigate their robustness to uncertainties stemming from the natural history of disease and sensitivity of colonoscopy.MethodsThis study uses the CRC-SPIN microsimulation model to project the life-years gained (LYG) under several colonoscopy CRC screening strategies. The model was extended to include birth cohort effects on adenoma risk. We estimated natural history parameters under 2 different assumptions about the youngest age of adenoma initiation. For each, we generated 500 parameter sets to reflect uncertainty in the natural history parameters. We simulated 26 colonoscopy screening strategies and examined 4 different colonoscopy sensitivity assumptions, encompassing the range of sensitivities consistent with prior tandem colonoscopy studies. Across this set of scenarios, we identify efficient screening strategies and report posterior credible intervals for benefits of screening (LYG), burden (number of colonoscopies), and incremental burden-effectiveness ratios.ResultsProjected absolute screening benefits varied widely based on assumptions, but strategies starting at age 45 y were consistently in the efficiency frontier. Strategies in which screening starts at age 50 y with 10-y intervals were never efficient, saving fewer life-years than starting screening at age 45 y and performing colonoscopies every 15 y while requiring more colonoscopies per person.ConclusionsDecennial colonoscopy screening initiation at age 45 y remained a robust recommendation. Colonoscopy screening with a 10-y interval starting at age 50 y did not result in an efficient use of colonoscopies in any of the scenarios evaluated.HighlightsColorectal cancer colonoscopy screening strategies initiated at age 45 y were projected to yield more life-years gained while requiring the least number of colonoscopies across different model assumptions about disease natural history and colonoscopy sensitivity.Colonoscopy screening starting at age 50 y with a 10-y interval consistently underperformed strategies that started at age 45 y.
Linking Patient Perceptions of Shared Decision Making to Satisfaction in Lung Cancer Screening Decisions.
Robinson SA, Barker AM, Fix GM … +4 more , Clayman ML, Herbst AN, White JC, Wiener RS
Med Decis Making · 2025 Jul · PMID 40292863 · Publisher ↗
IntroductionLung cancer is especially prevalent among US veterans, and lung cancer mortality can be reduced through lung cancer screening (LCS). LCS guidelines recommend shared decision making (SDM) to help patients weig...
IntroductionLung cancer is especially prevalent among US veterans, and lung cancer mortality can be reduced through lung cancer screening (LCS). LCS guidelines recommend shared decision making (SDM) to help patients weigh the benefits and harms of LCS and make informed, values-based decisions about screening. Yet some question whether SDM affects patient outcomes. This study evaluated US veterans' perceptions of LCS SDM quality and its relationship with satisfaction in LCS decisions.MethodsWe administered surveys via mail and phone to veterans in the VA New England Healthcare Network after recent LCS conversations. SDM quality was measured using CollaboRATE, with top scores indicating high quality. Decision satisfaction was assessed using the Satisfaction with Decision scale. Generalized linear models analyzed associations between perceived SDM quality and decision satisfaction, adjusting for demographics, health, and overall care satisfaction.ResultsAmong 1,033 patients who received surveys, 320 responded (31.0%), with 220 recalling the LCS conversation. Among those who answered the CollaboRATE questions, 34.0% (73/215) perceived SDM to be high quality ("top scorers"). Perceived high-quality SDM was significantly associated with greater decision satisfaction compared with lower perceived SDM quality (adjusted mean satisfaction on a 30-point scale = 26.75 v. 24.23; < 0.001). A greater proportion of patients who received, versus did not receive, patient education materials rated SDM as high quality (44.4% v. 27.7%, = 0.018).LimitationsThe sample was primarily White, male, and all US veterans, limiting generalizability to other LCS-eligible cohorts. The cross-sectional design prevents causal inferences and long-term follow-up.ConclusionsHigher perceived SDM quality was associated with greater patient satisfaction with the LCS decision. Improving SDM processes can enhance patient engagement and may improve LCS adherence and health outcomes.HighlightsHigher perceived shared decision making (SDM) quality in lung cancer screening (LCS) discussions leads to greater patient satisfaction with screening decisions.While the use of patient education materials was linked to higher perceived SDM quality, less than half of patients who received materials rated SDM as high quality. There remains room for improved design and delivery to ensure materials effectively support the SDM process and guidance to providers on how to effectively incorporate patient educational materials to support, rather than replace, high-quality SDM conversations.Enhancing SDM processes and aligning them with patient preferences can support patient satisfaction with their decision, which may have downstream benefits to patient engagement, adherence, and improved outcomes.
The "Stock of Time" Method: A New Approach to Calculate Indirect Costs and Benefits in Economic Evaluations.
Kok L, Koopmans C
Med Decis Making · 2025 Jul · PMID 40285335 · Full text
BackgroundHealth interventions affect people's welfare directly by impacting people's health but also indirectly via a change in consumption and leisure time caused by the change in health. This study discusses 2 ongoing...
BackgroundHealth interventions affect people's welfare directly by impacting people's health but also indirectly via a change in consumption and leisure time caused by the change in health. This study discusses 2 ongoing issues in the economic evaluation of health interventions. The first is how to value a change in the amount of leisure time of a patient. The second issue concerns the valuation of a change in production.MethodsWe present a theoretical model that assumes that individual utility depends on health, consumption, and leisure time. We assume that the total stock of time consists of 3 components: leisure time, working time, and recovery time. The model takes a societal perspective and assumes that individuals optimize their utility, conditional on time and budget restrictions.ResultsFor the first issue, the model indicates that the value of a change in the stock of time available for leisure and work has to be added to the direct effects of a health intervention, instead of only a change in work hours. For the second issue, the model indicates that in case of a change in longevity, only the change in taxes paid may be counted because the income change is included in the value of the change in quality-adjusted life-years. A numerical example shows that this approach may counterbalance the potential overestimation of the welfare effects of treatments with the human capital method and underestimation related to the friction cost method.ConclusionWe propose a new method that includes the welfare effects of health interventions due to a change in the amount of leisure time and avoids double counting of welfare changes, which are included in the direct effects.HighlightsWe present a theoretical model and use it to analyze 2 issues in the economic evaluation of health interventions: the inclusion of leisure time and the valuation of production.The model indicates that the effects of health changes on the amount of both work and leisure hours need to be taken into account in economic evaluation.As to the valuation of production, the model indicates that in case of a change in longevity, only the change in taxes may be counted.We propose the "stock of time" method to value changes in working hours and leisure hours, which may counterbalance potential overestimates and underestimates in established methods.
Co-designing a Structured Expert Elicitation with Clinicians to Enhance Health Care Decision Making in Exercise Oncology.
Wang Y, McCarthy AL, Tuffaha H
Med Decis Making · 2025 Jul · PMID 40285324 · Full text
BackgroundWhile structured expert elicitation (SEE) is gaining traction in health technology assessment in situations in which data are scarce, its application in practice remains limited. Co-designing a practical and fi...
BackgroundWhile structured expert elicitation (SEE) is gaining traction in health technology assessment in situations in which data are scarce, its application in practice remains limited. Co-designing a practical and fit-for-purpose SEE with experts could enhance its acceptability and feasibility in clinical research.ObjectivesAn SEE was co-designed with clinicians to elicit expert opinions on 3 uncertain quantities of interest (QoIs) for a decision-analytic model in exercise oncology.MethodsA series of co-design meetings was convened to design 6 elicitation stages. Individual elicitation was conducted using the variable interval method (VIM), via videoconferencing. Linear pooling was adopted to generate group estimates. Semi-structured interviews were conducted after the elicitation exercise to gather the experts' first-hand experience of the elicitation process and to identify areas for improvement. Qualitative data were transcribed and content analyzed.ResultsTwelve experts participated in the co-designed SEE. Three beta distributions were derived and estimated from the experts' responses: the relative risk reduction of cardiovascular events of exercise for women who survived early-stage endometrial cancer (Mean: 0.362, SD: 0.15), the probability that a clinician would refer a patient to the exercise program (Mean: 0.457, SD: 0.218), and the probability that a cancer patient would use such a health service upon referral (Mean: 0.446, SD: 0.203). Most of the experts' first-hand experience of the co-designed SEE was positive. The qualitative feedback highlighted critical aspects of the elicitation process that should be designed and executed with caution when targeting clinicians with no prior experience of SEE.ConclusionsThis is the first expert elicitation conducted in exercise oncology. Engaging diverse stakeholders through co-design meetings and incorporating qualitative feedback proved effective and practical in introducing expert elicitation into clinical research.HighlightsRecent SEE guidelines aim to facilitate the conduct of expert elicitation in model-based economic evaluation, but its application in practice remains limited.Engaging experts in the design of SEE could enhance its acceptability and feasibility in clinical research.This is the first co-designed expert elicitation involving clinicians in the field of exercise oncology.This practical approach to conducting SEE could promote a wider adoption to inform health care policy decisions when the evidence is lacking or uncertain.
Optimizing the Harms and Benefits of Cervical Screening in a Partially Vaccinated Population in Ontario, Canada: A Modeling Study.
de Bondt DD, Jansen EEL, Stogios C … +8 more , McCurdy BR, Kupets R, Murphy J, Costescu D, Rabeneck L, Truscott R, Hontelez JAC, de Kok IMCM
Med Decis Making · 2025 Jul · PMID 40260498 · Full text
ObjectivesIn Ontario, Canada, the first cohorts who were offered school-based human papillomavirus (HPV) vaccination are now eligible for cervical screening. We determined which screening strategies for these populations...
ObjectivesIn Ontario, Canada, the first cohorts who were offered school-based human papillomavirus (HPV) vaccination are now eligible for cervical screening. We determined which screening strategies for these populations would result in optimal harms-benefits ratios of screening.MethodsWe used the hybrid microsimulation model STDSIM- MISCAN-Cervix to determine the harms and cancers prevented of 309 different primary HPV screening strategies, varying by screening ages and triage methods. In addition, we performed an unstratified (i.e., uniform screening protocols) and stratified (i.e., screening protocols by vaccination status) analysis. Harms induced were quantified as a weighted combination of the number of primary HPV-based screens and colposcopy referrals at 1:10. A harms-benefit acceptability threshold of number of harms induced for each cancer prevented was set at the estimated ratio under current screening recommendations in unvaccinated cohorts in Ontario.ResultsFor the unstratified scenario, 5 lifetime screens with HPV16/18 genotyping was optimal. For the stratified scenario, the optimal scenario was 3 lifetime screens with HPV16/18/31/33/45/52/58 genotyping for vaccinated individuals versus 6 lifetime screens with HPV16/18 genotyping for unvaccinated individuals.ConclusionsWe determined the optimal cervical screening strategy in Ontario over the next decades. To maintain an optimal harms-benefits balance of screening, the Ontario Cervical Screening Program could adjust screening recommendations in the future to reduce the number of lifetime screens and extend screening intervals to account for vaccinated cohorts. Stratified screening by vaccination status could further improve this balance on an individual level.HighlightsPeople in cohorts who were offered HPV vaccination as part of Ontario's school-based program may achieve a better harms-benefits balance if cervical screening recommendations are updated to a less intensive protocol in future. This holds for the cohorts as a whole (i.e., unstratified screening) as well as for both vaccinated and unvaccinated individuals in these cohorts.Instead of using a cost-effectiveness threshold, it is possible to determine optimal screening protocols by calculating an acceptability threshold using alternative harms-benefits measures based on existing policy.Using univariate harms measures such as primary HPV screening tests or colposcopies per 1,000 people can yield biases in optimizing cervical screening programs. Alternatively, combining both primary screens and colposcopy referrals could provide a more accurate harms measure and result in optimal strategies with a better balance between harms and benefits.
Understanding Delayed Diabetes Diagnosis: An Agent-Based Model of Health-Seeking Behavior.
Taghikhah FR, Jabbari A, Desouza KC … +2 more , Malik A, Khorshidi HA
Med Decis Making · 2025 May · PMID 40183324 · Full text
BackgroundDiabetes is a rapidly growing global health issue, with the hidden burden of undiagnosed cases leading to severe complications and escalating health care costs.MethodsThis study investigated the potential of in...
BackgroundDiabetes is a rapidly growing global health issue, with the hidden burden of undiagnosed cases leading to severe complications and escalating health care costs.MethodsThis study investigated the potential of integrated behavioral frameworks to predict health-seeking behaviors and improve diabetes diagnosis timelines through the development of an agent-based model. Focusing on Narromine and Gilgandra in New South Wales, Australia, the model captured the integrative influence of 3 social theories-theory of planned behavior (TPB), health belief model (HBM), and goal framing theory (GFT)-on health care decisions across behavioral and nonbehavioral variables, providing a robust analysis of temporal diagnostic patterns, health care utilization, and costs.ResultsOur comparative experiments indicated that this multitheory framework improved predictive accuracy by 15% to 30% compared with single-theory models, effectively capturing the interplay of planned, belief-driven, and context-based health behaviors. Spatial-temporal analysis highlighted key regional and demographic variations in diagnosis behaviors. While early, planned medical visits were prevalent in regions with better access (Gilgandra), areas with limited infrastructure saw a reliance on hospital-based diagnoses (Narromine). Health care cost analysis demonstrated a nonlinear expenditure pattern, suggesting that these theories defy conventional linear cost trends. Scenario analysis demonstrated the impact of targeted interventions. Gender-specific awareness initiatives in Gilgandra reduced late-diagnosis rates among men by approximately 15%, while enhanced access to care in Narromine decreased hospital-based late diagnoses from a baseline of 80% to around 60%.ConclusionsThis study contributes an empirically grounded, policy-oriented decision support tool to inform targeted interventions, offering novel insights to improve diabetes management.HighlightsWe explored the delay in diabetes diagnosis, particularly within remote Australian communities, through looking into the health care-seeking behavior of individuals displaying diabetes symptoms.We developed an innovative agent-based model to craft a dynamic decision support tool for policy makers by providing unique insights into the health behaviors of diabetes patients.Our study contributes significantly to the understanding of public health management with particular concerns around diabetes, as well as equips the New South Wales Ministry of Health with impactful insights into the consequences of their decisions.
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