Mathematical models served a critical role in COVID-19 decision making throughout the pandemic. Model calibration is an essential, but often computationally burdensome, step in model development that provides estimates for difficult-to-measure parameters and establishes an up-to-date modeling platform for scenario analysis. In the evolving COVID-19 pandemic, frequent recalibration was necessary to provide ongoing support to decision makers. In this study, we address the computational challenges of frequent recalibration with a new calibration approach.

With advancing illness, some patients with heart failure (HF) opt to receive life-extending treatments despite their high costs, while others choose to forgo these treatments, emphasizing cost containment. We examined the association between patients' health status and their preferences for treatment cost containment versus life extension and whether their patients' awareness of disease incurability moderated this association.

To examine awareness of disease incurability among patients with heart failure over 24 mo and its associations with patient characteristics and patient-reported outcomes (distress, emotional, and spiritual well-being).

Evidence is lacking on the most effective values clarification methods (VCMs) in patient decision aids (PtDAs). We tested the effects of an adaptive conjoint analysis (ACA)-based VCM compared with a ranking-based VCM and no VCM on several decision-related outcomes, with the decisional conflict and its subscale "perceived values clarity" as primary outcomes.

Multi-indication cancer drugs receive licensing extensions to include additional indications, as trial evidence on treatment effectiveness accumulates. We investigate how sharing information across indications can strengthen the inferences supporting health technology assessment (HTA).

We aim to assess the performance of methods for adjusting estimates of treatment effectiveness for patient nonadherence in the context of health technology assessment using simulation methods.

Information disclosures are used in medicine to provide patients with relevant information. This research examines whether patients are less likely to discuss medical conditions with their physicians after seeing an insurance information disclosure.

Nudges have been proposed as a method of influencing prescribing decisions.

The Veterans Health Administration (VA) recommends lung cancer screening (LCS), including shared decision making between clinicians and veteran patients. We sought to characterize 1) veteran conceptualization of lung cancer risk and 2) veteran and clinician accounts of shared decision-making discussions about LCS to assess whether they reflect veteran concerns.

Estimating change in health-related quality of life (HRQOL) from pre- to poststroke is challenging because HRQOL is rarely collected prior to stroke. Leveraging HRQOL data collected both before and after stroke, we sought to estimate the change in HRQOL from prestroke to early poststroke.

Risk score calculators are a widely developed tool to support clinicians in identifying and managing risk for certain diseases. However, little is known about physicians' applied experiences with risk score calculators and the role of risk score estimates in clinical decision making and patient communication.

Medical maximizing orientation is a stable, traitlike inclination to actively use health care, often associated with pursuing low-value care. Despite attempts to reduce the overuse of low-value care by targeting this orientation directly, such interventions have not always been effective. To design effective interventions to reduce the overuse of low-value care, it is critical to understand the underlying mechanisms that govern the impact of medical maximizing orientation.

A theoretical interpretation of factors influencing time tradeoff (TTO) scores is lacking. In this conceptual study, we use a sociopsychological theory, terror management theory (TMT), to explain how death thoughts may play a role in the TTO method. TMT describes how respondents suppress death thoughts by invoking psychological defenses, such as self-esteem, and by bolstering cultural values.

Clinical uncertainty is associated with increased resource utilization, worsened health-related quality of life for patients, and provider burnout, particularly during critical illness. Existing data are limited, because determining uncertainty from notes typically requires manual, qualitative review. We sought to develop a consensus list of descriptors of clinical uncertainty and then, using a thematic analysis approach, describe how respondents consider their use in intensive care unit (ICU) notes, such that future work can extract uncertainty data at scale.

Machine learning (ML) methods can identify complex patterns of treatment effect heterogeneity. However, before ML can help to personalize decision making, transparent approaches must be developed that draw on clinical judgment. We develop an approach that combines clinical judgment with ML to generate appropriate comparative effectiveness evidence for informing decision making.

The net value of reducing decision uncertainty by collecting additional data is quantified by the expected net benefit of sampling (ENBS). This tutorial presents a general-purpose algorithm for computing the ENBS for collecting survival data along with a step-by-step implementation in R.The algorithm is based on recently published methods for simulating survival data and computing expected value of sample information that do not rely on the survival data to follow any particular parametric distribution and that can take into account any arbitrary censoring process.We demonstrate in a case study based on a previous cancer technology appraisal that ENBS calculations are useful not only for designing new studies but also for optimizing reimbursement decisions for new health technologies based on immature evidence from ongoing trials.

Infectious disease (ID) models have been the backbone of policy decisions during the COVID-19 pandemic. However, models often overlook variation in disease risk, health burden, and policy impact across social groups. Nonetheless, social determinants are becoming increasingly recognized as fundamental to the success of control strategies overall and to the mitigation of disparities.

(1) To demonstrate the use of quality-adjusted life-years (QALYs) as an outcome measure for comparing performance between simulation models and identifying the most accurate model for economic evaluation and health technology assessment. QALYs relate directly to decision making and combine mortality and diverse clinical events into a single measure using evidence-based weights that reflect population preferences. (2) To explore the usefulness of Q, the proportional reduction in error, as a model performance metric and compare it with other metrics: mean squared error (MSE), mean absolute error, bias (mean residual), and .