Medical Decision Making

Mitchell, Grace; Ramagopalan, Sreeram V.

doi: 10.1177/0272989x231208443pmid: 37950378

Alarid-Escudero, Fernando; Andrews, Jason R.; Goldhaber-Fiebert, Jeremy D.

doi: 10.1177/0272989x231205565pmid: 37953597

BackgroundCompartmental infectious disease (ID) models are often used to evaluate nonpharmaceutical interventions (NPIs) and vaccines. Such models rarely separate within-household and community transmission, potentially introducing biases in situations in which multiple transmission routes exist. We formulated an approach that incorporates household structure into ID models, extending the work of House and Keeling.DesignWe developed a multicompartment susceptible-exposed-infectious-recovered-susceptible-vaccinated (MC-SEIRSV) modeling framework, allowing nonexponentially distributed duration in exposed and infectious compartments, that tracks within-household and community transmission. We simulated epidemics that varied by community and household transmission rates, waning immunity rate, household size (3 or 5 members), and numbers of exposed and infectious compartments (1–3 each). We calibrated otherwise identical models without household structure to the early phase of each parameter combination’s epidemic curve. We compared each model pair in terms of epidemic forecasts and predicted NPI and vaccine impacts on the timing and magnitude of the epidemic peak and its total size. Meta-analytic regressions characterized the relationship between household structure inclusion and the size and direction of biases.ResultsOtherwise similar models with and without household structure produced equivalent early epidemic curves. However, forecasts from models without household structure were biased. Without intervention, they were upward biased on peak size and total epidemic size, with biases also depending on the number of exposed and infectious compartments. Model-estimated NPI effects of a 60% reduction in community contacts on peak time and size were systematically overestimated without household structure. Biases were smaller with a 20% reduction NPI. Because vaccination affected both community and household transmission, their biases were smaller.ConclusionsID models without household structure can produce biased outcomes in settings in which within-household and community transmission differ.HighlightsInfectious disease models rarely separate household transmission from community transmission. The pace of household transmission may differ from community transmission, depends on household size, and can accelerate epidemic growth.Many infectious disease models assume exponential duration distributions for infected states. However, the duration of most infections is not exponentially distributed, and distributional choice alters modeled epidemic dynamics and intervention effectiveness.We propose a mathematical framework for household and community transmission that allows for nonexponential duration times and a suite of interventions and quantified the effect of accounting for household transmission by varying household size and duration distributions of infected states on modeled epidemic dynamics.Failure to include household structure induces biases in the modeled overall course of an epidemic and the effects of interventions delivered differentially in community settings. Epidemic dynamics are faster and more intense in populations with larger household sizes and for diseases with nonexponentially distributed infectious durations. Modelers should consider explicitly incorporating household structure to quantify the effects of non-pharmaceutical interventions (e.g., shelter-in-place).

Singh, Ganeev; Corlin, Laura; Beninger, Paul R.; Neumann, Peter J.; Boumil, Marcia M.; Mehta, Shreya; Salem, Deeb N.

doi: 10.1177/0272989x231206750pmid: 37876181

BackgroundProfessional roles within a hospital system may influence attitudes behind clinical decisions.ObjectiveTo determine participants’ preferences about clinical decisions that either value equal health care access or efficiency.DesignDeidentified survey asking participants to choose between offering a low-cost screening test to a whole population (“equal access”) or a more sensitive, expensive test that could be given to only half of the population but resulting in 10% more avoided deaths (“efficient”). Data collection took place from August 18, 2021, to January 24, 2022. Study 1644 was determined to be exempt by Tufts Health Sciences Institutional Review Board (IRB).SettingTufts Medicine Healthcare System.ParticipantsApproximately 15,000 hospital employees received an e-mail from the Tufts Medicine Senior Vice President of Academic Integration.MeasurementsAnalysis of survey responses with chi-square and 1-sample t tests to determine the proportion who chose each option. Logistic regression models fit to examine relationships between professional role and test choice.ResultsA total of 1,346 participants completed the survey (∼9.0% response rate). Overall, approximately equal percentages of respondents chose the “equal access” (48%) and “efficient” option (52%). However, gender, professional role (categorical), and clinical role (dichotomous) were significantly associated with test choice. For example, among those in nonclinical roles, women were more likely than men to choose equal health care access. In multivariable analyses, having clinical roles was significantly associated with 1.73 times the likelihood of choosing equal access (95% confidence interval = 1.33–2.25).LimitationsGeneralizability concerns and survey question wording limit the study results.ConclusionClinicians were more likely than nonclinicians to choose the equal health care access option, and health care administrators were more likely to choose efficiency. These differing attitudes can affect patient care and health care quality.HighlightsDivergent preferences of valuing equal health care access and efficiency may be in conflict during clinical decision making.In this cross-sectional study that included 1,346 participants, approximately equal percentages of respondents chose the “equal access” (48%) and “efficient” option (52%), a nonsignificant difference. However, gender, professional role (categorical), and clinical role (dichotomous) were significantly associated with test choiceSince clinicians were more likely than nonclinicians to choose the equal health care access option and health care administrators were more likely to choose efficiency, these differing attitudes can affect patient care and health care quality.

Main, Caitlin; Haig, Madeleine; Chavez, Danitza; Kanavos, Panos

doi: 10.1177/0272989x231206803pmid: 37882333

ObjectivesHardly any value frameworks exist that are focused on provider-facing digital health technologies (DHTs) for managing chronic disease with diverse stakeholder participation in their creation. Our study aimed to 1) understanding different stakeholder opinions on where value lies in provider-facing technologies and 2) create a comprehensive value assessment framework for DHT assessment.MethodsMixed-methods comprising both primary and secondary evidence were used. A scoping review enabled a greater understanding of the evidence base and generated the initial indicators. Thirty-four indicators were proposed within 6 value domains: health inequalities (3), data rights and governance (6), technical and security characteristics (6), clinical characteristics (7), economic characteristics (9), and user preferences (3). Subsequently, a 3-round Web-Delphi was conducted to rate the indicators’ importance in the context of technology assessment and determine whether there was consensus.ResultsThe framework was adapted to 45 indicators based on participant contributions in round 1 and delivered 16 stable indicators with consensus after rounds 2 and 3. Twenty-nine indicators showed instability and/or dissensus, particularly the data rights domain, in which all 5 indicators were unstable, showcasing the novelty of the concept of data rights. Significant instability between important and very important ratings was present within stakeholder groups, particularly clinicians and policy experts, indicating they were unsure how different aspects should be valued.ConclusionsOur study provides a comprehensive value assessment framework for assessing provider-facing DHTs incorporating diverse stakeholder perspectives. Instability for specific indicators was expected due to the novelty of data and analytics integration in health technologies and their assessment. Further work is needed to ensure that, across all types of stakeholders, there is a clear understanding of the potential impacts of provider-facing DHTs.HighlightsCurrent health technology assessment (HTA) methods may not be well suited for evaluating digital health technologies (DHTs) because of their complexity and wide-ranging impact on the health system.This article adds to the literature by exploring a wide range of stakeholder opinions on the value of provider-facing DHTs, creating a holistic value framework for these technologies, and highlighting areas in which further discussions are needed to align stakeholders on DHTs’ value attributes.A Web-based Delphi co-creation approach was used involving key stakeholders from throughout the digital health space to generate a widely applicable value framework for assessing provider-facing DHTs. The stakeholders include patients, health care professionals, supply-side actors, decision makers, and academia from the United States, United Kingdom, and Germany.High levels of instability among stakeholders and value domains are demonstrated, indicating the novelty of assessing provider-facing DHTs and their impact on the health system.

Abellan-Perpiñan, Jose-Maria; Martinez-Perez, Jorge-Eduardo; Pinto-Prades, Jose-Luis; Sanchez-Martinez, Fernando-Ignacio

doi: 10.1177/0272989x231207814pmid: 37947086

ObjectiveThe main aim of this article is to test monotonicity in life duration. Previous findings suggest that, for poor health states, longer durations are preferred to shorter durations up to some threshold or maximum endurable time (MET), and shorter durations are preferred to longer ones after that threshold.MethodsMonotonicity in duration is tested through 2 ordinal tasks: choices and rankings. A convenience sample (n = 90) was recruited in a series of experimental sessions in which participants had to rank-order health episodes and to choose between them, presented in pairs. Health episodes result from the combination of 7 EQ-5D-3L health states and 5 durations. Monotonicity is tested comparing the percentage rate of participants whose preferences were monotonic with the percentage of participants with nonmonotonic preferences for each health state. In addition, to test the existence of preference reversals, we analyze the fraction of people who switch their preference from rankings to choices.ResultsMonotonicity is frequently violated across the 7 EQ-5D health states. Preference patterns for individuals describe violations ranging from almost 49% with choices to about 71% with rankings. Analysis performed by separate states shows that the mean rates of violations with choices and ranking are about 22% and 34%, respectively. We also find new evidence of preference reversals and some evidence—though scarce—of transitivity violations in choices.ConclusionsOur results show that there is a medium range of health states for which preferences are nonmonotonic. These findings support previous evidence on MET preferences and introduce a new “choice-ranking” preference reversal. It seems that the use of 2 tasks with a similar response scale may make preference reversals less substantial, although it remains important and systematic.HighlightsTwo procedures based on ordinal comparisons are used to elicit preferences: direct choices and rankings. Our study reports significant rates of nonmonotonic preferences (or maximum endurable time [MET]–type preferences) for different combinations of durations and EQ-5D health states.Analysis for separate health states shows that the mean rates of nonmonotonicity range from 22% (choices) to 34% (rankings), but within-subject analysis shows that nonmonotonicity is even higher, ranging from 49% (choices) to 71% (rankings). These violations challenge the validity of multiplicative QALY models.We find that the MET phenomenon may affect particularly those EQ-5D health states that are in the middle of the severity scale and not so much the extreme health states (i.e., very mild and very severe states).We find new evidence of preference reversals even using 2 procedures of a similar (ordinal) nature. Percentage rates of preference reversals range from 1.5% to 33%. We also find some (although scarce) evidence on violations of transitivity.

Baker, Stuart G.

doi: 10.1177/0272989x231208673pmid: 37990924

BackgroundThe test tradeoff curve helps investigators decide if collecting data for risk prediction is worthwhile when risk prediction is used for treatment decisions. At a given benefit-cost ratio (the number of false-positive predictions one would trade for a true positive prediction) or risk threshold (the probability of developing disease at indifference between treatment and no treatment), the test tradeoff is the minimum number of data collections per true positive to yield a positive maximum expected utility of risk prediction. For example, a test tradeoff of 3,000 invasive tests per true-positive prediction of cancer may suggest that risk prediction is not worthwhile. A test tradeoff curve plots test tradeoff versus benefit-cost ratio or risk threshold. The test tradeoff curve evaluates risk prediction at the optimal risk score cutpoint for treatment, which is the cutpoint of the risk score (the estimated risk of developing disease) that maximizes the expected utility of risk prediction when the receiver-operating characteristic (ROC) curve is concave.MethodsPrevious methods for estimating the test tradeoff required grouping risk scores. Using individual risk scores, the new method estimates a concave ROC curve by constructing a concave envelope of ROC points, taking a slope-based moving average, minimizing a sum of squared errors, and connecting successive ROC points with line segments.ResultsThe estimated concave ROC curve yields an estimated test tradeoff curve. Analyses of 2 synthetic data sets illustrate the method.ConclusionEstimating the test tradeoff curve based on individual risk scores is straightforward to implement and more appealing than previous estimation methods that required grouping risk scores.HighlightsThe test tradeoff curve helps investigators decide if collecting data for risk prediction is worthwhile when risk prediction is used for treatment decisions.At a given benefit-cost ratio or risk threshold, the test tradeoff is the minimum number of data collections per true positive to yield a positive maximum expected utility of risk prediction.Unlike previous estimation methods that grouped risk scores, the method uses individual risk scores to estimate a concave ROC curve, which yields an estimated test tradeoff curve.

van Krugten, Frédérique C. W.; Jonker, Marcel F.; Himmler, Sebastian F. W.; Hakkaart-van Roijen, Leona; Brouwer, Werner B. F.

doi: 10.1177/0272989x231208645pmid: 37981788

BackgroundHealth economic evaluations using common health-related quality of life measures may fall short in adequately measuring and valuing the benefits of mental health care interventions. The Mental Health Quality of Life questionnaire (MHQoL) is a standardized, self-administered mental health–related quality of life instrument covering 7 dimensions known to be relevant across and valued highly by people with mental health problems. The aim of this study was to derive a Dutch value set for the MHQoL to facilitate its use in cost-utility analyses.MethodsThe value set was estimated using a discrete choice experiment (DCE) with duration that accommodated nonlinear time preferences. The DCE was embedded in a web-based self-complete survey and administered to a representative sample (N = 1,308) of the Dutch adult population. The matched pairwise choice tasks were created using a Bayesian heterogeneous D-efficient design. The overall DCE design comprised 10 different subdesigns, with each subdesign containing 15 matched pairwise choice tasks. Each participant was asked to complete 1 of the subdesigns to which they were randomly assigned.ResultsThe obtained coefficients indicated that “physical health,”“mood,” and “relationships” were the most important dimensions. All coefficients were in the expected direction and reflected the monotonic structure of the MHQoL, except for level 2 of the dimension “future.” The predicted values for the MHQoL ranged from −0.741 for the worst state to 1 for the best state.ConclusionsThis study derived a Dutch value set for the recently introduced MHQoL. This value set allows for the generation of an index value for all MHQoL states on a QALY scale and may hence be used in Dutch cost-utility analyses of mental healthcare interventions.HighlightsA discrete choice experiment was used to derive a Dutch value set for the MHQoL.This allows the use of the MHQoL in Dutch cost-utility analyses.The dimensions physical health, mood, and relationships were the most important.The utility values range from −0.741 for the worst state to 1 for the best state.

Bos – van den Hoek, Danique W.; Smets, Ellen M. A.; Ali, Rania; Tange, Dorien; van Laarhoven, Hanneke W. M.; Henselmans, Inge

doi: 10.1177/0272989x231203693pmid: 37876223

PurposeTo examine the effects of training general practitioners and nurses in shared decision-making (SDM) support as perceived by cancer patients and survivors.DesignAn innovative, experimental design was adopted that included analogue patients (APs), that is, people who have or have had cancer and who imagine themselves in the position of the actor-patient presented in a video. Each AP assessed a video-recorded simulated consultation of a health care professional (HCP) conducted before or after an SDM support training program. The primary outcome was the APs’ perceived SDM support with 13 self-developed items reflecting the perceived patient benefit of SDM support as well as the perceived HCP support behavior. Secondary outcomes included an overall rating of SDM support, AP-reported extent of SDM (CollaboRATE), satisfaction with the communication (Patient Satisfaction Questionnaire), conversation appreciation and helpfulness, as well as decision-making satisfaction and confidence (visual analog scale, 0–100). In addition, patient and HCP characteristics associated with AP-perceived SDM support were examined.ResultsAPs (n = 131) did not significantly differentiate trained from untrained HCPs in their perceptions of SDM support nor in secondary outcomes. Agreement between APs’ perceptions was poor. The higher the perceived comparability of the consultation with APs’ previous personal experiences, the higher their rating of SDM support.LimitationsWe used a nonvalidated primary outcome and an innovative study design that should be tested in future work.ConclusionsDespite the limitations of the study design, the training seemed to not affect cancer patients’ and survivors’ perceived SDM support.ImplicationsThe clinical relevance of the training on SDM support needs to be established. The variation in APs’ assessments suggests patients differ in their perception of SDM support, stressing the importance of patient-tailored SDM support.HighlightsCancer patients and survivors did not significantly differentiate trained from untrained HCPs when evaluating SDM support, and agreement between their perceptions was poor.The clinical relevance of training GPs and nurses in SDM support needs to be established.Patient-tailored SDM support may be recommended, given the variation in APs’ assessments and their possible diverging perceptions of SDM support.This innovative study design (having patients watch and assess videos of simulated consultations made in the context of training evaluation) needs to be further developed.

van de Water, Loïs F.; Bos–van den Hoek, Danique W.; Kuijper, Steven C.; van Laarhoven, Hanneke W. M.; Creemers, Geert-Jan; Dohmen, Serge E.; Fiebrich, Helle-Brit; Ottevanger, Petronella B.; Sommeijer, Dirkje W.; de Vos, Filip Y. F.; Smets, Ellen M. A.; Henselmans, Inge

doi: 10.1177/0272989x231208448pmid: 37953598

Dukhanin, Vadim; McDonald, Kathryn M.; Gonzalez, Natalia; Gleason, Kelly T.

doi: 10.1177/0272989x231207829pmid: 37965762

ObjectivesIn the context of validating a measure of patient report specific to diagnostic accuracy in emergency department or urgent care, this study investigates patients’ and care partners’ perceptions of diagnoses as accurate and explores variations in how they reason while they assess accuracy.MethodsIn February 2022, we surveyed a national panel of adults who had an emergency department or urgent care visit in the past month to test a patient-reported measure. As part of the survey validation, we asked for free-text responses about why the respondents indicated their (dis)agreement with 2 statements comprising patient-reported diagnostic accuracy: 1) the explanation they received of the health problem was true and 2) the explanation described what to expect of the health problem. Those paired free-text responses were qualitatively analyzed according to themes created inductively.ResultsA total of 1,116 patients and care partners provided 982 responses coded into 10 themes, which were further grouped into 3 reasoning types. Almost one-third (32%) of respondents used only corroborative reasoning in assessing the accuracy of the health problem explanation (alignment of the explanation with either test results, patients’ subsequent health trajectory, their medical knowledge, symptoms, or another doctor’s opinion), 26% used only perception-based reasoning (perceptions of diagnostic process, uncertainty around the explanation received, or clinical team’s attitudes), and 27% used both types of reasoning. The remaining 15% used general beliefs or nonexplicated logic (used only about accurate diagnoses) and combinations of general reasoning with perception-based and corroborative.ConclusionsPatients and care partners used multifaceted reasoning in their assessment of diagnostic accuracy.ImplicationsAs health care shifts toward meaningful diagnostic co-production and shared decision making, in-depth understanding of variations in patient reasoning and mental models informs use in clinical practice.HighlightsAn analysis of 982 responses examined how patients and care partners reason about the accuracy of diagnoses they received in emergency or urgent care.In reasoning, people used their perception of the process and whether the diagnosis matched other factual information they have.We introduce “patient reasoning” in the diagnostic measurement context as an area of further research to inform diagnostic shared decision making and co-production of health.