MDM policy & practicePub Date : 2022-11-25eCollection Date: 2022-07-01DOI: 10.1177/23814683221140122
Joshua B Rager, Sandra Althouse, Susan M Perkins, Karen K Schmidt, Peter H Schwartz
{"title":"Measuring the Impact of Quantitative Information on Patient Understanding: Approaches for Assessing the Adequacy of Patient Knowledge about Colorectal Cancer Screening.","authors":"Joshua B Rager, Sandra Althouse, Susan M Perkins, Karen K Schmidt, Peter H Schwartz","doi":"10.1177/23814683221140122","DOIUrl":"https://doi.org/10.1177/23814683221140122","url":null,"abstract":"<p><p><b>Background.</b> Guidelines recommend that decision aids disclose quantitative information to patients considering colorectal cancer (CRC) screening, but the impact on patient knowledge and decision making is limited. An important challenge for assessing any disclosure involves determining when an individual has \"adequate knowledge\" to make a decision. <b>Methods.</b> We analyzed data from a trial that randomized 213 patients to view a decision aid about CRC screening that contained verbal information (qualitative arm) versus one containing verbal plus quantitative information (quantitative arm). We analyzed participants' answers to 8 \"qualitative knowledge\" questions, which did not cover the quantitative information, at baseline (T0) and after viewing the decision aid (T1). We introduce a novel approach that defines adequate knowledge as correctly answering all of a subset of questions that are particularly relevant because of the participant's test choice (\"Choice-Based Knowledge Assessment\"). <b>Results.</b> Participants in the quantitative arm answered a higher mean number of knowledge questions correctly at T1 than did participants in the qualitative arm (7.3 v. 6.9, <i>P</i> < 0.05), and they more frequently had adequate knowledge at T1 based on a cutoff of 6 or 7 correct out of 8 (94% v. 83%, <i>P</i> < 0.05, and 86% v. 71%, <i>P</i> < 0.05, respectively). Members of the quantitative group also more frequently had adequate knowledge at T1 when assessed by Choice-Based Knowledge Assessment (87% v. 76%, <i>P</i> < 0.05). <b>Conclusions.</b> Patients who viewed quantitative information in addition to verbal information had greater qualitative knowledge and more frequently had adequate knowledge compared with those who viewed verbal information alone, according to most ways of defining adequate knowledge. Quantitative information may have helped participants better understand qualitative or gist concepts. <b>Trial Registration:</b> ClinicalTrials.gov ID# NCT01415479.</p><p><strong>Highlights: </strong>Patients who viewed quantitative information in a decision aid about colorectal cancer screening were more knowledgeable about nonquantitative information and were more likely to have adequate knowledge according to a variety of approaches for assessing that, compared with individuals who viewed only qualitative information. This result supports the inclusion of quantitative information in decision aids.Researchers assessing patient understanding should consider a variety of ways to define adequate knowledge when assessing decision quality.</p>","PeriodicalId":520707,"journal":{"name":"MDM policy & practice","volume":" ","pages":"23814683221140122"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/73/3f/10.1177_23814683221140122.PMC9703495.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40518786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
MDM policy & practicePub Date : 2022-10-22eCollection Date: 2022-07-01DOI: 10.1177/23814683221134098
Charles Yan, Nathan McClure, Sean P Dukelow, Balraj Mann, Jeff Round
{"title":"Optimal Planning of Health Services through Genetic Algorithm and Discrete Event Simulation: A Proposed Model and Its Application to Stroke Rehabilitation Care.","authors":"Charles Yan, Nathan McClure, Sean P Dukelow, Balraj Mann, Jeff Round","doi":"10.1177/23814683221134098","DOIUrl":"https://doi.org/10.1177/23814683221134098","url":null,"abstract":"<p><p><b>Background.</b> Increasing demand for provision of care to stroke survivors creates challenges for health care planners. A key concern is the optimal alignment of health care resources between provision of acute care, rehabilitation, and among different segments of rehabilitation, including inpatient rehabilitation, early supported discharge (ESD), and outpatient rehabilitation (OPR). We propose a novel application of discrete event simulation (DES) combined with a genetic algorithm (GA) to identify the optimal configuration of rehabilitation that maximizes patient benefits subject to finite health care resources. <b>Design.</b> Our stroke rehabilitation optimal model (sROM) combines DES and GA to identify an optimal solution that minimizes wait time for each segment of rehabilitation by changing care capacity across different segments. sROM is initiated by generating parameters for DES. GA is used to evaluate wait time from DES. If wait time meets specified stopping criteria, the search process stops at a point at which optimal capacity is reached. If not, capacity estimates are updated, and an additional iteration of the DES is run. To parameterize the model, we standardized real-world data from medical records by fitting them into probability distributions. A meta-analysis was conducted to determine the likelihood of stroke survivors flowing across rehabilitation segments. <b>Results.</b> We predict that rehabilitation planners in Alberta, Canada, have the potential to improve services by increasing capacity from 75 to 113 patients per day for ESD and from 101 to 143 patients per day for OPR. Compared with the status quo, optimal capacity would provide ESD to 138 (<i>s</i> = 29.5) more survivors and OPR to 262 (<i>s</i> = 45.5) more annually while having an estimated net annual cost savings of $25.45 (<i>s</i> = 15.02) million. <b>Conclusions.</b> The combination of DES and GA can be used to estimate optimal service capacity.</p><p><strong>Highlights: </strong>We created a hybrid model combining a genetic algorithm and discrete event simulation to search for the optimal configuration of health care service capacity that maximizes patient outcomes subject to finite health system resources.We applied a probability distribution fitting process to standardize real-world data to probability distributions. The process consists of choosing the distribution type and estimating the parameters of that distribution that best reflects the data. Standardizing real-word data to a best-fitted distribution can increase model generalizability.In an illustrative study of stroke rehabilitation care, resource allocation to stroke rehabilitation services under an optimal configuration allows provision of care to more stroke survivors who need services while reducing wait time.Resources needed to expand rehabilitation services could be reallocated from the savings due to reduced wait time in acute care units. In general, the predicted optimal configuration","PeriodicalId":520707,"journal":{"name":"MDM policy & practice","volume":" ","pages":"23814683221134098"},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/f7/44/10.1177_23814683221134098.PMC9597031.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40670429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
MDM policy & practicePub Date : 2022-10-08eCollection Date: 2022-07-01DOI: 10.1177/23814683221131321
Torunn Heggland, Lars Johan Vatten, Signe Opdahl, Harald Weedon-Fekjær
{"title":"Interpreting Breast Cancer Mortality Trends Related to Introduction of Mammography Screening: A Simulation Study.","authors":"Torunn Heggland, Lars Johan Vatten, Signe Opdahl, Harald Weedon-Fekjær","doi":"10.1177/23814683221131321","DOIUrl":"https://doi.org/10.1177/23814683221131321","url":null,"abstract":"<p><p><b>Background.</b> Several studies have evaluated the effect of mammography screening on breast cancer mortality based on overall breast cancer mortality trends, with varied conclusions. The statistical power of such trend analyses is, however, not carefully studied. <b>Methods.</b> We estimated how the effect of screening on overall breast cancer mortality is likely to unfold. Because a screening effect is based on earlier treatment, screening can affect only new incident cases after screening introduction. To evaluate the likelihood of detecting screening effects on overall breast cancer mortality time trends, we calculated the statistical power of joinpoint regression analysis on breast cancer mortality trends around screening introduction using simulations. <b>Results.</b> We found that a very gradual increase in population-level screening effect is expected due to prescreening incident cases. Assuming 25% effectiveness of a biennial screening program in reducing breast cancer mortality among women 50 to 69 y of age, the expected reduction in overall breast cancer mortality was 3% after 2 y and reached a long-term effect of 18% after 20 y. In common settings, the statistical power to detect any screening effects using joinpoint regression analysis is very low (<50%), even in an artificial setting of constant risk of baseline breast cancer mortality over time. <b>Conclusions.</b> Population effects of screening on breast cancer mortality emerge very gradually and are expected to be considerably lower than the effects reported in trials excluding women diagnosed before screening. Studies of overall breast cancer mortality time trends have too low statistical power to reliably detect screening effects in most populations. <b>Implications.</b> Researchers and policy makers evaluating mammography screening should avoid using breast cancer mortality trend analysis that does not separate pre- and postscreening incident cases.</p><p><strong>Highlights: </strong>Population-level mammography screening effects on breast cancer mortality emerge gradually following screening introduction, resulting in very low statistical power of trend analysis.Researchers and policy makers evaluating mammography screening should avoid relying on population-wide breast cancer mortality trends.Expected mammography screening effects at population level are lower than those from screening trials, as many cases of breast cancer fall outside the screening age range.</p>","PeriodicalId":520707,"journal":{"name":"MDM policy & practice","volume":" ","pages":"23814683221131321"},"PeriodicalIF":0.0,"publicationDate":"2022-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/b8/54/10.1177_23814683221131321.PMC9549205.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33503924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
MDM policy & practicePub Date : 2022-10-08eCollection Date: 2022-07-01DOI: 10.1177/23814683221131317
Clara N Lee, Janessa Sullivan, Randi Foraker, Terence M Myckatyn, Margaret A Olsen, Crystal Phommasathit, Jessica Boateng, Katelyn L Parrish, Milisa Rizer, Tim Huerta, Mary C Politi
{"title":"Integrating a Patient Decision Aid into the Electronic Health Record: A Case Report on the Implementation of BREASTChoice at 2 Sites.","authors":"Clara N Lee, Janessa Sullivan, Randi Foraker, Terence M Myckatyn, Margaret A Olsen, Crystal Phommasathit, Jessica Boateng, Katelyn L Parrish, Milisa Rizer, Tim Huerta, Mary C Politi","doi":"10.1177/23814683221131317","DOIUrl":"https://doi.org/10.1177/23814683221131317","url":null,"abstract":"<p><p>Patient decision aids can support shared decision making and improve decision quality. However, decision aids are not widely used in clinical practice due to multiple barriers. Integrating patient decision aids into the electronic health record (EHR) can increase their use by making them more clinically relevant, personalized, and actionable. In this article, we describe the procedures and considerations for integrating a patient decision aid into the EHR, based on the example of BREASTChoice, a decision aid for breast reconstruction after mastectomy. BREASTChoice's unique features include 1) personalized risk prediction using clinical data from the EHR, 2) clinician- and patient-facing components, and 3) an interactive format. Integrating a decision aid with patient- and clinician-facing components plus interactive sections presents unique deployment issues. Based on this experience, we outline 5 key implementation recommendations: 1) engage all relevant stakeholders, including patients, clinicians, and informatics experts; 2) explicitly and continually map all persons and processes; 3) actively seek out pertinent institutional policies and procedures; 4) plan for integration to take longer than development of a stand-alone decision aid or one with static components; and 5) transfer knowledge about the software programming from one institution to another but expect local and context-specific changes. Integration of patient decision aids into the EHR is feasible and scalable but requires preparation for specific challenges and a flexible mindset focused on implementation.</p><p><strong>Highlights: </strong>Integrating an interactive decision aid with patient- and clinician-facing components into the electronic health record could advance shared decision making but presents unique implementation challenges.We successfully integrated a decision aid for breast reconstruction after mastectomy called BREASTChoice into the electronic health record.Based on this experience, we offer these implementation recommendations: 1) engage relevant stakeholders, 2) explicitly and continually map persons and processes, 3) seek out institutional policies and procedures, 4) plan for it to take longer than for a stand-alone decision aid, and 5) transfer software programming from one site to another but expect local changes.</p>","PeriodicalId":520707,"journal":{"name":"MDM policy & practice","volume":" ","pages":"23814683221131317"},"PeriodicalIF":0.0,"publicationDate":"2022-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/2f/e2/10.1177_23814683221131317.PMC9549192.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33503922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
MDM policy & practicePub Date : 2022-09-16eCollection Date: 2022-07-01DOI: 10.1177/23814683221124090
Alfred Kodjo Toi, Ali Ben Charif, Claudia Lai, Gérard Ngueta, Karine V Plourde, Dawn Stacey, France Légaré
{"title":"Difficult Decisions for Older Canadians Receiving Home Care, and Why They Are So Difficult: A Web-Based Decisional Needs Assessment.","authors":"Alfred Kodjo Toi, Ali Ben Charif, Claudia Lai, Gérard Ngueta, Karine V Plourde, Dawn Stacey, France Légaré","doi":"10.1177/23814683221124090","DOIUrl":"https://doi.org/10.1177/23814683221124090","url":null,"abstract":"<p><p><b>Background.</b> Older adults receiving home care services often face decisions related to aging, illness, and loss of autonomy. To inform tailored shared decision making interventions, we assessed their decisional needs by asking about the most common difficult decisions, measured associated decisional conflict, and identified factors associated with it. <b>Methods.</b> In March 2020, we conducted a cross-sectional survey with a pan-Canadian Web-based panel of older adults (≥65 y) receiving home care services. For a difficult decision they had faced in the past year, we evaluated clinically significant decisional conflict (CSDC) using the 16-item Decisional Conflict Scale (score 0-100) with a >37.5 cutoff. To identify factors associated with CSDC, we performed descriptive, bivariable, and multivariable analyses using the stepwise selection method with an assumed entry and exit significance level of 0.15 and 0.20, respectively. Final model selection was based on the Bayesian information criterion. <b>Results.</b> Among 460 participants with an average age of 72.5 y, difficult decisions were, in order of frequency, about housing and safety (57.2%), managing health conditions (21.8%), and end-of-life care (8.3%). CSDC was experienced by 14.6% (95% confidence interval [CI]: 11.5%, 18.1%) of respondents on all decision points. Factors associated with CSDC included household size = 1 (OR [95% CI]: 1.81 [0.99, 3.33]; <i>P</i> = 0.27), household size = 3 (2.66 [0.78, 8.98]; <i>P</i> = 0.83), and household size = 4 (6.91 [2.23, 21.39]; <i>P</i> = 0.014); preferred option not matching the decision made (4.05 [2.05, 7.97]; <i>P</i> < 0.001); passive role in decision making (5.13 [1.78, 14.77]; <i>P</i> = 0.002); and lower quality of life (0.70 [0.57, 0.87]; <i>P</i><0.001). <b>Discussion.</b> Some older adults receiving home care services in Canada experience CSDC when facing difficult decisions. Shared decision-making interventions could mitigate associated factors.</p><p><strong>Highlights: </strong>This is the first study in Canada to assess the decisional needs of older adults receiving care at home and to identify their most common difficult decisions.Difficult decisions most frequently made were about housing and safety. The most significant decisional conflict was experienced by people making decisions about palliative care.When their quality-of-life score was low, older adults experienced clinically significant decision conflict.</p>","PeriodicalId":520707,"journal":{"name":"MDM policy & practice","volume":" ","pages":"23814683221124090"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/df/11/10.1177_23814683221124090.PMC9483974.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33466970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
MDM policy & practicePub Date : 2022-08-11eCollection Date: 2022-07-01DOI: 10.1177/23814683221116304
Tania Lognon, Amédé Gogovor, Karine V Plourde, Paul Holyoke, Claudia Lai, Emmanuelle Aubin, Kathy Kastner, Carolyn Canfield, Ron Beleno, Dawn Stacey, Louis-Paul Rivest, France Légaré
{"title":"Predictors of Decision Regret among Caregivers of Older Canadians Receiving Home Care: A Cross-Sectional Online Survey.","authors":"Tania Lognon, Amédé Gogovor, Karine V Plourde, Paul Holyoke, Claudia Lai, Emmanuelle Aubin, Kathy Kastner, Carolyn Canfield, Ron Beleno, Dawn Stacey, Louis-Paul Rivest, France Légaré","doi":"10.1177/23814683221116304","DOIUrl":"https://doi.org/10.1177/23814683221116304","url":null,"abstract":"<p><p><b>Background.</b> In Canada, caregivers of older adults receiving home care face difficult decisions that may lead to decision regret. We assessed difficult decisions and decision regret among caregivers of older adults receiving home care services and factors associated with decision regret. <b>Methods.</b> From March 13 to 30, 2020, at the outbreak of the COVID-19 pandemic, we conducted an online survey with caregivers of older adults receiving home care in the 10 Canadian provinces. We distributed a self-administered questionnaire through Canada's largest and most representative private online panel. We identified types of difficult health-related decisions faced in the past year and their frequency and evaluated decision regret using the Decision Regret Scale (DRS), scored from 0 to 100. We performed descriptive statistics as well as bivariable and multivariable linear regression to identify factors predicting decision regret. <b>Results.</b> Among 932 participants, the mean age was 42.2 y (SD = 15.6 y), and 58.4% were male. The most frequently reported difficult decisions were regarding housing and safety (75.1%). The mean DRS score was 28.8/100 (SD = 8.6). Factors associated with less decision regret included higher caregiver age, involvement of other family members in the decision-making process, wanting to receive information about the options, and considering organizations interested in the decision topic and health care professionals as trustworthy sources of information (all <i>P</i> < 0.001). Factors associated with more decision regret included mismatch between the caregiver's preferred option and the decision made, the involvement of spouses in the decision-making process, higher decisional conflict, and higher burden of care (all <i>P</i> < 0.001). <b>Discussion.</b> Decisions about housing and safety were the difficult decisions most frequently encountered by caregivers of older adults in this survey. Our results will inform future decision support interventions.</p><p><strong>Highlights: </strong>This is one of the first studies to assess decision regret among caregivers of older adults receiving home and community care services and to identify their most frequent difficult decisions.Difficult decisions were most frequently about housing and safety. Most caregivers of older adults in all 10 provinces of Canada experienced decision regret.Factors associated with less decision regret included higher caregiver age, the involvement of other family members in the decision-making process, wanting to receive information about the options, considering organizations interested in the decision topic, and health care professionals as trustworthy sources of information. Factors associated with more decision regret included mismatch between the caregiver's preferred option and the decision made, the involvement of spouses in the decision-making process, higher decisional conflict, and higher burden of care.</p>","PeriodicalId":520707,"journal":{"name":"MDM policy & practice","volume":" ","pages":"23814683221116304"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/a5/ad/10.1177_23814683221116304.PMC9380233.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40426398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
MDM policy & practicePub Date : 2022-08-03eCollection Date: 2022-07-01DOI: 10.1177/23814683221113846
Kevin Chiu, Joanna P MacEwan, Suepattra G May, Katalin Bognar, Desi Peneva, Lauren M Zhao, Candice Yong, Suvina Amin, Bjorn Bolinder, Katharine Batt, James R Baumgardner
{"title":"Estimating Productivity Loss from Breast and Non-Small-Cell Lung Cancer among Working-Age Patients and Unpaid Caregivers: A Survey Study Using the Multiplier Method.","authors":"Kevin Chiu, Joanna P MacEwan, Suepattra G May, Katalin Bognar, Desi Peneva, Lauren M Zhao, Candice Yong, Suvina Amin, Bjorn Bolinder, Katharine Batt, James R Baumgardner","doi":"10.1177/23814683221113846","DOIUrl":"https://doi.org/10.1177/23814683221113846","url":null,"abstract":"<p><p><b>Background.</b> Traditional approaches to capturing health-related productivity loss (e.g., the human capital method) focus only on the foregone wages of affected patients, overlooking the losses caregivers can incur. This study estimated the burden of productivity loss among breast cancer (BC) and non-small-cell lung cancer (NSCLC) patients and individuals caring for such patients using an augmented multiplier method. <b>Design.</b> A cross-sectional survey of BC and NSCLC patients and caregivers measured loss associated with time absent from work (absenteeism) and reduced effectiveness (presenteeism). Respondents reported pre- and postcancer diagnosis income, hours worked, and time to complete tasks. Exploratory multivariable analyses examined correlations between respondents' clinical/demographic characteristics-including industry of employment-and postdiagnosis productivity. <b>Results.</b> Of 204 patients (104 BC, 100 NSCLC) and 200 caregivers (100 BC, 100 NSCLC) who completed the survey, 319 participants (162 BC, 157 NSCLC) working ≥40 wk/y prediagnosis were included in the analysis. More than one-third of the NSCLC (33%) and BC (43%) patients left the workforce postdiagnosis, whereas only 15% of caregivers did. The traditional estimate for the burden of productivity loss was 66% lower on average than the augmented estimate (NSCLC patients: 60%, BC patients: 69%, NSCLC caregivers: 59%, and BC caregivers: 73%). <b>Conclusions.</b> Although patients typically experience greater absenteeism, productivity loss incurred by caregivers is also substantial. Failure to account for such impacts can result in substantial underestimation of productivity gains novel cancer treatments may confer by enabling patients and caregivers to remain in the workforce longer. Our results underscore the importance of holistic approaches to understanding this impact on both patients and their caregivers and accounting for such considerations when making decisions about treatment and treatment value.</p><p><strong>Highlights: </strong>Cancer can have a profound impact on productivity. This study demonstrates how the disease affects not only patients but also the informal or unpaid individuals who care for patients.An augmented approach to calculating health-related productivity loss suggests that productivity impacts are much larger than previously understood.A more comprehensive understanding of the economic burden of cancer for both patients and their caregivers suggests the need for more support in the workplace for these individuals and a holistic approach to accounting for these impacts in treatment decision making.</p>","PeriodicalId":520707,"journal":{"name":"MDM policy & practice","volume":" ","pages":"23814683221113846"},"PeriodicalIF":0.0,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/2c/9d/10.1177_23814683221113846.PMC9354140.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40677639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
MDM policy & practicePub Date : 2022-07-26eCollection Date: 2022-07-01DOI: 10.1177/23814683221115416
Deniz Marti, Rana F Hamdy, David A Broniatowski
{"title":"Gist Representations and Decision-Making Processes Affecting Antibiotic Prescribing for Children with Acute Otitis Media.","authors":"Deniz Marti, Rana F Hamdy, David A Broniatowski","doi":"10.1177/23814683221115416","DOIUrl":"https://doi.org/10.1177/23814683221115416","url":null,"abstract":"<p><p><b>Objective.</b> To test the predictions of fuzzy-trace theory regarding pediatric clinicians' decision-making processes and risk perceptions about antibiotics for children with acute otitis media (AOM). <b>Methods.</b> We conducted an online survey experiment administered to a sample of 260 pediatric clinicians. We measured their risk perceptions and prescribing decisions across 3 hypothetical AOM treatment scenarios. Participants were asked to choose among the following options: prescribe antibiotics immediately, watchful waiting (\"hedging\"), or not prescribing antibiotics. <b>Results.</b> We identified 4 gists based on prior literature: 1) \"why not take a risk?\" 2) \"antibiotics might not help but can hurt,\" 3) \"antibiotics do not have harmful side effects,\" and 4) \"antibiotics might have harmful side effects.\" All 4 gists predicted risky choice (<i>P</i> < 0.001), and gist endorsements varied significantly between scenarios when antibiotics were indicated, <i>F</i>(2, 255) = 8.53, <i>P</i> < 0.001; <i>F</i>(2, 255) = 5.14, <i>P</i> < .01; and <i>F</i>(2, 255) = 3.56, <i>P</i> < 0.05 for the first 3 factors, respectively. In a logistic regression, more experienced clinicians were less likely to hedge (<i>B</i> = -0.05; <i>P</i> < 0.01). <b>Conclusion.</b> As predicted by fuzzy-trace theory, pediatric clinicians' prescription decisions are associated with gist representations, which are distinct from verbatim risk estimates. <b>Implications.</b> Antibiotic stewardship programs can benefit by communicating the appropriate gists to clinicians who prescribe antibiotics for pediatric patients.</p><p><strong>Highlights: </strong>We found clinicians' antibiotic prescription decisions were driven by gist representations of antibiotic risks for a given hypothetical patient scenario, and clinicians' gist representations and verbatim risk estimates about antibiotic-related risks were distinct from each other.We showed that the effect of patient scenarios on clinicians' antibiotic prescription decisions was mediated by clinicians' gist representations.Less experienced clinicians tend to \"hedge\" in their antibiotic prescription decisions compared with more experienced clinicians.The broader impact of our study is that antibiotic stewardship programs can benefit by communicating the appropriate gists to clinicians who prescribe antibiotics for pediatric patients, rather than solely focusing on closing potential knowledge deficits of the clinicians.</p>","PeriodicalId":520707,"journal":{"name":"MDM policy & practice","volume":" ","pages":"23814683221115416"},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/cc/13/10.1177_23814683221115416.PMC9335473.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40659778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
MDM policy & practicePub Date : 2022-07-25eCollection Date: 2022-07-01DOI: 10.1177/23814683221113573
Edmond Awad, Bence Bago, Jean-François Bonnefon, Nicholas A Christakis, Iyad Rahwan, Azim Shariff
{"title":"Polarized Citizen Preferences for the Ethical Allocation of Scarce Medical Resources in 20 Countries.","authors":"Edmond Awad, Bence Bago, Jean-François Bonnefon, Nicholas A Christakis, Iyad Rahwan, Azim Shariff","doi":"10.1177/23814683221113573","DOIUrl":"https://doi.org/10.1177/23814683221113573","url":null,"abstract":"<p><p><b>Objective.</b> When medical resources are scarce, clinicians must make difficult triage decisions. When these decisions affect public trust and morale, as was the case during the COVID-19 pandemic, experts will benefit from knowing which triage metrics have citizen support. <b>Design.</b> We conducted an online survey in 20 countries, comparing support for 5 common metrics (prognosis, age, quality of life, past and future contribution as a health care worker) to a benchmark consisting of support for 2 no-triage mechanisms (first-come-first-served and random allocation). <b>Results.</b> We surveyed nationally representative samples of 1000 citizens in each of Brazil, France, Japan, and the United States and also self-selected samples from 20 countries (total <i>N</i> = 7599) obtained through a citizen science website (the Moral Machine). We computed the support for each metric by comparing its usability to the usability of the 2 no-triage mechanisms. We further analyzed the polarizing nature of each metric by considering its usability among participants who had a preference for no triage. In all countries, preferences were polarized, with the 2 largest groups preferring either no triage or extensive triage using all metrics. Prognosis was the least controversial metric. There was little support for giving priority to healthcare workers. <b>Conclusions.</b> It will be difficult to define triage guidelines that elicit public trust and approval. Given the importance of prognosis in triage protocols, it is reassuring that it is the least controversial metric. Experts will need to prepare strong arguments for other metrics if they wish to preserve public trust and morale during health crises.</p><p><strong>Highlights: </strong>We collected citizen preferences regarding triage decisions about scarce medical resources from 20 countries.We find that citizen preferences are universally polarized.Citizens either prefer no triage (random allocation or first-come-first served) or extensive triage using all common triage metrics, with \"prognosis\" being the least controversial.Experts will need to prepare strong arguments to preserve or elicit public trust in triage decisions.</p>","PeriodicalId":520707,"journal":{"name":"MDM policy & practice","volume":" ","pages":"23814683221113573"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/a0/32/10.1177_23814683221113573.PMC9326829.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40659779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
MDM policy & practicePub Date : 2022-03-16eCollection Date: 2022-01-01DOI: 10.1177/23814683221086623
Alexandra K Kunzelmann, Karin Binder, Martin R Fischer, Martin Reincke, Leah T Braun, Ralf Schmidmaier
{"title":"Improving Diagnostic Efficiency with Frequency Double-Trees and Frequency Nets in Bayesian Reasoning.","authors":"Alexandra K Kunzelmann, Karin Binder, Martin R Fischer, Martin Reincke, Leah T Braun, Ralf Schmidmaier","doi":"10.1177/23814683221086623","DOIUrl":"https://doi.org/10.1177/23814683221086623","url":null,"abstract":"<p><p><b>Background.</b> Medical students often have problems with Bayesian reasoning situations. Representing statistical information as natural frequencies (instead of probabilities) and visualizing them (e.g., with double-trees or net diagrams) leads to higher accuracy in solving these tasks. However, double-trees and net diagrams (which already contain the correct solution of the task, so that the solution could be read of the diagrams) have not yet been studied in medical education. This study examined the influence of information format (probabilities v. frequencies) and visualization (double-tree v. net diagram) on the accuracy and speed of Bayesian judgments. <b>Methods.</b> A total of 142 medical students at different university medical schools (Munich, Kiel, Goettingen, Erlangen, Nuremberg, Berlin, Regensburg) in Germany predicted posterior probabilities in 4 different medical Bayesian reasoning tasks, resulting in a 3-factorial 2 × 2 × 4 design. The diagnostic efficiency for the different versions was represented as the median time divided by the percentage of correct inferences. <b>Results.</b> Frequency visualizations led to a significantly higher accuracy and faster judgments than did probability visualizations. Participants solved 80% of the tasks correctly in the frequency double-tree and the frequency net diagram. Visualizations with probabilities also led to relatively high performance rates: 73% in the probability double-tree and 70% in the probability net diagram. The median time for a correct inference was fastest with the frequency double tree (2:08 min) followed by the frequency net diagram and the probability double-tree (both 2:26 min) and probability net diagram (2:33 min). The type of visualization did not result in a significant difference. <b>Discussion.</b> Frequency double-trees and frequency net diagrams help answer Bayesian tasks more accurately and also more quickly than the respective probability visualizations. Surprisingly, the effect of information format (probabilities v. frequencies) on performance was higher in previous studies: medical students seem also quite capable of identifying the correct solution to the Bayesian task, among other probabilities in the probability visualizations.</p><p><strong>Highlights: </strong>Frequency double-trees and frequency nets help answer Bayesian tasks not only more accurately but also more quickly than the respective probability visualizations.In double-trees and net diagrams, the effect of the information format (probabilities v. natural frequencies) on performance is remarkably lower in this high-performing sample than that shown in previous studies.</p>","PeriodicalId":520707,"journal":{"name":"MDM policy & practice","volume":" ","pages":"23814683221086623"},"PeriodicalIF":0.0,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/6e/41/10.1177_23814683221086623.PMC8935422.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40317442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}