scholarly journals Shared Decision Making and Patient Reported Outcomes among Adults with Atherosclerotic Cardiovascular Disease, Medical Expenditure Panel Survey 2006-2015

2021 ◽  
pp. 100281
Author(s):  
Victor Okunrintemi ◽  
Javier Valero-Elizondo ◽  
Neil J. Stone ◽  
Ron Blankstein ◽  
Michael J. Blaha ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Decision Making ◽  
Shared Decision Making ◽  
Patient Reported Outcomes ◽  
Medical Expenditure Panel Survey ◽  
Medical Expenditure ◽  
Atherosclerotic Cardiovascular Disease ◽  
Shared Decision ◽  
Panel Survey ◽  
Patient Reported

Abstract 189: Income Level Disparities in Consumer Reported Domains of Healthcare Experience Among Those With Established Atherosclerotic Cardiovascular Disease in United States: Insights From Medical Expenditure Panel Survey 2010-2013

2017 ◽  
Vol 10 (suppl_3) ◽  
Author(s):  
Victor Okunrintemi ◽  
Erica Spatz ◽  
Joseph Salami ◽  
Paul D Capua ◽  
Haider Warraich ◽  
...  
Keyword(s):  
Decision Making ◽  
Shared Decision Making ◽  
Medical Expenditure Panel Survey ◽  
Medical Expenditure ◽  
Atherosclerotic Cardiovascular Disease ◽  
Shared Decision ◽  
Panel Survey ◽  
Provider Satisfaction ◽  
Healthcare Experience ◽  
Patient Reported

Background: While it is well established that significant health outcome disparities exist across patients of varying socio-economic status (SES) with established atherosclerotic cardiovascular disease (ASCVD), disparities in patients’ healthcare experiences are not well investigated. We explore income level differences in four central tenets of patient-reported healthcare experience (access to care, provider communication, shared decision making and provider satisfaction) as measured by the Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey, in a nationally representative adult US population with established ASCVD. Methods: The study population consisted of 8223 individuals (age ≥ 18 years) representing 21.6 million with established ASCVD (self-reported or ICD-9 diagnosis) reporting a usual source of care in the 2010-2013 pooled Medical Expenditure Panel Survey (MEPS) cohort. We assessed the responses for each item as: a) difficult access to care (always/almost difficult), b) ineffective communication and shared decision making (never/sometimes), and c) poor provider satisfaction (lowest quartile on a scale of 0-10). We examined the relationship between scores in the lowest quartile of each domain composite scores, derived using the weighted average response from each items scores, with patients’ SES, using the high-income group as reference. Results: Lower SES was consistently associated with greater perceived difficulties in access, poor provider-patient communication, less shared decision making, as well as lower provider satisfaction (Table). Participants classified as poor vs. high income were 47% (95% CI 1.17-1.83) more likely to report difficulty accessing care, 39% (95% CI 1.09-1.78) and 26% (95% CI 0.99-1.60) reported a higher likelihood of experiencing poor communication and shared decision making respectively, as well as a 66% (95% CI 1.31-2.11) higher likelihood of reporting lower provider satisfaction. Conclusion: Among patients with established ASCVD, significant SES disparities exist in all domains of patient reported healthcare experience quality of care metrics. Targeted policies focusing on improving communication, engagement and satisfaction are needed to enhance patient healthcare experience among high-risk vulnerable populations.

scholarly journals Nondietary Cardiovascular Health Metrics With Patient Experience and Loss of Productivity Among US Adults Without Cardiovascular Disease: The Medical Expenditure Panel Survey 2006 to 2015

2020 ◽  
Vol 9 (19) ◽  
Author(s):  
Martin Tibuakuu ◽  
Victor Okunrintemi ◽  
Nazir Savji ◽  
Neil J. Stone ◽  
Salim S. Virani ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Odds Ratio ◽  
Patient Reported Outcomes ◽  
Medical Expenditure Panel Survey ◽  
Medical Expenditure ◽  
Panel Survey ◽  
Health Related Quality ◽  
Patient Reported ◽  
Related Quality ◽  
Health Related

Background The American Heart Association 2020 Impact Goals aimed to promote population health through emphasis on cardiovascular health (CVH). We examined the association between nondietary CVH metrics and patient‐reported outcomes among a nationally representative sample of US adults without cardiovascular disease. Methods and Results We included adults aged ≥18 years who participated in the Medical Expenditure Panel Survey between 2006 and 2015. CVH metrics were scored 1 point for each of the following: not smoking, being physically active, normal body mass index, no hypertension, no diabetes mellitus, and no dyslipidemia, or 0 points if otherwise. Diet was not assessed in Medical Expenditure Panel Survey. Patient‐reported outcomes were obtained by telephone survey and included questions pertaining to patient experience and health‐related quality of life. Regression models were used to compare patient‐reported outcomes based on CVH, adjusting for sociodemographic factors and comorbidities. There were 177 421 Medical Expenditure Panel Survey participants (mean age, 45 [17] years) representing ~187 million US adults without cardiovascular disease. About 12% (~21 million US adults) had poor CVH. Compared with individuals with optimal CVH, those with poor CVH had higher odds of reporting poor patient‐provider communication (odds ratio, 1.14; 95% CI, 1.05–1.24), poor healthcare satisfaction (odds ratio, 1.15; 95% CI, 1.08–1.22), poor perception of health (odds ratio, 5.89; 95% CI, 5.35–6.49), at least 2 disability days off work (odds ratio, 1.39; 95% CI, 1.30–1.48), and lower health‐related quality of life scores. Conclusions Among US adults without cardiovascular disease, meeting a lower number of ideal CVH metrics is associated with poor patient‐reported healthcare experience, poor perception of health, and lower health‐related quality of life. Preventive measures aimed at optimizing ideal CVH metrics may improve patient‐reported outcomes among this population.

scholarly journals Shared Decision Making: From Decision Science to Data Science

2020 ◽  
Vol 40 (3) ◽  
pp. 254-265
Author(s):  
Azza Shaoibi ◽  
Brian Neelon ◽  
Leslie A. Lenert
Keyword(s):  
Decision Making ◽  
Shared Decision Making ◽  
Patient Reported Outcomes ◽  
Data Science ◽  
Treatment Effects ◽  
Predictive Analytics ◽  
Simulated Data ◽  
Treatment Recommendations ◽  
Shared Decision ◽  
Patient Reported

Background. Accurate diagnosis of patients’ preferences is central to shared decision making. Missing from clinical practice is an approach that links pretreatment preferences and patient-reported outcomes. Objective. We propose a Bayesian collaborative filtering (CF) algorithm that combines pretreatment preferences and patient-reported outcomes to provide treatment recommendations. Design. We present the methodological details of a Bayesian CF algorithm designed to accomplish 3 tasks: 1) eliciting patient preferences using conjoint analysis surveys, 2) clustering patients into preference phenotypes, and 3) making treatment recommendations based on the posttreatment satisfaction of like-minded patients. We conduct a series of simulation studies to test the algorithm and to compare it to a 2-stage approach. Results. The Bayesian CF algorithm and 2-stage approaches performed similarly when there was extensive overlap between preference phenotypes. When the treatment was moderately associated with satisfaction, both methods made accurate recommendations. The kappa estimates measuring agreement between the true and predicted recommendations were 0.70 (95% confidence interval = 0.052–0.88) and 0.73 (0.56–0.90) under the Bayesian CF and 2-stage approaches, respectively. The 2-stage approach failed to converge in settings in which clusters were well separated, whereas the Bayesian CF algorithm produced acceptable results, with kappas of 0.73 (0.56–0.90) and 0.83 (0.69–0.97) for scenarios with moderate and large treatment effects, respectively. Limitations. Our approach assumes that the patient population is composed of distinct preference phenotypes, there is association between treatment and outcomes, and treatment effects vary across phenotypes. Findings are also limited to simulated data. Conclusion. The Bayesian CF algorithm is feasible, provides accurate cluster treatment recommendations, and outperforms 2-stage estimation when clusters are well separated. As such, the approach serves as a roadmap for incorporating predictive analytics into shared decision making.

512-P: Desire for and Experience of Shared Decision-Making and Associations with Patient-Reported Outcomes in Those with Diabetes

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 512-P
Author(s):  
EMILY SYVERUD ◽  
SARAH T. MANSER ◽  
STEVEN ARRIAZA ◽  
ELIZABETH A. ROGERS
Keyword(s):  
Decision Making ◽  
Shared Decision Making ◽  
Patient Reported Outcomes ◽  
Shared Decision ◽  
Patient Reported
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