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Patient sharing and quality of care: measuring outcomes of care coordination using claims data.

ABSTRACT: With the goal of improving clinical efficiency and effectiveness, programs to enhance care coordination are a major focus of health care reform.To examine whether "care density"--a claims-based measure of patient sharing by office-based physicians--is associated with measures of quality. Care density is a proxy measure that may reflect how frequently a patient's doctors collaborate.Cohort study using administrative databases from 3 large commercial insurance plans.A total of 1.7 million adult patients; 31,675 with congestive heart failure, 78,530 with chronic obstructive pulmonary disease, and 240,378 with diabetes.Care density was assessed in 2008. Prevention Quality Indicators (PQIs), 30-day readmissions, and Healthcare Effectiveness Data and Information Set quality indicators were measured in the following year.Among all patients, we found that patients with the highest care density density--indicating high levels of patient sharing among their office-based physicians--had significantly lower rates of adverse events measured as PQIs compared with patients with low-care density (odds ratio=0.88; 95% confidence interval, 0.85-0.92). A significant association between care density and PQIs was also observed for patients with diabetes mellitus but not congestive heart failure or chronic obstructive pulmonary disease. Diabetic patients with higher care density scores had significantly lower odds of 30-day readmissions (odds ratio=0.68, 95% confidence interval, 0.48-0.97). Significant associations were observed between care density and Healthcare Effectiveness Data and Information Set measures although not always in the expected direction.In some settings, patients whose doctors share more patients had lower odds of adverse events and 30-day readmissions.

SUBMITTER: Pollack CE

PROVIDER: S-EPMC4384684 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

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Patient sharing and quality of care: measuring outcomes of care coordination using claims data.

Pollack Craig E CE Lemke Klaus W KW Roberts Eric E Weiner Jonathan P JP

Medical care 20150401 4

<h4>Background</h4>With the goal of improving clinical efficiency and effectiveness, programs to enhance care coordination are a major focus of health care reform.<h4>Objective</h4>To examine whether "care density"--a claims-based measure of patient sharing by office-based physicians--is associated with measures of quality. Care density is a proxy measure that may reflect how frequently a patient's doctors collaborate.<h4>Research design</h4>Cohort study using administrative databases from 3 lar ...[more]

PMID: 25719430

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Project description:BACKGROUND:Regional population management (PM) health initiatives require insight into experienced quality of care at the regional level. Unsolicited online provider ratings have shown potential for this use. This study explored the addition of comments accompanying unsolicited online ratings to regional analyses. OBJECTIVE:The goal was to create additional insight for each PM initiative as well as overall comparisons between these initiatives by attempting to determine the reasoning and rationale behind a rating. METHODS:The Dutch Zorgkaart database provided the unsolicited ratings from 2008 to 2017 for the analyses. All ratings included both quantitative ratings as well as qualitative text comments. Nine PM regions were used to aggregate ratings geographically. Sentiment analyses were performed by categorizing ratings into negative, neutral, and positive ratings. Per category, as well as per PM initiative, word frequencies (ie, unigrams and bigrams) were explored. Machine learning-naïve Bayes and random forest models-was applied to identify the most important predictors for rating overall sentiment and for identifying PM initiatives. RESULTS:A total of 449,263 unsolicited ratings were available in the Zorgkaart database: 303,930 positive ratings, 97,739 neutral ratings, and 47,592 negative ratings. Bigrams illustrated that feeling like not being "taken seriously" was the dominant bigram in negative ratings, while bigrams in positive ratings were mostly related to listening, explaining, and perceived knowledge. Comparing bigrams between PM initiatives showed a lot of overlap but several differences were identified. Machine learning was able to predict sentiments of comments but was unable to distinguish between specific PM initiatives. CONCLUSIONS:Adding information from text comments that accompany online ratings to regional evaluations provides insight for PM initiatives into the underlying reasons for ratings. Text comments provide useful overarching information for health care policy makers but due to a lot of overlap, they add little region-specific information. Specific outliers for some PM initiatives are insightful.

Dataset Information

Patient sharing and quality of care: measuring outcomes of care coordination using claims data.

Publications

Patient sharing and quality of care: measuring outcomes of care coordination using claims data.

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