Physician Payments from Industry Are Associated with Greater Medicare Part D Prescribing Costs

<div><p>Background</p><p>The U.S. Physician Payments Sunshine Act mandates the reporting of payments or items of value received by physicians from drug, medical device, and biological agent manufacturers. The impact of these payments on physician prescribing has not been examined at large scale.</p><p>Methods</p><p>We linked public Medicare Part D prescribing data and Sunshine Act data for 2013. Physician payments were examined descriptively within specialties, and then for association with prescribing costs and patterns using regression models. Models were adjusted for potential physician-level confounding features, including sex, geographic region, and practice size.</p><p>Results</p><p>Among 725,169 individuals with Medicare prescribing data, 341,644 had documented payments in the OPP data (47.1%). Among all physicians receiving funds, mean payment was $1750 (SD$28336); median was $138 (IQR$48-$394). Across the 12 specialties examined, a dose-response relationship was observed in which greater payments were associated with greater prescribing costs per patient. In adjusted regression models, being in the top quintile of payment receipt was associated with incremental prescribing cost per patient ranging from$27 (general surgery) to $2931 (neurology). Similar associations were observed with proportion of branded prescriptions written.</p><p>Conclusions</p><p>While distribution and amount of payments differed widely across medical specialties, for each of the 12 specialties examined the receipt of payments was associated with greater prescribing costs per patient, and greater proportion of branded medication prescribing. We cannot infer a causal relationship, but interventions aimed at those physicians receiving the most payments may present an opportunity to address prescribing costs in the US.</p></div

Incremental prescribing cost per patient, adjusted for proportion of branded prescriptions, by payment quintile and specialty.

<p>Incremental prescribing cost per patient, adjusted for proportion of branded prescriptions, by payment quintile and specialty.</p

Incremental proportion of branded prescriptions, by payment quintile and specialty.

<p>Incremental proportion of branded prescriptions, by payment quintile and specialty.</p

Characteristics of physicians among the top quintile for receipt of payments in Q3/4 2013^*.

<p>Characteristics of physicians among the top quintile for receipt of payments in Q3/4 2013^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155474#t001fn001" target="_blank">*</a>}.</p

CNVs from any region of the genome that show differences between MD_SA and MD_NO SA, with single point p-values<0.1 (Hg18 coordinates).

<p>CNVs from any region of the genome that show differences between MD_SA and MD_NO SA, with single point p-values<0.1 (Hg18 coordinates).</p

Burden of duplication CNVs >100 Kb in people that did (MD_SA) or did not attempt suicide (MD_NO SA).

<p>Abbreviations: CNVs: Number of segments; PROP: Proportion of sample with one or more segment; TOTKB: Total kb length spanned per indivudal; AVGKB: Average segment size. His: Hispanic; afr: African-American; cau: Caucasian.</p

Sentiment Measured in Hospital Discharge Notes Is Associated with Readmission and Mortality Risk: An Electronic Health Record Study

<div><p>Natural language processing tools allow the characterization of sentiment–that is, terms expressing positive and negative emotion–in text. Applying such tools to electronic health records may provide insight into meaningful patient or clinician features not captured in coded data alone. We performed sentiment analysis on 2,484 hospital discharge notes for 2,010 individuals from a psychiatric inpatient unit, as well as 20,859 hospital discharges for 15,011 individuals from general medical units, in a large New England health system between January 2011 and 2014. The primary measures of sentiment captured intensity of subjective positive or negative sentiment expressed in the discharge notes. Mean scores were contrasted between sociodemographic and clinical groups in mixed effects regression models. Discharge note sentiment was then examined for association with risk for readmission in Cox regression models. Discharge notes for individuals with greater medical comorbidity were modestly but significantly lower in positive sentiment among both psychiatric and general medical cohorts (p<0.001 in each). Greater positive sentiment at discharge was associated with significantly decreased risk of hospital readmission in each cohort (~12% decrease per standard deviation above the mean). Automated characterization of discharge notes in terms of sentiment identifies differences between sociodemographic groups, as well as in clinical outcomes, and is not explained by differences in diagnosis. Clinician sentiment merits investigation to understand why and how it reflects or impacts outcomes.</p></div

Association between sociodemographic features and sentiment in a psychiatric inpatient cohort.

<p>Coefficients refer to standard deviation units of positive or negative sentiment.</p

Cox regression examining association between sentiment and readmission hazard in a psychiatric inpatient cohort.

<p>Cox regression examining association between sentiment and readmission hazard in a psychiatric inpatient cohort.</p

Cox regression examining association between sentiment and mortality in a general medical cohort.

<p>Cox regression examining association between sentiment and mortality in a general medical cohort.</p