William Fleischman

Association between payments from manufacturers of pharmaceuticals to physicians and regional prescribing: cross sectional ecological study.

Objective To examine the association between payments made by the manufacturers of pharmaceuticals to physicians and prescribing by physicians within hospital referral regions. Design Cross sectional analysis of 2013 and 2014 Open Payments and Medicare Part D prescribing data for two classes of commonly prescribed, commonly marketed drugs: oral anticoagulants and non-insulin diabetes drugs, overall and stratified by physician and payment type. Setting 306 hospital referral regions, United States. Participants 45 949 454 Medicare Part D prescriptions written by 623 886 physicians to 10 513 173 patients for two drug classes: oral anticoagulants and non-insulin diabetes drugs. Main outcome measures Proportion, or market share, of marketed oral anticoagulants and non-insulin diabetes drugs prescribed by physicians among all drugs in each class and within hospital referral regions. Results Among 306 hospital referral regions, there were 977 407 payments to physicians totaling

Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach.

61 026 140 (£46 174 600; €54 632 500) related to oral anticoagulants, and 1 787 884 payments totaling

Industry relationships with pediatricians: Findings from the open payments sunshine act

108 417 616 related to non-insulin diabetes drugs. The median market share of the hospital referral regions was 21.6% for marketed oral anticoagulants and 12.6% for marketed non-insulin diabetes drugs. Among hospital referral regions, one additional payment (median value

Emergency Department Visits Without Hospitalization Are Associated With Functional Decline in Older Persons

13, interquartile range,

Improving emergency physician performance using audit and feedback: a systematic review

10-

The visit-data warehouse: enabling novel secondary use of health information exchange data.

18) was associated with 94 (95% confidence interval 76 to 112) additional days filled of marketed oral anticoagulants and 107 (89 to 125) additional days filled of marketed non-insulin diabetes drugs (P<0.001). Payments to specialists were associated with greater prescribing of marketed drugs than payments to non-specialists (212 v 100 additional days filled per payment of marketed oral anticoagulants, 331 v 114 for marketed non-insulin diabetes drugs, P<0.001). Payments for speaker and consulting fees for non-insulin diabetes drugs were associated with greater prescribing of marketed drugs than payments for food and beverages or educational materials (484 v 110, P<0.001). Conclusions and study limitations Payments by the manufacturers of pharmaceuticals to physicians were associated with greater regional prescribing of marketed drugs among Medicare Part D beneficiaries. Payments to specialists and payments for speaker and consulting fees were predominantly associated with greater regional prescribing of marketed drugs than payments to non-specialists or payments for food and beverages, gifts, or educational materials. As a cross sectional, ecological study, we cannot prove causation between payments to physicians and increased prescribing. Furthermore, our findings should be interpreted only at the regional level. Our study is limited to prescribing by physicians and the two drug classes studied.

The Association Between Physician Empathy and Variation in Imaging Use.

OBJECTIVES Predictive analytics in emergency care has mostly been limited to the use of clinical decision rules (CDRs) in the form of simple heuristics and scoring systems. In the development of CDRs, limitations in analytic methods and concerns with usability have generally constrained models to a preselected small set of variables judged to be clinically relevant and to rules that are easily calculated. Furthermore, CDRs frequently suffer from questions of generalizability, take years to develop, and lack the ability to be updated as new information becomes available. Newer analytic and machine learning techniques capable of harnessing the large number of variables that are already available through electronic health records (EHRs) may better predict patient outcomes and facilitate automation and deployment within clinical decision support systems. In this proof-of-concept study, a local, big data-driven, machine learning approach is compared to existing CDRs and traditional analytic methods using the prediction of sepsis in-hospital mortality as the use case. METHODS This was a retrospective study of adult ED visits admitted to the hospital meeting criteria for sepsis from October 2013 to October 2014. Sepsis was defined as meeting criteria for systemic inflammatory response syndrome with an infectious admitting diagnosis in the ED. ED visits were randomly partitioned into an 80%/20% split for training and validation. A random forest model (machine learning approach) was constructed using over 500 clinical variables from data available within the EHRs of four hospitals to predict in-hospital mortality. The machine learning prediction model was then compared to a classification and regression tree (CART) model, logistic regression model, and previously developed prediction tools on the validation data set using area under the receiver operating characteristic curve (AUC) and chi-square statistics. RESULTS There were 5,278 visits among 4,676 unique patients who met criteria for sepsis. Of the 4,222 patients in the training group, 210 (5.0%) died during hospitalization, and of the 1,056 patients in the validation group, 50 (4.7%) died during hospitalization. The AUCs with 95% confidence intervals (CIs) for the different models were as follows: random forest model, 0.86 (95% CI = 0.82 to 0.90); CART model, 0.69 (95% CI = 0.62 to 0.77); logistic regression model, 0.76 (95% CI = 0.69 to 0.82); CURB-65, 0.73 (95% CI = 0.67 to 0.80); MEDS, 0.71 (95% CI = 0.63 to 0.77); and mREMS, 0.72 (95% CI = 0.65 to 0.79). The random forest model AUC was statistically different from all other models (p ≤ 0.003 for all comparisons). CONCLUSIONS In this proof-of-concept study, a local big data-driven, machine learning approach outperformed existing CDRs as well as traditional analytic techniques for predicting in-hospital mortality of ED patients with sepsis. Future research should prospectively evaluate the effectiveness of this approach and whether it translates into improved clinical outcomes for high-risk sepsis patients. The methods developed serve as an example of a new model for predictive analytics in emergency care that can be automated, applied to other clinical outcomes of interest, and deployed in EHRs to enable locally relevant clinical predictions.

Financial Relationships between Urologists and Industry: an Analysis of Open Payments Data

BACKGROUND AND OBJECTIVES: Ties between physicians and pharmaceutical/medical device manufactures have received considerable attention. The Open Payments program, part of the Affordable Care Act, requires public reporting of payments to physicians from industry. We sought to describe payments from industry to physicians caring for children by (1) comparing payments to pediatricians to other medical specialties, (2) determining variation in payments among pediatric subspecialties, and (3) identifying the types of payment and the products associated with payments to pediatricians. METHODS: We conducted a descriptive, cross-sectional analysis of Open Payments data from January 1 to December 31, 2014. The primary outcomes included percent of physicians receiving payments, median total pay per physician, the types of payments received, and the drugs and devices associated with payments. RESULTS: There were 9 638 825 payments to physicians, totaling

Industry support of physician education in the USA

1 186 217 157. There were 244 915 payments to general pediatricians and pediatric subspecialists, totaling >

Why the Open Payments Program Is Likely to Provide Systematic and Transparent Data on Financial Relationships with Industry

32 million. The median individual payment to general pediatricians was