Accuracy of Physician Estimates of Out-of-Pocket Costs for Medication Filling.

Importance: One-third of US residents have trouble paying their medical bills. They often turn to their physicians for help navigating health costs and insurance coverage. Objective: To determine whether physicians can accurately estimate out-of-pocket expenses when they are given all of the necessary information about a drug's price and a patient's insurance plan. Design, Setting, and Participants: This national mail-in survey used a random sample of US physicians. The survey was sent to 900 outpatient physicians (300 each of primary care, gastroenterology, and rheumatology). Physicians were excluded if they were in training, worked primarily for the Veterans Administration or Indian Health Service, were retired, or reported 0% outpatient clinical effort. Analyses were performed from July to December 2020. Main Outcomes and Measures: In a hypothetical vignette, a patient was prescribed a new drug costing $1000/month without insurance. A summary of her private insurance information was provided, including the plan's deductible, coinsurance rates, copays, and out-of-pocket maximum. Physicians were asked to estimate the drug's out-of-pocket cost at 4 time points between January and December, using the plan's 4 types of cost-sharing: (1) deductibles, (2) coinsurance, (3) copays, and (4) out-of-pocket maximums. Multivariate linear regression was used to assess differences in performance by specialty, adjusting for attitudes toward cost conversations, demographics, and clinical characteristics. Results: The response rate was 45% (405 of 900) and 371 respondents met inclusion criteria. Among the respondents included in this study, 59% (n = 220) identified as male, 23% (n = 84) as Asian, 3% (n = 12) as Black, 6% (n = 24) as Hispanic, and 58% (n = 216) as White; 30% (n = 112) were primary care physicians, 35% (n = 128) were gastroenterologists, and 35% (n = 131) were rheumatologists; and the mean (SD) age was 49 (10) years. Overall, 52% of physicians (n = 192) accurately estimated costs before the deductible was met, 62% (n = 228) accurately used coinsurance information, 61% (n = 224) accurately used copay information, and 57% (n = 210) accurately estimated costs once the out-of-pocket maximum was met. Only 21% (n = 78) of physicians answered all 4 questions correctly. Ability to estimate out-of-pocket costs was not associated with specialty, attitudes toward cost conversations, or clinic characteristics. Conclusions and Relevance: This survey study found that many US physicians have difficulty estimating out-of-pocket costs, even when they have access to their patients' insurance plans. The mechanics involved in calculating real-time out-of-pocket costs are complex. These findings suggest that increased price transparency and simpler insurance cost-sharing mechanisms are needed to enable informed cost conversations at the point of prescribing.

Usefulness of a Novel Risk Score to Predict In-Hospital Mortality in Patients ≥ 60 Years of Age with ST Elevation Myocardial Infarction.

Numerous algorithms are available to predict short-term mortality in ST elevation myocardial infarction (STEMI) but none are focused on elderly patients or include invasive hemodynamics. A simplified risk score (LASH score) including left ventricular end diastolic pressure > 20 mm Hg, age > 75 years, systolic blood pressure < 100 mm Hg and heart rate > 100 bpm was tested in a retrospective, single-center study of 346 patients ≥ 60 years old who underwent primary percutaneous coronary intervention (PPCI). The median age was 70 years [IQR: 64, 79], 60.1% were men, and 77.8% identified as White. In-hospital all-cause mortality was 10.1%. Patients with a LASH score ≥ 3 (n = 34) had an in-hospital mortality rate of 44.1% compared to 6.4% for LASH score ≤ 2 (p < 0.0001). The odds ratio for in-hospital mortality for patients with LASH score ≥ 3 was 13.2 (95% CI 5.3-33.1) compared to patients with a LASH score ≤ 2 when adjusted for sex, cardiac arrest, heart failure, and prior cerebrovascular event. The LASH score had an area under the ROC curve for predicting in-hospital mortality of 0.795 [CI 0.716-0.872], as compared to TIMI-STEMI (0.881, CI 0.829-0.931; p = 0.01), GRACE (0.849, CI 0.778-0.920; p = 0.19), shock index (0.769, CI 0.667-0.871; p = 0.51) and modified shock index (0.765, CI 0.716-0.873; p = 0.48). In summary, a simplified, easy to calculate risk score that incorporates age and invasive hemodynamics predicts in-hospital mortality in patients ≥ 60 years old undergoing PPCI for STEMI.