Low-Value Health Care Services in a Commercially-Insured Population Academy Health Annual Research Meeting – June 28, 2016 Rachel O. Reid, MD, MS 1,2 ; Brendan Rabideau, BA 3 ; Neeraj Sood, PhD 3,4 1 RAND Corporation; 2 Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital; 3 Leonard D. Schaeffer Center for Health Policy and Economics, University of Southern California; 4 Department of Health Policy and Management, Sol Price School of Public Policy, University of Southern California
Background: Waste in the US Health Care System is Both Common and Expensive • $750 billion of US health care spending annually is waste • $200 billion is for overtreatment or overuse • Reducing waste • Decrease spending while improving quality and access • Championed by providers, policymakers, payers and patients • Choosing Wisely campaign and other initiatives
Background: Direct Assessments of Waste in Health Care are Needed Indirect assessments of Direct assessments of spending on waste wasteful, low-value services Based on geographic variation in Important to understand spending opportunities for improvement Provide scale of problem Have focused on • Medicare Cannot directly inform • Narrow set of measures improvement • Limited geographic area
Design: Retrospective Analysis of Commercial Insurance Claims Aim: : Assess utilization of and spending on set of low-value health care services in a large, national commercially insured population Design: n: Data: a: Cohor ort: • Retrospective analysis of • 25% random sample of Optum • Patients aged 18-64 commercial insurance claims Cliniformatics Datamart • Continuously enrolled in 2013 • Assessed care received in 2013 • 2011 to 2013
Analyses: Employed 28 Published Low-Value Health Care Service Measures Cardio iovas ascu cula lar Testin ing Diagnostic stic and Head and Neurolo logic ic Musculo loske skelet letal al Testin ing Preoper erat ativ ive e Testin ing and Proced edures es Preven entiv ive e Testin ing Testin ing and Proced edures es • Carotid endarterectomy • 1,25-OH Vitamin D • EEG for headaches • Arthroscopic surgery • Chest radiography for asymptomatic testing without for knee osteoarthritis • Head imaging for • Echocardiography adults hypercalcemia or CKD syncope • Frequent bone density • Pulmonary Function • IVC filters to prevent PE • Homocysteine testing testing • Head imaging for Testing in cardiovascular • Percutaneous coronary uncomplicated • Imaging for nonspecific • Routine Stress Tests disease intervention for stable headache low back pain coronary artery disease • HPV testing younger • Sinus CT for • Imaging for plantar than 30 • Renal artery uncomplicated acute fasciitis angioplasty or stent • Hypercoagulable rhinosinusitis • Spinal injection for testing for VTE • Screening for carotid lower-back pain artery disease for • Imaging for adnexal • Vertebroplasty or syncope cysts kyphoplasty for • Screening for carotid • PTH test for stage I-III osteoporotic vertebral artery disease in CKD fractures asymptomatic adults • T3 testing for • Stress testing for stable hypothyroidism coronary artery disease
Analyses: Measuring Low-Value Utilization and Spending in Study • Utilization • Unique patients receiving each low value service in 2013 • Spending • Patients 2013 standardized costs for each service • Accounting for spending on related services and procedures • Winsorized at 5 th and 95 th percentile to reduce impact of outliers
Analyses: Regression Analysis to Predict Disproportionate Low-Value Service Spending • Outcome • Adjusting for: • Low-value spending per • Patient characteristics $10,000 in total spending Age • • Regression: 2 Part-Model Sex • Race • 1. Probit Household Income • Estimate probability of any low- • Census Division • value spending • Plan Characteristics 2. GLM (γ -distribution, log link Health Plan Type function) • i.e., EPO, HMO, POS, PPO • Estimate low-value spending per • CDHP Options • $10,000 in overall spending i.e., High-Deductible plan with • Conditional on having any low • HRA or HSA value spending
Results: Low-Value Service Utilization and Spending in Commercially Insured Patients Patients in Study Overall Medical Cohort: Spending: $6.6 Billion 1,468,689
Results: Low-Value Service Utilization and Spending in Commercially Insured Patients Patients in Study Overall Medical Cohort: Spending: $6.6 Billion 1,468,689 Low-Value Service Patients Receiving Spending: Low-Value Services: $32.8 Million (0.5%) 114,732 (7.8%)
Results: Low-Value Service Utilization and Spending in Commercially Insured Patients 100% Preoperative Patients in Study Overall Medical 11,550 (0.8%) 90% Cohort: Spending: $6.6 Billion 1,468,689 80% Musculoskeletal 33,028 (2.3%) 70% 60% Low-Value Service Head and Neurologic Patients Receiving 20,194 (1.4%) Spending: 50% Low-Value Services: $32.8 Million (0.5%) 114,732 (7.8%) 40% 30% Diagnostic/Preventive 49,743 (3.4%) 20% 10% Cardiovascular 9,007 (0.6%) 0% Pati atients ents Sp Spen endin ing
Results: Low-Value Service Utilization and Spending in Commercially Insured Patients 100% Preoperative Preoperative Patients in Study Overall Medical 11,550 (0.8%) $1.3 Million (4.0%) 90% Cohort: Spending: $6.6 Billion 1,468,689 80% Musculoskeletal 33,028 (2.3%) Musculoskeletal 70% $16.8 Million (51.1%) 60% Low-Value Service Head and Neurologic Patients Receiving 20,194 (1.4%) Spending: 50% Low-Value Services: $32.8 Million (0.5%) 114,732 (7.8%) 40% Head and Neurologic $5.2 Million (15.9%) 30% Diagnostic/Preventive Diagnostic/Preventive 49,743 (3.4%) 20% $4.0 Million (12.2%) Cardiovascular 10% Cardiovascular $5.5 Million (16.8%) 9,007 (0.6%) 0% Pati atients ents Spen Sp endin ing
Results: Low-Value Services Accounting for Most Utilization and Spending Most Commonly Received Low-Value Services with Low-Value Services Greatest Spending T3 measurement in Imaging for Spinal injection for Imaging for hypothyroidism nonspecific low back lower-back pain uncomplicated pain headache • 1.5% • $12.1 million (37.0%) • 1.3% • $3.6 million (11.0%) Imaging for Imaging for uncomplicated nonspecific low back headache pain • 1.0% • $3.1 million (9.4%)
Regression Results: Older, Male, Non-White, Low-Income have Less Low-Value Spending $15 Low-Value Service Spending $10 otal Spending $5 $1.89 $0.08 $0 -$3.35** -$5 -$3.81** per $10k in T -$4.40* -$5.12** -$5.25** -$10 -$7.42** -$8.10** -$9.12** -$11.30** -$15 -$20 -$18.19** -$20.42** -$25 * p<0.05, **p<0.001
Regression Results: Selected Regions and CDHPs have Less Low-Value Spending $14.26** $15 $12.66** $11.92** $10.10** Low-Value Service Spending $10 $8.01** otal Spending $6.10* $5 $2.35 $2.30 $0.98 $0 -$5 -$2.73* -$3.19 -$4.60** per $10k in T -$5.86** -$10 -$15 -$20 -$25
Summary • In commercially-insured population, found • Modest use of low-value services assessed • Considerable corresponding savings • Compared to prior assessments of low-value services • Comparable care patterns • Similar regional variation • Younger, healthier population • Key groups have disproportionate low-value spending
Limitations • Cross-sectional analysis • Direct measures of low-value care can be: • Over-inclusive • Potentially capture instances where care was high-value • Minimized by using narrow, specific measure definitions • Under-inclusive • Set of measure can only address small fraction of all low-value care delivered and predictors of spending on other service may differ • Minimized by using a broad list of measures across multiple specialties, settings, and care types
Policy Implications • Higher-income and white patients have greater proportionate spending on low-value services • Could represent reverse disparities • Disparities contribute to waste in 2 ways, both warrant attention • CDHP enrollees less proportionate spending on low value services • Insurance benefit design could facilitate reduced overuse • However, finding should be weighed against potential for selection bias and parallel reductions in high-value care
Conclusions • Efforts to reduce waste in health care may be bolstered by • Measure development efforts that focus on overtreatment • Insurance benefit designs that discourage overuse • Programs targeting groups and regions at risk of low-value care
Thank you! rreid@rand.org Acknowledgements This work was funded by grants from the National Institutes of Health and from the Leonard D. Schaeffer RAND-USC Initiative in Health Policy and Economics.
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