Public Reporting and Self-Regulation: Hospital Compare s Effect on Sameday Brain and Sinus Computed Tomography Utilization
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1 Public Reporting and Self-Regulation: Hospital Compare s Effect on Sameday Brain and Sinus Computed Tomography Utilization Darwyyn Deyo and Danny R. Hughes December 1, 2017
2 Motivation Widespread concerns over rising health care costs 1 : Year Total Expenditures (billions) Share of GDP % % Average annual growth rate : 4.06% How to curb the growth? 1. Martin et al. (2016). Health Affairs 35:
3 CMS Efforts to Reduce Costs Reductions in physician and facility payments Private payers usually follow CMS Encouraging managed care utilization Alternative payment models Accountable Care Organizations Episodic Bundled Payments Resource utilization as quality measure in PQRS/MIPS Regulation targeting potential over-utilization of specific procedures 3
4 Imaging Efficiency Metrics (IEM) Established by CMS under ACA Why? 2014 Part B Imaging Expenditures: $9.52 billion 1 Targeted 6 imaging procedures seen as low value: OP-8: MRI lumbar spine for low back pain OP-9: Mammography follow-up rates OP-10: Abdomen CT Double scans (with & without contrast) OP-11: Thorax CT Double scans OP-13: Cardiac stress tests before low risk outpatient surgery OP-14: Simultaneous Brain & Sinus CT 1 Neiman Almanac: 4
5 IEM: Public Reporting with Self Regulation Hospitals must report IEMs to CMS Applied to outpatient and emergency departments 2% penalty for failure to report Public reporting on CMS Hospital Compare Allow comparisons between facilities Self-regulation: Hospitals decide whether and how to change behavior No established benchmark for quality No penalties to hospitals for IEM results 5
6 Interesting Questions Why do hospitals care about IEM? No one knows the appropriate volume of these services No penalties regardless of results Reputation? If hospitals care about IEM, what can they do? Physicians not hospitals order tests Most hospitals have limited control over nonemployee physicians How effective is this kind of regulation? 6
7 Contributions First to examine public reporting without report cards : Scores are not monotonic functions of quality Examine effects of IEMs on imaging utilization 7
8 OP-14: Simultaneous Brain & Sinus CT Same patient undergoes same day brain & sinus CTs: Calculated by hospital and by state not by physician! Incorporates known exclusions for appropriate use Only applies to hospital outpatient and ED claims 8
9 Comparison of Same day and Brain only CTs 9
10 Brain CT Utilization by Place of Service 10
11 Game Theoretic Model Each individual in a population is assigned a rank, xε 0,1 s. t. 100 x % expects to be affected more severely by disease 1 : x 1, individuals are healthier x 0, individuals are sicker 1 Yaesoubi & Roberts (2011). J Health Econ 30: Pauker & Kassirer (1980). NEJM 302:
12 Empirical Analysis Examine the effectiveness of OP-14 on reducing same day brain and sinus CT Two empirical approaches: Aggregate ( Reduce B-S CT utilization? ) Interrupted time series (ITS) analysis Physician Decisions ( Can hospitals influence? ) Multivariate difference-in-difference (DID) logistic regression Both exploit variation between outpatient and office place of service 12
13 Data CMS 5% Research Identifiable Files (RIF) Covers 5% national sample of Medicare enrollees All Part A & Part B claims associated with sample ~ 1.8 million fee-for-service enrollees per year Spans (DID analysis covers ) Study cohort: All patients receiving a brain CT Excluded: Patients < 65 years old Patients meeting same day exclusion criteria (n=942) 13
14 ITS Analysis Segmented regression: Y = β 0 + β 1 Time + β 2 IEM + β 3 Time post IEM + ε time Where: Y = Rate of same day brain sinus CTs per month β 0 estimates baseline rate at beginning of study period β 1 estimates baseline trend (slope) before IEM β 2 estimates change in rate (level) after IEM β 3 estimates change in trend (slope) after IEM 14
15 ITS Results: Monthly Outpatient Utilization Variable Estimate P-value Intercept <.0001 (0.0002) Trend pre-iem <.0001 (0.0000) Rate change (IEM) (0.0003) Trend change (IEM) <.0001 (0.0000) 15
16 ITS Results: Outpatient vs Office Variable Estimate P-value Intercept (0.0009) Trend pre-iem <.0001 (0.0000) Rate change (IEM) <.0001 (0.0012) Trend change (IEM) (0.0000) 16
17 DID Logistic Regression Multivariate logistic regression on lengthy panel of Medicare claims data: Likelihood a brain CT patient also receives a sinus CT on the same day Difference-in-difference model Controls for patient demographics, risk, geographic variation in care Robustness checks with different treatment and control groups 17
18 DID Identification Strategy Sample: All patient claims for brain CT Y ist = Outpatient is β 1 + OP14 t β 2 + Interaction st β 3 + Zβ + u ist Dependent Variable: Patient received same day sinus CT (binary) Treatment Group: Outpatient facility patients Control group: Physician office patients Treatment: Implementation of OP-14 metric Appropriate control variables 18
19 Data: Control Variables ACA Indicator (June ) Beneficiary demographics Age Race Sex Prospective Charlson Comorbidity Index (required 2007 data) Provider state indicator: geographic variation State Leading Index 1 ( ): macro trends Year: volume growth Month: seasonality 1. Philadelphia Federal Reserve 19
20 Summary Statistics Variable Mean SD Min Max IEM Indicator (January 1, 2011) DiD Indicator Same Day Rate ACA (June 23, 2010) Charlson Comorbidity Index Beneficiary Age Beneficiary Race White Black Other Beneficiary Sex Male Female Leading Index
21 Stats: Treatment & Control Groups Place of Service Total Brain CT Scans Sameday (SD) Scans % SD Scans Total SD Growth (%) Avg. Annual Growth (%) Outpatient 1,255,325 50, Inpatient 799,333 11, ER 931,600 41, Office 137,268 3,
22 Comparison Between Treatment and Control Groups Variable Mean Outpatient Office [t-test] OP-14 Indicator (0.50) (0.00) (0.00) ACA Indicator (0.49) (0.00) (0.00) Charlson Comorbidity Index (1.64) (0.00) (0.00) Beneficiary Age (8.21) (0.01) (0.02) Beneficiary Race White (0.39) (0.00) (0.00) Black (0.27) (0.00) (0.00) Other (0.30) (0.00) (0.00) Beneficiary Sex Male (0.47) (0.00) (0.00) Female (0.47) (0.00) (0.00) Leading Index (1.81) (0.00) (0.00) Note: Standard errors are listed below mean values. 22
23 Logistic DID Model: Marginal Effects Full Sample Introduction of OP *** (0.001) Outpatient 0.011*** (0.001) DiD 0.006*** (0.001) ACA 0.002** (0.001) Leading Index (0.000) Year 0.001*** (0.000) N 1,281,687 Note: Robust standard errors are in parentheses. * P <.10; ** P <.05; *** P <
24 Logistic DID Model: Marginal Effects Full Sample Pre-OP14 High Pre-OP14 Low Introduction of OP *** *** (0.001) (0.002) (0.001) Outpatient 0.011*** 0.033*** *** (0.001) (0.001) (0.001) DiD 0.006*** *** (0.001) (0.002) (0.001) ACA 0.002** 0.003*** (0.001) (0.001) (0.001) Leading Index ** (0.000) (0.000) (0.000) Year 0.001*** 0.001*** (0.000) (0.000) (0.000) N 1,281, , ,377 Note: Robust standard errors are in parentheses. * P <.10; ** P <.05; *** P <
25 Robustness Tests: Inpatient Control Group Inpatients not subject to OP-14 measure More comparable sample size Secondary hypothesis: Test for independence in hospital department management Global vs. departmental utilization policies 25
26 Marginal Effects: Inpatient Control Group Full Sample Introduction of OP (0.001) Outpatient 0.030*** (0.000) DiD (0.001) ACA 0.002*** (0.001) Leading Index (0.000) Year 0.001*** (0.000) N 1,860,936 Note: Robust standard errors are in parentheses. * P <.10; ** P <.05; *** P <
27 Marginal Effects: Inpatient Control Group Full Sample Pre-OP14 High Pre-OP14 Low Introduction of OP (0.001) (0.001) (0.001) Outpatient 0.030*** 0.042*** 0.010*** (0.000) (0.001) (0.000) DiD *** 0.005*** (0.001) (0.001) (0.001) ACA 0.002*** 0.002*** (0.001) (0.001) (0.001) Leading Index (0.000) (0.000) (0.000) Year 0.001*** 0.001*** 0.000*** (0.000) (0.000) (0.000) N 1,860,936 1,358,428 1,495,146 Note: Robust standard errors are in parentheses. * P <.10; ** P <.05; *** P <
28 Other Robustness Tests False announcement of IEM Not a result of a time trend Smaller time windows (6 months, 1 year) Qualitatively similar results to original model of outpatient and office claims Emergency Departments Large growth regardless of treatment and control groups 28
29 Conclusions ITS: OP-14 associated with small but significant reduction in same day outpatient B-S CT Drop in level but rate unchanged Outpatient B-S CT increases after OP-14 Incentives matter: Driven by increases from low utilizers ~$138 million of additional imaging Find independent management of hospital inpatient & outpatient departments 29
30 Thank You! Contact: Darwyyn Deyo, PhD Web:
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