Strategies to Reduce Non-emergent ER Use: Experience of an Employer Group in Northern VA

Transcription

1 Strategies to Reduce Non-emergent ER Use: Experience of an Employer Group in Northern VA Chia-hsuan Li Chun-lan Chang Manish Oza Heather Holleque Andrea DeVries 1

2 Introduction HealthCore is an independent research subsidiary of WellPoint, Inc. WellPoint is a parent company for 14 BCBS plans The work we are presenting today was carried out for Anthem BCBS of Virginia and funded by WellPoint Slide 2

3 Background Published literature 1 and previous internal analyses 2 within HealthCore found that 14%-27% of emergency room (ER) visits could be managed by community physicians or alternative care sites such as urgent care centers or retail health clinics Over the past 2 years, Anthem BCBS of Virginia has implemented an ER Utilization Management Initiative program (ERUMI) with the Prince William County employer group in northern VA 1. Weinick RM, Burns RM, Mehrotra A. Many emergency department visits could be managed at urgent care centers and retail clinics. Health Aff (Millwood.) 2010;29: Retail clinics offer cost-effective care for allergies, study says. URL: Dec 2010 accessed. Slide 3

4 Components of the Intervention Components directed across the employer group: ER copay increased from $100 to $200 Educational brochures regarding ER alternatives mailed out messaging to employees Members had access to information via Anthem BCBS nurse hotline, website, and a Google Map finder for ER alternatives Components specific to members with potential non-emergent ER visits Identified by claims data, using a list of 535 ICD-9 diagnosis codes Members received phone call follow-up Additional educational mailings were also sent to these members Slide 4

5 Study Goals Determine whether the ERUMI program launched within the employer group in northern VA can reduce the rate of nonemergent ER visits, as compared to a matched control group with the same geographic access to services Examine site of service choice for non-emergent conditions Slide 5

6 Methods Slide 6

7 Definition of Terms Site of Service: ER Emergency departments/rooms RHC Retail health clinics UCC Urgent care centers WI Walk-in clinics or physician offices offering after-hour services, listed on education brochure Categories of Care PT Program trigger conditions; Corresponds to the 535 ICD-9 diagnoses that triggered the member reinforcement education mailings RHC treatable A more narrow list of conditions that can be treated by RHC e.g. acute upper respiratory infections, urinary tract infection, conjunctivitis. A subset of PT list Slide 7

8 Study Design Retrospective claims-based study of claims for ER and ER alternatives Time Frame: Jan Jun 2009 (prior to program initiation) vs. Jan Jun 2010 Fall of 2009 had distorted utilization due to swine flu occurring during that time Study cohort description Cases: 14,224 Prince William County members living in northern VA Controls: 42,672 propensity score matched other northern VA members Age in categories Female Eligibility in months in 2009 Deyo-Charlson comorbidity index score (DCI) in 2009 Outcomes of interest: Utilization rate/1000 for ER and ER alternatives, categorized by diagnosis All cause, PT, and RHC treatable conditions ER alternatives include RHC, UCC, and WI Site of service for visits, categorized by diagnosis PT and RHC treatable conditions Slide

9 Statistical Approach Annualized utilization rate per 1,000 members for ER and ER alternatives Descriptive analyses Difference In Difference (DID) model ER utilization (Y/N)= β 0 + β 1 (case_grp) + β 2 (time) + β 3 (case_grp)*(post-time) + ε Generalized Estimating Equation Proc Genmod, dist=binomial, link=logit, repeated statement Trend analyses Applied trend observed in matched controls to cases Calculated the % of decrease/increase over the matched control trend Site of service for PT/ RHC treatable conditions Descriptive analyses Multinomial logistic model Site of service in 2010= β 0 + β 1 (case_grp) + β 2 (age) + β 3 (gender)+ β 4 (dci in 2010)+ ε Members go to a single site for PT or RHC treatable conditions in 2010 Slide

10 Results Slide 10

11 Population Characteristics 1:3 PS Matched Control Case Group Group N % N % P Value Number of members 14,224 42,672 Age* under 18 2, , , , , , >= , Female* 8, , Duration of eligibility in months in 2009* >= 4 13, , Duration of eligibility in months in < , >= 4 14, , Deyo-Charlson comorbidity index score in 2009* 0 12, , , , >= Deyo-Charlson comorbidity index score in , , < , , >= Slide 11

12 Results: Annualized ER Utilization Rate ER utilization Jan- Jun 2009 Jan- Jun 2010 Change % Change Relative Change* Statistical Significance** All cause Case % -2.66% Matched N VA % Program trigger Case % -1.45% Matched N VA % RHC treatable Case % % Matched N VA % * % difference between actual value and expected value given matched N VA trend ** Based on difference-in-difference analyses Slide 12

13 Results: Use of ER and ER Alternatives for RHC Treatable Conditions Case: Jan-Jun, 2009 ER UCC WI RHC 11% 15% Matched N VA: Jan-Jun, 2009 ER UCC WI RHC 6% 19% 20% 39% 35% 55% Case: Jan-Jun, 2010 ER UCC WI RHC 24% 28% 39% 9% Matched N VA: Jan-Jun, 2010 ER UCC WI RHC 9% 16% 17% 58% Note: Shows site of service mix for RHC treatable conditions outside regular doctor offices Slide 13

14 Results: Site of Service Choice for RHC Treatable Conditions Modeling likelihood of selecting ER alternatives vs. ER for RHC treatable conditions Case group was more likely to chose RHC or WI over ER in 2010, controlling for covariates (OR=4.7 and 2.7) RHC treatable conditions Case_grp Odd ratio estimate 95% CI RHC vs. ER UCC vs. ER WI vs. ER Slide 14

15 Conclusions Comparing the same period 2009 vs we saw a decrease in rate/1000 for ER visits for case group relative to the propensity score matched cohorts Other internal analysis showed a similar pattern Did not reach statistical significance may be due to sample size Case group was more likely to select ER alternatives vs. ER compared to matched cohort in 2010 We saw significant increase in RHC use as a proportion of care in first half of 2010 Difference from control did reach statistical significance The study suggests that education and financial incentives can shift member behavior in selecting site of service Slide 15

16 Limitations and Potential for Further Research Impact of copay change vs. educational outreach (and potential interaction between these factors) 6 months is a relatively short term follow-up; may need longer follow-up for full impact especially for members getting the targeted mailings Further evaluation of similar interventions across additional WellPoint health plans are planned to evaluate effectiveness Slide 16

17 Comments and Questions Slide 17

18 Appendix 1: RHC Treatable Conditions ICD-9 Diagnosis codes Infection type Sinusitis 461.xx Acute 473.xx Chronic Acute Pharyngitis 462.xx Acute Pharyngitis 463.xx Acute Tonsillitis 034.0x Streptococcal sore throat Otitis media and Other ear disorders 381.xx Otitis media, non-suppurative 382.xx Otitis media, suppurative and NOS 380.xx External ear disorders 388.xx Ear disorders NOS Acute and chronic bronchitis 466.xx Acute bronchitis 490.xx Bronchitis NOS 491.xx Chronic bronchitis General URI and Influenza/Viral infections 460.xx Acute nasopharyngitis (common cold 465.xx Acute URI NOS 487.xx Influenza 079.xx Viral infection Urinary tract infection 590.xx Pyelonephritis 595.xx Cystitis 599.0x Urinary tract infection Conjunctivitis (eye) 372.0x Acute conjunctivitis 372.1x Chronic conjunctivitis 372.2x Blepharoconjunctivitis 372.3x Conjunctivitis NOS 077.xx Other conjunctiva diseases 373.xx inflammation of eyelids Slide 18

19 Appendix 2: Population Characteristics Prior to PS Matching Case Group Control Group N % N % P Value Number of members 18,909/ 16, ,043/ 221,763 Age under 18 3, , < , , , , >= , Female 9, , <.0001 Duration of eligibility in months in , < , >= 4 18, , Duration of eligibility in months in , < , >= 4 15, , Deyo-Charlson comorbidity index score in , , < , , >= Deyo-Charlson comorbidity index score in , , < , , >= , Slide 19

20 Appendix 3: Use of ER and ER Alternatives for Program Trigger Conditions Case : Jan-Jun, 2009 ER UCC WI Matched N VA: Jan-Jun, 2009 ER UCC WI 16% 32% 38% 41% 43% 30% Case: Jan-Jun, 2010 ER UCC WI Matched N VA : Jan-Jun, 2010 ER UCC WI 15% 29% 33% 38% 43% 42% Note: Shows site of service mix for Program trigger conditions outside regular doctor offices Slide 20

21 Appendix 4: Site of Service Choice for Program Trigger Conditions Modeling likelihood of choosing ER alternatives vs. ER for program trigger conditions Case group was more likely to chose WI over ER in 2010, controlling for covariates (OR=2.7) PT conditions Case_grp Odd ratio estimate 95% CI UCC vs. ER WI vs. ER Slide 21