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Enhanced FFS Model and Patient Access: Evidence from FHG Model in Ontario Jasmin Kantarevic, Boris Kralj, Darrel Weinkauf Ontario Medical Association.

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Presentation on theme: "Enhanced FFS Model and Patient Access: Evidence from FHG Model in Ontario Jasmin Kantarevic, Boris Kralj, Darrel Weinkauf Ontario Medical Association."— Presentation transcript:

1 Enhanced FFS Model and Patient Access: Evidence from FHG Model in Ontario Jasmin Kantarevic, Boris Kralj, Darrel Weinkauf Ontario Medical Association

2 Outline 1. Patient Enrolment Models in Ontario 2. Comparison of FFS and FHG Models 3. Data and Empirical Framework 4. Results FHG physicians provide more services, visits, and see more patients than comparable FFS physicians. No adverse impact on referral rates or patient selection.

3 Primary Care Renewal in Ontario Started in early 2000s The focus is on how to pay family physicians Goals of new reform: Improved access Improved quality Lower cost Rejection of pure FFS, capitation, or salary Introduction of Patient Enrolment Models (PEM)

4 Patient Enrolment Models Base Payment (FFS, Capitation, or Salary) + Additional Elements:

5 Percent of Family Physicians in PEM,

6 Primary Care Physicians in Ontario, January 2010 Compensation ModelPhysicians % of Family Physicians Patient Enrolment Models (PEM) 1. Blended Capitation FHN - Family Health Network % FHO - Family Health Organization2, % 2. Enhanced FFS FHG - Family Health Group3, % CCM - Comprehensive Care Model % Fee-for-Service Model (FFS)3, %

7 In This Paper: Zoom in on Family Health Groups Introduced in 2003 Enhanced FFS model Most popular model for family physicians Usually the first stop from FFS to PEM Focus on access to physician services Services, visits, patients

8 Comparison of FFS and FHG Models FFS ModelFHG Model Organizational Elements Minimum Group Size1 3 Patient EnrolmentNoYes After-Hours RequirementNoYes Compensation Elements FFS Billings100% FFS PremiumsNo CC Premium No AH Premium 10% CC Premium 20% AH Premium Comprehensive Care FeeNoYes Incentives and Bonuses:NoYes Preventive Care Chronic Disease Unattached Patients Special Payments

9 Data Ontario Health Insurance Plan (OHIP) Claims Fiscal 1992/3 to 2008/9 Almost all family physicians in Ontario 11 years before and 5 years after introduction of FHG Lots of detail at the service level Payment/administrative data Minimal demographics (age, sex, postal code)

10 Empirical Strategy y log of outcome (services, visits, distinct patients) physician fixed effects year fixed effects physician-specific linear trend wtime-varying controls FHG=1 if FHG, = 0 if FFS Difference in difference estimate of FHG impact

11 Selecting Comparison Sample 1. Sample of all family physicians in Propensity to Ever Join FHG Covariates = age, gender, expected income gain, after-hour days, 14 geographic (LHIN) indicators 3. Selecting Comparison Sample Nearest neighbor matching Replacement Option 4. Follow this sample over period

12 Summary Statistics, 2002 Treatment (FHG) Group FFS Group Full Matched Number of Physicians5,2604,8511,734 Covariates: Average Age *46.4 Percent Male *0.66 Percent in Toronto Central Region *0.13 Expected Income Gain (C$)42,84418,222*42,629 Working Weekends and Holidays *32.1 Outcomes: Log of Annual Services *8.93 Log of Annual Visits *8.65 Log of Annual Distinct Patients *7.49

13 Common Trend Assumption: Log of annual services

14 Common Trend Assumption: Log of annual visits

15 Common Trend Assumption: Log of annual distinct patients

16 Initial Estimates Dependent Variable Specification Sample Size [Physicians] Log of Services Log of Visits Log of Patients OLS95, [6,938](0.0256)(0.0251)(0.0344) Fixed Effects95, [6,938](0.0219)(0.0212)(0.0237) Correlated Random Trend89, [6,929](0.0090) (0.0088)

17 Multiple Experiments Dependent Variable Sample Sample Size [Physicians] Log of Services Log of Visits Log of Patients 2003 Cohort44, [3,633](0.0192) (0.0188)(0.0177) 2004 Cohort39, [3,073](0.0213) (0.0204)(0.0196) 2005 Cohort39, [3,089] (0.0251)(0.0249)(0.0245)

18 Leads and Lags: Log of annual services

19 Leads and Lags: Log of annual visits

20 Leads and Lags: Log of annual distinct patients

21 Alternative Samples: Shadow Claims and Harmonized Physicians Dependent Variable Sample Sample Size [Physicians] Log of Services Log of VisitsLog of Patients Excluding Years with Shadow Claims 86,362 [6,865] (0.0092) (0.0091) (0.0086) Excluding Switchers to Harmonized Models 63,910 [4,659] (0.0115) (0.0114) (0.0110)

22 Alternative Samples: Income Restrictions Dependent Variable Sample Sample Size [Physicians] Log of Services Log of VisitsLog of Patients No Restriction 92,748 [6,981] (0.0127) (0.0129) (0.0120) > C$10,000 91,512 [6,964] (0.0101) (0.0101) (0.0095) > C$50,000 87,818 [6,879] (0.0084) (0.0083) (0.0083) > C$100,00080,140 [6,652] (0.0078) (0.0076) (0.0076)

23 Impact by Age and Gender Dependent Variable Sample Sample Size [Physicians] Log of Services Log of VisitsLog of Patients Males61, [4,608](0.0107) (0.0105) Females27, [2,321](0.0163)(0.0164)(0.0155) Age in 2002: < 4124, [2,324](0.0191)(0.0190)(0.0181) Age in 2002: 41 to 5128, [2,093](0.0134) (0.0133) Age in 2002: > 5136, [2,512](0.0123)(0.0121)(0.0122)

24 Impact by Location Dependent Variable Sample Sample Size [Physicians] Log of Services Log of VisitsLog of Patients South East Ontario 19, [1,577](0.0160)(0.0159)(0.0173) Central Ontario 37, [2,792](0.0128)(0.0125)(0.0115) South West Ontario 24, [1,898](0.0205)(0.0204) Northern Ontario 7, [682](0.0342)(0.0349)(0.0329)

25 Decomposition of Impact on Services Dependent Variable Coefficient on FHG Indicator Bootstrap Standard Error Log of Services (0.0090) Log of Services per Day (0.0046) Log of Annual Days (0.0067)

26 Impact on Referrals and Complexity Dependent Variable Coefficient on FHG Indicator Bootstrap Standard Error Log of Referrals per Service (0.0086) Log of Referrals per Visit (0.0081) Log of Referrals per Patient (0.0092) Log of Complexity Modifier0.0278(0.0019)

27 Implications How we pay physicians may affect patient access FHG a promising alternative to traditional FFS model Access important in many jurisdictions: Aging physician population Increasing number of female physicians Changing preferences

28 Future Research Impact of FHG incentives on cost and quality Study of entire spectrum of PEM models Determinants of transition Impact of transition on physician behaviour


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