Enhanced FFS Model and Patient Access: Evidence from FHG Model in Ontario

Jasmin Kantarevic, Boris Kralj, Darrel WeinkaufOntario Medical Association

Outline1. Patient Enrolment Models in Ontario2. Comparison of FFS and FHG Models3. Data and Empirical Framework4. Results

FHG physicians provide more services, visits, and see more patients than comparable FFS physicians.

No adverse impact on referral rates or patient selection.

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)

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

Percent of Family Physicians in PEM, 1999-2009

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 20090%

10%

20%

30%

40%

50%

60%

70%

80%

Primary Care Physicians in Ontario, January 2010

Compensation Model Physicians % of Family Physicians

Patient Enrolment Models (PEM)

1. Blended Capitation FHN - Family Health Network 430 3.8% FHO - Family Health Organization 2,839 25.0%

2. Enhanced FFS FHG - Family Health Group 3,414 30.1% CCM - Comprehensive Care Model 318 2.8%

Fee-for-Service Model (FFS) 3,796 33.5%

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

Comparison of FFS and FHG Models

FFS Model FHG ModelOrganizational Elements Minimum Group Size 1 ≥ 3 Patient Enrolment No Yes After-Hours Requirement No Yes

Compensation Elements FFS Billings 100% 100%

FFS Premiums No CC PremiumNo AH Premium

10% CC Premium20% AH Premium

Comprehensive Care Fee No Yes Incentives and Bonuses: No Yes Preventive Care Chronic Disease Unattached Patients Special Payments

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)

Empirical Strategy

y ititititiit uFHGwty '

y log of outcome (services, visits, distinct patients) physician fixed effects year fixed effects physician-specific linear trendw time-varying controlsFHG =1 if FHG, = 0 if FFS Difference in difference estimate of FHG impact

Selecting Comparison Sample1. Sample of all family physicians in 20022. 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 1992-2008 period

Summary Statistics, 2002Treatment

(FHG) Group

FFS Group

  Full Matched

Number of Physicians 5,260 4,851 1,734

Covariates: Average Age 46.5 50.3* 46.4 Percent Male 0.65 0.70* 0.66 Percent in Toronto Central Region 0.12 0.18* 0.13 Expected Income Gain (C$) 42,844 18,222* 42,629 Working Weekends and Holidays 31.6 21.0* 32.1

Outcomes: Log of Annual Services 8.92 7.83* 8.93 Log of Annual Visits 8.65 7.33* 8.65 Log of Annual Distinct Patients 7.49 6.37* 7.49

Common Trend Assumption: Log of annual services

.5

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1992 1994 1996 1998 2000 2002 2004 2006 2008

Treatment Comparison

Common Trend Assumption: Log of annual visits

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1992 1994 1996 1998 2000 2002 2004 2006 2008

Treatment Comparison

Common Trend Assumption: Log of annual distinct patients

.5

1

1.5

2

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7.3

7.4

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7.6

7.7

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1992 1994 1996 1998 2000 2002 2004 2006 2008

Treatment Comparison

Initial EstimatesDependent Variable

Specification Sample Size[Physicians]

Log of Services

Log of Visits

Log of Patients

OLS 95,890 0.1107 0.1012 0.0911[6,938] (0.0256) (0.0251) (0.0344)

Fixed Effects 95,890 0.1374 0.1209 0.1167[6,938] (0.0219) (0.0212) (0.0237)

Correlated Random Trend 89,741 0.0936 0.0682 0.0610[6,929] (0.0090) (0.0090) (0.0088)

Multiple “Experiments”Dependent Variable

SampleSample Size[Physicians]

Log of Services Log of Visits Log of

Patients

2003 Cohort 44,194 0.0875 0.0793 0.0635[3,633] (0.0192) (0.0188) (0.0177)

2004 Cohort 39,002 0.0857 0.0770 0.0777[3,073] (0.0213) (0.0204) (0.0196)

2005 Cohort 39,721 0.1799 0.1173 0.1036[3,089] (0.0251) (0.0249) (0.0245)

Leads and Lags:Log of annual services

-.05

0

.05

.1

.15

.2

t<-5 t-5 t-4 t-3 t-2 t-1 Switch t+1 t+2 t+3 t+4 t+5

Leads and Lags:Log of annual visits

-.05

0

.05

.1

.15

.2

t<-5 t-5 t-4 t-3 t-2 t-1 Switch t+1 t+2 t+3 t+4 t+5

Leads and Lags:Log of annual distinct patients

-.05

0

.05

.1

.15

.2

t<-5 t-5 t-4 t-3 t-2 t-1 Switch t+1 t+2 t+3 t+4 t+5

Alternative Samples:Shadow Claims and Harmonized Physicians

Dependent Variable

SampleSample Size[Physicians]

Log of Services Log of Visits Log of Patients

Excluding Years with Shadow Claims

86,362[6,865]

0.0892(0.0092)

0.0613(0.0091)

0.0568(0.0086)

Excluding Switchers to Harmonized Models

63,910[4,659]

0.1099(0.0115)

0.0789(0.0114)

0.0704(0.0110)

Alternative Samples:Income Restrictions

Dependent Variable

SampleSample Size[Physicians] Log of

ServicesLog of Visits Log of Patients

No Restriction  92,748[6,981]

0.1400(0.0127)

0.1158(0.0129) 

0.1014(0.0120)

> C$10,000 91,512[6,964]

0.1124(0.0101)

0.0873(0.0101)

0.0753(0.0095)

> C$50,000 87,818[6,879]

0.0823(0.0084)

0.0566(0.0083)

0.0541(0.0083)

> C$100,000 80,140[6,652]

0.0699(0.0078)

0.0441(0.0076)

0.0434(0.0076)

Impact by Age and GenderDependent Variable

SampleSample Size[Physicians] Log of

ServicesLog of Visits Log of Patients

Males 61,985 0.0872 0.0621 0.0575[4,608] (0.0107) (0.0107) (0.0105)

Females 27,756 0.1068 0.0802 0.0679[2,321] (0.0163) (0.0164) (0.0155)

Age in 2002: < 41 24,513 0.0990 0.0776 0.0684[2,324] (0.0191) (0.0190) (0.0181)

Age in 2002: 41 to 51 28,952 0.0842 0.0632 0.0558[2,093] (0.0134) (0.0134) (0.0133)

Age in 2002: > 51 36,276 0.0869 0.0539 0.0502[2,512] (0.0123) (0.0121) (0.0122)

Impact by LocationDependent Variable

SampleSample Size[Physicians] Log of

ServicesLog of Visits Log of Patients

South East Ontario 19,772 0.0630 0.0542 0.0504[1,577] (0.0160) (0.0159) (0.0173)

Central Ontario 37,502 0.1124 0.0703 0.0580[2,792] (0.0128) (0.0125) (0.0115)

South West Ontario 24,668 0.1007 0.0799 0.0729[1,898] (0.0205) (0.0204) (0.0204)

Northern Ontario 7,799 0.0672 0.0614 0.0729[682] (0.0342) (0.0349) (0.0329)

Decomposition of Impact on Services

Dependent VariableCoefficient

on FHG Indicator

Bootstrap Standard

Error

Log of Services 0.0936 (0.0090)

Log of Services per Day 0.0498 (0.0046)

Log of Annual Days 0.0448 (0.0067)

Impact on Referrals and Complexity

Dependent Variable Coefficient on FHG Indicator

Bootstrap Standard Error

Log of Referrals per Service -0.0421 (0.0086)

Log of Referrals per Visit -0.0169 (0.0081)

Log of Referrals per Patient -0.0098 (0.0092)

Log of Complexity Modifier 0.0278 (0.0019)

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

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