1. An Evaluation of Rhode Island’s CTC PCMH Program
Effect on Total Cost of Care & Utilization
Final Presentation - Results to Date
August 2, 2016

2. Agenda
Background
Objective
Methods
Results
Limitations
Next steps

3. Background
As of 2016, 14 states have active multi-payer PCMH initiatives
Recent evaluations of these initiatives have shown mixed results
Modest improvement in quality
Some reduced unnecessary utilization
Mixed effects on cost

4. Background
In 2008, RI launched the Care Transformation Collaborative (CTC) PCMH initiative
5 practices
By December 2014: 48 practices, >300,000 patients
Initial evaluation: 5 practices through 2010
Significant reduction in preventable ED visits
No effect on quality
Cost not assessed

5. Research Objective
To assess how the CTC program has affected total cost of care and utilization of services for CTC patients, compared to similar, non-CTC patients, across the 48 CTC practices from
Expands previous work by evaluating 43 practices that have since joined CTC and assessing effect on total cost of care

6. Methods

7. Data Sources
Total cost of care: Q – Q analytic files from claims data
Utilization: Q – Q analytic files from claims data
Claims data from plans:
Blue Cross Blue Shield
UnitedHealthcare
Neighborhood Health Plan
Medicaid FFS
Medicare FFS
Commercial
Medicaid managed care Medicare Advantage
- Plans include commercial, Medicaid managed care, Medicare Advantage, plus Medicaid and Medicare FFS
- Data reported at the individual level and analyzed at the person-quarter level

8. Study Population
Treatment group: patients attributed to CTC practice for entire study period
Control group: patients attributed to non-CTC practice for entire study period
Inclusion criteria: all covered lives who live in RI or who receive primary care services in RI
Exclusion criteria: non-continuous coverage in quarter before and after intervention; non-residents attributed to non-RI providers; patients in non-CTC PCMH practices; unattributed patients; patients switching between treatment and control groups; patients belonging to first five CTC practices
In utilization analyses only, additional exclusion criteria include utilization related to mental health, pregnancy, chemical dependency, and dental visits and events occurring less than 30 days after attribution.
More on attribution: Patients are attributed to the practice on a monthly basis . If a patient is assigned to a PCP, then the patient is assigned to the PCP’s practice. If there is no assigned PCP, the attributed PCP is derived by looking back 27 months and assigning the patient to the PCP with whom they had the most recent preventive visit. If there are no preventive visits, the PCP with the most eligible visits during the previous 27 months is attributed to the member for that month. If multiple PCPs have the same number of eligible visits, then the patient is attributed to the PCP seen latest during the 27 months. If more than one of those PCPs were seen on the most recent visit, the member is attributed to the one with the previous most recent eligible visit. If there are no eligible visits in the 27 months prior to the first day of the month, then the member is unattributed.

9. Treatment Groups Total Cost of Care Utilization Wave Pre-Period
Post-Period
# practices in CTC 1
1q2011-3q2012
4q2012-2q2014 3 2
1q2013-3q2013
4q2013-2q2014 32
Utilization
Wave
Pre-Period
Post-Period
# practices in CTC
1q2009-1q2010
2q2010-4q2014 8 1
1q2009-3q2012
4q2012-4q2014 3 2
1q2009-3q2013
4q2013-4q2014 32
Sample sizes vary by wave, by measure, and by method used (matching vs IPTW). Range from about 73k-236k individuals across both groups.
Readmission much lower.
Sample size varies by wave, by measure, and by method used (matching vs IPTW). Ranges from about 73k-236k unique individuals.

10. Study Population Characteristics Example: TCOC, wave 1 – Baseline Characteristics
variable
Unmatched
Matched
CTC
Control
Age (mean)
44.7
53.7
44.4
Female (%)
67%
57%
HCC score (mean)
2.28
3.15
2.29
2.23
Charlson score (mean)
0.35
0.60
Mean Income by Zip Code (%)
$4,599-26,150
39%
31%
$27,050-31,700
26%
24%
25%
$32,640-35,930
11%
15%
10%
$36,150-41,170
14%
$41,700-56,810 8% 9%
Out of state/other 2% 5%
Payer Type (%)
Commercial
43%
55%
44%
Dual eligible 7%
Medicaid FFS 0%
Medicare Advantage 6%
Medicare FFS
RHP 3%
RiteCare
29%
CSHCN 1%
Plan Type (%)
BCBS
32%
UHC
22%
NHP
34%
33%
13%
19%
all unmatched differences: p<0.01, all matched differences: p>0.10

11. Outcome Variables Total cost of care – price standardized, includes
Inpatient
Outpatient
Other professional
Prescription drug
Utilization
ED visits – all
ED visits - preventable
Inpatient admissions – all
Inpatient admissions – ACSC
30 day readmissions
TCOC: reported as mean patient cost per quarter. Price standardized total cost deflated by price adjustment factor and capped at $100k. The same prices are used for all payers and providers. Price standardized costs were developed using HealthPartners Total Cost of Care Methodology. 
Utilization: reported as mean count per 1000 patients per quarter
In utilization analyses only, exclude utilization related to mental health, pregnancy, chemical dependency, and dental visits and events occurring less than 30 days after attribution

12. Statistical Analysis, I
Outcomes calculated on quarterly basis, for each patient in the study population in quartern
Use difference-in-differences (DID) framework
Models are run in two ways:
DID with inverse probability of treatment (IPTW) weights, based on propensity score
DID with propensity matching

13. Effect of Intervention=A-B
Statistical Analysis, II: Difference-in-Differences Framework Hypothetical Example A
Before Intervention
After Intervention
Control group
CTC group
Effect of Intervention=A-B B
30 day readmissions

14. Statistical Analysis, III
Propensity score (PS): probability of being in a CTC practice given a vector of observable covariates. Mimics randomization.
Baseline covariates used in PS: age, sex, HCC score, Charlson index, payer type, plan type, median per capita income within zip code, pre-period outcome
IPTW: weight is equal to inverse probability of receiving the treatment that was actually received, stabilized to mean of 1 and trimmed to maximum weight of 10
Treatment group: IPTW=1/(PS)
Control group: IPTW=1/(1-PS)
Use full sample
e.g. Wave 1 ED, N=3,451,862 person quarters; Wave 2 ED, N=3,934,576 person quarters
Propensity matching: creates “matched” comparison group with similar distribution of characteristics as CTC group, uses 1:1 nearest neighbor matching with replacement and imposes caliper of <=0.0001
Use partial sample
e.g. Wave 1 ED, N=194,639 person quarters; Wave 2 ED, N=2,023,939
Matching: Using a logistic model, the propensity score is then calculated and used in the analytic model to create a “matched” comparison group that has a similar distribution of characteristics as the treatment group. Matching is done for individual i in pre-period, using nearest neighbor with replacement (in the area of common support), robust standard errors to account for replacement, and imposing a caliper to avoid poor matches. We validate the propensity-matched comparison cohort by checking for balance of covariates and outcome patterns using Wilcoxon-Mann-Whitney statistics. We also test for differences in quarterly trends for the matched groups.

15. Statistical Analysis, IV
Total cost of care
Standard two-part generalized estimating equation (GEE) models
Utilization
GEE models assuming negative binomial distribution with log link (count)
General model
Clustered at patient level
Robust SEs to account for weights
Model 1 applies IPTW
Model 2 uses matched sample
𝑂𝑢𝑡𝑐𝑜𝑚𝑒= β0 + β1*CTC Statusi,q + β2*Post Periodi,q + β3*(CTC Statusi,q*Post Periodi,q) + β4*Quarterq + β5*Member Monthsiq+ εiq
2 part model: breaks down the estimation into (1) the probability of having any cost and, (2) for those with costs greater than 0 costs, a model of the level of costs. The first part has a dependent variable of whether there is any cost greater than zero, where a binomial distribution with a logit link is used. The second part, where the dependent variable is the level of cost for those with greater than zero cost, uses gamma distribution with log link.

16. Results

17. Total Cost of Care

18. Total Cost of Care Difference-in- Differences, Wave 1
CTC
Control
DID
p-value
95% CI
Pre
Post
Difference
lower
upper
IPTW
Part 1
0.84
0.00
0.85
0.83
-0.02
1.14 (0.02)
1.08
1.20
Part 2
$1,617
$1,709
$92
$1,859
$1,993
$134
0.98 (-$42)
0.58
0.93
1.04
Matching
0.82
0.80
0.99 (0.00)
0.81
1.06
$1,626
$1,687
$61
$1,616
$1,688
$72
0.99 (-$11)
0.78
0.94
1.05
Pre and post estimates represent marginal effect at the mean
Note:
Part 1 represents the odds of having any quarterly cost, DID=Odds Ratio (absolute odds difference in parentheses)
Part 2 represents the mean quarterly costs of those with >$0 in costs, DID=Incident Rate Ratio (absolute $ difference in parentheses)

19. Odds of a patient having any cost – Wave 1 with IPTW
Total Cost of Care – IPTW, Wave 1
Odds of a patient having any cost – Wave 1 with IPTW
Note – rates are higher than actual because unattributed are excluded, and often patients are unattributed due to no utilization..
In CTC – more patients have costs (i.e. are using care) after implementation
Graph represents marginal effect at the mean for each quarter
2011, q1
2012, q1
2013, q1
2014, q1

20. Total Cost of Care – IPTW, Wave 1
Quarterly total cost/patient for patients with >$0 cost– Wave 1 with IPTW
Of those using some care, TOTAL costs do not change post CTC. This masks possible changes in cost for subcategories of cost…
Graph represents marginal effect at the mean for each quarter
2011, q1
2012, q1
2013, q1
2014, q1

21. Total Cost of Care Difference-in- Differences, Wave 2
CTC
Control
DID
p-value
95% CI
Pre
Post
Difference
lower
upper
IPTW
Part 1
0.86
0.84
-0.02
0.82
0.98(-0.01)
0.01
0.96
0.99
Part 2
$1,878
$1,969
$92
$1,915
$2,018
$102
1.00 (-$11)
0.68
0.97
1.02
Matching
0.85
0.98 (-0.01)
0.16
1.01
$1,839
$1,926
$87
$1,898
$2,019
$120
0.98(-$33)
0.18
Note:
Part 1 represents the odds of having any quarterly cost, DID=Odds Ratio
Part 2 represents the mean quarterly costs of those with >$0 in costs, DID=Rate Ratio

22. Odds of a patient having any cost – Wave 2 with IPTW
Total Cost of Care – IPTW, Wave 2
Odds of a patient having any cost – Wave 2 with IPTW
Note – rates are higher than actual because unattributed are excluded, and often patients are unattributed due to no utilization..
In CTC – SLIGHTLY fewer patients have costs after implementation
Graph represents marginal effect at the mean for each quarter
2013, q1
2013, q2
2013, q3
2013, q4
2014, q1
2014, q2

23. Total Cost of Care – IPTW, Wave 2
Quarterly total cost/patient for patients with >$0 cost– Wave 2 with IPTW
Of those using some care, TOTAL costs do not change post CTC. This masks possible changes in cost for subcategories of cost…
Graph represents marginal effect at the mean for each quarter
2013, q1
2013, q2
2013, q3
2013, q4
2014, q1
2014, q2

24. Utilization

25. Wave 0

26. All ED Visits Difference-in-Differences, Wave 0
Quarterly number of ED visits/1000 patients
CTC
Control
DID
p-value
95% CI
Pre
Post
Difference
lower
upper
IPTW
95.2
94.7
-0.5
90.1
96.2
6.1
-6.71
0.049
-13.40
-0.02
Matching
95.6
99.0
3.4
84.9
89.4
4.5
-2.68
0.358
-8.40
3.03
Note:
Model represents the mean # of ED visits/patient-quarter, DID=absolute difference (marginal effect)
In this wave, CTC is associated with a reduction in the # of ED visits
Lower rate translates into small overall effect, though, as baseline rates of ED visits are low – about 7 ED visits/1000 patients fewer
Pre and post estimates represent marginal effect at the mean

27. All ED Visits – IPTW, wave 0
Quarterly number of ED visits/1000 patients – Wave 0 with IPTW
Significant but small effect – CTC effect translates into less than 7 ED visits per 1000 patients on average
At the quarterly level, there is lots of variation – CIs are wide (not pictured due to crowded visualization..)
Graph represents marginal effect at the mean for each quarter
From model 𝑂𝑢𝑡𝑐𝑜𝑚𝑒 = β0 + β1*CTC Statusi,q + β2*Post Periodi,q + β3*(CTC Statusi,q* treat timei,q) + β4*Quarterq + β5*Member Monthsiq+ εiq
2009, q1
2010, q1
2011, q1
2012, q1
2013, q1
2014, q1

28. Preventable ED Visits Difference-in-Differences, Wave 0
Quarterly number of Preventable ED visits/1000 patients
CTC
Control
DID
p-value
95% CI
Pre
Post
Difference
lower
upper
IPTW
58.88
58.93
0.05
53.84
53.94
0.10
-3.56
0.098
-7.78
0.65
Matching
60.71
60.34
-0.37
55.33
52.61
-2.72
1.34
0.530
-2.85
5.53
Note:
Model represents the mean # of preventable ED visits/patient-quarter, DID=absolute difference (marginal effect)
In this wave, CTC is associated with a reduction in the # of ED visits
Lower rate translates into small overall effect, though, as baseline rates of ED visits are low – about 7 ED visits/1000 patients fewer
Pre and post estimates represent marginal effect at the mean

29. Preventable ED Visits – IPTW, wave 0
Quarterly number of Preventable ED visits/1000 patients – Wave 0 with IPTW
Significant but small effect – CTC effect translates into less than 7 ED visits per 1000 patients on average
At the quarterly level, there is lots of variation – CIs are wide (not pictured due to crowded visualization..)
Graph represents marginal effect at the mean for each quarter
From model 𝑂𝑢𝑡𝑐𝑜𝑚𝑒 = β0 + β1*CTC Statusi,q + β2*Post Periodi,q + β3*(CTC Statusi,q* treat timei,q) + β4*Quarterq + β5*Member Monthsiq+ εiq
2009, q1
2010, q1
2011, q1
2012, q1
2013, q1
2014, q1

30. All Inpatient Admissions Difference-in-Differences, Wave 0
Quarterly number of inpatient admissions/1000 patients
CTC
Control
DID
p-value
95% CI
Pre
Post
Difference
lower
upper
IPTW
44.4
53.4
8.9
44.7
56.4
11.7
-3.03
0.208
-7.75
1.68
Matching
38.2
49.3
11.1
32.3
47.4
15.1
-6.83
0.003
-11.32
-2.34
Note:
Model represents the mean # of inpatient admissions/patient-quarter, DID=absolute difference (marginal effect)
Pre and post estimates represent marginal effect at the mean

31. All Inpatient Admissions – IPTW, wave 0
Quarterly number of inpatient admissions/1000 patients – Wave 0 with IPTW
At the quarterly level, there is lots of variation – CIs are wide (not pictured due to crowded visualization..)
Graph represents marginal effect at the mean for each quarter
From model 𝑂𝑢𝑡𝑐𝑜𝑚𝑒 = β0 + β1*CTC Statusi,q + β2*Post Periodi,q + β3*(CTC Statusi,q* treat timei,q) + β4*Quarterq + β5*Member Monthsiq+ εiq
2009, q1
2010, q1
2011, q1
2012, q1
2013, q1
2014, q1

32. ACSC Inpatient Admissions Difference-in-Differences, Wave 0
Quarterly number of ACSC inpatient admissions/1000 patients
CTC
Control
DID
p-value
95% CI
Pre
Post
Difference
lower
upper
IPTW
2.0
2.5
0.4
2.1
2.6
0. 02
0.964
-0. 82
0. 86
Matching
1.4
2.3
0.9
1.8
2.7
0.11
0.836
-0.92
1.14
Note:
Model represents the mean # of inpatient admissions/patient-quarter, DID=absolute difference (marginal effect)
Pre and post estimates represent marginal effect at the mean

33. ACSC Inpatient Admissions – IPTW, wave 0
Quarterly number of ACSC inpatient admissions/1000 patients – Wave 0 with IPTW
At the quarterly level, there is lots of variation – CIs are wide (not pictured due to crowded visualization..)
Graph represents marginal effect at the mean for each quarter
From model 𝑂𝑢𝑡𝑐𝑜𝑚𝑒 = β0 + β1*CTC Statusi,q + β2*Post Periodi,q + β3*(CTC Statusi,q* treat timei,q) + β4*Quarterq + β5*Member Monthsiq+ εiq
2009, q1
2010, q1
2011, q1
2012, q1
2013, q1
2014, q1

34. 30 Day Readmissions Difference-in-Differences, Wave 0
Quarterly number of 30 Day Readmissions/1000 patients
CTC
Control
DID
p-value
95% CI
Pre
Post
Difference
lower
upper
IPTW
124.3
160.0
35.7
133.2
160.7
27.6
7.72
0.756
-40.90
56.33
Matching
97.4
141.8
44.3
73.2
130.2
57.0
-27.36
0.102
-60.16
5.45
Note:
Model represents the mean # of inpatient admissions/patient-quarter, DID=absolute difference (marginal effect)
Pre and post estimates represent marginal effect at the mean

35. 30 Day Readmissions – IPTW, wave 0
Quarterly number of 30 day readmissions/1000 patients – Wave 0 with IPTW
At the quarterly level, there is lots of variation – CIs are wide (not pictured due to crowded visualization..)
Graph represents marginal effect at the mean for each quarter
From model 𝑂𝑢𝑡𝑐𝑜𝑚𝑒 = β0 + β1*CTC Statusi,q + β2*Post Periodi,q + β3*(CTC Statusi,q* treat timei,q) + β4*Quarterq + β5*Member Monthsiq+ εiq
2009, q1
2010, q1
2011, q1
2012, q1
2013, q1
2014, q1

36. Wave 1

37. All ED Visits Difference-in-Differences, Wave 1
Quarterly number of ED visits/1000 patients
CTC
Control
DID
p-value
95% CI
Pre
Post
Difference
lower
upper
IPTW
59.18
64.17
4.98
70.30
76.93
6.62
-1.29
0.64
-6.68
4.11
Matching
81.08
82.84
1.76
93.65
108.38
14.73
-12.72
0.00
-18.39
-7.05
Note:
Model represents the mean # of ED visits/patient-quarter, DID= marginal difference in differences (absolute difference)
In this wave, CTC is associated with a reduction in the # of ED visits
Lower rate translates into small overall effect, though, as baseline rates of ED visits are low – about 7 ED visits/1000 patients fewer
Pre and post estimates represent marginal effect at the mean

38. Preventable ED Visits Difference-in-Differences, Wave 1
Quarterly number of Preventable ED visits/1000 patients
CTC
Control
DID
p-value
95% CI
Pre
Post
Difference
lower
upper
IPTW
34.98
37.61
2.63
41.52
44.45
2.93
-0.32
0.86
-3.75
3.11
Matching
51.53
51.34
-0.19
59.53
66.95
7.42
-3.59
0.01
-6.17
-1.01
Note:
Model represents the mean # of preventable ED visits/patient-quarter, DID= marginal difference in differences (absolute difference)
In this wave, CTC is associated with a reduction in the # of ED visits
Lower rate translates into small overall effect, though, as baseline rates of ED visits are low – about 7 ED visits/1000 patients fewer
Pre and post estimates represent marginal effect at the mean

39. Inpatient Admissions (all) Difference-in-Differences, Wave 1
Quarterly number of inpatient admissions/1000 patients
CTC
Control
DID
p-value
95% CI
Pre
Post
Difference
lower
upper
IPTW
17.01
21.87
4.87
25.64
32.72
7.08
0.12
0.95
-3.71
3.94
Matching
15.43
19.46
4.03
14.62
19.13
4.51
-0.86
0.49
-3.30
1.57
Note:
Model represents the mean # of inpatient admissions/patient-quarter, DID= marginal difference in differences (absolute difference)
In this wave, CTC is associated with a reduction in the # of ED visits
Lower rate translates into small overall effect, though, as baseline rates of ED visits are low – about 7 ED visits/1000 patients fewer
Pre and post estimates represent marginal effect at the mean

40. ACSC Admissions Difference-in-Differences, Wave 1
Quarterly number of inpatient admissions/1000 patients
CTC
Control
DID
p-value
95% CI
Pre
Post
Difference
lower
upper
IPTW
1.11
1.19
0.07
1.78
2.39
0.61
-0.38
0.44
-1.34
0.59
Matching
0.87
1.14
0.21
1.28
1.50
0.13
0.72
-0.57
0.83
Note:
Model represents the mean # of ACSC admissions/patient-quarter, DID= marginal difference in differences (absolute difference)
In this wave, CTC is associated with a reduction in the # of ED visits
Lower rate translates into small overall effect, though, as baseline rates of ED visits are low – about 7 ED visits/1000 patients fewer
Pre and post estimates represent marginal effect at the mean

41. 30 Day Readmissions Difference-in-Differences, Wave 1
Quarterly number of inpatient admissions/1000 patients
CTC
Control
DID
p-value
95% CI
Pre
Post
Difference
lower
upper
IPTW
78.28
142.88
64.60
99.64
133.99
34.34
38.26
0.31
-34.88
111.40
Matching
137.13
143.28
6.15
63.02
103.8
40.75
-24.91
0.18
-61.57
11.74
Note:
Model represents the mean # of 30 day readmissions/patient-quarter of those with an admission, DID= marginal difference in differences (absolute difference)
In this wave, CTC is associated with a reduction in the # of ED visits
Lower rate translates into small overall effect, though, as baseline rates of ED visits are low – about 7 ED visits/1000 patients fewer
Pre and post estimates represent marginal effect at the mean

42. Wave 2

43. All ED Visits Difference-in-Differences, Wave 2
Quarterly number of ED visits/1000 patients
CTC
Control
DID
p-value
95% CI
Pre
Post
Difference
lower
upper
IPTW
58.13
60.85
2.73
67.53
71.92
4.39
-1.50
0.11
-3.36
0.35
Matching
57.10
59.76
2.66
63.54
68.69
5.15
-2.40
0.00
-3.85
-0.95
Note:
Model represents the mean # of ED visits/patient-quarter, DID= marginal difference in differences (absolute difference)
In this wave, CTC is associated with a reduction in the # of ED visits
Lower rate translates into small overall effect, though, as baseline rates of ED visits are low – about 7 ED visits/1000 patients fewer
Pre and post estimates represent marginal effect at the mean

44. Preventable ED Visits Difference-in-Differences, Wave 2
Quarterly number of Preventable ED visits/1000 patients
CTC
Control
DID
p-value
95% CI
Pre
Post
Difference
lower
upper
IPTW
33.01
34.67
1.67
39.78
41.50
1.72
-0.02
0.970
-1.28
1.23
Matching
32.25
33.86
1.61
37.12
38.59
1.47
0.09
0.870
-1.02
1.21
Note:
Model represents the mean # of preventable ED visits/patient-quarter, DID= marginal difference in differences (absolute difference)
In this wave, CTC is associated with a reduction in the # of ED visits
Lower rate translates into small overall effect, though, as baseline rates of ED visits are low – about 7 ED visits/1000 patients fewer
Pre and post estimates represent marginal effect at the mean

45. Inpatient Admissions (all) Difference-in-Differences, Wave 2
Quarterly number of inpatient admissions/1000 patients
CTC
Control
DID
p-value
95% CI
Pre
Post
Difference
lower
upper
IPTW
23.61
28.94
5.33
24.56
29.90
5.34
0.07
0.901
-0.98
1.12
Matching
21.84
27.95
6.11
20.81
26.64
5.82
-1.74
0.000
-2.62
-0.86
Note:
Model represents the mean # of inpatient admissions/patient-quarter, DID = marginal difference in differences (absolute difference)
In this wave, CTC is associated with a reduction in the # of ED visits
Lower rate translates into small overall effect, though, as baseline rates of ED visits are low – about 7 ED visits/1000 patients fewer
Pre and post estimates represent marginal effect at the mean
DID for matched results is negative – differences in margins do not represent the effect of the model but are illustrative only for purposes of showing scale

46. ACSC Admissions Difference-in-Differences, Wave 2
Quarterly number of inpatient admissions/1000 patients
CTC
Control
DID
p-value
95% CI
Pre
Post
Difference
lower
upper
IPTW
1.62
2.00
0.38
1.65
2.01
0.37
-0.02
0.861
-0.27
0.23
Matching
1.28
0.34
1.45
1.87
0.42
0.02
0.889
-0.20
Note:
Model represents the mean # of ACSC admissions/patient-quarter, DID = marginal difference in differences (absolute difference)
In this wave, CTC is associated with a reduction in the # of ED visits
Lower rate translates into small overall effect, though, as baseline rates of ED visits are low – about 7 ED visits/1000 patients fewer
Pre and post estimates represent marginal effect at the mean

47. 30 Day Readmissions Difference-in-Differences, Wave 2
Quarterly number of inpatient admissions/1000 patients
CTC
Control
DID
p-value
95% CI
Pre
Post
Difference
lower
upper
IPTW
100.62
136.66
36.04
104.20
141.36
37.16
0.18
0.985
-18.68
19.03
Matching
98.35
133.84
35.49
88.60
122.76
34.17
-1.86
0.756
-13.59
9.88
Note:
Model represents the mean # of 30 day readmissions/patient-quarter of those with an admission, DID = marginal difference in differences (absolute difference)
In this wave, CTC is associated with a reduction in the # of ED visits
Lower rate translates into small overall effect, though, as baseline rates of ED visits are low – about 7 ED visits/1000 patients fewer
Pre and post estimates represent marginal effect at the mean

48. Discussion

49. Summary of Findings – Total Cost
Total Cost of Care Analyses
DID
IPTW
Matching
Wave 1
Part 1 (any cost)
increase 
Part 2 (level of cost for those w/ cost)
Wave 2
decrease 

50. Summary of Findings - Utilization
Utilization Analyses
DID
IPTW
Matching
Wave 0
ED visits (all)
Preventable ED visits
Inpatient Admissions (all)
ACSC Inpatient Admissions
30 Day Readmissions (all)
Wave 1
Wave 2

51. Summary of findings For the waves and time period examined:
No evidence of PCMH effect on total cost of care in the short term
Some evidence on reduced ED visits, preventable ED visits, and inpatient admissions
No effect found for ACSC inpatient admissions or 30 day readmissions

52. Limitations Non-CTC practices not identifiable in data
Cannot adjust for practice characteristics
Cannot conduct analyses at practice level or cluster within practice
Lack of pre-period data
No data for first 13 practices (cost)
No data for first 5 practices (utilization)
Lack of sufficient post-period data for Wave 2 cost analyses
Large number of unattributed patients (27% of person-quarters)
Intervention not randomized – participation bias
To address practice identifier issue, can create synthetic practices in next steps.
Also missing 2015 data, where bigger changes may be evident

53. Next Steps Complete analysis of survey results
Incorporate all results into final report
Sub-group analyses will take significant time because propensity scores will have to be re-calculated for each wave and each measure, given that the PS include plan and risk scores. Will focus on payer type and risk score – will include in report if ready