Clinical and Economic Impact of a

Pharmacist-Led Diabetes Collaborative

Drug Therapy Management Program in

a Medicaid ACO Setting

E. Biltaji1, C. McAdam Marx1, M. Yoo1,

B. Jennings1,2, K. Gunning1,3, J. Leiser3

1. Department of Pharmacotherapy, University of Utah College of Pharmacy;

2. Department of Pharmacy Services, University of Utah Hospitals and Clinics;

3. Department of Family and Preventive Medicine, University of Utah School of Medicine

Introduction

• In 2013, the University of Utah Heath Care

(UUHC) and Health Plan established a Medicaid

Accountable Care Organization (ACO).

• Effective management of chronic diseases

including type 2 diabetes mellitus (T2DM) is a

priority of this ACO.

CDTM: Overview

• A diabetes Collaborative Drug Therapy

Management (CDTM) 1 program is supported in

the UUHC Community Clinics

– Based on primary care provider’s referral

– Pharmacist working under collaborative practice

agreement with clinic physicians and advanced

practice clinicians

• Prescribe and modify diabetes medication therapy

• Order HbA1c and lipid monitoring tests

• Provide diabetes education

1. McAdam-Marx, C., Dahl A. et al. J Manag Care Pharm. Accepted

for Publication February 2, 2015.

CDTM: Previous Analyses

• CDTM has been shown to1

– Improve HbA1c by up to 2.0%

– No change in healthcare utilization for inpatient and

emergency services in the 12-month period after

index date than the prior 12 months

– Total medical charges (SD) were on average

$251(18,174) higher and inpatient charges (SD) were

on average $283 (12,336) lower, but the differences

were not significant.

1. McAdam-Marx, C., Dahl A. et al. J Manag Care Pharm. Accepted

for Publication February 2, 2015.

CDTM: Analysis Limitations

• Previous analysis used health system billing

data to estimate economic outcomes

– Does not reflect amount paid

– Does not include economic data for care delivered

outside UUHC

• It is also unknown if Medicaid patients achieve

the same CDTM outcomes, given barriers to

care and self-management.1

1.Call KT, McAlpine DD, et al. Medical care. 2014;52(8):720-7.

Objective

Thoroughly evaluate CDTM impact on economic &

clinical outcomes patients with T2DM

Pilot study in Medicaid patients to assess the feasibility of merging clinical & claims data

Data Source

UUHC

• EMR

• Administrative data

HealthyU

• Claims data

More comprehensive

use and cost data

Study Design

• Retrospective cohort study

– 1/1/2008 to 12/31/2012

CDTM vs. Comparison

HbA1c Charges Amt Paid

Eligibility Criteria

• Inclusion

– Age ≥18 years with

T2DM and HbA1c

≥7.0%

– Medicaid coverage

any time during the

observation period

– ≥1 HbA1c reading

90+ days after index

date

• Exclusion

– Diagnosis for type 1

diabetes or polycystic

ovary syndrome

(PCOS) without

diabetes diagnosis

Index date

Intervention patients:

• First CDTM visit

Comparison patients:

• HbA1c value 6+ months after first activity date during study period

Study Outcomes

• Mean change in HbA1c

– At baseline vs at 6-months follow-up

• Change in UUHC charges

– 6-months pre-index vs. 6 months post-index date

• Change in amount paid by Medicaid

– 6-months pre-index vs. 6 months post-index date

Patient Identification Flowchart: CDTM Group

Adults with T2DM in CDTM program 2008-2012 (N= 305)

Primary Study Cohort

(N=79)

HealthyU Subset (N=46)

Patient Identification Flowchart: Comparison Group

Adults with T2DM treated in a UUHS community clinic 2008-2012 (N=8,900)

Primary Study Cohort

(N=131)

HealthyU Subset (N=67)

Baseline Characteristics

Variable CDTM

N=79

Comparison

N=131 p-value

Age (mean, SD) 53.2 (12.9) 53.9 (13.0) 0.698

Male (n, %) 24 (30.4) 49 (37.4) 0.756

Diabetes Medications (n, %)

Metformin

Insulin

44 (55.7)

60 (76.0)

53 (40.5)

22 (16.8)

0.032

<0.001 HbA1C (mean, SD)

≥8.0% (n, %)

10.31 (1.70)

72 (91.1)

8.63 (1.76)

65(49.6)

<0.001

<0.001

HealthyU Subset (n, %) 46 (58.2) 67 (51.1) 0.319

Medications Prescribed vs. Dispensed

52%

33%

78%

15%

54%

30%

52%

15%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

CDTM (n=46) Comparison(n=67)

CDTM (n=46) Comparison(n=67)

Perc

en

t o

f P

ati

en

ts

Prescribed by UUHC With HealthyU Claim

Metformin

Insulin

Change in HbA1c

-2.04** (n=79) -2.19**

(n=72)

-0.9** (n=131)

-1.81** (n=65)

-3

-2.5

-2

-1.5

-1

-0.5

0

All Baseline HbA1c ≥8.0%

Ch

an

ge i

s H

bA

1C

CDTM Comparison

Paired t-test *p<0.05, **p≤0.001

p≤0.001

p=0.32

Mean Inpatient Admissions

0.16 (n=79)

0.15 (n=131)

0.09 (n=79)

0.09 (n=131)

0.43 (n=46)

0.76 (n=67)

1.76 (n=46)

1.73 (n=67)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

CDTM Comparison CDTM Comparison

EDW HealthyU

6 months pre-index

6 months post-index

Mean Emergency Department Visits

0.04 (n=79)

0.08 (n=131)

0.05 (n=79)

0.1 (n=131)

3.24 (n= 46)

1.31 (n=67)

3.3 (n= 46)

1.73 (n=67)

0

0.5

1

1.5

2

2.5

3

3.5

4

CDTM Comparison CDTM Comparison

EDW HealthyU

6 months pre-index 6 months post-index

Change in Mean Charges for Care Provided by UUHS

$4,494

$9,528

CDTM $4,392

$8,887

$6,661

Comparison -$2,867

-$4,000

-$2,000

$0

$2,000

$4,000

$6,000

$8,000

$10,000

CDTM (n=79) Comparison (n=131) Difference

6-months pre-index 6-months post-index

p=0.396 p=0.212

Paired t-tests

p=0.157

Change in Mean Paid Amount by Medicaid to all Providers

$2,509

$3,346

CDTM $822

$3,331

$5,227

Comparison $1,881

$0

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

CDTM (n=46) Comparison (n=67) Difference

6-months pre-index 6-months post-index

p=0.112

p=0.304

Paired t-tests

p=0.636

Study Conclusion

• Incorporating claims data provided much larger estimates of inpatient and ED use

• Comparing prescription orders to claims highlighted potential primary non-adherence to insulin

• CDTM program was not associated with greater improvement in clinical outcomes & cost trends vs usual care

Linking Database Conclusion

Benefits • Expanded cost &

utilization data

• Charges vs. amount paid

• Prescription orders vs claims

Limitations • Reduced “linkable”

cohort size

• Inability to control for all factors that could influence outcomes

Acknowledgment

• The authors thank Brian Oberg, MBA, for data management assistance

• At the time of the study, Dr. McAdam-Marx received funding from the National Cancer Institute (award no. KM1CA156723). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.