1. Informing Practice Redesign with the Patient Assessment of Chronic Illness Care in a Diabetes Collaborative
Daniel Mullin, PsyD
Department of Family Medicine and community health
University of Massachusetts Medical school
Courtney Jarvis, PHARMD
Department of pharmacy practice
MASSACHUSETTS COLLEGE OF PHARMACY AND HEALTH SCEINCES

2. Outline Brief review of Chronic Care Model
Review of Center for the Advancement of Primary Care (CAPC)
Limitations of collaborative without patient engagement outcomes
Introduction to Patient Assessment of Chronic Illness Care (PACIC)
Results of PACIC from a “Wave 2 Diabetes Collaborative”
Challenges and Implications for future use

4. Chronic Care Model (CCM)
The Institute of Medicine concluded that patient-centered collaborative care is essential to improving chronic illness care
CCM widely accepted as a framework for developing and implementing evidence-based activities to improve care for chronic illnesses
Applicable for a variety of chronic illnesses,including diabetes
Few instruments assess the level of CCM-congruent activities that patients receive.
To inform quality improvement programs, compare different health care settings, and evaluate intervention studies, assessment tools are necessary it to evaluate the delivery of CCM activities

5. CAPC Diabetes Learning Collaborative Goals
Improve the quality of care provided to diabetics through the provision of care that is
Evidence-based
Comprehensive
Patient-Centered
Team-Based
Coordinated
Improve the capacity to reliably provide the right care to every patient every time

6. CAPC Diabetes Learning Collaborative Rationale
High (and rising) prevalence of DM
High morbidity and mortality associated with DM
Clear evidence-based recommendations exist
Opportunities for improvement
P4P $$$
Excellent first step towards PCMH capabilities and skills

7. Diabetes Learning Collaborative Participants
Wave 1: September June 2009
5 Practices (3 Family Med, 2 Internal Med)
52 MDs/NPs
~1800 Patients with diabetes (types 1 and 2)

8. Review of Structure of the Collaborative
CAPC Diabetes Collaborative Team
Practice Improvement Facilitator
Practice Leadership Teams
Practice physicians, nurses, and staff
Patients and families

9. Learning Collaborative Components
Informed Patient
Activated Patient
Patient education in office by care team
Monthly newsletter (paper and electronic)
Web-based education resources
Group medical visits
Diabetes care supplies
Health behavior change counseling
Visit planner
Motivational interviewing
Group medical visits
Pedometers/walking kits

10. Learning Collaborative Components
Prepared Practice Team
Proactive Practice Team
Nursing responsibilities/checklist
Establish efficient workflows
Development of written protocols
DM specific visit type in EMR
Pre-visit lab appointments
Standing orders for labs
Facilitated documentation in EMR
Group medical visits
Registry development
Outreach to patients
Weekly meetings
Patient advisor

11. Wave 1 Clinical Measures
Process
Outcome
Immunization: Flu, Pneumococcal
A1C < 7
Eye/Dental Exams
Mean A1C
Testing: A1C, LDL, ACR
LDL < 100
Prescribing: ASA, Statin, ACE-I
Mean LDL
ACR < 30

12. A1c Measured Within Last 6 Months
For those patient’s who are coming in, we are doing well getting these labs ~100%
Green line tells us that some patients are not coming in –
Either call them and get them in
Consider whether they are truly a patient of the practice

13. Wave 1 Outcomes Baseline - 2009 Most recent - 2010 Mean (SD) 7.6 (1.9)
7.3 (1.5)
A1c < 7.0
662 (42.7%)
758 (48.9%)
7.5 > A1c ≥ 7.0
249 (16.1%)
277 (17.9%)
8.0 > A1c ≥ 7.5
170 (11.0%)
149 (9.6%)
9.0 > A1c≥ 8.0
176 (11.4%)
184 (11.9%)
10.0 > A1c ≥ 9.0
112 (7.2%)
77 (5.0%)
A1c ≥ 10.0
104 (6.7%)
Test not performed
6 (0.4%)
2 (0.1%)

14. “The adage in quality improvement that “what gets measured, gets done” is true in diabetes as well as its corollary— what is not measured does not get done. It is now time to expand the content of diabetes performance measures to include patient-reported psychosocial and behavioral measures. Inclusion of such measures would make several important contributions in addition to aligning measurement with current evidence and values.” Glasgow, R. Peeples, M., & Skovlund, S. (2008) Where is the patient in diabetes performance measures. Diabetes Care, 31(5)

15. Assessment of Chronic Illness Care (ACIC)
Is an assessment used to measure CCM
Completed by health care team members
Particularly useful for helping teams identify gaps and generate innovations.
Subject to clinician over reporting
Bonomi AE, Wagner EH, Glasgow RE, VonKorff M. Assessment of chronic illness care (ACIC): a practical tool to measure quality improvement. Health Serv Res 2002; 37(3):

16. Patient Assessment of Chronic Illness Care (PACIC)
Designed to provide a patient perspective on receipt of CCM- related chronic illness care
Collects patient reports of the extent to which they have received specific actions and care during the past 6 months
Emphasizes key elements of self-management support (eg, collaborative goal settings, problem-solving and follow- up) and planned, proactive, and population-based care

17. PACIC 20-item instrument
Respondents rate how often they experienced the content described in each item during the past 6 months
Each item was scored on a 5-point scale ranging from 1 (no or never) to 5 (yes or always)
Patients rate care received from their primary health care team for the chronic illness that they identified as most impacting their life
The written version takes 2–5 minutes to complete. Phone administration times highly variable but averaged 7–8 minutes.

18. PACIC
Glasgow RE, Wagner EH, Schaefer J, Mahoney LD,  Reid RJ, Greene SM. Development and Validation of the Patient Assessment of Chronic Illness Care (PACIC). Med Care  2005; 43(5):

19. PACIC Scoring
Each scale is scored by averaging of items completed within that scale
An overall PACIC is scored by averaging scores across all 20 items

20. PACIC Validation
Only slightly correlated with age, gender and number of chronic conditions
Unrelated to education
Correlated moderately with measures of primary care and activation
Demonstrated moderate test-retest reliability
Glasgow RE, Wagner EH, Schaefer J, Mahoney LD,  Reid RJ, Greene SM. Development and Validation of the Patient Assessment of Chronic Illness Care (PACIC). Med Care  2005; 43(5):

24. Respondents, by Practice
Pre
Post
Westboro Med-Peds 14 15
Westboro IM 40 48
Shrewsbury IM 36 27
Shrewsbury FM 4 19
Hahnemann IM 42 38
Family Medicine Associates 31 12
Plumley Village HS 16 2
Total
183
161

25. Respondent Characteristics
Pre
Post
Years with DM: Mean
10.05
10.87
Years with DM: Range
<1 - 42
<1 - 50
Age: Mean
61.23
61.65
Age: Range
26-90
27-89
Gender: Male (#, %)
95 (51.9)
96 (59.6%)
Gender: Female (#, %)
88 (48.1)
65 (40.4)
Smoker (#, %)
23 (12.6)
17 (10.6)
HTN (#, %)
117 (63.9)
104 (64.6)
Hypercholesterolemia (#, %)
81 (44.3)
86 (53.4)
CAD (#, %)
23 (14.3)
CKD (#, %)
14 (7.7)
7 (4.3)

26. Respondent Ethnicity Pre # (%) Post # (%) White 163 (89.1) 142 (88.2)
Hispanic/Latino
16 (8.7)
3 (1.9)
Black or African-American
5 (2.7)
6 (3.7)
Asian
8 (5.0)

27. Significance
T-tests did not find a statistically significant difference between pre and post performance of practices for any items, scales, or total score

28. Patient Activation Pre SD Post 1. Asked my ideas re: Tx 3.40 3.54
2. Given Tx choices
3.58
3.62
3. Asked about med. problems
3.92
3.96
Mean
3.63
1.11
3.69
1.10

29. Delivery System/Practice Design
Pre SD
Post
4. Given written To-Do list
3.43
3.48
5. Care was well-organized
4.42
4.40
6. Shown how my actions influence my condition
3.88
4.00
Mean
3.91
0.94
3.96
0.89

30. Goal Setting/Tailoring
Pre SD
Post
7. Asked about my goals
3.68
3.69
8. Helped to set specific goals: eating/exercise
3.72
3.88
9. Given copy of my Tx plan
3.03
3.28
10. Encouraged to go to a specific group/class
2.76
2.77
11. Asked questions re: my health habits
3.49
3.76
Mean
3.30
1.11
3.46
0.97

31. Problem Solving/Contextual
Pre SD
Post
12. My Dr. considered my values/traditions
3.92
4.03
13. Helped to make Tx plan that I could do
3.68
3.94
14. Helped to plan ahead
3.37
3.36
15. Asked how diabetes affects my life
3.24
3.28
Mean
3.55
1.15
3.65
0.98

32. Follow-up/Coordination
Pre SD
Post
16. Contacted after a visit
3.02
2.95
17. Encouraged to attend community programs
2.57
2.58
18. Referred to dietitian, health educator, or counselor
3.31
3.25
19. Told how my visits with other doctors helped my treatment
3.66
3.96
20. Asked how visits with other doctors were going
3.95
Mean
3.22
1.04
3.34
0.91

33. Summary Pre SD Post Total PACIC 3.44 0.96 3.57 0.87
Patient Activation (Items 1-3)
3.62
1.11
3.69
1.10
Delivery System Design (4-6)
3.91
0.94
3.96
0.89
Goal Setting (7-11)
3.30
3.46
0.98
Problem Solving/Contextual (12-15)
3.55
1.15
3.65
Follow-Up/Coordination (16-20)
3.22
1.04
3.34
0.91

34. Comparison with 2005 Validation Diabetes Sample
Umass-Pre
Umass- Post
2005
Total PACIC
3.44
3.57
2.83
Patient Activation (Items 1-3)
3.62
3.69
2.79
Delivery System Design (4-6)
3.91
3.96
3.21
Goal Setting (7-11)
3.30
3.46
2.57
Problem Solving/Contextual (12-15)
3.55
3.65
3.09
Follow-Up/Coordination (16-20)
3.22
3.34
2.65
Glasgow RE, Wagner EH, Schaefer J, Mahoney LD,  Reid RJ, Greene SM. Development and Validation of the Patient Assessment of Chronic Illness Care (PACIC). Med Care  2005; 43(5):

35. % of Patients who Know what A1C is
Pre
Post
% of respondents
76.0
83.0 N
167
141

36. % of Patients who Know what LDL is
Pre
Post
% of respondents
79.8
89.7 N
168
145

37. What’s next?
Currently starting another collaborative wave with 4 new practices
Improve collection of data in the pre-intervention period
Increase number of completed surveys in each practice, targeting a minimum of 50 from each practice

38. Ongoing challenges
Continuing tensions between researchers and QI specialists, ie. external validity vs. quickly facilitating practice redesign
PACIC data is not collected as part of routine care, like A1c and LDL
Also this data is not in the EMR

39. Ongoing challenges
No agreement on what constitutes clinically significant change on PACIC
Our data analyst was unprepared to manage this data
Collection of this data is time intensive relative to other measures