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Evaluating the Efficacy of a Bundled Chronic Condition Management Program
Sean Bowman, MPH; Mazi Rasulnia, PhD, MBA, MPH; Dhiren Patel, PharmD, CDE, BC-ADM, BCACP; David Masom, BSc; Margaret Belshé, BA; and William Wright, MBA, MPH
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Evaluating the Efficacy of a Bundled Chronic Condition Management Program

Sean Bowman, MPH; Mazi Rasulnia, PhD, MBA, MPH; Dhiren Patel, PharmD, CDE, BC-ADM, BCACP; David Masom, BSc; Margaret Belshé, BA; and William Wright, MBA, MPH
A chronic disease management company presents promising preliminary results from their remote intensive behavioral counseling intervention, aimed at addressing type 2 diabetes.
For all numerical measures, the mean and 95% confidence intervals (CIs) were calculated at baseline and at 12 weeks. For all measures described above as binary, a score of 1 or 0 was given to the participant, and the percentage scoring a 1 was presented. Then, all 12-week means were compared with baseline means using 1-sided t tests, not assuming equal variance, to discover or disprove statistically significant differences. One-sided t tests were used in place of 2-sided because all of our outcomes had an ideal direction of improvement (either up or down), except for prediabetes A1C, in which we wanted to either affect a decrease or affect the prevention of an increase. Weight change, although not necessarily directionally tied to diabetes management, was also measured directionally for a decrease because the average BMI of our participants was above 30. In the future, we would like to perform and report analyses of variance and covariance between our behavioral and clinical measures; however, for this preliminary report, we only wanted to examine the efficacy of the intervention on each measure individually. All analyses were conducted using Microsoft Excel and Google Spreadsheets.


Of 167 individuals consenting to participate in the intervention, 122 were retained and provided data on 1 or more secondary measures throughout the program (73.05% participant retention), with 101 providing baseline data on the primary measure (A1C). Of those 101, 56 self-reported the 12-weeks’ primary assessment (55.45% primary reporting retention). Participants were inconsistent in their responsiveness to secondary measures. Numerical measures are presented in Table 1 and management threshold measures are presented in Table 2. Where CIs passed the minimum or maximum value possible, they were presented as the minimum or maximum.


Intervention participants saw significantly improved behavioral and clinical measures. In the context of our model condition, A1C and diabetes management behaviors significantly improved, signifying better glycemic control. Although this decrease was seen mostly in participants with diabetes, we did not detect a significant change (2-sided t test P = .2282) in A1C for participants with prediabetes, signifying that the progression to diabetes may have been delayed or prevented. Also of note is that intervention participants saw a meaningful improvement in A1C, but not in weight. We believe this to be due to the fact that we did not focus on rapid weight loss, but rather on small and sustainable goals that would increase self-efficacy and reduce blood sugar, such as appropriate medication adherence and moderate physical activity. Longer-term iterations of this intervention may indeed yield weight loss, but that remains to be seen and was not a focus of our intervention.

For our secondary measures on individual aspects of diabetes management, results were quite favorable. We saw significant differences post intervention in the percent of participants meeting management thresholds for all related aspects except cost management understanding and ED use reduction. These results seem promising to our hypothesis that our intensive counseling intervention would produce positive behavioral and clinical results. Although we hypothesized that self-efficacy, medication adherence, diet, and physical activity were the arms of our intervention most correlated with clinical improvement, in a future formal study, we hope to test the covariance between all secondary values and A1C.

Limitations and Strengths

Our hope was that, in providing the participant with the authority to choose his or her time of contact and making the intervention 12 weeks (instead of the common 24-week intensive behavioral counseling intervention), we would see high engagement and retention throughout the intervention. However, health advisors were unable to systematically connect with participants at previously agreed-upon times and by previously agreed-upon means; thus, some participants consented with the physician to enroll, and then were unreachable by health advisors. In these cases, information was delivered and participant-reported measures were requested by mail, but response rates were low. Qualitatively, health advisors determined that common reasons for this were the lack of a static schedule and that baseline measures were collected during the same call in which the program was explained, resulting in sometimes long initial calls. Although we thought this would make the data-collecting process more personal, and we explained that ensuing calls would be much shorter, this could have been a factor in the loss to follow-up.

Another method to be improved on was that we did not ask participants to ensure they had up-to-date A1C scores preceding baseline and shortly following 12 weeks. This may have increased our sample size for the primary measure, and is now standard protocol for all interventions following this analysis. A final limitation was that we did not conduct follow-up assessments at 24 and 48 weeks (approximately 6 months and 12 months) to assess the sustainability hypothesis. Since this iteration of the intervention was completed, all of these changes have been implemented with the intent to strengthen the efficacy of the intervention and our power to measure that efficacy. 

Despite the aforementioned limitations, our feasibility study had several promising strengths. One is that, for the participants that did engage, we were able to provide extremely personalized counseling with multiple contact points and a conversational environment.  Participants routinely commented in an unsolicited fashion that they appreciated the accountability, the nonjudgmental setting in which to voice problems and brainstorm solutions, and the empathy and compassion of the health advisor, and they rated satisfaction with the program an average at 2.97 of 3 stars. Another advantage of the intervention was that it demonstrated successful outcomes for a meaningful percentage of participants. This is meaningfully different than fee-for-service reimbursements, such as the Chronic Care Management codes that focus on activity and process rather than effectively driving for better outcomes. Our cost per intervention per participant was approximately $300, which is 37.5% less than the annual cost of reimbursement for face-to-face chronic condition management programs sponsored by CMS.10 Furthermore, the clinicians received regular reports on participant outcomes, but put forth no extra time outside the participant’s normal visits. If we can replicate these results from remote counseling in a larger study group, we believe we can prove the scalability of remote counseling over face-to-face counseling in terms of clinician time spent and cost-effectiveness.


Our preliminary results from a remote intensive behavioral counseling intervention to improve diabetes management showed significant clinical and behavioral results at lower than usual financial cost while requiring less time on the part of the clinician. Although loss to follow-up and sustainability are being addressed in current iterations of the intervention, the preliminary results, in terms of clinical differences and scalability, are promising. A full-scale study comparing the intervention with usual care is underway.


The authors acknowledge with the utmost gratitude the following individuals/groups: esteemed colleague Hala Fawal, MBA, MPH, whose scrupulous work and keen perspectives were paramount to the development of this project; the health advisor team, including Barbara Schuler, MPH; M’Kayl Lewis, BS; Camilla Green, BS; Michael McMorris, MSW; Tamara Wilson, MS; Beth Vaughan, MPH; Tina Lu, MPH; Brass Bralley, MA; and Hazeza Kochi, MPH; the operations and development team, including Robert Ginter, MBA; Leigh Anne Gilbert, BA; Uma Srivastava, MS; and Maya Madden; all physician partners; and all the program participants who were partners with us in the pursuit of better health.

Author Affiliations: Pack Health, LLC (MB, SB, DP, MR, DM, WW), Birmingham, AL; MCPHS University, School of Pharmacy Practice (DP), Boston, MA.

Source of Funding: The implementation was supported in part by an unrestricted educational grant from Eli Lilly and Med-IQ.

Author Disclosures: Drs Patel and Rasulnia, Ms Belshe, Mr Bowman, Mr Masom, and Mr Wright are employees of Pack Health, LLC, which uses the evaluated model in their practice. Dr Patel is on the advisory board for Novo Nordisk and Sanofi, and has received lecture fees for speaking at the invitation of commercial sponsors (Novo Nordisk, Sanofi, and Merck).

Authorship Information: Concept and design (DP, MR, WW); acquisition of data (SB, DM, WW); analysis and interpretation of data (SB, DM, DP, WW); drafting of the manuscript (SB, MR, WW); critical revision of the manuscript for important intellectual content (SB, MB, DP, MR, WW); statistical analysis (SB); provision of study materials or patients (MR, WW); obtaining funding (MR, WW); administrative, technical, or logistic support (MB, DM, DP, WW); and supervision (DP, MR, WW). 

Send Correspondence to: Sean Bowman MPH, Pack Health, LLC, 3613 6th Ave S, Birmingham, AL, 35222. E-mail:

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