Currently Viewing:
The American Journal of Managed Care October 2018
Putting the Pieces Together: EHR Communication and Diabetes Patient Outcomes
Marlon P. Mundt, PhD, and Larissa I. Zakletskaia, MA
Primary Care Physician Resource Use Changes Associated With Feedback Reports
Eva Chang, PhD, MPH; Diana S.M. Buist, PhD, MPH; Matt Handley, MD; Eric Johnson, MS; Sharon Fuller, BA; Roy Pardee, JD, MA; Gabrielle Gundersen, MPH; and Robert J. Reid, MD, PhD
From the Editorial Board: Bruce W. Sherman, MD
Bruce W. Sherman, MD
Recent Study on Site of Care Has Severe Limitations
Lucio N. Gordan, MD, and Debra Patt, MD
The Authors Respond and Stand Behind Their Findings
Yamini Kalidindi, MHA; Jeah Jung, PhD; and Roger Feldman, PhD
The Characteristics of Physician Practices Joining the Early ACOs: Looking Back to Look Forward
Stephen M. Shortell, PhD, MPH, MBA; Patricia P. Ramsay, MPH; Laurence C. Baker, PhD; Michael F. Pesko, PhD; and Lawrence P. Casalino, MD, PhD
Nudging Physicians and Patients With Autopend Clinical Decision Support to Improve Diabetes Management
Laura Panattoni, PhD; Albert Chan, MD, MS; Yan Yang, PhD; Cliff Olson, MBA; and Ming Tai-Seale, PhD, MPH
Currently Reading
Medicare Underpayment for Diabetes Prevention Program: Implications for DPP Suppliers
Amanda S. Parsons, MD; Varna Raman, MBA; Bronwyn Starr, MPH; Mark Zezza, PhD; and Colin D. Rehm, PhD
Medicare Savings From Conservative Management of Low Back Pain
Alan M. Garber, MD, PhD; Tej D. Azad, BA; Anjali Dixit, MD; Monica Farid, BS; Edward Sung, BS, BSE; Daniel Vail, BA; and Jay Bhattacharya, MD, PhD
CMS HCC Risk Scores and Home Health Patient Experience Measures
Hsueh-Fen Chen, PhD; J. Mick Tilford, PhD; Fei Wan, PhD; and Robert Schuldt, MA
An Early Warning Tool for Predicting at Admission the Discharge Disposition of a Hospitalized Patient
Nicholas Ballester, PhD; Pratik J. Parikh, PhD; Michael Donlin, MSN, ACNP-BC, FHM; Elizabeth K. May, MS; and Steven R. Simon, MD, MPH
Gatekeeping and Patterns of Outpatient Care Post Healthcare Reform
Michael L. Barnett, MD, MS; Zirui Song, MD, PhD; Asaf Bitton, MD, MPH; Sherri Rose, PhD; and Bruce E. Landon, MD, MBA, MSc

Medicare Underpayment for Diabetes Prevention Program: Implications for DPP Suppliers

Amanda S. Parsons, MD; Varna Raman, MBA; Bronwyn Starr, MPH; Mark Zezza, PhD; and Colin D. Rehm, PhD
The actual costs of implementing the evidence-based Diabetes Prevention Program (DPP) were compared with the latest reimbursement rates provided by CMS.

Overall, the costs of implementing DPP at MHS were $177,976, or $553 per participant attending 1 or more sessions in 2016. Figure 1B shows the breakdown of costs per participant. Twenty-eight percent of costs ($153/participant) were for direct instruction, whereas 24% were for telephonic outreach and orientation, as well as confirming patient eligibility ($133/participant). Filling a class with 15 to 20 participants requires placing about 30 in each class and reaching out to 60 to 70 eligible patients. The curriculum guide (5.1% of total costs), staff training and teaching guides (3.4%), and student incentives (1.7%) were modest costs. About 5.6% of the costs were for data management and reporting, and 17.4% were for direct supervision and staff management. An additional 15% of the costs were for other program coordination activities (eg, scheduling classes/rooms, sending materials to sites).

Based on our program experience, the average number of classes attended per patient was 6.4. Among all individuals, including those who attended only a single session and, therefore, had no follow-up weights, 14.3% of patients lost at least 5% of their initial weight. Fifty percent of participants attended 4 or more sessions and 33% attended 9 or more (Figure 2). Based on these attendance and weight loss outcomes, MHS would receive $34,625, or $108 per patient, which would cover 19.5% of costs.

Additional analyses modeled reimbursement assuming that our outcomes had been aligned with national outcomes presented by CDC.4 If MHS had achieved these outcomes, the reimbursement would be $61,270, or $190 per patient, covering 34.4% of costs.


Based on our program experience, Medicare reimbursement would cover just 19.5% of program costs. This shortfall is only partially explained by the relatively poorer outcomes of our program. Compared with other DPP implementations, our program had poorer attendance, driven mostly by a large number of drop-outs between sessions 1 and 2. A recent evaluation of NDPP data found that the median number of sessions attended was 14 and that 86.6% attended 4 or more sessions.4 Comparatively, MHS’ mean attendance was 6.4 sessions and the median was 4, due to a large number of drop-outs after the first class (among those attending ≥4 sessions, median attendance was 12 sessions). Half of the patients dropped out before class 4 compared with 13.4% nationally.

These differences in attendance outcomes may be driven by geographic and/or demographic differences in attendance and/or differences in program delivery. For instance, some programs hold a “class zero” orientation session prior to the first session, which reduces early drop-outs. Implications for poorer attendance in certain patient populations mean that DPP suppliers who serve them have to expend additional resources conducting outreach, sending reminders, and providing meaningful incentives. It is critical to note that the intensity and length of DPP may not be feasible for many patients, including those with inconsistent work schedules and complex family demands. Given that nationally only one-third of patients reached the 5% weight loss threshold across all of the implementations, it is imperative that CMS recognize this in its pay-for-performance methodology. Even if our program had outcomes identical to the national CDC-reported data, there would still be a considerable shortfall in revenue of about $363 per patient (34.4% of program costs would be covered).

Notably, in these national data, Hispanic and non-Hispanic black participants had lower median attendance (11 and 13 sessions, respectively) than non-Hispanic white participants (16 sessions).4 Furthermore, outcomes among Hispanic (median weight loss of 3.0%) and non-Hispanic black (2.6%) participants were worse than for non-Hispanic whites (4.4%) nationally. At MHS, more than 90% of DPP participants are Hispanic or non-Hispanic black, compared with 23.8% nationally.4 This major demographic difference may be an additional explanation for the poorer outcomes among MHS participants.

It is important to quantify the true costs of delivering programs like DPP, which sit outside of the traditional clinical encounter. Program costs do not decrease when patients do not attend classes. Instructors are still paid, the encounter is still documented, materials are still printed, and the program is still managed. Therefore, the CMS payment, which underpays for both attendance and performance, will be insufficient for most, if not all, DPP suppliers. Furthermore, given the strong correlation between attendance and weight loss, the payment structure is paying for the same outcome twice. Lastly, so few patients achieve 9% weight loss that this payment benchmark has little utility.

DPP program costs are significant and most are not variable, because activities such as program coordination, outreach, EHR documentation, instruction, and reporting all occur whether or not participants attend classes. Placement- and instruction-related costs account for just over half (52%) of costs. Another 38% of costs are for program coordination, staff management, and data reporting; 9% are for instruction materials for coaches and patients; and 2% are for patient incentives. Because of significant fixed/semivariable program costs, DPP is not well suited for reimbursement based on a threshold number of classes or largely skewed toward pay-for-performance for weight loss thresholds that exceed common experience. Rather, a baseline of fee-for-service reimbursement on a per class basis with a potential pay-for-performance bonus for each percentage point of weight loss would align payments with costs. This payment structure also acknowledges the diabetes risk reduction with weight loss less than the 5% threshold.9 For our program to break even at current levels of performance with regard to attendance and weight loss, we would need $88.71 per patient per session attended. If we achieved the national outcomes, break-even revenue would be $42.28 per session attended.


It is important to analyze, document, and disseminate the costs associated with DPP implementation to inform prospective suppliers and payers and to advocate for appropriate reimbursement. As healthcare systems wrestle with the relative value of prevention versus treatment, it is important for NDPP suppliers to quantify outcomes beyond weight loss. Additional value can be demonstrated by documenting reductions in glycated hemoglobin, blood pressure, and/or cholesterol and, more importantly, long-term outcomes like diabetes incidence. It is important for CMS to consider the real-world costs of program delivery when setting reimbursement rates.


The authors wish to thank Nicole Hollingsworth, Melinda Marquez, and Elizabeth Spurrell-Huss for contributing their input and expertise to the estimation of program costs.

Author Affiliations: Montefiore Health System (ASP, CDR), Bronx, NY; Department of Family & Social Medicine (ASP) and Department of Epidemiology & Population Health (CDR), Albert Einstein College of Medicine, Bronx, NY; Columbia Business School (VR), New York, NY; New York State Health Foundation (BS, MZ), New York, NY.

Source of Funding: None.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (ASP, VR, MZ, CDR); acquisition of data (VR, CDR); analysis and interpretation of data (VR, BS, MZ, CDR); drafting of the manuscript (ASP, BS, MZ, CDR); critical revision of the manuscript for important intellectual content (ASP, BS, CDR); statistical analysis (CDR); provision of patients or study materials (CDR); administrative, technical, or logistic support (ASP, CDR); and supervision (ASP, CDR).

Address Correspondence to: Colin D. Rehm, PhD, MPH, Office of Community & Population Health, Montefiore Health System, 3514 Dekalb Ave, Bronx, NY 10467. Email:

1. Knowler WC, Barrett-Connor E, Fowler SE, et al; Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393-403. doi: 10.1056/NEJMoa012512.

2. Ackermann RT, Finch EA, Brizendine E, Zhou H, Marrero DG. Translating the Diabetes Prevention Program into the community: the DEPLOY pilot study. Am J Prev Med. 2008;35(4):357-363. doi: 10.1016/j.amepre.2008.06.035.

3. Hernan WH, Brandle M, Zhang P, et al; Diabetes Prevention Program Research Group. Costs associated with the primary prevention of type 2 diabetes mellitus in the Diabetes Prevention Program. Diabetes Care. 2003;26(1):36-47. doi: 10.2337/diacare.26.1.36.

4. Ely EK, Gruss SM, Luman ET, et al. A national effort to prevent type 2 diabetes: participant-level evaluation of CDC’s National Diabetes Prevention Program. Diabetes Care. 2017;40(10):1331-1341. doi: 10.2337/dc16-2099.

5. National Diabetes Prevention Program: registry of all recognized organizations. CDC website. Accessed November 1, 2017.

6. CMS, HHS. Medicare program; revisions to payment policies under the physician fee schedule and other revisions to Part B for CY 2018; Medicare Shared Savings Program requirements; and Medicare Diabetes Prevention Program. Fed Regist. 2017;82(219):52976-53371.

7. Rehm CD, Marquez ME, Spurrell-Huss E, Hollingsworth N, Parsons AS. Lessons from launching the Diabetes Prevention Program in a large integrated health care delivery system: a case study. Popul Health Manag. 2017;20(4):262-270. doi: 10.1089/pop.2016.0109.

8. Chambers EC, Rehm CD, Correra J, et al. Factors in placement and enrollment of primary care patients in YMCA’s Diabetes Prevention Program, Bronx, New York, 2010-2015. Prev Chronic Dis. 2017;14:E28. doi: 10.5888/pcd14.160486.

9. Maruthur NM, Ma Y, Delahanty LM, et al; Diabetes Prevention Program Research Group. Early response to preventive strategies in the Diabetes Prevention Program. J Gen Intern Med. 2013;28(12):1629-1636. doi: 10.1007/s11606-013-2548-4.
Copyright AJMC 2006-2020 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
Welcome the the new and improved, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up