Currently Viewing:
The American Journal of Managed Care April 2016
Single- Versus Multiple-Tablet HIV Regimens: Adherence and Hospitalization Risk
S. Scott Sutton, PharmD; James W. Hardin, PhD; Thomas J. Bramley, RPh, PhD; Anna O. D'Souza, BPharm, PhD; and Charles L. Bennett, MD, PhD, MPP
Medicaid Managed Care Reduces Readmissions for Youths With Type 1 Diabetes
Kathleen Healy-Collier, CSSBB, DHA; Walter J. Jones, PhD; James E. Shmerling, DHA, FACHE; Kenneth R. Robertson, MD, MBA; and Robert J. Ferry, Jr, MD, FAAP
Assessing the Impact of an Integrated Care System on the Healthcare Expenditures of Children With Special Healthcare Needs
Mircea I. Marcu, PhD; Caprice A. Knapp, PhD; David Brown, PhD; Vanessa L. Madden, BSc; and Hua Wang, MS
The Role of Health IT and Delivery System Reform in Facilitating Advanced Care Delivery
Jennifer King, PhD; Vaishali Patel, PhD; Eric Jamoom, PhD; and Catherine DesRoches, DrPH
Lost in Translation: Healthcare Utilization by Low-Income Workers Receiving Employer-Sponsored Health Insurance
Bruce W. Sherman, MD; Wendy D. Lynch, PhD; and Carol Addy, MD, MMSc
Patient Safety Intervention to Reduce Unnecessary Red Blood Cell Utilization
Scott Hasler, MD; Amanda Kleeman MS; Richard Abrams, MD; Jisu Kim, MD; Manya Gupta, MD; Mary Katherine Krause, MS; and Tricia J. Johnson, PhD
Impact of Clinical Pharmacy Services on Outcomes and Costs for Indigent Patients With Diabetes
Marissa Escobar Quinones, PharmD, CDE; Margaret Youngmi Pio, PharmD, BCPS, CDE; Diem Hong Chow, PharmD, CDE; Elizabeth Moss, PharmD, CDE, BCACP; Jeffrey Lynn Hulstein, PharmD, CDE; Steven Micheal Boatright, PharmD, CDE; and Annie Mathew, PharmD, CDE
Adding Glucose-Lowering Agents Delays Insulin Initiation and Prolongs Hyperglycemia
Courtney Hugie, PharmD, BCPS; Nancee V. Waterbury, PharmD, BCACP; Bruce Alexander, PharmD; Robert F. Shaw, PharmD, MPH, BCPS, BCNSP; and Jason A. Egge, PharmD, MS, BCPS
Currently Reading
Costs for a Health Coaching Intervention for Chronic Care Management
Todd H. Wagner, PhD; Rachel Willard-Grace, MPH; Ellen Chen, MD; Thomas Bodenheimer, MD, MPH; and David H. Thom, MD, PhD, MPH
Patient Perceptions of Clinician Self-Management Support for Chronic Conditions
Peter Cunningham, PhD

Costs for a Health Coaching Intervention for Chronic Care Management

Todd H. Wagner, PhD; Rachel Willard-Grace, MPH; Ellen Chen, MD; Thomas Bodenheimer, MD, MPH; and David H. Thom, MD, PhD, MPH
On average, the health coach intervention cost $483 per participant per year. There was no evidence that the coaching intervention saved money at 1 year.

Objectives: Health coaches can help patients gain knowledge, skills, and confidence to manage their chronic conditions. Coaches may be particularly valuable in resource-poor settings, but they are not typically reimbursed by insurance, raising questions about their budgetary impact.

Study Design: The Health Coaching in Primary Care (HCPC) study was a randomized controlled trial that showed health coaches were effective at helping low-income patients improve control of their type 2 diabetes, hypertension, and/or hyperlipidemia at 12 months compared with usual care.

Methods: We estimated the cost of employing 3 health coaches and mapped these costs to participants. We tested whether the added costs of the coaches were offset by any savings in healthcare utilization within 1 year. Healthcare utilization data were obtained from 5 sources. Multivariate models assessed differences in costs at 1 year controlling for baseline characteristics.

Results: Coaches worked an average of 9 hours with each participant over the length of the study. On average, the health coach intervention cost $483 per participant per year. The average healthcare costs for the coaching group was $3207 compared with $3276 for the control group (P = .90). There was no evidence that the coaching intervention saved money at 1 year.

Conclusions: Health coaches have been shown to improve clinical outcomes related to chronic disease management. We found that employing health coaches adds an additional cost of $483 per patient per year. The data do not suggest that health coaches pay for themselves by reducing healthcare utilization in the first year.

Am J Manag Care. 2016;22(4):e141-e146
Take-Away Points

Studies have shown that health coaches yield modest clinical benefits. Many clinics, especially those with limited resources, have questions about the budgetary impact of hiring additional staff. Linked with a coaching intervention, we found that:
  • Health coaches cost $483 per participant per year.
  • There was no evidence that coaches saved money at 1 year (through a hypothesized reduction in care).
  • In 2015, CMS approved a monthly payment of $42.60 for chronic care management per qualified patient (defined as having 2 or more significant chronic conditions). Such payments would enable many clinics to train coaches to provide essential services for patients with multiple chronic illnesses.
Primary care clinicians, especially in resource-poor settings, often face competing demands and resource constraints that impede their ability to provide high-value care that improves patient outcomes while minimizing the use of resources. These clinicians are tightly scheduled to manage patients, many of whom have multiple chronic conditions, limited means, and low health literacy. Frequently, a clinician cannot address a patient’s needs in a single visit.1,2 New evidence-based models of care are needed to provide self-management support in primary care that is culturally and linguistically appropriate, as well as financially sustainable in resource-poor settings.3

Health coaches represent a unique resource for self-management support in primary care. They help patients gain the knowledge, skills, and confidence to manage their chronic conditions.4 Coaches are trained in collaborative communication to improve understanding of, and adherence to, mutually agreed upon care plans. They may provide patients with health-related information, navigational support though the healthcare system, connections to community resources, and emotional support. Although health coaches may come from a variety of training backgrounds, medical assistants are emerging as a common and relatively economical workforce that may meet the demand for self-management support. Health coaching has been proposed as an inexpensive and effective means to improve control of chronic conditions, including risk factors for cardiovascular disease, such as diabetes, hypertension, and hyperlipidemia.5 Coaches may be particularly valuable in resource-poor settings, where minority and low-income communities bear a disproportionate burden of chronic disease and its complications and are less likely to engage in effective self-management of their conditions.6,7 In these settings, clinics can employ coaches that culturally and linguistically match the patients’ characteristics.8

Previous studies have found health coaching to be effective in improving outcomes for chronic conditions, including diabetes, hypertension, asthma, and hyperlipidemia.9-15 However, for health coaching to be adopted on a wider basis, more must be understood about the budgetary impact for clinics interested in this model. For this reason, we analyzed cost data from a randomized controlled trial of health coaches versus usual care. Our goal was to determine the added costs associated with implementing a health coaching program in 2 primary care clinics and then to examine whether the added costs of the program were offset by any changes in short term healthcare utilization (within 1 year).


The Health Coaching in Primary Care (HCPC) study was a randomized controlled trial testing the efficacy of health coaching versus usual care to help low-income patients with uncontrolled type 2 diabetes, hypertension, and/or hyperlipidemia to better manage their condition(s) at 12 months. The study was conducted at 2 San Francisco safety net clinics from April 2011 to June 2012. Patients were eligible if they were between the ages of 18 and 75, spoke Spanish or English, could be reached by phone, and had poorly controlled diabetes (glycated hemoglobin [A1C] ≥8%), hypertension (systolic blood pressure [SBP] ≥140 mm Hg), and/or hyperlipidemia (low-density lipoprotein cholesterol [LDL-C] ≥160 mg/dL for patients without diabetes or ≥100 mg/dL for patients with). A total of 441 (66.4%) patients provided informed consent, completed baseline measures, and were randomized to usual care plus health coaching (n = 224) or usual care alone (n = 217). More details on the HCPC study design and methods have previously been published.3

The study employed 3 medical assistant health coaches. Each coach attended 40 hours of training over 6 weeks based on a curriculum of: active listening and nonjudgmental communication; self-management skills for diabetes, hypertension, and hyperlipidemia; social and emotional support; lifestyle change; medication understanding and adherence; clinic navigation; and community resources. The study team developed the curriculum, which is available online.16 Each health coach managed a panel of 40 to 60 patients; no limitations were placed on the time that a health coach could spend with a patient. The coaches attended medical visits with patients, met with them before and after the visit, and called or met with them between visits. Health coaches helped patients review their medications, ensured that they understood their lab results and their goals, assisted in creating action plans for personalized behavior change, and assisted with navigation of clinic and community resources. Patients faced no co-payments or other financial barriers for meetings with their health coach. The coaches were paid a salary, and the study did not employ any financial incentives (eg, bonuses) for coaches to meet performance targets.

After 12 months, patients randomized to health coaching were more likely to have met the primary outcome of control for 1 or more of the conditions for which they were enrolled than patients in usual care (46.4% vs 34.3%; P = .02).17 This result was driven primarily by the benefit of health coaching on control of diabetes (change in A1C of –1.2% vs –0.5%) and, to a lesser extent, by control of hyperlipidemia (change in LDL-C of –28 mg/dL vs –18 mg/dL).

Costs of the Health Coach Program

Our first goal was to assess the cost of implementing a health coach program. We estimated the cost of the health coaches based on labor, training, supplies, and space. We tracked the hours of work spent by the health coaches as related to the intervention, excluding research-specific activities. The total time spent coaching (estimated as 5376 hours, or 60% of the total full-time equivalent [FTE]) was calculated based on time studies conducted at 3 intervals during the study; it excluded time that was spent assisting with other study activities, such as chart review for eligibility screening or assisting with trainings in other locations.

The amount of health coaching time spent per patient was derived from interaction forms that health coaches completed after each encounter. The health coaches were paid $19 per hour, plus benefits (30% of the FTE). The coaching program included 40 hours of training time for each health coach and the course trainer. In addition, there was ongoing mentoring and observation, estimated at 1 hour per month per coach and the trainer. Because wages are higher in San Francisco compared with the national median, cost analyses were also run using median salary data from the Bureau of Labor Statistics, which was $13.28 per hour, plus an additional 30% for benefits. The coaches shared an office of 222 square feet, which was the equivalent of three 8×9 cubicles with additional filing space; this space is consistent with government benchmarks.18 Office space was valued at a cost of $25 per square foot. Accounting databases from San Francisco General Hospital were queried to estimate the costs associated with telephones ($24/FTE/month), computer hardware, network and support ($90/FTE/month), and supplies ($50/FTE/month).

Healthcare Utilization and Costs

Our second goal was to determine the budgetary impact of the health coaching program. We tested whether the added costs of the coaches were offset by any savings in healthcare utilization within 1 year. Utilization data were obtained from clinics, health systems, and insurers. Of the 2 clinics involved in this trial, one (Site A) was an independent federally qualified health center; we obtained utilization data for primary care services from its practice management and pharmacy system. At the second clinic (Site B), utilization data—including primary care, specialty, inpatient, and emergency department (ED) care—were drawn from the health system.

The inpatient and ED care for Site A that occurred within the health system were also available through this data set; however, pharmacy data were not available for Site B. Visits to external hospitals and EDs were identified through patient report, and diagnosis and visit information was abstracted from discharge reports. For the utilization data, we estimated Medicare payments based on Current Procedural Terminology codes, when available. California Medicaid (MediCal) estimated payments were used for services not covered by Medicare, namely maternal services.

Payer information was also collected for 239 participants who were covered under either MediCal or Healthy San Francisco, a local health insurance system funded by the county. This data set included inpatient, ED, outpatient, and pharmacy claims across all reporting hospitals and clinics. Of these 239 patients, 192 were covered by one of these programs for at least half of the year (at least 182 days). These data included payments or charges. To estimate costs, we used payments when available; otherwise we cost-adjusted the charges using Medicare’s hospital-specific cost-to-charge ratio.

In total, the analysis pooled utilization data from 5 data sets. We extracted data for the year prior to the intervention and for the year following randomization. We standardized costs to 2013 using the US general consumer price index. Given the possibility of duplicate records across these data sets, we examined claims for an individual based on dates, and we examined records based on the providing clinician and date. All data cleaning was blind to treatment assignment.


We compared participants randomized to usual care or to health coaches.  We examined differences based on gender, age, whether they were born in the United States, race/ethnicity, marital status, formal educational attainment, yearly income, English as a first language, and employment status. We used bivariate statistics to compare treatment groups’ health services costs at 1 year; we examined the cost across the 5 data sources to ensure consistency of results. We also estimated counts of services from the claims data, but were less confident that each claim represented a unique visit or stay—we treated this as a check on the cost data.

We used multivariate models to control for possible observed differences and to run sensitivity analyses, and ordinary least squares for the primary multivariate analysis, controlling for clinic and the covariates listed above. Sensitivity analyses used semi-log and generalized linear models with a log link and a gamma distribution. We ran 2 subsample analyses: first, we examined whether there were differential effects by clinic; second, we examined the impact of the intervention on patients who had healthcare costs in the top quartile in the year prior to the intervention to determine whether the intervention had a differential effect for high-cost patients.


Participants had an average age of 52.7 years, with a broad range of 22 to 75 years (Table 1). Approximately half of the participants were women. The population exemplified those seeking care at resource-poor settings, with over half reporting a household income of $10,000 or less and only 12% reported an income of more than $20,000 per year. There were no significant differences in characteristics of the sample between the intervention and the control groups.

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