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The American Journal of Managed Care February 2015
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Leveraging Remote Behavioral Health Interventions to Improve Medical Outcomes and Reduce Costs
Reena L. Pande, MD, MSc; Michael Morris; Aimee Peters, LCSW; Claire M. Spettell, PhD; Richard Feifer, MD, MPH; William Gillis, PsyD
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Leveraging Remote Behavioral Health Interventions to Improve Medical Outcomes and Reduce Costs

Reena L. Pande, MD, MSc; Michael Morris; Aimee Peters, LCSW; Claire M. Spettell, PhD; Richard Feifer, MD, MPH; William Gillis, PsyD
Successful patient engagement in a nationally available, remotely delivered behavioral health intervention can significantly improve medical outcomes and lower healthcare costs.
By targeting individuals at a moment when they may be particularly receptive to change (ie, after a recent medical event), by focusing on achieving successful patient engagement, and by ensuring high-quality and consistent program delivery, our intervention was able to reduce all-cause hospital admissions and total days spent in the hospital, and produce a corresponding significant cost savings. The actual healthcare savings that accrue from our behavioral health intervention delivers on the promise that by virtue of improved well-being, high-quality behavioral healthcare can indeed lead to measurable improvements in medical health and lower healthcare costs. These results serve as a reminder that helping patients to overcome their barriers to change can improve overall health and well-being and reduce the cost of care simultaneously.


There are several limitations to this study that should be considered. The study was designed as a retrospective observational study, and as such we cannot exclude the possibility of participation bias. However, the comparison population had completed the initial intake consultation and was remarkably similar to the intervention group with respect to almost all baseline measurements: demographics; comorbid clinical conditions; baseline medical utilizations and medical expenditures; and baseline depression, stress, and anxiety scores. One significant difference noted in the baseline characteristics was a differential in utilization of outpatient behavioral health services in the 6-month period prior to intervention. We theorize that this difference might have been one of the reasons why individuals in the comparison group chose not to participate in the AbilTo intervention. However, it is important to consider the impact that this difference might have had on utilization in the follow-up period. Given that baseline utilization of behavioral health services in many medical conditions is recognized to result in greater medical utilization, we accounted for these differences in several ways in our analysis.

First, all regression analyses were adjusted for this baseline utilization data. Second, analyses also adjusted for a prospective risk score—a measure to predict current and future healthcare usage15—and this score was no different at baseline between the 2 groups. Moreover, the absolute rate of pre-period utilization (1544 per 1000 members per year) was small compared with the utilization in the post period (19,713 per 1000 members per year), which was largely accounted for by AbilTo program participation. As such, while there were statistical differences, the absolute rates may not have been large enough to have a clinically meaningful impact on outcomes. Even after adjusting for these differences, our analysis shows significant reductions in hospital admissions and total days in the hospital, even after full multivariable adjustment for many potentially confounding factors.

In addition, though the sample size allowed adequate power to see significant reductions in the primary end point, the small sample size may have limited the ability to detect differences in secondary end points. Finally, the study included only individuals with primary commercial insurance and did not include individuals with Medicaid or Medicare, or the dual-eligible population. While we anticipate that similar benefits would accrue in these populations, the study does not allow us to generalize to this wider population.


These data demonstrate that a high-quality, short-duration, remotely delivered population health strategy utilizing a behavioral health intervention can lead to demonstrable benefits in behavioral health, medical health outcomes, and overall cost of care. A scalable intervention of this nature has the potential to reach a wide population of individuals in need. Successful patient engagement and the meaningful behavior change that results are necessary prerequisites to improving medical health and reducing the burgeoning costs of healthcare in the United States.


The authors would like to thank the AbilTo provider network and team and the Aetna nursing and behavioral health teams for their contributions to clinical care and program support. The authors would also like to thank Patrick Kerr, PhD, West Virginia University, and Stephen Schleicher, MD, Brigham and Women’s Hospital, for their contributions.

AbilTo and Aetna were both directly involved in the design and conduct of the study, as well as in collection, analysis, and interpretation of the data. All authors contributed to study concept and design.

Author Affiliations: AbilTo, Inc (RLP, AP), New York, NY; Cardiovascular Division, Brigham and Women’s Hospital (RLP), Boston, MA; and Aetna (MM, CMS, RAF, WG), Hartford, CT.

Source of Funding: None.

Author Disclosures: Dr Pande reports being an employee of and holding an equity interest in AbilTo. She serves as AbilTo’s chief medical officer, and she is also a physician in the Cardiovascular Division at Brigham and Women’s Hospital, and an instructor at Harvard Medical School. Ms Peters reports being an employee of and holding an equity interest in AbilTo; she serves as AbilTo’s chief clinical officer. Dr Feifer reports being an employee and shareholder of Aetna, where he serves as national medical director and leads the Department of Clinical Consulting, Strategy, and Analysis. Ms Spettell reports being an employee and shareholder of Aetna, where she serves as executive director in the Data Science Department. Mr Gillis reports being an employee and shareholder of Aetna and serves as the director of clinical health services. Mr Morris reports being an employee and shareholder of Aetna, where he holds the position of senior informatics manager in the Data Science Department. No other potential conflicts of interest relevant to this article were reported.

Authorship Information: Concept and design (RAF, MM, AP, RLP, CMS); acquisition of data (RAF, MM, AP, RLP); analysis and interpretation of data (RAF, MM, RLP, CMS); drafting of the manuscript (RAF, MM, RLP, CMS); critical revision of the manuscript for important intellectual content (RAF, WG, MM, AP, RLP, CMS); statistical analysis (MM, SPM, RLP); provision of study materials or patients (AP, RLP); administrative, technical, or logistic support (RAF, MM, RLP, CMS); and supervision (RAF, WG, MM, RLP).

Address correspondence to: Reena L. Pande, MD, MSc, AbilTo, Inc, 320 37th St, 7th Fl, New York, NY 10018. Phone: 617-512-9597. Fax: 646-626-7549. E-mail:
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