Adoption of Digital Health Technologies in the Practice of Behavioral Health: Qualitative Case Study of Glucose Monitoring Technology.

BACKGROUND: Evaluation of patients with serious mental illness (SMI) relies largely on patient or caregiver self-reported symptoms. New digital technologies are being developed to better quantify the longitudinal symptomology of patients with SMI and facilitate disease management. However, as these new technologies become more widely available, psychiatrists may be uncertain about how to integrate them into daily practice. To better understand how digital tools might be integrated into the treatment of patients with SMI, this study examines a case study of a successful technology adoption by physicians: endocrinologists' adoption of digital glucometers. OBJECTIVE: This study aims to understand the key facilitators of and barriers to clinician and patient adoption of digital glucose monitoring technologies to identify lessons that may be applicable across other chronic diseases, including SMIs. METHODS: We conducted focus groups with practicing endocrinologists from 2 large metropolitan areas using a semistructured discussion guide designed to elicit perspectives of and experiences with technology adoption. The thematic analysis identified barriers to and facilitators of integrating digital glucometers into clinical practice. Participants also provided recommendations for integrating digital health technologies into clinical practice more broadly. RESULTS: A total of 10 endocrinologists were enrolled: 60% (6/10) male; a mean of 18.4 years in practice (SD 5.6); and 80% (8/10) working in a group practice setting. Participants stated that digital glucometers represented a significant change in the treatment paradigm for diabetes care and facilitated more effective care delivery and patient engagement. Barriers to the adoption of digital glucometers included lack of coverage, provider reimbursement, and data management support, as well as patient heterogeneity. Participant recommendations to increase the use of digital health technologies included expanding reimbursement for clinician time, streamlining data management processes, and customizing the technologies to patient needs. CONCLUSIONS: Digital glucose monitoring technologies have facilitated more effective, individualized care delivery and have improved patient engagement and health outcomes. However, key challenges faced by the endocrinologists included lack of reimbursement for clinician time and nonstandardized data management across devices. Key recommendations that may be relevant for other diseases include improved data analytics to quickly and accurately synthesize data for patient care management, streamlined software, and standardized metrics.

Challenges and opportunities in insomnia disorder.

BACKGROUND: Insomnia disorder is a prevalent, often unrecognized condition that affects millions. This clinical disorder is characterized by difficulty initiating or maintaining sleep over a sustained period. In fact, insomnia disorder affects much more than sleep; it increases the risk of developing serious medical and psychiatric comorbidities and can exacerbate existing conditions. The association between insomnia disorder and serious medical and psychiatric comorbidities are complex and directionality is not yet fully understood. There remain gaps in the treatment landscape for insomnia disorder. METHODS: We performed a narrative review of the published literature to identify challenges, unmet needs, and burden associated with insomnia disorder. RESULTS: In this article, we describe the substantial burden that insomnia disorder poses on patients, the healthcare system, and society in the US. This article explores the factors attributable to this burden including limited provider knowledge, inadequate treatment options, and unknown long-term impacts of off-label treatments. CONCLUSIONS: Several recommendations are proposed to address these challenges and improve patient outcomes through efforts to: (1) establish the societal value of treatment; (2) improve the clinical understanding of insomnia disorder; and (3) prioritize development of and access to effective treatments that do not pose addiction potential or tolerability issues.

Incremental Health Care Burden of Treatment-Resistant Depression Among Commercial, Medicaid, and Medicare Payers.

OBJECTIVE: This study compared health care use and costs among patients with treatment-resistant versus treatment-responsive depression across Medicaid, Medicare, and commercial payers. METHODS: A retrospective cohort study was conducted by using Truven Health Analytics' commercial (2006-2017; N=111,544), Medicaid (2007-2017; N=24,036), and Medicare supplemental (2006-2017; N=8,889) claims databases. Participants were adults with major depressive disorder who had received one or more antidepressant treatments. Treatment resistance was defined as failure of two or more antidepressant treatments of adequate dose and duration. Annual use (hospitalizations and outpatient and emergency department [ED] visits) and costs were compared across patients by treatment-resistant status in each payer population. Incremental burden of treatment-resistant depression was estimated with regression analyses. Monthly changes in costs during 1-year follow-up were assessed to understand differential cost trends by treatment-resistant status. RESULTS: In the three payer populations, patients with treatment-resistant depression incurred higher health care utilization than those with treatment-responsive depression (hospitalization, odds ratios [ORs]=1.32-1.76; ED visits, ORs=1.38-1.45; outpatient visits, incident rate ratio=1.29-1.54; p<0.001 for all). Compared with those with treatment-responsive depression, those with treatment resistance incurred higher annual costs (from $4,093 to $8,054 higher; p<0.001). Patients with treatment-resistant depression had higher costs at baseline compared with patients with treatment-responsive depression and incurred higher costs each month throughout follow-up. CONCLUSIONS: Treatment-resistant depression imposes a significant health care burden on insurers. Treatment-resistant depression may exist and affect health care burden before a patient is identified as having treatment-resistant depression. Findings underscore the need for effective and timely treatment of treatment-resistant depression.