EVALUATING THE QUALITY OF CLINICAL EVIDENCE FOR DIGITAL HEALTH SOLUTIONS ACROSS FIVE THERAPEUTIC AREAS: FINDINGS FROM PHTI ASSESSMENTS
Author(s)
Ashley Kang, MPH1, Corinna Cline, BS2, Noemi F. Brennan, MPH, MSc1, Josh Carlson, MPH, PhD3;
1Curta, Seattle, WA, USA, 2Peterson Health Technology Institute, New York, NY, USA, 3University of Washington, Seattle, WA, USA
1Curta, Seattle, WA, USA, 2Peterson Health Technology Institute, New York, NY, USA, 3University of Washington, Seattle, WA, USA
OBJECTIVES: Digital health solutions have the potential to improve health and lower costs, but stakeholders often face an information gap on their clinical impact. The Peterson Health Technology Institute (PHTI) addresses this gap by providing independent, evidence-based assessments of these solutions’ clinical benefits and economic impact. The evidence underlying these assessments is highly variable and has not been comprehensively evaluated. Our objective was to characterize the clinical evidence supporting published PHTI assessments.
METHODS: The analysis set included five PHTI assessments: diabetes, musculoskeletal (MSK) disorders, hypertension (HTN), depression/anxiety, and opioid use disorder (OUD). Data was abstracted on study designs, study duration, and study quality. Abstracted data were analyzed using descriptive statistics.
RESULTS: Across PHTI assessments, 363 citations (articles) were reviewed, including 164 interventional and 199 observational study designs. Among assessments that reported counts for comparative versus single-arm studies, 41% were comparative and 59% were single-arm.
Of the 51 unique companies assessed across five reports, 30 submitted evidence for review. Follow-up durations were generally short, particularly in depression/anxiety and MSK studies, which most often reported outcomes at 6-12 weeks, whereas hypertension, diabetes, and OUD assessments more frequently included follow-up periods of 6-12 months. A total of 250 unique studies were evaluated for risk of bias, including 80 studies assessed using RoB2 and 170 studies assessed using NOS. Approximately half of the studies (57%, n=142) were rated moderate or high risk of bias; 43% of studies were rated good or low risk of bias.
CONCLUSIONS: The evidence base supporting PHTI assessments is substantial but heterogeneous, with variability in study design, duration, and quality. Future research on digital solutions should prioritize rigorous methodology with appropriate comparators and adequate follow-up, when possible, to generate more robust and generalizable evidence.
METHODS: The analysis set included five PHTI assessments: diabetes, musculoskeletal (MSK) disorders, hypertension (HTN), depression/anxiety, and opioid use disorder (OUD). Data was abstracted on study designs, study duration, and study quality. Abstracted data were analyzed using descriptive statistics.
RESULTS: Across PHTI assessments, 363 citations (articles) were reviewed, including 164 interventional and 199 observational study designs. Among assessments that reported counts for comparative versus single-arm studies, 41% were comparative and 59% were single-arm.
Of the 51 unique companies assessed across five reports, 30 submitted evidence for review. Follow-up durations were generally short, particularly in depression/anxiety and MSK studies, which most often reported outcomes at 6-12 weeks, whereas hypertension, diabetes, and OUD assessments more frequently included follow-up periods of 6-12 months. A total of 250 unique studies were evaluated for risk of bias, including 80 studies assessed using RoB2 and 170 studies assessed using NOS. Approximately half of the studies (57%, n=142) were rated moderate or high risk of bias; 43% of studies were rated good or low risk of bias.
CONCLUSIONS: The evidence base supporting PHTI assessments is substantial but heterogeneous, with variability in study design, duration, and quality. Future research on digital solutions should prioritize rigorous methodology with appropriate comparators and adequate follow-up, when possible, to generate more robust and generalizable evidence.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
MT32
Topic
Medical Technologies
Topic Subcategory
Digital Health
Disease
SDC: Cardiovascular Disorders (including MI, Stroke, Circulatory), SDC: Diabetes/Endocrine/Metabolic Disorders (including obesity), SDC: Musculoskeletal Disorders (Arthritis, Bone Disorders, Osteoporosis, Other Musculoskeletal), STA: Multiple/Other Specialized Treatments