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Building a Science-Driven Business: How National Institutes of Health Funding Enabled an Evidence-Based Approach to Maternal Mental Health Innovation

The digital mental health (DMH) industry has grown drastically over the last decade; yet, many DMH products have failed to demonstrate meaningful clinical outcomes, in large part due to lack of scientific evidence. This viewpoint paper highlights an example of how early-stage DMH companies can prioritize science as a strategic advantage. We discuss Moment for Parents, an artificial intelligence–driven maternal mental health app built entirely with support from the National Institutes of Health (NIH) Small Business Innovation Research (SBIR) program. We illustrate the advantages and challenges of building a science-backed product with federal funding. Benefits include credible evidence generation, independence in product development, and enhanced market differentiation. We also discuss the challenges of navigating the SBIR ecosystem, including grant writing and administrative demands, and aligning business objectives with federal research priorities. By showcasing both the promise and complexity of SBIR funding, this viewpoint paper offers actionable insights for founders and chief executive officers who aim to prioritize science in the DMH space.
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Understanding Collaborative CT and MRI Utilization Through Network Analysis: Retrospective Study Using Administrative Claims Data

<strong>Background:</strong> Japan has one of the highest densities of computed tomography (CT) and magnetic resonance imaging (MRI) scanners globally, yet efficient resource allocation remains a challenge amid demographic shifts and regional health care disparities. <strong>Objective:</strong> This study aimed to develop an analytic framework using network analysis techniques to understand the collaborative use of CT and MRI devices across health care facilities in a Japanese prefecture. <strong>Methods:</strong> A retrospective observational study was conducted using outpatient receipt data from Japan’s National Health Insurance and the Late-Stage Elderly Medical System, covering fiscal years 2016 to 2019. Network analysis techniques were used to identify patterns of shared use among medical institutions. Network graphs with community detection were developed to visualize collaborative relationships, and density and reciprocity metrics were calculated to assess interinstitutional cooperation. <strong>Results:</strong> CT examinations increased from 287,782 (2016) to 307,029 (2019), while MRI examinations increased from 107,876 to 115,929 over the same period. Collaborative examinations also increased for both modalities. Network density remained relatively stable (CT: 3.10-3.50×10<sup>-3</sup>; MRI: 3.20-3.70×10<sup>-3</sup>), while reciprocity decreased (CT: 9.74×10<sup>-2</sup> to 7.79×10<sup>-2</sup>; MRI: 2.82×10<sup>-2</sup> to 1.56×10<sup>-2</sup>). Community detection analysis showed differences in the distribution of medical institutions across clusters over time. <strong>Conclusions:</strong> Network analysis revealed structural changes in collaborative CT and MRI use patterns, including declining reciprocity, which suggests a shift toward more unidirectional referral patterns. This analytic framework provides a method for health care planners to assess interinstitutional collaboration and inform resource allocation strategies for shared diagnostic equipment.
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Development of a Child Articulation Screening Test Within Digital Therapeutics: Delphi Study

Background: Speech sound disorders are common in children and are associated with an increased risk of academic reading difficulties. The COVID-19 pandemic further highlighted the need for remote and digitalized assessment tools. In South Korea, standardized instruments such as the Urimal Test of Articulation and Phonation and Assessment of Phonology and Articulation for children are widely used but have limitations, including reliance on face-to-face evaluation, and the absence of automated scoring. Objective: This study aimed to develop and establish the content validity of an articulation assessment tool that can overcome these limitations and be integrated into digital therapeutics (DTx). Methods: A 3-round modified Delphi survey was conducted between July and September 2025 with 92% (23/25) of the invited experts, including 52.2% (12/23) physiatrists and 47.8% (11/23) speech-language pathologists, with a mean professional experience of 10.69 (SD 5.09) years. All participants (23/23, 100%) completed all rounds. Panelists evaluated the appropriateness of word lists, phonological environments, and scoring criteria. Quantitative analyses, including calculations of content validity ratio (CVR), content validity index (CVI), and median and IQR, were performed. Consensus thresholds were set at a CVR of ≥0.39, a CVI of ≥0.78, a median of ≥3.5, and an IQR of ≤1.0. Items were retained only when all 4 criteria were satisfied. While formal qualitative analysis was not performed, the research team internally reviewed and synthesized core keywords and themes from the experts’ open-ended responses to guide the refinement of items. Results: These findings were summarized into four key areas: (1) modernization of word stimuli, (2) expansion of phonological coverage, (3) refinement of scoring criteria to reduce ambiguity, and (4) enhancement of result interpretability through visualization. In round 2, a revised 35-word list was evaluated across 25 items, of which 20 (80%) met all consensus criteria. In total, 20% (5/25) of the items failed to meet at least one threshold, including phonological environment adequacy (CVR=0.48; CVI=0.74), scoring redundancy (CVR=0.13; CVI=0.57), usefulness of proportion of whole-word correctness or percentage of word proximity (CVR=0.39; CVI=0.70), contribution of mean phonological length (CVR=0.22; CVI=0.61), and usefulness of feature-based indexes (CVR=0.30; CVI=0.65; IQR 2). Items that reached consensus showed CVR values of 0.57 to 0.91, CVI values of 0.78 to 0.96, a median score of 4, and IQR values of 0 to 1. In round 3, all remaining items achieved consensus. Conclusions: This Delphi study developed a novel articulation assessment tool with robust content validity. This tool includes updated word stimuli, diverse analysis indexes, and visualization features, thereby enhancing its clinical utility and suitability for integration into artificial intelligence–based DTx. By standardizing and digitalizing articulation assessments, this tool has the potential to support personalized and accessible interventions for children with speech sound disorders.
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