Remote Therapeutic Monitoring (RTM) represents an approach to enhance patient care by coordinating medication and treatment adherence without requiring a separate administrative department. The system helps patients understand their monitoring plans, addresses participation barriers, and documents activities while routing clinical concerns to treating physicians. RTM employs various device options tailored to specific therapies, including medication compartment and bottle-opening trackers for pillboxes, inhaler-use monitoring systems, and patient-reported outcome measures for pain and function. MySeema coordinates support services while qualified clinicians retain treatment direction. The framework emphasizes that device selection must align with prescribed therapy, patient routines, and applicable monitoring pathways; notably, opening a medication container does not confirm actual dose consumption, and inhaler events alone cannot validate proper technique. CMS billing includes qualifying management time with interactive communication requirements, with codes such as 98980 applicable for RTM management exceeding twenty minutes per eligible patient. Reimbursement varies based on location, setting, clinician type, and coverage parameters. This structured approach aims to improve treatment outcomes by supporting adherence monitoring while maintaining clinical oversight and appropriate documentation standards.

Patient engagement transforms patients from passive care recipients into active participants in managing their health, with evidence showing this approach significantly improves outcomes. Research from the CDC and NCBI identifies the highest-evidence strategies as group-based chronic disease education, SMS and web-based interventions, and telehealth-supported self-monitoring. A critical challenge in private clinics is patient retention between visits rather than during them; automated recall systems, post-care follow-up, and patient portal access emerge as the most effective retention tools. Private clinics and aesthetic practices lose an estimated 20-30% of their active patient base annually, often through passive drift rather than competitor defection. Platforms like Pabau address this by automating recall workflows, delivering pre- and post-care instructions, and providing self-service portals, reducing manual team effort. The Patient Activation Measure, a validated scale measuring patient activation levels, demonstrates that higher activation correlates with lower hospitalization rates, better chronic disease management, and improved treatment adherence. Rather than treating patient engagement as a marketing challenge, successful clinics embed evidence-based engagement strategies throughout their operational workflows, from pre-visit intake through post-visit follow-up, making engagement a sustainable operational priority that increases patient retention without expanding staffing needs.

Remote therapeutic monitoring powered by wearable devices is transforming how physical therapists, occupational therapists, and speech-language pathologists deliver care. Wearable technologies—from smartwatches to specialized sensors—provide real-time data on patient activity, movement, and progress, enabling therapists to tailor interventions effectively. Telehealth platforms facilitate virtual therapy sessions across rehabilitation exercises, cognitive activities, and speech therapy, while integration of virtual and augmented reality creates immersive, engaging experiences. Artificial intelligence and machine learning algorithms analyze vast patient datasets to identify patterns, predict complications, and personalize treatment plans by assessing movement, cognitive function, and speech patterns. Security and privacy protection remain critical priorities, requiring advanced encryption and compliance with healthcare regulations. Patient engagement is enhanced through interactive applications and gamification elements that motivate active participation in therapy. Sophisticated outcome measurement tools and analytics provide data-driven insights, allowing therapists to track progress, make informed adjustments, and demonstrate intervention effectiveness. Collectively, these technologies address traditional care limitations by delivering continuous monitoring of patient activity and health status, revolutionizing therapeutic practice.

Remote therapeutic monitoring (RTM) and remote physiologic monitoring (RPM) represent distinct virtual care modalities designed for different clinical purposes within hybrid rehabilitation therapy. While RPM tracks physiological data such as weight, blood pressure, and pulse oximetry for chronic disease management by physicians and certain non-physician practitioners, RTM monitors non-physiological data including therapy adherence, patient engagement, and musculoskeletal treatment response between clinical visits. RTM emerged as a Medicare-approved care model in response to a gap identified during the COVID-19 pandemic, when RPM codes proved valuable for physicians but excluded musculoskeletal, respiratory, and cognitive behavioral therapy providers. The American Medical Association's CPT Editorial Panel introduced RTM-specific codes in late 2020, which received CMS approval and went live in 2022. RTM primarily supports musculoskeletal rehabilitation, respiratory therapy, and cognitive behavioral therapy by monitoring subjective metrics and behavioral markers reflecting how patients manage recovery at home. Understanding these distinctions enables physical and occupational therapy practices to select the appropriate monitoring model for their patient populations and operational workflows, optimizing virtual care pathways and treatment outcomes.

Software as a Medical Device (SaMD) represents software designed for medical purposes that operates independently of hardware devices, as defined by the International Medical Device Regulators Forum (IMDRF). The FDA categorizes SaMD as one of three software types related to medical devices, governed by the Federal Food, Drug, and Cosmetic Act. A significant regulatory framework emerged from the 21st Century Cures Act (enacted December 13, 2016), which added section 520(o) to establish exemptions for clinical decision support (CDS) software meeting four specific criteria: it must not analyze medical images or diagnostic signals, must display or analyze medical information, must support healthcare professional recommendations, and must allow independent professional review of recommendations. The FDA reissued its SaMD guidance on January 29, 2026, clarifying enforcement positions regarding single-option recommendations and patient-directed software. The IMDRF risk framework categorizes SaMD into four risk categories based on healthcare decision significance and situation acuity, with Category IV representing highest impact. Device authorization occurs through three FDA pathways: the 510(k) premarket notification route, De Novo classification for novel devices, or the traditional premarket approval process for Class III devices, enabling appropriate regulation based on risk levels.

Digital health implementation in clinical trials has evolved beyond technical deployment to focus on actual operational value realization. While platforms meeting FDA guidance on remote data acquisition, decentralized trial elements, and electronic systems—alongside ICH E6(R3) standards—represent necessary technical achievement, they frequently fail to deliver meaningful outcomes if poorly integrated into workflows or inconsistently adopted. Dr. Harsh Shah proposes a five-layer framework addressing this "last mile" implementation challenge: Technical Fitness, Workflow Fit, Adoption, Data Reliability, and Outcome Conversion. Traditional success metrics measuring configuration completion, validation passage, and site activation mask deeper failures, such as electronic clinical outcome assessment platforms deployed to all sites yet generating high participant support needs and parallel paper workarounds, or analytics platforms producing unactionable alerts. Shah's diagnostic model—Realized Value equals the multiplication of all five layers—demonstrates that weakness in any single component compromises overall value delivery. This framework shifts evaluation emphasis from binary go-live milestones toward sustained measurement of whether technology genuinely improves trial execution and decision-making, establishing necessary standards for mature digital implementation in drug development.

Researchers from Chinese universities conducted a scoping review examining how digital phenotyping technologies can monitor and predict mental health outcomes in people with diabetes. The review searched six English and four Chinese databases through September 2026, ultimately analyzing ten studies. The evidence reveals that digital phenotyping enables real-time mental health monitoring through continuous glucose monitoring devices, passive smartphone sensing, voice analysis, and ecological momentary assessment. Machine learning algorithms were commonly employed for risk prediction modeling; however, most studies operated at the group level rather than individual level, with limited use of n-of-1 analysis or subgroup prediction for personalized outcomes. The review identified significant gaps, including insufficient external validation of models and an absence of intervention studies. Given that depression, anxiety, and diabetes-related distress frequently co-occur and directly impact glycemic control and complication risk, this technology offers promising potential for more objective, real-time mental health assessment compared to traditional subjective scales. Future development should prioritize individualized risk prediction models, robust validation protocols, and intervention trials to fully realize digital phenotyping's clinical utility in diabetes care.

Digital health app engagement remains a critical challenge, with industry benchmarks revealing that approximately fifty percent of users remain active after thirty days, declining to just one in five after a year. This engagement problem extends beyond mere app retention metrics, significantly impacting patient outcomes and clinical effectiveness. When patients perceive that tracking effort outweighs perceived benefits—particularly with repetitive reminders, manual data entry, and limited feedback—disengagement follows, leading to missed treatment actions, reduced clinician visibility between appointments, and lost patient-generated data valuable for real-world evidence. Successful patient companion app development requires embedding engagement strategies from inception rather than treating them as afterthoughts. Effective approaches must account for evolving patient motivations, integration with daily routines, caregiver involvement, alignment with clinical workflows, and clear evidence requirements. This comprehensive design perspective transforms patient engagement from a retention metric into a fundamental design challenge, ensuring digital health products deliver sustained value throughout patients' treatment journeys.

The Centers for Medicare & Medicaid Services has launched the ACCESS (Advancing Chronic Care with Effective, Scalable Solutions) Model, a voluntary 10-year initiative beginning July 5, 2026, designed to expand technology-supported chronic care delivery for Medicare beneficiaries. The program addresses chronic conditions affecting over two-thirds of Medicare enrollees, including hypertension, diabetes, chronic musculoskeletal pain, and depression. ACCESS introduces Outcome-Aligned Payments (OAPs), a novel reimbursement approach that provides participating organizations recurring payments for managing qualifying conditions, with full compensation contingent upon achieving measurable health outcomes. This addresses a critical gap in Original Medicare's fee-for-service model, which historically cannot adequately support technology-enabled care delivery. New disease tracks for heart failure, COPD, substance use disorder, tobacco cessation, and chronic musculoskeletal pain launch April 1, 2027. Leading medical societies and major health plans have pledged support for ACCESS-aligned payment options. The model particularly benefits patients in underserved communities with limited local care access, including rural areas, by enabling continuous digital health support beyond traditional office visits.

The article examines the urgent need for a sovereign approach to artificial intelligence in healthcare, beginning with a diagnosis of systemic failures in centralized digital platforms. Drawing on Cory Doctorow's concept of "platform enshitification," the piece describes how digital health ecosystems have become extractive silos that undermine patient agency and erode trust through data breaches and behavioral manipulation. In response, the "Building Together" platform has been developed as a sovereign mental health ecosystem designed to address these vulnerabilities. The article emphasizes that rapid AI advancement—exemplified by Anthropic's Claude Mythos 5 deployment through Project Glasswing—is outpacing regulatory strategies and fundamentally threatening traditional cybersecurity for healthcare data. The Mythos model's capacity for autonomous vulnerability discovery and network reconnaissance has rendered centralized firewall protections obsolete. Consequently, healthcare systems must transition toward decentralized, cryptographically verifiable trust frameworks managed at the data source. Canadian technology leaders Don and Alex Tapscott have advocated for establishing a national digital identity framework to help the nation adapt to this technological shift, highlighting the critical importance of building resilient healthcare infrastructure.

Attention-deficit/hyperactivity disorder affects millions of school-aged children globally, presenting with inattention, executive dysfunction, and motor impulsivity that often lead to academic underachievement and social difficulties. While pharmacotherapy remains a primary treatment approach, concerns about adverse effects like appetite suppression and growth inhibition, combined with limited access to specialized behavioral therapies, have prompted exploration of alternative interventions. A comprehensive meta-analysis synthesizing thirty-seven randomized controlled trials examined digital therapeutics as scalable treatment options for pediatric ADHD. The analyzed interventions included four technological categories: computer-based cognitive programs, serious video games, exergames, and virtual reality environments. Unlike passive screen use, these structured digital tools leverage neuroplasticity through adaptive cognitive stimulation and closed-loop feedback mechanisms, potentially modulating dopaminergic pathways to improve attention spans without pharmacologic toxicity. The pooled evidence demonstrates statistically significant improvements in both core ADHD symptoms and broader neuropsychological performance metrics. These findings are particularly significant as digital therapeutics offer objective behavioral tracking, scalability across diverse settings, and accessible alternatives to traditional treatment modalities, representing a valuable complement to existing clinical approaches in pediatric ADHD management.

Data privacy represents a critical yet underrecognized public health crisis with profound mental health implications. According to Dr. Patrick Fisher, a licensed clinician and founder of TheraPetic® Healthcare Provider Group, millions of individuals make consequential mental health decisions—including whether to pursue therapy or fill prescriptions—based on justified fears about their digital data exposure. Fisher's doctoral research on support animal therapeutic outcomes revealed that invisible stressors produce measurable suffering, directly informing his development of MyDataKey™ and his contribution to Volume 6 of The Invisible Series titled "The Invisible Data." The core argument extends beyond conventional data security concerns like identity theft to encompass intimate psychological vulnerability. Health records, search histories, therapy app usage, and prescription data collectively create detailed psychological profiles; unauthorized exposure violates psychological safety and control. In clinical terms, loss of control ranks among the strongest predictors of anxiety and depression. The contemporary digital environment systematically engineered chronic low-grade loss of control affecting billions through data-harvesting applications, telehealth platforms, and behavioral tracking by advertisers. Fisher identifies this pervasive condition as "surveillance anxiety"—chronic psychological distress resulting from awareness of monitoring without meaningful consent. This perspective fundamentally reframes data privacy from a technical issue to an essential mental health imperative requiring urgent clinical and ethical attention.

Digital healthcare has transformed patient engagement through apps offering tracking, coaching, diagnostics, and therapy. At the core is Software as a Medical Device (SaMD), which differs fundamentally from consumer digital health tools based on intended purpose. SaMD targets medical outcomes such as diagnosis, treatment, or disease monitoring, while consumer apps focus on wellness and lifestyle. This distinction determines regulatory requirements, evidence standards, engineering controls, and commercialization strategies. The global medtech market has surpassed $680 billion in 2025 and is projected to reach $1.3 trillion by 2029, with SaMD representing one of the fastest-growing segments. Real-world examples include LumineticsCore for diabetic retinopathy detection, reSET for substance use disorder treatment, EndeavorRx for ADHD attention improvement, and BlueStar for diabetes management—all FDA-authorized products. Understanding whether software serves medical or wellness purposes is critical, as products often evolve into regulatory gray zones. This classification fundamentally shapes product development, validation approaches, and market strategy, making clarity essential for successful deployment.

Negative symptoms in schizophrenia-spectrum disorders, particularly avolition and asociality, remain highly disabling and treatment-resistant. While clinician-rated scales like the Clinical Assessment Interview for Negative Symptoms and the Brief Negative Symptom Scale have improved assessment, they rely on infrequent interviews and retrospective reporting, missing real-world behavioral dynamics. This narrative review examines whether artificial intelligence can transform passive digital phenotyping—continuous data collection via smartphones and wearable sensors capturing mobility, physical activity, sleep, social rhythms, and speech acoustics—into clinically valid endpoints for negative symptoms. The authors propose a construct-to-endpoint pipeline integrating construct definition, multimodal representation learning, and clinical validation to create interpretable digital markers enabling just-in-time adaptive interventions. Current evidence demonstrates consistent but modest associations between passive sensing modalities and negative symptoms; however, single-modality correlations alone cannot establish valid endpoints. A qualified digital endpoint requires demonstrating analytical validity, clinical validity, longitudinal sensitivity, external transportability, and patient meaningfulness. Significant challenges remain across data-level issues such as resolution mismatch and device heterogeneity, inferential risks including environmental confounding and algorithmic bias, and ethical-regulatory concerns, underscoring the complexity of translating sensor data into clinically actionable insights.

PROCLE represents an innovative advancement in remote therapeutic monitoring through an AI-powered, mobile-first platform designed to personalize patient care delivery. The system utilizes artificial intelligence algorithms to identify optimal care programs tailored to individual patient needs, enabling healthcare providers to coordinate comprehensive treatment through a single unified interface. This approach eliminates fragmentation by consolidating multiple care pathways and multidisciplinary teams into one cohesive platform. A distinctive feature of PROCLE is its flexibility to support both traditional Fee-for-Service and Value-Based Care models, making it adaptable across diverse healthcare economic structures. The mobile-first architecture ensures accessibility and engagement by allowing patients to access monitoring and care coordination through smartphone and portable devices. By automating the matching process between patients and appropriate care programs, PROCLE reduces clinical decision-making burden while potentially improving outcomes through evidence-based program selection. The platform's ability to orchestrate coordinated care across multiple providers addresses a critical gap in fragmented healthcare systems. This solution holds particular significance for organizations transitioning to value-based models and those seeking to enhance remote patient engagement and monitoring capabilities in increasingly digital healthcare environments.

Artificial intelligence is transforming the management of neuropsychiatric disorders by moving beyond diagnostic assistance toward adaptive intervention and personalized disease management. This comprehensive review examines AI-driven approaches for neuropsychiatric assessment and treatment, encompassing computer vision-based behavioral analysis, wearable sensing technologies, neuroimaging biomarkers, digital therapeutics, virtual reality, social robotics, and brain-computer interfaces. A key innovation discussed is the emergence of closed-loop frameworks that combine real-time patient state estimation with adaptive intervention strategies, enabling continuous therapeutic optimization rather than passive observation. However, significant challenges persist, including integration of phenotyping with intervention systems, limited translation from experimental settings to clinical practice, multimodal data representation, interpretability, and generalizability across diverse populations and devices. The research, authored by scientists from Wuhan University, Guangzhou Women and Children's Medical Center, and other Chinese institutions, identifies that prediction accuracy is no longer the primary bottleneck in AI-driven neuropsychiatry. Instead, the critical challenge lies in connecting continuous patient-state estimation with clinically actionable adaptive responses. The review concludes by proposing future directions toward developing clinically grounded, human-AI collaborative neuropsychiatric systems that can effectively address the substantial global disease burden caused by conditions including Alzheimer's disease and Parkinson's disease.

Digital phenotyping leverages smartphone passive sensing to detect depression relapse before clinical symptoms emerge by analyzing behavioral patterns like typing rhythm, movement, and sleep cycles. Rather than relying on population averages, this approach establishes individual baselines to identify meaningful deviations signaling potential relapse. The field has matured from consumer wellness applications toward hospital-affiliated and specialty clinic deployments using vetted platforms under explicit protocols. However, significant implementation barriers remain: designing consent models patients genuinely understand, reducing alert fatigue for clinicians, and establishing clear FDA regulatory pathways for software as a medical device that distinguish clinical tools from wellness trackers. Early research demonstrated linkages between inactive smartphone features and mood state, though high-profile startup failures highlighted privacy and business model vulnerabilities. Recent progress emphasizes reproducibility across diverse populations, as phenotyping models developed in one cohort frequently fail when applied elsewhere due to variations in keyboard layouts, commute patterns, and cultural phone-use norms. Published research in journals like npj Digital Medicine increasingly standardizes features, preprocessing methods, and outcome definitions. By 2026, digital phenotyping for depression represents a narrower but more clinically grounded proposition with genuine potential for preventive psychiatry at scale, contingent on resolving regulatory and workflow integration challenges.

Remote Therapeutic Monitoring (RTM) represents a frequently underutilized yet highly valuable component of CMS care-management programs. Introduced in 2022, RTM monitors non-physiologic therapeutic data across three anchor domains: musculoskeletal status, respiratory status, and cognitive behavioral therapy, enabling physical therapists, occupational therapists, and speech-language pathologists to serve as billing practitioners. Unlike Remote Patient Monitoring, RTM operates with national non-facility averages and allows concurrent billing alongside other programs for the same patient with different practitioners. Sansera's CCM360 platform collects patient-reported outcomes including pain scores, exercise completion, and validated instruments through conversational SMS and app-based flows, with natural language processing identifying clinical signals from free-text responses. The platform enforces strict billing separation, ensuring compliance with the 16-day rule and maintaining independent time ledgers for each program. Automated administration of validated instruments, trending, and integration into therapist documentation provide both clinical value and audit evidence. Organizations offering rehabilitation, respiratory care, or behavioral health services can capture significant unrealized revenue while generating meaningful clinical data through RTM implementation.

CPT code 99457 is the billing code for remote physiologic monitoring treatment management services covering the first 20 minutes of clinical care per calendar month. This code requires involvement from a physician, qualified healthcare professional, or clinical staff under general supervision, and mandates at least one real-time interactive communication with the patient or caregiver. The code is part of the remote patient monitoring family alongside 99453, 99454, 99458, and from January 1, 2026, codes 99445 and 99470. Device setup and supply are billed separately under distinct codes, while additional 20-minute increments use add-on code 99458. Practices cannot report 99091 during the same period as 99457. New regulations expanding the RPM family include 99470 for months with 10-19 minutes of management time and 99445 for device supply covering 2-15 days of data. Accurate coding is critical for clean claims and timely reimbursement, making automation tools valuable for reducing denials and ensuring compliance with evolving CMS guidelines.

Remote Patient Monitoring (RPM) enables patients to collect their own health data using connected medical devices that automatically transmit information to healthcare providers for treatment and condition management. While RPM services—including Digital Mental Health Treatment, Remote Physiologic Monitoring (RPM), and Remote Therapeutic Monitoring (RTM)—are not classified as telehealth under Medicare definitions, the Centers for Medicare and Medicaid Services (CMS) established separate reimbursement pathways beginning in 2018. Remote physiologic monitoring codes (99091, 99453, 99454, 99457, 99458) cover the collection and interpretation of physiologic data, while remote therapeutic monitoring codes (98975, 98976, 98977, 98980, 98981) serve psychiatrists, nurse practitioners, and physical therapists, allowing non-physiological data collection. Each code carries specific requirements outlined in code descriptions. Additionally, chronic care management, principal care management, and transitional care management services may incorporate remote monitoring applications. CMS has progressively expanded coverage across multiple calendar years, with detailed billing processes and policies documented in the CY 2021, 2022, 2024, and 2026 Physician Fee Schedule Final Rules, reflecting the growing integration of remote monitoring into standard clinical practice.