International Journal of Health Technology and Innovation

Perspectives on Overcoming Challenges in Translating Evidence into Practice and Policy in Digital Health

Mahesh Shirke — 2025-08-18

Introduction: Digital health technologies, including telemedicine, mobile health (mHealth), artificial intelligence (AI), and health information exchange platforms, have the potential to revolutionize healthcare by improving access, efficiency, and patient outcomes. However, translating research evidence into practice and policy remains challenging due to barriers such as complex healthcare systems, data privacy concerns, regulatory hurdles, resource constraints, and organizational resistance.
Methods: This article synthesizes insights from field experience and peer-reviewed literature to identify key barriers and propose strategies for effective translation of digital health evidence. References were selected based on their relevance to digital health implementation, focusing on high-impact studies and real-world examples published between 2013 and 2024, sourced from journals like Health Affairs, Healthcare, and Nature Medicine.
Results: Key challenges include the complexity of healthcare systems, data privacy and security concerns, regulatory and policy barriers, resource limitations, and resistance to cultural change. Proposed strategies encompass strengthening data governance and interoperability, enhancing stakeholder collaboration, implementing robust policy frameworks, investing in education and training, and fostering cultural change. Case studies, such as telemedicine adoption during the COVID-19 pandemic and blockchain applications for data security, illustrate successful implementation when these barriers are addressed.
Discussion: The integration of digital health into practice requires a multifaceted approach that addresses systemic, regulatory, and cultural challenges. Future directions include streamlining regulatory processes, enhancing interoperability, addressing health disparities, and scaling solutions without compromising quality. Collaborative, evidence-based strategies are essential to realize the full potential of digital health for equitable and efficient healthcare delivery.

Medical Devices: Powering India’s Health-Tech Future

Shashi Bala Singh — 2025-08-18

India – An Emerging Hub for Medical Devices Manufacturing

Satyaki Banerjee — 2025-08-18

India is transitioning from an import-dependent medical device market to a global manufacturing hub. Building on its pharmaceutical success, the country is now focusing on MedTech self-reliance. Government initiatives like the PLI scheme and dedicated MedTech parks are driving this shift. Innovation by startups and alignment with global regulatory standards are enhancing competitiveness. India is poised to play a major role in making quality medical devices accessible worldwide.

SHESHA: Standardization of Health Education for Sustainable Healthcare Activities

Arjun Thimmaiah — 2025-08-18

Introduction: The Standardization of Health Education for Sustainable Healthcare Activities (SHESHA) addresses the challenge of integrating wellness-focused interventions with diverse healthcare systems, including modern medicine and AYUSH (Ayurveda, Yoga, Unani, Siddha, Homeopathy), to enhance quality of life (QOL) and longevity. The research question is: “How can a digital platform standardize health education and wellness practices across diverse healthcare systems to improve QOL and longevity?”
Methods: SHESHA employs a blockchain-based digital platform for secure, anonymous data collection, focusing on milestone-based interventions across developmental stages (prenatal to late adulthood). The methodology includes stakeholder engagement (healthcare providers, educators, communities), user-friendly interface design (React/Flutter), scalable backend architecture (MongoDB, RESTful/GraphQL APIs), and pilot testing in urban and rural Indian settings. The platform aligns with India’s National Education Policy (NEP) 2020 and the United Nations’ Sustainable Development Goals (SDGs), integrating AYUSH and global wellness practices. AI-driven analytics enable personalized interventions, with data validation through interdisciplinary collaboration.
Results and Discussion: SHESHA is a white paper. As a conceptual framework, SHESHA is yet to be implemented. Preliminary case studies indicate that milestone-based interventions, such as infant visual stimulation, traditional lifestyle interventions, and geriatric yoga, improve developmental outcomes and mobility. The open-source data model and blockchain integration promise enhanced data privacy and interdisciplinary research coordination. SHESHA’s strength lies in bridging traditional and modern healthcare systems through standardized milestones and community-sourced data, fostering sustainable practices. Challenges include data privacy, stakeholder resistance, and funding constraints. Blockchain ensures compliance with privacy regulations, while partnerships with government and innovation hubs support scalability. Alignment with NEP 2020 and SDGs enhances global relevance.
Conclusion: SHESHA offers a novel framework for standardizing health education and promoting sustainable healthcare. Future efforts will focus on prototype development, pilot studies, and stakeholder collaboration to assess its impact on QOL and longevity, with potential for global adoption.

Lessons Learned, Actions Needed: Preparing India for the Next Pandemic Wave

Sai Venkat Mandalapu — 2025-08-18

The COVID-19 pandemic exposed critical vulnerabilities and strengths within India’s health system, prompting an urgent need for comprehensive pandemic preparedness. This study systematically analyzed India’s pandemic response, drawing on global best practices and adapting them to the Indian context. This investigation encompassed public health infrastructure, digital health integration, supply chain resilience, innovations in care delivery, information management, and governance frameworks. Data revealed that India’s rapid vaccine development and deployment, supported by digital platforms and community health workers, enabled the administration of over 1.6 billion doses and improved rural coverage. However, chronic underfunding, fragmented data systems, and supply chain dependencies led to hospital overloads, oxygen shortages, and inequitable care, particularly in rural and marginalized populations. The study found that digital health initiatives, including the creation of unique health IDs and telemedicine expansion, enhanced surveillance and access but were limited by interoperability and connectivity gaps. Localized manufacturing and strategic stockpiling improve supply security, while mobile health units and community-based m care models reduce mortality in underserved areas. Misinformation emerged as a significant barrier, necessitating trusted communication channels and media literacy interventions. The research highlighted the importance of integrating public health and clinical care, establishing reserve health workforces, and prioritizing equity for vulnerable groups, including women, migrant workers, and persons with disabilities. Effective governance, federal-state collaboration, international data sharing, and sustained civil society engagement were identified as essential for future resilience. The findings underscore that a multisectoral, equity-focused approach – grounded in sustained investment, digital innovation, and inclusive governance – is imperative to prepare India for future pandemics

Digital Health Innovation: Integrating Blockchain, Point-of-Care Diagnostics and AI for Rural Telemedicine Delivery

Rohit Jampani — 2025-08-18

This paper presents a conceptual and implementation-focused framework for a next-generation telehealth system tailored to underserved and rural populations. The proposed system leverages blockchain for secure data exchange and consent management, HL7-FHIR interoperability with a national health ID (UHI) framework, modular architecture, edge computing, and AI diagnostics integrated with point-of-care (POC) devices. By combining teleconsultations with local POC testing and AI support, the model aims to improve early diagnosis and continuity of care in remote regions. We highlight how blockchain’s immutable ledgers and smart contracts can enforce patient consent and audit trails, while a standards-compliant FHIR-based data layer and Unique Health IDs (e.g. India’s ABHA) enable seamless record sharing. This approach addresses interoperability and privacy (aligned with WHO and national guidelines) and could dramatically expand access: for example, India’s national telemedicine platform recorded over 318 million remote consults by 2024. The framework is evaluated conceptually for security, performance, and public health impact. Ethical compliance (Declaration of Helsinki 2000/2008) is ensured and all authors declare original work with no conflicts.

Exploring Statistical Relationships for Risk Assessment and Value Computation of Digital Health Technologies in India

Sarada Ghosh — 2025-08-18

In this research, we explore the statistical models that have contributed to determining the value of digital health technologies (DHTs) in global contexts and demonstrate their critical application using diverse datasets within the Indian context. We highlight the incorporation of several key healthcare analytical approaches, such as propensity score matching to evaluate treatment effects in cardiovascular research, structural equation modeling to examine psychosocial factors contributing to academic burnout among college students, and random survival forest classification methods for identifying genetic markers associated with breast cancer prognosis. We utilize a college-level social burnout survey and a comprehensive Kaggle dataset to show the application of these approaches. This is the first study of its kind to highlight both these datasets and key analytical methods, tools that are underutilized in India, while showing their practical relevance for guiding digital health investments and addressing healthcare challenges in the country. This study draws on several case studies with datasets to present a future perspective, where key statistical methodologies play a central role in improving healthcare productivity and promoting personalized care.

Enhanced Fall Detection for Seniors with Sensor Fusion and Optimized AI Techniques

Dwadasi Venkata Sushma Chandra — 2025-08-18

Senior citizens often prefer privacy and may live alone in secluded homes, making fall-related injuries a serious concern, especially during nighttime or in washrooms, where immediate assistance is unavailable. This study proposes an AI-based fall detection approach utilizing particle swarm optimization (PSO) to enhance the accuracy of a sensor fusion mechanism integrated with biomarkers for improved fall assessment. The PSO algorithm optimizes feature selection, refining sensor data interpretation to reduce false alarms while ensuring reliable detection. The analysis is conducted using MATLAB, demonstrating promising insights into the effectiveness of the proposed method in real-world elder care applications.

Discriminative EEG Feature Extraction Using the Adaptive Synchrosqueezing Wavelet Transform for Epileptic Seizure Detection

Arpish R. Solanki — 2025-08-18

This study investigated using the adaptive synchrosqueezing wavelet transform (ASST) for feature extraction in EEG-based epileptic seizure detection. Traditional time-frequency methods, notably the continuous wavelet transform (CWT) and short-time fourier transform (STFT), were used to extract similar features and compared against ASST for seizure detection performance. Classification experiments using support vector machines (SVM), random forest (RF), and K-nearest neighbors (KNN) showed that ASST-derived features led to strong classification performance for distinguishing seizure and non-seizure EEG segments. The random forest classifier achieved the best performance using ASST features, with an accuracy of 99.09%, an F1-score of 97.71%, and an AUC-ROC of 0.9990 after applying class balancing with SMOTE. The results showed that ASST achieved slightly higher classification performance compared to STFT and CWT, suggesting its potential as an effective and adaptive tool for non-stationary EEG analysis in seizure detection.

The Evolution of Electronic Health Records in India: Progress, Challenges, and Future Prospects (2020-2025)

Mandalapu Sai Venkat — 2025-08-18

This study provides a comprehensive evaluation of the evolution of Electronic Health Records (EHRs) in India from 2020 to 2025. Employing a mixed-methods approach, the research synthesized policy analysis, literature review, and secondary data from government reports, peer-reviewed studies, and digital health platforms. The study assesses progress in HER implementation, policy developments, and adoption rates, while critically examining challenges and opportunities that have emerged during this period. The impact of EHRs on public health management, epidemiological insights, and healthcare delivery is investigated, with a focus on implications for universal health coverage. The findings identify key barriers and facilitators to EHR adoption and offer evidence-based recommendations for enhancing EHR effectiveness and scalability in India’s unique healthcare context. This work aims to inform ongoing discourse on digital health transformation in developing nations.
Objective: To comprehensively evaluate the evolution of Electronic Health Records (EHRs) in India from 2020 to 2025, analyzing the progress in implementation, policy developments, and adoption rates; to critically assess the challenges and opportunities that have emerged during this period; and to investigate the impact of EHRs on public health management, epidemiological insights, and healthcare delivery. This study aims to identify key barriers and facilitators to EHR adoption, examine the implications for achieving universal health coverage, and provide evidence-based recommendations for enhancing the effectiveness and scalability of EHR systems in addressing India’s unique healthcare needs, thereby contributing to the ongoing discourse on digital health transformation in developing nations.

Advancing Chemotherapy Care: Integrated Self-Management Tools for Symptom Monitoring and Personalized Interventions

Srimathi E — 2025-08-18

Chemotherapy remains a cornerstone of cancer treatment; however, its administration is often associated with debilitating side effects, including fatigue, nausea, alopecia, neuropathy, and immunosuppression, which significantly impact patients’ quality of life. With the increasing shift toward outpatient chemotherapy, there is a critical need for effective symptom management strategies. This study underscores the role of self-management tools in enhancing patient care during chemotherapy. Existing digital health solutions, including mHealth applications, telemedicine, and drug-tracking platforms, have shown promise in symptom monitoring and remote care. This paper presents an integrated solution that combines a wearable device with a mobile application to enable real-time monitoring of vital signs and symptoms, facilitating timely interventions. By enhancing personalized care and improving treatment outcomes, this approach empowers patients in managing their health more effectively while supporting healthcare providers in delivering proactive and responsive care.