Introduction

Healthcare is an essential aspect of any society, and in India, it holds a unique place due to its immense population and diverse healthcare needs. The integration of technology into healthcare has the potential to be a transformative force, promising improved accessibility, quality, and efficiency. As technology continues to advance at a rapid pace, it is imperative for India to leverage this opportunity and develop a comprehensive roadmap for the adoption of healthcare technology. Technology has the potential to revolutionise healthcare in India by improving the delivery of medical services, enhancing patient outcomes, and increasing the efficiency of healthcare processes (Davenport & Kalakota, 2019). However, realising this potential is not without its hurdles. India faces challenges that must be navigated to fully harness the benefits of healthcare technology. The process of technology adoption in healthcare is influenced by factors such as ease of use, perceived usefulness, and regulatory pressures, all of which need careful consideration (Holden & Karsh, 2010). Through this study we scrutinize the adoption trends and the formidable barriers, including regulatory requirements.

Theoretical Foundation 

The Technology Adoption Model (TAM), proposed by Fred Davis, provides a robust theoretical foundation for examining the acceptance and adoption of new technologies within specific contexts. 

TAM posits that perceived ease of use and perceived usefulness are critical determinants influencing users' attitudes and, consequently, their intention to adopt a particular technology (Davis, 1989). In the context of Indian healthcare, understanding the applicability and relevance of TAM is essential for assessing how healthcare professionals and organizations perceive and embrace technological innovations. Various studies have demonstrated the applicability of TAM in healthcare settings globally, emphasizing the importance of user perceptions in technology adoption (Holden & Karsh, 2010). In the Indian healthcare context, where cultural, infrastructural, and resource-related factors play a significant role, TAM provides a lens through which the unique challenges and opportunities of technology adoption can be analysed. Recognising the relevance of TAM in this context is crucial for developing targeted strategies that address the specific concerns of healthcare professionals and organisations in India (Venkatesh & Davis, 2000).

Methodology

We systematically searched literature databases including PubMed, Google Scholar, Embase and JSTOR to gather articles pertaining to various themes such as technology acceptance in healthcare, the role of local governments, AI in healthcare, telemedicine, and electronic health records in India. By using "AND" and "OR" operators in our search strategy, we obtained a total of 636 articles. These articles underwent screening based on specific inclusion criteria, including English language, publication dates ranging from 2000 to 2023, and content types such as empirical data analysis, systematic and scoping literature reviews. Ultimately, 37 articles aligned with these criteria and were subjected to thorough review focusing on the aforementioned themes. We extensively analyzed the findings of these articles through thematic coding to identify key patterns and themes related to technology adoption in Indian healthcare. We also tried to map the evolution of technology integration across three distinct phases spanning from 2000 to 2023. This analysis forms the cornerstone of our study, delineating the progression and assimilation of technology within the landscape of Indian healthcare. This analysis serves as the basis for generating policy recommendations.

Technology Adoption in Indian Healthcare

We attempt to trace the evolution of the technology integration in healthcare. For this purpose we classify the periods as a) early adoption phase pre 2010, b) Initial adoption phase 2010-2020, and c) current phase 2020- till date. The reason being, the first decade of the 21st century saw the birth of new technology and their application in various fields. Nevertheless, the use of this technology was very limited in healthcare. The second decade saw application of the technology with significant healthcare outcomes. Table 1 elaborates the phases.

Table 1: Phases of technology evolution in Indian healthcare

Artificial Intelligence and Healthcare

Artificial Intelligence (AI) is rapidly transforming the healthcare across the world, presenting a huge potential for diagnostic accuracy, treatment advancements, and enhanced patient care. Projections indicate a significant upsurge in AI investment within India, expected to reach $11.78 billion by 2025, thereby potentially contributing $1 trillion to the nation's economy by 2035, according to a World Economic Forum report. AI in Healthcare is forecasted to grow from $14.6 billion in 2023 to $102.7 billion by 2028. 

Application of AI in healthcare

AI technology has made significant strides in revolutionizing healthcare across various domains. Medical Imaging applications leverage Convolutional Neural Networks (CNNs) and Image Segmentation to interpret and analyse medical images, enhancing diagnostic accuracy. In Drug Discovery, predictive models, Molecular Docking, and Virtual Screening expedite the development of new drugs. Electronic Health Records (EHR) benefit from AI-driven Natural Language Processing (NLP) and Predictive Analytics, aiding in patient data management and analysis. Predictive Analytics utilizes Data Mining and Predictive Modeling to forecast patient outcomes and identify high-risk cases (Bohr & Memarzadeh, 2020). Telemedicine integrates AI for remote diagnosis and treatment, utilizing Telehealth Platforms, Video Conferencing, and AI Chatbots. 

Stakeholder Perception on AI 

We try to explore the acceptance and challenges associated with the stakeholders of healthcare technology using the TAM. AI holds a crucial role in the future of healthcare, especially in precision medicine through machine learning, yet its widespread adoption faces challenges including regulatory approval, integration with EHR systems, standardisation, clinician training, funding, and continuous updates, leading to anticipated limited use in clinical practice (Davenport & Kalakota, 2019). Particularly when it comes to the stakeholders’ perception on safety, responsible innovation and adoption of healthcare AI that extends beyond technology-centric perspectives to legal, and societal implications. Further when it comes to precision medicine through AI, it has yielded breakthroughs according to F. Wang & Preininger, 2019 but necessitates a collaborative, inclusive, and data-driven approach to ensure equitable access and implementation for diverse populations in a learning healthcare system.

According to (Thakkar & Bharathi, 2023) Medical specialists view AI in healthcare as a promising technology with potential adoption intentions, suggesting a need for longitudinal studies and comprehensive consideration of factors influencing their behavioural intentions for AI adoption. Similarly, studies conducted by Al-Dhaen et al., 2021 reveal that the healthcare professionals' continuous intention to use AI-based IoMT hinges on factors like relative advantage, complexity, and compatibility, moderated by motivation, training, and awareness, promoting responsible usage in healthcare settings. Despite studies revealing the healthcare professionals’ intent to adapt to AI in Healthcare, there still exists a little skepticism on the “Blackbox” nature of AI. Weighing on the advantages of AI, experts show inclination towards AI-assisted diagnosis and treatment. Research also highlights the influence of expectancy factors, social influence, and Human-Computer Trust (HCT) (Cheng et al., 2022), providing insights for enhancing technology and crafting policies for reliable AI implementation across diverse sectors.

Regulatory requirement

The increasing integration of AI in healthcare demands moral accountability, emphasizing the need to mitigate data bias, ensure diverse and inclusive programming, and conduct regular algorithm audits. AI complements clinical judgment, aiding in decision-making, particularly in contexts with limited medical expertise. While AI decisions are systematic, the accountability lies with both creators and users, even without clear legal frameworks. 

Despite moral dilemmas, AI's potential to coexist or replace existing systems signifies the advent of an AI-driven healthcare era, where not utilising AI could be deemed unscientific and unethical (Naik, Hameed, Shetty, et al., 2022; Siala & Wang, 2022). Challenges in AI adoption within healthcare systems include various issues such as data privacy, ethical considerations, and technological limitations, while the pursuit of a more comprehensive, universal AI aims to address current limitations and revolutionise healthcare with sophisticated algorithms and expanded capabilities (Bajwa et al., 2021). Prakash et al., 2022 in their article provide justifications that aim to navigate the ethical challenges associated with AI in healthcare by promoting regulatory oversight, ethics education, transparency, inclusivity in ethical debates, and continuous evaluation to foster responsible and ethical AI implementation.

Telemedicine in practice

Telemedicine is defined by the World Health Organisation as “the delivery of healthcare services, where distance is a critical factor, by all healthcare professionals using information and communication technologies for the exchange of valid information for the diagnosis, treatment and prevention of disease and injuries, research and evaluation, and for the continuing education of healthcare providers, all in the interests of advancing the health of individuals and their communities.” (WHO Global Observatory for eHealth, 2010)

Telemedicine in India has undergone a remarkable progress, driven by technological advancements and healthcare accessibility needs. Its adoption gained attention with the establishment of the Apollo Telemedicine Networking Foundation with the aid of ISRO, pioneering tele-consultations and diagnostic services across remote areas (Ganapathy & Ravindra, 2009). Subsequently, the government launched initiatives like the National Telemedicine Taskforce and the National Telemedicine Grid, aiming to extend healthcare services to underserved regions (National Health Portal, Government of India). The COVID-19 pandemic further accelerated telemedicine adoption in India, prompting regulatory changes, such as the Telemedicine Practice Guidelines in 2020 facilitating remote consultations. 

Telemedicine in primary care is generally feasible and accepted, preferred by patients over healthcare providers, with varying demographic acceptance, while showing comparable effectiveness to traditional care and increasing evidence of cost-effectiveness (Bashshur et al., 2016). Telemedicine's comprehensive implementation demonstrates increased public interest during the COVID-19 pandemic, generating high user satisfaction due to improved accessibility, especially for distant consultations, yet necessitating transitions like video consultations for better acceptance (Hincapié et al., 2020). When it comes to the ethical dimension, Aneja & Arora, 2021, emphasize the need for comprehensive education, legal frameworks, and ethical considerations to facilitate remote healthcare services in India.

Telemedicine has also demonstrated effectiveness as a reliable and cost-efficient patient-care tool, particularly benefiting individuals facing medical conditions leading to embarrassment, social exclusion, and diminished self-esteem, thereby enhancing health-related quality-of-life (Naik, Hameed, Nayak, et al., 2022). Its successful implementation spans across diverse healthcare settings and specialities. In urban areas, telemedicine shows high acceptance and satisfaction, yet its limited adoption in developing countries stems from technology availability and awareness gaps, necessitating further regional studies for targeted improvement (Kumar et al., 2022).

Electronic Health Records (EHR) implementation and challenges

India has been striving to establish standardized guidelines and protocols concerning EHRs. The Ministry of Health and Family Welfare introduced the EHR Standards in 2016 to streamline the digitization of health records across the country. These standards aim to define a uniform framework for the creation, storage, exchange, and retrieval of health information in electronic formats. They encompass various aspects such as data structure, coding systems, privacy and security measures, interoperability, and guidelines for healthcare providers to ensure consistent and secure handling of patient information. It seeks to promote interoperability among different healthcare systems and facilitate the seamless sharing of patient data while prioritizing patient confidentiality and data security (Zayas-Cabán & Wald, 2020).

The Health Record IT Standards delineated in Health Informatics - Identification of Subjects of Health Care and MDDS - Demographic (Person Identification and Land Region Codification) by the Government of India's E-Governance Standards, establish the criteria for recording patient demographics and specific identifiers. These standards foster uniformity, completeness, and compatibility in capturing patient demographic data within health record systems, offering multiple identification options based on availability and compliance with regulations. The implementation of EHR offers substantial benefits such as increased productivity, enhanced data quality, and improved data management efficiency, albeit with challenges like missing data, interoperability standards absence, and productivity loss due to training and data-entry requirements (Kruse et al., 2018). It is found that the perception influences the identification of factors as both facilitators and barriers in healthcare technology adoption, exemplified by instances such as error, cost, time-related issues, and organizational characteristics like size and location. (Kruse et al., 2016).

Interconnectivity and data sharing

Achieving seamless connectivity among various healthcare systems and facilitating secure data sharing are critical for improving patient care, optimizing resources, and fostering innovation in the healthcare sector (Amjad et al., 2023). Fragmented systems, varying standards, and concerns about data security impede the seamless exchange of patient information. Initiatives like the National Digital Health Mission (NDHM) aim to address these challenges by establishing a unified health record for each citizen (NDHM, 2020). However, Abouelmehdi et al., 2018 emphasize the need for ensuring standardized protocols, data privacy, and secure sharing mechanisms as determinants for successful implementation.

(Kim & Choi, 2019) in their research on privacy and data sharing find that the older adults exhibit selective information sharing preferences in healthcare technologies, emphasizing the need for personalized control over data sharing while expressing lower trust in government agencies, urging the prioritization of privacy considerations and diverse needs, in designing healthcare technology and services.

The Pivotal Role of Local Governments

Local governments play a crucial role in facilitating and supporting the adoption of technology in Indian healthcare. Their involvement spans infrastructure development, community engagement, and essential support at the grassroots level, significantly impacting the success of healthcare technology implementation. Local governments are instrumental in providing the necessary infrastructure for healthcare technology integration. This includes ensuring reliable power supply, internet connectivity, and establishing IT frameworks essential for the seamless functioning of digital health systems (Rao, 2019).  

Engaging with communities is vital for successful technology adoption in healthcare. Local governments facilitate community outreach programs to educate and create awareness about the benefits of technological interventions. They collaborate with healthcare providers to ensure that technology is accessible and understood by the diverse population, overcoming potential barriers of language, culture, or socioeconomic status (Haldane et al., 2019). Several local government initiatives across India showcase successful implementations of healthcare technology. For instance, the Health Management Information System (HMIS) implemented by the Government of Kerala has significantly enhanced healthcare data management and patient care delivery. 

Local government-led telemedicine programs in states like Karnataka and Maharashtra have demonstrated improved access to healthcare services in remote areas, reducing the burden of travel for patients. Local governments in various Indian states have initiated innovative public-private partnerships (PPPs) to enhance healthcare infrastructure, leveraging technology to expand access and improve healthcare delivery (Dwivedi & Bhargava, 2022).  The involvement of local governments is paramount for the successful implementation and sustainability of healthcare.

Thematic coding and policy suggestions

Discussion

The comprehensive exploration of healthcare technology adoption in India reveals a landscape marked by promising advancements and persistent challenges. The evolution across phases highlights significant strides made in adopting technologies like telemedicine, Electronic Health Records (EHR), and Artificial Intelligence (AI), However, this progress isn't uniform, characterized by disparities in adoption rates influenced by factors like infrastructure, awareness, and regulatory complexities. The role of local governments emerges as pivotal in overcoming barriers, fostering community engagement, and providing essential infrastructure for successful technology integration. The literature review underscores the critical need for tailored approaches considering India's socio-economic diversity, cultural nuances, and the digital divide, emphasizing the importance of inclusive strategies to ensure equitable access to healthcare technology.

Conclusion

The exploration of healthcare technology adoption in India signifies the significant strides made and the multifaceted challenges encountered in embracing digital solutions. The analysis underlines the transformative potential of technology in revolutionising healthcare accessibility, quality, and efficiency. Yet, the realisation of this potential demands concerted efforts, encompassing regulatory harmonisation, infrastructural advancements, and addressing the varied perceptions and challenges faced by stakeholders. Local governments emerge as crucial catalysts in steering technology integration, playing pivotal roles in infrastructure development, community engagement, and public-private collaborations. The formulation of a comprehensive roadmap for sustained technology adoption must address the complex relationship between technological innovations, regulatory frameworks, and socio-economic factors, while leveraging the proactive involvement of local governments to ensure equitable and impactful healthcare technology implementation across diverse regions of India.

References

1. Abouelmehdi, K., Beni-Hessane, A., & Khaloufi, H. (2018). Big healthcare data: Preserving security and privacy. Journal of Big Data, 5(1), 1.  https://doi.org/10.1186/s40537-017-0110-7

2. Al-Dhaen, F., Hou, J., Rana, N. P., & Weerakkody, V. (2021). Advancing the Understanding of the Role of Responsible AI in the Continued Use of IoMT in Healthcare. Information Systems Frontiers. https://doi.org/10.1007/s10796-021-10193-x

3. Amjad, A., Kordel, P., & Fernandes, G. (2023). A Review on Innovation in Healthcare Sector (Telehealth) through Artificial Intelligence. Sustainability, 15(8). https://doi.org/10.3390/su15086655

4. Aneja, J., & Arora, S. (2021). Telemedicine and ethics: Opportunities in India. Indian Journal of Medical Ethics, 314–320.  https://doi.org/10.20529/IJME.2021.042

5. Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthcare Journal, 8(2), e188–e194. https://doi.org/10.7861/fhj.2021-0095

6. Bashshur, R. L., Howell, J. D., Krupinski, E. A., Harms, K. M., Bashshur, N., & Doarn, C. R. (2016). The Empirical Foundations of Telemedicine Interventions in Primary Care. Telemedicine and E-Health, 22(5), 342–375. https://doi.org/10.1089/tmj.2016.0045

7. Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. In Artificial Intelligence in Healthcare (pp. 25–60). Elsevier. https://doi.org/10.1016/B978-0-12-818438-7.00002-2

8. Cheng, M., Li, X., & Xu, J. (2022). Promoting Healthcare Workers’ Adoption Intention of Artificial-Intelligence-Assisted Diagnosis and Treatment: The Chain Mediation of Social Influence and Human–Computer Trust. International Journal of Environmental Research and Public Health, 19(20), 13311. https://doi.org/10.3390/ijerph192013311

9. Dash, S., Aarthy, R., & Mohan, V. (2021). Telemedicine during COVID-19 in India—A new policy and its challenges. Journal of Public Health Policy, 42(3), 501–509. https://doi.org/10.1057/s41271-021-00287-w

10. Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. https://doi.org/10.7861%2Ffuturehosp.6-2-94

11. Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008

12. Dawkins, E., André, K., Axelsson, K., Benoist, L., Swartling, Å. G., & Persson, Å. (2019). Advancing sustainable consumption at the local government level: A literature review. Journal of Cleaner Production, 231, 1450–1462. https://doi.org/10.1016/j.jclepro.2019.05.176

13. Dwivedi, N., & Bhargava, R. (2022, September 9). Public-private partnerships can benefit healthcare in India. World Economic Forum.  https://www.weforum.org/agenda/2022/09/public-private-partnerships-india-healthcare-ecosystem/

14. Ganapathy, K., & Ravindra, A. (2009). Telemedicine in India: The Apollo Story. Telemedicine and E-Health, 15(6), 576–585.  https://doi.org/10.1089/tmj.2009.0066

15. Garavand, A., Mohseni, M., Asadi, H., Etemadi, M., Moradi-Joo, M., & Moosavi, A. (2016). Factors influencing the adoption of health information technologies: A systematic review. Electronic Physician, 8(8), 2713–2718. 

https://doi.org/10.19082/2713

16. Gupta, A., Thorpe, C., Bhattacharyya, O., & Zwarenstein, M. (2016). Promoting development and uptake of health innovations: The Nose to Tail Tool. F1000Research, 5, 361. https://doi.org/10.12688/f1000research.8145.1

17. Haldane, V., Chuah, F. L. H., Srivastava, A., Singh, S. R., Koh, G. C. H., Seng, C. K., & Legido-Quigley, H. (2019). Community participation in health services development, implementation, and evaluation: A systematic review of empowerment, health, community, and process outcomes. PLOS ONE, 14(5), e0216112. https://doi.org/10.1371/journal.pone.0216112

18. Hincapié, M. A., Gallego, J. C., Gempeler, A., Piñeros, J. A., Nasner, D., & Escobar, M. F. (2020). Implementation and Usefulness of Telemedicine During the COVID-19 Pandemic: A Scoping Review. Journal of Primary Care & Community Health, 11, 215013272098061.  https://doi.org/10.1177/2150132720980612

19. Holden, R. J., & Karsh, B.-T. (2010). The Technology Acceptance Model: Its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159–172. https://doi.org/10.1016/j.jbi.2009.07.002

20. Kim, T. K., & Choi, M. (2019). Older adults’ willingness to share their personal and health information when adopting healthcare technology and services. International Journal of Medical Informatics, 126, 86–94.  https://doi.org/10.1016/j.ijmedinf.2019.03.010

21. Kruse, C. S., Kothman, K., Anerobi, K., & Abanaka, L. (2016). Adoption Factors of the Electronic Health Record: A Systematic Review. JMIR Medical Informatics, 4(2), e19. https://doi.org/10.2196/medinform.5525

22. Kruse, C. S., Stein, A., Thomas, H., & Kaur, H. (2018). The use of Electronic Health Records to Support Population Health: A Systematic Review of the Literature. Journal of Medical Systems, 42(11), 214.  https://doi.org/10.1007/s10916-018-1075-6

23. Kumar, S., Kumar, A., Sinha, S., Qureshi, O. A., Aggarwal, N., Khan, K., & Jameel, J. (2022). Patient Preferences Regarding Telemedicine to In-person Consultation: A Questionnaire-Based Survey. Indian Journal of Orthopaedics, 56(12), 2202–2209. https://doi.org/10.1007/s43465-022-00750-0

24. Naik, N., Hameed, B. M. Z., Nayak, S. G., Gera, A., Nandyal, S. R., Shetty, D. K., Shah, M., Ibrahim, S., Naik, A., Kamath, N., Mahdaviamiri, D., D’costa, K. K., Rai, B. P., Chlosta, P., & Somani, B. K. (2022). Telemedicine and Telehealth in Urology—What Do the ‘Patients’ Think About It? Frontiers in Surgery, 9, 863576. https://doi.org/10.3389/fsurg.2022.863576

25. Naik, N., Hameed, B. M. Z., Shetty, D. K., Swain, D., Shah, M., Paul, R., Aggarwal, K., Ibrahim, S., Patil, V., Smriti, K., Shetty, S., Rai, B. P., Chlosta, P., & Somani, B. K. (2022). Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility? Frontiers in Surgery, 9, 862322. https://doi.org/10.3389/fsurg.2022.862322

26. NDHM. (2020). [Gov]. National Digital Health Mission | Make in India. https://www.makeinindia.com/national-digital-health-mission

27. Prakash, S., Balaji, J. N., Joshi, A., & Surapaneni, K. M. (2022). Ethical Conundrums in the Application of Artificial Intelligence (AI) in Healthcare—A Scoping Review of Reviews. Journal of Personalized Medicine, 12(11), 1914. https://doi.org/10.3390/jpm12111914

28. Prinja, S., Downey, L. E., Gauba, V. K., & Swaminathan, S. (2018). Health Technology Assessment for Policy Making in India: Current Scenario and Way Forward. PharmacoEconomics - Open, 2(1), 1–3.  https://doi.org/10.1007/s41669-017-0037-0

29. Saeed, S. A., & Masters, R. M. (2021). Disparities in Health Care and the Digital Divide. Current Psychiatry Reports, 23(9), 61. https://doi.org/10.1007/s11920-021-01274-4

30. Siala, H., & Wang, Y. (2022). SHIFTing artificial intelligence to be responsible in healthcare: A systematic review. Social Science & Medicine, 296, 114782. https://doi.org/10.1016/j.socscimed.2022.114782

31. Srivastava, S. K. (2016). Adoption of Electronic Health Records: A Roadmap for India. Healthcare Informatics Research, 22(4), 261.  https://doi.org/10.4258/hir.2016.22.4.261

32. Thakkar, B., & Bharathi, S. V. (2023). Medical Specialists’ Perception About Adoption of Artificial Intelligence in the Healthcare Sector. CARDIOMETRY, 25, 426–434. https://doi.org/10.18137/cardiometry.2022.25.426434

33. Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

34. Wang, F., & Preininger, A. (2019). AI in Health: State of the Art, Challenges, and Future Directions. Yearbook of Medical Informatics, 28(01), 016–026. https://doi.org/10.1055/s-0039-1677908

35. Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3–13.  https://doi.org/10.1016/j.techfore.2015.12.019

36. WHO Global Observatory for eHealth. (2010). Telemedicine: Opportunities and developments in Member States: Report on the second global survey on eHealth. WHO IRIS. https://iris.who.int/handle/10665/44497

37. Zayas-Cabán, T., & Wald, J. S. (2020). Opportunities for the use of health information technology to support research. JAMIA Open, 3(3), 321–325. https://doi.org/10.1093/jamiaopen/ooaa037