Digital health: trends, opportunities and challenges in medical devices, pharma and bio-technology

Springer Science and Business Media LLC - Tập 11 - Trang 11-30 - 2023
Naresh Kasoju1, N. S. Remya1, Renjith Sasi1, S. Sujesh1, Biju Soman1, C. Kesavadas1, C. V. Muraleedharan1, P. R. Harikrishna Varma1, Sanjay Behari1
1Sree Chitra Tirunal Institute for Medical Science and Technology, Thiruvananthapuram, India

Tóm tắt

Digital health interventions refer to the use of digital technology and connected devices to improve health outcomes and healthcare delivery. This includes telemedicine, electronic health records, wearable devices, mobile health applications, and other forms of digital health technology. To this end, several research and developmental activities in various fields are gaining momentum. For instance, in the medical devices sector, several smart biomedical materials and medical devices that are digitally enabled are rapidly being developed and introduced into clinical settings. In the pharma and allied sectors, digital health-focused technologies are widely being used through various stages of drug development, viz. computer-aided drug design, computational modeling for predictive toxicology, and big data analytics for clinical trial management. In the biotechnology and bioengineering fields, investigations are rapidly growing focus on digital health, such as omics biology, synthetic biology, systems biology, big data and personalized medicine. Though digital health-focused innovations are expanding the horizons of health in diverse ways, here the development in the fields of medical devices, pharmaceutical technologies and biotech sectors, with emphasis on trends, opportunities and challenges are reviewed. A perspective on the use of digital health in the Indian context is also included.

Tài liệu tham khảo

Digital Health Market Size, Share & Trends Report, 2030. https://www.grandviewresearch.com/industry-analysis/digital-health-market Digital Health—India. https://www.statista.com/outlook/dmo/digital-health/india Smartphone-based patient monitoring global market report 2022. https://www.businesswire.com/news/home/20230104005482/en/Smartphone-Based-Patient-Monitoring-Global-Market-Report-2022-Featuring-Leading-Players---Apple-Boston-Scientific-Cerner-Medtronic-and-Phillips-Healthcare---ResearchAndMarkets.com Dunn J, Runge R, Snyder M (2018) Wearables and the medical revolution. Pers Med 15:429–448. https://doi.org/10.2217/pme-2018-0044 Venkatesan M, Mohan H, Ryan JR et al (2021) Virtual and augmented reality for biomedical applications. Cell Rep Med 2:100348. https://doi.org/10.1016/j.xcrm.2021.100348 Vinolo Gil MJ, Gonzalez-Medina G, Lucena-Anton D et al (2021) Augmented reality in physical therapy: systematic review and meta-analysis. JMIR Serious Games 9:e30985. https://doi.org/10.2196/30985 Chen M, Decary M (2020) Artificial intelligence in healthcare: an essential guide for health leaders. Healthc Manag Forum 33:10–18. https://doi.org/10.1177/0840470419873123 Li L, Lou Z, Chen D et al (2018) Recent advances in flexible/stretchable supercapacitors for wearable electronics. Small 14:1702829. https://doi.org/10.1002/smll.201702829 Choi S, Lee H, Ghaffari R et al (2016) Recent advances in flexible and stretchable bio-electronic devices integrated with nanomaterials. Adv Mater 28:4203–4218. https://doi.org/10.1002/adma.201504150 Gous N, Boeras DI, Cheng B et al (2018) The impact of digital technologies on point-of-care diagnostics in resource-limited settings. Expert Rev Mol Diagn 18:385–397. https://doi.org/10.1080/14737159.2018.1460205 Salem M, Elkaseer A, El-Maddah IAM et al (2022) Non-invasive data acquisition and iot solution for human vital signs monitoring: applications. Limit Future Prospects Sens 22:6625. https://doi.org/10.3390/s22176625 Belushkin A, Yesilkoy F, Altug H (2018) Nanoparticle-enhanced plasmonic biosensor for digital biomarker detection in a microarray. ACS Nano 12:4453–4461. https://doi.org/10.1021/acsnano.8b00519 Kar A, Ahamad N, Dewani M et al (2022) Wearable and implantable devices for drug delivery: applications and challenges. Biomaterials 283:121435. https://doi.org/10.1016/j.biomaterials.2022.121435 Long Y, Li J, Yang F et al (2021) Wearable and implantable electroceuticals for therapeutic electrostimulations. Adv Sci 8:2004023. https://doi.org/10.1002/advs.202004023 Pilotto A, Rizzetti MC, Lombardi A et al (2021) Cerebellar rTMS in PSP: a double-blind sham-controlled study using mobile health technology. Cerebellum 20:662–666. https://doi.org/10.1007/s12311-021-01239-6 Farahani M, Shafiee A (2021) Wound healing: from passive to smart dressings. Adv Healthc Mater 10:2100477. https://doi.org/10.1002/adhm.202100477 Gore JC (2020) Artificial intelligence in medical imaging. Magn Reson Imaging 68:A1–A4. https://doi.org/10.1016/j.mri.2019.12.006 Pugliese L, Marconi S, Negrello E et al (2018) The clinical use of 3D printing in surgery. Update Surg 70:381–388. https://doi.org/10.1007/s13304-018-0586-5 Sun L, Wong Y (2019) Personalized three-dimensional printed models in congenital heart disease. J Clin Med 8:522. https://doi.org/10.3390/jcm8040522 Sirajuddin A, Mirmomen SM, Kligerman SJ et al (2021) Ischemic heart disease: noninvasive imaging techniques and findings. Radiographics 41:E990–E1021. https://doi.org/10.1148/rg.2021200125 Yang D, Martinez C, Visuña L et al (2021) Detection and analysis of COVID-19 in medical images using deep learning techniques. Sci Rep 11:19638. https://doi.org/10.1038/s41598-021-99015-3 Shan R, Sarkar S, Martin SS (2019) Digital health technology and mobile devices for the management of diabetes mellitus: state of the art. Diabetologia 62:877–887. https://doi.org/10.1007/s00125-019-4864-7 Ong DSY, Poljak M (2020) Smartphones as mobile microbiological laboratories. Clin Microbiol Infect 26:421–424. https://doi.org/10.1016/j.cmi.2019.09.026 Coons SJ, Eremenco S, Lundy JJ et al (2015) Capturing patient-reported outcome (PRO) data electronically: the past, present, and promise of epro measurement in clinical trials. Patient Cent Outcomes Res 8:301–309. https://doi.org/10.1007/s40271-014-0090-z Dinh-Le C, Chuang R, Chokshi S, Mann D (2019) Wearable health technology and electronic health record integration: scoping review and future directions. JMIR MHealth UHealth 7:e12861. https://doi.org/10.2196/12861 Shen Y-T, Chen L, Yue W-W, Xu H-X (2021) Digital technology-based telemedicine for the COVID-19 pandemic. Front Med 8:646506. https://doi.org/10.3389/fmed.2021.646506 Guo C, Ashrafian H, Ghafur S et al (2020) Challenges for the evaluation of digital health solutions—a call for innovative evidence generation approaches. NPJ Digit Med 3:110. https://doi.org/10.1038/s41746-020-00314-2 Kim J, Campbell AS, de Ávila BE-F, Wang J (2019) Wearable biosensors for healthcare monitoring. Nat Biotechnol 37:389–406. https://doi.org/10.1038/s41587-019-0045-y Koydemir HC, Ozcan A (2018) Wearable and implantable sensors for biomedical applications. Annu Rev Anal Chem 11:127–146. https://doi.org/10.1146/annurev-anchem-061417-125956 Wang C, Xia K, Wang H et al (2019) Advanced carbon for flexible and wearable electronics. Adv Mater 31:1801072. https://doi.org/10.1002/adma.201801072 Correia DM, Fernandes LC, Fernandes MM et al (2021) Ionic liquid-based materials for biomedical applications. Nanomaterials 11:2401. https://doi.org/10.3390/nano11092401 Choi DY, Kim MH, Oh YS et al (2017) Highly stretchable, hysteresis-free ionic liquid-based strain sensor for precise human motion monitoring. ACS Appl Mater Interfaces 9:1770–1780. https://doi.org/10.1021/acsami.6b12415 Yamada S, Toshiyoshi H (2020) Temperature sensor with a water-dissolvable ionic gel for ionic skin. ACS Appl Mater Interfaces 12:36449–36457. https://doi.org/10.1021/acsami.0c10229 Zhang H, Lowe A, Kalra A, Yu Y (2021) A flexible strain sensor based on embedded ionic liquid. Sensors 21:5760. https://doi.org/10.3390/s21175760 Yu Z, Wu P (2021) Water-resistant ionogel electrode with tailorable mechanical properties for aquatic ambulatory physiological signal monitoring. Adv Funct Mater 31:2107226. https://doi.org/10.1002/adfm.202107226 Esteves C, Palma SICJ, Costa HMA et al (2022) Tackling humidity with designer ionic liquid-based gas sensing soft materials. Adv Mater 34:2107205. https://doi.org/10.1002/adma.202107205 Curto VF, Fay C, Coyle S et al (2012) Real-time sweat pH monitoring based on a wearable chemical barcode micro-fluidic platform incorporating ionic liquids. Sens Actuators B Chem 171–172:1327–1334. https://doi.org/10.1016/j.snb.2012.06.048 Zandu SK, Chopra H, Singh I (2020) Ionic liquids for therapeutic and drug delivery applications. Curr Drug Res Rev 12:26–41. https://doi.org/10.2174/2589977511666191125103338 Jian M, Wang C, Wang Q et al (2017) Advanced carbon materials for flexible and wearable sensors. Sci China Mater 60:1026–1062. https://doi.org/10.1007/s40843-017-9077-x Castro KPR, Colombo RNP, Iost RM et al (2023) Low-dimensionality carbon-based biosensors: the new era of emerging technologies in bioanalytical chemistry. Anal Bioanal Chem. https://doi.org/10.1007/s00216-023-04578-x Das S, Pal M (2020) Review—non-invasive monitoring of human health by exhaled breath analysis: a comprehensive review. J Electrochem Soc 167:037562. https://doi.org/10.1149/1945-7111/ab67a6 Pang J, Bachmatiuk A, Yang F et al (2021) Applications of carbon nanotubes in the internet of things era. Nano-Micro Lett 13:191. https://doi.org/10.1007/s40820-021-00721-4 Yi J, Xianyu Y (2022) Gold nanomaterials-implemented wearable sensors for healthcare applications. Adv Funct Mater 32:2113012. https://doi.org/10.1002/adfm.202113012 Ali I, Chen L, Huang Y et al (2018) Humidity-responsive gold aerogel for real-time monitoring of human breath. Langmuir 34:4908–4913. https://doi.org/10.1021/acs.langmuir.8b00472 Haine AT, Niidome T (2017) Gold nanorods as nanodevices for bioimaging, photothermal therapeutics, and drug delivery. Chem Pharm Bull (Tokyo) 65:625–628. https://doi.org/10.1248/cpb.c17-00102 Jin H, Jin Q, Jian J (2018) Smart materials for wearable healthcare devices. In: Ortiz JH (ed) Wearable technologies. InTech Choo-Smith L-P, Edwards HGM, Endtz HP et al (2002) Medical applications of Raman spectroscopy: from proof of principle to clinical implementation. Biopolymers 67:1–9. https://doi.org/10.1002/bip.10064 Kothari R, Jones V, Mena D et al (2021) Raman spectroscopy and artificial intelligence to predict the Bayesian probability of breast cancer. Sci Rep 11:6482. https://doi.org/10.1038/s41598-021-85758-6 Deore AB, Dhumane JR, Wagh R, Sonawane R (2019) The stages of drug discovery and development process. Asian J Pharm Res Dev 7:62–67. https://doi.org/10.22270/ajprd.v7i6.616 Hughes J, Rees S, Kalindjian S, Philpott K (2011) Principles of early drug discovery: principles of early drug discovery. Br J Pharmacol 162:1239–1249. https://doi.org/10.1111/j.1476-5381.2010.01127.x Showell GA, Mills JS (2003) Chemistry challenges in lead optimization: silicon isosteres in drug discovery. Drug Discov Today 8:551–556. https://doi.org/10.1016/S1359-6446(03)02726-0 Vemula D, Jayasurya P, Sushmitha V et al (2023) CADD, AI and ML in drug discovery: a comprehensive review. Eur J Pharm Sci 181:106324. https://doi.org/10.1016/j.ejps.2022.106324 Kist R, Timmers LFSM, Caceres RA (2018) Searching for potential mTOR inhibitors: ligand-based drug design, docking and molecular dynamics studies of rapamycin binding site. J Mol Graph Model 80:251–263. https://doi.org/10.1016/j.jmgm.2017.12.015 Aparoy P, Kumar Reddy K, Reddanna P (2012) Structure and ligand based drug design strategies in the development of novel 5- LOX inhibitors. Curr Med Chem 19:3763–3778. https://doi.org/10.2174/092986712801661112 Kanakaveti V, Shanmugam A, Ramakrishnan C et al (2020) Computational approaches for identifying potential inhibitors on targeting protein interactions in drug discovery. In: Advances in protein chemistry and structural biology. Elsevier, pp 25–47 Chikhale RV, Gupta VK, Eldesoky GE et al (2021) Identification of potential anti-TMPRSS2 natural products through homology modelling, virtual screening and molecular dynamics simulation studies. J Biomol Struct Dyn 39:6660–6675. https://doi.org/10.1080/07391102.2020.1798813 Li K, Du Y, Li L, Wei D-Q (2019) Bioinformatics approaches for anti-cancer drug discovery. Curr Drug Targets 21:3–17. https://doi.org/10.2174/1389450120666190923162203 Yu T, Cheng L, Yan X et al (2020) Systems biology approaches based discovery of a small molecule inhibitor targeting both c-Met/PARP-1 and inducing cell death in breast cancer. J Cancer 11:2656–2666. https://doi.org/10.7150/jca.40758 Aldewachi H, Al-Zidan RN, Conner MT, Salman MM (2021) High-throughput screening platforms in the discovery of novel drugs for neurodegenerative diseases. Bioengineering 8:30. https://doi.org/10.3390/bioengineering8020030 Ferdowsian HR, Beck N (2011) Ethical and scientific considerations regarding animal testing and research. PLoS ONE 6:e24059. https://doi.org/10.1371/journal.pone.0024059 Achary PGR (2020) Applications of quantitative structure-activity relationships (QSAR) based virtual screening in drug design: a review. Mini-Rev Med Chem 20:1375–1388. https://doi.org/10.2174/1389557520666200429102334 Staszak M, Staszak K, Wieszczycka K et al (2022) Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship. WIREs Comput Mol Sci. https://doi.org/10.1002/wcms.1568 Raies AB, Bajic VB (2016) In silico toxicology: computational methods for the prediction of chemical toxicity: computational methods for the prediction of chemical toxicity. Wiley Interdiscip Rev Comput Mol Sci 6:147–172. https://doi.org/10.1002/wcms.1240 Workman P (2003) How much gets there and what does it do?: The need for better pharmacokinetic and pharmacodynamic endpoints in contemporary drug discovery and development. Curr Pharm Des 9:891–902. https://doi.org/10.2174/1381612033455279 Bouzom F, Ball K, Perdaems N, Walther B (2012) Physiologically based pharmacokinetic (PBPK) modelling tools: how to fit with our needs?: PBPK MODELLING TOOLS. Biopharm Drug Dispos 33:55–71. https://doi.org/10.1002/bdd.1767 Mahan VL (2014) Clinical trial phases. Int J Clin Med 05:1374–1383. https://doi.org/10.4236/ijcm.2014.521175 Gold M, Amatniek J, Carrillo MC et al (2018) Digital technologies as biomarkers, clinical outcomes assessment, and recruitment tools in Alzheimer’s disease clinical trials. Alzheimers Dement Transl Res Clin Interv 4:234–242. https://doi.org/10.1016/j.trci.2018.04.003 Bennett AV, Jensen RE, Basch E (2012) Electronic patient-reported outcome systems in oncology clinical practice. CA Cancer J Clin 62:336–347. https://doi.org/10.3322/caac.21150 Gong K, Yan Y-L, Li Y et al (2020) Mobile health applications for the management of primary hypertension: a multicenter, randomized, controlled trial. Medicine (Baltimore) 99:e19715. https://doi.org/10.1097/MD.0000000000019715 Gordon S, Crager J, Howry C et al (2022) Best practice recommendations: user acceptance testing for systems designed to collect clinical outcome assessment data electronically. Ther Innov Regul Sci 56:442–453. https://doi.org/10.1007/s43441-021-00363-z Galsky MD, Shahin M, Jia R et al (2017) Telemedicine-enabled clinical trial of metformin in patients with prostate cancer. JCO Clin Cancer Inform. https://doi.org/10.1200/CCI.17.00044 Beg S, Handa M, Shukla R et al (2022) Wearable smart devices in cancer diagnosis and remote clinical trial monitoring: transforming the healthcare applications. Drug Discov Today 27:103314. https://doi.org/10.1016/j.drudis.2022.06.014 Patel VN, Kaelber DC (2014) Using aggregated, de-identified electronic health record data for multivariate pharmacosurveillance: a case study of azathioprine. J Biomed Inform 52:36–42. https://doi.org/10.1016/j.jbi.2013.10.009 Needamangalam Balaji J, Prakash S, Park Y et al (2022) A scoping review on accentuating the pragmatism in the implication of mobile health (mHealth) technology for tuberculosis management in India. J Pers Med 12:1599. https://doi.org/10.3390/jpm12101599 Holmén C, Piehl F, Hillert J et al (2011) A Swedish national post-marketing surveillance study of natalizumab treatment in multiple sclerosis. Mult Scler J 17:708–719. https://doi.org/10.1177/1352458510394701 Antonijević Z, Beckman RA (2019) Platform trial designs in drug development: umbrella trials and basket trials. CRC Press, Boca Raton Battaglini D, Al-Husinat L, Normando AG et al (2022) Personalized medicine using omics approaches in acute respiratory distress syndrome to identify biological phenotypes. Respir Res 23:318. https://doi.org/10.1186/s12931-022-02233-0 Poirion OB, Jing Z, Chaudhary K et al (2021) DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data. Genome Med 13:112. https://doi.org/10.1186/s13073-021-00930-x Tran KA, Kondrashova O, Bradley A et al (2021) Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med 13:152. https://doi.org/10.1186/s13073-021-00968-x Turek C, Wróbel S, Piwowar M (2020) OmicsON—integration of omics data with molecular networks and statistical procedures. PLoS ONE 15:e0235398. https://doi.org/10.1371/journal.pone.0235398 Galeone C, Scelfo C, Bertolini F et al (2018) Precision medicine in targeted therapies for severe asthma: is there any place for “omics” technology? BioMed Res Int 2018:1–15. https://doi.org/10.1155/2018/4617565 Big data analytics in healthcare market. https://www.factmr.com/report/369/big-data-analytics-healthcare-market Hemingway H, Asselbergs FW, Danesh J et al (2018) Big data from electronic health records for early and late translational cardiovascular research: challenges and potential. Eur Heart J 39:1481–1495. https://doi.org/10.1093/eurheartj/ehx487 Wang L, Alexander CA (2020) Big data analytics in medical engineering and healthcare: methods, advances and challenges. J Med Eng Technol 44:267–283. https://doi.org/10.1080/03091902.2020.1769758 Koppad S, B A, Gkoutos GV, Acharjee A, (2021) Cloud computing enabled big multi-omics data analytics. Bioinforma Biol Insights 15:1177932221. https://doi.org/10.1177/11779322211035921 Morris MA, Saboury B, Burkett B et al (2018) Reinventing radiology: big data and the future of medical imaging. J Thorac Imaging 33:4–16. https://doi.org/10.1097/RTI.0000000000000311 Ngiam KY, Khor IW (2019) Big data and machine learning algorithms for health-care delivery. Lancet Oncol 20:e262–e273. https://doi.org/10.1016/S1470-2045(19)30149-4 Ho D, Quake SR, McCabe ERB et al (2020) Enabling technologies for personalized and precision medicine. Trends Biotechnol 38:497–518. https://doi.org/10.1016/j.tibtech.2019.12.021 Sadler D, Okwuosa T, Teske AJ et al (2022) Cardio oncology: Digital innovations, precision medicine and health equity. Front Cardiovasc Med 9:951551. https://doi.org/10.3389/fcvm.2022.951551 Vajawat B, Varshney P, Banerjee D (2021) Digital gaming interventions in psychiatry: evidence. Appl Chall Psych Res 295:113585. https://doi.org/10.1016/j.psychres.2020.113585 Raijada D, Wac K, Greisen E et al (2021) Integration of personalized drug delivery systems into digital health. Adv Drug Deliv Rev 176:113857. https://doi.org/10.1016/j.addr.2021.113857 Montanhesi PK, Coelho G, Curcio SAF, Poffo R (2022) Three-dimensional printing in minimally invasive cardiac surgery: optimizing surgical planning and education with life-like models. Braz J Cardiovasc Surg. https://doi.org/10.21470/1678-9741-2020-0409 Jamróz W, Szafraniec J, Kurek M, Jachowicz R (2018) 3D printing in pharmaceutical and medical applications—recent achievements and challenges. Pharm Res 35:176. https://doi.org/10.1007/s11095-018-2454-x Bedford J, Farrar J, Ihekweazu C et al (2019) A new twenty-first century science for effective epidemic response. Nature 575:130–136. https://doi.org/10.1038/s41586-019-1717-y Canfell OJ, Davidson K, Woods L et al (2022) Precision public health for non-communicable diseases: an emerging strategic roadmap and multinational use cases. Front Public Health 10:256 Cubillos-Ruiz A, Guo T, Sokolovska A et al (2021) Engineering living therapeutics with synthetic biology. Nat Rev Drug Discov 20:941–960. https://doi.org/10.1038/s41573-021-00285-3 Davies JA (2016) Synthetic biology: rational pathway design for regenerative medicine. Gerontology 62:564–570. https://doi.org/10.1159/000440721 Sridhar S, Ajo-Franklin CM, Masiello CA (2022) A framework for the systematic selection of biosensor chassis for environmental synthetic biology. ACS Synth Biol 11:2909–2916. https://doi.org/10.1021/acssynbio.2c00079 Jain KK (2013) Synthetic biology and personalized medicine. Med Princ Pract 22:209–219. https://doi.org/10.1159/000341794 McNerney MP, Doiron KE, Ng TL et al (2021) Theranostic cells: emerging clinical applications of synthetic biology. Nat Rev Genet 22:730–746. https://doi.org/10.1038/s41576-021-00383-3 Zupanic A, Bernstein HC, Heiland I (2020) Systems biology: current status and challenges. Cell Mol Life Sci 77:379–380. https://doi.org/10.1007/s00018-019-03410-z Lopatkin AJ, Collins JJ (2020) Predictive biology: modelling, understanding and harnessing microbial complexity. Nat Rev Microbiol 18:507–520. https://doi.org/10.1038/s41579-020-0372-5 Irmisch A, Bonilla X, Chevrier S et al (2021) The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support. Cancer Cell 39:288–293. https://doi.org/10.1016/j.ccell.2021.01.004 McEwen SC, Merrill DA, Bramen J et al (2021) A systems-biology clinical trial of a personalized multimodal lifestyle intervention for early Alzheimer’s disease. Alzheimers Dement Transl Res Clin Interv. https://doi.org/10.1002/trc2.12191 Wolstencroft K, Owen S, Krebs O et al (2015) SEEK: a systems biology data and model management platform. BMC Syst Biol 9:33. https://doi.org/10.1186/s12918-015-0174-y Kobeissy FH, Guingab-Cagmat JD, Razafsha M et al (2011) Leveraging biomarker platforms and systems biology for rehabilomics and biologics effectiveness research. PM&R 3:S139–S147. https://doi.org/10.1016/j.pmrj.2011.02.012 Brown S-A (2015) Building SuperModels: emerging patient avatars for use in precision and systems medicine. Front Physiol. https://doi.org/10.3389/fphys.2015.00318 Cummins N, Schuller BW (2020) Five crucial challenges in digital health. Front Digit Health 2:536203. https://doi.org/10.3389/fdgth.2020.536203 Board of Governors in Supersession of the Medical Council of India (2020) Telemedicine practice guidelines-enabling registered medical practitioners to provide healthcare using telemedicine Latifi R, Doarn CR (2020) Perspective on COVID-19: finally, telemedicine at center stage. Telemed E-Health 26:1106–1109. https://doi.org/10.1089/tmj.2020.0132 Gudi N, Lakiang T, Pattanshetty S et al (2021) Challenges and prospects in india’s digital health journey. Indian J Public Health 65:209. https://doi.org/10.4103/ijph.IJPH_1446_20 Dash S, Aarthy R, Mohan V (2021) Telemedicine during COVID-19 in India—a new policy and its challenges. J Public Health Policy 42:501–509. https://doi.org/10.1057/s41271-021-00287-w Srivastava SK (2016) Adoption of electronic health records: a roadmap for India. Healthc Inform Res 22:261. https://doi.org/10.4258/hir.2016.22.4.261 Madanian S, Parry DT, Airehrour D, Cherrington M (2019) mHealth and big-data integration: promises for healthcare system in India. BMJ Health Care Inform 26:e100071. https://doi.org/10.1136/bmjhci-2019-100071 Al Dahdah M, Mishra RK (2023) Digital health for all: the turn to digitized healthcare in India. Soc Sci Med 319:114968. https://doi.org/10.1016/j.socscimed.2022.114968 Katyayan A, Katyayan A, Mishra A (2022) Enhancing India’s health care during COVID era: role of artificial intelligence and algorithms. Indian J Otolaryngol Head Neck Surg 74:2712–2713. https://doi.org/10.1007/s12070-020-02101-7 National Digital Health Mission. https://www.sanskritiias.com/current-affairs/national-digital-health-mission Soman B (2014) Participatory GIS in action, a public health initiative from Kerala, India. ISPRS Int Arch Photogramm Remote Sens Spat Inf Sci XL–8:233–237. https://doi.org/10.5194/isprsarchives-XL-8-233-2014 Babu AN, Niehaus E, Shah S et al (2019) Smartphone geospatial apps for dengue control, prevention, prediction, and education: MOSapp, DISapp, and the mosquito perception index (MPI). Environ Monit Assess 191:393. https://doi.org/10.1007/s10661-019-7425-0 Chaudhary S, Soman B (2022) Spatiotemporal analysis of environmental and physiographic factors related to malaria in Bareilly district, India. Osong Public Health Res Perspect 10:10. https://doi.org/10.24171/j.phrp.2021.0304 Singh G, Mitra A, Soman B (2022) Development and use of a reproducible framework for spatiotemporal climatic risk assessment and its association with decadal trend of dengue in India. Indian J Community Med 47:50. https://doi.org/10.4103/ijcm.ijcm_862_21 Valson JS, Soman B (2017) Spatiotemporal clustering of dengue cases in Thiruvananthapuram district. Kerala’ Indian J Public Health 61:74 Sarma PS, Sadanandan R, Thulaseedharan JV et al (2019) Prevalence of risk factors of non-communicable diseases in Kerala, India: results of a cross-sectional study. BMJ Open. https://doi.org/10.1136/bmjopen-2018-027880 Ulahannan SK, Wilson A, Chhetri D et al (2022) Alarming level of severe acute malnutrition in Indian districts. BMJ Glob Health 7:e007798. https://doi.org/10.1136/bmjgh-2021-007798 Valson JS, Kutty VR, Soman B, Jissa VT (2019) Spatial clusters of diabetes and physical inactivity: do neighborhood characteristics in high and low clusters differ? Asia Pac J Public Health. https://doi.org/10.1177/1010539519879322 Mitra A, Soman B, Singh G (2021) An interactive dashboard for real-time analytics and monitoring of COVID-19 outbreak in india: a proof of Concept. arXiv:210809937Cs Mitra A, Soman B, Gaitonde R et al (2023) Data science approaches to public health: case studies using routine health data from India Singh G, Patrikar S, Sarma PS, Soman B (2020) Time-dependent dynamic transmission potential and instantaneous reproduction number of COVID-19 pandemic in India. medRxiv. https://doi.org/10.1101/2020.07.15.20154971 Singh G, Srinivas G, Jyothi EK et al (2020) Containing the first outbreak of COVID-19 in a healthcare setting in India: the sree chitra experience. Indian J Public Health 64:240. https://doi.org/10.4103/ijph.IJPH_483_20 Singh G, Soman B (2021) Spatiotemporal epidemiology and forecasting of dengue in the state of Punjab, India: study protocol. Spat Temp Epidemiol 39:100444. https://doi.org/10.1016/j.sste.2021.100444