Balancing Innovation and Safety in Digital Healthcare

Main Article Content

Shalini Sharma
Maninder Singh
Keerti Bhusan Pradhan

Keywords

Digital Health, Artificial Intelligence, Telemedicine, Cybersecurity Risks, Patient Safety, Healthcare innovation

Abstract

In an era of rapid digital transformation, patient safety is increasingly intertwined with technological advancements in healthcare. This article explores the dual nature of these innovations, where tools like telemedicine, artificial intelligence (AI), and electronic health records (EHRs) offer significant potential to enhance care delivery and introduce new risks such as algorithmic bias, cybersecurity threats, and challenges in minimizing patient risks. A balanced approach focusing on robust safety protocols and continuous learning is required to ensure technology enhancement without undermining patient safety. The paper aims to advance the discourse on integrating technology with patient-centric care, proposing future research and policy development strategies to sustain a high safety standard in an increasingly digital healthcare environment.

Downloads

Download data is not yet available.
Abstract 38 | PDF Downloads 7

References

Nair, M., Wen, X., Lin, X., et al. Barriers and enablers for implementation of an artificial intelligence-based decision support tool to reduce the risk of readmission of patients with heart failure: stakeholder interviews. JMIR Form Res. 2023;7:e47335. https://doi.org/10.2196/47335.
Lin, M.C.M., Kim, T.H., Kim, W.S., et al. Involvement of frontline clinicians in healthcare technology development: lessons learned from a ventilator project. Health Technol. 2022;12(3):597–606. https://doi.org/10.1007/s12553-022-00655-w.
Taylor, M.L., Thomas, E.E., Snoswell, C.L., et al. Does remote patient monitoring reduce acute care use? A systematic review. BMJ Open. 2021;11:e040232. https://doi.org/10.1136/bmjopen-2020-040232.
Gajarawala, S.N. and Pelkowski, J.N. Telehealth benefits and barriers. J Nurse Pract. 2021;17(2):218–221. https://doi.org/10.1016/j.nurpra.2020.09.013.
Kruk, M.E., Gage, A.D., Joseph, N.T., et al. Mortality due to low-quality health systems in the universal health coverage era: a systematic analysis of amenable deaths in 137 countries. Lancet. 2018;392:2203–2212. https://doi.org/10.1016/S0140-6736(18)31668-4.
Rodriguez, N.M., Burleson, G., Linnes, J.C., et al. Thinking beyond the device: an overview of human- and equity-centered approaches for health technology design. Annu Rev Biomed Eng. 2023;25:257–280. https://doi.org/10.1146/annurev-bioeng-081922-024834.
Isherwood, P. and Waterson, P. To err is system; a comparison of methodologies for the investigation of adverse outcomes in healthcare. J Patient Saf Risk Manag. 2021;26(2):64–73. https://doi.org/10.1177/2516043521990261.
Sharifian, R., Ghasemi, S., Kharazmi, E., et al. An evaluation of the risk factors associated with implementing projects of health information technology by fuzzy combined ANP-DEMATEL. PLoS One. 2023;18(2):e0279819. https://doi.org/10.1371/journal.pone.0279819.
Topol, E.J. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. https://doi.org/10.1038/s41591-018-0300-7.
Crigger, E., Reinbold, K., Hanson, C., et al. Trustworthy augmented intelligence in health care. J Med Syst. 2022;46(2):12. https://doi.org/10.1007/s10916-021-01790-z.
Hodkinson, A., Tyler, N., Ashcroft, D.M., et al. Preventable medication harm across health care settings: a systematic review and meta-analysis. BMC Med. 2020;18(1):313. https://doi.org/10.1186/s12916-020-01774-9.
Klein, D.O., Rennenberg, R.J.M.W., Koopmans, R.P., et al. A systematic review of methods for medical record analysis to detect adverse events in hospitalized patients. J Patient Saf. 2021;17(8):e1234-e1240. https://doi.org/10.1097/PTS.0000000000000670.
Sauro, K.M., Machan, K.M., Whalen-Browne, L., et al. Evolving factors in hospital safety: a systematic review and meta-analysis of hospital adverse events. J Patient Saf. 2021;17. https://doi.org/10.1097/PTS.0000000000000889.
Vasudevan, A., Plombon, S., Piniella, N., et al. Effect of digital tools to promote hospital quality and safety on adverse events after discharge. J Am Med Inform Assoc. 2024;31(10):2304–2314. https://doi.org/10.1093/jamia/ocae176.
Subbe, C.P., Tellier, G., Barach, P. Impact of electronic health records on predefined safety outcomes in patients admitted to hospital: a scoping review. BMJ Open. 2021;11(1):e047446. https://doi.org/10.1136/bmjopen-2020-047446.
Vikan, M., Haugen, A.S., Bjørnnes, A.K., et al. The association between patient safety culture and adverse events: a scoping review. BMC Health Serv Res. 2023;23(1):300. https://doi.org/10.1186/s12913-023-09332-8.
Hadjiat, Y. Healthcare inequity and digital health—a bridge for the divide, or further erosion of the chasm? PLoS Digit Health. 2023;2(6):e0000268. https://doi.org/10.1371/journal.pdig.0000268.
Eruchalu, C.N., Pichardo, M.S., Bharadwaj, M., et al. The expanding digital divide: digital health access inequities during the COVID-19 pandemic in New York City. J Urban Health. 2021;98(2):183–186. https://doi.org/10.1007/s11524-020-00508-9.
Spanakis, P., Peckham, E., Mathers, A., et al. The digital divide: amplifying health inequalities for people with severe mental illness in the time of COVID-19. Br J Psychiatry. 2021;219(4):529–531. https://doi.org/10.1192/bjp.2021.56.
Rodriguez, J.A., Betancourt, J.R., Sequist, T.D., et al. Differences in the use of telephone and video telemedicine visits during the COVID-19 pandemic. Am J Manag Care. 2021;27(1):21–26. https://doi.org/10.37765/ajmc.2021.88573.
Daniels, B., McGinnis, C., Topaz, L.S., et al. Bridging the digital health divide—patient experiences with mobile integrated health and facilitated telehealth by community-level indicators of health disparity. J Am Med Inform Assoc. 2024;31(4):875–883. https://doi.org/10.1093/jamia/ocae007.
Clare, C.A. Telehealth and the digital divide as a social determinant of health during the COVID-19 pandemic. Netw Model Anal Health Inform Bioinform. 2021;10(1):26. https://doi.org/10.1007/s13721-021-00300-y.
Jerjes, W. and Harding, D. Telemedicine in the post-COVID era: balancing accessibility, equity, and sustainability in primary healthcare. Front Digit Health. 2024;6:1432871. https://doi.org/10.3389/fdgth.2024.1432871.
Hollander, J.E. and Carr, B.G. Virtually perfect? Telemedicine for COVID-19. N Engl J Med. 2020;382(18):1679–1681. https://doi.org/10.1056/NEJMp2003539.
Bashshur, R., Doarn, C.R., Frenk, J.M., et al. Telemedicine and the COVID-19 pandemic, lessons for the future. Telemed J E Health. 2020;26(5):571–573. https://doi.org/10.1089/tmj.2020.29040.rb.
De Simone, S., Franco, M., Servillo, G., et al. Implementations and strategies of telehealth during COVID-19 outbreak: a systematic review. BMC Health Serv Res. 2022;22(1):833. https://doi.org/10.1186/s12913-022-08235-4.
Mee, P., Gussy, M., Huntley, P., et al. Digital exclusion as a barrier to accessing healthcare: a summary composite indicator and online tool to explore and quantify local differences in levels of exclusion. Univers Access Inf Soc. 2024. https://doi.org/10.1007/s10209-024-01148-5.
Paik, K.E., Hicklen, R., Kaggwa, F., et al. Digital determinants of health: health data poverty amplifies existing health disparities—a scoping review. PLOS Digit Health. 2023;2(10):e0000313. https://doi.org/10.1371/journal.pdig.0000313.
Bentley, S.V., Naughtin, C.K., McGrath, M.J., et al. The digital divide in action: how experiences of digital technology shape future relationships with artificial intelligence. AI Ethics. 2024;4(4):901–915. https://doi.org/10.1007/s43681-024-00452-3.
Liu, J.C., Cheng, C.Y., Cheng, T.H., et al. Unveiling the potential: remote monitoring and telemedicine in shaping the future of heart failure management. Life. 2024;14(8):936. https://doi.org/10.3390/life14080936.
Ekstedt, M., Nordheim, E.S., Hellström, A., et al. Patient safety and sense of security when telemonitoring chronic conditions at home: the views of patients and healthcare professionals—a qualitative study. BMC Health Serv Res. 2023;23(1):581. https://doi.org/10.1186/s12913-023-09428-1.
Hilty, D.M., Armstrong, C.M., Edwards-Stewart, A., et al. Sensor, wearable, and remote patient monitoring competencies for clinical care and training: scoping review. J Technol Behav Sci. 2021;6(2):252–277. https://doi.org/10.1007/s41347-020-00190-3.
Panch, T., Mattie, H., Atun, R. Artificial intelligence and algorithmic bias: implications for health systems. J Glob Health. 2019;9(2):020318. https://doi.org/10.7189/jogh.09.020318.
Seyyed-Kalantari, L., Zhang, H., McDermott, M.B.A., et al. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in underserved patient populations. Nat Med. 2021;27(12):2176–2182. https://doi.org/10.1038/s41591-021-01595-0.
Ghassemi, M., Oakden-Rayner, L., Beam, A.L. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health. 2021;3(11):e745–e750. https://doi.org/10.1016/S2589-7500(21)00208-9.
Celi, L.A., Cellini, J., Charpignon, M.L., et al. Sources of bias in artificial intelligence that perpetuate healthcare disparities—A global review. PLOS Digit Health. 2022;1(3):e0000022. https://doi.org/10.1371/journal.pdig.0000022.
Obermeyer, Z., Powers, B., Vogeli, C., et al. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447–453. https://doi.org/10.1126/science.aax2342.
Vyas, D.A., Eisenstein, L.G., Jones, D.S. Hidden in plain sight—reconsidering the use of race correction in clinical algorithms. N Engl J Med. 2020;383(9):874–882. https://doi.org/10.1056/NEJMms2004740.
Khera, R., Simon, M.A., Ross, J.S. Automation bias and assistive AI: risk of harm from AI-driven clinical decision support. JAMA. 2023;330(23):2255–2257. https://doi.org/10.1001/jama.2023.22557.
Alanazi, A. Clinicians’ views on using artificial intelligence in healthcare: opportunities, challenges, and beyond. Cureus. 2023;15(9):e45255. https://doi.org/10.7759/cureus.45255.
Abdelwanis, M., Alarafati, H.K., Tammam, M.M.S., et al. Exploring the risks of automation bias in healthcare artificial intelligence applications: A Bowtie analysis. J. Saf. Sci. Resil. 2024;5(4):460–469. https://doi.org/10.1016/j.jnlssr.2024.06.001.
Ratwani, R.M., Bates, D.W., Classen, D.C. Patient safety and artificial intelligence in clinical care. JAMA Health Forum. 2024;5(2):e235514. https://doi.org/10.1001/jamahealthforum.2023.5514.
Argaw, S.T., Troncoso-Pastoriza, J.R., Lacey, D., et al. Cybersecurity of hospitals: discussing the challenges and working towards mitigating the risks. BMC Med Inform Decis Mak. 2020;20(1):146. https://doi.org/10.1186/s12911-020-01161-7.
Neprash, H.T., McGlave, C.C., Cross, D.A., et al. Trends in ransomware attacks on US hospitals, clinics, and other health care delivery organizations, 2016–2021. JAMA Health Forum. 2022;3(12):e224873. https://doi.org/10.1001/jamahealthforum.2022.4873.
Mejía-Granda, C.M., Fernández-Alemán, J.L., Carrillo-de-Gea, J.M., et al. Security vulnerabilities in healthcare: an analysis of medical devices and software. Med Biol Eng Comput. 2024;62(1):257–273. https://doi.org/10.1007/s11517-023-02912-0.
Nemec Zlatolas, L., Welzer, T., Lhotska, L. Data breaches in healthcare: security mechanisms for attack mitigation. Clust. Comput. 2024;27(7):8639–8654. https://doi.org/10.1007/s10586-024-04507-2.
Investigation: WannaCry cyber attack and the NHS. Available online: https://www.nao.org.uk/wp-content/uploads/2017/10/Investigation-WannaCry-cyber-attack-and-the-NHS.pdf.
Ghafur, S., Grass, E., Jennings, N.R., et al. The challenges of cybersecurity in health care: the UK National Health Service as a case study. Lancet Digit Health. 2019;1(1):e10–e12. https://doi.org/10.1016/S2589-7500(19)30005-6.
Ghafur, S., Kristensen, S., Honeyford, K., et al. A retrospective impact analysis of the WannaCry cyberattack on the NHS. NPJ Digit Med. 2019;2:98. https://doi.org/10.1038/s41746-019-0161-6.
Frati, F., Darau, G., Salamanos, N., et al. Cybersecurity training and healthcare: the AERAS approach. Int. J. Inf. Secur. 2024;23:1527–1539. https://doi.org/10.1007/s10207-023-00802-y.
Tomlinson, E.W., Abrha, W.D, Kim, S.D., et al. Cybersecurity access control: framework analysis in a healthcare institution. J Cybersecur Priv. 2024;4(3):762–776. https://doi.org/10.3390/jcp4030035.
Yang, J., Soltan, A.A., Eyre, D.W., et al. An adversarial training framework for mitigating algorithmic biases in clinical machine learning. NPJ Digit Med. 2023;6(1):55. https://doi.org/10.1038/s41746-023-00805-y.
Abràmoff, M.D., Tarver, M.E., Loyo-Berrios, N., et al. Considerations for addressing bias in artificial intelligence for health equity. NPJ Digit Med. 2023;6(1):170. https://doi.org/10.1038/s41746-023-00913-9.
Ritoré, Á., Jiménez, C.M., González, J.L., et al. The role of open access data in democratizing healthcare AI: a pathway to research enhancement, patient well-being, and treatment equity in Andalusia, Spain. PLOS Digit Health. 2024;3(9):e0000599. https://doi.org/10.1371/journal.pdig.0000599.
Bird, M., McGillion, M., Chambers, E.M., et al. A generative co-design framework for healthcare innovation: development and application of an end-user engagement framework. Res Involv Engagem. 2021;7(1):1–12. https://doi.org/10.1186/s40900-021-00252-7.
Holden, R.J., Boustani, M.A., Azar, J. Agile innovation to transform healthcare: innovating in complex adaptive systems is an everyday process, not a light bulb event. BMJ Innov. 2021;7(1):399–505. https://doi.org/10.1136/bmjinnov-2020-000574.
Liao, F., Adelaine, S., Afshar, M., et al. Governance of clinical AI applications to facilitate safe and equitable deployment in a large health system: key elements and early successes. Front Digit Health. 2022;4:931439. https://doi.org/10.3389/fdgth.2022.931439.
Benson, T. and Grieve, G. Principles of health interoperability. Springer: Cham, Switzerland; 2021; pp. 21–40. https://doi.org/10.1007/978-3-030-56883-2.
Li, E., Clarke, J., Ashrafian, H., et al. The impact of electronic health record interoperability on safety and quality of care in high-income countries: systematic review. J Med Internet Res. 2022;24(9):e38144. https://doi.org/10.2196/38144.
Turbow, S., Hollberg, J.R., Ali, M.K. Electronic health record interoperability: how did we get here and how do we move forward? JAMA Health Forum. 2021;2(3):e210253. https://doi.org/10.1001/jamahealthforum.2021.0253.
Prabha, P.D., Kumar, N.S., Shree N.N., et al. Cybersecurity in healthcare: safeguarding patient data. In Proceedings of the 2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), Chennai, India, 9–10 May 2024; IEEE: New York, USA; 2024; pp.1–6. https://doi.org/10.1109/ACCAI61061.2024.10602188.
Bhuyan, S.S., Kabir, U.Y., Escareno, J.M., et al. Transforming healthcare cybersecurity from reactive to proactive: current status and future recommendations. J Med Syst. 2020;44(1):1–9. https://doi.org/10.1007/s10916-019-1507-y.
Boutros, P., Kassem, N., Nieder, J. et al. Education and training adaptations for health workers during the COVID-19 pandemic: a scoping review of lessons learned and innovations. Healthcare. 2023;11(21):2902. https://doi.org/10.3390/healthcare11212902.
Li, F., Ruijs. N., Lu, Y. Ethics & AI: a systematic review on ethical concerns and related strategies for designing with AI in healthcare. AI. 2022;4(1):28-53. https://doi.org/10.3390/ai4010003.
Tahri-Sqalli M., Aslonov, B., Gafurov, M., et al. Humanizing AI in medical training: ethical framework for responsible design. Front Artif Intell. 2023;6:1189914. https://doi.org/10.3389/frai.2023.1189914.
Ueda, D., Kakinuma, T., Fujita, S., et al. Fairness of artificial intelligence in healthcare: review and recommendations. Jpn J Radiol. 2024;42(1):3–15. https://doi.org/10.1007/s11604-023-01474-3.
Foy, R., Skrypak, M., Alderson, S., et al. Revitalising audit and feedback to improve patient care. BMJ. 2020;368:m213. https://doi.org/10.1136/bmj.m213.
Boehnke, J.R. and Rutherford, C. Using feedback tools to enhance the quality and experience of care. Qual Life Res. 2021;30(11):3007–3013. https://doi.org/10.1007/s11136-021-03008-8.
Sittig D.F., Sengstack P., Singh H. Guidelines for US hospitals and clinicians on assessment of electronic health record safety using SAFER guides. JAMA. 2022;327(8):719–720. https://doi.org/10.1001/jama.2022.0085.
Mökander, J. Auditing of AI: legal, ethical and technical approaches. Digit Soc. 2023;2:49. https://doi.org/10.1007/s44206-023-00074-y.
Ayers, J.W., Desai, N., Smith, D.M. Regulate artificial intelligence in health care by prioritizing patient outcomes. JAMA. 2024;331(8):639–640. https://doi.org/10.1001/jama.2024.0549.
Alami, H., Rivard, L., Lehoux, P., et al. Artificial intelligence in health care: laying the foundation for responsible, sustainable, and inclusive innovation in low- and middle-income countries. Glob Health. 2020;16(1):52. https://doi.org/10.1186/s12992-020-00584-1.
Torab-Miandoab, A., Samad-Soltani, T., Jodati, A., et al. Interoperability of heterogeneous health information systems: a systematic literature review. BMC Med Inform Decis Mak. 2023;23(1):18. https://doi.org/10.1186/s12911-023-02115-5.
Brotherton, T., Brotherton, S., Ashworth, H., et al. Development of an offline, open-source, electronic health record system for refugee care. Front Digit Health. 2022;4:847002. https://doi.org/10.3389/fdgth.2022.847002.
Antoniotti, N.M. Standards and guidelines in telehealth: creating a compliance and evidence-based telehealth practice. In Telemedicine, Telehealth and Telepresence: Principles, Strategies, Applications, and New Directions; Latifi, R., Doarn, C.R., Merrell, R.C., eds. Springer: Cham, Switzerland; 2021; pp. 97–113. https://doi.org/10.1007/978-3-030-56917-4_7.