Maximizing Artificial Intelligence for Patient Satisfaction: Marketing Strategies in The Digital Health Era
Abstract
The advent of the Fourth Industrial Revolution has catalyzed transformative changes in the healthcare sector, highlighting the need to optimize Artificial Intelligence (AI) to improve patient satisfaction. This research examines the role of AI in revolutionizing the healthcare market and its potential to improve patient outcomes. Using a systematic literature review, we collected and analyzed studies published from 2017 to 2022 from various databases such as ProQuest, Science Direct, PubMed, CINAHL, and Scopus, focusing on AI applications in healthcare and its impact on patient satisfaction. Inclusion criteria for the initial screening were articles that were freely available in full text, had open access, and were published in English or Indonesian, as well as ensuring no duplication in the records found. Significant findings were synthesized through thematic and descriptive analysis to ascertain the efficacy of AI in patient care. Of the 25 articles relevant for further evaluation, 12 studies met the inclusion criteria and were included in the qualitative analysis. The chosen methodology allowed the authors to conduct a comprehensive and systematic review. A practical suggestion that can be drawn from these findings is the importance of investing in the development of artificial intelligence (AI) in healthcare. This requires adequate training for staff to understand and use these technologies effectively. In addition, healthcare institutions should ensure that the use of AI remains compliant with applicable privacy and ethical regulations. Collaboration between various parties such as healthcare institutions, technology providers, and researchers is also necessary to accelerate innovation and knowledge exchange. By utilizing real-time data analysis powered by AI, healthcare can be improved, and governments can support innovation in AI by providing incentives, supportive regulations, and adequate research funding.
Keywords: Artificial Intelligence, Digital Transformation, Healthcare Revolution, Patient Satisfaction, Service Optimization
Full Text:
PDFReferences
Aceto, G., Persico, V., & Pescapé, A. (2018). The role of Information and Communication Technologies in healthcare: taxonomies, perspectives, and challenges. Journal of Network and Computer Applications, 107, 125–154. https://doi.org/10.1016/j.jnca.2018.02.008
Agrawal, A., Gans, J. S., & Goldfarb, A. (2019). Exploring the impact of artificial Intelligence: Prediction versus judgment. Information Economics and Policy, 47(June), 1–6. https://doi.org/10.1016/j.infoecopol.2019.05.001
Alfian, G., Syafrudin, M., Ijaz, M. F., Syaekhoni, M. A., Fitriyani, N. L., & Rhee, J. (2018). A personalized healthcare monitoring system for diabetic patients by utilizing BLE-based sensors and real-time data processing. Sensors (Switzerland), 18(7). https://doi.org/10.3390/s18072183
Appio, F. P., Lima, M., & Paroutis, S. (2018). Understanding Smart Cities: Innovation ecosystems, technological advancements, and societal challenges. Science Direct.
Ardito, L., Messeni, A., & Ghisetti, C. (2019). Technological Forecasting & Social Change The impact of public research on the technological development of industry in the green energy fi eld. Technological Forecasting & Social Change, 144(April), 25–35. https://doi.org/10.1016/j.techfore.2019.04.007
Aruba. (2017). Change the conversation in Healthcare: Atmosphere 2017 EMEA (Issue September).
Ayaad, A. O., Alloubani, A., & Abu, E. (2019). The Role of Electronic Medical Records in Improving the Quality of Health Care Services: Comparative Study.
Balogun, B., & Ogunnaike. (2017). Healthcare Organisations in a Global Marketplace: A Systematic Review of the Literature on Healthcare Marketing JEL Classification. Journal of Marketing Management and Consumer Behavior, 1(5), 36–52. https://ssrn.com/abstract=3047747Electroniccopyavailableat:https://ssrn.com/abstract=3047747Electroniccopyavailableat:https://ssrn.com/abstract=3047747
Balta, M., Valsecchi, R., Papadopoulos, T., & Bourne, D. J. (2021). Digitalization and co-creation of healthcare value: A case study in Occupational Health. Technological Forecasting and Social Change, 168(July), 120785. https://doi.org/10.1016/j.techfore.2021.120785
Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. In Artificial Intelligence in Healthcare (Issue January). https://doi.org/10.1016/B978-0-12-818438-7.00002-2
Butt, H., Eid, A., Momin, A. A., Bazin, J., Crespi, M., Arold, S. T., & Mahfouz, M. M. (2019). CRISPR directed evolution of the spliceosome for resistance to splicing inhibitors. Genome Biology, 20(1), 1–9. https://doi.org/10.1186/s13059-019-1680-9
Eckrich, D. W., & Schlesinger, W. (2011). An application of the marketing concept in health-care services planning : a case report. Journal of Management and Marketing Research, 6(1), 1–9.
Ferreira, J. J., Fernandes, C. I., Veiga, P. M., & Hughes, M. (2021). Prevailing theoretical approaches predicting sustainable business models: a systematic review. International Journal of Productivity and Performance Management, 71(3), 790–813. https://doi.org/10.1108/IJPPM-12-2020-0653
Flessa, S., & Huebner, C. (2021). Innovations in health care—a conceptual framework. International Journal of Environmental Research and Public Health, 18(19). https://doi.org/10.3390/ijerph181910026
Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, April(69S), S36–S40. https://doi.org/10.1016/j.metabol.2017.01.011
Hong, K. S., & Lee, D. (2018). Impact of operational innovations on customer loyalty in the healthcare sector. Service Business, 12(3), 575–600. https://doi.org/10.1007/s11628-017-0355-4
Jatoba, M., Ferreira, J. J. M., Fernandes, P. O., & Teixeira, J. P. (2023). Intelligent human resources for the adoption of artificial intelligence: a systematic literature review. Journal of Organizational Change Management, 36(1). https://doi.org/10.1108/JOCM-03-2022-0075
Korzh, O. (2021). Digital Technologies in healthcare : promises and challenges. Technium, 3(1), 202–211.
Krakowski, S., Luger, J., & Raisch, S. (2023). Artificial intelligence and the changing sources of competitive advantage. Strategic Management Journal, 44(6), 1425–1452. https://doi.org/10.1002/smj.3387
Kumar, Y., Koul, A., Singla, R., & Ijaz, M. F. (2023). Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. Journal of Ambient Intelligence and Humanized Computing, 14(7), 8459–8486. https://doi.org/10.1007/s12652-021-03612-z
Lee, H., & Yoon, No, S. (2021). Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges. International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022 - Proceedings, 18, 1521–1524. https://doi.org/10.1109/IIHC55949.2022.10059767
Lee, J. J., Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., Nguyen-Viet, T. A., Bowers, P., Sidorenko, J., Karlsson Linnér, R., Fontana, M. A., Kundu, T., Lee, C., Li, H., Li, R., Royer, R., Timshel, P. N., Walters, R. K., Willoughby, E. A., … Turley, P. (2018). Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. In Nature Genetics (Vol. 50, Issue 8). https://doi.org/10.1038/s41588-018-0147-3
Leone, D., Schiavone, F., Appio, F. P., & Chiao, B. (2018). How does artificial intelligence enable and enhance value co-creation in industrial markets? An exploratory case study in the healthcare ecosystem. Journal of Business Research.
Martins, S. M., Ferreira, F. A. ., Ferreira, J. J. M., & Marques, C. S. E. (2020). An artificial-intelligence-based method for assessing service quality: insights from the prosthodontics sector. Journal of Service Management, 291–312. https://doi.org/10.1108/JOSM-03-2019-0084
Pfotenhauer, S., & Jasanoff, S. (2017). Panacea or diagnosis? Imaginaries of Innovation and the “MIT model” in three political cultures. Sage Journals, 47(6), 783–810.
Prado-Prado, J. C., Fernández-González, A. J., Mosteiro-Añón, M., & García-Arca, J. (2020). Increasing competitiveness through the implementation of lean management in healthcare. International Journal of Environmental Research and Public Health, 17(14), 1–26. https://doi.org/10.3390/ijerph17144981
Rialti, R., Lamberto Zollo, Ferraris, A., & Alon, I. (2019). Big data analytics capabilities and performance: Evidence from a moderated multi-mediation model. Technological Forecasting and Social Change, 149(Desember), 119781. https://doi.org/10.1016/j.techfore.2019.119781
Rodríguez-Rodríguez, D., Sebastiao, J., Tierra, Á. E. S., & Martinez-Vega, J. (2019). Effect of protected areas in reducing land development across geographic and climate conditions of a rapidly developing country, Spain. Land Degradation and Development, 30(8). https://doi.org/10.1002/ldr.3286
Rodriguez, J., Ren, G., Day, C. R., Zhao, K., Chow, C. C., & Larson, D. R. (2019). Intrinsic Dynamics of a Human Gene Reveal the Basis of Expression Heterogeneity. Pubmed, 1(2), 213–226.
Stoumpos, A. I., Kitsios, F., & Talias, M. A. (2023). Digital Transformation in Healthcare: Technology Acceptance and Its Applications. International Journal of Environmental Research and Public Health, 20(4), 2–44. https://doi.org/10.3390/ijerph20043407
Teece, D. J. (2018). Business models and dynamic capabilities*. Long Range Planning, 51(1), 40–49. https://doi.org/10.1016/j.lrp.2017.06.007
Terry, Beth, M., & Zeinomar, N. (2019). Response to Lee et al 2019: Essential to frame study implications within the context of prior findings from enriched cohorts for underlying familial risk of breast cancer. Occupational and Environmental Medicine, 76(8), 592–592.
Thuemmler, C., & Bai, C. (2017). Health 4.0: How virtualization and big data are revolutionizing healthcare. Health 4.0: How Virtualization and Big Data Are Revolutionizing Healthcare, 1–254. https://doi.org/10.1007/978-3-319-47617-9
Wamba-Taguimdje, S. L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893–1924. https://doi.org/10.1108/BPMJ-10-2019-0411
Wang, Q., Su, M., Zhang, M., & Li, R. (2021). Integrating digital technologies and public health to fight covid-19 pandemic: Key technologies, applications, challenges and outlook of digital healthcare. International Journal of Environmental Research and Public Health, 18(11). https://doi.org/10.3390/ijerph18116053
Waring, B., Neumann, M., Prentice, I. C., Adams, M., Smith, P., & Siegert, M. (2020). Forests and Decarbonization – Roles of Natural and Planted Forests. Frontiers in Forests and Global Change, 3(May), 1–6. https://doi.org/10.3389/ffgc.2020.00058
Wilson, M. K., Boag, R. J., & Strickland, L. (2019). All models are wrong, some are useful, but are they reproducible? Commentary on Lee et al. (2019). Computational Brain and Behavior, 2(3–4), 239–241. https://doi.org/10.1007/s42113-019-00054-x
Ziyadin, S., Omarova, A., Doszhan, R., Saparova, G., & Zharaskyzy, G. (2018). Diversification of R and D results commercialization. Problems and Perspectives in Management, 16(4), 331–343. https://doi.org/10.21511/ppm.16(4).2018.27
DOI: http://dx.doi.org/10.30829/contagion.v6i1.19198
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Anggi Parsaoran Hotmangatur, Adang Bachtiar
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
