Abstract
Information Technology has created new channels by which patients can access data and communicate with their health care providers. Innovations such as self-diagnostic tools, telehealth, and mobile health are changing the landscape of health care. Health care professionals who are actively managing these changes can improve patient care through implementation of technology. This chapter will provide clinicians with an understanding of the types of technologies that are affecting the patient–provider relationship, how communication channels are being affected, and how health care software service companies have been adapting to these changes. At the conclusion of this chapter, the reader will have a framework for assessing possible strategies for using information technologies to strengthen efficiency and health outcomes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The book, The Innovator’s Prescription, distinguishes between conditions treated with rules-based health care and conditions treated with intuitive care. Conditions treated with rules-based care are diagnosed using a straightforward test or readily observed symptoms, and treatment is guided by straightforward algorithmic treatment recommendations. Conditions requiring intuitive care are more difficult to diagnose, and they may require treatment regimens that are tailored to meet patient-specific requirements. The authors of The Innovator’s Prescription argue that disruptive technologies may be useful for conditions that require rules-based diagnosis and treatment, but they are less likely to be useful for conditions requiring intuitive care. (Christensen, C, Grossman, J., and Hwang, J. (2009) The Innovator’s Prescription. McGraw-Hill Education.
- 2.
A substantive complaint voiced by personal care aides (PCAs) is gaps in communication between health care providers and PCAs. PCAs help patients implement health care recommendations, but they do not always receive information about these recommendations directly. (Osterman, P. [2017] Who will care for us? Long-term care and the long-term workforce. Russell Sage Foundation. 232 pages. ISBN-13: 978-0871546395).
- 3.
The academic discipline that focuses on the adoption and usage of technology by individuals and organizations is known as Information Systems. University Information Systems departments are typically located within Colleges of Business.
- 4.
See Chap. 5 (Using Computer Technology to Support Clinical Decision Making) for additional discussion of EHR systems and issues posed by patient-generated data and behavioral health data.
References
Abdolkhani, R., Gray, K., Borda, A., & DeSouza, R. (2019). Patient-generated health data management and quality challenges in remote patient monitoring. JAMIA Open, 2(4), 471–478. https://doi.org/10.1093/jamiaopen/ooz036
Amisha, P. M., Pathania, M., & Rathaur, V. K. (2019). Overview of artificial intelligence in medicine. Journal of Family Medicine and Primary Care, 8(7), 2328. https://doi.org/10.4103/jfmpc.jfmpc_440_19
Aungst, T., Clauson, K., Misra, S., Lewis, T., & Husain, I. (2014). How to identify, assess and utilise mobile medical applications in clinical practice. International Journal of Clinical Practice, 68(2), 155–162. https://doi.org/10.1111/ijcp.12375
Beisecker, A. E., & Beisecker, T. D. (1990). Patient information-seeking behaviors when communicating with doctors. Medical Care, 28, 19–28. https://doi.org/10.1097/00005650-199001000-00004
Dick, S., O’Connor, Y., Thompson, M. J., O’Donoghue, J., Hardy, V., Wu, T.-S. J., O’Sullivan, T., Chirambo, G. B., & Heavin, C. (2020). Considerations for improved mobile health evaluation: Retrospective qualitative investigation. JMIR mHealth and uHealth, 8(1), e12424. https://doi.org/10.2196/12424
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 and uHealth, 7(9), e12861. https://doi.org/10.2196/12861
Escriche-Escuder, A., De-Torres, I., Roldán-Jiménez, C., MartÃn-MartÃn, J., Muro-Culebras, A., González-Sánchez, M., Ruiz-Muñoz, M., Mayoral-Cleries, F., Biró, A., Tang, W., et al. (2020). Assessment of the quality of mobile applications (Apps) for management of low back pain using the mobile app rating scale (MARS). International Journal of Environmental Research and Public Health, 17(24), 9209. https://doi.org/10.3390/ijerph17249209
Fan, W., Liu, J., Zhu, S., & Pardalos, P. M. (2020). Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS). Annals of Operations Research, 294(1), 567–592. https://doi.org/10.1007/s10479-018-2818-y
Genes, N., Violante, S., Cetrangol, C., Rogers, L., Schadt, E. E., & Chan, Y.-F. Y. (2018). From smartphone to EHR: A case report on integrating patient-generated health data. NPJ Digital Medicine, 1(1), 1–6. https://doi.org/10.1038/s41746-018-0030-8
Gruessner, V. (2015). FDA oversight on high-risk apps in mobile health industry. mHealth Intelligence. https://mhealthintelligence.com/news/fda-oversight-on-high-risk-apps-in-mobile-health-industry
Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69, S36–S40. https://doi.org/10.1016/j.metabol.2017.01.011
Hochberg, I., Allon, R., & Yom-Tov, E. (2020). Assessment of the frequency of online searches for symptoms before diagnosis: Analysis of archival data. Journal of Medical Internet Research, 22(3), e15065. https://doi.org/10.2196/15065
Holzinger, A., & Errath, M. (2007). Mobile computer Web-application design in medicine: Some research based guidelines. Universal Access in the Information Society, 6(1), 31–41.
Hurst, E. J. (2020). Web conferencing and collaboration tools and trends. Journal of Hospital Librarianship, 20(3), 266–279. https://doi.org/10.1080/15323269.2020.1780079
Jacobs, W., Amuta, A. O., & Jeon, K. C. (2017). Health information seeking in the digital age: An analysis of health information seeking behavior among US adults. Cogent Social Sciences, 3(1), 1302785. https://doi.org/10.1080/23311886.2017.1302785
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101
Kerr, D., Serrano, J. A., Ray, P., et al. (2018). The role of a disruptive digital technology for home-based healthcare of the elderly: Telepresence robot. Digital Medicine, 4(4), 173.
Knitza, J., Tascilar, K., Messner, E.-M., Meyer, M., Vossen, D., Pulla, A., Bosch, P., Kittler, J., Kleyer, A., Sewerin, P., et al. (2019). German mobile apps in rheumatology: Review and analysis using the Mobile Application Rating Scale (MARS). JMIR mHealth and uHealth, 7(8), e14991. https://doi.org/10.2196/14991
Lambert, S. D., & Loiselle, C. G. (2007). Health information—Seeking behavior. Qualitative Health Research, 17(8), 1006–1019. https://doi.org/10.1177/1049732307305199
Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., Mahendiran, T., Moraes, G., Shamdas, M., Kern, C., et al. (2019). A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis. The Lancet Digital Health, 1(6), e271–e297. https://doi.org/10.1016/S2589-7500(19)30123-2
Llorens-Vernet, P., & Miró, J. (2020). Standards for mobile health–related apps: Systematic review and development of a guide. JMIR mHealth and uHealth, 8(3), e13057. https://doi.org/10.2196/13057
Major, I. (2019). Two-sided information asymmetry in the healthcare industry. International Advances in Economic Research, 25(2), 177–193. https://doi.org/10.1007/s11294-019-09732-9
Mihelj, M., Novak, D., Milavec, M., Ziherl, J., Olenšek, A., & Munih, M. (2012). Virtual rehabilitation environment using principles of intrinsic motivation and game design. Presence Teleoperators and Virtual Environments, 21(1), 1–15. https://doi.org/10.1162/PRES_a_00078
Morgan, A. J., & Trauth, E. M. (2013). Socio-economic influences on health information searching in the USA: The case of diabetes. Information Technology & People, 26(4), 324–346. https://doi.org/10.1108/ITP-09-2012-0098
Munzner, T. (2014). Visualization analysis and design. CRC Press. https://doi.org/10.1201/b17511
Nouri, R., Niakan Kalhori, S. R., Ghazisaeedi, M., Marchand, G., & Yasini, M. (2018). Criteria for assessing the quality of mHealth apps: A systematic review. Journal of the American Medical Informatics Association, 25(8), 1089–1098. https://doi.org/10.1093/jamia/ocy050
Osborn, C. Y., & Egede, L. E. (2010). Validation of an Information–Motivation–Behavioral Skills model of diabetes self-care (IMB-DSC). Patient Education and Counseling, 79(1), 49–54. https://doi.org/10.1016/j.pec.2009.07.016
Peek, S. T., Wouters, E. J., Van Hoof, J., Luijkx, K. G., Boeije, H. R., & Vrijhoef, H. J. (2014). Factors influencing acceptance of technology for aging in place: A systematic review. International Journal of Medical Informatics, 83(4), 235–248.
Provoost, S., Ruwaard, J., van Breda, W., Riper, H., & Bosse, T. (2019). Validating automated sentiment analysis of online cognitive behavioral therapy patient texts: An exploratory study. Frontiers in Psychology, 10, 1065. https://doi.org/10.3389/fpsyg.2019.01065
Rajput, A. (2020). Natural language processing, sentiment analysis, and clinical analytics. In Innovation in health informatics (pp. 79–97). Elsevier. https://doi.org/10.1016/B978-0-12-819043-2.00003-4
Robeznieks, A. (2019). Mobile health gets road map for high-quality apps. American Medical Association. https://www.ama-assn.org/practice-management/digital/mobile-health-gets-road-map-high-quality-apps
Salim, M. H. M., Ali, N. M., & Noah, S. A. M. (2017). Mobile application on healthy diet for elderly based on persuasive design. International Journal on Advanced Science, Engineering and Information Technology, 7(1), 222–227.
Sekaran, R. (2020). FDA policy for mobile medical applications. Nossaman LLP. https://www.thehealthlawticker.com/fda-policy-for-mobile-medical-applications
Shin, G., Feng, Y., Jarrahi, M. H., & Gafinowitz, N. (2019). Beyond novelty effect: A mixed-methods exploration into the motivation for long-term activity tracker use. JAMIA Open, 2(1), 62–72. https://doi.org/10.1093/jamiaopen/ooy048
Smith, A. (2021). Mobile fact sheet. Pew Research Center. https://www.pewresearch.org/internet/fact-sheet/mobile/
Steinhubl, S. R., Muse, E. D., & Topol, E. J. (2015). The emerging field of mobile health. Science Translational Medicine, 7(283), 283rv3–283rv3. https://doi.org/10.1126/scitranslmed.aaa3487
Stoyanov, S. R., Hides, L., Kavanagh, D. J., & Wilson, H. (2016). Development and validation of the user version of the Mobile Application Rating Scale (uMARS). JMIR mHealth and uHealth, 4(2), e5849. https://doi.org/10.2196/mhealth.5849
US Food and Drug Administration. (2019). Policy for device software functions and mobile medical applications. US Department of Health and Human Services. https://www.fda.gov/media/80958/download
Varshney, U. (2014). Mobile health: Four emerging themes of research. Decision Support Systems, 66, 20–35. https://doi.org/10.1016/j.dss.2014.06.001
Xcertia. (2019). MHealth app guidelines. Xcertia Board of Directors. https://www.himss.org/sites/hde/files/media/file/2020/04/17/xcertia-guidelines-2019-final.pdf
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Yang, A., Lebedoff, S. (2022). Advancements in Health Care Communication. In: James, L.C., O’Donohue, W., Wendel, J. (eds) Clinical Health Psychology in Military and Veteran Settings. Springer, Cham. https://doi.org/10.1007/978-3-031-12063-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-12063-3_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-12062-6
Online ISBN: 978-3-031-12063-3
eBook Packages: Behavioral Science and PsychologyBehavioral Science and Psychology (R0)