Journal:Public health informatics, human factors, and the end-users
| Full article title | Public health informatics, human factors, and the end-users |
|---|---|
| Journal | Health Services Research and Managerial Epidemiology |
| Author(s) | Matthews, Sarah D.; Proctor, Michael D. |
| Author affiliation(s) | University of Central Florida |
| Primary contact | Email: sarah dot matthews at knights dot ucf dot edu |
| Year published | 2021 |
| Volume and issue | 8 |
| Article # | 23333928211012226 |
| DOI | 10.1177/23333928211012226 |
| ISSN | 2333-3928 |
| Distribution license | Creative Commons Attribution-NonCommercial 4.0 International |
| Website | https://journals.sagepub.com/doi/10.1177/23333928211012226 |
| Download | https://journals.sagepub.com/doi/pdf/10.1177/23333928211012226 (PDF) |
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This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed. |
Abstract
There is an unspoken assumption in public health informatics that “if you build it, they will come.” In this commentary, we argue that building it is not enough. Without end-user focus on human factors issues that are identified and resolved prior to implementation, “they may come, but they won’t stay!” We argue that to engage public health professionals with new innovative technology, one must continually ask during the development process, “who are we building this product for, and do we have the right information to back up our theories on implementation and use?” With the myriad of public health informatics introduced amid the COVID-19 pandemic, there are now many choices. For those languishing, we note that this question may not have been sufficiently pursued, resulting in situations where “they may come, but they won’t stay!”
Keywords: public health informatics, public health professionals, Technology Acceptance Model, Health Belief Model, human factors research
Public health informatics evolution and COVID-19
Over two decades ago, Yasnoff et al.[1] defined the discipline of public health informatics (PHI) as the “systematic application of information and computer science and technology to public health practice, research, and learning.”[1] The prevailing impression at that time was that all stakeholders must be engaged in coordinated activities related to PHI, but the public health workforce were deemed to not have the training and experience to make decisions about IT.[2] If public health professionals were left out of the PHI decision making, is it any surprise that this practice resulted in some high risk level of technology failures, and in other cases slow adoption of the technology.[2] Besides bringing public health professionals into the decision making circle, what can be done to help PHI flourish?
In the last two decades, we have seen a myriad of innovative technologies applied to public health practice in areas such as disease surveillance, immunization registries, electronic health record (EHR) integration, vital statistics, etc. Public health organizations such as the Association of State and Territorial Health Officials (ASTHO), Public Health Informatics Institute (PHII), and Centers for Disease Control and Prevention (CDC) recognized that the decentralized United States public health system is enormous in scale and immense in diversity, making it difficult to implement innovative technology successfully.[3][4][5] Complicating successful implementations are informatics factors and organizational culture barriers that do not allow for evidence-based public health to be implemented in practice.[2][6]
References
- ↑ 1.0 1.1 Yasnoff, William A.; OʼCarroll, Patrick W.; Koo, Denise; Linkins, Robert W.; Kilbourne, Edwin M. (2000). "Public Health Informatics: Improving and Transforming Public Health in the Information Age:" (in en). Journal of Public Health Management and Practice 6 (6): 67–75. doi:10.1097/00124784-200006060-00010. ISSN 1078-4659. http://journals.lww.com/00124784-200006060-00010.
- ↑ 2.0 2.1 2.2 Yasnoff, W. A.; Overhage, J. M.; Humphreys, B. L.; LaVenture, M. (1 November 2001). "A National Agenda for Public Health Informatics: Summarized Recommendations from the 2001 AMIA Spring Congress" (in en). Journal of the American Medical Informatics Association 8 (6): 535–545. doi:10.1136/jamia.2001.0080535. ISSN 1067-5027. PMC PMC130064. PMID 11687561. https://academic.oup.com/jamia/article-lookup/doi/10.1136/jamia.2001.0080535.
- ↑ Beck, Angela J.; Boulton, Matthew L.; Coronado, Fátima (1 November 2014). "Enumeration of the Governmental Public Health Workforce, 2014" (in en). American Journal of Preventive Medicine 47 (5): S306–S313. doi:10.1016/j.amepre.2014.07.018. PMC PMC6944190. PMID 25439250. https://linkinghub.elsevier.com/retrieve/pii/S0749379714003857.
- ↑ Hilliard, Tracy M.; Boulton, Matthew L. (1 May 2012). "Public Health Workforce Research in Review" (in en). American Journal of Preventive Medicine 42 (5): S17–S28. doi:10.1016/j.amepre.2012.01.031. https://linkinghub.elsevier.com/retrieve/pii/S074937971200092X.
- ↑ Tao, Donghua; Evashwick, Connie J.; Grivna, Michal; Harrison, Roger (19 February 2018). "Educating the Public Health Workforce: A Scoping Review". Frontiers in Public Health 6: 27. doi:10.3389/fpubh.2018.00027. ISSN 2296-2565. PMC PMC5826052. PMID 29515988. http://journal.frontiersin.org/article/10.3389/fpubh.2018.00027/full.
- ↑ Novick, Lloyd F. (1 September 2020). "JPHMP and The Guide to Community Preventive Services" (in en). Journal of Public Health Management and Practice 26 (5): 399–400. doi:10.1097/PHH.0000000000001217. ISSN 1078-4659. https://journals.lww.com/10.1097/PHH.0000000000001217.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. Some grammar and paragraph spacing was updated for improved readability. In some cases important information was missing from the references, and that information was added.
