Right Information, Right Care, Right Patient, Right Time: Community Preferences to Inform a Self-Management Support Tool for Upper Respiratory Symptoms

Abstract

Objectives During and since the coronavirus disease 2019 (COVID-19) pandemic, communities have needed to cope with several conditions that cause similar upper respiratory symptoms but are managed differently. We describe community reactions to a self-management toolkit for patients with upper respiratory symptoms to inform mobile e-health app development. The toolkit is based on the “4R” (Right Information, Right Care, Right Patient, Right Time) care planning and management model.

Methods The 4R Cold, Flu, and COVID-19 Information Tool (4R-Toolkit) along with a brief evaluation survey were distributed in three ways: through a Bronx NY Allergy/Asthma clinic, through the Bronx Borough President's Office listserv, and through peer recruitment. The survey assessed respondents' perceptions of the 4R-Toolkit's accessibility, preferences for sharing symptoms with clinicians, social media use, and e-health literacy.

Results We obtained a diverse sample of 106 Bronx residents, with 83% reporting personal or a social contact with symptoms suggestive of COVID-19. Respondents varied in the information sources they preferred: computer (39%), smartphone (28%), paper (11%), and no preference (22%). Most (67%) reported that social media had at least some impact on their health care decisions. Regardless of media preferences, respondents were positive about the 4R-Toolkit. Out of 106 respondents, 91% believed the 4R-Toolkit would help people self-manage upper respiratory symptoms and 85% found it easy to understand. Respondents strongly endorsed retention of all 4R-Toolkit content domains with 81% indicating that they would be willing to share symptoms with providers using a 4R-Toolkit smartphone app.

Conclusion The 4R-Toolkit can offer patients and community members accurate and up-to-date information on COVID-19, the common cold, and the flu. The user-friendly tool is accessible to diverse individuals, including those with limited e-health literacy. It has potential to support self-management of upper respiratory symptoms and promote patient engagement with providers.

Keywords

COVID-19 - infectious disease - domain - clinical care - 4R-tool - eHealth - respiratory - literacy

Authors' Contributions

D.G. (conception, design, interpretation of data, drafting, and critical revision of the article); C.L. (interpretation and analysis of data, drafting, and critical revision of the article); K.M. (interpretation of data and drafting and critical revision of the article); B.R. (conception, design, interpretation of data, drafting, and critical revision of the article); S.K. (acquisition of data); S.P.J. (conception, design, interpretation of data, drafting, and critical revision of the article); C.W. (conception and design, data analysis/interpretation, drafting, and critical revision of the article); J.F. (conception, design); J.T. (conception and design, data analysis/interpretation, drafting, and critical revision of the article); K.J. (conception and design, data analysis/interpretation, drafting, and critical revision of the article).

Protection of Human and Animal Subjects

This study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research involving Human Subjects and was reviewed by the Albert Einstein College of Medicine Institutional Review Board.

Publikationsverlauf

Eingereicht: 10. Februar 2024

Angenommen: 14. Oktober 2024

Accepted Manuscript online:
29. Oktober 2024

Artikel online veröffentlicht:
19. Februar 2025

• References

• 1 Podder P, Das SR, Mondal MRH. et al. LDDNet: a deep learning framework for the diagnosis of infectious lung diseases. Sensors (Basel) 2023; 23 (01) 480
• 2 Miller IF, Becker AD, Grenfell BT, Metcalf CJE. Disease and healthcare burden of COVID-19 in the United States. Nat Med 2020; 26 (08) 1212-1217
• 3 Soria A, Lapadula G, Bonfanti P. COVID-19 mortality and stress to the hospital system from high patient load. JAMA Intern Med 2021; 181 (08) 1134-1134
• 4 Soria A, Galimberti S, Lapadula G. et al. The high volume of patients admitted during the SARS-CoV-2 pandemic has an independent harmful impact on in-hospital mortality from COVID-19. PLoS One 2021; 16 (01) e0246170
• 5 Roy D, Tripathy S, Kar SK, Sharma N, Verma SK, Kaushal V. Study of knowledge, attitude, anxiety & perceived mental healthcare need in Indian population during COVID-19 pandemic. Asian J Psychiatr 2020; 51: 102083
• 6 Purkayastha D, Vanroelen C, Bircan T, Vantyghem MA, Gantelet Adsera C. Work, health and COVID-19: a literature review. SSRN Electronic J 2021; 25: 634-653
• 7 Stephenson-Hunter C, Yusuf Y, Larson R, Campanella J, Gutnick DN. What matters to us: bridging research and accurate information through dialogue (BRAID) to build community trust and cultivate vaccine confidence. Prev Med Rep 2023; 34: 102253
• 8 Pollard MS, Davis LM. Decline in trust in the Centers for Disease Control and Prevention during the COVID-19 pandemic. Rand Health Q 2022; 9 (03) 23
• 9 Aleligne YK, Appiah D, Ebong IA. Racial disparities in coronavirus disease 2019 (COVID-19) outcomes. Curr Opin Cardiol 2021; 36 (03) 360-366
• 10 Brakefield WS, Olusanya OA, White B, Shaban-Nejad A. Social determinants and indicators of COVID-19 among marginalized communities: a scientific review and call to action for pandemic response and recovery. Disaster Med Public Health Prep 2022; 17: e193
• 11 Benitez J, Courtemanche C, Yelowitz A. Racial and ethnic disparities in COVID-19: evidence from six large cities. J Econ Race Policy 2020; 3 (04) 243-261
• 12 Golinelli D, Boetto E, Carullo G, Nuzzolese AG, Landini MP, Fantini MP. Adoption of digital technologies in health care during the COVID-19 pandemic: systematic review of early scientific literature. J Med Internet Res 2020; 22 (11) e22280
• 13 Wosik J, Fudim M, Cameron B. et al. Telehealth transformation: COVID-19 and the rise of virtual care. J Am Med Inform Assoc 2020; 27 (06) 957-962
• 14 Keesara S, Jonas A, Schulman K. COVID-19 and health care's digital revolution. N Engl J Med 2020; 382 (23) e82
• 15 Trosman JR, Weldon CB, Rapkin BD. et al. Evaluation of the novel 4R oncology care planning model in breast cancer: impact on patient self-management and care delivery in safety-net and non-safety-net centers. JCO Oncol Pract 2021; 17 (08) e1202-e1214
• 16 COVID-19 en casa—COVID-19 at home. Accessed at: https://www.home-covid.me/espanol/
• 17 COVID-19 at home—for people with mild symptoms of COVID-19. Accessed at: https://www.home-covid.me/
• 18 Weldon C, James B, Jinnett K. et al. 4R COVID-19 toolkit for patient self-management of mild or moderate COVID-19 symptoms. SSRN Electronic Journal 2021;19
• 19 Norman CD, Skinner HA. eHEALS: the eHealth literacy scale. J Med Internet Res 2006; 8 (04) e27
• 20 Guo X, Han X, Zhang X, Dang Y, Chen C. Investigating m-Health acceptance from a protection motivation theory perspective: gender and age differences. Telemed J E Health 2015; 21 (08) 661-669
• 21 Reiter PL, Katz ML. Racial/ethnic differences in knowledge, attitudes, and beliefs about COVID-19 among adults in the United States. Front Public Health 2021; 9: 653498
• 22 Lane S, Fitzsimmons E, Zelefksy A. et al. Assessing electronic health literacy at an urban academic hospital. Appl Clin Inform 2023; 14 (02) 365-373
• 23 El Benny M, Kabakian-Khasholian T, El-Jardali F, Bardus M. Application of the eHealth literacy model in digital health interventions: scoping review. J Med Internet Res 2021; 23 (06) e23473
• 24 Lobach DF, Boxwala A, Kashyap N. et al. Integrating a patient engagement app into an electronic health record-enabled workflow using interoperability standards. Appl Clin Inform 2022; 13 (05) 1163-1171
• 25 Hernandez S, George T, Dharod A, Feiereisel K, Bundy R, Williams D. The future of community outreach: using patient portals to provide voter resources during the coronavirus disease 2019 pandemic. Appl Clin Inform 2023; 14 (02) 300-309
• 26 Xie SJ, Mah NR, Chew L. et al. Improving vaccine equity: how community engagement and informatics facilitate health system outreach to underrepresented groups. Appl Clin Inform 2024; 15 (01) 129-144
• 27 Mani K, Canarick J, Ruan E, Liu J, Kitsis E, Jariwala SP. Effect of telemedicine and the COVID-19 pandemic on medical trainees' usage of the electronic health record in the outpatient setting. Appl Clin Inform 2023; 14 (02) 309-320
• 28 Ahmed MH, Guadie HA, Ngusie HS. et al. Digital health literacy during the COVID-19 pandemic among health care providers in resource-limited settings: cross-sectional study. JMIR Nurs 2022; 5 (01) e39866
• 29 Guo Z, Zhao SZ, Guo N. et al. Socioeconomic disparities in eHealth literacy and preventive behaviors during the COVID-19 pandemic in Hong Kong: cross-sectional study. J Med Internet Res 2021; 23 (04) e24577
• 30 Malik AA, McFadden SM, Elharake J, Omer SB. Determinants of COVID-19 vaccine acceptance in the US. EClinicalMedicine 2020; 26: 100495
• 31 Çalıca Utku A, Budak G, Karabay O, Güçlü E, Okan HD, Vatan A. Main symptoms in patients presenting in the COVID-19 period. Scott Med J 2020; 65 (04) 127-132
• 32 Alimohamadi Y, Sepandi M, Taghdir M, Hosamirudsari H. Determine the most common clinical symptoms in COVID-19 patients: a systematic review and meta-analysis. J Prev Med Hyg 2020; 61 (03) E304-E312
• 33 Kamal M, Abo Omirah M, Hussein A, Saeed H. Assessment and characterisation of post-COVID-19 manifestations. Int J Clin Pract 2021; 75 (03) e13746
• 34 Wang PW, Lu WH, Ko NY. et al. COVID-19-related information sources and the relationship with confidence in people coping with COVID-19: Facebook survey study in Taiwan. J Med Internet Res 2020; 22 (06) e20021
• 35 Research-Brief.pdf (nyc.gov). Google Search. https://www.google.com/search?q=Research-Brief.pdf+(nyc.gov)&oq=Research-Brief.pdf+(nyc.gov)&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIKCAEQABiABBiiBDIKCAIQABiABBiiBDIKCAMQABiABBiiBDIKCAQQABiiBBiJBTIKCAUQABiABBiiBNIBBzI2OWowajSoAgCwAgE&sourceid=chrome&ie=UTF-8
• 36 Demographics of Mobile Device Ownership and Adoption in the United States. Pew Research Center. https://www.pewresearch.org/internet/fact-sheet/mobile/
• 37 Benetoli A, Chen TF, Aslani P. How patients' use of social media impacts their interactions with healthcare professionals. Patient Educ Couns 2018; 101 (03) 439-444
• 38 Graham AL, Cobb CO, Cobb NK. The internet, social media, and health decision-making. Handbook of Health Decision Science 2016: 335-355
• 39 Patrick M, Venkatesh RD, Stukus DR. Social media and its impact on health care. Ann Allergy Asthma Immunol 2022; 128 (02) 139-145
• 40 Trosman JR. et al. Does the 4R oncology model improve clinicians' effectiveness in patient-facing planning of complex cancer care?. J Clin Oncol 2022; 40: 1542-1542
• 41 Trosman J, Weldon C, Kircher S, Gradishar W, Benson III A. Innovating cancer care delivery: the example of the 4R oncology model for colorectal cancer patients. Curr Treat Options Oncol 2019; 20 (02) 11
• 42 Liu R, Weldon CB, Linehan E. et al. Fostering a high-functioning team in cancer care using the 4R oncology model: assessment in a large health system and a blueprint for other institutions. JCO Oncol Pract 2023; 19 (01) e125-e137
• 43 U.S. Census Bureau QuickFacts. Bronx County, New York. https://www.census.gov/quickfacts/fact/table/bronxcountynewyork/PST0452222022
• 44 Hsia BC, Wu S, Mowrey WB, Jariwala SP. Evaluating the asthmaxcel mobile application regarding asthma knowledge and clinical outcomes. Respir Care 2020; 65 (08) 1112-1119