Skip to main content

Online Health Communities: an alternative feasible data registry tool for developing countries

Abstract

Given the many challenges facing healthcare access in many developing countries and the added limitations observed in emergencies like COVID-19 pandemic, the authors here discuss an alternative and feasible approach to overcome all these limitations.

Peer Review reports

A recent study by Adams and colleagues showed that medical teams should validate Online Health Communities (OHCs) as they do not threaten the parent–provider relationship [1]. The idea of OHCs is broadly accepted to increase engagement, disease awareness and management. Successful OHCs exist in many nations; in the USA, PatientsLikeMe has more than 850,000 members with more than 2800 diseases [2]. The UK has HealthUnlocked with 1.5 million members, covering more than 250 conditions [3]. More than 420 million people are registered on China's OHC Ping A Good Doctor, and the platform has close to 1.27 billion consultations overall [4].

The construction of health registries in developing countries has been challenging. Some developing countries suffer from a lack of disease registries and others face the failure of their registry projects. Significant barriers include; inconsistent documentation and archiving system (absence of Electronic Health Records), low quality of the data collected, lack of budget, scarcity of trained and qualified personnel, the poor performance of managers, low stakeholders' interest/motivation, and absence of funding [5,6,7].

Since internet users are significantly increasing in developing countries, for example, approximately 73% of the population in Egypt has access to the internet, the OHCs could provide an excellent opportunity to develop online disease registries [8]. They offer advantages to the healthcare sector in many ways, such as being accessible to the population, achieving high levels of engagement, and removing physical and location barriers [9]. These platforms offer patients customized disease-specific reports and visualization tools to help patients understand and share information about their condition which result in better disease management [10]. This improves the quality of life and decreases the sense of loneliness [11]. OHCs may offer a chance to replace conventional health information systems in the developing countries. They might serve as source of data for research, patient care, and policy shaping. Additionally, OHCs can help these countries overcome the issue of health illiteracy by supplying patients and caregivers with reliable information and knowledge [12]. In addition, they will offer new insights for researchers to learn about the processes and outcomes of care from a patient perspective [13]. As a result, patients will be shifted from passive recipients of medical care to experts in their conditions, leading to patient-driven innovations. Since social support is one of the fundamental motives behind individuals engaging in OHCs, the broad reach will attract governments and policymakers to consider disease registries and might end up as collaborators in these OHCs [14].

OHCs are considered a reliable and valid resource for medical research [15]. This is evidenced by hundreds of studies that have been published in peer-reviewed medical and scientific journals, which have validated the utility of OHCs as a research tool. PatientsLikeMe is one of the most OHCs with high publication output. The literature with OHCs data has enhanced our knowledge of various disease conditions. These include neurodegenerative diseases such as Amyotrophic Lateral Sclerosis (ALS) [16,17,18], Huntington’s disease [19], Parkinson’s disease [20,21,22], Multiple sclerosis [23,24,25], and Osteogenesis imperfecta [26], as well as neurological diseases such as Epilepsy [27, 28], Bipolar disorder, depression [29], and Insomnia [30]. Additionally, OHCs have also provided insights into autoimmune diseases like Rheumatoid Arthritis [31, 32], Neuromyelitis Optica [33], cancer types such as Non-Small Cell Lung Cancer [34], Ovarian Cancer [35], metabolic diseases such as Diabetes Mellitus [35, 36], and cardiovascular diseases [37]. Several studies revealed OHCs improved population health. One study showed that OHC played an important role in promoting healthy behavior. The results of the study showed that social integration support from online social relationships have a positive relationship with users’ health behavior and increased informational support [38]. Other studies indicated that OHC increased patient empowerment which consequently improved health outcomes [39]. Additionally, the studies also address various themes and perspectives such as patient perception [40], patients reported outcomes (PROs) [41], crowdsourcing, patient-centeredness [42, 43], pharmacovigilance and adverse drug reactions reporting [44], drug development process [45] and off-label prescribing [46].

OHCs also provide a unique opportunity for real-world data and observational studies. They are considered new tools for collecting and analyzing data for epidemiological research. Using Ping A Good Doctor, a cross-sectional survey study analyzed 35.3 million consultations and inquisitions over the course of 1 year [4]. The study found that the most frequently consulted departments were gynecology and obstetrics, dermatology, and pediatrics. The most common diseases were acute upper respiratory infections, pregnancy, and dermatitis. Most users were female and between the ages of 19 and 35. The study found that online healthcare services can relieve the stress on hospitals and provide good user experiences [47]. So, Online health communities can reduce the prevalence and incidence of diseases by providing access to accurate and up-to-date information about different health conditions and treatments. Also, OHCs are heavily used by patients with long- term conditions. It is thought that OHCs have potential to promote health, usage of healthcare resources, and facilitate self-management of illness [48]. Also, OHCs provided social support for ongoing health-related problems especially at the onset of COVID-19 pandemic [49].

Like any virtual community, privacy invasion or personal health information (PHI) disclosure is a crucial challenge facing the OHCs. Qualified researchers in universities from different disciplines can work collaboratively to manage the platform activities from data acquisition to data analysis, generate reports and publications, and maintain privacy. Also, it will open the door for pharmaceutical companies to understand patients' needs in a specific population leading to personalized medicine and better evaluating of drug effects [50].

Digital platforms have contributed to the spread of misinformation online, which can have a detrimental effect on people's health, as stated by the WHO [51]. Therefore, it is important to note that certain OHCs, such as PatientsLikeMe and HealthUnlocked, operate as patient-driven platforms where information is reported and shared by members [52, 53]. It is crucial to understand that the information provided within these communities does not replace the need for guidance and consultation from healthcare professionals. In adherence to this, terms and conditions of use for these types of communities typically stipulate that the reliance on any information provided is the responsibility of the individual member [54, 55]. It is also important to be aware of the limitations in distinguishing between credible and unreliable information from these patient-driven platforms, and to exercise caution when interpreting the information provided. In contrast, other online health communities such as Ping An Health, which is also known as Ping An Good Doctor, function as a national internet-based hospital and provide online healthcare services, which may have more rigorous standards for the information provided [47].

To combat misinformation, OHC should have a team of researchers and experts in the field to make sure that all information posted are from a reliable source with scientific evidence and platforms with evidence-based data [51]. Although artificial intelligence is used unethically to spread misinformation, it also plays a crucial role in fighting misinformation and infodemics [56]. With support from the government and acceptance by the public, online health care services could develop quickly and greatly benefit people's daily lives and help in solving health problems [47].

Although OHC is crucial for developing countries due to the economic situation, there are some limitations. Low-Middle Income Countries (LMICs) have limited access to the internet; about 35% of the population have access to the internet compared to 80% in the developed countries. According to the World Bank, "Connecting for inclusion, high-speed internet access is not a luxury, but a basic necessity for economic and human development in both developed and developing countries" [57].

In conclusion, OHCs offer an alternative and workable strategy to get over the restrictions of developing disease registries. Also, it provides an accessible solution for healthcare access in light of the numerous obstacles that many developing countries face in their health systems and during emergencies.

Availability of data and materials

Not applicable.

References

  1. Adams SY, Tucker R, Lechner BE. The new normal: parental use of online health communities in the NICU. Pediatr Res. 2021. https://doi.org/10.1038/s41390-021-01684-3.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Live better, together! | PatientsLikeMe. https://www.patientslikeme.com/. Accessed 25 May 2022.

  3. HealthUnlocked. https://about.healthunlocked.com/. Accessed 25 May 2022.

  4. Company overview—ping an good doctor. https://www.pagd.net/allPage/aboutUs/47?lang=EN_US. Accessed 25 May 2022.

  5. Aljurf M, et al. Challenges and opportunities for HSCT outcome registries: perspective from international HSCT registries experts. Bone Marrow Transplant. 2014;49(8):1016–21. https://doi.org/10.1038/bmt.2014.78.

    Article  CAS  PubMed  Google Scholar 

  6. Sawe HR, Sirili N, Weber E, Coats TJ, Wallis LA, Reynolds TA. Barriers and facilitators to implementing trauma registries in low- and middle-income countries: qualitative experiences from Tanzania. Afr J Emerg Med. 2020;10:S23–8. https://doi.org/10.1016/J.AFJEM.2020.06.003.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Lazem M, Sheikhtaheri A. Barriers and facilitators for the implementation of health condition and outcome registry systems: a systematic literature review. J Am Med Inform Assoc. 2022;29(4):723–34. https://doi.org/10.1093/JAMIA/OCAB293.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Africa number of internet users by country 2022 | Statista. https://www.statista.com/statistics/505883/number-of-internet-users-in-african-countries/. Accessed May 25, 2022.

  9. Tseng HT, Ibrahim F, Hajli N, Nisar TM, Shabbir H. Effect of privacy concerns and engagement on social support behaviour in online health community platforms. Technol Forecast Soc Change. 2022;178: 121592. https://doi.org/10.1016/J.TECHFORE.2022.121592.

    Article  Google Scholar 

  10. Frost JH, Massagli MP. Social uses of personal health information within PatientsLikeMe, an online patient community: what can happen when patients have access to one another’s data. J Med Internet Res. 2008. https://doi.org/10.2196/JMIR.1053.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Wicks P, Thorley EM, Simacek K, Curran C, Emmas C. Scaling PatientsLikeMe via a ‘generalized platform’ for members with chronic illness: web-based survey study of benefits arising. J Med Internet Res. 2018. https://doi.org/10.2196/JMIR.9909.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Meherali S, Punjani NS, Mevawala A. Health literacy interventions to improve health outcomes in low- and middle-income countries. Health Lit Res Pract. 2020;4(4):e251–66. https://doi.org/10.3928/24748307-20201118-01.

    Article  PubMed  PubMed Central  Google Scholar 

  13. The Emerging World of Online Health Communities. https://ssir.org/articles/entry/the_emerging_world_of_online_health_communities . Accessed 25 May 2022.

  14. Wang X, Zhao K, Street N. Social support and user engagement in online health communities. Lecture Notes Comp Sci (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2014;8549:97–110. https://doi.org/10.1007/978-3-319-08416-9_10.

    Article  Google Scholar 

  15. Eichler GS et al. Exploring concordance of patient-reported information on PatientsLikeMe and medical claims data at the patient level. J Med Internet Res 2016;18(5):e110. https://www.jmir.org/2016/5/e110. 2016;18(5): e5130. https://doi.org/10.2196/JMIR.5130.

  16. Wicks P, Vaughan TE, Massagli MP, Heywood J. Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm. Nat Biotechnol. 2011;29(5):411–4. https://doi.org/10.1038/nbt.1837.

    Article  CAS  PubMed  Google Scholar 

  17. Bedlack RS, et al. How common are ALS plateaus and reversals? Neurology. 2016;86(9):808–12. https://doi.org/10.1212/WNL.0000000000002251.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Wicks P, Albert SM. It’s time to stop saying ‘the mind is unaffected’ in ALS. Neurology. 2018;91(15):679–81. https://doi.org/10.1212/WNL.0000000000006303.

    Article  PubMed  Google Scholar 

  19. Thorley EM, et al. Understanding how chorea affects health-related quality of life in Huntington disease: an online survey of patients and caregivers in the United States. Patient. 2018;11(5):547. https://doi.org/10.1007/S40271-018-0312-X.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Venkataraman V, Donohue SJ, Biglan KM, Wicks P, Dorsey ER. Virtual visits for Parkinson disease: a case series. Neurol Clin Pract. 2014;4(2):146. https://doi.org/10.1212/01.CPJ.0000437937.63347.5A.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Wicks P, MacPhee GJA. Pathological gambling amongst Parkinson’s disease and ALS patients in an online community (PatientsLikeMe.com). Mov Disord. 2009;24(7):1085–8. https://doi.org/10.1002/MDS.22528.

    Article  PubMed  Google Scholar 

  22. LouJackson M, Bex PJ, Ellison JM, Wicks P, Wallis J. Feasibility of a web-based survey of hallucinations and assessment of visual function in patients with Parkinson’s disease. Interact J Med Res. 2014;3(1): e2744. https://doi.org/10.2196/IJMR.2744.

    Article  Google Scholar 

  23. Wicks P, Rasouliyan L, Katic B, Nafees B, Flood E, Sasané R. The real-world patient experience of fingolimod and dimethyl fumarate for multiple sclerosis. BMC Res Notes. 2016;9(1):1–9. https://doi.org/10.1186/S13104-016-2243-8/TABLES/6.

    Article  Google Scholar 

  24. Bove R, et al. Evaluation of an online platform for multiple sclerosis research: patient description, validation of severity scale, and exploration of BMI effects on disease course. PLoS ONE. 2013;8(3): e59707. https://doi.org/10.1371/JOURNAL.PONE.0059707.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Wicks P, Brandes D, Park J, Liakhovitski D, Koudinova T, Sasane R. Preferred features of oral treatments and predictors of non-adherence: two web-based choice experiments in multiple sclerosis patients. Interact J Med Res. 2015;4(1):e6. https://doi.org/10.2196/IJMR.3776.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Brownstein CA, Wicks P. The potential research impact of patient reported outcomes on osteogenesis imperfecta. Clin Orthop Relat Res. 2010;468(10):2581. https://doi.org/10.1007/S11999-010-1373-X.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Hixson JD, et al. Patients optimizing epilepsy management via an online community: The POEM Study. Neurology. 2015;85(2):129. https://doi.org/10.1212/WNL.0000000000001728.

    Article  PubMed  PubMed Central  Google Scholar 

  28. de la Loge C, et al. PatientsLikeMe® online epilepsy community: patient characteristics and predictors of poor health-related quality of life. Epilepsy Behav. 2016;63:20–8. https://doi.org/10.1016/J.YEBEH.2016.07.035.

    Article  PubMed  Google Scholar 

  29. Tonozzi TR, Braunstein GD, Kammesheidt A, Curran C, Golshan S, Kelsoe J. Pharmacogenetic profile and major depressive and/or bipolar disorder treatment: a retrospective, cross-sectional study. Pharmacogenomics. 2018;19(15):1169–79. https://doi.org/10.2217/PGS-2018-0088.

    Article  CAS  PubMed  Google Scholar 

  30. Katic B, et al. New approach for analyzing self-reporting of insomnia symptoms reveals a high rate of comorbid insomnia across a wide spectrum of chronic diseases. Sleep Med. 2015;16(11):1332–41. https://doi.org/10.1016/J.SLEEP.2015.07.024.

    Article  PubMed  Google Scholar 

  31. Kelman A, et al. Communicating laboratory test results for rheumatoid factor: what do patients and physicians want? Patient Prefer Adher. 2016;10:2501. https://doi.org/10.2147/PPA.S104396.

    Article  Google Scholar 

  32. Costello R, Jacklin C, Evans MJ, McBeth J, Dixon WG. Original article: Representativeness of a digitally engaged population and a patient organisation population with rheumatoid arthritis and their willingness to participate in research: a cross-sectional study. RMD Open. 2018;4(1): e000664. https://doi.org/10.1136/RMDOPEN-2018-000664.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Eaneff S, et al. Patient perspectives on neuromyelitis optica spectrum disorders: data from the PatientsLikeMe online community. Mult Scler Relat Disord. 2017;17:116–22. https://doi.org/10.1016/J.MSARD.2017.07.014.

    Article  PubMed  Google Scholar 

  34. Rodriguez AM, Braverman J, Aggarwal D, Friend J, Duus E. The experience of weight loss and its associated burden in patients with non-small cell lung cancer: results of an online survey. JCSM Clin Rep. 2017;2(2):1–12. https://doi.org/10.17987/JCSM-CR.V2I2.18.

    Article  Google Scholar 

  35. Simacek K, Raja P, Chiauzzi E, Eek D, Halling K. What do ovarian cancer patients expect from treatment?: Perspectives from an online patient community. Cancer Nurs. 2017;40(5):E17–27. https://doi.org/10.1097/NCC.0000000000000415.

    Article  PubMed  Google Scholar 

  36. Lopez JMS, Katic BJ, Fitz-Randolph M, Jackson RA, Chow W, Mullins CD. Understanding preferences for type 2 diabetes mellitus self-management support through a patient-centered approach: a 2-phase mixed-methods study. BMC Endocr Disord. 2016;16(1):1–11. https://doi.org/10.1186/S12902-016-0122-X/FIGURES/6.

    Article  Google Scholar 

  37. Antman EM, et al. Acquisition, analysis, and sharing of data in 2015 and beyond: a survey of the landscape a conference report from the American heart association data summit 2015. J Am Heart Assoc. 2015. https://doi.org/10.1161/JAHA.115.002810.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Li Y, Yan X. How could peers in online health community help improve health behavior. Int J Environ Res Public Health. 2020;17:2995. https://doi.org/10.3390/IJERPH17092995.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Johansson V, Islind AS, Lindroth T, Angenete E, Gellerstedt M. Online communities as a driver for patient empowerment: systematic review. J Med Internet Res. 2021;23(2): e19910. https://doi.org/10.2196/19910.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Simacek K, Curran C, Fenici P, Garcia-Sanchez R. Patient perceptions of their glycemic control and its influence on type 2 diabetes outcomes: an international survey of online communities. Patient Prefer Adher. 2019;13:295–307. https://doi.org/10.2147/PPA.S186801.

    Article  Google Scholar 

  41. Brownstein CA, Brownstein JS, Williams DS, Wicks P, Heywood JA. The power of social networking in medicine. Nat Biotechnol. 2009;27(10):888–90. https://doi.org/10.1038/nbt1009-888.

    Article  CAS  PubMed  Google Scholar 

  42. Richards T, Coulter A, Wicks P. Time to deliver patient centred care. BMJ. 2015. https://doi.org/10.1136/BMJ.H530.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Lavallee DC, Wicks P, Alfonso Cristancho R, Mullins CD. Stakeholder engagement in patient-centered outcomes research: high-touch or high-tech? Expert Rev Pharmacoecon Outcomes Res. 2014;14(3):335–44. https://doi.org/10.1586/14737167.2014.901890.

    Article  PubMed  Google Scholar 

  44. Blaser DA, et al. Comparison of rates of nausea side effects for prescription medications from an online patient community versus medication labels: an exploratory analysis. AAPS Open. 2017;3(1):1–10. https://doi.org/10.1186/S41120-017-0020-Y.

    Article  Google Scholar 

  45. Anand A, Brandwood HJ, JamesonEvans M. Improving patient involvement in the drug development process: case study of potential applications from an online peer support network. Clin Ther. 2017;39(11):2181–8.

    Article  PubMed  Google Scholar 

  46. Frost J, Okun S, Vaughan T, Heywood J, Wicks P. Patient-reported outcomes as a source of evidence in off-label prescribing: analysis of data from PatientsLikeMe. J Med Internet Res 2011;13(1):e6 https://www.jmir.org/2011/1/e6. 2011;13(1): e1643. doi: https://doi.org/10.2196/JMIR.1643.

  47. Jiang X, et al. Characteristics of online health care services from China’s largest online medical platform: cross-sectional survey study. J Med Internet Res. 2021;23(4): e25817. https://doi.org/10.2196/25817.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Joglekar S et al. How online communities of people with long-term conditions function and evolve: network analysis of the structure and dynamics of the asthma UK and British Lung Foundation Online Communities. J Med Internet Res. 2018;20(7):e238. https://www.jmir.org/2018/7/e238. 2018;20(7): e9952. https://doi.org/10.2196/JMIR.9952.

  49. Jong W, Liang OS, Yang CC. The exchange of informational support in Online Health Communities at the onset of the COVID-19 pandemic: content analysis. Jmirx Med. 2021;2(3): e27485. https://doi.org/10.2196/27485.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Heywood J. Straight talk with...Jamie Heywood. Nat Med. 2014;20(5):457–457. https://doi.org/10.1038/nm0514-457.

    Article  CAS  PubMed  Google Scholar 

  51. do Nascimento IJB, et al. Infodemics and health misinformation: a systematic review of reviews. Bull World Health Organ. 2022;100(9):544. https://doi.org/10.2471/BLT.21.287654.

    Article  Google Scholar 

  52. Brajovic S, et al. Validating a framework for coding patient-reported health information to the medical dictionary for regulatory activities terminology: an evaluative study. JMIR Med Inform. 2018. https://doi.org/10.2196/MEDINFORM.9878.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Costello RE, Anand A, Evans MJ, Dixon WG. Associations between engagement with an Online Health Community and changes in patient activation and health care utilization: longitudinal web-based survey. J Med Internet Res. 2019. https://doi.org/10.2196/13477.

    Article  PubMed  PubMed Central  Google Scholar 

  54. User agreement | PatientsLikeMe. https://www.patientslikeme.com/about/user_agreement. Accessed 23 Jan 2023.

  55. How communities are safeguarded?—HealthUnlocked Help Center. https://support.healthunlocked.com/article/11-community-guidelines. Accessed 23 Jan 2023.

  56. Benzie A, Montasari R, Benzie A, Rodham H, Montasari R. Artificial intelligence and the spread of mis- and disinformation. Artif Intell Natl Secur. 2022. https://doi.org/10.1007/978-3-031-06709-9_1.

    Article  Google Scholar 

  57. Kelly T, Rossotto CM. Broadband Strategies Handbook. 2012. https://doi.org/10.1596/978-0-8213-8945-4.

Download references

Acknowledgements

Not applicable.

Funding

Not applicable.

Author information

Authors and Affiliations

Authors

Contributions

OA, DGS conceptualized and drafted the manuscript. MS revised and edited the final version. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Mohamed Salama.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

All authors consent to publish this work upon acceptance.

Competing interests

Authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abdelraheem, O., Sami, D.G. & Salama, M. Online Health Communities: an alternative feasible data registry tool for developing countries. Health Res Policy Sys 21, 28 (2023). https://doi.org/10.1186/s12961-023-00976-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12961-023-00976-w

Keywords