Implementation of a Cloud-Based Electronic Medical Record to Reduce Gaps in the HIV Treatment Continuum in Rural Kenya
Open accessAbstract
BACKGROUND: Electronic medical record (EMR) systems are increasingly being adopted to support the delivery of health care in developing countries and their implementation can help to strengthen pathways of care and close gaps in the HIV treatment cascade by improving access to and use of data to inform clinical and public health decision-making. METHODS: This study implemented a novel cloud-based electronic medical record system in an HIV outpatient setting in Western Kenya and evaluated its impact on reducing gaps in the HIV treatment continuum including missing data and patient eligibility for ART. The impact of the system was assessed using a two-sample test of proportions pre- and post-implementation of EMR-based data verification and clinical decision support. RESULTS: Significant improvements in data quality and provision of clinical care were recorded through implementation of the EMR system, helping to ensure patients who are eligible for HIV treatment receive it early. A total of 2,169 and 764 patient records had missing data pre-implementation and post-implementation of EMR-based data verification and clinical decision support respectively. A total of 1,346 patients were eligible for ART, but not yet started on ART, pre-implementation compared to 270 patients pre-implementation. CONCLUSION: EMR-based data verification and clinical decision support can reduce gaps in HIV care, including missing data and eligibility for ART. A cloud-based model of EMR implementation removes the need for local clinic infrastructure and has the potential to enhance data sharing at different levels of health care to inform clinical and public health decision-making. A number of issues, including data management and patient confidentiality, must be considered but significant improvements in data quality and provision of clinical care are recorded through implementation of this EMR model.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Previous studies suggest that poor completion or accuracy of key data items required for clinical care is likely to limit the benefit of EHR systems in day-to-day patient management, such as the functioning of clinical decision support system in this study and that reported by Haskew et al.
“Previous studies suggest that poor completion or accuracy of key data items required for clinical care is likely to limit the benefit of EHR systems in day-to-day patient management, such as the functioning of clinical decision support system in this study and that reported by Haskew et al [41].”
Haskew et al showed that clinical alerts reduced missing data and improved the quality of care, with missing data significantly reduced for key variables including "patient source" (375 to 69 patients), "first CD4 count" (826 to 354 patients), and "first WHO stage" (2258 to 479 patients; all P<.001).
“They showed that clinical alerts reduced missing data and improved the quality of care. Missing data were significantly reduced for key variables (before and after alerts implemented) including “patient source” (375 to 69 patients), “first CD4 count” (826 to 354 patients), and “first WHO stage” (2258 to 479 patients; all P<.001).”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Oluoch, T., Santas, X., Kwaro, D., Were, M., Biondich, P., Bailey, C., Abu-Hanna, A., & de Keizer, N. (2012) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2012.07.010 · Cited by 120 · see citing works
Flagged first, then by citations
Research works printed but not linked: 17
- [1]Fraser HSF, Biondich P, Moodley D, Choi S, Mamlin BW, Szolovits P. Implementing electronic medical record systems in developing countries. Inform Prim Care. 2005; 13: 83– 95. 15992493
- [2]Fraser HS, Blaya J. Implementing medical information systems in developing countries, what works and what doesn't. AMIA Annual Symposium proceedings / AMIA Symposium AMIA Symposium. 2010; 2010: 232– 236. 21346975 PMC3041413
- [3]Were MC, Emenyonu N, Achieng M, Shen C, Ssali J, Masaba JPM, et al Evaluating a scalable model for implementing electronic health records in resource-limited settings. J Am Med Inform Assoc. 2010; 17: 237– 244. 10.1136/jamia.2009.002303 20442140 PMC2995711 · DOI · Paper page
- [7]Rosen S, Fox MP. Retention in HIV Care between Testing and Treatment in Sub-Saharan Africa: A Systematic Review. 2011; 8: e1001056 10.1371/journal.pmed.1001056 21811403 PMC3139665 · DOI
- [11]Badri M, Lawn SD, Wood R. Short-term risk of AIDS or death in people infected with HIV-1 before antiretroviral therapy in South Africa: a longitudinal study. 2006; 368: 1254– 1259. 10.1016/S0140-6736(06)69117-4 17027731 · DOI
- [12]Fox MP, Rosen S. Patient retention in antiretroviral therapy programs up to three years on treatment in sub-Saharan Africa, 2007–2009: systematic review. Trop Med Int Health. 2010; 15 Suppl 1: 1– 15. 10.1111/j.1365-3156.2010.02508.x 20586956 PMC2948795 · DOI
- [13]Castilla J, Sobrino P, la Fuente de L, Noguer I, Guerra L, Parras F. Late diagnosis of HIV infection in the era of highly active antiretroviral therapy: consequences for AIDS incidence. 2002; 16: 1945 12351955
- [14]Sabin CA, Smith CJ, Gumley H, Murphy G, Lampe FC, Phillips AN, et al Late presenters in the era of highly active antiretroviral therapy: uptake of and responses to antiretroviral therapy. 2004; 18: 2145 15577647
- [15]Nsigaye R, Wringe A, Roura M, Kalluvya S, Urassa M, Busza J, et al From HIV diagnosis to treatment: evaluation of a referral system to promote and monitor access to antiretroviral therapy in rural Tanzania. J Int AIDS Soc. 2009; 12: 31 10.1186/1758-2652-12-31 19906291 PMC2788344 · DOI
- [16]Decroo T, Panunzi I, Dores das C, Maldonado F, Biot M, Ford N, et al Lessons learned during down referral of antiretroviral treatment in Tete, Mozambique. J Int AIDS Soc. 2009; 12: 6 10.1186/1758-2652-12-6 19419543 PMC2685122 · DOI
- [17]Nordberg E, Holmberg S, Kiugu S. Exploring the interface between first and second level of care: referrals in rural Africa. Trop Med Int Health. 1996; 1: 107– 111. 8673814
- [18]Were MC, Shen C, Bwana M, Emenyonu N, Musinguzi N, Nkuyahaga F, et al Creation and evaluation of EMR-based paper clinical summaries to support HIV-care in Uganda, Africa. Int J Med Inform. 2010; 79: 90– 96. 10.1016/j.ijmedinf.2009.11.006 20036193 PMC2818680 · DOI
- [19]Menendez MD, Alonso J, Rancaño I, Corte JJ, Herranz V, Vazquez F. Impact of computerized physician order entry on medication errors. Rev Calid Asist. 2012; 27: 334– 340. 10.1016/j.cali.2012.01.010 22465826 · DOI
- [20]Eslami S, de Keizer NF, Abu-Hanna A. The impact of computerized physician medication order entry in hospitalized patients—a systematic review. Int J Med Inform. Elsevier; 2008; 77: 365– 376. 10.1016/j.ijmedinf.2007.10.001 18023611 · DOI
- [21]Williams F, Boren SA. The role of electronic medical record in care delivery in developing countries. International Journal of Information Management. 2008; 28: 503– 507. 10.1016/j.ijinfomgt.2008.01.016 PMC6374042 30774175 · DOI
- [23]Mamlin BW, Biondich PG, Wolfe BA, Fraser H, Jazayeri D, Allen C, et al Cooking up an open source EMR for developing countries: OpenMRS—a recipe for successful collaboration. AMIA Annual Symposium proceedings / AMIA Symposium AMIA Symposium. 2006; 529– 533. 17238397 PMC1839638
- [26]Ministry of Medical Services, NASCOP. Guidelines for antiretroviral therapy in Kenya 4th edition 2011. Ministry of Medical Services, National AIDS and STI Control Program; 2011.
Cited by
Sorted by citations
Wahl, B., Cossy-Gantner, A., Germann, S., & Schwalbe, N. R. (2018) · BMJ Global Health · doi:10.1136/bmjgh-2018-000798 · Cited by 796 · see citing works
Kruse, C. S., Stein, A., Thomas, H., & Kaur, H. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-1075-6 · Cited by 403 · see citing works
Asi, Y. M., & Williams, C. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.11.003 · Cited by 187 · see citing works
Fraser, H. S. F., Mugisha, M., Bacher, I., Ngenzi, J. L., Seebregts, C., Umubyeyi, A., & Condo, J. (2024) · JMIR Public Health and Surveillance · doi:10.2196/49127 · Cited by 20 · see citing works
- Authors
- Haskew, J., Rø, G., Turner, K., Kimanga, D., Sirengo, M., & Sharif, S.
- Affiliations
- Durham University (GB)
- British Antarctic Survey (GB)
- Ministry of Health (KE)
- Corresponding author
- Haskew, John
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-08-07
- Volume
- 10
- Issue
- 8
- Pages
- e0135361
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4529204
- OpenAlex
- W1752899526
- Cited by
- 52
- References
- 1
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 3 · 2023: 9 · 2022: 9 · 2021: 3 · 2020: 5 · 2019: 4 · 2018: 3 · 2017: 9 · 2016: 1
- FWCI
- 8.35
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- NERC Open Research Archive (Natural Environment Research Council) (acceptedVersion)
- Durham Research Online (Durham University) (acceptedVersion, other-oa)
- Durham Research Online (Durham University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Natural Environment Research Council (bas010011)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Infectious Diseases › HIV/AIDS Research and Interventions
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Keywords
- electronic health records, HIV care gaps, antiretroviral therapy eligibility, data quality, missing data, clinical decision support, data verification, rural health care, Western Kenya
- MeSH terms
- Adolescent, Adult, Aged, Continuity of Patient Care, Decision Making, Developing Countries, Female, Humans, Kenya, Male, Middle Aged, Public Health, Rural Population, HIV Infections, Rural Health Services, Decision Support Systems, Clinical, Internet, Access to Information, Young Adult, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
