Volume 98, Issue 1
  • ISSN: 0002-9637
  • E-ISSN: 1476-1645



Most health care in low-income countries is delivered at a primary care level by health workers who lack quality training and supervision, often distant from more experienced support. Lack of knowledge and poor communication result in a poor quality of care and inefficient delivery of health services. Although bringing great benefits in sectors such as finance and telecommunication in recent years, the Digital Revolution has lightly and inconsistently affected the health sector. These advances offer an opportunity to dramatically transform health care by increasing the availability and timeliness of information to augment clinical decision-making, based on improved access to patient histories, current information on disease epidemiology, and improved incorporation of data from point-of-care and centralized diagnostic testing. A comprehensive approach is needed to more effectively incorporate current digital technologies into health systems, bringing external and patient-derived data into the clinical decision-making process in real time, irrespective of health worker training or location. Such dynamic clinical algorithms could provide a more effective framework within which to design and integrate new digital health technologies and deliver improved patient care by primary care health workers.

[open-access] This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Article metrics loading...

The graphs shown below represent data from March 2017
Loading full text...

Full text loading...



  1. Dheda K, Theron G, Welte A, , 2014. Cost-effectiveness of Xpert MTB/RIF and investing in health care in Africa. Lancet Glob Health 2: e554e556. [Google Scholar]
  2. Theron G, 2014. Feasibility, accuracy, and clinical effect of point-of-care Xpert MTB/RIF testing for tuberculosis in primary-care settings in Africa: a multicentre, randomised, controlled trial. Lancet 383: 424435. [Google Scholar]
  3. Cohen GM, Drain PK, Noubary F, Cloete C, Bassett IV, , 2014. Diagnostic delays and clinical decision making with centralized Xpert MTB/RIF testing in Durban, South Africa. J Acquir Immune Defic Syndr 67: e88e93. [Google Scholar]
  4. Puchalski Ritchie LM, 2016. Low- and middle-income countries face many common barriers to implementation of maternal health evidence products. J Clin Epidemiol 76: 229237 [Google Scholar]
  5. African Strategies for Health, 2015. mHealth Compendium, Vol. 5. Arlington, VA: African Strategies for Health, Management Sciences for Health. Available at: http://www.africanstrategies4health.org/uploads/1/3/5/3/13538666/mhealthvol5_final_15jun15_webv.pdf. Accessed May 30, 2017.
  6. Fritz F, Tilahun B, Dugas M, , 2015. Success criteria for electronic medical record implementations in low-resource settings: a systematic review. J Am Med Inform Assoc 22: 479488. [Google Scholar]
  7. Jawhari B, Ludwick D, Keenan L, Zakus D, Hayward R, , 2016. Benefits and challenges of EMR implementations in low resource settings: a state-of-the-art review. BMC Med Inform Decis Mak 16: 116. [Google Scholar]
  8. Braa J, Sahay S, . The DHIS2 open source software platform: evolution over time and space. In: Celi LAG, Fraser HSF, Nikore V, Osorio JS, Paik K, editors. Global Health Informatics. Principles of eHealth and mHealth to Improve Quality of Care. Cambridge, MA: The MIT Press.
  9. Purkayastha S, Braa J, , 2013. Big data analytics for developing countries – using the cloud for operational BI IN health. Electron J Inf Syst Dev Ctries 59: 117. [Google Scholar]
  10. Kariuki JM, Manders EJ, Richards J, Oluoch T, Kimanga D, Wanyee S, Kwach JO, Santas X, , 2016. Automating indicator data reporting from health facility EMR to a national aggregate data system in Kenya: an Interoperability field-test using OpenMRS and DHIS2. Online J Public Health Inform 8: e188. [Google Scholar]
  11. Moja L, 2014. Effectiveness of computerized decision support systems linked to electronic health records: a systematic review and meta-analysis. Am J Public Health 104: e12e22. [Google Scholar]
  12. Roshanov PS, 2013. Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials. BMJ 346: f657. [Google Scholar]
  13. Chaudhry B, Wang J, Wu S, Maglione M, Mojica W, Roth E, Morton SC, Shekelle PG, , 2006. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med 144: 742752. [Google Scholar]
  14. Ash JS, McCormack JL, Sittig DF, Wright A, McMullen C, Bates DW, , 2012. Standard practices for computerized clinical decision support in community hospitals: a national survey. J Am Med Inform Assoc 19: 980987. [Google Scholar]
  15. Ash JS, 2012. Recommended practices for computerized clinical decision support and knowledge management in community settings: a qualitative study. BMC Med Inform Decis Mak 12: 6. [Google Scholar]
  16. Kaushal R, Shojania KG, Bates DW, , 2003. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch Intern Med 163: 14091416. [Google Scholar]
  17. Liao PH, Hsu PT, Chu W, Chu WC, , 2015. Applying artificial intelligence technology to support decision-making in nursing: a case study in Taiwan. Health Informatics J 21: 137148. [Google Scholar]
  18. DTree International. Better Decisions Save Lives. Available at: www.d-tree.org. Accessed April 6, 2017.
  19. Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, Sam J, Haynes RB, , 2005. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 293: 12231238. [Google Scholar]
  20. Kawamoto K, Houlihan CA, Balas EA, Lobach DF, , 2005. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 330: 765. [Google Scholar]
  21. Cancedda C, Farmer PE, Kerry V, Nuthulaganti T, Scott KW, Goosby E, Binagwaho A, , 2015. Maximizing the impact of training initiatives for health professionals in low-income countries: frameworks, challenges, and best practices. PLoS Med 12: e1001840. [Google Scholar]
  22. Global HWA, World Health Organization; , 2013. A Universal Truth: No Health without a Workforce: Third Global Forum on Human Resources for Health Report. Geneva, Switzerland: World Health Organization.
  23. World Health Organization, 2007. Community Health Workers: What do we Know About Them? The State of the Evidence on Programmes, Activities, Costs and Impact on Health Outcomes of Using Community Health Workers. Available at: http://www.who.int/hrh/documents/community_health_workers.pdf. Accessed April 6, 2017.
  24. Celi LA, Zimolzak AJ, Stone DJ, , 2014. Dynamic clinical data mining: search engine-based decision support. JMIR Med Inform 2: e13. [Google Scholar]
  25. Fine AM, Nizet V, Mandl KD, , 2011. Improved diagnostic accuracy of group A streptococcal pharyngitis with use of real-time biosurveillance. Ann Intern Med 155: 345352. [Google Scholar]
  26. Jorgensen P, Nambanya S, Gopinath D, Hongvanthong B, Luangphengsouk K, Bell D, Phompida S, Phetsouvanh R, , 2010. High heterogeneity in Plasmodium falciparum risk illustrates the need for detailed mapping to guide resource allocation: a new malaria risk map of the Lao People’s Democratic Republic. Malar J 9: 59. [Google Scholar]
  27. National Academies Press, 2010. Biometric Recognition: Challenges and Opportunities. National Research Council (US) Whither Biometrics Committee; Pato JN, Millett LI, editors. Washington, DC: The National Academies Press.
  28. Unique Identification Authority of India. Available at: https://uidai.gov.in/. Accessed April 5, 2017.

Data & Media loading...

  • Received : 16 Jun 2017
  • Accepted : 26 Sep 2017
  • Published online : 06 Nov 2017

Most Cited This Month

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error