Investments in this strategy aim to improve decision-making throughout the health system – from providers to policymakers – by collecting more data, ensuring it is shared by and among providers and policymakers, and using it to better coordinate and deliver health care. The sections below include an overview of the strategy for achieving desired goals, supporting evidence, core metrics that help measure performance toward goals, and a curated list of resources to support collecting, reporting on, and using data for decision-making.
Decisions are at the core of a healthcare system, from government administration to the point of care. At every level, poorly informed decision-making leads to stagnant or worsened health outcomes.
Providers, policymakers, and network managers can struggle to collect and act on data—whether examining a patient's history for accurate diagnosis or collecting immunization data to prepare for potential outbreaks. Also, the siloed nature of many health systems stunts coordination. Whether because of geographic remoteness, misaligned incentives, or myriad other factors, governments and private providers struggle to communicate adequately with one another to coordinate care and systems-level policy.
This strategy seeks to address these coordination challenges to build a stronger health system by: (a) improving the collection of data, (b) improving health stakeholders' ability to make decisions based on that data, and (c) creating innovative network models to facilitate coordination.
Investments in this strategy can improve health outcomes by:
Gaps in data and coordination challenges abound in emerging markets, even at the highest level of administration. Each year, for instance, roughly 40% of births and 29 million deaths worldwide are not formally registered (2). At the point of care, meanwhile, although governments and providers in both emerging and established markets are investing in Electronic Health Records (EHR), many still use paper-based systems (3). Related to that, within and between components of health systems, providers and policymakers have difficulty coordinating to facilitate referrals and provide high-quality care.
This strategy benefits stakeholders throughout the healthcare system. This includes:
Patients: Whether the results of improved data and greater coordination accrue to the overall system (as when using data or coordination mechanisms to improve policy) or the point of care (as with electronic health records (EHR)and other provider-centric interventions), patients benefit.
Policymakers and Network Managers: Better data helps policymakers better manage health systems. In the private sector, data helps network administrators better manage provider networks. Improved data and coordination can help these stakeholders plan strategically, allocate resources, and contain and respond to outbreaks.
Facility Administrators: With greater access to patient history through EHR, administrators can more easily facilitate referrals to higher levels of care and make better decisions about resource allocation.
Providers: Better data collection and storage systems reduce inefficiencies in service provision at the point of care, helping providers better serve their patients.
This strategy generally seeks to improve health outcomes for the poorest populations (e.g., by more strongly containing viral outbreaks in emerging markets) and in areas with minimal connectivity. The potential marginal impact is greatest in rural, poor areas.
Without investment, governments will likely continue to invest in improved decision-making through savvier data collection and overall system coordination. However, many governments will likely be resource-constrained. Investments targeting innovative data collection and coordination mechanisms in poorer areas will likely be most impactful, because authorities in these areas will likely have fewer resources to invest themselves.
Focusing on the private sector can also magnify this strategy’s impact, because, in the private sector, coordination and oversight by governments are often minimal. In promising models like social franchising, independent parties organize private providers by offering incentives in return for coordination and adherence to standards (1).
Individuals in poverty most acutely experience the challenges that result from poor use of data and coordination. In that sense, the people who benefit the most from strengthening health systems are the 766 million people (10.7% of world population) who live on less than USD 1.90 per day (4). Challenges of coordination and data are most concentrated in rural areas, where most people living in extreme poverty reside (5).
The amount of change this strategy can deliver for each beneficiary depends on the scale of the intervention; more-concentrated applications (e.g., data platforms targeted geographically or by disease) will likely have greater individual impact, while system-wide interventions (e.g., innovative coordination platforms sold to governments) will likely have more diffuse impact.
Consider several examples of impact from investments to improve coordination and data in health systems:
Risk factors for this strategy include the following:
Evidence Risk could inhibit lessons learned and scalability, and Stakeholder-Participation Risk could dilute the impact of any potential investment, but neither will likely lead to negative impact.
The organization Living Goods trains and equips community-health workers as self-sustaining entrepreneurs, who then provide critical services in their communities, which are often in remote areas. Living Goods trains its entrepreneurs to focus on four key priorities, maximizing impact while minimizing costs: (1) treatment of childhood diseases; (2) free pregnancy and newborn checkups; (3) nutrition improvement; and (4) referral of acute cases to qualified facilities. Most importantly, the organization selected these priority areas based on population need, demonstrating how this strategy’s emphasis on coordination and the use of data to design policies and programs can magnify impact. The model has scaled quickly and impactfully; a three-year, randomized evaluation of Living Goods’ program conducted by the Children's Investment Fund Foundation showed a 27% reduction in under-five mortality (8).
“About Social Franchises.” Social Franchising for Health. http://sf4health.org/about-social-franchises.
Lee, Bruce Y. “Mike Bloomberg Wants to Know Why More than 29 Million People Are Dying Each Year.” Forbes (contributor blog), May 15, 2018.https://www.forbes.com/sites/brucelee/2018/05/15/mike-bloomberg-wants-to-know-why-over-29-million-are-dying-each-year/.
Kalogriopoulos, Nicholas A., Jonathan Baran, Amit J. Nimunkar, and John G. Webster. “Electronic Medical Record Systems for Developing Countries: Review.” Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1730–33. Minneapolis, MN: September 2009. https://doi.org/10.1109/IEMBS.2009.5333561.
“1. No Poverty.” SDG Atlas. Washington, DC: World Bank, 2018. http://datatopics.worldbank.org/sdgatlas/SDG-01-no-poverty.html
Roser, Max, and Esteban Ortiz-Ospina. “Global Extreme Poverty.” Our World in Data, March 27, 2017. https://ourworldindata.org/extreme-poverty#the-demographics-of-extreme-poverty.
Beyeler, Naomi, Anna York De La Cruz, and Dominic Montagu. “The Impact of Clinical Social Franchising on Health Services in Low- and Middle-Income Countries: A Systematic Review.” PLOS ONE8, no. 4 (2013): e60669. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0060669.
Microsoft Devices Team. “Nokia Data Gathering: A Mobile Tool with Social Impact.” Windows Blogs, July 19, 2012. https://blogs.windows.com/devices/2012/07/19/nokia-data-gathering-a-mobile-tool-with-social-impact/.
“Living Goods Invests in Best in Class Impact Measurement." Living Goods. https://livinggoods.org/what-we-do/measuring-impact/.
This mapped evidence shows what outcomes and impacts this strategy can have, based on academic and field research.
Joos O, Silva R, Amouzou A, Moulton LH, Perin J, Bryce J, et al. (2016) Evaluation of a mHealth Data Quality Intervention to Improve Documentation of Pregnancy Outcomes by Health Surveillance Assistants in Malawi: A Cluster Randomized Trial. PLoS ONE 11(1): e0145238.
McConnell M, Ettenger A, Rothschild CW, Muigai F, Cohen J. Can a community health worker administered postnatal checklist increase healthseeking behaviors and knowledge?: evidence from a randomized trial with a private maternity facility in Kiambu County, Kenya. BMC Pregnancy Childbirth. 2016 Jun 04;16(1):136.
Mitchell M, Hedt B, Msellemu D, Mkaka M, Lesh N. Improvement in Integrated Management of Childhood Illness (IMCI) Implementation through use of Mobile Technology: Evidence from a Pilot Study in Tanzania. BMC Med Inform Decis Mak. 2013;13:95.
Haberer JE, Musiimenta A, Atukunda EC, Musinguzi N, Wyatt MA, Ware NC, et al. Short message service (SMS) reminders and real?time adherence monitoring improve antiretroviral therapy adherence in rural Uganda. AIDS. 2016;30(8): 1295.
Biering-Sorensen S, Andersen A, Ravn H, Monterio I, Aaby P, Benn CS. Early BCG vaccine to low-birth-weight infants and the effects on growth in the first year of life: a randomised controlled trial. BMC Pediatr. 15, 137 (2015).
Mbonye AK, Magnussen P, Lal S, Hansen KS, Cundill B, Chandler C, et al. (2015) A Cluster Randomised Trial Introducing Rapid Diagnostic Tests into Registered Drug Shops in Uganda: Impact on Appropriate Treatment of Malaria. PLoS ONE 10(7): e0129545.
Das J, Chowdhury A, Hussam R, Banerjee AV. The impact of training informal health care providers in India: A randomized controlled trial. Science2016;354:aaf7384.
Nyqvist, M. B., Guariso, A., Svensson, J., Yanagizawa-Drott, D. Effect of a Micro Entrepreneur Based Community Health Delivery Program on Under-Five Mortality in Uganda: A Cluster-Randomized Controlled Trial (CEPR Discussion Paper Series DP 11515). London: Centre for Economic Policy Research.
Sharma S, Van Teijlingen E, Belizán JM, Hundley V, Simkhada P, Sicuri E. Measuring What Works: An impact evaluation of women’s groups on maternal health uptake in rural Nepal. PloS one. 2016;11(5):e0155144.
Boston Consulting Group (BCG). The Advance market commitment pilot for Pneumococcal Vaccines: Outcomes and impact evaluation, 2015.
Ross, et al, 2013. A Low-Cost Ultrasound Program Leads to Increased Antenatal Clinic Visits and Attended Deliveries at a Health Care Clinic in Rural Uganda. PloS One. 2013.
Each resource is assigned a rating of rigor according to the NESTA Standards of Evidence.
(Number of patients completing treatment within the clinically recommended time frame during the reporting period) / (Number of patients who started treatment and who were expected to complete treatment during the reporting period)