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.


Dimensions of Impact: WHAT

Investors interested in deploying this strategy should consider the scale of the addressable problem, what positive outcomes might be, and how important the change would be to the people (or planet) experiencing it.

Key questions in this dimension include:

What is the problem the investment is trying to address? For the people experiencing the problem, how important is this change?

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:

  1. helping providers make better clinical decisions through simple platforms for healthcare centers to store and manage data internally;
  2. giving providers simple platforms to upload data to centralized systems so policymakers and network managers can make better decisions based on that data;
  3. assisting policymakers and health network managers to better prepare for outbreaks;
  4. reducing fraud, counterfeit products, and other forms of malpractice;
  5. monitoring systemic, often previously unmeasurable health-related variables, such as the social determinants of health; and
  6. organizing public or private providers in networks to facilitate coordination, improve standards, and stimulate demand through a common brand, which is commonly termed social franchising (1).
Interested in understanding how gender relates to this strategy? Check out the gender lens summary and metrics created to complement this theme: Improving Health Systems through Gender Equitable Policies and Decision-Making.

What is the scale of the problem?

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.


Dimensions of Impact: WHO

Investors interested in deploying this strategy should consider whom they want to target, as almost every strategy has a host of potential beneficiaries. While some investors may target women of color living in a particular rural area, others may set targets more broadly, e.g., women. Investors interested in targeting particular populations should focus on strategies that have been shown to benefit those populations.

Key questions in this dimension include:

Who/What is helped through this strategy?

This strategy benefits stakeholders throughout the healthcare system. This includes: 

PatientsWhether 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 ManagersBetter 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 AdministratorsWith 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.

ProvidersBetter data collection and storage systems reduce inefficiencies in service provision at the point of care, helping providers better serve their patients.

What are the geographic attributes of those who benefit?

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.


Dimensions of Impact: CONTRIBUTION

Investors considering investing in a company or portfolio aligned with this strategy should consider whether the effect they want to have compares to what is likely to happen anyway. Is the investment's contribution ‘likely better’ or ‘likely worse’ than what is likely to occur anyway across What, How much and Who?

Key questions in this dimension include:

Is the investment’s contribution ‘likely better’ or ‘likely worse’ than what is likely to occur anyway across What, How Much and Who?

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).

How Much

Dimensions of Impact: HOW MUCH

Investors deploying capital into investments aligned with this strategy should think about how significant the investment's effect might be. What is likely to be the change's breadth, depth, and duration?

Key questions in this dimension include:

How many can receive the outcome through this strategy?

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).

How much change can beneficiaries experience through this strategy?

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:

  • A study in Vietnam of a social franchise, which organized providers into a network in which they adhered to common standards in exchange for support generating demand and other incentives, found that participation in the franchise increased total client volume by 40%. Certain services experienced even greater increases, such as reproductive health services (51% increase). Similar results were found in India, Ethiopia, and Pakistan (6).
  • A data-collection platform, Nokia Data Gathering, was deployed in Brazil to facilitate the monitoring of Dengue fever. Field workers upload data gathered by survey to a centralized platform, where it may be analyzed to identify potential outbreaks. The government health department deploying the system used the data to facilitate a 90% decline in reported cases of Dengue fever between 2008 and 2009 (7).


Dimensions of Impact: RISK

Key questions in this dimension include:

What risks do investments in this strategy run in terms of either people/planet experiencing impact or society as a whole? What is the probability that those risks happen?

Risk factors for this strategy include the following:

  1. Evidence Risk: Some impacts of this strategy are fairly easy to measure (for example, by comparing data for completeness before and after intervention), but for other, relatively indirect applications (most notably, those intervening systemically), measuring impact on ultimate health outcomes may be more difficult. Carefully and realistically designed evaluative methodologies should be designed prior to the investment to mitigate this risk.

  2. Stakeholder-Participation Risk: Ultimately, since this strategy aims to facilitate stronger decision-making, it assumes high-quality participation by stakeholders ranging from providers (for point-of-care interventions) to policymakers or network managers (for systemic interventions). Less than full participation by stakeholders will dilute the impact of this strategy. To mitigate this risk, investors should carefully consider incentives and engage stakeholders consistently throughout the design of the investment.

What are likely consequences of these risk factors?

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.

Illustrative Investment

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).

Draw on Evidence

This mapped evidence shows what outcomes and impacts this strategy can have, based on academic and field research.

Evaluation of a mHealth Data Quality Intervention to Improve Documentation of Pregnancy Outcomes by Health Surveillance Assistants in Malawi: A Cluster Randomized Trial

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.

Can a community health worker administered postnatal checklist increase health-seeking behaviors and knowledge?: evidence from a randomized trial with a private maternity facility in Kiambu County, Kenya

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.

Improvement in Integrated Management of Childhood Illness (IMCI) Implementation through use of Mobile Technology: Evidence from a Pilot Study in Tanzania

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.

Short message service (SMS) reminders and real-time adherence monitoring improve antiretroviral therapy adherence in rural Uganda

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.

Early BCG vaccine to low-birth-weight infants and the effects on growth in the first year of life: a randomised controlled trial

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).

Introducing rapid diagnostic tests for malaria to drug shops in Uganda: a cluster-randomized controlled trial

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.

The impact of training informal health care providers in India: A randomized controlled trial

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.

Effect of a micro entrepreneur-based community health delivery program on under-five mortality in Uganda: a cluster-randomized controlled trial

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.

Measuring What Works: An Impact Evaluation of Women’s Groups on Maternal Health Uptake in Rural Nepal

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.

The Advance Market Commitment Pilot for Pneumococcal Vaccines: Outcomes and impact evaluation

Boston Consulting Group (BCG). The Advance market commitment pilot for Pneumococcal Vaccines: Outcomes and impact evaluation, 2015.

A low-cost ultrasound program leads to increased antenatal clinic visits and attended deliveries at a health care clinic in rural Uganda

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.

Define Metrics

Core Metrics

This starter set of core metrics — chosen from the IRIS catalog with the input of impact investors who work in this area — indicate performance toward objectives within this strategy. They can help with setting targets, tracking performance, and managing toward success.

Additional Metrics

While the above core metrics provide a starter set of measurements that can show outcomes of a portfolio targeted toward this goal, the additional metrics below — or others from the IRIS catalog — can provide more nuance and depth to understanding your impact.