Overview

Digital Health Tools

As COVID-19 strained health systems around the world, many countries adopted and adapted digital health tools to detect and respond to the novel coronavirus.

While there are a wide range of use cases for digital tools in pandemic response, this work focuses on a subset of tools most commonly used by the health care system managers, providers, and clients in low- and lower-middle-income countries.

Digital technologies for COVID-19

Focus areas shown in blue

Contents

In 2020, Exemplars in Global Health launched a series of short- and long-term research projects to help us understand the impact of the COVID-19 pandemic in countries and communities around the world. One of these, a short-term research project in partnership with McKinsey & Company on digital tools for COVID-19 response, aims to explain the implementation factors that led to the scale-up of digital tools developed or adapted for use during the COVID-19 pandemic.

Table: Implementation stories profiled

Abbreviation: SORMAS, Surveillance Outbreak Response Management and Analysis System
Implementation story Country Digital tool User group Use case 

Adapting a fully integrated surveillance system to track COVID-19 

Nigeria SORMAS Health system managers

Routine surveillance 

Early action to track and prevent COVID-19 

Sri Lanka DHIS2  Health system managers

Data assets (Health management information system) 
A decade of scaling digital health tool enabled rapid deployment across country  Burkina Faso  CommCare  Health care providers  Case management 
Community health workers in Uganda provide critical health services during COVID-19 pandemic  Uganda  Smart Health (Medic)  Health care providers  Case management 
A chatbot tool for pandemic response  South Africa  HealthConnect  Clients  Risk communication & community engagement 
Government launches digital health apps to contain COVID-19  Vietnam  NCOVI, Bluezone, others  Clients  Contact tracing, risk communication & community engagement 

We looked at the broad landscape of digital health solutions in low- and lower-middle-income countries (LMICs) across three user groups: health care providers, health system managers, and the end users of the health system such as patients and caregivers. We then conducted heavier research on six implementations and incorporated lighter research on seven other implementations as part of the synthesis. This research focuses on assessing the implementation of the tools rather than the technology itself (see the Resources section for more information). We aim to supplement the growing knowledge base surrounding best practices in digital health and to use what we learn to sustain and strengthen health systems more generally.

Methodology and Limitations
The six case studies draw on desk research and stakeholder interviews with developers, implementers and government representatives, and local experts. However, our findings are limited because: the research was not conducted by academic partners, the urgency of COVID-19 accelerated the urgency of the research, and we needed to understand the implementations in real time. The selection process was driven by scale, impact, and sustainability as well as availability of information to conduct desk research. The tools represented here are not necessarily successes by all metrics, nor do they capture the full scope of work done by countries worldwide to implement digital tools in the COVID-19 response.

What Is Digital Health?

"Digital health is the systematic application of information and communications technologies, computer science, and data to support informed decision-making by individuals, the health workforce, and health institutions, to strengthen resilience to disease and improve health and wellness for all."

- US Agency for International Development, A Vision for Action in Digital Health1

Developments in smartphone connectivity, new point-of-care diagnostics, and declining costs and improvements in data-system infrastructure are changing the way health systems across LMICs operate. The COVID-19 pandemic has highlighted the importance of digital technologies such as telemedicine and digital contact-tracing systems for health emergencies—but even under ordinary circumstances, digital health has great potential to support and improve health systems around the globe.

Even so, implementing digital health programs at scale will be a substantial challenge across countries at all income levels. Obstacles include:

  • Outdated governance and policies: A digitally enabled future for primary care is reliant on governance and regulations that can ensure high-quality care, patient data protection, and the interoperability of dispersed information systems. This enabling environment does not yet exist in most countries and will require substantial support to become a reality. In the Global Digital Health Index survey of 22 countries in 2019, data standards and interoperability was assessed as the lowest maturity component on average, and half of the countries had not set up a national data architecture or information exchange.2
  • Fragmented landscapes of digital innovation: As care shifts to mobile and virtual provision, either with or without a provider (e.g., via artificial intelligence chatbots), the public and private sectors will need to work together to ensure continuity of care. This is counter to the optimization of services, which often seeks to minimize costs for acute care. Compounding this is the sense of “pilotitis,” where many tools are developed and few persist. A series of US Agency for International Development reports from 2016 summarizing more than 160 mobile health tools noted that most of the programs profiled had ceased operations.3
  • Limited and uneven levels of digital literacy and access: For digital primary care to be successful, it needs to be accessible to all—and especially to women, who are often the primary care seekers for themselves and their families. This will require broader and more affordable access to internet and mobile phones and a more widespread capacity to use new tools. Prices for phones and data plans are falling, but costs still pose a substantial barrier to access—the average price of a 1 GB phone data plan is more than 4 percent of the average monthly gross domestic product in LMICs.2
  • Weak supporting infrastructure: Each technology required to realize a vision of digital health rests on a strong infrastructure of e-health and connectivity, which in turn rests on interoperable and connected systems (such as health information management systems). Nationally connected systems have not yet arrived in many low-income countries. For example, approximately half of the sub-Saharan African population had access to a smartphone in September 2020, but the usability of these phones is limited by slow connection speeds.4

What Role Has Digital Health Played in the Response to COVID-19?

Because digital health tools make it possible to create efficiencies and scale relatively quickly, to share data near-instantaneously, and to quickly perform data aggregation and analysis—all while limiting in-person contact—the COVID-19 pandemic has been a catalytic opportunity for digital health.

“The pandemic of COVID-19 underscores the critical need to use digital tools and data together. The response to COVID-19 requires detailed, often granular-level understanding of the disease, its spread, and its immediate and second-order impacts, as well as situational awareness of what resources are available and where they are located within a country, to enable their effective deployment. Digital technologies are essential to generating and analyzing data to inform the preparation for, and response to, infectious diseases in a timely manner.”

- US Agency for International Development, A Vision for Action in Digital Health1

As COVID-19 has challenged health systems around the world, innovators have adopted and adapted new and existing digital tools for:

  • Case management: Identifying and tracking known or suspected cases, which could include COVID-19 screening, symptom monitoring, exposure, and lab results.
  • Contact tracing: Identifying and notifying contacts of a known COVID-19 case.
  • Event-based surveillance: Tracking known and suspected cases, including demographic data, to ensure health system administrators can better understand the spread of the disease and enact data-based policies to stop it.
  • Learning and training: Giving health care workers tools to check patients for symptoms and keep themselves safe.
  • Risk communication: Providing clear, accurate information via push or pull messaging systems to boost participation in COVID-19-response measures.

Use of digital tools throughout a pandemic

Adapted from Digital Square

What Do We Know About What It Takes to Implement and Scale Digital Health Tools?

When the COVID-19 pandemic began in 2020, many digital health programs were already in place across the globe, including those designed to aid in response to outbreaks of epidemic disease. For example, digital health applications such as CommCare and Surveillance Outbreak Response Management and Analysis System (SORMAS) gained traction in the wake of the 2014–2016 Ebola outbreaks in West Africa. Some of these tools—including CommCare and SORMAS—have been scaled and repurposed for COVID-19 response and put to use in high-income countries and the LMICs they were designed for.

Scaling and sustaining digital innovations is an enormous challenge. It is relatively easy to start a digital health pilot program, but bringing a tool into widespread use is extremely difficult. A 2016 study on digital tools for frontline health workers in LMICs found that only 11 of the 150 active projects under review had more than 1,000 users.5

Throughout the past decade, the World Health Organization and partners including PATH and the United Nations Children’s Fund put together several guides on how to scale and implement sustainable digital health programs, and how to decide whether a digital health intervention is appropriate.6,7 The framework we used to anchor our analyses, from the mHealth Assessment and Planning for Scale Toolkit, identifies six axes for assessing the implementation and scalability of a given program (see below).

The mHealth Assessment and Planning for Scale framework

World Health Organization

In general, researchers have identified a few core lessons for successfully scaling up digital health applications in LMICs:

  • The initiative must meet an unmet need in tangible ways, and it must be developed from the beginning with end-user input.
  • All stakeholders must be engaged, trained, and motivated to implement the initiative.
  • The initiative should be designed for simplicity, interoperability, and adaptability.
  • The initiative must be aligned with its broader policy environment and must have sustainable public and/or private funding to support its long-term growth.
  • The broader health care ecosystem must include the appropriate infrastructure to support the use of the digital initiative at scale.8

In order for today’s digital innovations to endure past the current COVID-19 crisis, we must, as one expert noted, “solve for the market failures that are currently creating the digital divide.”9 For instance, health care providers, clients, and managers across and within countries have inequitable access to stable internet and smartphone technology. In 2018, a little more than half of the world’s population did not have mobile internet, and connectivity rates varied according to age (e.g., more young people used smartphones than older people did), location (e.g., cities were more connected than rural areas), and other factors such as race, income, and gender.10

Public policy can also aid or hinder the adoption and scale-up of digital health programs—and regulations regarding digital health vary widely. Many countries still require support in development of a long-term national digital health strategy and have not yet established a set of robust privacy guidelines. In South Korea, for example, the government enacted a set of privacy laws with exceptions for disease outbreaks. Establishing these goals helps policy makers and implementers work together and set up tools for scale at the outset.10,11

Finally, digital health is not a panacea. If a country faces substantial challenges within its health care system, such as a lack of providers or facilities, digital health cannot fully close the gap in quality of or access to care.12

How Has the Impact of Digital Health Been Measured During the COVID-19 Pandemic?

Digital health tools and applications have a great deal of potential to improve health systems—and user experiences within these systems—around the world. But, most research on impact is conducted on smaller implementations (i.e., pre-scale pilot programs) or on specific, easier-to-study digital health applications, such as SMS reminders.13 Researchers have found that while some interventions have their desired impact on the health system, many have not been measured or have been found to have minimal impact. This is one of the core challenges facing digital health, especially in resource-constrained environments where much of the health funding goes toward vertical programs with proven benefits, such as antimalaria or immunization campaigns.

A concerted effort to evaluate and document the impact of digital health interventions has occurred in recent years. This growing body of evidence suggests impact across client behaviors, quality of care, health worker performance, and program efficiency. Examples of digital health interventions that have quantified impact include the following:

  • In a randomized controlled trial in India, clients of health care workers using CommCare saw a 73 percent increase in antenatal care visits.14 A study in Burkina Faso found that the use of CommCare improved adherence to Integrated Management of Childhood Illness protocols by 50 percent.15
  • A randomized controlled trial in Mali found that performance reviews using supervision dashboards for community health workers increased monthly home visits by approximately 40 visits and had a positive (though not statistically significant) impact on timeliness and quality above the implementation of monthly performance reviews without a dashboard.16
  • Implementation of an electronic immunization-registration logistics-management information system in Tanzania found that monthly stock-outs of vaccines decreased from 7.1 percent to 2.1 percent at facilities using the new system.17

Throughout the pandemic, measuring the impact of programs on pandemic response has been particularly challenging. The need to focus efforts on the response as quickly as possible limits the ability to evaluate tools’ usage. In the absence of traditional measurements, alternative metrics and evidence can serve as evidence of impact. The time to deploy a solution, the number of laboratories integrated into a system, or people reached, for example, are all used in the evaluations of the six COVID-19 implementations.

Despite these challenges, it is clear that digital health can increase access to health care, increase the quality of health care, diminish the costs of providing care, and empower patients to manage their own health.” 18  

  1. 1
    US Agency for International Development (USAID). A Vision for Action in Digital Health 2020–2024: Accelerating the Journey to Self-Reliance Through Strategic Investments in Digital Technologies. Washington, DC: USAID; 2020. https://www.usaid.gov/sites/default/files/documents/USAID-A-Digital-Health-Vision-for-Action-v10.28_FINAL_508.pdf
  2. 2
    Global Development Incubator (GDI). The State of Digital Health. Washington, DC: GDI; 2019. Accessed April 11, 2021. https://static1.squarespace.com/static/5ace2d0c5cfd792078a05e5f/t/5d4dcb80a9b3640001183a34/1565379490219/State+of+Digital+Health+2019.pdf
  3. 3
  4. 4
    GSM Association (GSMA). Global Mobile Trends 2021: Navigating Covid-19 and Beyond. London: GSMA; 2020. Accessed April 6, 2021. https://data.gsmaintelligence.com/api-web/v2/research-file-download?id=58621970&file=141220-Global-Mobile-Trends.pdf
  5. 5
    Africa Health. Using Digital Tools at Scale: The Integrated e-Diagnostic Approach in Burkina. Kampala, Uganda: Africa Health; 2018. Accessed April 11, 2021. https://africa-health.com/wp-content/uploads/2018/07/13.-July-Scaling-digitally-in-Burkina.pdf
  6. 6
    World Health Organization (WHO). The MAPS Toolkit: mHealth Assessment and Planning for Scale. Geneva: WHO; 2015. Accessed April 11, 2021. https://apps.who.int/iris/bitstream/handle/10665/185238/9789241509510_eng.pdf
  7. 7
    World Health Organization (WHO). Digital Implementation Investment Guide (DIIG): Integrating Digital Interventions into Health Programmes. Geneva: WHO; 2020. Accessed April 11, 2021. https://www.who.int/publications/i/item/9789240010567
  8. 8
    Labrique AB, Wadhwani C, Williams KA, et al. Best practices in scaling digital health in low and middle income countries. Global Health. 2018;14(1):103. https://doi.org/10.1186/s12992-018-0424-z
  9. 9
    As COVID-19 accelerates digital health innovation, 'Who will we leave behind?' Devex website. Published January 15, 2021. Accessed April 11, 2021. https://www.devex.com/news/as-covid-19-accelerates-digital-health-innovation-who-will-we-leave-behind-98911
  10. 10
    Budd J, Miller BS, Manning EM, et al. Digital technologies in the public-health response to COVID-19. Nat Med. 2020;26(8):1183–1192. https://doi.org/10.1038/s41591-020-1011-4
  11. 11
    Johns Hopkins Bloomberg School of Public Health. Digital Solutions for COVID-10 Response: An Assessment of Digital Tools for Rapid Scale-Up for Case Management and Contact Tracing. Baltimore, MD: Johns Hopkins Bloomberg School of Public Health; 2020. Accessed April 22, 2021. https://www.jhsph.edu/departments/international-health/news/johns-hopkins-researchers-publish-assessment-of-digital-solutions-for-covid-19-response-in-low-and-middle-income-countries.html
  12. 12
    Digital health and COVID-19. Bull World Health Organ. 2020;98(11):731–732. https://doi.org/10.2471/BLT.20.021120
  13. 13
    Long L-A, Pariyo G, Kallander K. Digital technologies for health workforce development in low- and middle-income countries: a scoping review. Glob Health Sci Pract. 2018;6(Suppl 1):S41–S48. https://doi.org/10.9745/GHSP-D-18-00167
  14. 14
    Mathematica. Evaluation of the Information and Communication Technology (ICT) Continuum of Care Services (CCS) Intervention in Bihar. Princeton, NJ: Mathematica Policy Research; 2015. Accessed April 11, 2021. https://www.mathematica.org/our-publications-and-findings/publications/evaluation-of-the-information-and-communication-technology-ict-continuum-of-care-services-ccs
  15. 15
    Can digital technology help reinvent primary healthcare in support of universal health coverage? BMJ Global Health Blog. Published May 17, 2019. Accessed April 11, 2021. https://blogs.bmj.com/bmjgh/2019/05/17/can-digital-technology-help-reinvent-primary-healthcare-in-support-of-universal-health-coverage/
  16. 16
    Whidden C, Kayentao K, Liu JX, et al. Improving community health worker performance by using a personalised feedback dashboard for supervision: a randomised controlled trial. J Global Health. 2018;8(2):020418. https://doi.org/10.7189/jogh.08.020418
  17. 17
    Gilbert SS, Bulula N, Yohana E, et al. The impact of an integrated electronic immunization registry and logistics management information system (EIR-eLMIS) on vaccine availability in three regions in Tanzania: a pre-post and time-series analysis. Vaccine. 2020;38(3):562–569. https://doi.org/10.1016/j.vaccine.2019.10.059
  18. 18
    Broadband Commission for Sustainable Development. Digital Health: A Call for Government Leadership and Cooperation between ICT and Health. Geneva: Broadband Commission for Sustainable Development; 2017. Accessed April 11, 2021. https://www.novartisfoundation.org/news/media-library/digital-health-call-government-leadership-and-cooperation-between-ict-and-health