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How Artificial Intelligence Can Reimagine Global Healthcare

By Wayan Vota on December 9, 2020

global health artificial intelligence
Artificial Intelligence is revolutionizing healthcare with game-changing capabilities. AI-powered solutions are participating in more and more medical tasks traditionally performed by healthcare practitioners.

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Artificial Intelligence capabilities can assist with tasks performed by highly skilled medical workers and tasks that go beyond human cognition (e.g., processing big data to diagnose illness), as well as tasks that support humans but are often subject to inattention, cause fatigue, or are physically difficult to perform. Across the entire care spectrum, AI capabilities in healthcare are performing hard work.

Artificial Intelligence should not replace health workers, but the profession will undoubtedly evolve. As AI increasingly enters healthcare, good governance should ensure that physicians will see the upside of less administrative work and more patient- facing time. Humans and machines should work together to improve patient outcomes, strengthen health systems, and drive progress towards universal health coverage (UHC). AI will also create new jobs across sectors.

Yet, the new jobs will not necessarily emerge within the same professional specialization or same geography where old jobs are being replaced by AI. Therefore, sequenced action and policy timing are crucial, with measures taken to ensure workforce disruption is accompanied by appropriate change management, social safety nets, and professional retraining opportunities.

Reimagining Global Health through Artificial Intelligence, the third report of the Broadband Commission for Sustainable Development Working Group on Digital and AI in Health, explores the way AI can support global and national health goals and Sustainable Development Goal 3.

AI Addresses Key Global Health Issues

In low- and middle-income countries (LMICs), artificial Intelligence has the potential to address longstanding, systemic health issues with new advanced capabilities. Many LMICs are dealing with:

  • A shortage of health workers: The doctor-to-population ratio is markedly lower in LMICs, especially in rural settings.
  • Emerging threats: From COVID-19 to climate change and antimicrobial resistance (AMR), emerging threats present new challenges to existing health systems.
  • A dual burden of disease: Rising rates of noncommunicable diseases (NCDs) and infectious diseases combine to present a dual burden of disease in many LMICs.
  • Underserved populations: Over 734 million people live on less than USD 1.9 per day and lack access to essential health and care services.
  • Rapid urbanization: Urbanization is increasing rapidly, with nearly 70% of people projected to live in cities by 20503, which is expected to widen health inequities.
  • Misinformation & disinformation: Adequately managing Ebola, COVID-19, or other outbreaks and diseases is often hindered by a deluge of misinformation and disinformation, causing unnecessary suffering.

To combat today’s growing health challenges, we need to systematically integrate AI-enabled tools into the way healthcare is delivered and expand access for all. Without AI, UHC may not be achieved.

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Yet, there are also risks associated with the use of artificial Intelligence in health, particularly for vulnerable populations, youth, and children. Successful integration of AI into health and care delivery depends on appropriate risk management processes that have been defined and operationalized for AI development, deployment, and continuous improvement.

Top 5 Use Cases for AI in Health

To deliver on its promise, artificial Intelligence boasts an arsenal of capabilities. The report identifies five use cases for how AI is applied to address global and public health priorities, strengthen health systems, and improve outcomes for patients.

  1. AI-enabled population health: Solutions that use AI to monitor and assess the health of a human population, and select and target public health interventions based on AI-enabled predictive analytics.
  2. AI-enabled preclinical research & clinical trials: Solutions that use AI to assist drug discovery and design, omics technologies for highly personalized treatments, and AI tools for clinical trial design and execution.
  3. AI-enabled clinical care pathways: AI-based solutions that can be integrated into existing and new clinical workflows.
  4. AI-enabled patient-facing solutions: AI solutions that interact directly with patients, including personalized health coaching and life-style advice, the delivery of (mostly non-clinical) therapies, chatbots, interventions without the risk of harm, and information provision.
  5. AI-enabled optimization of health operations: AI solutions that optimize back- end processes in healthcare, such as procurement, logistics, staff scheduling, emergency service dispatch management, automated medical notes, and patient experience analyses.

The results so far are encouraging. AI has a proven track record across all five use cases, having improved or saved patient lives, augmented the capabilities of health workers, and strengthened health systems across the world. Yet, we have only scratched the surface!

6 AI Enabling Environment Investments

To bring artificial Intelligence capabilities to the next level for health systems, universal health coverage, and patients, countries need to be proactive and foster a robust enabling environment for needs-driven AI. To build and continuously improve a healthcare innovation ecosystem, the report identifies six areas for AI maturity in health that countries should prioritize to advance on their journey to AI maturity.

1. People & workforce

Countries should prioritize AI and data science in their national health education curricula for pre- and in-service training and formal education. They should strengthen level-appropriate offerings for both technical and non-technical roles and prioritize the soft aspects of technology solutions: human-centric design and behavioral aspects.

2. Data & technology

Countries should prioritize the foundations: robust technology architecture, connectivity, access to quality and representative data, data privacy and security layers, data stewardship, interoperability, fair and transparent algorithms and AI models, and explainability.

Critical to achieving this are consent-driven policy frameworks, a strong data
and AI strategy, robust technology implementation roadmaps, and the formulation of relevant best practices.

3. Governance & regulatory

Leadership is critical to establish the robust governance structures and regulations necessary to ensure AI innovation targets national health priorities. Keys to good AI governance are:

  • a national strategy and budget development,
  • clear costing and implementation plans,
  • privacy-and security-preserving regulations that put people first while balancing innovation,
  • and the integration of human rights and a social contract.
  • regulators that continuously develop appropriate clinical and scientific validation pathways.

4. Design & processes

Existing national health systems and clinical workflows are not always ready to integrate AI solutions.

Relevant stakeholders may want to perform gap analyses for technical and user requirement specifications and collaborate broadly to ensure successful integration. Professional societies and academies, government agencies, and the private sector should collaborate to streamline the integration of AI into health systems.

The localization of solutions into deployment contexts, performance measurements for outcomes-based validation, and human-centric design should also be prioritized to enable AI impact in healthcare.

5. Partnerships & stakeholders

Countries should support effective, goal-oriented partnerships (e.g., multi- sectoral public-private partnerships, data collaboratives), coordinated prototyping, stakeholder engagement and participation in international working groups or task forces, and strong relationships with local partners and patient organizations.

Perhaps most important of all, progress on the path to AI maturity requires the cultivation of high-level political support across ministries and the head of state.

6. Business models

Innovative and sustainable business models should be a priority for countries and stakeholders across the healthcare, tech, and life sciences industries, and for the public sector. Countries should foster a diverse set of funding mechanisms guided by
a long-term outlook for AI in health solutions. They can develop incentive mechanisms, experiment with novel pricing models and monetization strategies for assets, and advance the use of innovative financing mechanisms for social impact.

Action Points for AI in Global Health

To create an artificial Intelligence-enabling environment, policymakers, donors, private companies, and other stakeholders should proactively invest in the six areas for AI maturity in health. The recommendations in the report’s last chapter detail specific action points and recommendations for each stakeholder group, and can help navigate challenges, pursue best practices, and strengthen AI-enablers.

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Policymakers, the private sector, international non-governmental organizations (INGOs), and other stakeholders have a wealth of experience and knowledge capital. To truly maximize the impact of AI on health, collaboration is essential to cultivate an AI-enabling environment that accelerates the achievement of Sustainable Development Goal 3 and health for all, and facilitates progress towards extending and improving people’s lives and health.

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Written by
Wayan Vota co-founded ICTworks and is the Digital Health Director at IntraHealth International. He also co-founded Technology Salon, MERL Tech, ICTforAg, ICT4Djobs, ICT4Drinks, JadedAid, Kurante, OLPC News and a few other things. Opinions expressed here are his own and do not reflect the position of IntraHealth International or other ICTWorks sponsors.
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