India AI Impact Summit 2026

workforce-readiness-for-artificial-intelligence-in-primary-health-care

6 min read

TRACK:Human Capital
SPEAKERS
  • Dr Mona Duggal, ICMR
  • Dr Monika Kochar, Health, DAKSHIN, RIS
  • Dr Ranjana Kumar, Nashik
  • Dr Smisha Agarwal, Center for Global Digital Health Innovation, Johns Hopkins University
  • Dr Titipol Phakdeewanich, Regional Center for Human Rights, Faculty of Political Science, Ubon Ratchathani University, Thailand
  • Professor Anurag Agrawal, Biosciences and Health Research, Ashoka University
  • Professor Fuad, Embassy of the Republic of Indonesia, New Delhi
SESSION OVERVIEW

Digital health innovations and AI are reshaping triage, diagnosis, and population health management, but their impact on primary health care depends on whether frontline workers across cadres can adopt these tools safely, ethically, and effectively. This panel brings together senior experts from government, academia, industry, and think tanks to define AI readiness for primary health care and translate it into role-based competency pathways and practical implementation levers.

VIDEO RECORDING

Workforce Readiness for Artificial Intelligence in Primary Health Care

Abstract

The panel explored how rapid advances in artificial‑intelligence (AI)–driven digital health tools are outpacing the readiness of primary‑care workforces across the Global‑South. Participants mapped six inter‑related dimensions—organizational actors, personnel categories, training modalities, equity considerations, systemic barriers, and governance—and illustrated these with country‑specific experiences from India, Indonesia, Thailand, and the Chakra initiative in Maharashtra. Consensus emerged around the need for new competency pathways, blended pre‑service and in‑service training, stronger cross‑sectoral coordination, and trust‑building with frontline providers. The discussion closed with rapid‑fire suggestions for immediate curriculum shifts and a brief audience Q&A on model bias and accountability.

Detailed Summary

1. Opening Remarks

  • Dr Sachin Sharma (Director General, Production) welcomed the audience, acknowledged the diversity of the panel, and introduced the overarching challenge: the “gap between the pace of AI innovation and the readiness of our health‑care workforce.”

2. Framing the Challenge

  • Dr Smisha Agarwal (Johns Hopkins) described how traditional learning—“thinking until your brain hurts”—is being undermined by the convenience of single‑prompt answers. She highlighted:

    • The pace of technology (last two years) outstripping educators’ ability to adapt curricula.
    • The resource disparity: elite institutions struggle to keep up, implying even greater difficulty for frontline workers in underserved districts.

  • Dr Monika Kochar (DAKSHIN, RIS) added a Global‑South lens:

    • Many low‑ and middle‑income countries still face a digital divide; AI‑driven solutions risk widening inequities.
    • Primary health‑care workers are the “frontier” where basic services must be guaranteed before AI can be layered on.

3. Six Interconnected Dimensions

3.1 Organizational Actors

  • Dr Mona Duggal (ICMR) enumerated the required stakeholders:

    • Ministry of Health, education ministries, professional bodies, software vendors, engineering schools, and technology associations.

3.2 Personnel & Skill Gaps

  • Prof Anurag Agrawal (Ashoka) argued that classic “information‑retention” skills are losing relevance; instead, the workforce must develop:

    • Critical‑thinking that survives when AI fails.
    • An engineering mindset—breaking problems into solvable parts and using AI as a tool, not a crutch.

  • Dr Ranjana Kumar (Chakra) emphasized the generational gap in senior leadership (less tech‑savvy) versus younger staff, stressing the need for continuous up‑skilling across all levels, including program managers who review research grants.

3.3 Training Modalities

  • Prof Fuad (Indonesia) highlighted two tracks:

    • Pre‑service training (curricula redesign in universities).
    • In‑service rapid‑upskilling for current frontline staff.

  • Dr Titipol Phakdeewanich (Thailand) illustrated a blended‑learning model for village health volunteers using mobile platforms (WhatsApp‑like apps) and a “Doctor‑at‑Home” service, noting that digital‑naïve workers often lack basic device maintenance skills.

3.4 Equity & Gender Considerations

  • Dr Monika Kochar warned that gender biases are often ignored when implementing AI tools, calling for explicit equity audits in every rollout.

3.5 Structural Barriers

  • Dr Mona Duggal listed cultural, structural, and resource barriers, stressing that trust is the core “platform” on which AI must be built.

3.6 Governance & International Collaboration

  • Prof Fuad outlined Indonesia’s multi‑ministerial coordination (Health, Higher Education, Finance, Digital & Communications) and the importance of a central government direction to ensure AI platforms do not increase workload.

  • Dr Monika Kochar introduced the Dakshin initiative (India’s Global‑South think‑tank network) as a mechanism for South‑South policy exchange, evidence‑based guideline development, and joint research.

4. Country‑Specific Illustrations

4.1 India (Academic & Policy View)

  • Dr Smisha Agarwal (Johns Hopkins) argued for a re‑imagined curriculum that integrates AI literacy without sacrificing core biomedical knowledge.

  • Prof Anurag Agrawal suggested that health education should move beyond medical schools, fostering interdisciplinary teams that include data scientists and engineers.

4.2 Indonesia (Governance Model)

  • Prof Fuad explained Indonesia’s large health‑insurance system and the need for inter‑ministerial alignment to embed AI tools while preserving trust and avoiding extra burden on clinicians.

4.3 Thailand (Volunteer‑Centric Model)

  • Dr Titipol Phakdeewanich described the village health volunteer network, the use of simple mobile apps, and the challenge of misinformation spread by social‑media influencers.

4.4 Maharashtra – Chakra Initiative

  • Dr Ranjana Kumar detailed Chakra’s activities:

    • An online Digital‑Health Foundation course (open‑source, hosted on ePrabodhini & iGOT).
    • A six‑month certificate with capstone projects that link to national schemes (e.g., Ayushman Bharat).
    • Partnerships with IIT Mumbai and the Coida Foundation to provide technology‑focused modules to health‑workers.

5. Recommendations & Calls to Action

RecommendationProponentTarget
Embed critical‑thinking assessments that can be performed without digital tools.Dr Smisha Agarwal, Prof Anurag AgrawalAcademic curricula
Create interdisciplinary “engineering‑mindset” modules for health students.Prof Anurag AgrawalUniversities & training institutes
Develop pre‑service AI literacy pathways for all health‑professional tracks.Prof Fuad, Dr Monika KocharEducation ministries
Conduct equity and gender audits before AI deployment.Dr Monika KocharGovernment & NGOs
Establish cross‑sector governance bodies (health, finance, digital) to oversee AI rollout.Prof FuadNational policy
Promote South‑South collaboration via Dakshin to share best practices and co‑create guidelines.Dr Monika KocharInternational think‑tank network
Upskill program managers and peer reviewers to evaluate AI‑centric research proposals.Dr Ranjana KumarFunding agencies
Provide basic digital‑literacy support (device maintenance, password management) for frontline volunteers.Dr Titipol PhakdeewanichRural health volunteers
Offer rapid‑upskilling in‑service workshops (short courses, e‑learning) for existing staff.Dr Ranjana KumarHealth workforce
Foster trust‑building through transparent AI design, explainability and community engagement.Multiple panelistsAll stakeholders

6. Rapid‑Fire “One‑Shift” Round

SpeakerSuggested Immediate Shift
Dr Mona DuggalFlatten hierarchical structures in health systems so that all cadres can interact freely.
Dr Titipol PhakdeewanichDecentralize decision‑making to empower village health volunteers with local authority.
Dr Ranjana KumarEliminate the fear‑complex around AI; encourage “befriending” tools rather than fearing them.
Prof FuadEmphasize human accountability – clinicians must retain ultimate decision‑making authority.
Prof Anurag AgrawalIntegrate liberal‑arts critical thinking into health curricula to mitigate over‑reliance on AI.
Dr Monika KocharBuild trust in digital systems among frontline workers through sustained engagement.

7. Audience Q&A (Selected Highlights)

  • Question – How do we communicate the inherent biases and limitations of AI models to health workers?
    Answer – Panelists agreed that training must include explicit modules on model bias, explainability, and the notion that “all models are flawed; some are useful.”

  • Question – What job categories are most at risk of automation?
    Answer – Prof Anurag Agrawal warned that any role solely performed via keyboard (e.g., routine data entry, basic analytics) is vulnerable, echoing broader industry sentiment.

  • Question – How can we ensure reproducible, evidence‑based AI policies across countries?
    Answer – Dr Monika Kochar highlighted the Dakshin platform as a conduit for joint research, policy drafting, and think‑tank collaboration.

8. Closing Remarks

The moderator thanked the panelists for their “insightful, cross‑cutting perspectives,” underscored the urgency of building a workforce that can safely, ethically, and effectively adopt AI, and invited the audience to continue the dialogue during the conference’s networking breaks.

Key Takeaways

  • Training must balance AI proficiency with core clinical and critical‑thinking skills; assessments should be able to be completed without digital tools.
  • Interdisciplinary curricula (medicine + data science + engineering) are essential for future health professionals.
  • Equity audits, especially gender‑sensitive reviews, should precede any AI deployment in primary health care.
  • Governance requires multi‑ministerial coordination (health, education, finance, digital) to embed AI without overburdening clinicians.
  • South‑South collaboration (Dakshin) offers a scalable model for sharing best practices and co‑creating evidence‑based policies.
  • Frontline digital literacy (device maintenance, password handling) is a hidden barrier that must be addressed alongside high‑level AI training.
  • Decentralization—empowering local health volunteers with decision‑making authority—enhances relevance and adoption of AI tools.
  • Human accountability remains paramount; AI should aid, not replace, clinical judgment.
  • Rapid‑upskilling and open‑source online courses (e.g., Chakra’s Digital‑Health Foundation) can bridge the skill gap at scale.
  • Jobs that are purely keyboard‑based are most vulnerable to automation; health systems should anticipate and reskill those roles.

Prepared from the verbatim transcript of the panel “Workforce Readiness for Artificial Intelligence in Primary Health Care” held in Delhi.

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