JUNIOR NATIONAL STTA, AI MACHINE LEARNING SPECIALIST/ DEVELOPER

(E4H FED TA: National Digital Health Hub - Provincial Expansion and AI-enabled Practice Hub - Phase II)

Programme Overview

Evidence for Health (E4H) is a Foreign, Commonwealth & Development Office (FCDO)-funded programme aimed at strengthening Pakistan's healthcare system, thereby decreasing the burden of illness and saving lives. E4H provides technical assistance (TA) to the Federal, Khyber Pakhtunkhwa (KP), and Punjab governments, and is being implemented by Palladium along with Oxford Policy Management (OPM).

Through its flexible, embedded, and demand-driven model, E4H supports the government to achieve a resilient health system that is prepared for health emergencies, responsive to the latest evidence, and delivers equitable, quality, and efficient healthcare services. Specifically, E4H delivers TA across three outputs:

Output 1: Strengthened integrated health security, with a focus on preparing and responding to health emergencies, including pandemics.

Output 2: Strengthened evidence-based decision-making to drive health sector performance and accountability.

Output 3: Improved implementation of Universal Health Coverage, with a focus on ending preventable deaths.

Background

The Health Information System (HIS) in Pakistan is critical for enabling data-driven decision-making and improving health outcomes in a population exceeding 262 million. HIS supports evidence-based policymaking, equitable resource allocation, and health system monitoring, aligning with the upcoming National Health and Population Policy (2026-35) and the goals of universal health coverage (UHC) and Sustainable Development Goals (SDGs).

Despite multiple initiatives, including the District Health Information System (DHIS2), Lady Health Worker MIS, Expanded Programme on Immunisation (EPI) MIS, disease-specific information systems, and the Integrated Disease Surveillance and Response System (IDSRS), fragmentation, limited interoperability and data-quality constraints persist. Devolution under the 18th Constitutional Amendment also requires clear federal-provincial governance and data-sharing arrangements.

A unified, consolidated HIS that integrates administrative, clinical, epidemiological, and surveillance data is essential to provide a holistic view of the health sector. Such integration will enhance healthcare monitoring, optimise resource allocation, eliminate redundancies, and strengthen responses to public health emergencies and climate-related threats. Central to this vision is the development of a national dashboard that offers real-time insights into health system performance, supported by advanced analytics and AI-powered visualisation tools, such as heat maps and trend analyses. This will enable localised, evidence-based interventions and more effective monitoring of key health indicators.

In response, the Ministry of National Health Services, Regulations and Coordination (M/o NHSR&C) has advanced the National Digital Health Hub (NDHH) blueprint as a transformational architecture to unify Pakistan’s health information ecosystem into an interoperable, analytics-driven platform and to promote evidence-informed decision-making.

Phase I of the NDHH established a strong proof of concept and the core NDHH foundations, including the Centralised Dashboard, Knowledge Hub, initial Practice Hub functionality, core architecture, national metadata framework, HL7/FHIR-aligned integration models and data-governance instruments, hosted at the National Health Data Centre (NHDC), NIH and under the administrative oversight of M/o NHSR&C. Phase I secured API-based integration of Sindh and Balochistan DHIS2 data and data-sharing arrangements involving M/o NHSR&C, NIH/NHDC, ICT and the Sindh and Balochistan Departments of Health.

Phase II is a continuation and scale-up of the NDHH, and not a new platform. It will adopt a reuse-first approach by applying Phase I architecture, governance instruments, metadata, integration standards, data-sharing arrangements and operational lessons as the baseline. Phase II will begin with a rapid Phase I asset, gap and reuse assessment to confirm what is already fit for purpose, what requires limited enhancement, and what residual gaps require financing. The explicit geographic priority for this assignment is KP and Punjab. Phase II will also strengthen integration of priority federal MIS and GHI-supported programme datasets, including CMU/ATM, EPI, polio and surveillance platforms, where additional integration, validation or operational use is required. It will avoid parallel reporting channels and will not duplicate or recreate existing systems.

• addressing the need to complete provincial onboarding by integrating and linking the data from remaining two provinces within the agreed pilot scope, and making these data streams fully live,
• strengthening interoperability by institutionalising routine, secure, and scalable data exchange through agreed interoperability mechanism, and
• developing and operationalising the Practice Hub with AI-enabled advanced analytics and decision support layer that converts integrated data and current knowledge into interpretable, explainable, and actionable outputs to support routine planning and decision-making cycles across the health sector.

Additionally, AKU and PATH are planning to undertake data ecosystem assessment and Enterprise Architecture/Business Architecture (EA/BA) work. The findings will help identify relevant linkages, complementarities and areas for future alignment with NDHH if needed.

Goal and Objective(s)

The overall goal is to scale and institutionalise the Phase I NDHH proof of concept into a secure, production-ready and government-owned national digital health platform by completing agreed onboarding and integrations, operationalising the AI-enabled Practice Hub, and establishing the governance, capacity, maintenance and support arrangements required for routine use and sustainability.

The specific objectives are to:

1.     Confirm the Phase I close-out evidence required for Phase II scale-up is available and identify any unresolved dependencies.

2.     Complete agreed onboarding of Punjab and KP and any other outstanding federating-area linkages confirmed at inception through secure, standards-aligned interoperability mechanisms and validated data pipelines.

3.     Complete priority federal MIS and GHI-supported programme-system integrations, including CMU/ATM for HIV/AIDS, TB and malaria, EPI, polio, surveillance and other agreed GHI-linked datasets, using the Phase I architecture, metadata and interoperability approach and avoiding parallel or duplicate reporting channels.

4.     Operationalise the Practice Hub to deliver explainable AI-enabled analytics and decision support, while institutionalising NDHH within NIH/NHDC through clear ownership, operational SOPs, training, maintenance/support arrangements and progressive handover.

Scope of Work and Methodology

The consultant(s) will deliver the following scope, using Phase I outputs as the baseline and refining the detailed sequencing at inception in agreement with M/o NHSR&C, NIH/NHDC and E4H

1. Governance, Coordination, and Data Sharing

Building on Phase 1 governance structures, the consultant(s) will:

At inception, complete a Phase I asset, gap and reuse matrix as a prerequisite for Phase II, covering existing dashboard, Knowledge Hub, Practice Hub functionality, metadata, APIs, governance instruments, data-sharing agreements, hosting/security arrangements and costed investments. The matrix should identify reusable components, residual gaps, risks/dependencies, potential savings and an activity-wise rationalised Phase II cost plan linked to NHSP-supported digital health strategy.

Operationalise the HIS Technical Working Group (HIS-TWG) as the principal governance mechanism for Phase II, with defined membership, decision rights, meeting cadence, action tracking, approval/escalation routes, and clear ownership roles across M/o NHSR&C, NIH/NHDC and participating provinces/federating areas.

Verify and complete only outstanding data-sharing/governance agreements required for Phase II participation, clarifying data ownership, permissible use, privacy/security responsibilities and operational accountability; completed Phase I agreements will not be recreated.

Establish practical governance for AI-enabled decision support, including approval of use cases, safeguards, change control and auditability, and maintain alignment with the NHSP-supported national digital health strategy and other relevant architecture work.

2.      Provincial Onboarding and Integration

Confirm onboarding and DSA status across all federating units using Phase I close-out evidence, then complete the agreed Phase II onboarding of Punjab and KP and any other outstanding federating-area linkages confirmed at inception, without redoing completed Phase I work. This workstream will also prioritise integration of federal MIS and GHI-supported programme systems agreed through the Steering Committee/HIS-TWG.

Complete priority linkages between vertical programme systems and NDHH, including CMU/ATM data (HIV/AIDS, TB and malaria), EPI, polio and other agreed sources, using interoperable exchange and harmonised indicator definitions to reduce parallel/duplicate reporting.

Implement, validate and document secure data-exchange pipelines, including scheduled ingestion, reconciliation, error handling, alerting and data-quality checks, consistent with the Phase I architecture and agreed standards.

Reuse and apply existing national metadata packages, indicator mappings, API specifications and HL7/FHIR-aligned standards, modifying them only where a verified Phase II requirement exists.

Conduct integration testing and provincial/federating-area UAT with documented issue resolution, acceptance criteria and formal sign-off before go-live.

Generate and validate sample federal and provincial reports/dashboards that demonstrate interoperability and practical decision-use; where agreed, also develop the NDHH donor-investment mapping view to identify geographic, thematic and programmatic gaps and overlaps.

3.      Practice Hub Development (AI-enabled Advanced Analytics)

Strengthen and operationalise the Practice Hub within NDHH around priority government and programme-management use cases agreed with intended users and the HIS-TWG. The Practice Hub will:

Enable authorised users to query integrated datasets and receive interpretable outputs such as trends, comparisons, alerts, scenario-oriented analyses, briefs and short summaries.

Connect analytics with the Knowledge Hub so that outputs can be linked to relevant national and provincial policies, strategies, guidelines and other approved evidence resources.

Apply ethical, privacy and safety controls, including role-based access, explainability, source traceability, bias/risk checks and appropriate human review of AI-generated outputs.

Use only approved AI/analytics services through secure interfaces and documented configurations, with safeguards to prevent inappropriate exposure of sensitive data.

4.     Security, Identity, and Access Management

Implement role-based access control, multi-factor authentication, least-privilege permissions, audit logs and account-management procedures for provincial users and Practice Hub functions.

Validate production hosting and security controls, including encryption, secure configuration, backup/recovery, vulnerability/security testing, data-protection requirements and documented approval of the production environment.

5.     Testing, Quality Assurance, and Deployment

Execute and document a structured test plan covering unit, integration, end-to-end, performance/load, security and UAT, with traceable test results and an issue-resolution log.

Complete UAT for newly onboarded jurisdictions and Practice Hub use cases, close critical defects, and produce a production-readiness checklist and formal go-live/acceptance sign-off.

Establish operational monitoring, alerts, routine health checks and incident/escalation procedures required for stable production operation after go-live.

 6.     Documentation, Training, Capacity Building, and Handover

Produce and hand over up-to-date technical documentation, including architecture, APIs, interoperability mechanisms, end-to-end data flows, security/configuration records, SOPs, operational runbooks, user guides and training materials.

Deliver structured training and hands-on mentoring for NIH/NHDC administrators, federal/programme users and participating provincial/federating-area focal points; document attendance, completion and practical competency/user acceptance.

Implement progressive operational handover to NIH/NHDC using a clear ownership and responsibility matrix covering system administration, data stewardship, security, user management, monitoring, support and change control.

Define and agree a maintenance and support model, including service levels/SLA, support ownership, incident and change-management arrangements, escalation routes, update/patch responsibilities and indicative recurrent resource requirements for sustainability.

Sustainability: Capacity Building, Institutionalisation, and/or Transition Planning

Capacity Building: Phase II will combine structured training with supervised, hands-on operation of NDHH. Competency and user acceptance will be documented so that NIH/NHDC and designated federal/provincial users can independently perform their assigned functions.

Institutionalisation: NDHH will remain housed at NHDC/NIH, with governance through the HIS-TWG and a documented ownership/responsibility matrix covering Ministry, NIH/NHDC, programme and provincial roles. Phase II will institutionalise routine decision-making, data stewardship, security and change-control arrangements rather than relying on consultant-managed processes.

Transition Planning: Phase II will conclude with progressive handover of operational functions, an agreed maintenance/support and SLA model, technical and user documentation, trained counterparts, and a sustainability plan that identifies recurrent resource needs and leverages relevant NHSP PC-1 or other government-funded components where appropriate.

Timeline and Days

The proposed LOE is 30 days from Oct 01, 2026 – Mar 31, 2027.

Requirement

Technical Expertise

Minimum 05 years of experience in machine learning and natural language processing. Experience with OpenAI, Google AI APIs, and model integration. - Experience applying AI in healthcare/public health data systems.

Competencies

Innovative Thinking, Strong algorithmic and coding skills and data literacy.