We invite submissions for the workshop “From Health Data to Action – AI for District Health Service Monitoring and Planning”.
We invite short papers, extended abstracts and practice case studies on how artificial intelligence (AI) and routine health data can help district health and management teams monitor services and plan action, for an interactive workshop at the Digital Public Health Conference (DPH 2027) in London. The workshop uses experience from district health management in Sindh, Pakistan, as a starting point and welcomes contributions from any health system, especially low- and middle-income countries (LMICs).
District health managers turn policy and resources into services, yet routine health information is rarely converted into insight on service gaps, priorities, resource allocation and performance. AI and accessible analytical tools offer new options, particularly where resources are limited, but they also bring risks: poor data quality, algorithmic bias, confidentiality breaches, unequal access to tools and over-reliance on AI-generated recommendations. The workshop treats AI as a decision-support tool that complements local knowledge, professional judgment and accountability, not as a replacement for them.
The workshop aims to:
- Demonstrate practical methods for turning routine health data into actionable insight for district-level service monitoring and planning.
- Explore accessible AI-enabled tools for data interpretation, report summarization, trend identification and service planning.
- Engage participants in solving realistic district health management problems through data-driven exercises.
- Examine critically the ethical, operational and governance considerations of using AI in public health decision-making.
The call for papers seeks real-world evidence, tools and lessons that will enrich these discussions and help participants adapt the approach to their own settings.
Click here for full information on the workshop.
!! Deadlines !!
| Workshop Call for Papers (CFP) – Submission deadline | 23 December 2026 |
| Workshop Call for Papers (CFP) – Notification deadline | 28 January 2027 |
| Camera-ready deadline | 7 April 2027 |
Themes of interest
Submissions should address AI or data analytics for district or sub-national health management. Topics include, but are not limited to:
- Turning routine health information system data (for example DHIS2 or similar) into insight for district service monitoring, review meetings and planning
- Practical use of AI and generative AI tools for data interpretation, trend detection, report summarisation, forecasting and anticipating service needs
- Dashboards and data visualisation that support district decision-making, including low-cost and spreadsheet-based approaches
- Applications in maternal and child health, immunisation, facility readiness, essential medicines and other service areas
- Data quality assessment and improvement as a foundation for AI use
- Evaluating AI-generated outputs for accuracy, contextual relevance, bias and feasibility
- Ethics, confidentiality, data protection and governance of AI in public health decision-making
- Capacity building and training of district managers and health information staff in data and AI literacy
- Integration of AI-supported analysis into existing review processes without creating parallel reporting systems
- Barriers, enablers and lessons from implementation in resource-constrained settings
- Equity and access: unequal digital infrastructure, language and skills
Submission guidelines
We encourage interested author(s) to submit either a full paper or an extended abstract. The author(s) of an accepted paper/ abstract, which is presented in-person at DPH 2027 in London, will be included in the conference proceedings – extended abstracts will be included in Frontiers proceedings, and full papers will be submitted for possible inclusion into IEEE Xplore.
Accepted authors will present a short lightning talk (about 5 minutes) during the workshop. Their cases, datasets and lessons will inform the group exercises and the closing discussion on integrating AI-supported analysis into routine district review meetings.
For submitting a Full Paper, please follow the instructions:
- All submissions must be original and not simultaneously submitted to another journal or conference.
- The papers must range between 4‑8 pages, including references and any appendices.
- Papers should conform to the IEEE double column format: https://www.ieee.org/conferences/publishing/templates.html
- Papers must be anonymised: the names and affiliations removed, and any references to previous works either substituted with “anonymised” or rephrased.
- All submissions must be submitted in PDF format using the Easychair submission system. Click on the submission button below and choose the “DPH2027 Workshop – From Health Data to Action – AI for District Health Service Monitoring and Planning” track.
- Please paste your Abstract in the Abstract field in the Easychair system for ease of processing and review allocation.
NOTE that:
- Full Paper submissions will be reviewed by three reviewers from the PC and SPC committees and assessed in terms of their quality, relevance to the conference, originality, significance, correctness, and clarity.
- All accepted papers will have their own DOI.
- Accepted papers will need to incorporate reviewers’ comments and re-submit their paper by the Camera-ready deadline. Instructions will be provided.
- Papers accepted for publication must have at least one author registered and must be presented in person at the conference in order to be considered for publication by IEEE Xplore. Papers from no-show authors will be removed from the proceedings.
- An accepted paper may be selected for either oral or poster presentation depending on review score and topic specificity.
For submitting an Extended Abstract, please follow the instructions:
- Please format your submission using Times New Roman, 12 pt font size, with justified alignment. The submission must not exceed 1000 words, excluding the reference list. References are compulsory and should be included in the submission. A maximum of two figures or tables may be included.
- Extended Abstracts could follow the standard structure: Introduction, Background, Methods / Datasets, Results, and Conclusion. Alternatively, computer science and engineering abstracts can follow alternative structure such as Introduction, Background, Framework / Architecture, Implementation, Results (if available), Novelty and Challenges and Conclusion.
- Submissions must be anonymised: the names and affiliations removed, and any references to previous works either substituted with “anonymised” or rephrased.
- All submissions must be submitted in PDF format using the Easychair submission system. Click on the submission button below and choose the “DPH2027 Workshop – From Health Data to Action – AI for District Health Service Monitoring and Planning” track.
- Paste your first section (Aims/Abstract/Overview) to the Abstract field in the Easychair system for ease of processing and review allocation.
NOTE that:
- Abstract submissions will be reviewed by two-three reviewers from the PC and SPC committees and assessed in terms of their quality, relevance to the conference, originality, significance, correctness, and clarity.
- Accepted Extended Abstracts will have to incorporate reviewers’ comments and re-submit their abstract by the Camera-ready deadline. Instructions will be provided.
- An accepted abstract may be selected for either oral or poster presentation depending on review score and topic specificity.
Review process and publication
Every submission will be reviewed by the workshop organizers and a programme committee of public health, health informatics and digital health experts. Reviewers will assess relevance to the workshop, originality, methodological soundness, practical value for district-level decision-making, clarity and attention to ethics and equity in line with DPH 2027 requirements. Authors will be notified by email.
Contact
Questions about this call can be sent to Farhana.shahid@zu.edu.pk.
General conference enquiries: support@dphconf.org.
Please help us reach district health teams and digital health colleagues by sharing this call with your networks.
SUBMISSION
Submission is via EasyChair.