ScanForm

Quantitative Engineering Design (QED.ai)

ScanForm

Comprehensive epidemic detection to protect entire countries by using AI to instantly digitize handwritten data on paper

Lilongwe

Malawi

Hybrid of for-profit and nonprofit

One of the most critical challenges in achieving equitable pandemic preparedness is that high-quality data does not flow rapidly from all sites, as would be needed for rapid, informed decision-making. Artificial intelligence (AI) has the potential to transform global health security, but no level of analytical sophistication can overcome large gaps in data timeliness, quality, and completeness—problems which are pervasive in many countries. More than 1 billion people are served at health facilities with unreliable or nonexistent electricity (WHO 2023). While relatively better-resourced sites might support an “electronic-first” (e-first) solution, data digitization is often completely lacking in other, often less-resourced sites which poses a significant risk: the next pandemic is likely to first pass through rural primary healthcare centers or communities, the very sites that are lower-resourced and less likely to have immediate data digitization. Pandemic prevention cannot be a privilege reserved only for better-resourced sites, serving better-resourced people. True global health security requires equitable digitization of data from all sites, with high quality and in near real-time. This requires context-appropriate tech solutions. Doing this well unleashes the power of advanced analytics, including robust detection of aberrations. We propose an AI-driven, context-appropriate, sustainable solution to meet these challenges.

With ScanForm, handwriting on paper is transcribed into digital data with a single smartphone photo, saving time and eliminating manual data entry. Artificial intelligence (AI) extracts all information to auto-calculate custom summary statistics, data quality assessments (DQA) and visual analytics in seconds. All data digitized by ScanForm integrates into existing data systems (e.g., DHIS2, EMRs). The custom-built AI is calibrated to local handwriting from each country deploying ScanForm, with a baseline accuracy of >99%. Human verification of this fraction of a percent of uncertain fields results in 100% accuracy. Especially in the Global South, health information is still recorded on paper first because e-first solutions cannot be supported or maintained without consistent electricity and network. ScanForm is complementary and familiar because paper and pen is the universal primary method of data collection, simple to use regardless of education level, and can function at the last mile where there is poor or nonexistent infrastructure. After official MOH registers and paper tools are redesigned and printed to be compatible with ScanForm, all data and tools are still owned by the MOH but now enhanced with ScanForm AI, reducing multi-month manual data processing cycles to near real-time. https://scanform-movie.qed.ai

Detecting early signs of epidemic outbreaks is critical to ensure uninterrupted supply chains of test kits, antiretroviral drugs, and other treatments. Currently, most governments are unable to detect outbreaks quickly, and are often forced to make time-sensitive decisions without access to critical data, placing entire countries at risk. ScanForm addresses this gap by empowering MOHs to make decisions based on near real-time epidemiological data captured at national scale. This protects communities everywhere, especially in under-resourced populations. ScanForm saves frontline workers over 25% of their time, allowing them to focus on providing high-quality care rather than manual data entry and summarization. ScanForm is unique in its ability to provide high-tech sensing in low-resource or underserved settings, which might typically be restricted to low-tech sensing or no solution at all. ScanForm does not require a stable internet connection and is designed to work in the most rural environments, in contrast to e-first solutions that have underdelivered in the Global South despite billions of dollars in investment. In an era of shrinking foreign aid, future digital health investments must demonstrate measurable return on investment, operational resilience, and true local ownership to ensure long-term sustainability within host nations. ScanForm was built for this moment.