Speaker

Leah Goeke

Leah Goeke

Epidemiologist @ QED.ai

Pittsburgh, Pennsylvania, United States

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Leah Goeke, MPH, CPH is an Epidemiologist at QED.ai with 8+ years of experience in global health, program implementation and diplomacy. She previously worked in the Strategic Information Branch in the Division of Global HIV and TB for CDC Tanzania, focusing on surveillance and monitoring & evaluation activities. As a Peace Corps Volunteer in Zambia, she worked with community health volunteers on sustainable agriculture practices and malnutrition workshops. She earned her BAs in Biology and Anthropology from Bucknell University, and MPH in Epidemiology and Global Health Certificate from the University of Pittsburgh.

Area of Expertise

  • Health & Medical

Topics

  • Epidemiology
  • Global Health
  • Data Science & AI

ScanForm: Digital Data from Paper in Under 60 Seconds

Timely and accurate data is a critical need around the world, but is hindered by inadequate infrastructure. With ~600 million people lacking access to electricity in Sub-Saharan Africa alone, how can data be collected at scale in areas without reliable power or network?

ScanForm is a sustainable and non-disruptive solution that transcribes handwriting on paper forms into digital data with a single smartphone photo. Artificial intelligence (AI) extracts all information to auto-calculate custom summary statistics and visual analytics in under 60 seconds, saving time and eliminating human errors. If network is unavailable, data collection can still occur offline by taking photos, which are queued for automatic upload once internet is restored.

In collaboration with governments and organizations, paper tools and source registers are redesigned and printed to be compatible with ScanForm, ensuring all original data elements are captured. Additionally, ScanForm handles end-to-end processing of data, including not only data collection, but also cleaning, imputation, summary statistics, data quality assessments and phone management. As a result, ScanForm delivers comprehensive confidence in data quality and reduces multi-month data processing cycles to near real-time.

Compared to alternatives, ScanForm is more affordable to deploy and maintain, particularly in developing countries, while saving money by eliminating manual data entry and physical source data verification. Interoperable with other systems like DHIS2 and electronic medical records (EMRs), ScanForm can create a complete picture of national health systems from paper and electronic-based methods of data collection.

ScanForm has been successfully used for: epidemiological surveillance (HIV, malaria, COVID-19), routine health reporting, clinical trials, influenza vaccine delivery, school attendance, agricultural data collection in the field, and refugees and human rights. With over 20 million records processed since 2018, any information collected on paper that needs to be analyzed quickly and correctly is suitable for ScanForm.

NetHope Global Summit 2022 Sessionize Event

October 2022

Leah Goeke

Epidemiologist @ QED.ai

Pittsburgh, Pennsylvania, United States

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