Implementation of Duke Machine Learning Data Quality Assurance Framework in a Pakistani Healthcare Institution

Purpose High-quality data are critical for developing healthcare machine learning (ML) models yet remain a major challenge in low- and middle-income countries (LMICs). This study evaluated the applicability of the Duke Institute for Health Innovation’s (DIHI) Machine Learning Data Quality Assurance (ML-DQA) framework in an LMIC setting using two ML projects at a tertiary care hospital in Pakistan. Methods We applied the ML-DQA framework to two ML projects, Sepsis Watch (SW) and In-hospital Mortality Prediction (IMP). The framework comprises four phases: pre-DQA, data element pre-processing, ML-DQA checks, and ML-DQA adjudication, and was used to evaluate data quality and usability for both models. Results In the pre-DQA phase, required data elements were identified through consultation with clinicians and project teams. During data element pre-processing, transformations and harmonization were performed, including consolidation of differently labelled glucose variables. ML-DQA checks assessed plausibility, conformance, and completeness, identifying elements that met quality standards. In the adjudication phase, reports were reviewed by two independent clinicians to determine suitability for model development. Conclusions The Duke ML-DQA framework effectively identified data limitations and informed model development, demonstrating its utility for assessing data quality and supporting ML implementation in resource-constrained settings.