Predicting Multi-Morbidity Progression and Identifying Key Determinants in Chronic Disease Patients Using a Longitudinal Data-Driven Information Systems Approach: A Research Protocol for a Cohort Study in Bandung Regency, Indonesia

Lusianto Lusianto ; Zaenal Arifin Hasibuan ; Sri Supatmi ; Adnan Shahid Khan
Indonesian Journal of Infomatics 📅 2026 Vol 1, No 2 🔗 DOI: 10.66472/iji.v1i2.388
Abstract
Multimorbidity represents a critical challenge for primary healthcare systems in low- and middle-income countries (LMICs). In Indonesia, fragmented electronic health record (EHR) infrastructure limits effective chronic disease management. This research protocol presents an end-to-end information systems approach to: (1) design a validated ETL framework for heterogeneous health data; (2) develop and compare machine learning models (Random Forest, XGBoost, LSTM) for predicting multimorbidity risk; (3) identify critical determinants in an Indonesian population; and (4) evaluate a Clinical Decision Support System (CDSS) prototype. A mixed-methods, three-phase design will analyze 150,000 chronic disease patients from SIMPUS EHR data (2021-2025). Phase 2 focuses on CDSS development using explainable AI (XAI), while Phase 3 evaluates user acceptance using the Technology Acceptance Model (TAM). The study expects to produce a predictive model with AUC-ROC $\ge0.75$ and an operational CDSS prototype integrated with the Satu Sehat platform. This protocol addresses gaps in Southeast Asian LMIC data, implementation, and interpretability.
Keywords
Clinical Decision Support; Health Information Systems; Longitudinal Ehr Data; Machine Learning; Multimorbidity Prediction
How to Cite

Lusianto Lusianto, et al. (2026). Predicting Multi-Morbidity Progression and Identifying Key Determinants in Chronic Disease Patients Using a Longitudinal Data-Driven Information Systems Approach: A Research Protocol for a Cohort Study in Bandung Regency, Indonesia. Indonesian Journal of Infomatics, 1(2). https://doi.org/10.66472/iji.v1i2.388

Lusianto Lusianto; Zaenal Arifin Hasibuan; Sri Supatmi; Adnan Shahid Khan, "Predicting Multi-Morbidity Progression and Identifying Key Determinants in Chronic Disease Patients Using a Longitudinal Data-Driven Information Systems Approach: A Research Protocol for a Cohort Study in Bandung Regency, Indonesia," Indonesian Journal of Infomatics, vol. 1, no. 2, 2026.

Lusianto Lusianto; Zaenal Arifin Hasibuan; Sri Supatmi; Adnan Shahid Khan. "Predicting Multi-Morbidity Progression and Identifying Key Determinants in Chronic Disease Patients Using a Longitudinal Data-Driven Information Systems Approach: A Research Protocol for a Cohort Study in Bandung Regency, Indonesia." Indonesian Journal of Infomatics, vol. 1, no. 2, 2026.

Lusianto Lusianto; Zaenal Arifin Hasibuan; Sri Supatmi; Adnan Shahid Khan. "Predicting Multi-Morbidity Progression and Identifying Key Determinants in Chronic Disease Patients Using a Longitudinal Data-Driven Information Systems Approach: A Research Protocol for a Cohort Study in Bandung Regency, Indonesia." Indonesian Journal of Infomatics 1, no. 2 (2026).

Lusianto Lusianto, et al. (2026) 'Predicting Multi-Morbidity Progression and Identifying Key Determinants in Chronic Disease Patients Using a Longitudinal Data-Driven Information Systems Approach: A Research Protocol for a Cohort Study in Bandung Regency, Indonesia', Indonesian Journal of Infomatics, 1(2). doi: 10.66472/iji.v1i2.388.

Lusianto Lusianto; Zaenal Arifin Hasibuan; Sri Supatmi; Adnan Shahid Khan. Predicting Multi-Morbidity Progression and Identifying Key Determinants in Chronic Disease Patients Using a Longitudinal Data-Driven Information Systems Approach: A Research Protocol for a Cohort Study in Bandung Regency, Indonesia. Indonesian Journal of Infomatics. 2026;1(2).

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