SMH Institute is an independent research institute dedicated to advancing digital health innovation, quality and patient safety, and clinical governance across Indonesia's healthcare ecosystem.
SMH Institute was established to bridge the gap between healthcare practice, management science, and digital innovation in Indonesia.
To become a leading independent research institute that shapes sustainable, evidence-based, and digitally empowered healthcare systems in Indonesia and Southeast Asia.
We generate high-impact research, develop clinical decision support tools, and translate evidence into practical governance frameworks — enabling healthcare institutions to improve quality, safety, and sustainability of care delivery.
Every output is grounded in validated methodology, peer-reviewed evidence, and transparent reporting standards.
We design with, not for, clinicians and patients — participatory approaches drive all our system development.
Research outcomes must be implementable, scalable, and financially viable within real-world healthcare constraints.
We actively share tools, datasets, and findings openly to accelerate collective progress across institutions.
Patient safety, dignity, and satisfaction are non-negotiable anchors in every research and design decision.
We set high standards for quality in research output, clinical tool development, and knowledge translation activities.
Our research portfolio spans three interconnected domains that together form the foundation of high-quality, sustainable healthcare.
Developing and evaluating AI-assisted tools that enhance diagnostic reasoning, clinical documentation quality, and nursing decision-making — grounded in Indonesia's national nursing standards.
Designing and validating instruments that measure nursing quality indicators, patient satisfaction, and system usability — enabling data-driven quality improvement in hospital settings.
Translating national healthcare standards into organizational governance frameworks — including compliance monitoring, professional accountability systems, and integrated care coordination models.
SMH Institute operates through four specialized divisions, each with distinct program mandates and cross-divisional collaboration protocols.
Develops AI-assisted clinical tools, health informatics solutions, and electronic health record integrations. Flagship project: a hybrid rule-based and ML-powered nursing decision support system covering all national nursing diagnoses.
Designs and validates measurement tools for nursing quality, patient satisfaction, and adverse event prevention. Conducts clinical trials using standardized instruments adapted to the Indonesian healthcare context.
Develops governance frameworks, clinical audit systems, and policy briefs aligned with national nursing and health standards. Partners with hospital accreditation bodies and regulatory institutions across Indonesia.
Manages clinical data annotation workflows, builds training datasets for AI models, and ensures data quality and interoperability. Utilizes professional annotation platforms for structured clinical labeling at scale.
Across all divisions, SMH Institute delivers research, tools, and capacity-building programs for the Indonesian healthcare community.
End-to-end design, training, and clinical validation of AI-powered decision support tools for bedside nursing and medical staff.
Primary research, systematic reviews, and instrument validation studies published in indexed national and international journals.
Expert-annotated clinical case datasets for AI model training, validated by multi-rater panels with inter-rater reliability ≥ 0.80.
Design and psychometric validation of nursing quality indicators, patient experience surveys, and system usability instruments.
Policy brief development, clinical governance framework design, and accreditation readiness support for Indonesian healthcare institutions.
Training workshops, FGD facilitation, and think-aloud protocol implementation for clinical staff and health informatics teams.
High-quality annotated datasets are the foundation of any reliable clinical AI system. SMH Institute operates a structured clinical annotation program with expert nursing panels.
We welcome collaboration from researchers, clinicians, institutions, and technology partners aligned with our research mission.
Location
East Java, Indonesia
Email
research@smh.ac.id
Website
smh.ac.id
Research areas open for collaboration
Digital health systems · Clinical AI · Quality measurement · Nursing informatics · Clinical governance