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Longitudinal Research

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...

Longitudinal Studies

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...

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Trajectory Data Analyses for Pedestrian Space-time Activity Study

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Analysis of Patients' Mobility Patterns: Insights From a Process Mining-Based Longitudinal Study.

Mansoureh Yari Eili¹, Fatemeh Hajialiasgari², Mohammad Hossein Roozbahani³

¹University of Qom, Iran.

Inquiry : a Journal of Medical Care Organization, Provision and Financing

|March 3, 2026

Summary

This summary is machine-generated.

Alzheimer

Keywords:

Alzheimer’s disease care process digital health health facilities medical tourism patient mobility process mining

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Area of Science:

Healthcare Management
Data Science in Healthcare
Process Mining Applications

Background:

Patient mobility is key for healthcare resource allocation.
Uneven healthcare facility distribution drives patient movement.
Understanding Alzheimer's disease patient mobility is vital for Iran.

Purpose of the Study:

To map Alzheimer's disease patient mobility patterns across Iranian provinces.
To analyze inter-provincial care-seeking behaviors using health insurance data.
To identify key healthcare destinations and underserved regions for AD patients.

Main Methods:

Longitudinal study utilizing process mining techniques.
Analysis of 28,425 physician office visits from the Salamat Health Insurance database (2019-2023).
Construction of inter-provincial patient mobility patterns and care flows.

Main Results:

Ilam, Alborz, Sistan, and North/South Khorasan show highest out-of-province mobility (>50%).
Tehran, Alborz, and Isfahan are preferred destinations for 70% of Alzheimer's disease treatment.
Qom, Yazd, Fars, Gilan, Isfahan, E. Azerbaijan, and K. Razavi exhibit minimal patient mobility (<1%).

Conclusions:

Process mining effectively reveals complex patient mobility patterns.
Disparities in healthcare resource allocation are evident across Iranian provinces.
Recommendations for balanced medical resource distribution nationwide are crucial.