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Updated: May 13, 2026

Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit

Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit

Published on: September 30, 2020

Developing an adaptive policy for long-term care capacity planning.

Yue Zhang¹, Martin L Puterman

¹College of Business and Innovation, University of Toledo, Toledo, OH 43606, USA. yue.zhang@utoledo.edu

Health Care Management Science

|March 21, 2013

Summary

This study introduces an adaptive policy for long-term care (LTC) capacity planning, improving resource utilization. The new method dynamically adjusts capacity based on previous service levels, overcoming limitations of static approaches.

Related Experiment Videos

Last Updated: May 13, 2026

Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit

Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit

Published on: September 30, 2020

Area of Science:

Healthcare Management
Operations Research
Health Services Research

Background:

Determining optimal long-term care (LTC) capacity is crucial for meeting service level criteria.
Previous static capacity planning methods faced limitations in dynamic healthcare environments.
The need for adaptive strategies in LTC resource management is increasingly recognized.

Purpose of the Study:

To present a refined methodology for multi-year LTC capacity planning using an adaptive policy.
To improve upon a previous study's approach by incorporating system state for capacity determination.
To enhance resource utilization in long-term care systems through dynamic capacity adjustments.

Main Methods:

Integration of discrete event simulation to model the LTC system dynamics.
Development of an optimization algorithm to determine capacity levels based on adaptive policy.
Aggregation of service levels into manageable groups for computational tractability.

Main Results:

The refined methodology successfully addresses issues identified in prior studies.
Demonstrated significant improvements in resource utilization within the LTC system.
The adaptive policy proved effective in meeting wait time service level criteria over a multi-year horizon.

Conclusions:

The proposed adaptive policy offers a more effective approach to LTC capacity planning than static methods.
Dynamic adjustment of capacity based on system state enhances operational efficiency and resource use.
Practical implementation necessitates the availability of flexible or temporary LTC capacity resources.