Decision-support systems for ambulatory care, including pandemic requirements: using mathematically optimized...

Area of Science:

• Operations Research
• Health Informatics
• Public Health Policy

Background:

• Healthcare planning faces challenges due to fragmented, historically developed structures, leading to missed optimization opportunities.
• Existing planning tools often lack interactive exploration capabilities, hindering the adoption of innovative solutions.

Purpose of the Study:

• To implement optimized decision-support systems for ambulatory care planning across strategic, tactical, and operational levels.
• To demonstrate the utility of interactive cartographic representations and mathematical optimization in solving real-world healthcare planning problems.

Main Methods:

• Developed and implemented decision-support systems for four distinct case studies: COVID-19 vaccination center positioning, emergency doctor placement, out-of-hours pharmacy planning, and patient transport route planning.
• Utilized mathematical optimization models and interactive cartographic representations, developed in collaboration with domain experts.

Main Results:

• Mathematical optimization provides a robust framework for modeling and solving complex healthcare planning problems.
• Interactive decision-support tools are crucial for making mathematical solutions comprehensible and tangible to decision-makers and public health experts.

Conclusions:

• Innovative decision-support tools, particularly those with interactive features, can overcome hurdles in adopting new planning solutions within the healthcare sector.
• The successful implementation of these tools can lead to significant optimization potential in healthcare planning.