Optimizing hospital logistics by digiting service functions and using Machine Learning for workload predictions

The hospitals of the future are bigger and more complex than hospitals built years ago. The pressure on internal logistic is increasing, as levelling capacity with patient needs is challenging due to capacity depending on availability of equipment as well as clinical treatment flexibility.
In the hospitals it is therefore important to implement solutions that support hospital logistics (porters, cleaners, AGV’s, warehouse etc.) to meet the needs of the clinical flow. We need to assure that the right patient is met with the right personnel, equipment and treatment at the right time.
Transparency and empowerment are key factors when implementing the solution Columna Service Logistics. It provides service employees as well as clinical staff with an overview of the various needs for support across an entire hospital. With the right tool, employees can make the necessary decisions in an impulsive hospital environment.
Columna Service Logistics support patient-related service tasks (e.g. patient transports and patient assists), as well as logistical functions such as bed handling, equipment handling and other transport tasks related to the hospital flow (e.g. food, waste, linen, medicine etc).
Predicting workload by use of Machine Learning is a key element in the future of hospitals to further increase transparency. Machine Learning gives the ability to predict the number of tasks needed in specific areas. This means that service logistics can be proactive.
The result is an overall increase of quality in the clinical processes, as well as an increase in efficiency.

Dieser Vortrag ist Bestandteil des Programmblocks "Prozessmanagement" mit den weiteren Themen:

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Christian Schläpfer, UniversitätsSpital Zürich

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