Randers Regional Hospital tests new digital solution
A new planning and forecasting tool that uses advanced machine learning is able to predict the need for beds and identify risk patients who may need special attention. This solution is now being tested at Randers Regional Hospital in Denmark – and has great potential.
Up until now, staff at Randers Regional Hospital has organized patient care based on a combination of expectations and experience, but unfortunately this is not always a sufficient method to avoid overcrowding, inappropriate transfers and re-admissions.
In a forthcoming pilot project, Randers Regional Hospital will test a new planning tool that can predict future admissions with considerable accuracy by using advanced machine learning. The pilot project is part of the Danish national Big Data research project called DABAI (Danish Center for Big Data Analytics driven Innovation), and the solution based on this data, Columna Patientflow, is being developed by Danish software company Systematic in collaboration with Central Denmark Region.
The solution works by feeding a computer with large amounts of historical data covering several years of hospital admissions. The computer then identifies patterns and deviations in the data material and combines the findings with current data from the hospital departments to provide solid data on which to base forecasts about future admissions and courses of hospital treatment.
The new solution can also identify patients likely to be re-admitted or to experience complications during their stay in hospital. This makes it possible for the Columna Patientflow system to provide data to support the decisions that doctors and nurses need to make when planning the course of treatment for a patient at risk.
Randers Regional Hospital is the first place in Denmark to use such an advanced solution. It is expected that staff at the hospital will be able to make use of the system during the autumn of 2019, and the solution will be tested until February 2020.
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