Better planning through data based forecasts
Achieve better Case Management with our Casematch’s length of stay prediction. The algorithm expects input from data that is known shortly after admission. These include demographic data (age, sex, etc.), diagnoses with POA flag (present on admission) and planned interventions (CHOP). With the input of this data and the learned knowledge from the eonum data pool, this results in the predicted length of stay of a patient. A hospital can therefore better schedule its interventions. Thus, for example, expensive weekend stays can be avoided, better bed and duty scheduling can be carried out and, in the end, costs can be saved.
Please read our blog posts about this:
- Medizinische Einflussfaktoren auf die Verweildauerprognose (available only in German)
- Besseres Case Management dank Verweildauerprognose und künstlicher Intelligenz (available only in German)
The use of a robot (00.99.50) can be identified here as a shortening factor.
A more advanced application of the algorithm is the prediction of possible complications that may occur during or after a hospital stay. Detailed information on this application can be found in our blog post:
- Vorhersage von Komplikationen während des Spitalaufenthaltes (available only in German)
Other possible applications of the algorithm are: Prediction of the final DRG, possible DRG changes, identification of high-risk patients, assessment of the risk of re-admission or transfer. These developments are of interest for quality management. In this way, precautions can be taken in advance for high-risk patients in order to prevent possible complications. Any prognosis can be used both during pre-coding and during coding.
In addition to data-based forecasts, our Casematch software offers many other features. Learn more about them on the corresponding pages.
For our counselling and analysis services, we use our own Casematch software. You can use this software too – find out more about it and get in touch with us!
