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AI-POWERED PREDICTIONS
IN THE EMERGENCY DEPARTMENT

Intelligent solutions are transforming hospitals operations

Emergency department crowding and congestion is a global problem. Part of the solution lies in better planning, which requires forecasting. We forecast emergency department visits and patient admissions with unsurpassed accuracy, delivering a powerful tool for hospital planning and operational excellence. 

CUTTING EDGE INSIGHTS AND PREDICTIONS

We help hospitals adopt data science and AI technologies to accurately forecast emergency department visits, patient admissions, predicting length of stay and risk of readmission. Sumo Analytics works with hospitals to establish cutting edge analytics environments for ED’s to foster better decision making, by turning ever-growing volumes of data into actionable insights.

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Less crowding, less congestion, reduced boarding and shorter waiting times

FORECASTING ED VISITS WITH SUPERIOR ACCURACY

ED congestion is a global problem where hospitals have difficulties forecasting the level of visits. Not knowing how many visits are expected can both lead to understaffing scenarios which can cause major crowding and congestion, and overstaffing which leads to waste of resources. ​

 

Since about 70% of patients admitted each day come through the ED, it's critical for hospitals to have accurate forecasts of how many are arriving. ​

 

Leverage SUMO Analytics' Automated AI for hospitals to forecast emergency department visits with superior accuracy.

ACCURATELY FORECASTING PATIENT ADMISSIONS

Bed management is only based on the current situation, i.e. the constant monitoring of hospital admissions, discharges and patient flow within a hospital at any given moment. But that doesn’t allow for any planning since the future is always unknown, which causes major organisational challenges within hospitals. ​

 

About 18% of all ED visitors are admitted and become inpatients, but accurately knowing how many each and every day is extremely valuable for bed management and general resource planning.  ​

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PATIENT LENGTH-OF-STAY PREDICTIONS 

Patient Length of Stay (LOS) is considered one of the most critical monitoring and performance indicators in hospitals.
 

Difficulties with predicting LOS leads to the consumption of hospital resources, manpower, and equipment. Predicting LOS, especially in ICUs, is beneficial from different aspects in terms of the hospital organisational efficiency and planning, the patient's medical plan and family, and insurance companies.

With SUMO Analytics' advanced AI-driven LOS models, hospitals have all patients' LOS times updated hourly for effective patient management. 

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READMISSION PREDICTIONS

Unplanned readmission after discharge is an important measure of hospital’s service and quality levels, as it often indicates problems with patient care during the hospitalisation, and is a key indicator of the effectiveness of a patient's treatment plan.

 

Readmissions can be dramatically decreased, if additional attention is on patients with high readmission risk and with preventive measures taken.

 

Early prediction of hospital readmissions risks with SUMO Analytics' advanced AI models significantly improve operational planning, reduce unnecessary cost, and improve patient experience.

Hospital Corridor

As hospitals strive to increase operational efficiency and remove bottlenecks in patient flow, data-driven technologies are playing an important supporting role.

BLOOD DEMAND FORECASTING

Preventing blood shortages by accurately forecasting blood demand

Our AI-driven approach takes multiple variables into account and forecasts short- and long-term blood demand with superior accuracy, allowing blood banks to proactively prevent blood shortages of different blood types. 

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We pioneer innovation and development in forecast technology and lead the field when it comes to forecast accuracy across industries.

Learn more about our AI-powered forecast technologies here HERE

See how our advanced AI-driven technology can help at your hospital