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The healthcare sector is currently undergoing a major transformation. Of course, it is nothing new that increasingly high service quality is supposed to be offered at ever more competitive terms. However, in recent years ther has been another decisive factor that has the potential to bring about far-reaching improvements: artificial intelligence.

With the help of AI and machine learning, valuable insights can already be gained from the vast amounts of existing data in the healthcare system and more are being added every day. This has significant advantages in numerous fields. Precise predictive models enable service providers to improve their service quality and save costs, while achieving better results for patients.

Our BI expertise in the healthcare sector

Use cases AI and Machine Learning in hospitals

Machine learning and artificial intelligence in general can contribute in a variety of ways to achieving a healthcare system that offers better services at lower prices: smart solutions can help with clinical, operational and financial decisions.

The frequency of many cost-driving events can already be reduced by using intelligent predictive models: the likelihood of a patient returning can be better predicted and prevented. The utilisation of the intensive care units and emergency departments can be predicted more precisely and resources can be planned accordingly. The risk of infection during hospitalisation can be calculated more precisely for each patient, which means that preventive measures can be initiated early on. Length of stay can be more precisely predicted as a cost-driving factor, which in turn leads to more flexible planning and lower costs

Automated Machine Learning - Use Cases mit DataRobot

  • Clinical

    • Risk stratification
    • Medication adherence prediction
    • Disease propensity
    • Patients hospital-acquired conditions risk
    • Clinical pathway predictions
    • Treatment effectiveness
    • Outcomes prediction (based on treatment)
    • Patient outcomes prediction (based on social determinants) 
    • Acute vs chronic episodic care forecasting


  • Clinical

    • Early disease detection
    • Non-adherent patient prediction
    • Error propensity (clinical or medication) 
    • Adverse event prediction
    • Procedure complication prediction
    • Sepsis, A-Fib, CHF, NICU, transplant cases prediction
    • Infection rate prediction
    • Bed sores risk
  • Services

    • A&E utilization forecasting
    • No show forecasting
    • Patient volume forecasting by service type (for staffing models)
    • GP visits forecasting
    • IP visits and bed days, ALOS by bed type forecasting
    • Surgery/procedure til optimization
    • Advoidable readmits prediction
    • Utilization forecasting
    • ICU occupancy forecasting
    • Patient booking optimization
  • Marketing

    • Patient churn
    • Propensity to take a survey
    • Targeted marketing
    • Target marketing geography prediction
    • Call center optimization
    • Prospecting
    • Lead optimization
    • Lead scoring / propensity
    • Campaign optimization
    • Patient lifetime value
    • Message optimization
  • Substance Abuse & Mental Health

    • SAddiction risk (ex. opioid )
    • Likelihood of relapse
    • Likelihood of mental health diagnosis
    • Mental health counseling needs /utilization forecasting
    • Patient exacerbation prediction
  • Revenue Cycle Mgmt.

    • Claim triage and prioritization
    • Claims collection optimization
    • Revenue prediction (by patient type) 
    • Revenue prediction (based on department) 
    • Patients costs prediction (by visit and procedure type)

Impressions DataRobot for Healthcare

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  • Datarobot Printscreen Healthcare
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  • Datarobot Printscreen Healthcare

Hidden costs in the hospital environment

Areas that appear trivial at first glance but nevertheless significantly drive costs up can also be optimised. Thanks to machine learning, invoicing can be improved – a process that used to be extremely complex and depended on numerous factors. To this day up to 90% of invoices are consequently erroneous, with an average error of $1,300 contained in invoices of more than $10,000. Intelligent solutions not only lead to cost savings but also to greater patient satisfaction.

Artificial intelligence also helps predict a patient no-show. If a patient does not show up for treatment, this can be expensive not only for the service provider and the payer: In such cases, diseases and other patient complaints are often not treated, which can lead to an exacerbation of the condition at a later date. This in turn requires more elaborate and sophisticated treatment. Machine learning can help decision makers better predict which patients are most likely not to turn up. This allows them to intervene in a more targeted manner, to set new appointments and to engage the patients more actively. This could save up to $150 billion annually in the United States alone.

DataRobot Healthcare Infographic

Machine Learning Healthcare

Limitless Potential

Artificial intelligence also has the potential to make major breakthroughs in medical research and practice. Whether in diagnosing heart problems, the prognosis of liver diseases or in radiology: machine learning is likely to play a role in almost all areas of life in the future. Health care is still in the early stages of its data-driven transformation and the possibilities seem endless. Thanks to intelligent systems, enormous costs can already be saved, service quality optimised and patient results improved.

Our AI/ML solutions with DataRobot

With Informatec and our preferred AI solution DataRobot, your company is also well prepared for the future with artificial intelligence. The platform brings together the knowledge, experience and best practices of world leading data scientists, offering an unparallelled level of automation, precision, transparency and collaboration in order assist you in building an AI-controlled organisation.

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