Performance Monitoring in Hospitals BThe UKBB relies on data-driven hospital management using a management information system (MIS) based on Qlik Sense and SAP.
More efficiency and structure The JURA Materials Group uses iVIEW to implement an efficient ETL link between SAP FICO and Qlik
Performance Monitoring in Hospitals BThe UKBB relies on data-driven hospital management using a management information system (MIS) based on Qlik Sense and SAP.
More efficiency and structure The JURA Materials Group uses iVIEW to implement an efficient ETL link between SAP FICO and Qlik
Artificial intelligence (AI) in the healthcare sector Learn more about the potential of Automated Machine Learning and Artificial Intelligence (AI) in the healthcare sector. WEBINAR ON-DEMAND - Predictions about patient re-admissions 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 0 1 2 3 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 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. Automated Machine Learning with DataRobot AutoML (Automated Machine Learning) is a technology invented by DataRobot to automate many of the tasks required to develop artificial intelligence (AI) and machine learning applications. DataRobot combines the knowledge and experience of some of the world's leading data scientists and enables more users in an organization to succeed with machine learning by simply using their understanding of their data and business and letting DataRobot do the rest. Automatisation and Democratisation of AI and ML Models and predictions via the intuitive user interface DataRobot provides an easy-to-use visual interface suitable for business users and experienced data scientists. Simply select the target variable and click the start button to get the data models that are relevant to you. Discover DataRobot Email Facebook Instagram LinkedIn Twitter Xing