Decision intelligence in healthcare encompasses various critical use cases, including the optimization of patient care and treatment through personalized recommendations, efficient resource allocation for hospitals, predictive analytics for disease outbreaks to aid public health responses, and the optimization of clinical trials for pharmaceutical companies. These applications empower healthcare professionals and organizations to make data-driven decisions that enhance patient outcomes, resource efficiency, and overall healthcare delivery.
Clinical Decision Support: AI systems provide real-time clinical decision support to healthcare professionals, offering diagnostic and treatment recommendations based on patient data and medical literature, thereby enhancing the accuracy and effectiveness of care.
Telemedicine and Remote Monitoring: Advanced analytics and AI enable remote patient monitoring and telemedicine applications. They facilitate continuous monitoring of vital signs and patient data, allowing healthcare providers to intervene early and manage chronic conditions more effectively.
Healthcare Fraud Detection: Advanced analytics can identify fraudulent billing and insurance claims by analyzing patterns and anomalies in healthcare transactions, helping to reduce healthcare fraud and save costs.
Population Health Management: AI and decision intelligence tools help healthcare organizations analyze population health data to identify at-risk groups, develop preventive strategies, and allocate resources efficiently to improve overall community health.
Natural Language Processing (NLP) for Electronic Health Records: NLP algorithms can extract valuable information from unstructured clinical notes and narratives in electronic health records, making this data accessible for research, analysis, and decision-making.
Drug Adverse Event Monitoring: AI models can monitor and analyze adverse events associated with medications by mining electronic health records, social media, and other sources, assisting in drug safety monitoring and regulatory compliance.
Supply Chain Optimization: AI-driven demand forecasting and inventory management improve the supply chain for pharmaceuticals and medical equipment, ensuring timely availability and cost-effective procurement.
Genomic Analysis: Advanced analytics and AI assist in analyzing genomic data to identify genetic markers for diseases, predict disease risk, and develop targeted therapies in the field of precision medicine.
Patient Engagement and Chatbots: AI-powered chatbots and virtual assistants engage with patients, providing information, appointment scheduling, medication reminders, and answering health-related queries, enhancing patient experience and adherence to treatment plans.
Our Platforms
Healthcare Insights
The DI Fabric Healthcare Analytics solution is designed to help healthcare organizations of all sizes gain valuable insights into various aspects, such as individual or multiple facilities, patient data, departments, clinical records, revenue, and more. This tool enables organizations to respond effectively, enhance patient care, and facilitate organizational growth..
Patient Readmission Risk Prediction
Predictive analytics can assess the risk of patients being readmitted to the hospital shortly after discharge. By analyzing historical patient data and demographics, machine learning models can identify individuals at higher risk for readmission due to factors like chronic conditions, medication adherence, or social determinants of health.
Get a demo of Decision-Centric Approach Solution
Decision makes need every instrument availably in order to help them ask the right questions about their data. Decision intelligence can assist leaders in establishing meaningful, actionable business insights, and recommendations. As data and insights are becoming more and more important, having a helping hand to make smart decisions and provide predictive outcomes will be the next form of digital transformation.