Unlock insights faster and drive innovation with AI in health care

Use predictive modeling to anticipate the post-surgical care pathway for patients ahead of hospital discharge to build a personalized care path that optimizes a patient’s health.

The value of this solution:

• Improved health outcomes.
• Higher patient satisfaction.
• Reduced risk of readmission.

AI techniques used in this solution:

• Predictive risk modeling estimates the likelihood that a patient will experience a future health-related event. Risk modeling is often specific to a health condition and is based on both health and non-health data. 
• Large language models can be applied to create a report that summarizes the recommended care program based on the model. A case worker can quickly review and approve the program materials.
• AI agents retrieve, organize and provide quality and outcome data to satisfy regulatory and governing body requirements.

How AI helps:

• A more holistic plan can reduce future medical visits related to surgical recovery.
• Overall reduced burden of care for complex procedures and improved health outcomes.
• Higher satisfaction for the patient and increased confidence in the personalized care plan.

The AI models provide:

• Quick analysis of historic claims data, along with demographic and financial data.
• Ensemble models apply various techniques depending on the underlying data structure.
• Quantification of various risk factors helps pinpoint the care programs that will mitigate any harm.

Use the power of document vision and intelligent AI agents to allow medical reviewers to make decisions more efficiently and accurately. AI agents summarize high-impact information, declutter complex records, and simplify information.

The value of this solution:

• Greater productivity.
• Better health outcomes.
• Maximized operational efficiency.

AI techniques used in this solution:

• Proprietary machine learning and advanced Optical Character Recognition techniques extract information.
• Document vision agents identify and create a catalog of individual medical forms and handwritten and copied documents.
• Machine learning models, including natural language processing and text analytics, are used to provide contextual clues and relevant information in summaries alongside powerful data visualizations.
• Periodically assess readiness and performance across various programs, such as the Joint Commission, HEDIS, HCAHPS/CAHPS and regulatory governing bodies.

How AI helps:

• Reduces the need for manual reviews and laborious search processes, enhancing employee satisfaction and productivity.
• Quickly pinpoints and extracts information from paper files.
• Better and more effective patient outcomes from faster identification of care trends and reactions.

The AI models provide:

• Automation of the extraction of key information from images or documents into a structured format.
• Automation of current Optical Character Recognition/Robotic Process Automation processes to significantly improve the accuracy and quality of the information extraction, especially with tougher forms like blurry documents and forms with checkboxes or handwriting.

Deploy AI agents to prevent, detect and manage payment integrity issues from every angle and at every stage of the claims process to stop improper payments before claims are paid.

The value of this solution:

• Fraud detection and prevention.
• Risk mitigation.
• Cost savings.

AI techniques used in this solution: 

• Machine learning algorithms review health insurance claims for processing. The algorithms detect duplication and identify fraud easier, faster and more accurately.
• AI agents build dynamic risk profiles and adapt to emerging fraud tactics using real-time data.
• Intelligent decisioning provides a transparent and automated workflow that meets business requirements and guides the AI agents.

How AI helps:

• Detect fraud faster, reduce loss and optimize payment integrity.
• Gain a consolidated view of fraud risk.
• Build social network diagrams with sophisticated data mining capabilities for a better understanding of new threats, preventing big losses early.

The AI models provide:

• Reduction in false positives while boosting efficiency, including components for fraud detection, alert management and case handling.
• Outcome-based analytics help monitor newer value-based payment models.

Use machine learning to understand, predict and visualize threats to human health faster to save lives. Forecast trends for seasonal and chronic diseases for response and resource management.

The value of this solution:

• Faster decision making.
• Highly accurate forecasting.
• Maximized operational efficiency.

AI techniques used in this solution:

• Machine learning models can learn from data that is ingested into them and identify infectious disease patterns. The more data provided, the more accurate the model.
• Machine learning ingests massive amounts of data, extracts key features, determines a method of analysis, writes the code to execute that analysis and produces an intelligent output – all through an automated process.
• Predictive modeling helps you forecast trends for infectious diseases to predict threats to human health faster and improve response and resource management.

How AI helps:

• Identify, forecast and respond to infectious diseases faster.
• Reduce hospital-acquired infections and mortality.
• Efficiently mitigate future public health crises.
• Improve resource and response management.

The AI models provide:

• Visualization and prediction of disease patterns and threats to human health.
• Comprehensive alert generation process, enabling public health stakeholders to anticipate public health emergencies before they happen.
• Automated insights, including summary reports, that empower public health agencies and providers to improve response and resource management.
• Ability to embed open source code within the coding environment.

Elevate your patient and member engagement with intelligent AI agents, using natural language processing (NLP) and machine learning to understand, organize and act on vast volumes of health-related data.

The value of this solution:

• Faster issue resolution.
• Greater customer engagement.
• Greater productivity.

AI techniques used in this solution:

• NLP enables AI agents to scale the human act of processing (reading or hearing), organizing and extracting useful information from huge volumes of textual data to improve care coordination, data-driven outreach and engagement.
• Machine learning works to process structured and unstructured qualitative and quantitative data connections.
• Intelligent decisioning guides AI agents and provides transparent and automated workflows to improve engagement.

How AI helps:

• Optimize resources and improve engagement effectiveness.
• Increase consumer and stakeholder satisfaction while maintaining data privacy and ensuring AI transparency.
• Be better prepared to respond quickly in times of disruption and uncertainty.

The AI models provide:

• Analysis of large volumes of unstructured text data.
• Examination of information and gaining meaningful insights.
• Improved engagement by helping to draft scripts that increase satisfaction.

Use AI agents to evaluate patients at highest risk for chronic disease progression, using integrated data from patients, providers and payers.

The value of this solution:

• Reduce risk of readmission.
• Improve chronic disease management.
• Early detection of emerging health issues.

AI techniques used in this solution:

• AI agents are trained using natural language processing and managed by an intelligent decisioning workflow.
• Predictive analytics to detect emerging health risks and the likelihood of disease progression.
• Machine language for pattern recognition in patient data.
• AI-embedded analytics to integrate data from multiple data sources.

How AI helps:

• Provide support for chronic disease management.
• Improve adherence and support mental/chronic care.
• Act as virtual assistants to answer patient questions, schedule follow-ups and provide medication reminders with provider approval.
• Monitor patient vitals and alert clinicians to anomalies.
• Summarize patient data and recommend the next best actions.
• Reduce clinician workload by automating routine tasks.
• Identify high-risk patients for prioritized outreach and accelerated interventions.

The AI models provide:

• Real-time analysis of health and behavioral data.
• Personalized care recommendations.
• Alerts for critical health changes.
• Summarized insights for care teams to act on quickly.

Use predictive modeling and analytics to forecast demand, identifying opportunities to improve efficiency, manage costs and optimize resources.

The value of this solution:

• Maximized operational efficiency.
• Highly accurate forecasting.
• Cost savings.
• Better customer and staff experience.

AI techniques used in this solution:

• Predictive modeling to predict the demand for medical devices, beds, mobility aids and other equipment.
• Machine learning to recommend the optimal utilization of assets and optimal product inventory.
• AI-embedded IoT analytics to track assets and visually explore them through a business-focused interface.

How AI helps:

• Greater productivity.
• Optimized resource management.
• Real-time location tracking of medical devices.
• Reduced hospital costs.
• Optimized purchasing and maintenance.

The AI models provide:

• Visualizations and prediction of hospital demand, including staff, medical devices and other equipment.
• Automated insights, including summary reports, that empower hospitals to optimize medical resources, processes, workflow and throughput.
• Transparent and fair decision support about patient care through built-in bias monitoring and repeatable explanations of data, models and predictions in use in ICUs globally.

Quantify the impact of policy changes on the health care triple aim of cost, quality and outcomes. Government health policies impact the availability and cost of care in our communities. From ensuring provider coverage to promoting wellness programs, policies drive behaviors for patients and providers.

The value of this solution:

• Faster decision making.
• Better outcomes.
• Effective health care policies.

AI techniques used in this solution:

• Machine learning quickly transforms and organizes data so that it is useful for policy analysis.
• Simulation can help quantify and visualize the impacts of various policies on cost and coverage.
• Synthetic data generation may be needed to protect citizen privacy or complete gaps in available data.

How AI helps:

• Using data and evidence-based analysis reduces the human bias in policymaking related to health care.
• The ability to move from reactive to proactive policy-making decisions by simulating policy change effects.

The AI models provide:

• Faster evaluations of policy changes or new proposed policies to enhance manual analysis.
• More robust analysis and visualizations of the impact on stakeholders.
• Creation of synthetic data to enrich available data or mask private data if needed.