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AI analytics

Healthcare analytics help stakeholders across clinical, operations, finance, and compliance teams learn from their data and make decisions that drive efficiency and patient care. Healthcare companies use healthcare analytics BI platforms to collect all their data and explore it using various visualization and analysis tools.

Today’s most powerful healthcare analytics solutions do far more than generate reports and static dashboards. They combine many data sources and formats from a complex ecosystem of healthcare solutions—EHR systems, billing, asset management, and many others—and apply advanced machine learning models capable of surfacing the most important findings and predictions from a huge volume of real-time healthcare data.

Hospitals and healthcare systems rely on a vast network of solutions to deliver patient care and manage their facilities. To understand the entire patient journey or learn what staff needs to be successful, they need to see all of the moving parts at once.

Modern healthcare analytics software can process data from all of these systems in real time, analyzing everything from streaming indoor sensor data to detailed patient histories in an EHR. Using healthcare analytics, doctors, nurses, and support staff can make better decisions about patient care and prevent risk during patient stays.

Healthcare analytics also drive efficiency, aiding decision-making for operations and facilities teams. It’s used to streamline workflows around intake, billing, bed turnover, equipment usage, and more, leveraging cloud-based storage or data warehouses and ML models tuned specifically for the healthcare industry.

By analyzing historical trends, identifying patterns, and predicting future outcomes with AI-powered healthcare analytics, healthcare organizations can enhance clinical workflows, reduce costs, and improve patient outcomes.

The top benefits of AI healthcare analytics include:

• Improved patient outcomes - Clinical teams use data-driven insights to ensure staff has the right equipment to treat patients when they need it, reduce risk during inpatient stays, and predict negative outcomes like wandering, elopement, and falls. Using a modern healthcare analytics platform, providers can blend operational data with clinical data from an electronic health records (EHR) system, gaining a new level of visibility into the factors that lead to excellent patient outcomes.
• Better patient engagement - Healthcare teams learn from AI-powered analytics to improve the patient experience both on-site and virtually. This may include using sentiment analysis and patient survey results to find areas of friction, setting up automated alerts for important events and reminders, or using real-time data to simplify complex processes like insurance claims or at-home care. They can also embed analytics into telehealth apps and patient portals, empowering users to explore their own data without having to reach out to a support rep.
• Accelerated R&D - Researchers and analysts use AI-powered healthcare insights to identify new connections and trends that further their research. Using an enterprise BI platform, they can consolidate all of their data and explore it in the format that’s best for their skillset—SQL, R, Python, or a completely self-service, no-code workflow.
• Cost savings - Healthcare providers and insurers use healthcare analytics to find inefficiencies, reduce costs, and improve the bottom line. This might involve pinpointing the causes of wait times and claims delays, identifying customer segments that aren’t profitable, or reducing rentals and replacement costs through better equipment allocation across a portfolio of hospitals.
• Improved staff morale - Healthcare data analytics improve the workplace environment and make it easier for clinicians and support staff to focus on care. AI insights and self-service access to data help staff find the assets they need, quickly get up to date on the patients they’re caring for, and stop spending time on manual, repetitive work.
• New business models and revenue streams - By having secure, reliable access to patient and operational data in one place, providers can more easily adopt new virtual or value-based care models. Business stakeholders and leadership can use self-service analytics to benchmark their organization against external clinical data sources.

The top features to look for in a healthcare data analytics solution include:

• AI and advanced analytics, including AI agents and natural language search for deeper exploration—no SQL required.
• Self-service analytics tools that provide access to operational and health data, with visualizations and charts that are easy to click into and explore.
• Interoperability with existing healthcare solutions and datasets, with ready-to-use integrations and APIs.
• Live, interactive dashboards that data stakeholders can use to share the metrics that matter to each team.
• Enterprise-grade BI features, including role-based access, global governance, and security controls, built to meet strict healthcare regulations.