Health care analytics directly addresses the rise in consumer demand for high-quality, safe and affordable care. With the right combination of technology, skills and strategy, health care providers are gaining insights from health data to help them improve patient outcomes and experience while minimizing costs.
Through descriptive, predictive and prescriptive analytics that incorporates machine learning and artificial intelligence (AI), health care organizations are able to strengthen financial performance, deepen consumer relationships and improve the way health care is conceived and delivered for better outcomes across the entire spectrum of health industries:
SAS helps life sciences companies transform data into life-changing insights. We understand what’s at stake for your business and for patients. That's why we deliver innovative technologies you can trust to help you get better, safer therapies to patients faster in a highly regulated landscape. SAS can help you rise to the challenges of digital health, improving the way you discover, develop, manufacture and commercialize therapies.
Health analytics can propel your life sciences business forward by:
- Getting new medications to patients faster.
- Providing regulatory bodies with evidence of drug safety and efficacy.
- Integrating evidence throughout the life sciences value chain.
- Accelerating therapeutic discovery with clinical trial data.
- Improving manufacturing processes and sales and marketing efforts.
- Identifying and developing the next generation of health care treatments.
- Developing optimal strategies to commercialize treatments.
By providing industry-leading data management, predictive analytics, AI, and visualization software and expertise, SAS has become the trusted leader in health analytics. That's why 100% of life sciences companies on the Fortune 500 rely on SAS for drug discovery, clinical trials, sales and marketing, and manufacturing.
SAS offers a flexible platform that enables us to analyze sensor data, generate forecasts, and reduce downtimes and maintenance times. Torben Scaffidi Head of Lab Operation Analytics Siemens Healthineers
Health insurance analytics
As the health insurance sector grows more complex, regulated and customer-centric, insurers are adopting data-driven technologies to cope with new realities. Health analytics and data management software offer a proven approach to streamline areas such as health and condition management, actuarial analysis and customer retention.
But one area where health analytics is revolutionizing the insurance industry is fraud detection and prevention. Fraud, waste and abuse divert billions away from patient care annually. Faster, more aggressive investigation and detection of key risk indicators at every stage of the process are key to controlling costs and protecting patients.
SAS enables you to view the cost of care and associated patient outcomes at a level of detail never possible before. Our advanced analytics with embedded AI capabilities optimizes the detection, management and prevention of payment integrity issues from every angle to help you:
- Gain insights for value-based care and payment models.
- Report accurate risk adjustment information.
- Get a consolidated view of fraud risk.
- Reduce false positives and increase efficiency.
We have some providers that may not be doing the right services at the right time or are doing too many services. We want to make sure our dollars are being allocated to more preventive and diagnostic services. Dean Webb Senior Manager of Analytics DentaQuest
Health analytics for health care providers
Health analytics informs optimal care pathways and digital workflows to help health care organizations provide better and safer care at a lower cost. The use cases are piling up. Computer vision helps radiologists improve the speed and accuracy of cancer diagnostics. Predictive analytics helps patients get placed in the right care setting and get seen by the right clinical staff. And automation technologies like robotic process automation and intelligent automation bring efficiencies to internal processes.
Of the many ways that health analytics is changing health care, the biggest gains are expected in three main areas:
- Transitioning to outcomes-based funding and value-based care.
- Raising patient expectations for personalization.
- Improving access to care through new community partnerships and innovative commercial ventures.
For health care providers, advanced analytics and AI can augment efforts to personalize health care while improving overall population health one patient at a time. And technologies like the Internet of Things (IoT) only quicken this process by infusing more insight-rich data into the equation. With the right focus, analytics will enable health – not just health care.
AI will help us save lives ... I'm absolutely sure about that. Dr. Geert Kazemier Professor of Surgery and Director of Surgical Oncology Amsterdam UMC
Health analytics for government
Governments make thousands of policy decisions each year that affect the health and well-being of millions of citizens. As agencies strive to improve health outcomes while controlling costs, they’re increasingly using data and health analytics to ensure the right care is provided at the right time and at the right costs.
Health analytics has proven effective for the triple aim of improving the patient experience, improving overall population health and reducing the per capita cost of health care. Not only does analytics help operationally, but it provides a means for tracking progress. By combining health care data with clinical and socioeconomic data, agencies can demonstrate if program investments are meeting citizen needs. And as data volumes grow, analytics will become even more critical in managing costs and allocating scarce resources.
Health analytics from SAS help government agencies with:
- Population health outcomes. Analyze big data to identify health disparities, target health care programs most effectively and ensure the proper allocation of health care resources.
- All-payer claims databases (APCD). View how and where the cost and quality of health care can be improved – and scale your health data infrastructure to meet your state’s needs.
- Transparency. Explore and analyze cost, quality, access and utilization data to gain insights. Identify patterns and trends unique to your geographic area. And share that information with the public.
- Health care fraud, waste and abuse. Rapidly detect and investigate suspicious claims – and identify collusive behavior among criminal networks – before improper payments are issued.
We need to make good decisions about the community we're serving, and the best way to do that is to collect, manage and analyze data. Sarah Eberhardt-Rios Deputy Director for Program Support Services San Bernardino County
Recommended health analytics solutions from SAS
- What is a data lake and why does it matter?A data lake is a storage repository that quickly ingests large amounts of raw data in its native format. As containers for multiple collections of data in one convenient location, data lakes allow for self-service access, exploration and visualization. In turn, businesses can see and respond to new information faster.
- As AI accelerates, focus on 'road' conditionsAI technology has made huge strides in a short amount of time and is ready for broader adoption. But as organizations accelerate their AI efforts, they need to take extra care, because as any police officer will tell you, even small potholes can cause problems for vehicles traveling at high speeds.
- ModelOps: How to operationalize the model life cycleModelOps is where analytical models are cycled from the data science team to the IT production team in a regular cadence of deployment and updates. In the race to realizing value from AI models, it’s a winning ingredient that only a few companies are using.