The health care industry has more data than it can utilize in meaningful decision-support capabilities. Whether it is the volume, the velocity, or the variety of the data, wrangling insights from this incessant stream is a never-ending and complex task.
Enter the age of AI, where an agent can synthesize insights from a much larger set of preprocessed data, maintaining privacy and security. Data engineers can streamline workflows to optimize efficiency and bring valuable data points to clinicians and care support teams to anticipate health concerns before they impact quality of life and deliver better care.
AI helps automate repetitive administrative tasks, freeing up valuable time for clinicians and supporting teams. Automation can also improve timely communication and personalized recommendations.
Choosing the right approach to AI
Once thought the art of the possible, today’s health care CIO is swimming in a sea of possibilities to increase efficiency and value from data-driven analytics. The challenge arises when deciding whether to build or buy the capabilities that enable AI-powered health analytics.
Which decision workflows are proprietary to your organization? What skillsets do you employ to develop, test and deploy AI? Is your governance structure in place to monitor bias and performance? The answers to these questions help determine your strategy for engaging with a trusted technology partner.
SAS models for health care
In response to this market need, SAS now offers industry-tuned packaged offerings that complement our trusted health care solutions portfolio. Ready-made AI models are portable, industry-focused offerings designed to tackle business challenges with precision and efficiency. Models are built from decades of expertise in applying scalable and trustworthy AI to real-world use cases.
- Improve adherence to medication therapies: Utilize a patented machine learning approach to identify patients at risk of being non-adherent when starting new medication therapy.
Since it is estimated that only half of patients take their medications as prescribed, the medication adherence risk model helps health care organizations improve patient outcomes and reduce financial losses through easily understood visuals for timely personalized interventions.
- Enhance analysis of unstructured claims data: Extract and surface key information into summarized data for medical reviewers, allowing them to evaluate records more effectively and efficiently.
The SAS® Document Analysis is an intelligent document processing (IDP) pipeline focused on extracting contextual information from scanned document images and generating structured data assets. The AI model converts scanned images into consumable data optimized for downstream reporting and analytics, already showing a 400% efficiency gain over manual review in one of the largest US health insurers.
Trust industry models from SAS
With SAS’ health care solutions and models, you can break down common barriers to AI adoption. Confidently deploy packaged models in SAS® Health on SAS® Viya®, unlocking insights faster and with greater productivity. From operational and financial analytics to clinical risk predictions, AI is transforming the health industry to benefit patients and clinicians.
