Multisource data aggregation means collecting information from many places like electronic health records (EHRs), health information exchanges (HIEs), labs, pharmacy records, insurance claims, and other systems. This data helps healthcare providers and AI systems get a better view of patient health.<\/p>\n
<\/p>\n
One example of this is Availity Fusion\u2122, a platform that organizes and standardizes data into useful forms. It follows healthcare quality standards like HEDIS\u00ae (Health Effectiveness Data and Information Set) and data sharing rules such as FHIR (Fast Healthcare Interoperability Resources). This process, called data normalization or \u201cupcycling,\u201d changes raw, mixed-up data into helpful information. This helps healthcare groups find and fix care gaps more accurately.<\/p>\n
<\/p>\n
In the U.S., most health plans\u2014over 90%\u2014use HEDIS\u00ae measures to report quality. These reports affect billions of dollars in payments and bonuses. Closing care gaps on time is very important for following rules and doing well financially. For example, a national health plan saw a 20% rise in meeting HEDIS targets after it used normalized data. This helped them find patients who needed screenings like mammograms and colonoscopies.<\/p>\n
<\/p>\n
Care gaps happen when patients miss screenings, vaccines, or doctor visits for long-term conditions. AI-powered healthcare tools examine data from many sources to find these gaps.<\/p>\n
<\/p>\n
For example, Essentia Health uses the Healthy Planet platform. It combines insurance claims with Epic electronic health records every night. This creates real-time reports that help doctors see which patients are at high risk. They can then focus on patients who need cancer screenings, vaccines, or care for chronic diseases. Debbie Welle-Powell, Chief Population Health Officer at Essentia Health, said mixing different data types helped teams spot patients who need care and organize outreach better.<\/p>\n
<\/p>\n
AI programs for population health also check social factors like problems with transportation, food, or housing. They suggest local help or programs to support patients. For instance, the San Francisco Department of Public Health used AI-supported care coordination to help homeless people by combining medical and social services.<\/p>\n
<\/p>\n
During big preventive care efforts, AI systems send messages by phone, text, email, or patient portals. If patients don\u2019t answer at first, the system tries other ways to get in touch. This keeps patients involved and lowers care gaps over time.<\/p>\n
<\/p>\n
Repeating tests unnecessarily wastes money and causes extra work for patients and healthcare workers. This happens when doctors don\u2019t have access to all the patient\u2019s records.<\/p>\n
<\/p>\n
AI helps by gathering and organizing clinical data from many sources. With a full patient picture, doctors avoid ordering repeat tests. For example, Availity Fusion\u2019s system helped identify 110% more patients who completed mammograms by combining data from different places, stopping extra screening orders.<\/p>\n
<\/p>\n
Onduo, a virtual care platform that focuses on diabetes, uses data from devices like glucose monitors, labs, medicines, and doctor notes to improve care. Its AI figures out risk levels and important clinical signals to guide personalized care and prevent repeated tests. This helped reduce average A1C levels by 1.6% in four months for adults with type 2 diabetes, and by 2.4% for high-risk patients.<\/p>\n
<\/p>\n
Linking pharmacy and clinical data also helps manage medicines better. AI finds when patients don\u2019t take medicines as prescribed, have side effects, or face cost issues. The system promotes medication reviews that improve treatment and avoid extra tests or doctor visits.<\/p>\n
<\/p>\n
Health plans and providers join quality programs like HEDIS\u00ae and Medicare Star Ratings, which reward good preventive care. AI-driven multisource data aggregation makes data more complete and accurate. This helps raise performance scores.<\/p>\n
<\/p>\n
The Centers for Medicare & Medicaid Services (CMS) now requires digital quality measures (dQMs) reported with Electronic Clinical Data Systems (ECDS). These use real-time, organized data rather than only past claims. This speeds up finding and fixing care gaps, lowers paperwork, and improves accuracy.<\/p>\n
<\/p>\n
If health plans drop their CMS Star Ratings, they face big financial losses. One drop could mean losing up to $800 million. AI tools with predictive analytics and natural language processing (NLP) help health plans find members at risk, predict care gaps, and send personalized messages. This improves care quality and patient satisfaction, measured by CAHPS (Consumer Assessment of Healthcare Providers and Systems).<\/p>\n
<\/p>\n
Dr. Adnan Masood, PhD, says AI is now a must-have in the Medicare market to earn bonuses and avoid losing money. New AI technology called agentic AI starts to automate quality improvement from start to finish. This cuts down manual work and helps make timely care changes.<\/p>\n
\n<\/p>\n