When subject matter experts help train AI algorithms to detect and categorize certain data patterns that reflect how language is actually used in their part of the health industry, this natural language processing (NLP) enables the algorithm to isolate meaningful data. This helps decision makers find the information they need to make informed care or business decisions quickly.

Healthcare payers

For healthcare payers, this NLP capability can take the form of a virtual agent using conversational AI to help connect health plan members with personalized answers at scale. View the resource.

Government health and human service professionals

For government health and human service professionals, a case worker can use AI solutions to quickly mine case notes for key concepts and concerns to support an individual's care.

Clinical operations and data managers

Clinical operations and data managers executing clinical trials can use AI functionality to accelerate searches and validation of medical coding, which can help reduce the cycle time to start, amend, and manage clinical studies.

Inundated with massive volumes of health data and growing responsibilities, clinicians are struggling to find the time to keep up with the latest medical evidence and still provide patient-centered care. By applying machine learning technologies to the latest biomedical data and electronic health records, healthcare professionals can quickly mine accurate, relevant, evidence-based information that has been curated by medical professionals. Some AI-powered clinical decision support tools feature natural language processing and domain-based training – enabling users to type questions as if they were asking a medical colleague in everyday conversation and receive fast, reliable answers.

By supplementing labor-intensive image scanning and case triage, AI solutions used in medical imaging enable cardiologists and radiologists by surfacing relevant insights that can help them identify critical cases first, make more accurate diagnoses and potentially avoid errors while taking advantage of the breadth and complexity of electronic health records. A typical clinical study can produce vast datasets containing thousands of images, leading to incredible amounts of data in need of review. Using AI algorithms, studies from across the healthcare industry can be analyzed for patterns and hidden relationships, which can help imaging professionals find critical information fast.

The healthcare IT industry has a responsibility to create systems that help ensure fairness and equality in data science and clinical studies, which leads to optimal health outcomes for everyone. AI and machine learning algorithms can be trained to help reduce or eliminate bias by promoting data diversity and transparency to help address health inequities. For example, minimizing bias in healthcare research can help combat health outcome disparities based on gender, race, ethnicity or income level.

There are challenges to adopting AI in healthcare, including having to meet regulatory requirements and overcoming trust issues with machine learning results. Despite these challenges, bringing AI and machine learning to the healthcare industry has brought numerous benefits to healthcare organizations and those they serve alike. AI improves operations by streamlining workflows and helping with mundane tasks, as well as by helping users to quickly find answers to their pressing questions, leading to better experiences for patients, members, citizens and consumers.