Color Health, a genetic testing company, is using OpenAI’s newest, less expensive large language model to equip doctors with pre-treatment expertise that can advance Expediting prior authorization requirements for cancer screening diagnostics and getting patients into treatment faster.
The company has also partnered with the University of California San Francisco to study how the cancer monitor works in detecting early warning signs, seemingly inappropriate warning signs, and details. Other pertinent details may be scattered deep within electronic health records and other patient information.
WHY IS IT IMPORTANT?
Although the determinants for different types of cancer are different, testing of the technology helped providers analyze patient profiles within five minutes, according to the company.
“Primary care physicians don’t tend to have the time or sometimes even the expertise to risk-adjust patients,” said Othman Laraki, co-founder and CEO of Color Health. Everyone’s Screening Guide”. Wall Street Journal reported Monday.
The UCSF Helen Diller Family Comprehensive Cancer Center is testing Color’s secondary pilot system to perform pre-treatment cancer diagnostics by comparing it with retrospective analyzes of cancer patient charts. letters.
Although that research is in the early stages, according to a Color Spokesperson, if AI can ultimately reduce wait times for cancer treatments by connecting the dots, it’s a win in care. patient care.
In Color’s announcement Monday, Laraki said the company designed the tool to address the gap in providing oncology expertise to determine pre-treatment management for patients with confirmed cancer. malignant disease.
The goal is to provide primary care physicians and other clinicians with an AI service that can determine what tests are needed to inform a patient’s cancer treatment without need to wait for the patient to see an oncologist before requesting a pre-treatment diagnosis and starting the prior authorization process, He explained.
“That way, by the time the patient sees her oncologist for the first time, she has a much better chance of being ready to start treatment and we hope to save weeks of valuable time.”
Laraki also emphasized the clinician’s role in decision-making when using this tool.
“One of the most important design decisions behind our work is that the tools are built from the ground up to be based on a human-in-the-loop model,” he said.
The company said it will share the results of the first use case tested – which focuses on automating the analysis of a person’s underlying risk factors and then applying regulatory principles. tailor their screening plan – first with individuals in their cancer program, then deliver key results. Give your doctor an opportunity to review the information.
Color estimates that doctors using cancer copilots will assist more than 200,000 patient cases in creating personalized care plans using AI by the end of the year.
BIGGER TREND
Before focusing on tools to help doctors improve cancer patient outcomes, Color launched a proactive patient-initiated testing model in 2015. These tests focus on genes that are known to increase an individual’s risk of cancer, such as BRCA1 and BRCA2 for breast, ovarian and pancreatic cancers. .
Within a few years, the unicorn, along with 23andMe and other companies, broke down barriers for patients to get cancer screening that were previously impossible by providing testing kits. Low-cost, over-the-counter at-home tests can detect major genetic risk factors.
Using AI for new decision support services that empower PCPs to get their cancer patients into treatment faster is a budding area in healthcare AI where automation Physician note-taking and reducing clinical administrative burdens have accounted for the majority of mainstream LLM uses.
However, applying machine learning to health data represents a major opportunity to improve health outcomes for individuals and communities.
Xin Wang, an assistant professor in the department of epidemiology and biostatistics at the University of Albany, said AI can be instrumental in disease management.
“By analyzing patient data over time, AI algorithms can predict each patient’s risk, recommend personalized treatment plans, and even alert providers,” he said. health care services about early signs of complications. Healthcare IT news in January.
“This proactive approach can lead to earlier interventions, better disease management and ultimately improved health outcomes.”
ON PROFILE
“We see a perfect fit between AI technology and language models,” said Brad Lightcap, OpenAI’s chief executive officer. WSJ story. “They can give clinicians more tools to understand medical records, understand data, understand labs and diagnoses.”
Andrea Fox is a senior editor at Healthcare IT News.
Email: [email protected]
Healthcare IT News is a publication of HIMSS Media.
The HIMSS AI in Healthcare Forum is scheduled to take place September 5-6 in Boston. Learn more and register.
