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Is your doctor providing the right treatment?This Healthcare AI Tool Can Help

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In the modern world, how can medical professionals maintain awareness of the correct procedures and treatment of their patients’ diseases ? While many people often rely on experience, there is another way to save lives. The trick is that it relies heavily on the power of artificial intelligence (AI).

New York-based healthcare startup H1 today released a new update to its HCP Universe platform to infuse healthcare AI into medical intelligence. The HCP Universe platform is currently used by the medical affairs teams of life sciences companies to ensure physicians are aware of and use the latest science and medicine.

With HCP Universe, the medical affairs team can target the right physicians, educating them about the latest and greatest medical care and which patients should receive this treatment.

H1 co-founder and CEO Ariel Katz told VentureBeat, “Our mission with this product is to ensure that the latest medicines are used in the right patients so that patients get the right treat.”


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Using Healthcare AI to Increase Adoption of New Therapies

For H1, the use of healthcare AI is to provide intelligence to help medical affairs staff proactively find the right doctor.

“What we’ve done in the past is give users a platform to search and find doctors in a particular area or treatment, but that’s not the right way to do it,” Katz explained road.

Instead, identifying and reaching the right physician in a given field of medicine is key. Katz said the updated HCP Universe platform has AI-driven capabilities that can highlight and help drive the use of evidence-based medicine around the world.

The hardest part of making the data medically useful, Katz explained, is that the transaction is linking the data together and putting it into the right taxonomy. For example, if a user searches for “obesity,” any number of medical professionals may be involved, including endocrinologists, nutritionists, and even psychiatrists.

“If a patient searches for obesity, they don’t just want to find a doctor who specializes in diet, they want to find a doctor that’s relevant to that person’s needs,” Katz said . “So it correlates data together, and the machine learning library learns from user behavior to improve correlation.”

Why Graph Databases Are Not Enough

Relational The idea of ​​connecting together is a common concept in graph databases. In fact, the HCP Universe platform is built with a graph database. But on its own, Katz said, his company found that it wasn’t accurate enough when it came to making important health care treatment decisions.

“If the recommendation engine is 80 percent accurate for restaurants where you only want bagels, you’re probably going to be happy,” Katz said. “If it’s 80% accurate and you’re trying to find a doctor and you should be diagnosed with cancer, that’s not going to work.”

Using a machine learning library , H1 is able to learn from data and can relate complex relationships that are not recognized by graph databases themselves. H1 uses AWS machine learning tools, including Sagemaker, to help drive its healthcare AI efforts, Katz said.

When H1 launched, Katz noted that the biggest problem it had to solve was aggregating and collecting sources of information about medical professionals.

“We started by solving a data problem and making sure all the information was accurate, credible and reliable,” Katz said. “How does the next generation of products we’re launching actually make it smart and turn data into insights?”

Looking ahead, H1 will train its AI to be Medical professionals provide clinical quality scores. For example, if a user wants to determine the best medical professional to treat bladder cancer, the system will help determine the best doctor based on a variety of factors, including patient surveys and related factors such as hospital readmission rates.

“This information about who is a better doctor and what is a better hospital will change the experience for many people involved in the healthcare ecosystem,” Katz said.

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