Artificial intelligence (AI) has become increasingly integrated into healthcare settings, but the shortcomings of the technology mean the potential for errors is more consequential than ever.

One study suggests that the AI healthcare market hit $6.6 billion in 2021. Soon, the University of California, San Francisco (UCSF) Division of Clinical Informatics and Digital Transformation and UCSF Health’s new, real-time and continuous AI monitoring tool—dubbed the Impact Monitoring Platform for AI in Clinical Care (IMPACC)—could help clinicians understand the efficacy, safety, and equity of this new technology in relation to their patients.

IMPACC is designed to report if AI is doing what it is designed to do, and flag any AI-powered tech that could be unsafe or widen health disparities. With the report results, healthcare leaders can then decide if they want to keep using a certain tool or phase it out altogether, Julia Adler-Milstein, professor of medicine and chief of the UCSF Division of Clinical Informatics and Digital Transformation, told Healthcare Brew.

Keep reading here.—CM

Are there new allergy profiles and test codes that HCPs should be utilizing? Yep. Do all HCPs know about these new profiles and test codes? Nope, not really. This could lead to ordering out-of-date codes (i.e., not the most comprehensive profile).

Fortunately, Thermo Fisher Scientific can help. This super-helpful Lab Ordering Guide has tons of info on the most up-to-date test codes that HCPs need to meet their patients’ needs and order accurate allergy profiles.

So, why are the new test codes so important? It’s crucial for HCPs to order profiles with these new test codes because they offer , which can better support patients. And that’s always the end goal.

Help improve patient care with this Lab Ordering Guide.

Digital health tools could help patients trying to lower their risk for heart disease, a recent study published in the Journal of the American Heart Association found.

Researchers analyzed more than 100,000 people who used digital therapeutic company Hello Heart’s mobile app and heart health monitor over a span of five years and saw reductions in blood pressure (BP), total cholesterol, low-density lipoprotein cholesterol (LDLC), and weight—all major risk factors for heart disease, according to the study.

These researchers included Edo Paz—lead author of the study, Hello Heart’s SVP of medical affairs, and a cardiologist at White Plains Hospital in New York—and clinical leaders from Hello Heart, CVS Health, Aetna, Johns Hopkins University School of Medicine, and the American Heart Association’s Center for Health Technology and Innovation.

Heart disease is the leading cause of death across genders and most racial and ethnic groups, according to the CDC. It’s also expensive for the healthcare industry: In one year between 2018 and 2019, for example, heart disease cost an estimated $239 billion in healthcare services, medicines, and lost productivity, according to one study cited by the CDC.

How did they do it?

The Hello Heart heart mobile app uses artificial intelligence and behavioral science to provide personalized coaching to participants that aligns with clinical guidelines. For the research, the app received data from multiple sources: Through the connected heart monitor, it recorded a user’s BP and heart rate. It also integrated data from medical records and apps like fitness trackers, and depended on users to manually input information about their medications, according to the study.

Keep reading here.—NB

Artificial intelligence (AI) helped UC San Diego Health reduce its sepsis mortality rate by 17%, according to a study the health system published in January.

Sepsis is a leading cause of death in US hospitals—at least 1.7 million people develop the condition annually and of that group, about 350,000 patients die in the hospital or are discharged to hospice, CDC data shows. Sepsis also costs the US healthcare system roughly $62 billion annually, according to an article published in journal Critical Care Medicine.

Clinicians at UC San Diego Health developed an AI model called Composer that can detect sepsis in patients roughly four to six hours before a clinician would be able to diagnose it, Gabriel Wardi, the medical director of hospital sepsis at the health system, told Healthcare Brew.

“If you can predict when sepsis is going to happen ahead of time, that might be able to improve patient care and thereby have better outcomes for patients,” Wardi said.

Sepsis treatments, like antibiotics, “have greater benefits” when given earlier, the study authors noted.

Keep reading here.—MA

Interpretation is everything. Some HCPs don't order specific IgE blood testing because they are not fully comfortable interpreting the results. Fortunately, Thermo Fisher Scientific is here to help. These super-comprehensive ImmunoCAP Testing Interpretation Guides can aid in your diagnosis and patient management plans. See for yourself.