The role of artificial intelligence in healthcare continues to evolve as does the definition of what it is and is not. This state of flux has contributed to slower than desired adoption, unmet expectations, and gaps between deployment and value realization. If clinical artificial intelligence more specifically is to transform patient care, it must deliver insights that are unique, individualized, can be tied to community and align with existing workflows. Join Jvion, a market leader in clinical AI, along with leadership from Microsoft, for a one-hour webinar that will provide clarity and guidance to aid in addressing patient risk and improving population health across communities.

With late 2019 marking the 20th anniversary of the landmark report on medical errors "To Err is Human," now is time for a renewed focus on novel ways to improve patient safety. The report launched the modern patient safety movement by shedding some much-needed light on the prevalence of medical errors and preventable deaths in the U.S., spawning many improvements to patient safety over the subsequent two decades. But before the healthcare industry gets too self-congratulatory, we could use a quick reality check. Patient safety remains a persistent global issue that exacts a huge human cost, as well as a financial one, as a recent report from the World Health Organization (WHO) starkly illustrates. While it is estimated that there is a one in 3 million risk of dying while travelling by airplane, the risk of patient death while receiving healthcare due to a preventable medical accident is estimated to be one in 300, according to the WHO.

Medial EarlySign are a leader in machine-learning based solutions in the early detection and prevention of high burden diseases using AI technology to detect the early warning signals and health risks associated with major diseases. The peer-reviewed retrospective data study, Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention, published in the Journal of the American College of Cardiology: "Cardiovascular Interventions, evaluated the ability of machine learning models to assess risk for patients who underwent percutaneous coronary intervention (PCI) inside the hospital and following their discharge. The analysed algorithm was developed by Medial EarlySign data scientists to identify patients at highest risk of complications and hospital readmission after undergoing PCI, one of the most frequently performed procedures in U.S. hospitals. "Contemporary risk models have traditionally had little success in identifying patients' post-PCI risks for complications, in-patient mortality, and hospital readmission. This study shows that machine learning tools may enable cardiology care teams to identify patients who may be on high-risk trajectories," said Rajiv Gulati, MD, Ph.D., Interventional Cardiologist at Mayo Clinic.

Hospitals continue to grapple with clostridium difficile infections, caused by bacteria that are resistant to many common antibiotics and that kill about 30,000 Americans each year. However, machine learning can help predict patient risk in developing C. difficile much earlier than current methods of diagnosis. Using electronic health records for nearly 257,000 patients, researchers from Massachusetts General Hospital, MIT and Michigan Medicine have built hospital-tailored machine learning models that they contend are an improvement over a "one-size-fits-all" approach that ignores important factors specific to medical facilities. "When data are simply pooled into a one-size-fits-all model, institutional differences in patient populations, hospital layouts, testing and treatment protocols, or even in the way staff interact with the EHR can lead to differences in the underlying data distributions and ultimately to poor performance of such a model," says Jenna Wiens, assistant professor of computer science and engineering at U-M. "To mitigate these issues, we take a hospital-specific approach, training a model tailored to each institution." De-identified EHR data from 191,014 adult admissions to Michigan Medicine and 65,718 adult admissions to MGH were analyzed using separate machine learning algorithms tailored to each healthcare institution with different types of variables. "These hospital-specific models allow for earlier and more accurate identification of high-risk patients and better targeting of infection prevention strategies," conclude the authors in an article appearing in the April issue of the journal Infection Control and Hospital Epidemiology.

Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer. Electronic Health Record (EHRs) systems are now used in 80% of doctors offices and contain a rich source of patient data available to innovate and improve healthcare. A team at New York University's Courant Institute of Mathematical Sciences developed algorithms and a system to extract EHR data to faster diagnose patients and provide a thorough understanding of the patient's health.