Empowering Physicians via Advanced AI and ML Algorithms

Empowering Physicians via Advanced AI and ML Algorithms

  • industry Highlights
  • 1 month ago

The rapid acceleration of AI and ML algorithms could be the right force for clinical decision support tools (CDST) to become of better use for physicians.

New world reality demands physicians, patients, and technology to be connected. It became inevitable for medical software technology to be accurately blended into everyday healthcare delivery and to better aid physicians. 

Clinical decision support tools can provide vast functionalities including diagnostic, alarm systems, disease management, prescriptions, drug control, and much more.

Main benefits:

  • enhance patient outcomes
  • improve diagnostics
  • lead to higher-quality health care
  • implement evidence-based decision support
  • avoid errors and adverse event
  • cost-benefit

A healthcare CDST is a technology designed to provide clinical support to physicians backed by evidence-based data via sophisticated AI and ML algorithms. 

The idea of interaction with such devices is to be at the point of care and to assist analyze and reach a diagnosis based on patient data.

In a recent article, Mayo Clinic announced the launch of a new platform designed to deliver advanced, AI-powered clinical decision support through remote monitoring. 

"Undiagnosed heart disease affects millions of Americans and people across the globe," said Dr. Paul Friedman, chair of the Department of Cardiovascular Medicine at Mayo Clinic.

The key is to detect a disease before symptoms occur and prevent acute events from happening. 

Mayo Clinic has set up these goals to achieve:

  1. Improve early detection and treatment of heart disease by designing new neural-network algorithms based on troves of heart health data in Mayo's Clinical Data Analytics Platform, including raw ECG signals.
  2. Provide the platform which collects, orchestrates, and curates data from any device via ML algorithms for integrating AI-powered diagnostic insights.

Sqilline is also on this trend.

We developed a Danny Cardio application tool to improve the identification of FH (Familial Hypercholesterolemia) in patients with Acute Coronary Syndrome (ACS) during hospitalization. 

Our key objective is to merge all available patient data from multiple departments in one place so that patients pathway to diagnosis is accelerated and physicians can approve/reject FH diagnosis during patients' hospitalization or limited to 48-72 h.

Early diagnosis and treatment are crucial for improving outcomes in FH patients.

More benefits of Danny Cardio:

  • support physicians to use the full scope available data merged from different department
  • improve communication between different working shift
  • direct link to FH Dutch Score while patients are hospitalized
  • smart notifications to physicians

In our recent observational study, published in Springers' Advances in Therapy we pointed out the urging need for supporting tools to unburden clinicians in the emergency department of interventional cardiology clinics. Such a tool may remind if a patient carries a high probability of FH diagnosis. In-hospital screening of FH would allow for rapid and effective lipid management and prevent recurrent/critical events.  

The continuous advancement in AI evolves the complexity of clinical decision support tools. But the physicians' involvement in the development process should be compulsory. The evaluation and final acceptance from specialists are important. After all, they are the main user of these devices.

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