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The Role of Machine Learning in Medical Decision-Making

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How introducing cutting-edge technologies such as Artificial Intelligence (AI) and Machine Learning (ML) has the potential to amplify the functionalities of ePrescribing systems.Read More...
HealthTech news roundup
How Zigzagging Is Undermining US Healthcare

U.S. healthcare is in a financial crisis, and patients feel it acutely. Nearly 90 million Americans are a part of Medicaid, the public health insurance program for low-income groups. Medical bills are a leading cause of bankruptcy, as 100 million Americans remain saddled with medical debt.

AI-Driven Medtech Begins To Deliver On Its Potential

Today, healthcare professionals use AI-enabled devices and platforms every day, from performing robot-assisted joint replacements to analyzing X-rays and flagging anomalies for further review by a human radiologist.

U.S. Military Embraces Telecritical Care Technology for Service Members and Veterans

The program equips intensive care unit rooms with telehealth technologies, allowing care providers to remotely monitor patients in real-time and provide remote support to onsite medical staff.

How Digitization And Partnership Can Address Health Inequities

By leveraging the collective power of stakeholders and governments, member states can advance health equity worldwide, says Dr. Hans Kluge, regional director for Europe at the World Health Organization.

1Beyond The Hype
Defining AI, ML, and DL

Understanding the foundations of AI and its subfields, Machine Learning (ML) and Deep Learning (DL), is crucial for appreciating their impact on medical decision-making.

ML involves the development of algorithms that continuously improve through experience by learning from data. These ML models can get conditioned to recognize patterns, make predictions, and adapt their behavior as they process new information. In telemedicine, ML algorithms play a crucial role in analyzing patient data, medical images, and other pertinent information. They assist healthcare providers in diagnosing diseases, tailoring treatments to individual patients, and closely monitoring patient health.

On the other hand, DL represents a more advanced approach. It leverages the power of artificial neural networks to model and solve problems. These networks are similar to the intricate structure and functionality of the human brain. With these neural networks, deep learning models can effectively process and interpret data hierarchically. DL excels at handling vast volumes of data, including unstructured data like images and text, which makes it particularly well-suited for tasks such as medical image analysis, natural language processing, and advanced predictive analytics in the healthcare domain.

2Leveraging AI for Stroke Detection:
A Case in Point
In addition to these specific examples, AI-driven methods in stroke detection can utilize real-time data from electronic health records (EHRs) and other sources. By integrating with EHR systems, AI algorithms can access a patient's complete medical history, including laboratory results, medications, and previous diagnoses. This comprehensive information allows AI systems to provide more accurate assessments and predictions regarding stroke risk, prognosis, and treatment options.

Stroke detection is where AI-driven methods have shown great promise in healthcare. By utilizing various algorithms and machine learning techniques, AI systems can analyze patient data and assist healthcare professionals in accurately detecting and diagnosing strokes.

Key tools and frameworks that are significant in stroke detection
  • NIHSS (National Institutes of Health Stroke Scale):

    The NIHSS is a standardized assessment tool used to evaluate the severity of stroke symptoms. This automated approach can save time for clinicians and provide consistent and objective evaluations.

  • CHA2DS2-VASc:

    This scoring system assesses the risk of stroke in patients with atrial fibrillation (a common heart rhythm disorder). AI algorithms can analyze patient data, including medical history, age, sex, and comorbidities, and calculate the CHA2DS2-VASc score. By automating this process, AI can help identify individuals at a higher risk of stroke, enabling healthcare providers to intervene and provide appropriate treatments.

  • ABCD2:

    ABCD2 is a scoring system used to assess the risk of stroke in individuals who have experienced a transient ischemic attack (TIA), often considered a warning sign of an impending stroke. Helping healthcare professionals determine the urgency of follow-up care and interventions.

3EHR Integration:
Key to Accurate Stroke Detection
Tackling Alert FatigueThe refined algorithms would strike a balance between sensitivity and specificity, reducing the occurrence of unnecessary alerts while ensuring crucial notifications are promptly conveyed to healthcare professionals.

The integration of AI-driven tools for stroke detection has been a promising development. However, their implementation in clinical settings has encountered a significant obstacle in the form of alert fatigue. This phenomenon occurs when healthcare providers get bombarded with numerous notifications and alerts, a considerable portion of which may be false positives or hold minimal clinical relevance. The consequence is the gradual desensitization and diminished responsiveness of healthcare professionals to genuinely critical notifications.

To tackle this issue, we must refine the algorithms powering these tools, focusing on minimizing false positives and prioritizing alerts based on their clinical significance.

4Machine Learning for Telemedicine-
A Winning Combination?
ML algorithms can play a pivotal role in achieving this by assisting physicians in developing personalized treatment plans. By analyzing a patient's medical history, genetic factors, and other relevant data, ML algorithms can help identify the most effective treatment options, minimize adverse effects, and optimize patient outcomes.

Telemedicine relies heavily on remote patient monitoring, which is greatly enhanced by ML algorithms.

ML algorithms can analyze data from wearable devices, mobile apps, and other monitoring tools, allowing them to detect changes in patient's health, predict potential complications, and deliver alerts to the appropriate healthcare professionals. This proactive approach, known as preventative care, enables timely interventions, helps prevent complications, and significantly improves overall patient satisfaction.

Real-time decision support powered by ML algorithms allows for appropriate interventions or adjustments to treatment plans based on genetic factors.

The amalgamation of telemedicine, remote patient monitoring, and ML algorithms can reshape healthcare delivery and empower physicians with the tools they need to deliver exceptional care in a rapidly evolving healthcare landscape.

5The Road Ahead

AI's rapid growth has transformed patient care, introducing innovations like remote monitoring and personalized treatments.

With machine learning algorithms set to play a pivotal role in enhancing medical decision-making. However, alongside these exciting developments, we must address the challenges and ethical considerations arising from integrating AI into healthcare. Responsible practices and robust frameworks that uphold patient safety and privacy are essential before fully embracing AI-driven medical decision-making tools.

By navigating these complexities and prioritizing improved patient care and outcomes, we can pave the way for a future where AI revolutionizes healthcare, improves patient outcomes, sets new standards for quality care, and works in favor of both patients and providers.

That’s a wrap
Hope you enjoyed it!

Live from #HIMSS23 in Chicago: Ionixx's CMIO, Dr. Maanas Samant, MD, is connecting with healthcare innovators from around the world!

Live from #HIMSS23 in Chicago: Ionixx's CMIO, Dr. Maanas Samant, MD, is connecting with healthcare innovators from around the world!