The digital health space refers to the integration of technology and health care services to improve the overall quality of health care delivery. It encompasses a wide range of innovative and emerging technologies such as wearables, telehealth, artificial intelligence, mobile health, and electronic health records (EHRs). The digital health space offers numerous benefits such as improved patient outcomes, increased access to health care, reduced costs, and improved communication and collaboration between patients and health care providers. For example, patients can now monitor their vital signs such as blood pressure and glucose levels from home using wearable devices and share the data with their doctors in real-time. Telehealth technology allows patients to consult with their health care providers remotely without having to travel to the hospital, making health care more accessible, particularly in remote or rural areas. Artificial intelligence can be used to analyze vast amounts of patient data to identify patterns, predict outcomes, and provide personalized treatment recommendations. Overall, the digital health space is rapidly evolving, and the integration of technology in health

Friday, September 13, 2024

OpenAI and ChatGPT,DougallMD, Doximity and most Medical applications will use AI Breaking the rules of medical technology: A conversation with Dr. Eric Topol | LinkedIn

History has proven that society moves forward when innovators question and defy the accepted financial, cultural, or technological norms. This is especially true in medicine.

To encourage more technological innovation in medicine, the 10th season of the
Fixing Healthcare podcast focuses on (a) which technologies will disrupt the status quo in medical practice and (b) who will lead these much-needed changes.

1. Personalized Preventive Care

The traditional approach to preventive care uses broad, universal categories like age and sex to determine what medical screenings and preventive measures people should receive. While this approach works on a large—and mostly probabilistic—scale, it overlooks individual risk factors, such as genetic profiles and specific biomarkers. Humans can’t possibly retain and recall all the data that affects each individual patient, but they will be able to apply the totality of information using GenAI. These tools can almost instantaneously analyze vast amounts of data to identify those individuals at highest risk for serious conditions like cancer and heart disease and recommend targeted approaches to improve patient outcomes.

2. Reducing Diagnostic Errors
Traditionally, doctors rely on memory and experience to diagnose new symptoms, drawing from their medical training and past cases. However, this approach risks overlooking rare or complex conditions, and it is vulnerable to cognitive errors like confirmation and proximity bias. GenAI offers a powerful solution by integrating vast amounts of medical data—combining patient history, symptoms and real-time imaging—and correlating it with comprehensive medical literature, including obscure case reports. In the future, combining a dedicated clinician with a generative AI application will produce more accurate diagnoses than either alone.
3. Enhancing Doctor-Patient Interactions
In today’s healthcare, doctors often spend more time inputting data into electronic health records (EHRs) than engaging with patients, leading to impersonal and transactional experiences. GenAI is changing this dynamic by automatically transcribing and organizing doctor-patient conversations into accurate, high-quality EHR entries. This technology not only frees up to two hours a day for clinicians, but also improves the quality of care and helps reduce burnout.

A Fourth Opportunity: Accelerating Medical Research
In addition to Dr. Topol’s three points, I’d add a fourth: the ability of GenAI to accelerate research. In medical science today, it can take years to gather enough data to drive meaningful advances. GenAI can dramatically shorten this timeline by analyzing vast amounts of patient data quickly, leading to faster breakthroughs and more timely application of new treatments.
Clinical research conventionally starts with a question, followed by lengthy data collection and analysis. This approach is time-consuming and limited by the volume of data that researchers can analyze and manage. GenAI alters the calculus by enabling doctors to sift through enormous datasets. Today, U.S. hospitals produce up to 50 petabytes of data each year, 97% of which currently goes unused. By mining this data, GenAI will be able to uncover patterns and insights that would take years to find with traditional methods. One of the first practical applications will be identifying hospitalized patients who are likely to deteriorate over the next 24 hours, allowing clinicians to intervene earlier and potentially save lives.

Challenges and Risks
Of course, breaking the rules of medicine comes with challenges. Security is a major concern, especially when clinicians use generative AI for EHR data entry. However, the reality is that this danger already exists in the current EHRs, which can easily be hacked, but fail (alone) to offer the advantages that GenAI solutions will provide.
The evolution of medical technology always includes trade-offs. The advent of CT scans, MRIs, and laparoscopic tools, for example, led doctors to lose their skill in physical exams, but the lives saved by these innovations are undeniable. No clinician would go back to the past.
Within the next five years, Dr. Topol predicts that GenAI will become a standard tool for creating electronic health records (EHRs). Other applications will follow soon after. I’m confident that the old rule “the doctor knows best” will be replaced by a new reality—one in which the best outcomes come from a collaboration between a dedicated clinician, an empowered patient, and GenAI. Together, they will achieve more than any of the three could accomplish alone.


You will be there.
 


Breaking the rules of medical technology: A conversation with Dr. Eric Topol | LinkedIn

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