17 Feb 2023

The use of AI in Population Health as authored by….

Artificial intelligence (AI) is revolutionizing many industries, including healthcare. One area where AI is making a significant impact is population health. Population health focuses on improving the health outcomes of entire populations, rather than just individual patients. AI can assist in this mission by providing valuable insights into healthcare trends, identifying at-risk populations, and assisting with resource allocation.

One of the primary ways AI is being used in population health is through predictive analytics. Using machine learning algorithms, AI can analyze vast amounts of health data to identify patterns and make predictions about future health outcomes. For example, AI can analyze health records and social determinants of health to predict which populations are at high risk for chronic diseases like diabetes and heart disease. This information can then be used to develop targeted interventions to prevent these conditions from developing.

AI can also help with resource allocation by identifying areas with the greatest need for healthcare services. For example, if an AI algorithm identifies that a particular neighborhood has a high prevalence of a specific disease, healthcare providers can allocate resources to that area to ensure that patients receive timely and appropriate care.

Another area where AI is making a significant impact is in improving clinical decision-making. AI algorithms can analyze medical images, lab results, and other clinical data to assist healthcare providers in making accurate diagnoses and developing effective treatment plans. This can lead to better health outcomes for patients and can help reduce healthcare costs by minimizing unnecessary tests and treatments.

However, it is essential to note that AI is not a replacement for healthcare providers. AI algorithms are only as good as the data they are trained on, and they can make mistakes or provide biased recommendations if the data used to train them is not representative of the population they are serving. Therefore, it is crucial to ensure that AI systems are regularly audited and monitored to ensure that they are providing accurate and unbiased recommendations.

In conclusion, the use of AI in population health has the potential to improve health outcomes, reduce healthcare costs, and help healthcare providers make more informed decisions. However, it is important to use AI responsibly and to ensure that it is complementing, rather than replacing, healthcare providers’ expertise. By doing so, we can harness the power of AI to improve the health of entire populations.

As written by ChatGPT
So how’d it do?
Well, the use of AI/ML for predictive analytics is one of the main areas of use.  As far as using it to predict geographic areas with issues, we are already seeing that without the use of AI, so I’m not sure how much more insight it will provide.
Clinical decision-making is one that everyone hyped for quite some time but given the issues uncovered and by the way pointed out by ChatGPT on the data (or lack thereof)the model is trained on, we have a ways to go. But as the data sets get larger and the requirements for decision-making include these diverse data sets, the ability to sift through them will require the augmentation of a clinician with AI of some sort.
Overall ChatGPT hit the obvious high points, but it missed areas such as impacts on the individual by identifying persona, behavior change, messaging, interventions, nudges, etc, where I believe there will be interventions better targeted to the person, such as those used in retail and other places that get us to buy certain products or select movies we may like. It also did not mention the more mundane clerical tasks, such as document conversion, where we have seen great efficiency improvements applied to faxes for referral scheduling, or perhaps applying AI to authorizations, and other similar areas. Another area demonstrated about 4 years ago at HIMSS was the use of NLP to transcribe in real-time a patient visit with a physician, including loading the documentation into the chart, creating orders, and writing a prescription.  Another area where efficiency can be drastically improved.
It’s not a bad start but more is needed.

 

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