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Now is the Time to Learn the Basics of Artificial Intelligence in Healthcare

Artificial intelligence (AI) and Machine Learning (ML) are already game-changers for early adopters in healthcare. They are and will continue to rapidly change how we care for patients. While there are some guardrails needed, AI and ML will improve patient safety and quality of care and be an indispensable tool for healthcare delivery.

Here are some of definitions:

Artificial Intelligence (AI) and Machine Learning (ML)

AI, in its simplest form, is the development of computer systems capable of performing tasks that generally require human intelligence, such as understanding natural language, recognizing patterns, and problem-solving. It’s easiest to think of this as analyzing existing data for insights. ML is a subset of AI, and involves the use of statistical techniques to enable machines to improve with experience.

Artificial Intelligence (AI) and Machine Learning (ML) are increasingly being used in the healthcare sector. Here are a few common examples of simple AI and ML in use:

• AI-powered chatbots: These are used to provide quick responses to patients' questions, reducing the burden on healthcare providers and providing real-time assistance to patients.

• Predictive analytics: Machine learning algorithms are used to predict patient outcomes, such as the likelihood of readmission or the risk of certain diseases, based on their medical history and other factors.

• Image analysis: AI and ML are used to analyze medical images like X-rays, MRIs and CT scans to detect diseases such as cancer, pneumonia, etc. at an early stage.

• Drug discovery: Machine learning algorithms can analyze vast amounts of data to identify potential drug candidates and predict their effectiveness.

• Personalized medicine: AI and ML are used to analyze a patient's unique genetic makeup and prescribe treatments that are most likely to work for them.

Natural Language Processing

Natural Language Processing (NLP) refers to the interaction between computers and humans through natural language. The ultimate goal of NLP is to read, decipher, understand, and make sense of human language in a valuable way. It is a key component of artificial intelligence, enabling computers to understand and respond to text or spoken words, just like a human would. One of its practical applications is in healthcare, where it is used to interpret clinical data, streamlining patient records and potentially helping in the diagnosis and treatment process. Some examples of NLP in healthcare:

1. Clinical Decision Support: NLP can be used to analyze clinical notes and reports, extracting relevant information and providing physicians with real-time decision support. For instance, NLP systems can alert physicians to potential drug interactions or allergies based on a patient's medical history.

2. Disease Prediction and Surveillance: NLP can analyze social media posts, web searches, and many other forms of unstructured text to predict disease outbreaks and monitor their spread. This has been used for diseases like influenza and dengue.

3. Patient-Provider Communication: NLP can be used to analyze patient-provider communication, identifying areas where communication can be improved, which can lead to better treatment adherence and patient outcomes.

4. Clinical Trial Matching: NLP can be used to match patients with appropriate clinical trials based on their medical history, potentially providing them with access to novel treatments.

Generative AI

Generative AI goes a step further. It differs from regular AI in its ability to create new, realistic data based on what it has learned. While traditional AI systems are designed to interpret and learn from data, generative AI is designed to create new data that mirrors the original data it has learned from. This ability is being leveraged in healthcare already to improve diagnoses, predict patient outcomes, and personalize treatments, ultimately leading to better patient care.

In Part II, I’ll talk more about Gen AI and how it relates to controversy and policy considerations.

AI Tools are Already Saving Lives

AI tools are already making a big difference for providers, patients, hospitals, researcher, and more. Check out a few examples:

AI algorithms can spot diseases like cancer very early. IBM's Watson for Oncology is a good example. This AI system is capable of analyzing a patient's medical information and then cross-referencing this with a vast database of clinical data and medical literature to suggest potential treatment options. Watson for Oncology is commonly used to assist in diagnosing lung, breast, and colorectal cancers, among others. This application of AI not only aids in early detection but also helps in formulating a personalized treatment plan based on a patient's unique medical history.

Google’s DeepMind developed an AI system that’s just as good as human doctors at diagnosing eye diseases.

Zebra Medical Vision, an AI that can detect lung cancer from CT scans and is used in hospitals worldwide. And let’s not forget predictive analytics – AI can predict patient risk factors and give healthcare providers a heads-up so they can intervene early.

Aidoc uses AI to analyze medical imaging to detect issues faster and more accurately.

PathAI uses AI to assist pathologists in making more accurate diagnoses.

AI in Primary Care

AI is transforming the field of advanced primary care by addressing major challenges in the primary care space.

1. Provider burnout is a widespread issue in healthcare that can compromise patient safety and quality of care. It’s leading to physicians and healthcare providers of all types leaving clinical practice and creating shortages of primary care providers across the country. By automating time-consuming administrative tasks, AI can significantly reduce the workload of healthcare providers, allowing them to focus more on patient care and less on paperwork.

2. Patient safety: In primary care, one way AI can make healthcare safer is by providing a more thorough and nuanced understanding of patient information. By interpreting patients’ language and words, it can detect subtleties and patterns that are easy to overlook or incorporate into assessments by human providers. This AI use case can lead to early detection of health issues, better communication with patients, and ultimately, improved health outcomes.

3. Reshaping communication into one that’s patient-centered is another way generative AI can improve primary care. It can create a “no wrong door” approach for patients who want to access the practice through any modality. It can also help identify when outreach should be made proactively, when a patient is at a higher risk of a negative event, and more. Timely, seamless interventions that are stress free for patients is crucial to success in advanced primary care.

4. Point of care solutions like clinical decision support, automated notetaking and assessments, and preventing missed opportunities to close gaps in care allow healthcare providers to spend time with patients and do the things the human expert can only do and should be doing more of. Listening, teaching, coaching, explaining, and supporting patients becomes the norm during a patient visit.

The potential of AI in healthcare is vast. There’s all kinds of information stored in various formats and locations, and other information not yet captured anywhere, that can be analyzed and made actionable in healthcare to improve quality, cost, safety, and the patient experience. (Sounds like value-based care concepts, right?)

The interaction between data sets, documentation, EHRs, Health Information Exchanges (HIEs), Admission/Discharge/Transfer (ADT) feeds is much more valuable and actionable than each piece of data standing alone. Humans are not well-suited to capture the opportunity. But AI is. AI can serve up what providers need, when they need it to meet patients’ needs even before they know they have them.

Here’s a recent podcast that uses a conversational style and great examples to bring more life to practical applications of AI 👇️ 

That’s it for Part I. In Part II, we’ll cover controversy, federal and state policy, and value-based care and AI. And of course, we’ll talk about some predictions.