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A Guide to the Successful Implementation of AI in Healthcare

Artificial Intelligence (AI) has tremendous power to revolutionize healthcare delivery when approached responsibly, thoughtfully, and strategically. This article serves as a guide to understanding the foundational principles, practical applications, and essential considerations required to pave the way for successful AI implementation.

Understanding AI at a High Level

The goal of AI is the creation of a computer system capable of performing tasks that traditionally require human intelligence. These tasks include learning, reasoning, problem-solving, perception, and language understanding. While we may be a far distance from this goal, we can already realize a lot of value from the fruits of the pursuit.  

AI encompasses a spectrum of technologies, with Machine Learning (ML) a prominent subset. ML has revolutionized how we approach complex tasks, enabling computers to learn patterns and rules from data and make informed decisions without explicit programming. It consists of many ‘parameters,’ as the model’s foundation, that get adjusted as it is trained. 

Many conversations surrounding AI today refer to generative AI, or GenAI, a technology that has gained significant attention in recent years. A core element of generative AI is using Large Language Models (LLMs), a complex statistical method that processes natural language inputs and attempts to predict the next best word to respond to a prompt based on the data it’s been trained on. It then predicts the next word, and so on, until its answer or response is complete.

While the ability of LLMs to understand language and generate answers relevant to the conversation’s context is remarkable, they still have many limitations. They are prone to “hallucinations,” where they generate factually incorrect responses that may seem very reliable. They are also limited to the information provided to them at the time they were trained. For example, when ChatGPT launched in November 2022, it could only answer questions based on data up to September 2021 because of training limitations.

Generative AI has also raised concerns as it is known to perpetuate societal biases. Generative AI systems make determinations based on training data. Because of the vast information available on the internet, for example, the training data that many AI models have learned from reflect human biases within society, including current and historical social inequality. Furthermore, there are growing concerns over the “black box” nature of some generative AI systems, which stems from the inability to see how those AI systems make their decisions. 

Despite these limitations, AI has great potential and is already transforming many industries, including healthcare. As AI technology evolves and more work is done to determine how to overcome current limitations, we will continue seeing more innovative applications.

The Power of Artificial Intelligence in Healthcare

In healthcare, AI is poised to make substantial advancements that can help solve longstanding challenges and address some of the biggest drivers of waste and inefficiency. In fact, the technology is already making significant strides in revolutionizing healthcare delivery to enhance patient outcomes, streamline processes, and optimize healthcare operations in applications including:

Diagnosis and Treatment Planning: AI’s ability to analyze vast amounts of data, including electronic patient records and diagnostic images such as X-rays and MRIs, can be used to detect abnormalities and assist healthcare professionals in sifting through the tsunami of data. Furthermore, personalized medicine, driven by AI, tailors treatment plans based on patient data, genetic factors, and medical history, improving the precision and effectiveness of medical interventions.

Predictive Analytics: AI technologies can help predict high-risk patients by analyzing demographic, medical history, and social determinants of health data (SDoH).AI could potentially predict disease outbreaks, patient deterioration, and resource utilization trends. Early identification enables proactive interventions, leading to better outcomes and optimized resource allocation.

Patient Engagement and Adherence: Successful healthcare outcomes are enhanced when patients proactively participate in their care. Healthcare providers use clinical expertise to develop care plans to improve chronic or acute conditions. However, that is only effective if the patient makes the necessary behavioral adjustments, e.g., complying with a treatment plan, scheduling essential follow-up visits, filling prescriptions, and more. AI technologies are proving effective in personalizing and contextualizing care—fostering active patient participation through targeted content, messaging alerts, and nuanced interventions—addressing the challenge of non-compliance, and enhancing the overall healthcare experience.

Reducing Clinical Data Complexities: The complexity of healthcare data is a longstanding challenge hampering the incredible potential of its effective use to support new strategic initiatives. AI presents an opportunity to cut through this complexity and make health information more accessible. By processing highly specific terminology and staying current with the ever-expanding and changing field, AI can help facilitate better information management and decision-making. 

Administrative Efficiency: AI has the potential to enhance many administrative workflows in healthcare. AI is already streamlining and enhancing administrative efficiency and reducing burdensome manual processes for tasks, including prior authorizations, billing, and claims processing, allowing healthcare providers to focus more on patient care.

Resource Allocation and Coordination: Optimizing resource allocation is a significant benefit of AI in healthcare. By personalizing patient care, automating scheduling, and enhancing communication, AI helps healthcare organizations operate more efficiently. Improved coordination contributes to a smoother healthcare experience for both providers and patients.

While AI will continue to have a significant impact in helping to address numerous challenges that the healthcare industry currently faces, it’s essential to recognize that AI is not a panacea. There will always be a need for skills beyond the capabilities of AI, such as abstract problem-solving and understanding context. Treating patients isn’t about just diagnosing a problem and giving them medication. It is about working with them to understand their circumstances and what they need to improve their lives. AI should complement human expertise, fostering a collaborative approach to healthcare that integrates technological advancements with compassionate patient care.

Visit our AI in Healthcare 101 landing page to learn more.

Author Bio:

Sam Schifman is the Principal Architect for Innovation at Availity. He has been working as a software developer and architect for over 25 years, in a number of different industries. These include Finance, HR, Education, back to Finance, and some years doing No-code Development Platforms. During that time, he has worked at companies like ADP, Pearson, Sungard, and some less recognizable names.

A little over four years ago he joined Diameter Health which was acquired by Availity in 2023. At Availity, he is helping design next-generation architecture, raise awareness about product capabilities, support sales, and engage in research into future initiatives. In this last role, he has developed a special interest in NLP and healthcare. He is currently advancing standards for AI / NLP outputs as well as investigating how Availity Fusion and NLP can complement each other. Also, he is interested in the impact of Home Hospital and Wearables on the future of healthcare. All of this leads to his passion for the Internet of Health: building an effective, responsible partnership between patients, providers, payers, and loved ones.