The Role of AI in Patient Care

In recent years, advances in artificial intelligence (AI) are transforming virtually every industry, including healthcare. Though it’s not a new concept, new kinds of AI have become increasingly accessible to the public. Commercial generative AI (Gen AI) platforms like ChatGPT and Claude for language generation have increased the number of AI users by millions. These tools offer the unprecedented ability to generate realistic text and deliver analyses in mere moments, enabling organizations to make more accurate data-driven decisions faster and more efficiently than ever before. The promise of AI lies in automating mundane and manual processes while quickly adapting to changing conditions and circumstances. 

In healthcare, having the tools to deliver quick, accurate, and efficient care to the masses is critical, particularly amid numerous challenges. Research from Mercer shows that the United States could have shortages of nearly 450,000 home health aides, 95,000 nursing assistants, and almost 100,000 medical technologists and technicians by 2025. Many healthcare workers are experiencing high levels of anxiety, burnout, and stress in the wake of the global pandemic. 

Healthcare is at a crossroads, and implementing AI offers scalable solutions that can augment and complement human expertise and optimize care delivery. Its implementation is not merely a response to workforce challenges but a proactive step toward a more efficient and effective healthcare ecosystem. 

Examples of Applications of AI in Healthcare

The essence of healthcare lies in the personal touch and understanding that healthcare professionals bring to patient interactions. AI complements this by empowering these professionals with advanced tools to assist with diagnosis, developing personalized treatment plans, medical research, and pharmaceutical development. AI-driven natural language processing (NLP) and machine learning (ML) can assist the healthcare industry in providing more holistic and personalized care. A few examples of its use include:

• Personalizing Chronic Disease Management: ML gives software the ability to learn and adapt to patterns using data. Unlike humans, these platforms don’t require sleep, so they can constantly evaluate data from electronic health records (EHRs), medical devices, wearables and other sources to identify patterns or risk factors of severe conditions. The technology is also empowering healthcare providers with predictive analytic capabilities and helping to identify patients at risk of developing chronic diseases, as just one example. With the ability to analyze large data sets and quickly predict treatment efficacy, its helping clinicians decide whether to change or continue a patient’s care plan. In addition to tailored treatment interventions, ML is also helping clinicians to identify at-risk patients via population health to optimize patient outcomes by providing the right treatment at the right time, while reducing healthcare costs by avoiding unnecessary treatment.

• Researching and Developing Pharmaceuticals: Drug development is risky, costly, and time-consuming. AI is now being used to optimize the process significantly, by helping to identify novel drug targets, optimizing lead compounds, and predicting drug-target interactions. By also learning from patient data, AI can identify correlates between drug safety & efficacy to help develop more effective, personalized, safer therapies. 

• Predictive Analytics: Further advancements in predictive analytics have been made possible through the integration of artificial intelligence and ML. Language models allow for predictive modeling from unstructured data, or from data from diverse and multimodal sources. AI’s ability to process data that was previously not easy to incorporate into predictive modeling yield better results in identifying early signs of disease, thereby enabling preventative care and early intervention.

The Benefits of AI 

Healthcare is complex because there are many stakeholders, including patients, providers, and payers. It can be nearly impossible for patients to parse through the myriad care options and find the best solution, while many healthcare systems struggle to balance the duty of care with efficient resource allocation and their direct relationship with payers. Powerful AI platforms have a much lower barrier to entry than they once did and can deliver immediate benefits for any healthcare organization, including:

• Predict Desired Output Based on Input Data: Physicians can reduce time spent researching and use AI as a “co-pilot” to help sort through millions of data points and summarize the information to gain more in-depth insights. AI-powered medical imaging has reached specialist-level accuracy in several disciplines, including dermatology, ophthalmology, and oncology. 

In one practice, ophthalmologists and computer scientists worked together to test and deploy automated image categorization to provide diagnostic support for diabetic retinopathy in patients with diabetes. The AI system was trained on thousands of images in electronic health records to diagnose diabetic retinopathy. The system screened millions of retinal photographs of patients with diabetes, saving time and resources. AI and deep-learning algorithms are transforming the field of radiology, helping to assist radiologists to automate tasks and quickly analyze thousands of images.

• Improved Accuracy and Error Detection: While computers aren’t 100% perfect, combining AI’s analytical skills with humans’ emotional intelligence is a powerful and complementary tool. Research found that AI can help improve human error detection, thus improving patient outcomes.

• Improved Clinical Understanding: AI-based language models are trained to better understand human language and through speech recognition, translation, analysis and other language-related items. These models can use that understanding to extract information from unstructured clinical notes in electronic health records, to provide a more holistic picture of a patient population.

One example is a biopharma organization using technology to analyze unstructured qualitative provider notes in the electronic health record during visits with patients with atopic dermatitis. Through the capabilities, they demonstrated that the encounters focused primarily on patient symptoms rather than relief, and the meetings rarely included burden on daily functioning or quality of life. This demonstrates how combining structured and unstructured data and applying natural language processing, offers potential to broaden the understanding of the disease impact and management moving forward.

• Reduced Costs: One of the most significant issues with healthcare today is a lack of accessibility. Patients will sometimes forgo necessary treatment because it’s cost-prohibitive or they lack trust in the medical system. One study found that a quarter of healthcare spending is considered wasteful, with contributing factors ranging from fraud and abuse to failure of care delivery and misuse of materials. AI can address these issues by supporting high-quality personalized patient care plans, improving fraud detection and prevention, and optimizing administrative and clinical processes.

Achieving Better Health Outcomes With Responsible AI

The pandemic only accelerated an already-existing trend – the need for digital transformation. Patients are looking for convenience and efficiency without losing the expertise of healthcare professionals. With AI that is responsibly trained, it’s possible to achieve continuous improvement while reducing costs and improving access. AI systems will not and cannot replace healthcare professionals, but they will improve their work and help patients become the healthiest version of themselves. One of the most exciting parts about AI in healthcare is that it’s still developing rapidly, with innovations arriving almost daily. The time is now to support our health care providers with the power of AI to deliver the highest quality care, and steer its development and application for the benefit of all patients.