Top 4 Applications for Generative AI in Healthcare

Artificial intelligence (AI) holds transformative implications across various sectors and stakeholders, including grantmakers in the mission-driven industry, teachers and administration in education, and, notably, health professionals across the care continuum.

Generative AI uses advanced algorithms to create new data and insights, making it a revolutionary tool for improving decision-making and streamlining workflows in healthcare. According to Arcadia’s guide on the topic, generative AI is not yet accepted across the healthcare sector, but it does offer many promising applications, such as:

1. Enhancing operational efficiency
2. Accelerating medical research
3. Deepening patient engagement
4. Personalizing care

In this article, we’ll review each application and its potential impact on the healthcare sector.

1. Enhancing operational efficiency

Staffing shortages and significant administrative workloads result in health professionals having more work than help. Not to mention, Gen Z’s entrance into the workforce is characterized by heavy technology use, increasing the demand for innovative tools that streamline workflows.

Generative AI can handle many administrative tasks in healthcare, reducing workloads for health professionals while meeting the expectations of the emerging workforce. AI can boost operational efficiency by:

• Automating clinical documentation and paperwork
• Generating billing codes
• Processing insurance claims
• Managing new patient intake and registration
• Scheduling patient appointments

These functionalities profoundly impact health professionals, allowing them to save time, reduce costs, and minimize human error. For instance, large language models (LLMs) can read through electronic health records (EHRs) and summarize patient interactions. As a result, generative AI automatically generates comprehensive records, reducing manual data entry and the opportunity for human error.

2. Accelerating medical research

The ability of advanced algorithms to analyze vast amounts of complex data plays a pivotal role in medical research. By automating data analysis and generating hypotheses, this technology supports research through:

• Drug discovery and testing: Generative AI analyzes molecular structures and simulates their interactions to accelerate the identification of new drug candidates
• Synthetic data generation: By creating synthetic data that mimics real-world data, generative AI can test hypotheses without compromising data privacy
• Personalized simulations: Generative models can predict the outcomes of specific therapies for patient populations based on specific conditions

By expediting processes like drug discovery and care simulations, generative AI accelerates the research cycle and minimizes the expenses associated with traditional research. Traditional research often includes trial-and-error processes or patient recruitment for drug testing, which tend to be resource- and time-intensive.

Additionally, generative AI produces groundbreaking discoveries by identifying insights and patterns easily overlooked by human researchers. As a result, this tool fosters innovation in treatments and therapies.

3. Deepening patient engagement

While generative AI is beneficial in supporting internal tasks for health organizations, this technology also supports many patient-facing tasks. By enhancing patient communications, these models improve patient engagement and understanding throughout the healthcare journey.

Here are a few practical ways generative AI supports patient engagement:

• AI-powered chatbots and virtual assistants: Generative models power virtual assistants, which provide 24/7 patient support by answering non-diagnostic questions, scheduling appointments, and automating relevant reminders
• Personalized health content: AI can generate educational resources tailored to a patient’s literacy level and learning preferences to help them better understand their diagnoses, treatment plans, health management strategies, and more
• Automated follow-up and communications: Generative AI can create personalized communications based on individual patient data, such as post-visit summaries, treatment reminders, and wellness tips

These tools make healthcare more accessible, helping patients receive instant answers to relevant questions and proactive communications. As a result, generative AI helps patients become more satisfied with their care providers, more involved in their care journeys, and more likely to adhere to care plans. In the end, generative AI ultimately helps improve health outcomes.

4. Personalizing care

While AI enables personalized communications across various industries, its unique impact on healthcare is its ability to personalize patient care. Using powerful analytics capabilities, generative AI can evaluate patient data, predict individual needs, and generate tailored solutions, such as:

• Customized treatment plans: Generative AI can process vast amounts of patient data, including medical histories and genetic information, to develop individual treatment plans. AI-generated treatment plans enable providers to select the most effective interventions without a trial-and-error approach.
• Preventive health measures: When paired with other analytics models, AI can predict a patient’s future health needs and responses to various treatments, helping providers identify measures to prevent avoidable health risks. For example, providers can leverage predictive analytics in healthcare to forecast a patient’s risk of heart disease progression and implement lifestyle changes to address the disease before it escalates.
• Tailored patient monitoring: Generative AI can provide real-time feedback on a patient’s health by monitoring data from wearable devices, health apps, and other resources. Based on the collected data, this could include reminders, wellness tips, and early warnings.

With this personalized approach, generative AI drives improved outcomes by tailoring treatments to patients’ needs. Not only does this increase precision in treatment selection, but it also helps prevent avoidable health situations, such as hospital readmissions.

Final thoughts on implementing generative AI

Generative AI’s impacts are widespread across the care continuum, yielding benefits for providers, payers, and patients alike. Adopting this technology requires organizations to first prioritize data governance by centralizing disparate data and verifying their database’s accuracy. With a strong foundation of reliable data, generative AI can transform the health industry’s workflows, research, patient communications, and care plans.