Generative AI in healthcare: Opportunities & use cases

Generative AI can improve business resilience, speed up research, improve claims management, and support advanced diagnostic tools. Using it strategically, healthcare organizations can boost customer care, clinical operations, and back-office tasks like accounting and purchasing.

With rising healthcare costs due to burnout, staff shortages, and disputes between payers and providers, the industry is turning to generative AI for solutions. 

This article will explore its use cases, importance, implementation strategies, and how to measure success.

What is generative AI in healthcare?

Generative AI in healthcare is the process of using a type of AI technology to create new medical data, like text or images, from existing patient information. 

This technology is important as it improves diagnostic accuracy by finding patterns in large amounts of patient data. It also reduces drug development costs by simulating trials, saving time and resources. 

Generative AI boosts research by generating synthetic data which promotes faster drug discovery. This also helps researchers study complex health conditions safely when real data is limited.

Additionally, generative AI enhances patient-centered care by creating personalized treatment plans. This tailored approach improves health outcomes and enhances the patient experience.

How is generative AI used in healthcare? 

Here are some specific use cases showcasing how generative AI is used in healthcare: 

Improving clinical decision-making and reducing errors

Big AI models like GPT-4 can help doctors.

These AI models can quickly read and understand patient information, like medical records and test results. This helps doctors make faster and more accurate clinical decisions.

Here’s how:

• Faster and more accurate: AI can quickly find important information in patient records, like diagnoses, treatments, and past medical history. This saves time and helps doctors avoid missing important details.

• Less paperwork: AI can help doctors with paperwork, like writing notes and reports. This frees up more time for doctors to spend with patients.

• Fewer mistakes: AI can help spot potential mistakes in patient records, such as incorrect diagnoses or dangerous drug combinations, which helps keep patients safer.

By using AI, doctors can spend more time caring for patients and less time on paperwork.

Enhancing patient care through personalized treatment plans

Generative AI can help doctors by looking at a lot of patient information, like medical records and genetic data. This helps doctors create treatment plans for each patient.

It does this through:

• Personalized plans: AI can find patterns in a patient’s health to predict future problems and suggest the best treatments.

• Better outcomes: By using AI, doctors can give treatments that work better and have fewer side effects.

• Early detection: AI can track health data from wearables, like smartwatches, to spot problems early. This helps doctors intervene quickly and provide more personalized care.

Accelerating drug discovery and time-to-market

AI is helping scientists find new medicines faster and more efficiently. It can design new molecules, like those found in drugs, and predict how well they will work.

By studying the structure of successful drugs, generative AI can create similar molecules with slight changes. This helps scientists quickly identify potential drug candidates, saving time and money. AI can even discover drugs that traditional methods might miss.

AI also helps predict the safety and effectiveness of new drugs. By analyzing large amounts of data, AI can spot potential problems before clinical trials, reducing costs and speeding up drug development. Additionally, AI can help identify the biological processes involved in diseases, leading to new drug targets and potentially lifesaving treatments.

Optimizing operational efficiency and reducing costs

Generative AI can help healthcare by automating tasks and saving time. This makes operations more efficient and cost-effective. For example, AI models like GPT-4 can summarize patient data. 

It can quickly analyze information from electronic health records (EHRs), such as medical history and lab results. In this way, it can create a concise summary of important details like diagnoses and treatments.

AI can also automate tasks like progress notes and discharge summaries. It can draft documents for doctors to review and approve, saving time and reducing stress.

LLMs can also reduce medical errors by providing extra checks in EHRs. Many errors happen due to misdiagnosis or missed follow-ups. AI can cross-check patient data and flag potential issues, helping doctors make better decisions.

Revolutionizing medical imaging and diagnostics

Generative AI can help medical imaging and diagnostics in several ways. First, hospitals can use it to improve traditional AI’s diagnostic abilities by turning low-quality scans into high-resolution images with more detail. The technology can also detect anomalies and present findings to radiologists.

Next, generative AI models can be trained on medical images, lab tests, and patient data to detect early signs of health conditions. These models can identify diseases such as skin cancer, lung cancer, fractures, Alzheimer’s, and diabetic retinopathy. AI can also find biomarkers linked to diseases and predict how they will progress.

Then, when doctors have questions, they can turn to generative AI instead of searching through medical books. AI can quickly process large amounts of data and provide answers, saving valuable time.

How to implement generative AI in healthcare

Here’s a roadmap for senior leaders to assess and plan generative AI integration for their healthcare organization:

Assess the readiness of your existing data infrastructure

Leaders should review their existing AI tech stack and improve it for using large language models (LLMs). Investing now will expand AI’s future potential. To maximize value, improve data quality, and consider partnerships with providers, payers, or tech vendors.

Leaders need to choose and fine-tune models for business needs. Test models in different scenarios to ensure accuracy and cost-effectiveness. Set up an interface to easily access the best models from various vendors.

Decide whether to build custom AI models or use pre-trained ones. This depends on resources, data, and costs. Find partners for data, model support, and development. Minimize risks by protecting data and following privacy regulations. Regularly assess AI models for bias and other risks.

Monitor AI systems for accuracy and efficiency. Retrain models with updated data and collect user feedback to improve them.

Select the right AI technologies and vendor partners

Before choosing an AI partner, make sure they follow rules like HIPAA and keep your data safe. Check if they use your data for their own AI models. Look for experience in healthcare and past successes.

Assess the partner’s technical skills, including AI algorithms, and how well they can work with your systems. They should offer solutions that can grow with your business. Verify data security, privacy, and customer support. Ask for demos, case studies, and feedback from other clients to see if they’re a good fit.

Navigate regulatory and ethical challenges

To use generative AI safely and ethically in healthcare, stay updated on laws like HIPAA and GDPR. Create clear rules and be honest with patients to build trust. Protect patient privacy and make sure AI follows healthcare data rules. Use a variety of data to avoid bias in AI models. Regularly update AI models to improve accuracy. Involve experts in ethics, healthcare, and government to discuss AI’s impact. 

Make sure AI helps the relationship between doctors and patients, not replace it. Get clear permission from patients before using AI in their care. Give employees a way to report concerns about AI. Address patient worries about AI, such as how it makes decisions and protects their data.

Build organizational alignment and prepare staff

To use AI effectively, companies should connect AI to their business goals. Identify problems that AI can solve and explain AI’s role to everyone in the company. Create a cross-functional team of experts in AI, data, and business to lead AI projects. 

Provide employee training on how to use AI tools and apply them to their jobs. This will help them learn new skills and take on more challenging tasks.

How to measure success and ROI from generative AI in healthcare

To measure GenAI’s impact on healthcare, businesses should look at high-performing and underperforming areas. Focusing on these helps improve weak areas and boost effective ones. 

Setting ROI metrics helps businesses track performance and make real-time adjustments. It also ensures that they align with their goals. 

Let’s talk about the main metrics to track: 

• Initial costs: These include hardware, software, data acquisition, and expert salaries. Training AI models can add significant expenses, especially with complex data.

• Time to value (TTV): TTV shows how quickly the business can see returns. A shorter TTV means a faster ROI, which can appeal to stakeholders. Calculating TTV involves tracking from the start of the project to the point when benefits emerge.

• Intangible benefits: These include improved customer satisfaction, employee productivity, and brand reputation, which require different approaches. These can be measured through surveys, employee feedback, and sentiment analysis.

Then, here are some additional metrics you should also consider post-implementation:

Clinical performance

This can be measured by how accurately AI diagnoses compared to humans. It also includes AI’s success in early disease detection and its ability to develop effective treatment plans.

Operational efficiency 

Here, metrics include reductions in administrative work through automation. They can also examine increases in the number of patients treated over a specific period and improvements in clinical processes through AI support. 

Patient experience 

This metric can be assessed through feedback on AI-assisted care, patient compliance with treatments, and an improved understanding of health information.

Financial impact

Businesses can track reductions in overall cost per patient to assess the above metric. They can also look at ROI by comparing financial gains from AI to its costs and monitor preventable hospital readmissions reduced by AI-driven proactive care.

Leading your organization into the AI-driven healthcare future

In healthcare, generative AI helps overcome data shortages and privacy concerns by creating synthetic data to complement real data. This improves disease diagnosis, drug discovery, patient care, and research. It enhances precision, efficiency, and personalization, leading to better healthcare solutions. 

But, challenges like protecting patient data and ensuring ethical use remain. Generative AI is a powerful tool, but not a complete solution. Healthcare organizations must assess data quality, clean and structure it, and start with simple use cases. Building infrastructure and training staff to use AI effectively will help integrate it into healthcare systems and support patient care.