Generative AI in healthcare: Opportunities & use cases

Generative AI can enhance business resilience, expedite research, improve claims management, and support advanced diagnostic tools. Strategically used, it 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 increasingly turning to generative AI for solutions. 

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

What is generative AI in healthcare?

Generative AI in healthcare uses AI technology to create new medical data, like text or images, from existing patient information.

This technology is crucial as it enhances diagnostic accuracy by identifying patterns in vast amounts of patient data. It also reduces drug development costs by simulating trials, saving time and resources.

Generative AI accelerates research by generating synthetic data, promoting faster drug discovery. It helps researchers study complex health conditions safely when real data is limited.

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

How is generative AI used in healthcare?

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

Improving clinical decision-making and reducing errors

Large AI models like GPT-4 can assist doctors.

These AI models can quickly interpret patient information, such as medical records and test results, helping doctors make faster and more accurate clinical decisions.

Here’s how:

• Faster and more accurate: AI can swiftly locate key information in patient records, like diagnoses, treatments, and medical history, saving time and preventing oversight.

• Less paperwork: AI can assist with paperwork, such as writing notes and reports, freeing up more time for patient care.

• Fewer mistakes: AI can identify potential errors in patient records, such as incorrect diagnoses or dangerous drug combinations, enhancing patient safety.

By leveraging AI, doctors can devote more time to patient care and less to paperwork.

Enhancing patient care through personalized treatment plans

Generative AI aids doctors by analyzing extensive patient information, like medical records and genetic data, to create individualized treatment plans.

It achieves this through:

• Personalized plans: AI identifies patterns in a patient’s health to predict future issues and recommend optimal treatments.

• Better outcomes: AI enables treatments that are more effective and have fewer side effects.

• Early detection: AI monitors health data from wearables, like smartwatches, to detect issues early, allowing for timely intervention and personalized care.

Accelerating drug discovery and time-to-market

AI is revolutionizing the discovery of new medicines, making the process faster and more efficient. It can design new molecules, like those found in drugs, and predict their efficacy.

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

AI also predicts the safety and effectiveness of new drugs. By analyzing large datasets, AI can identify potential issues before clinical trials, reducing costs and accelerating drug development. Additionally, AI can uncover biological processes involved in diseases, leading to new drug targets and potentially lifesaving treatments.

Optimizing operational efficiency and reducing costs

Generative AI enhances healthcare by automating tasks and saving time, making operations more efficient and cost-effective. For instance, AI models like GPT-4 can summarize patient data.

It can swiftly analyze information from electronic health records (EHRs), such as medical history and lab results, creating concise summaries of crucial details like diagnoses and treatments.

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

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

Revolutionizing medical imaging and diagnostics

Generative AI significantly enhances medical imaging and diagnostics. Hospitals can use it to improve traditional AI’s diagnostic capabilities by converting low-quality scans into high-resolution images with more detail. The technology can also detect anomalies and present findings to radiologists.

Furthermore, 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 discover biomarkers linked to diseases and predict their progression.

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 evaluate their current AI tech stack and enhance 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 select and fine-tune models for business needs. Test models in various scenarios to ensure accuracy and cost-effectiveness. Set up an interface to easily access the best models from different vendors.

Decide whether to build custom AI models or use pre-trained ones, depending 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, ensure they comply with regulations like HIPAA and safeguard your data. 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 their compatibility with your systems. They should offer scalable solutions. Verify data security, privacy, and customer support. Request demos, case studies, and feedback from other clients to evaluate their fit.

Navigate regulatory and ethical challenges

To use generative AI safely and ethically in healthcare, stay updated on laws like HIPAA and GDPR. Establish clear guidelines and maintain transparency with patients to build trust. Protect patient privacy and ensure AI compliance with healthcare data regulations. Use diverse data to avoid bias in AI models. Regularly update AI models to enhance accuracy. Involve experts in ethics, healthcare, and government to discuss AI’s impact. 

Ensure AI supports the doctor-patient relationship, not replaces it. Obtain clear consent from patients before using AI in their care. Provide employees with a way to report AI concerns. Address patient worries about AI, such as decision-making processes and data protection.

Build organizational alignment and prepare staff

To use AI effectively, align it with business goals. Identify problems AI can solve and communicate its role to everyone in the company. Form a cross-functional team of experts in AI, data, and business to lead AI projects. 

Provide employee training on using AI tools and applying them to their jobs. This will help them acquire new skills and tackle more challenging tasks.

How to measure success and ROI from generative AI in healthcare

To measure GenAI’s impact on healthcare, businesses should focus on both high-performing and underperforming areas. This approach helps improve weak areas and enhance effective ones.

Setting ROI metrics helps businesses track performance and make real-time adjustments, ensuring alignment with their goals.

Let’s discuss the main metrics to track:

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

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

• Intangible benefits: These include improved customer satisfaction, employee productivity, and brand reputation, requiring different measurement approaches. They can be assessed through surveys, employee feedback, and sentiment analysis.

Here are additional metrics to consider post-implementation:

Clinical performance

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

Operational efficiency

Metrics here include reductions in administrative work through automation, 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 improved understanding of health information.

Financial impact

Businesses can track reductions in overall cost per patient to assess this metric. They can also evaluate 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 address data shortages and privacy concerns by creating synthetic data to complement real data. This enhances disease diagnosis, drug discovery, patient care, and research, leading to better healthcare solutions. 

However, 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.