15 Top Use Cases of Generative AI in Healthcare and Adoption Challenges to Overcome

Healthcare is currently struggling with many challenges, from labor shortages, clinician burnout, and declining profitability to worsened health outcomes. Leveraging innovative technologies is crucial to navigating the issues and ensuring medical companies’ sustainability and patients’ well-being. Generative AI in healthcare can efficiently address these obstacles, offering solutions for both enterprise and direct-to-consumer applications.

In recent years, we have witnessed the application of natural language processing (NLP) and machine learning (ML) in various medical use cases. However, the advent of generative AI models is propelling healthcare tech to unprecedented heights. These models boast remarkable capabilities in natural language generation, translation, insight retrieval, reasoning, and handling unstructured, unlabeled data. This power of generative AI in healthcare can enrich experts’ knowledge, enhance interoperability, accelerate medical research, and enable true personalization within the industry.

Forbes predicts that generative AI in Healthcare has the potential to save the US medical sector a staggering $200 billion annually. This, coupled with the fact that 75% of major healthcare companies are either experimenting with or planning to scale generative AI, is a testament to the financial pros it can bring. Leaders of these companies believe that generative AI can improve practices and enable quicker decision-making. This article will discover how generative AI in healthcare can support healthcare practices’ operations by analyzing its most common use cases and key challenges.

“By integrating AI into clinical and administrative workflows, the SPsoft team has enabled multiple healthcare providers to enhance patient care, streamline operations, and reduce costs. Our commitment to innovation ensures that we stay at the forefront of this technology, helping our clients address the complexities of AI implementation and achieve remarkable outcomes.”
 
Mike Lazor
CEO, SPsoft

“Integrating generative AI in healthcare practices has transformed how we approach data and patient management. At SPsoft, we’ve developed solutions that leverage AI to automate routine tasks, generate insightful clinical data, and support decision-making processes. Our expertise in gen AI tech allows us to create customized solutions that meet the unique needs of medical providers, ultimately improving efficiency and patient engagement.”

Romaniya Mykyta
Head of Product Management, SPsoft

What Is Generative AI in Healthcare?

With its reliance on historical data to identify patterns and make predictions, generative AI performs well in environments characterized by high repetition and low risk. This approach is reassuring when used in low-risk situations where errors have a minor effect. It helps healthcare practices and patients gradually understand AI’s capabilities and build trust in its usage. Also, it provides AI developers with opportunities to rigorously test and refine their systems in a controlled environment before deployment in higher-stakes scenarios.

One of the most remarkable aspects of generative AI in healthcare, powered by deep-learning algorithms, is its ability to create new content, such as text, audio, code, etc. However, its true potential shines when analyzing challenging unstructured data sets. That shows a breakthrough for healthcare operations rich in unstructured information, such as clinical notes, diagnostic images, medical charts, and recordings. With generative AI in healthcare solutions, you can use these data sets independently or combine them with large, structured ones, like insurance claims.

Generative AI has the potential to unlock a staggering $1 trillion improvement in the medical industry. While healthcare businesses have been leveraging AI tech for years, gen AI brings a fresh perspective. It automates tedious and error-prone operational tasks, provides instant access to clinical data, and modernizes health systems infrastructure. That leads to substantial cost savings and operational efficiencies.

Finally, implementing generative AI in healthcare can help enhance diagnostic accuracy, create personalized treatment plans, accelerate drug discovery, and improve patient outcomes. While certain healthcare business operations (for example, managing relationships with healthcare systems) require a human touch, generative AI helps you augment the processes. Furthermore, regardless of the volume of data collected from back-office tasks, generative AI can in Healthcare summarize this information, freeing up valuable time for medical providers.

Leverage the potential of integrating gen AI into healthcare with SPsoft. Our expertise can help you enhance patient care and improve operational efficiency!

Get in Touch

15 Crucial Generative AI Healthcare Use Cases

Nowadays, insurance executives, hospital administrators, and physician group operators can leverage gen AI tech across the healthcare value chain. Its applications range from enhancing continuity of care to gaining valuable market insights and supporting quality value-based care. Below are the most critical generative AI use cases in healthcare for three main categories of stakeholders: private payers, clinics, and physician groups. 

Private Payers

Consumers are increasingly seeking personalized and convenient services from their health insurance providers. Meanwhile, private payers are under pressure from rising competition and escalating medical costs. Fortunately, generative AI in healthcare can enhance operational efficiency and improve patient and customer service quality.

Generative AI, a robust tool, can swiftly summarize large volumes of data, allowing personnel to concentrate on more intricate tasks. Also, AI streamline administrative functions and interactions with members and providers, involving laborious data sorting, saving valuable time for more critical activities.

Besides, gen AI presents numerous opportunities to enhance the quality of member services. For instance, inquiries about benefits, which typically require manual confirmation by insurance experts, can be expedited with healthcare AI solutions. These solutions can swiftly retrieve pertinent data from various plan types and files, supporting digital resources and call center experts. Ultimately, that improves the overall efficiency of member services.

Similarly, with gen AI, you can improve and accelerate resolving claims denials, an often frustrating process. AI models summarize denial letters, consolidate denial codes, highlight essential reasons, and suggest next steps, enhancing the overall member experience.

After all, generative AI in healthcare can optimize prior authorization and claims processing, which are typically time-consuming and costly for private payers. On average, verifying prior authorization takes ten days. AI solutions can convert unstructured data into structured formats, providing near-real-time benefits verification and accurate calculations of out-of-pocket costs. That is performed using health providers’ contracted rates and patients’ specific benefits.

Gen AI Applications for Private Payers

When it comes to private payers, the potential generative AI in Healthcare use cases are the following:

• Healthcare operations management. AI synthesizes clinical notes for care managers, compiles medical and referral data, and creates detailed care plans for members. 
• Robust member services. Gen AI generates custom coverage summaries for specific benefits questions that are accessible online and via call centers. It offers call scripts and other content for outbound nonclinical communications, deploys adaptive chatbots, and uses intelligent routing. That helps answer service questions for payers and providers. AI also suggests clinicians based on coverage, location, preferences, and conditions.
• Provider relationship management. The tech compares plan and product features, develops standard communications like welcome letters and reports, and identifies gaps in provider directories. Besides, it creates performance reports and observations for providers and vendors, helping to address gaps and improve service delivery.
• Corporate functionality. Healthcare generative AI brings HR self-serve functions, like first-line interactions and onboarding videos. Moreover, Gen AI can synthesize proposal requests (RFPs), create responses, draft vendor communications, and automate accounting by extracting relevant numbers. The tech provides updates for policyholders and generates complex reports and KPIs across functions, improving decision-making.
• Claims management. Implementing AI in healthcare administration helps develop summaries of manual and denied claims issues. The tech is essential for identifying solutions, aggregating data for complex claims to reduce processing time, and creating responses to appeals and other inquiries. 
• Marketing & Sales. Generative AI analyzes consumer distribution for marketing and sales teams to develop personalized plans and products. AI healthcare solutions help summarize and extract themes from online text and images to analyze client feedback and improve sales support chatbots. At the same time, AI allows for creating initial drafts of marketing materials and product overviews for brokers and employers, ensuring they comply with guidelines and necessary reviews.

Clinics and Medical Practitioner Groups

Within clinics and medical practitioner groups, generative AI technology can reshape everything from continuity of care and clinical operations to contracting and various corporate functions.

Back-office and administrative tasks, such as finance and staffing, are vital for the proper performance of a clinical system. However, these functions often work in silos, relying on manual inputs across fragmented systems that hinder easy data sharing and synthesis. Generative AI in healthcare leverages unstructured purchasing and accounts payable data, while AI chatbots address common IT and HR questions from clinic employees. This integration enhances their experience and reduces the time and money spent on hospital administration.

Clinical operations are another area where gen AI brings efficiencies. Now, medical providers and administrative staff must complete numerous forms per patient, along with post-visit notes, employee shift notes, etc. These responsibilities consume hours and contribute to employee burnout. Physician groups face similar administrative burdens.

With clinician oversight, gen AI generates discharge summaries or instructions in a patient’s native language, ensuring better comprehension. It can synthesize care coordination notes or shift hand-off notes and create real-time checklists, lab summaries, and clinical orders. 

Moreover, generative AI’s language generation capabilities enhance electronic health records (EHRs). While EHRs allow you to access and update patient data, they may require manual inputs and are prone to human error. The technology is being tested to create visit summaries in EHRs, suggest documentation changes, and provide research for decision support. Some health systems have already integrated this technology into their operations.

Discover how FHIR AI is reshaping patient care, diagnostics, and remote monitoring. Learn about the innovative integration of both technologies!

Read More

Gen AI Applications for Clinics and Medical Practitioner Groups

Hospitals and physicians can benefit from the following use cases of generative AI in Healthcare:

• Continuity of healthcare services. Gen AI summarizes discharge data and follow-up needs for post-acute care and synthesizes specialist notes for primary-care teams. 
• Care quality and patient safety. The tech can recommend tailored risk considerations based on a patient’s medical history and existing medical literature.
• Value-based care. Generative AI improves documentation accuracy and leverages structured and unstructured information to create patient education materials, such as videos and texts. It can develop standard value-based care contracts and carve out agreements based on market characteristics.
• Network & marketing insights. AI creates provider segmentation summaries by specialty and analyzes market performance using external resources and data. 
• Reimbursement. The technology develops prior authorization documentation, lists of current conditions, and potential codes from various data sources. With AI, developers can identify coding errors and automate coding checks based on physician notes.
• Medical operations. With gen AI, you can synthesize care coordination notes, update electronic medical records (EMRs), develop workflow materials and schedules, and bring personalized training journeys for clinicians.
• Corporate functionality. This area also benefits from AI, with applications in IT, such as code development, cybersecurity testing, and quality assurance (QA). In procurement, AI assists in creating RFPs, contracts, reports and KPIs, vendor communications, and purchase orders. In talent management, AI aids in hiring, writing offer letters, creating SOPs, providing onboarding education, and developing chatbots for HR queries. Also, AI can generate reports for legal, compliance, and regulatory departments.
• Clinical analytics. AI provides insights through conversational language and automates repetitive tasks using AI-assisted coding. 
• Patient engagement. Gen AI helps analyze patient feedback, create personalized care instructions and communications, improve chatbots for nonclinical member services, etc. 

Enhance your healthcare services with our cutting-edge AI solutions. With SPsoft’s deep expertise in AI tech, you will enhance patient care and streamline operations!

Learn More

Key Challenges of Adopting Generative AI in Healthcare

Medical organizations face dozens of complex challenges, ranging from data privacy concerns to stringent compliance requirements and issues with treatment plans. The following section will explore the main hurdles to integrating gen AI into health systems.

Possible Biases

Potential biases in AI models may result from the inappropriate quality and representativeness of the training information. The dataset’s lack of diversity and inclusivity can provoke relevant biases against underrepresented groups. Meanwhile, generative AI tools, which depend on extensive data from patient records, can perpetuate them. 

Healthcare providers should establish stringent algorithm development and validation protocols to mitigate this issue. Continuous testing for biases and diversifying training data are vital to ensure fairness and accuracy in healthcare AI solutions.

Information Privacy

Generative AI in healthcare, which relies on extensive data, including sensitive patient data, to train models and produce insights, is at the forefront of medical advancements. However, the importance of data privacy and security must be addressed, as it poses several challenges.

Medical companies are responsible for adhering to the existing data protection regulations. That entails implementing robust encryption, access controls, and auditing mechanisms to prevent unauthorized access or breaches. Equally important, your healthcare practice should take the lead in establishing transparent data-sharing policies and ensuring that data used for AI purposes is anonymized or de-identified. This not only mitigates privacy risks but also instills confidence in patients that their data is being handled with utmost care and respect for privacy.

Regulatory Requirements

Utilizing gen AI in healthcare administration should meet strict regulatory scrutiny and compliance requirements. Healthcare organizations must ensure that AI solutions:

• Align with industry standards and guidelines
• Secure necessary regulatory approvals for AI algorithms
• Adhere to medical device regulations (such as those from the FDA)
• Comply with data protection laws 

The regulatory environment surrounding the adoption of gen AI in healthcare administration is complex and intricate. This complexity requires detailed planning and adherence to ensure the safe and effective deployment of AI technologies in the healthcare sector.

Integration with Current Health Systems

Healthcare organizations often rely on legacy systems that may need to be compatible with AI technologies. However, adopting AI can disrupt established workflows and processes, leading to resistance from healthcare professionals.

Medical practices must thoroughly assess their IT infrastructure to identify suitable integration points for AI solutions and address such challenges. Ensuring interoperability between AI systems and existing healthcare IT platforms is crucial. Moreover, providing robust training and support to staff members is essential to familiarize them with AI tools and workflows. That helps you facilitate a smoother transition and reduce resistance to new technologies.

Accuracy Considerations

In healthcare, the consequences of AI-related mistakes can be severe. For example, large language models (LLMs) can generate outcomes that, while syntactically credible, are factually incorrect. Healthcare organizations must determine when to accept errors and demand the AI model to explain its conclusions. For instance, if generative AI is utilized to diagnose cancer, doctors are unlikely to use the tool unless it can substantiate its recommendations.

Diagnosis Issues

Achieving accurate diagnoses with AI remains challenging, particularly for rare diseases with limited data representation. Data scarcity hinders AI’s effectiveness in diagnosing uncommon conditions, resulting in suboptimal performance because of an insufficient learning sample. Even for common conditions with ample data, the crucial need for AI systems to have access to complex datasets cannot be overstated. That helps improve their accuracy and prevent the development of a fragmented AI landscape. 

Large health systems with extensive proprietary data can gain disproportionate advantages over smaller entities in such a landscape. Current generative AI techs, like ChatGPT, are trained only on publicly available data. Relying on these generic AI models for medical diagnostics without incorporating the rich medical histories from electronic health records will be inefficient. Thus, you should exercise caution in deploying generative AI in healthcare for diagnostics until the AI can be trained on extensive medical datasets.

Treatment Plans

The application of AI in treatment also faces substantial challenges. They include accountability and liability issues, patient trust and acceptance, and technological and practical limitations. Healthcare providers are ultimately responsible for administering treatments and must justify their decisions in malpractice cases. Additionally, patient trust in AI-managed treatments has yet to reach a level that would support widespread adoption.

AI needs advanced technology capability to replicate physicians’ nuanced tasks beyond simple medication management. Treatments are often highly individualized, not aligning with AI’s strengths in high-repetition, low-risk tasks. However, with further advancements in AI tech and the development of more sophisticated algorithms, its integration into medical treatment processes may become a reality.

See how our knowledge in AI is transforming healthcare. Read our case study showing successful AI integration and its remarkable impact on patient outcomes!

Read More

How to Adopt AI Solutions in Healthcare Effectively?

Integrating generative AI in healthcare businesses can reshape the entire industry. However, such transformation requires medical leaders to first assess their operations, talent, and tech capabilities. So, you should take the following strategic actions to harness the power of gen AI.

Step 1. Assessing the Landscape

At the outset, executives must gain a clear understanding of the most effective applications of healthcare generative AI. That establishes the basis for a cross-functional team, which includes leaders responsible for data and tech. Together, they can assess the potential value of gen AI across various divisions, ensuring a comprehensive and effective adoption.

Organizations can integrate these applications into a broader AI strategy by carefully evaluating and prioritizing use cases. Such a systematic process allows for a more cohesive and impactful adoption of generative AI in medicine, aligning with your company’s overall AI roadmap.

Step 2. Sizing Up Medical Data

To maximize the potential of generative AI, healthcare leaders must focus on securing broad and high-quality data sets. They should consider enhancing the fidelity and accuracy of their data through strategic partnerships. These may include cooperation with providers, payers, or technology vendors and investing in interoperability.

Additionally, you should evaluate your AI tech stack, covering apps, models, APIs, and other critical technology infrastructure. That will help identify areas needing enhancement to leverage large language models effectively. Investing in the proper AI tech stack now will position medical organizations to expand their use of healthcare AI solutions in the future. 

Equally important is the need for medical practices to ensure that the data used to train generative AI models is processed within secure firewalls. This is a critical step in data security, and after a thorough assessment of their internal capabilities, leaders may opt to outsource certain aspects of their tech stack to further enhance data protection.

Step 3. Navigating Risks and Biases

For private payers, hospitals, and physician groups, utilizing generative AI in healthcare solutions comes with several potentially costly risks, especially as technology evolves.

Ensuring the security of patients’ personally identifiable information is a paramount concern when it comes to generative AI in healthcare. Open-source gen AI tools may not always provide the necessary level of security, and there is a risk that generative AI might use this sensitive information to refine its models. If the data sets used by a gen AI platform disproportionately represent specific patient populations, the generated patient care plans may be biased. That leads to inaccurate, unhelpful, or potentially harmful recommendations. 

Integrating generative AI platforms with other hospital systems, such as billing systems, can lead to inefficiencies and errors if not properly executed. However, with the crucial element of human oversight, the potential for generative AI in medicine to produce inaccurate results can be effectively managed. That helps provide a level of control and reassurance.

To balance the benefits and risks of the technology, you must establish robust risk and legal frameworks governing its use. These complex frameworks, addressing data security, bias and fairness, regulatory compliance, and accountability, will ensure a secure foundation for responsible utilization. Organizations that can swiftly and effectively adopt generative AI in healthcare will likely reap significant benefits. These may cover enhanced efficiency, improved patient outcomes, and better overall experiences.

Step 4. Investing in Staff and Partners

Introducing generative AI in healthcare administration will transform how tasks are performed and who performs them. Healthcare professionals will experience role evolution as the tech streamlines certain aspects of their work. Despite the tremendous changes in processes and workflows, a human-in-the-loop approach will be crucial. Meanwhile, human oversight and intervention will remain essential in all areas influenced by generative AI.

To successfully integrate this technology, you must learn to use the platforms, evaluate their recommendations, and intervene when necessary. The goal is to augment operations with AI, not replace them. That may require providing learning resources and guidelines to upskill employees. For hospitals and physician groups, leaders should ensure that generative AI applications are designed to be user-friendly and do not add to the workload or detract from patient care time. Instead, they must enhance the efficiency and quality of work.

While some healthcare organizations may be able to develop their gen AI capabilities, most will need to establish strategic partnerships with tech vendors. The selection of the right partner is a critical step. Thus, you should evaluate the partner’s adherence to regulatory compliance, like HIPAA in the United States, as well as their data privacy and security measures. It is also critical to consider whether the medical organization’s data will be used to improve future foundational models, ensuring a mutually beneficial partnership.

Private payers and healthcare providers may also have opportunities to collaborate with companies with rich data sets, enhancing gen AI outputs for all parties involved.

Final Thoughts

Gen AI will revolutionize healthcare in unprecedented ways. As the technology evolves, it can converge with other emerging techs, like virtual and augmented reality or other forms of AI, to transform care delivery. For example, physicians may compare treatment approaches for a patient against the complete corpus of their history and similar cases with positive outcomes.

At the same time, healthcare stakeholders, including private payers, clinics, and physician group leaders, must prioritize the responsible and safe use of generative AI in medicine. Protecting patient privacy, ensuring equitable clinical outcomes, and enhancing the experience of medical providers are top priorities. So, you should evaluate each potential generative AI application case-by-case basis, carefully analyzing the benefits against the associated hazards.

Ultimately, given the tech’s rapid advancement and complexity, implementing generative AI in healthcare effectively can be challenging for medical organizations. That is where the role of a trusted third party becomes invaluable. By collaborating with specialists who have an in-depth understanding of gen AI, healthcare leaders can navigate its complexities, maximize their return on investment, and ensure the technology is tailored to their unique needs.

Improve your healthcare services and optimize operations with our generative AI solutions. Contact us now to learn more about our capabilities and start implementing advanced techs!

Contact Us

FAQ