The advent of Generative AI in healthcare marks a pivotal shift in the industry, heralding a new era of technological advancement and innovation. As we stand at the cusp of this transformation, it’s imperative to grasp the full spectrum of possibilities that GenAI brings to the table.
This article aims to unravel the intricacies of GenAI implementation, guiding healthcare organizations through the maze of choices and strategies available. From understanding the basic models of application to mastering frameworks for integration, we’re set to explore how generative AI technology can revolutionize patient interaction, operational efficiency and the overall landscape of healthcare services. Embarking on this journey requires not just a technological shift but a holistic reevaluation of operational, strategic, and human resource paradigms. For GenAI to be effectively integrated into healthcare, leadership must drive the strategy and champion the vision. Equally important, however, is securing widespread buy-in across the organization – from frontline healthcare professionals and administrative teams to the patients themselves
Watch Tarun’s talk on Implementing Generative AI in Healthcare from the Emids Healthcare Summit ’23
Implementation Flavors: 3 Ways to Implement Generative AI into Healthcare
The adoption of GenAI in healthcare presents a range of implementation strategies, each tailored to fit different organizational needs and capabilities. As we explore the specifics of these strategies, it’s crucial to understand the context and framework within which they operate.
These models represent three different levels of complexity to the adoption of GenAI in healthcare, that can enable any organization to integrate GenAI into their operations to some degree, regardless of their budget, requirements or level or knowledge.
Consume Existing GenAI Technologies
This is the most accessible approach for businesses. It involves leveraging pre-built applications powered by large language models (LLMs) such as GPT-4. This model is ideal for small to medium businesses or those new to generative AI, offering a low barrier to entry. This model is best for those looking to enhance existing processes or offer improved customer service through AI-driven chatbots, content generation, and automated workflows.
Implement Hybrid GenAI Solutions
A blend of off-the-shelf solutions and customized applications, this approach appeals to businesses seeking a middle ground. Companies might use a base model from providers like OpenAI but customize it with their own training data, tweaking it to suit specific business needs or to address unique market demands. This model allows for greater flexibility and differentiation from competitors who might also use off-the-shelf technologies.
Design Native Tech Stacks
This is the most advanced and resource-intensive approach. Large enterprises or technology-focused companies might opt to develop their own LLMs and GenAI stack. This provides unparalleled customization and control over data and algorithms, making it ideal for businesses with unique needs that cannot be met with existing solutions. However, it requires significant investment in terms of money, time, and technical expertise.
Essentially anything is possible with native generative AI systems – from mapping the patient journey or improving how healthcare organizations deal with data collection or sensitive information, through to leveraging artificial intelligence in ways that can reduce human errors, assist with drug discovery and development, and enable medical institutions to offer innovative solutions to patient care.
Choosing the Right Implementation Flavor
Selecting the right approach depends on factors like budget, desired speed of deployment, privacy and security concerns, and the intended impact on business processes, healthcare providers and patient experiences. Small businesses might prefer the ‘Consume’ model for its cost-effectiveness, while larger enterprises may lean towards the ‘Native’ model for its customization capabilities and data control.
5 Point GenAI Implementation Framework for Healthcare Organizations
As you venture into the realm of GenAI, a structured and strategic approach becomes pivotal for successful integration.
In this section we’ll introduce a 5 point framework, designed to guide healthcare organizations through the complexities of embedding generative AI into your products and services. This framework encompasses critical aspects such as financial planning, user experience redesign, data quality, risk management, operational maturity and workforce transformation.
Each point in this framework addresses a fundamental area of focus, helping organizations mitigate associated challenges and adapt to the rapid pace at which AI is evolving.
Baseline Investment Appetite
When contemplating the incorporation of GenAI the primary consideration should be to baseline your investment appetite. A common misconception is that integrating GenAI into existing web and mobile apps is as straightforward and cost-effective as attaching an API. However, the reality is far different. Deploying GenAI, especially machine learning models like GPT-4, can quickly become a significant financial commitment.
For instance, consider an application with just 10,000 daily active users interacting ten times per day with a large language model like GPT-4. This scenario would result in around 100,000 daily transactions. While the daily costs might seem manageable at first glance, amounting to approximately $4,500, the annual expenditure escalates to about $1.6 million. This figure is substantial, even though it might be less than the cost of employing a team of machine learning engineers or developing a proprietary model.
Therefore, it’s crucial to engage in thorough worst-case scenario planning.
Reimagine UX Strategy
The integration of GenAI in healthcare demands a fundamental reimagining of your user experience (UX) strategy. Unlike traditional web and mobile app development, incorporating large language models (LLMs) into healthcare organizations introduces new process, provider, payer and patient
LLMs are stochastic by nature; meaning their responses are probabilistic and not guaranteed to be consistent. This variability poses a challenge in maintaining a uniform user experience, and in ensuring accuracy when executing tasks such as assessing and diagnosing patients, and creating treatment plans. However, as the technology matures, new frameworks are emerging to address these challenges. One notable example is guardrails ai, which allows developers to quantify expected responses from LLMs. If the response deviates from the anticipated outcome, a subsequent call can be made to adjust it.
In addition to technical solutions, a significant aspect of reimagining UX involves adopting a ‘defensive UX’ approach. This approach involves designing systems that clearly communicate to users that they are interacting with AI. Major cloud providers and tech companies like Google, Microsoft, and Apple provide guidelines and strategies for building AI-powered UX. These resources, including detailed papers and design strategies can be invaluable for teams looking to integrate GenAI into their web and mobile applications.
Understand and Manage Risks of GenAI in Healthcare
Addressing and managing the risks associated with GenAI in healthcare is a critical aspect of its implementation. While there are numerous potential risks, a few recurring ones are particularly noteworthy, such biased data, ethical concerns, and patient privacy:
- Bias in AI Responses: Since generative AI models are trained on vast datasets, they can inadvertently produce biased results. The exact composition of these datasets isn’t always transparent, leading to uncertainty in the AI’s responses. In healthcare, this can lead to consequences such as biased diagnoses or treatment recommendations based on incomplete or non-representative patient data. For instance, if the training data lacks diversity in terms of race, gender, or socioeconomic status, the AI could produce less accurate recommendations for underrepresented groups, exacerbating disparities in the delivery of healthcare.
- Prompt Injection Attacks: Similar to the older concept of SQL injection attacks in databases, LLMs face the risk of prompt injection attacks. Here, the prompts can be manipulated or injected with certain inputs to make the AI respond in unintended ways. This can be especially dangerous when it comes to healthcare, where malicious prompts could cause the AI to generate harmful medical advice or release private patient information.
- Data Leaks and Confidentiality: When sharing sensitive or confidential company data with LLMs, there’s a risk of data leaks, potentially compromising important information such as patient data. Unauthorized access to this data could result in privacy breaches, regulatory penalties under laws like HIPAA (Health Insurance Portability and Accountability Act), and erosion of patient trust.
- Hallucinations: This refers to the AI providing convincing, grammatically correct, but factually incorrect responses. These ‘hallucinations’ can be particularly challenging as they often appear credible. Rigorous validation of AI outputs is crucial to prevent the propagation of such errors in clinical settings.
To mitigate these risks, several strategies can be adopted, focusing on maintaining privacy around medical data, safeguarding patient outcomes, and ensuring the integrity of clinical decision-making processes:
- Reinforcement Learning Through Human Feedback (RLHF): This involves training the model to reduce biases and improve accuracy based on human inputs, for example by incorporating feedback from clinicians, medical researchers, and subject matter experts. For example, if an AI system tends to recommend certain treatments more frequently to certain demographic groups due to biased training data, RLHF can help correct this by prioritizing equitable and evidence-based care decisions.
Tools like guardrails ai can assist in this process.
- Adopting SOC 2 Compliant Models: For concerns about data leaks, using SOC 2 compliant enterprise versions of AI models, such as the one offered by OpenAI for ChatGPT, ensures better security and data privacy.
- Defensive User Experience: Implementing a UX design that prepares users for AI interactions and manages their expectations can help mitigate the risks of misleading AI responses. This includes clear disclaimers, transparency in how AI-derived information should be used, and built-in checkpoints to encourage clinicians to manually evaluate AI responses.
- Mature Large Language Model Operations Tools: Enhancing the tools used to monitor and manage LLMs can help in identifying and rectifying hallucinations or other inaccuracies in AI responses before they impact patient care.
Understanding these risks and deploying appropriate strategies is essential for any healthcare organization looking to integrate GenAI into their operations. This ensures not only effective use of the technology but also safeguards against potential pitfalls, and the impact this can have on healthcare providers, and the patients they care for.
Mature your LLMOps Stack
The fourth crucial aspect in effectively implementing GenAI is the maturation of your Large Language Model Operations (LLMOps) stack. As this field continues to grow, a plethora of tools are emerging, each designed to enhance the operational management of LLMs. These tools play a pivotal role in evaluating and refining the performance of your GenAI models based on various parameters such as cost, latency, parameter size,and the fine-tuning process.
Key functionalities offered by these LLMOps tools in healthcare include:
- Prompt Management: This feature allows technical and non-technical staff, including healthcare product managers, to experiment with different prompts, observe the responses generated by the LLM
,and understand the nuances of AI interactions. This exercise is crucial for fine-tuning the prompts to achieve the desired outcomes, and the high standards of accuracy required in patient care. - Version Control and Deployment: LLMOps tools facilitate versioning of these prompts, allowing healthcare providers to maintain a history of changes and deploy the most effective versions directly through the LLMOps platform.
- Performance Monitoring: Another critical feature is the ability to monitor the performance of healthcare applications that utilize LLMs. This includes tracking the effectiveness of prompts and responses, as well as the overall impact on app functionality and user experience. In healthcare, real-time monitoring can alert administrators to issues like hallucinations in critical areas like diagnosis assistance or drug interaction, allowing for rapid intervention before they impact patient care.
The continuous evolution of the LLMOps space promises more advanced and diverse tools, making it increasingly vital for stakeholders across all healthcare verticals to stay informed and adapt their LLMOps stack accordingly. By doing so, they can ensure their GenAI implementations remain efficient and cost-effective while being aligned with operational goals and tailored to enhance patient care.
Rebuild Workforce Plan
The final key element in integrating GenAI into your healthcare organization is addressing the human angle: rebuilding your workforce plan. This goes beyond the technical aspects of GenAI and focuses on how roles and skillsets will evolve in a healthcare setting increasingly shaped by AI technologies.
When deploying GenAI applications, whether to improve patient care, streamline administrative process, or boost developer productivity, it’s essential to consider:
- Role Evolution: How will existing roles transform with the introduction of GenAI? Understanding this will help in planning future organizational structures and functions.
- Training and Skill Development: What new skills and training will your workforce need to adapt to these changes and continue to progress in their careers? As GenAI becomes more prevalent, the demand for skills in AI management and interaction will rise.
- Career Progression: Consider how career paths might change with the integration of GenAI. This involves identifying new opportunities and career trajectories that GenAI can create for your employees.
The process of rebuilding your workforce plan is not just about adapting to new technologies; it’s about preparing your human resources to thrive in an AI-augmented environment. This preparation includes upskilling, reskilling, and sometimes even creating entirely new roles to handle the unique challenges and opportunities that GenAI presents.
In summary, while technical considerations like budgeting, UX strategy, risk management, and LLMOps are critical, equally important is the focus on the human aspect. Rebuilding your workforce plan in the context of GenAI is about harmonizing technology with human potential to ensure sustainable growth and innovation in your organization, as well as the highest standard of patient care.
Emids is here to support you on this journey. With our expertise in GenAI and healthcare technology, we can help you navigate these choices, ensuring that the integration of GenAI into your services and products is both seamless and impactful. Reach out to Emids and let us help you transform healthcare delivery with the power of Generative AI. let us help you transform healthcare delivery with the power of Generative AI.