Reimagining the Digital Patient Journey with AI Assistants and Agents
Reading time: 10 min 54 sec | Mar 12, 2024
Introduction
AI assistants and agents differentiate primarily in their level of autonomy and the roles they play in digital environments. AI assistants act as intermediaries, facilitating tasks through user commands in a highly interactive, but user-dependent manner, focusing on enhancing user experience and productivity with minimal autonomous decision-making. Conversely, AI agents possess a higher degree of autonomy, capable of making decisions and executing actions without direct human input, tailored towards achieving specific goals within their operational domain. This fundamental distinction marks the boundary between AI assistants, which augment human actions, and AI agents, which independently navigate and interact with their environments to fulfill their objectives.
Online booking in the digital age
In the digital age, the healthcare industry has begun to embrace online appointment booking. However, the patient journey often remains fragmented, especially when seeking the right specialist for their needs. This article explores the transformative potential of AI in revolutionizing this journey, not just as a convenience but as a crucial step towards timely and accurate healthcare.
The Traditional Patient Journey: A Path Riddled with Inefficiencies
Traditionally, patients navigate through a healthcare system that offers online booking but falls short in delivering comprehensive information about specialists and clinics. This gap can result in prolonged wait times for appropriate treatment, with patients left in a loop of research and uncertainty. Such inefficiencies underscore the necessity for a more integrated approach to patient care.
Bridging the Gaps with AI
AI-powered solutions promise to fill these gaps. By providing detailed profiles of specialists and matching patients with the right care based on their specific needs, AI can offer a more targeted and efficient booking experience. Moreover, AI can alleviate the common bottleneck at clinic receptions through digital check-in systems, enhancing patient satisfaction and reducing unnecessary wait times.
Personalization at the Forefront with AI Assistants
An AI assistant goes beyond scheduling to facilitate a comprehensive pre-consultation process. Patients can articulate their symptoms, medical history, and concerns in detail, ensuring that healthcare providers receive a clear picture ahead of the appointment. This not only enriches the quality of care but also streamlines the consultation, making it more effective and personalized.
Breaking Down Barriers: AI as a Multilingual Communicator
Language barriers can significantly impact patient care. AI's ability to communicate across languages ensures that every patient's voice is heard and understood, directly translating into the healthcare provider's language. This level of inclusivity is vital for a system that caters to a diverse population.
Supporting Statistics: The Case for AI in Healthcare
Recent studies highlight the critical demand for AI in healthcare:
83% of patients report poor communication as the worst part of their healthcare experience, demonstrating a strong need for clearer communication between patients and providers.
AI technologies like natural language processing (NLP), predictive analytics, and speech recognition could help healthcare providers have more effective communication with patients.
AI could deliver more specific information about a patient’s treatment options, allowing the healthcare provider to have more meaningful conversations with the patient for shared decision-making.
According to Harvard’s School of Public Health, although it’s early days for this use, using AI to make diagnoses may reduce treatment costs by up to 50% and improve health outcomes by 40%.
AI brings to health systems the benefit of making gathering and sharing information easier.
AI can help providers keep track of patient data more efficiently.
For diabetes, which the Centers for Disease Control and Prevention states affects 10% of the US population, patients can now use wearable and other monitoring devices that provide feedback about their glucose levels to themselves and their medical team.
AI can help providers gather that information, store and analyze it, and provide data-driven insights from vast numbers of people.
Leveraging this information can help healthcare professionals determine how to better treat and manage diseases.
AI provides opportunities to help reduce human error, assist medical professionals and staff, and provide patient services 24/7.
More than any other industry, 28% of healthcare respondents and 25% in life sciences believe an AI copilot will reduce their workload.
The research also points out that organizations believe they will need help to skill up employees to use AI safely and effectively.
Ninety-seven percent of healthcare and life science professionals said that investments in AI will be increasing up to 26%, and 56% reported using AI daily in their current roles.
54% believe AI is a top priority for life science professionals in decentralized clinical trials and supply chain efficiency and accuracy.
67% state that improving the digital entry point is the top use case worthy of future investment for healthcare professionals.
60% believe workplace platforms and tools are the top digital investment for scaling AI throughout the organization.
Driving efficiency and increasing satisfaction is believed to be the main impact of AI on people-focused processes and roles.
How to Integrate AI into Healthcare: A Step-by-Step Action Plan
In the following blogs, I will focus on specific use cases. To reimagine the patient journey through AI, we can start with this blueprint of a step-by-step action plan:
Prerequisites:
Before diving into the action plan, it's essential to note that implementing such a strategy requires pre-existing groundwork, including a well-defined digital patient journey and a thorough comprehension thereof. This entails identifying potential pain points and specific areas within the journey that could benefit from integration with an AI assistant.
Step 1: Identify the applicable Use Cases
What is a use case?
A use case is a concept to describe how a system can be used to achieve specific goals or tasks. It outlines the interactions between users or actors and the system to achieve a specific outcome.
What is the purpose of a Use Case?
To manage scope, establish requirements, outline ways a user will interact with the system, visualize the system architecture, communicate technical requirements to business stakeholders, and manage risk.
Step 2: Assemble a cross-functional team
What is a cross-functional team?
A cross-functional team is a workgroup made up of employees from different functional areas within an organization who collaborate to reach a stated objective. Such teams help to produce better results quicker.
What could a cross-functional team look like in this case? It could include:
Physicians and nurses: Provide insights into clinical workflows, data requirements, and patient care needs.
IT specialists: Ensure the project is technically sound, secure, and interoperable with existing systems.
Administrators: Oversee budgeting, resource allocation, and change management strategies.
Project Managers: Coordinate tasks, timelines, and communications among team members.
*Gen AI specific: Prompt engineers: Responsible for translating the business persona's needs actions and output into prompts for the generative AI model(s).
*Gen AI specific: ML Operations Lead: Responsible for building and operating the application in production.
Step 3: Define intentions, objectives, and the output you are trying to achieve
Ensure you have a human in the loop to oversee the first use cases and provide oversight.
The value of a generative AI project can come from a number of sources.
Consider the following outcomes, which other organizations have experienced after adopting AI.
Define your budget.
Step 4: Define responsible AI guidelines
Governance: The foundation for Responsible AI is end-to-end enterprise governance. At its highest level, AI governance should enable an organisation to answer critical questions about results and decision-making of AI applications, including: some text
Who is accountable?
How does AI align with the business strategy?
What processes could be modified to improve the outputs?
What controls need to be in place to track performance and pinpoint problems?
Are the results consistent and reproducible?
Ethics and regulation: Organizations should strive to develop, implement, and use AI solutions that are both morally responsible and also legal and ethically defensible.
Interpretability and explainability: At some point, any business using AI will need to explain to various stakeholders why a particular AI model reached a particular decision. These explanations should be tailored to the different stakeholders, including regulators, data scientists, business sponsors, and end consumers.
Robustness and security: To be effective and reliable, AI systems need to be resilient, secure, and safe.
Bias and fairness: Bias is often identified as one of the biggest risks associated with AI.
Step 5: Choose the right model for your organization
When choosing the right model think of:
Intended Use Cases: What will the LLM be used for? Will it be used for content generation, classification, translation, etc.? Which are the core tasks you expect it to perform?
Data Domain: If you are a healthcare organization, you will want to have a model that is pre-trained on relevant and highest-quality data.
Ethical considerations: Educate yourself on potential biases, safety considerations, and risk of misuse depending on your use case.
Accuracy: Will you need high precision or is moderate accuracy acceptable?
Scalability: Are you considering a large number of users? How many thousands of queries per second do you anticipate? Scaling can be costly.
Cloud vs On-premise: Cloud API access can bring you ease and convenience but on premise offers greater control and opportunity for customization.
Budget: Consider the range of pricing based on compute usage, queries and model size.
Inference speed: Smaller models may be faster. How important is real-time low latency to you?
Step 6: Determine the data sources and data sets
The generative AI model will be trained on the data you gather.
This data should be honed for the specific business or domain level problem it is trying to solve, and accessible through enterprise data sources.
By having the right data fed into the model and fine-tuning it, your organization will be able to mitigate hallucinations and enhance the explainability of AI.
Additionally, it's crucial to analyze the existing data infrastructure within the organization and determine what needs to be changed in data governance to make it feasible to work with AI.
Step 7: Build a Language Model operations plan
Develop a plan for productionizing and monitoring the AI model's output to ensure it functions effectively and safely.
Some key questions to think of:
Can you quickly evaluate and experiment with generative AI?
Do you have cost controls during experimentation and evaluation?
How are you measuring impact? Do you have targeted goals and frequent checkpoints to ensure progress?
Do you have a mechanism for continuous improvement? Are you able to assess, evaluate, and re-engage to go deeper within existing use cases or expand to more use cases?
What your plan should include:
Infrastructure setup
Deployment and monitoring
Clear measurement of KPIs: some text
Accuracy: Measure the accuracy of the generative AI model in producing relevant and correct outputs. This can be quantified using metrics such as precision, recall, F1 score, or mean squared error, depending on the nature of the use case.
Productivity: Assess the impact of generative AI on the productivity of the target persona or department. This could include metrics like the number of tasks completed per unit of time, response time, or reduction in manual effort required.
Customer satisfaction: If the generative AI use case involves customer-facing applications, use customer satisfaction surveys or feedback to gauge how well the AI system meet customer needs and expectations.
Cost savings: Measure the cost savingsachieved through the use of generative AI. This may involve comparing the costs of employing the AI system to the expenses associated with traditional manual processes or outsourcing.
Turnaround time: Evaluate the time taken forthe generative AI model to generate responses or outputs compared to traditional methods. Faster turnaround times can lead to increased efficiency and improved customer experience.
Quality of output: Assess the quality of thegenerative AI outputs against predefined criteria. This can be done through manual review or automated quality checks, depending on the use case.
Error rate: Quantify the rate at which the generative AI model produces incorrect or undesirable outputs. Minimizing error rates is crucial for maintaining accuracy and reliability.
Business impact: Identify specific business metrics that are directly impacted by the generative AI use case, such as increased sales, reduced customer complaints, or improved employee retention.
Training time and cost: Measure the time and resources required to train and fine-tune the generative AI model. Efficient training processes can lead to faster implementation and quicker time-to-value.
Human-in-the-loop metrics: If human intervention isinvolved in the generative AI process, track metrics related to the efficiency and effectiveness of human oversight.
Output and quality
Regular audits and evaluation for expansion
Continuous performance improvement and model updates
Security and compliance
Human-in-the-loop oversight
Incident response and remediation
Step 8: Define Personas and Input Sources
Before creating prompts for interactions, it's crucial to precisely define the personas that will interact with these assistants. This ensures understanding what input they will receive.
Inputs can vary depending on the persona interacting with the system, whether it's a patient, doctor, or AI assistant.
Additionally, agents may receive input directly from an assistant rather than a human, highlighting the importance of knowing the exact personas interacting with the system to determine prompts effectively.
Step 9: Design prompts together with the cross-functional team
Work collaboratively with the cross-functional team to design prompts that will guide the generative AI model's response.
Step 10: Choose Development Approach
At this stage, the decision needs to be made whether to create the assistant or agent from scratch, custom-built, or to opt for a platform/solution that offers faster development capabilities.
Various factors come into play here, such as the specific use-case requirements and the level of customization needed for the assistant or agent. Choosing a platform requires consideration of trade-offs between customization, speed, efficiency, and control.
It's essential to assess the existing IT landscape and how our intended solution integrates into it.
Step 11: Designing the AI Assistant/Agent Architecture
It's crucial to establish the backbone of your AI system: the backend architecture. This step involves carefully crafting the architecture for your AI assistant or agent, which serves as the foundation for its functionality and performance.
There are various architectural approaches to consider, each offering unique benefits. For example, you may opt for a microservices architecture, breaking down your AI system into smaller, independently deployable services. This approach promotes scalability and flexibility, allowing for easier management and updates of individual components. Alternatively, an event-driven architecture offers real-time responsiveness by enabling components to communicate through events, facilitating seamless integration and decoupling of services.
Whether you're starting from scratch or leveraging different platforms, it's vital to prioritize architectural principles that ensure reliability, scalability, and maintainability. Carefully designing the backend architecture sets the stage for a robust and efficient AI system that can adapt to evolving needs and scale effectively.
Step 12: Build a user experience (UX) and user interface (UI)
Create user-friendly experiences and interfaces that will run the generative AI model in production for the chosen persona's use case.
Tips:
Keep design and interface simple.
Create a logical and intuitive user flow that guides users through the AI model's functionality.
Consider how the new interface fits within the larger ecosystem of existing apps.
Ensure the UX/UI is responsive and accessible across different devices and screen sizes.
Step 13: Expand usage to additional individuals
Once you are getting acceptable results from tuning, invite a few other individuals within the chosen persona to start using the model.
Continue testing, measuring, and tuning with a group until you are getting consistent, quality outputs.
With each new individual, make sure you understand the different ways each user interacts with the generative AI model.
Step 14: Expand usage to additional use cases within the same domain
With each use case added to the model, it can become more accurate in the domain.
Conclusion
AI stands poised to redefine the patient journey, driving efficiency, and increasing satisfaction across the board. By investing in AI, healthcare facilities can ensure a patient-centric approach that meets the needs of the modern patient and addresses the challenges faced by healthcare providers.
Take the Next Step in Healthcare Innovation
The potential of AI to transform the patient journey is immense, but realizing that potential starts with a conversation. We're at the forefront of integrating AI into healthcare to make patient care more efficient, personalized, and accessible. If you're ready to explore how AI can revolutionize your practice or healthcare organization, we want to hear from you.
Reach out to us today to start the dialogue on transforming healthcare together. Let's make the future of patient care brighter, together. Contact us now to begin shaping the future of healthcare.
