Step-by-Step Guide to Building Your First Healthcare AI Agent

The new buzzword from the tech industry has probably got everyone excited. You see, the re-introduction of AI agents has opened the door to many possibilities in our day-to-day lives. Though many people consider this as a new thing in the market, it has actually been in existence for quite some time.

One of the most prominent examples of this can be seen in caller bots. Over the years, AI agents have become more advanced, and today, they can perform your tasks just like you would perform. And its inclusion in the healthcare industry is sought out to be one of the most anticipated.

The healthcare industry is in dire need of automation and efficiency due to the increasing pressure and decreasing healthcare workforce. In the midst of this, custom healthcare AI agent development has become somewhere a practical solution to optimize healthcare workflow.

But how does one develop a custom healthcare AI agent?

Well, that’s why you’ve come to this blog post. In this blog, let’s discuss healthcare agency strategy and a step-by-step guide to custom healthcare AI agent development.

So, without further ado, let’s get started!

Understanding Healthcare AI Agents: Your Digital Workforce

Before developing an AI workflow agent, let’s first understand the intricacies of healthcare AI agents. Typically, healthcare chatbots or automation tools that are used to perform tasks can be considered AI agents. However, they are typically limited to certain usability.

However, modern AI agents that you want to include in your practice are autonomous software programs that use artificial intelligence to perform certain tasks and achieve goals on behalf of a user – hence the name AI agents. Now, as these programs will be designed to perform tasks related to healthcare delivery or typically work in a healthcare setting, they will be called healthcare AI agents.

Types of Healthcare AI Agents

Depending on the outputs these agents give, here are some of the different types of AI agents that you can use in your healthcare practice:

1. Conversation AI Agent: These are an evolved version of AI chatbots, or you can even consider this as a type of generative AI. These agents are typically useful for analysis, research, etc.

2. Task-based AI Agent: As the name suggests, these AI agents are designed to perform certain tasks that are typically complex.

3. Decision Support AI Agent: Decision Support AI agents are designed to help healthcare providers make better decisions quickly and accurately on the basis of data.

4. Hybrid Agents: Hybrid agents are a combination of multiple agents and have grown in demand, given their wide capabilities and use cases.

Core Capabilities & Intelligence Levels

Some of the core capabilities and intelligence levels of AI agents must be known and considered if you are considering including them in your clinical and administrative workflows. For building an AI workflow agent or clinic automation AI, a simple rule-based response mechanism can be implemented.

On the other hand, for decision support or analysis, advanced GenAI in clinics can be used. These systems would not only provide healthcare providers with reasoning and learning from the data that is being flowing into your system.

The capabilities of AI agents might spark the argument of AI Agents taking jobs. Well, AI agents augment the process. So, even if you bring EHR-integrated AI agents into your workflow, human intervention is required, as the human touch has significant importance in patient care.

Strategic Use Cases: Where AI Agents Deliver Maximum Impact

Custom healthcare AI agent development sounds lucrative. However, if you don’t know what that AI agent is going to do, the whole purpose of developing an AI agent is simply pointless. To help you find the use case that suits your healthcare practice, here are some of the strategic use cases that you need to know so that your AI agent can bring maximum impact to the growth of your practice.

1. Administrative Automation: Certain tasks in the healthcare practice’s workflow can be automated, such as patient scheduling, appointment reminders, insurance verification, and billing follow-ups. AI workflow agents to automate administrative tasks can be considered as they reduce the burden of administrative staff and streamline the workflows for them.

2. Clinical Support Functions: GenAI in clinics to provide support to the clinical staff during consultation or for diagnosis can also be considered. Functions like symptom triage, pre-visit questionnaires, medical adherence monitoring, and care plan coordination are some of the use cases of clinical support functions that an AI agent for clinical staff can use.

3. Revenue Cycle Enhancement: Some of the EHR-integrated AI agents can be used to enhance your revenue cycle as well. For instance, they can automatically track and report claims status, claim denial management, prior authorization, and payment collection.

4. Patient Engagement Optimization: One of the major use cases of AI agents in healthcare can be to keep the patient engaged and empower them to proactively participate in their care journey. Some of the use cases in this can include 24/7 support, health education and awareness, chronic care management, and satisfaction surveys.

Finding Your Starting Point: Assessment & Prioritization

Now that you know what a healthcare AI agent is and what are the tasks and things that it can do for your healthcare practice, it is time to find a way to bring it into your system.

But the question is – where should you get started?

You need to have a healthcare agent strategy in place before you start development. But even before that, you need to assess your system and see where the AI Agent can fit in if it can fit it. Here are some steps that can help you in assessing and prioritize which AI agent development:

1. Workflow Analysis: The first thing that you need to do is study your workflow and data flow for both clinical and administrative aspects. During this analysis, you can easily identify the automation opportunities using time and motion studies. Step-by-step process mapping techniques for identifying opportunities in workflows are one of the best ways.

2. Pain Point Prioritization Matrix: After identifying the opportunities, you need to prioritize which opportunities you need to capitalize on. For this, you need to evaluate the processes based on the frequency of the task, the complexity of the tasks, the margin of errors that occur in it, and the impact it has on staff satisfaction. For instance, if you have identified healthcare chatbot and voice-driven AI assistant as your opportunities, the above evaluation criteria can give you a clear idea of which one suits your practice the best.

3. Resource Readiness Assessment: Once the AI agent opportunities are identified and prioritized, it is now time to see if your practice is ready to integrate it. In the assessment of your resource readiness, you need to check for your technology infrastructure, staff capabilities, and budget. 

4. Quick Wins Vs. Strategic Initiatives: Certain aspects of AI agent implementation can be lucrative in nature and can be immediately incorporated into your system. However, for long-term transformation goals, you need to balance the immediate impact it can have on the projects and the long-term impact on your entire practice.

Build Vs. Buy: Choosing Your Development Path

If you are still reading this and have followed the process till now, then before going for a custom healthcare AI agent development, you must consider the option of buying. You see, certain AI agents, like healthcare chatbots, are readily available in the market. 

However, if you have a varied need, custom healthcare AI agent development is the way to go. But still, to help you make your building or buying choice better, refer to this table below:

Architecture & Workflow Design: Building for Success

If you’re still reading this, then you must have probably decided to move ahead with the custom healthcare AI agent development. You must know that the architecture and workflow design are the crucial aspects that determine the success of your healthcare AI agent. So, here are some considerations in this that you must know:

1. System Architecture Planning: If you have identified the opportunities beforehand, then system architecture planning would be really simple. For instance, during planning, you need to consider the data flow design and where AI will be to optimize, analyze, and suggest in the process. Along with that, integration points, security layers, and scalability considerations are some of the aspects that you must be at the top of your healthcare agent strategy while planning the system architecture.

2. Conversational Design Principles: In your healthcare AI agent, especially with a voice-driven AI assistant, conversational design principles are going to play a crucial role. This is because the user will directly interact with the agent. To make the agent usable, you need to create a delightful design with healthcare-appropriate dialogue flows. Also, if you have a fairly large healthcare staff, then including personality traits can be extremely useful.

3. Integration Strategy: The healthcare delivery is collaborative, and even for your practice to run smoothly, it needs to rely on other healthcare systems. The same systems can be used by AI agents to enhance the process and help you make the most. Depending on your needs, having an integration strategy with certain systems can go a long way. Some of the systems that you must consider integration are EHR systems, practice management software, and other third-party tools.

4. Workflow Orchestration: While you are at it, you need to orchestrate the workflow to make the most out of the AI agent. Some of the considerations in this are designing multi-step processes with exception handling and human handoff protocols.

From Pilot to Production: Deployment & Optimization

After designing the workflow and architecture, your development partner will get on with developing the AI agent for you. But while it is in the development phase, you can plan for the pilot or deployment of the AI agent into your workflow or system.

Here is a healthcare agent strategy for deployment that you can use:

1. Pilot Program Strategy: Before deploying the AI agent for everyone, first plan for a pilot program. This means deploying the AI agent in a controlled testing environment for selected users. This will help you determine the success metric and gather crucial feedback.

2. Phased Rollout Approach: Once your pilot program succeeds, rather than the option for a one-shot release, use a phased approach so that gradual expansion from pilot to practice-wide deployment is easier. Moreover, it will help you understand things better, and the risk factor is also mitigated.

3. Performance Monitoring: After implementing the AI agent, it’s time to monitor its performance. Determine the key metrics for tracking and compare them with the user satisfaction levels. This will give you a clear idea about what’s wrong with the performance and give you a nudge to improve in the right direction.

Frequently Asked Questions