AI Automation for Healthcare

AI is making the healthcare industry more efficient and accessible, through back-office automation and better knowledge management. It is reducing the time burden on healthcare professionals and improving patient care.

In contrast with previous digital efforts, Generative AI is having a particularly strong early adoption. We are seeing CIOs, enterprise architects, ops teams, physicians, and other roles being very receptive to this new technology.

A Myriad of Opportunities

There is a wide range of applications for GenAI in healthcare, including those shown above:

• Reporting: Automating SOAP notes, radiology reports, and other documentation by transcribing and summarizing patient visits.
• Knowledge Management: Saving CSR time by providing instant answers to common questions.
• Patient Care: Providing personalized care plans and recommendations based on patient data.
• Training: Creating interactive training materials for medical students and professionals.

Two Categories of AI Solutions

Starting with Generative AI in healthcare can be daunting due to the numerous paths.

The following structure clarifies the commonalities, differences, and implementation challenges of each use case.

At Stack AI, we categorize solutions into two categories: i) knowledge management ii) back-office automation.

Knowledge management

Companies typically start their GenAI journey with solutions that fall into this category, as it is the most intuitive and less complex to implement. Users interact with the Large Language Model (LLM) via a conversation interface, asking questions and receiving answers. The LLM retrieves information from a knowledge base or from documents that the user uploads.

Back Office automation

This category requires more connections with other systems and is intended to replace manual tasks that are part of an operational process. Think of the automation of SOAP notes. The LLM receives the recording, extracts the relevant information, generates the report, and sends it to the EHR system. This category tends to be more impactful. The value can be more easily measured, but it is also harder to implement.

We recommend starting with knowledge management solutions, as they are easier to implement and require less integration with other systems. Once you have a few of these solutions running, you can start with Back Office automation solutions which are generally more impactful.

High-Impact Solutions Built Easily in Stack AI

What are some examples of impactful solutions that can be built in Stack AI? Below we explain three popular solutions:

1. Physician Co-pilot

This use case falls into the knowledge management category. The physician asks questions about the patient's history, the treatment plan, and the patient's progress. The LLM retrieves the information from the EHR system and the knowledge base and provides answers.

Physician Co-pilot architecture in Stack AI.

2. Hospital CSR Assistant

This is a back-office automation use case. An LLM receives emails from patients, then drafts a response based on the patient's query and the hospital's policies. The draft is sent to the CSR for review and approval.

CSR Assistant architecture in Stack AI.

3. SOAP Report Generator

This is also a back-office automation use case. The physician records the patient's visit, and sends it to the LLM. The LLM transcribes the recording, extracts the relevant information, uses existing templates, and generates the SOAP notes. The SOAP notes are automatically uploaded into the EHR system after review.

SOAP Report Generator architecture in Stack AI.

Important Considerations for GenAI in Healthcare

Professionals in the industry know that the adoption of new technologies in healthcare is not easy. There are many regulations and compliance requirements that need to be met. The focus on privacy and security is particularly strong.

When adopting Generative AI in healthcare, it is important to consider the following:

Privacy

Ensure that the AI system is compliant with HIPAA among other regulations such as SOC 2 and GDPR. Additionally, if inclined to use AI Builders such as Stack AI, request on-premise deployment options. Stack AI can be deployed in your Virtual Private Cloud (VPC) or in your own servers, so the data never leaves your environment.

Moreover, when using AI models, it's important to use HIPAA-compliant models such as Anthropic ones. Ask for Data Processing Addendums with AI providers to ensure that your data will not be used to train the models. In Stack AI, you can find these HIPAA-compliant models and DPAs.

Finally, ensure the platform you use has PII (Personal Identifiable Information) detection and encryption when needed for extra protection.

Data Governance

Implement a robust Data Governance structure so that you can track the data used by the AI models and by specific individuals. Make sure the system has access and group control features. You can enable SSO (Single Sign-On) as well as MFA (Multi-Factor Authentication) to ensure that only authorized users have access to the system.

Use of AI

Finally, it's important to safeguard how your users (CSRs, physicians, etc.) interact with the AI system. Make sure that you implement guardrails to restrict the LLM's responses to specific domains.

In Stack AI, we built our platform around these dimensions. You'll find dedicated models and features to have a smooth experience building secure custom Generative AI solutions.

Getting Started

We love hearing your use cases and working together to design an implementation roadmap that fits your organization. When working with enterprises, we start with a pilot program where we focus on 1 to 3 low-hanging fruit that can be implemented fast (few weeks) to show the value inside your organization.

Don't wait any longer, schedule a meeting with us today and start your Generative AI journey!