Introduction of a Local Large/Small Language Model (LLLM/LSLM) for Mid-Sized Healthcare Companies
Introduction
Overview of Local LLMs
Local Language Models (LLMs) are AI-driven models deployed within an organization’s infrastructure, ensuring data privacy and customization. Unlike cloud-based LLMs, local LLMs or SLMs allow for complete control over data, reducing security risks. For instance, Phi-3, a recent open source LLM (some labels it a SLM), offers advanced language processing capabilities and an entity can tailor it to meet with specific organizational needs.
Importance for Healthcare
The healthcare industry handles sensitive patient data and requires precise, reliable, and secure solutions. Local LLMs/SLMs can revolutionize how healthcare entities manage information, deliver patient care, and streamline operations. By ensuring that all data processing happens within the organization's secure environment, local LLMs/SLMs help meet stringent compliance requirements and enhance data privacy.
Purpose
This write-up aims to provide CTOs and CIOs of mid-sized healthcare companies with insights into the benefits and implementation of a local LLM/SLM using “phi3” model, enhancing their operational efficiency and patient care. We will explore specific use cases, implementation strategies, and the potential impact on the company's bottom line.
1. The Case for Local LLMs/SLMs in Healthcare
1.1 Enhanced Data Privacy and Security
Regulatory Compliance: Local LLMs/SLMs help in adhering to strict healthcare regulations such as HIPAA by ensuring that patient data does not leave the organization’s secure environment. Data Sovereignty: Organizations retain full control over their data, preventing unauthorized access and ensuring compliance with data sovereignty laws.
1.2 Customization and Specialization
Tailored Solutions: Local LLMs can be fine-tuned to meet specific needs of the healthcare industry, including medical terminology and unique patient care scenarios. Integration with Existing Systems: Local LLMs/SLMs can seamlessly integrate with existing Electronic Health Records (EHR), Laboratory Information Management Systems (LIMS), and other healthcare IT systems.
2. Key Use Cases for Local LLMs in Healthcare
2.1 Internal Knowledge Management
Streamlining Information Access: Local LLMs/SLMs can organize and retrieve information efficiently, reducing time spent searching for data.
Improving Decision-Making: Enhanced access to information leads to better clinical decisions, improving patient outcomes.
Very powerful, however, you may consider this use case as Phase II project because of technology complexity, for instance, the implementation of RAG (Retrieval Augmented Generation).
2.2 Patient Care and Support
Personalized Care Plans: Generating tailored rehabilitation and treatment plans based on standardized guidelines and patient data. Real-Time Support for Caregivers: Providing immediate advice and support for home-based and facility-based caregivers. It falls under the Phase I project category.
2.3 Diagnostic Laboratories
Interpreting Test Results: Offering detailed interpretations of complex test results using vast medical literature. Standardized Reporting: Generating customized, compliant, and comprehensive reports that meet specific healthcare provider needs.
Another use case would be new employees training of the medical/healthcare knowledge with an interactive interface into the local LLM/SLM especially for those new employees who have no prior medical/healthcare training.
These two use cases also fall under the Phase I project category.
3. Industry-Specific Applications and Implementation Samples
3.1 Home Health Agencies and Rehabilitation Centers
Supporting Caregivers: Providing real-time advice and support for home-based caregivers through local LLMs/SLMs. Enhancing Patient Care: Tailored recommendations and monitoring for chronic conditions, improving overall patient care.
Once you get a local language model like ‘phi3’ up and running, the very first thing you want to do is to check its text-generation ability and how good the output is.
Your python code would load the model with parameters, provides some input and have the model generate text(response).? See the following screenshot for the core code with Python, not the final code, but it would give you an idea how it might be.
See the following screenshot for command line “prompt” and “response” when you have a fully functional code to execute.
For your readability, let me first copy/paste the “prompt” and “response” below:
Prompt:
“I am currently with Emily P. Supresor, who is experiencing chest pain and shortness of breath. Can you provide step-by-step instructions on what I should do immediately, including any first aid measures and when to call for emergency services?”
Response:
I'm here to help you, but please remember that I'm an AI and can't replace professional medical advice. However, I can provide general guidance. It's crucial that you call emergency services immediately if Emily is experiencing chest pain and shortness of breath. Here's a general guide:
1. Call emergency services: Dial the local emergency number (e.g., 911 in the United States) right away. Provide them with Emily's location and a clear description of her symptoms.
2. Stay with Emily: Keep Emily calm and comfortable while waiting for emergency services to arrive.?
3. Monitor vital signs: If you are trained to do so, check Emily's pulse and respiration. If she becomes unresponsive, perform CPR if you're trained and comfortable doing so.
4. Positioning: If Emily is conscious and able to sit up, help her sit comfortably, with her back supported. Avoid having her lay flat, as this may exacerbate her symptoms.
5. Reassure her: Talk to Emily, reassuring her that help is on the way.
6. Do not give her anything to eat or drink: It's essential that she doesn't ingest anything before the medical professionals arrive.
7. Prepare for the arrival of emergency personnel: Make sure the pathway is clear and accessible for the emergency team.
Remember, it's crucial to act quickly in such situations. The specifics of the situation might require additional steps, so it's always best to seek professional medical advice.
But we understand our users won’t be able to use prompt to do their work. Besides, they can’t access a server directly.? Thus, we need to develop web interface or web application for the same task.
Flask library can help us for development and for production, we would need another web server.? Let’s go with development first.? See the following screenshots for the interface and the web output for user questions/queries/prompts (zoom in to view larger image).
You’ll see it has “legs” and it is “kicking”!?
Now you’re getting your feet wet, let’s dive deep for actual business application delivery.
I call this Phase I implementation. See below.
3.2 Diagnostic Laboratories
See screenshots below for this type of medical service when leveraging a local LLM.
领英推荐
?
This is your local LLM/SLM in action!
And you can, certainly, take a few steps further, for instance, automatically turn the response from the LLM/SLM into speech/voice, thus, it frees up the staffer's hands and faculty (vision) so that she/he can focus on the patient while seeking valuable help at the same time. And yes, I've done that, it's just amazing!
4. Implementing a Local LLM/SLM
4.1 Hardware and Software Requirements
Infrastructure Needs: Necessary hardware configurations for deploying a local LLM/SLM, including a server and storage solution.
For running the Phi-3-mini (3.8 billion parameters) model, which was trained on 3.3 trillion tokens, on Windows 10 or 11, you have several hardware options depending on your performance and budget requirements. Here are some recommended configurations:
1. High-Performance Desktop Workstations:
Option A: High-End Configuration
Option B: Mid-Range Configuration
2. High-End Laptops:
Option A: Gaming Laptops
Option B: Workstation Laptops
3. Small Form Factor (SFF) PCs:
Option A: Compact Desktop
Option B: Mini PC
4. Edge Devices for On-the-Go Applications:
Option A: Edge Computing Devices
Considerations for Running Phi-3-mini:
By choosing the right hardware, you can effectively run and deploy the Phi-3-mini model on your Windows 10 or 11 system, enabling powerful AI capabilities locally.
Software Requirements: Install Python and relevant Machine Learning libraries and of course the "phi-3" model.
Hardware recommendation:
Buy a computer with 64 GB of RAM, most desirable to have GPU or even better, a GPU-based computer, otherwise with dual CPUs for it can also perform parallel processing. Processing speed and RAM are very important.
I have a computer with 16 GB of RAM, it’s able to run ‘phi3’ but slow, you should not deploy it as a production server.
4.2 Best Practices for Deployment
Phased Implementation: Steps for a successful rollout, including pilot projects and scaling to full deployment. Training and Support: Ensuring staff are well-trained to utilize LLMs effectively and ongoing support is available.
Your best bet is to hire a competent AI/LLM/SLM professional who’d been there done that as a consultant.? The money would be well spent.
5. Challenges and Considerations
Cost and ROI
Initial Investment: Evaluating the cost of implementing a local LLM/SLM with phi3, including hardware, software, and training expenses.
A powerful computer estimates at $4000. Hiring the above-mentioned AI/LLM/SLM professional to implement it including a production-grade web application for the local LLM/SLM for your entity for one week, at a total cost of $5000.
Your employees training cost would be very low since they just use local LLM/SLM via web applications (everyone is familiar with web applications).
Thus, the estimates of the total cost of about $9000 or slightly more for a local LLM/SLM deployment.
Long-Term Benefits: Assessing the return on investment through improved efficiency, reduced errors, and enhanced patient care.?
Assumptions:
Initial Investment Costs
Annual Operational Costs
Operational Cost Savings
Revenue Increases
Total Annual Benefits
ROI Calculation
With regard to the ROI, I'd recommend you choose a formula that has been serving your organization well.
Conclusion
With the further reduced training and maintenance costs, the initial investment in a local LLM/SLM like Phi-3 becomes even more cost-effective. The payback period is short, and a very impressive ROI return. This scenario illustrates that even with modest initial and ongoing costs, the deployment of a local LLM/SLM can yield significant cost savings and revenue increases, making it a highly attractive investment for certain mid-sized healthcare entities.
A side note: don't get hung up on the nomenclature of LLM vs. SLM. According to some literature, when a language model has 1 billion to 10 billion parameters it's considered a large language model. "phi-3-mini" has 3.8 billion parameters, hence, calling it an LLM is not a stretch.
Hope this write-up has been informative and if you have any questions, please dm me or connect with me.
IT manager went Renewables. Branding & Strategy for the sustainability/EV industry??Moderator "AI Small Language Models"??New book "From Lohner-Porsche to Autonomous Driving: 125y of electric mobility" coming soon
5 个月Great posting, (Don) Chunshen Li. We also shared it in the Linkedin group exlusively for SLMs as a great example. You are welcome??https://www.dhirubhai.net/groups/9859028
C-Suite Transformation Leader | Board of Directors Chair & Candidate | 25+ Years Turning Vision to Action | Chief Digital Officer | Chief Technology Officer | Executive Advisor | Critical Thinker | Motivating Storyteller
6 个月Curious as to what people think about the following question (which I think is interesting): Is there a fundamental difference between local LLMs and SLMs (small language models). Personally, I suspect there is, although the definition of both concepts are still somewhat fluid. Thoughts?
Idea Man | Entrepreneur | Technologist (past)
6 个月fyi FTI Consulting Tanner Kaufman
Idea Man | Entrepreneur | Technologist (past)
6 个月fyi Paolo Pelizzoli Jim Wiegand Jeff Thomas Hui Huang Jimi Li David Hirschfeld Elie Levy Bryant Maroney Lukas Kairys Kevin Yu Elizabeth Xu, Ph.D. James Moberg Dave Goldberg