Creating an AI Strategy for Community Health Centers
Artificial intelligence is not just a buzzword in healthcare anymore—it’s a practical toolkit that can shave minutes off routine tasks, shorten queues, and ultimately free clinicians to spend more time with patients. Effectively implementing and utilizing AI, however, is going to prove a challenge for many healthcare organizations
The issue isn’t about whether to use AI, but how to do it purposefully, safely, and in ways that stick. This requires a strategic playbook to guide your healthcare organization through the steps to implement AI innovation in a sustainable, fruitful way.
Telcion is at the forefront of AI integration in healthcare. Here’s our advice to help organizations build an AI vision and turn it into results.
Start with a clear vision
It’s important to write guiding documentation for AI in your organization to build a pathway for efficient usage. High-level intentions prevent scattershot pilots and “AI for the sake of AI.” Your strategy document should address the following:
Why you’re adopting AI (e.g., better patient experience, fewer administrative bottlenecks).
Where AI is appropriate—and where it isn’t.
How you’ll train people and protect privacy.
It helps to have both a broad AI vision statement and an AI strategy document. This provides the perspective for AI implementation while placing it in a practical context.
Sample Vision Statement
“Our community health center intends to use AI technology wherever feasible to provide better patient experiences, reduce administrative burdens, automation of workflows, and a safe environment for our people to use AI without security risks.”
Sample Strategy
“Our community health center will implement our AI vision as comprehensively as we can. We will revisit this strategy annually, or as new opportunities present themselves, to validate new tools and whether they fit our vision for the organization.”
Something to consider when creating these documents is governance:
Acceptable use: what data can/can’t be entered; examples of prohibited prompts; escalation paths for uncertain cases.
Privacy & security: vendor risk assessment, BAA requirements, data retention, and audit logs.
Clinical oversight: who reviews scribe output, who validates model-generated suggestions, and how disagreements are resolved.
Change management: short training modules, job-aids, and champions embedded in each department.
Once you’ve created your vision and strategy documents, commit to reviewing and updating them regularly (at least annually) as tools and regulations evolve. You want to stay in front of the curve, not behind it.
AI Readiness By Department
AI implements best when it’s contextualized in real use cases. The implementation of AI will depend on what department it is being rolled out in, so it’s important to consider where and when AI is being utilized in your organization.
Here are some examples of how AI can streamline operations for different departments in your healthcare organization.
Customer Service
Imagine a call center agent that is always available to answer patient inquiries, from appointment scheduling to payment—that’s the impact of AI on your customer service team.
AI call center agents can revolutionize your customer support. They are available 24/7, accessible through multiple channels, including text and voice, can collect payment, and can be programmed to answer basic or frequent questions in order to free up your human agents for more complex queries. This can reduce your overall labor cost and make your call center much more efficient and effective.
Medical/Clinical operations
Medicine is a complex practice, and AI support can augment clinicians’ workflows in order to provide better patient care. AI scribes, for example, can transcribe provider dictation directly to patient charts. This expedites the charting process and frees up providers to focus on the patient experience instead of notetaking.
Additionally, AI healthcare models are helpful tools that can assist providers in researching a diagnosis and creating treatment plans. With provider approval and oversight these models can take patient information and make recommendations for diagnosis and care plans. They can also be integrated into certain EHR platforms!
It is important to note that artificial intelligence should never be the final say on patient treatment. It is absolutely critical that clinicians have the responsibility of professional judgement. The power of AI is in augmenting and supporting clinician expertise, never in replacing it.
Administration
Administration is probably the easiest department to empower with AI. Because of the abundance of AI tools already integrated into standard office and productivity software (e.g., Copilot in Microsoft Office applications), it’s often only a matter of turning on AI functionality or upgrading a subscription to get AI capabilities for your administrative team.
Another less obvious area for AI support in administration is inbound faxing. While faxing is declining in other industries, it is still frequently used in healthcare environments. AI-powered faxing tools can automatically accept, read, and file inbound faxes, sending appropriate faxes to patient charts as necessary. These tools are supported by certain EHRs, which makes chart integration particularly useful.
IT & Help Desk
Artificial intelligence can significantly change your IT operations. In cybersecurity, AI can augment your security team to analyze data from disparate security tools in real time to identify threats. Where a human analyst can only digest data from one tool at a time, AI security tools can synthesize data from many tools at once in order to provide a comprehensive, timely response to potential security threats.
Network operations can also benefit from AI augmentation. Similarly to cybersecurity, AI tools can monitor network data for issues and fix them in real time. They can also synthesize logs for easier analysis, which frees up network engineers for project work and more complex tasks.
Questions to ask before rolling out AI
Are we truly ready for AI, or do we need to shore up identity, access, and data quality first?
What training and change support will each role need?
Which tools fit our stack (EHR, contact center, productivity suite), and what’s the total cost of ownership including integration and oversight?
What IT shifts are necessary to make AI a sustained priority (ownership, budget, SLAs, and security monitoring)?
AI is not a silver bullet, but with a written vision, clear guardrails, and department-specific pilots, community health centers can capture real time and cost savings—without compromising privacy or care quality. Start small, measure relentlessly, and scale what works.