The following is a guest article by Chandresh Patel from Bacancy Technology<\/p>\n
Agentic AI is no longer just a concept for healthcare. Many health systems are already exploring how AI agents in healthcare can automate administrative and clinical workflows. However, the bigger question today is whether to build an agentic AI solution in-house, buy a ready-made one, or combine both approaches.<\/p>\n
Choosing the right approach has become one of the biggest challenges for healthcare leaders as they move from AI experimentation to enterprise-wide adoption.<\/p>\n
According to research conducted by Microsoft and The Health Management Academy, published in the NEJM AI<\/a> in January 2026, found that 43% of health system leaders are testing or piloting agentic AI. Yet only 3% have deployed an AI agent into a live clinical or operational workflow.<\/p>\n That gap between the pilot and deployment is where most organizations struggle, and it\u2019s rarely a technology problem. It\u2019s a trust problem.<\/p>\n Giving an AI agent the ability to schedule patients, update an EHR, submit claims, or coordinate care is very different from deploying a chatbot. Every action it takes must be accurate, secure, easy to track, and meet clinical and regulatory requirements.<\/p>\n For CIOs evaluating this shift right now, the real decision is how much operational responsibility they are ready to hand an AI agent, what governance needs to be in place, and which implementation approach best fits the organization\u2019s long-term strategy.<\/p>\n These are the conversations we have most often with healthcare leaders exploring enterprise AI. Thus, before choosing a platform or starting development, let\u2019s understand why agentic AI in healthcare changes the way health systems approach the build-versus-buy decision.<\/p>\n What Makes Agentic AI in Healthcare Different From Traditional Build vs. Buy Decisions?<\/p>\n Healthcare organizations have been making build-versus-buy decisions for years. Agentic AI changes that decision because it does more than provide information. It can take action and complete tasks on its own.<\/p>\n While a chatbot answers a query, an agent understands the purpose, makes a decision on what must be done, reaches into multiple systems, and executes the process without manual monitoring.<\/p>\n Think of a denied insurance claim, for example. Rather than just recognizing the problem, an AI agent could confirm eligibility, retrieve the required documents, fix coding mistakes, ensure the claim follows the right approval process, and refile the claim.<\/p>\n That shifts the evaluation from comparing software features to evaluating operational responsibility.<\/p>\n Before selecting any solution, CIOs should answer three fundamental questions:<\/p>\n Can the agent securely interact with every system required to complete the workflows, whether that\u2019s Epic, Oracle Health, or a claims platform? Where the Evaluation Usually Breaks Down<\/p>\n The biggest mistake CIOs make isn\u2019t choosing the wrong vendor or investing in the wrong technology. It\u2019s assuming that if an AI agent can complete a workflow, it\u2019s ready for production.<\/p>\n A Black Book Research survey<\/a> of 182 U.S. hospital leaders, reported by Presidio following HIMSS26, found that only 22% feel confident they could produce a complete, auditable explanation of an AI agent\u2019s decision for a regulator within 30 days.<\/p>\n That\u2019s the number CIOs should be reacting to, not the adoption hype. If a vendor can\u2019t show approval logs, role-based access controls, and a clear record of what an agent did and why, the deployment speed they\u2019re selling doesn\u2019t matter. Ask for the audit trail before you ask for the demo.<\/p>\n Build, Buy, or Hybrid: How Do the Options Actually Compare?<\/p>\n The table below is the framework we walk through before any technical conversation starts.<\/p>\n Decision Factor Deployment time Workflow fit EHR integration Compliance & audit Maintenance Flexibility Upfront cost Vendor lock-in Best for Note: Budget often influences the first decision. Buying is usually the less expensive option to get started, while building costs more initially but gives you greater ownership over time. However, without a skilled AI and integration team, maintaining a custom solution can quickly become a challenge.<\/p>\n Why the Hybrid Model Wins for Most Health Systems<\/p>\n Few health systems need to commit to one approach forever. What tends to work best is using a platform to handle standardized, high-volume workflows like patient intake, eligibility checks, and appointment scheduling, while relying on custom development for the workflows tied to a specific payer contract, clinical protocol, or care pathway that no vendor platform was designed to handle.<\/p>\n This is exactly the gap we close; we offer custom healthcare software development services<\/a> to build and extend AI solutions around your existing platforms, giving you the flexibility to support unique workflows without replacing the systems you already use.<\/p>\n Instead of forcing you to choose between an off-the-shelf platform and a fully custom build, we help you combine both approaches to gain speed without giving up control.<\/p>\n How Bacancy Technology Helps Health Systems Build Production-Ready Agentic AI<\/p>\n With more than a decade of expertise in offering Healthcare IT services<\/a>, we\u2019ve learned that getting an agent to work in a demo is one thing; deploying it inside a health system\u2019s real compliance rules and real EHR systems is another. That\u2019s the biggest difference between a successful pilot and enterprise-wide adoption, and it\u2019s where our expertise delivers the most value.<\/p>\n Instead of replacing the systems you already use, we integrate with Epic, Oracle Health, MEDITECH, Cerner, FHIR R4 APIs, HL7, and your existing scheduling and revenue cycle platforms. This allows AI to work within your current environment without forcing you to replace the tools your teams already depend on. When the development and implementation of your healthcare system has advanced beyond the pilot phase, this is the point where our discussions typically start. We help you determine whether a build, buy, or hybrid approach best fits your operational, technical, and compliance goals.<\/p>\n If you\u2019re evaluating agentic AI in healthcare for production, our healthcare AI specialists at Bacancy Technology can help you assess your workflows, existing systems, and compliance requirements to determine whether a build, buy, or hybrid strategy best fits your organization.<\/p>\n Conclusion<\/p>\n When it comes to agentic AI in healthcare now, the issue is not whether to build or buy; the critical question is whether the model you choose will be safe to use, able to interact with your existing systems, and sustainable beyond the pilot stage.<\/p>\n The decision should come from your workflows and your compliance requirements, not from which option looks fastest on paper.<\/p>\n At Bacancy Technology, we help health systems plan and implement healthcare AI solutions<\/a> that fit their workflows, existing systems, and compliance requirements. Whether you need to evaluate a vendor, build a custom solution, or consider a hybrid approach, our team can help you choose the right path and deliver solutions that work in real-world healthcare environments.<\/p>\n About Chandresh Patel<\/p>\n
\nCan every action it takes be reconstructed later for a compliance review?
\nAnd does the workflow reflect something specific to how your organization delivers care, or is it a problem every health system solves the same way?<\/p>\n
\nBuy
\nBuild
\nHybrid<\/p>\n
\nWeeks
\nMonths
\nWeeks to months<\/p>\n
\nStandard workflows
\nCustom workflows
\nStandard + custom<\/p>\n
\nVendor-supported
\nFully customized
\nMix of both<\/p>\n
\nVendor-managed
\nFully controlled
\nShared responsibility<\/p>\n
\nVendor handles it
\nInternal team
\nShared<\/p>\n
\nLimited
\nHigh
\nHigh<\/p>\n
\nLower
\nHigher
\nModerate<\/p>\n
\nHigher
\nNone
\nLower<\/p>\n
\nScheduling, intake, basic RCM
\nClinical workflows, payer rules, care pathways
\nFast deployment with custom logic<\/p>\n
\nApprovals, logging, role-based access control (RBAC), and security guardrails are included in the initial release. This is how we achieve compliance with zero delays in rollouts or rollbacks.
\nPrior authorization, denial management, patient access, scheduling, clinical documentation, and care coordination all have different operational challenges. We build around those workflows rather than trying to fit healthcare into a generic AI platform.
\nWhether the right fit is Azure OpenAI, AWS Bedrock, Google Vertex AI, LangGraph, CrewAI, MCP, or a secure RAG architecture, we choose the technology based on your business and clinical requirements, not the other way around.
\nSome organizations need help evaluating commercial platforms. Others need custom development tied to payer requirements or clinical protocols. Most projects combine both, using a vendor platform with a custom layer to move faster without giving up control of critical workflows.<\/p>\n