Analyzing the balance between building custom AI tools internally versus procuring third-party solutions for healthcare enterprises to optimize domain-specific needs

In 2024, the healthcare sector in the United States finds itself at a critical point in adopting generative artificial intelligence (AI) technologies to improve patient care, streamline operations, and reduce administrative burdens.

Hospitals, medical practices, and health systems are investing more in AI solutions that help with tasks like clinical documentation, patient intake, coding, and managing money cycles. But healthcare organizations have to decide whether to build AI tools themselves or buy them from outside vendors. This choice depends on things like cost, how much they can change the tools, how well tools work with current systems, support after buying, and rules about healthcare data.

The healthcare industry traditionally has been slow to adopt new technology, partly due to regulatory concerns, data privacy, and the complexity of clinical workflows.

However, spending on AI in healthcare has grown to $13.8 billion in 2024. This is six times more than the year before. Healthcare leads in AI use with more than $500 million spent on AI tools made for clinical and administrative jobs. Because AI went from being an experiment to something critical, health leaders now face new decisions about how to get AI tools.

This article intends to examine the pros and cons of building AI tools internally versus buying third-party solutions within the U.S. healthcare system.

It looks closely at healthcare-specific needs, challenges in putting AI to work, and what automation can do. The main readers are medical practice managers, clinic owners, and IT staff who decide on technology in healthcare.

The Growing Role of AI in Healthcare Enterprises

Before considering whether to build or buy AI tools, we should understand what AI does in healthcare. Companies like Eleos Health, Abridge, Ambience, and Heidi have created technology that listens to doctor-patient talks and writes notes automatically. This saves doctors time on paperwork and lets them focus on patients.

AI also automates money-related tasks like patient sorting and medical coding. Tools from SmarterDx, Codametrix, Adonis, and Rivet do this. They work with electronic health records (EHR) to turn spoken words into notes fast, so healthcare workers spend less time on paperwork.

Healthcare providers see many uses for AI—one study found about 10 ways per healthcare organization. Many are moving from testing AI to using it quickly. Almost a quarter of providers want to start AI tools right away. This shows broad interest in AI in U.S. clinics and hospitals.

Building AI Tools Internally: Advantages and Challenges

In 2024, about 47% of healthcare groups decide to make their own AI tools. This is more than before. These organizations trust their IT teams and leaders to create tools that fit their needs exactly.

Advantages

• Customization to Specific Workflows: Building AI in-house lets hospitals shape software to fit their own processes exactly. AI can handle the special needs of different medical fields and follow local rules.
• Better Integration with Existing Infrastructure: Internal teams can make AI that fits well with current EHRs, money software, and communication tools. Older systems can work better with custom-made AI.
• Data Security and Privacy: Controlling AI development reduces risks from outside data handlers. Meeting HIPAA rules and patient privacy is easier when building and keeping AI in-house, avoiding cloud storage if needed.
• Long-Term Cost Management: Although starting costs can be large, making AI internally helps avoid paying ongoing fees or depending too much on vendors over time.

Challenges

• High Implementation Costs: Making AI tools takes money, experts, computer power, and time. In 2024, about 26% of AI trials failed because of poor budgeting, which worries healthcare leaders.
• Talent Shortages: There are not enough AI experts who also know healthcare well. Their salaries are much higher than usual, which makes hiring hard and expensive.
• Scalability and Maintenance: As AI gets more complex, IT teams may have trouble keeping it running well. Without dedicated AI staff, systems might slow down or have security risks.
• Longer Time to Market: Making AI from scratch takes longer than buying ready-made products. This can slow down improvements in care and operations.

Procuring Third-Party AI Solutions: Pros and Cons

On the other hand, 53% of healthcare groups in 2024 buy AI from outside vendors. Many fast-growing AI products, like ambient scribes and workflow automations, come from companies outside the usual healthcare system.

Advantages

• Faster Deployment: Vendor tools are often ready to use or easy to customize. This helps hospitals fix paperwork backlogs or care problems quickly.
• Proven Performance and ROI: Vendors give proof like results and case studies that show their tools work. Eleos Health, for example, has software that saves doctors time and works well with EHRs.
• Ongoing Support and Updates: Outside companies keep improving AI based on new research and user feedback. Hospitals get these updates without doing the work themselves.
• Regulatory and Compliance Expertise: Vendors know healthcare rules and build tools to follow them. This helps reduce work for hospital compliance teams.

Disadvantages

• Limited Customization: Vendor products may not fit unique hospital workflows exactly, which can cause problems or need workarounds.
• Vendor Lock-In Risks: Relying on vendors can be risky if contracts change, service worsens, or prices rise. Changing vendors later can be hard and costly.
• Data Privacy Concerns: Sending patient data to third parties can bring legal and ethical risks. Some hospitals worry about storing data in the cloud.
• Integration Challenges: Even if vendors say their tools work with other systems, real-life integration often needs more IT effort.

AI and Workflow Automations Relevant to the Build vs. Buy Question

Automation is a key part of how AI helps healthcare. Knowing what tasks AI automates shows why hospitals must carefully choose build or buy options.

Hospitals use AI to handle tasks that take a lot of time, like documentation, patient registration, sorting patients, coding, and billing. Tools that listen to conversations and write notes help avoid doctor burnout and improve patient care. AI tools for managing money cycles speed up claims and reduce errors, helping finances.

Most healthcare groups in 2024 use retrieval-augmented generation (RAG) systems that help AI find and mix large amounts of data like EHRs and patient notes. About 12% use agentic architectures, which let AI perform multi-step tasks on its own, moving towards doing complex jobs without help.

Because of this, AI workflow automation must be strong, can grow when needed, and can be changed to fit. Medical teams should think about:

• Integration Capacity: Will the AI work smoothly with current software like EHRs and management tools? Custom tools often fit better, but some vendors have APIs and connectors.
• Adaptability to Clinical Specialties: Medical fields like general care, cancer, and heart care all have different needs. AI must fit these to help.
• Security and Privacy Controls: AI tools must follow rules like HIPAA and protect patient data with encryption, access limits, and logging.
• User Experience: Doctors and staff must find AI easy to use and not disruptive.

Choosing to build or buy depends on which option fits these needs better for a healthcare group.

Considerations for Healthcare Enterprises in the United States

People who manage medical practices, own clinics, or handle IT face special U.S. healthcare challenges when choosing AI tools.

• Regulatory Environment: U.S. healthcare must follow laws like HIPAA and HITECH. This makes following rules very important and influences whether they pick internal or external AI.
• Cost Constraints and Budgets: Many AI budgets are split between special innovation funds and normal budgets, sometimes moved from other areas. Many places need AI to show clear money benefits and avoid extra costs.
• Healthcare Ecosystem Complexity: Healthcare ranges from small clinics to big hospital systems. What works in a large hospital may not fit a small clinic. This makes customizable or flexible AI important.
• Data Privacy Sensitivity: Because patients and providers want privacy, some hospitals want full control over how AI handles data, choosing in-house or local setups.
• Talent and Expertise Availability: The high need for AI experts makes many groups prefer buying tools, especially if they lack skilled IT workers.

Trends to Watch in Build vs. Buy Dynamics

Healthcare is changing how it buys AI. In 2023, 80% of groups bought AI from vendors, but this fell to 53% in 2024. This shows more organizations want to build their own tools. They weigh specific needs against costs and speed.

New AI types, like agentic AI that can work alone, might change choices. Hospitals may one day want AI that handles complex jobs like helping decisions, monitoring patients, or managing care after discharge.

Companies like Anthropic have grown in U.S. healthcare by focusing on security and fitting in with current systems. The battle between buying and building AI tools will likely continue as groups look for the best fit.

Summary

Medical managers and IT staff must think about what their organization needs and can do when choosing between making AI or buying it. They should consider how much they need to customize tools, their current IT setup, rules they must follow, budgets, and available staff. Healthcare leads in using generative AI in the U.S. The main goal is to pick a path that helps doctors and operations work better, whether by building or buying AI tools.