NeoTek Solutions builds healthcare AI for providers, payers, revenue cycle firms and health tech companies, from our headquarters in Nashville. We apply generative AI, automation and machine learning to prior authorizations, referrals, coding support and patient intake, so your staff spend less time on paperwork. Clinicians and coders stay in charge of every decision, and we design for HIPAA requirements from the first conversation.

Nashville is a national hub for the healthcare industry. Hospital systems, physician groups, payers, revenue cycle firms and health tech startups all operate here. Many are headquartered in Nashville, Brentwood and Franklin. We design for privacy from the first conversation.


The Pressures Healthcare Organizations Face

Healthcare teams in Middle Tennessee face the same strains seen across the country. Clinical and administrative staff are hard to hire and harder to keep. Prior authorizations, referrals and payer rules create steady piles of documents. Denials slow cash flow, and patients expect faster answers by phone and online.

AI can help with much of this work. It has to fit tightly controlled systems, though, and it must protect patient data. It also has to keep licensed professionals in charge of clinical and coding decisions.


How Can AI Help Healthcare Organizations?

Prior Authorization and Referral Summarization

Problem Staff read long faxes, notes and payer forms to find the details a request needs.

What AI does We build generative AI that extracts diagnoses, procedures and supporting notes, then drafts a structured summary against payer criteria.

Human in the loop We route every summary to your authorization staff, who check it against the source pages before anything is submitted.

Patient Intake and Scheduling Agents

Problem Call volumes spike, and simple scheduling requests tie up front-desk staff.

What AI does We build an AI agent, meaning software that can take approved actions in your systems, that collects intake details and offers open slots.

Human in the loop We design it to send clinical questions, urgent symptoms and anything unclear straight to a staff member.

Revenue Cycle and Coding Assistance

Problem Coders work through dense documentation under time pressure.

What AI does We build an assistant that suggests codes and points to the supporting text in the chart.

Human in the loop We route every suggestion to your certified coders, who accept, change or reject it, and we log each decision.

Clinical Documentation Summarization

Problem Clinicians spend long hours reviewing records and writing notes.

What AI does We build on a large language model (LLM), an AI model trained to read and write text, to draft visit summaries or chart overviews.

Human in the loop We design it so the clinician edits and signs every note, and nothing enters the record unreviewed.

Contact Center Assistants

Problem Agents search many systems to answer benefit, billing or policy questions.

What AI does We build retrieval-augmented generation (RAG), which grounds answers in your approved documents, to suggest responses with source links.

Human in the loop We leave the wording to your agents and give them a way to flag wrong answers so your team can fix the content. Learn more about RAG for healthcare contact centers.

Denial Analysis

Problem Denials pile up, and root causes hide across payers, codes and locations.

What AI does We build machine learning that groups denials by pattern and predicts which claims are at risk before submission.

Human in the loop We put the patterns in front of your revenue cycle leaders, who choose which fixes to make.

Staffing and Demand Forecasting

Problem Patient volume swings make shift planning a guessing game.

What AI does We build forecasting models that use historical volume, seasonality and schedules to project demand by unit or clinic.

Human in the loop We design forecasts as an input, and your managers still set the final schedule.


Data, Security and Compliance Considerations

Healthcare AI starts with safeguarding protected health information (PHI). We design for HIPAA requirements and work within your compliance program and privacy office. Key practices include:

  • PHI minimizationWe send models only the data a task needs, and we de-identify data where the use case allows.
  • BAAs with model providersWe support business associate agreements with cloud and model providers where required.
  • Private deploymentSolutions run in your cloud tenant on Azure, AWS or Google Cloud, or on-premises where needed.
  • Access and auditRole-based access, logging and retention rules match your existing policies.
  • No training on your dataYour data is never used to train public models.

We do not provide legal advice, so your compliance and legal teams make the final calls. For a deeper look, read using generative AI in healthcare without putting PHI at risk.


How Can NeoTek Solutions Help Your Organization?

Most healthcare teams know where the paperwork piles up. What they need is a partner who can build something safe, prove it works and support it afterward.

  • Work we deliverPrior authorization and referral summarization, contact center assistants, coding support, patient intake agents and denial analysis.
  • Built on your systemsWe integrate with your EHR and practice management systems through the interfaces and exports they support.
  • Designed for PHIWe work in your own cloud tenant with PHI minimization, role-based access and audit logs, and support BAAs with model providers where required.
  • How we workShort cycles with AI-assisted delivery and human review, so you see something working on your own data early rather than after months of analysis.
  • Skills on the teamAI and machine learning engineers, data engineers, cloud and security specialists and QA, plus people who have worked with healthcare data and its constraints.
  • What makes us differentOne team covers strategy, build and staffing, so we can hand the system to your people or stay on with them, and your data is never used to train public models.

Your compliance and legal teams make the final calls, and we build to the requirements they set. Book a free AI consultation to talk through one workflow and what it would take.


How Do Healthcare Organizations Start With AI?

  1. Assessment

    We review your workflows, data sources, systems and risk tolerance. Then we rank use cases by value and feasibility.

  2. Focused proof of concept

    We build one use case, such as referral summarization, in a controlled environment. Staff test it against real work.

  3. Production

    We integrate with your systems, add monitoring and train your team. Then we expand to the next use case.


Related Services


Frequently Asked Questions

Is your healthcare AI HIPAA certified?

There is no official HIPAA certification for software or vendors. We design solutions for HIPAA requirements and work within your compliance program. We also support BAAs with model providers where required.

Will AI make clinical or coding decisions?

No. In our designs, AI drafts, suggests and summarizes. Clinicians, coders and authorization staff review the output and make every final decision.

Can we use generative AI without sending PHI to a public chatbot?

Yes. We deploy models in your own cloud environment or through providers that sign BAAs where required. We also limit and de-identify the data each task uses.

Can AI work with our EHR and practice management systems?

Usually, yes. We integrate through the interfaces and exports your systems support. We confirm the options during the assessment before committing to a design.

How long does a first healthcare AI project take?

A focused proof of concept often takes weeks rather than months. The timeline depends on data access, security reviews and how quickly staff can test.


Start Your Healthcare AI Plan

Find out which use cases fit your organization and your privacy requirements. Take the free AI readiness assessment or book a free AI consultation.