We design, build and run HIPAA-compliant AI solutions for healthcare, with every project tied to a result your team agrees on before work begins and a person approving every decision that matters.
Trusted by 100+ businesses and AI-native startups
Almost every health system now runs some form of healthcare AI, yet few have the strategy, testing and measurement to take it past the pilot stage and run it reliably in production. Closing that gap, more than choosing the right model, is what turns AI healthcare investment into measurable results.
4%
of health systems describe their AI strategy as advanced. 63% are still developing or working ad hoc, even though 93% already run third-party AI.
18%
of health systems and plans scaling AI can reliably measure its financial return, even as 75% of them plan to spend more on AI next year.
88%
of physicians say proof of safety and efficacy is essential before AI is used in their practice, and 86% say data privacy is critical.
$48.4B
in hospital revenue lost to claim denials and uncollected bills in 2025, much of it paperwork and follow-up that AI can materially reduce.
Claim denials, prior authorization, documentation, missed calls, staffing and data security come up in almost every conversation with provider leaders, and the numbers behind them keep getting worse.
Hospital revenue leakage from denials rose 25% in a year. Initial denial rates run almost four times final rates, so most denials are paperwork and documentation problems that get paid only after costly rework.
Physicians complete an average of 40 prior authorizations a week, taking about 13 hours of physician and staff time. 95% say prior authorization delays access to necessary care.
Physician burnout has eased to 41.9%, but after-hours EHR time has not moved since 2022. More than a third of physicians say EHR inboxes and documentation requirements take time from patients.
National no-show rates run 5% to 8% and exceed 30% in some specialties. At peak hours many practices lose one in ten callers or more, and each lost call is a patient booking elsewhere.
Only 47% of nurses report job satisfaction, labor is 84.4% of physician enterprise expense, and Medicaid cuts expected from 2027 are already tightening budgets. Hiring your way out is not an option for most.
Healthcare has had the highest average breach cost of any industry for 14 years running, and breaches take 279 days to contain. Every new AI tool is another data flow your security team must approve.
Bring the pain point that costs you most, and we will map it to one workflow you can measure within a single discovery call.
Choose the view that fits you. Each use case names the job AI does, the checkpoint a person keeps, and the number you would measure.
For medical groups, specialty practices, health systems and billing companies that need claims paid, authorizations through and hours back for clinicians.
Checks each claim against payer edits, eligibility and chart documentation, and scores denial risk before it is sent.
Reads remittance codes, groups denials by root cause and drafts appeal letters citing chart evidence and payer policy.
Flags orders that need authorization, pulls notes, imaging and labs into the payer's criteria, and tracks status.
Books, confirms and reschedules by phone, SMS or web chat at any hour, writing straight to your scheduling system.
Scores each appointment's no-show risk, targets reminders at risky slots and offers openings to waitlisted patients.
Sorts portal messages by urgency, drafts routine replies and prepares cited pre-visit summaries.
From workflow automation to patient engagement and clinical documentation, each service targets one or more of the problems above.
Revenue cycle and authorization teams
AI agents take on the repetitive steps in billing and authorization and hand every exception to your staff.
Front office and contact center
Voice and chat assistants answer routine patient requests at any hour and write straight to your scheduling system.
Clinicians and medical directors
Generative AI prepares the drafts and summaries clinicians review, and reads the documents staff currently retype.
Operations and finance leaders
Forecasts and risk scores built on your own history, so staffing and follow-up go where the workload and the money are.
Healthtech and medtech product teams
We engineer the AI features your product roadmap promises and validate them on real clinical data before release.
Security and compliance teams
Every service above is delivered under these controls, so your security review has what it needs on day one.
Comparing AI companies in healthcare? Start with results. These case studies show Pinnasys systems handling claims, reading documents and automating follow-up at scale, the same core workflows behind healthcare AI solutions. They come from insurance and financial services, and are shown because the workflows match healthcare admin work so closely.
40%
Faster Patient Intake Processing
20+
Admin Hours Saved Weekly
2x
handling inspection
Single
Platform for the Whole Operation
80%
Faster Research Retrieval
3x
Faster Literature Reviews
76%
Reduced Contract Review Time
30%
Lower Legal Cost per Contract Cycle
AI systems in production today, described by the teams operating them
Stephanie Liu
CTO, Nourish
Pinnasys clearly had experience, and it spared us a lot of trial and error. We needed FHIR R4 and R5 conversion, two-way EHR sync, SMART on FHIR auth, and immutable audit logging before Nourish could pull a patient's clinical history without manual record requests. They delivered the gateway in six months
Patient data, clinical decision support and medical imaging carry some of the highest stakes in software. Every system we deliver, from provider workflows to medtech AI solutions, is engineered from day one to the privacy, security and AI governance frameworks your compliance team and auditors expect.
HIPAA
Privacy and Security Rules for protected health information
HITECH Act
Breach notification and stronger HIPAA enforcement
HITRUST CSF
Healthcare-specific security and privacy controls
SOC 2
Security, availability and confidentiality controls
ISO 27001
Information security management
42 CFR Part 2
Confidentiality of substance use disorder records
NIST AI RMF
Trustworthy AI risk management
ISO 42001
AI management systems and governance