GLACEIQ | THE AI SUBSTRATE
The AI Woven Into the Platform, Not Bolted On Top of It
30+ AI features wired into the chart, the claim, and the call inside GlaceEMR and GlaceRCM. Every output is a suggestion a physician or biller reviews before it touches the record. Not a chatbot, not a separate product.
30+
AI Features Shipping Today
5
AI Providers Under BAA
30+
Years Serving Practices
100%
Physician Review on Clinical Output
WHY YOU STOPPED TRUSTING AI
The AI Bolted Onto Your EHR Is the Problem
A wall of AI vendors that do not talk to each other is why most practices tried AI and quietly stopped trusting it. Four failure modes come up on almost every evaluation call. GlaceIQ exists because of them.
The bot cannot see the chart
An API hands the model a redacted summary, not the live problem list, orders, and labs. Output reads plausible and is often wrong, so you stop trusting it in a week.
The integration tax compounds
Every AI vendor is another contract, another renewal, another audit log, another security review. Five overlays on a legacy EHR and the glue is somebody’s full-time job.
Your data feeds their training
A clause in the order form lets your patient data train the next model. Your patients never consented, and your privacy officer inherits a problem the legacy EMR never had.
No model lineage on paper
Ask which model, which version, and whether the BAA covers inference. The answer is a marketing sentence, which means your audit posture is too.
WHAT GLACEIQ ACTUALLY IS
An AI Substrate, Not a Product You Plug In
GlaceIQ is the AI capability layer inside the platform, with first-party access to the chart, the orders, the labs, the problem list, and the billing context at once. Three architectural facts define it.
1. Built in, with full context
Every model reads the same data the physician reads: active chart, open orders, recent labs, problem list, billing context. No gateway redacting context to fit a contract limit, which is why the suggestions are useful instead of plausible-but-wrong.
2. Multi-vendor by design
Built on HIPAA-compliant AI infrastructure from Amazon, Google, Anthropic, Deepgram, ElevenLabs, and other enterprise AI providers. If one provider raises prices or sunsets a model, the substrate routes to an alternative. Your contract is with Glenwood; the BAA chain runs through every provider.
3. Physician in the loop
Every clinical output is a suggestion the physician or biller reviews, edits, and accepts or rejects before it reaches the chart, the bill, the order, or the patient. The model never finalizes anything, and every action writes to the audit trail with the model and version that produced it.
THE 30+ FEATURE SURFACE
30+ AI Features, Grouped the Way Your Day Is
Listing 30 features is the wall-of-features problem GlaceIQ was built to fix. Here they are grouped by the part of your day they affect. Every output is a suggestion a human reviews and selects.
Clinical AI
Charting suggestions, ICD-10 and CPT coding suggestions, documentation-completeness review, problem-list and medication-reconciliation prompts, lab-interpretation context. The physician reviews and signs.
Revenue AI
Pre-submission claim scrubbing, denial root-cause and appeal drafts, line-level underpayment detection, prior-auth tracking, A/R prioritization. Where GlaceBillSmart lives, inside GlaceRCM’s PMS. The biller reviews.
Voice AI
Ambient documentation that becomes the note (GlaceScribe), the voice receptionist that books and collects (GlacePhoneSmart), and dictation into any field. Both ride the substrate, which is why call routing knows the schedule.
Patient AI
Intake-form pre-population of the HPI, portal-message routing with a drafted reply, pre-visit summaries, plain-language post-visit instructions, English and Spanish. The clinician reviews each draft before it reaches the patient.
Administrative AI
KPI summaries the owner reads in 30 seconds, document classification and routing, payer-contract flagging, a credentials calendar, payer-policy change monitoring. The owner decides what to act on.
Always in the loop
Across all five categories the substrate never signs a note, never finalizes a code, never closes an order, never sends a patient message on its own. The discipline is the product, not a setting in the admin console.
Substrate inside the platform vs AI bolted onto a legacy EHR
The difference is structural, not cosmetic. The rows below are why a bolted-on overlay reads plausible and gets rejected, while a built-in substrate earns its place in the workflow.
| AI bolted onto a legacy EHR | GlaceIQ substrate | |
|---|---|---|
| Context the model sees | Redacted summary via API | Live chart, orders, labs, problem list |
| Who finalizes the output | Often the model, then you fix it | Physician or biller, every time |
| Model lineage and BAA chain | Marketing PDF, no version | Documented, audited, shareable |
| Training on your patient data | Buried clause in the order form | Contractually prohibited |
| Audit trail of suggestions | Lives in the vendor’s tool | In your chart, queryable |
| Cost model | Per-feature, per-output, per-vendor | Bundled, no per-output charge |
GlaceIQ is bundled into GlaceEMR and into GlaceRCM, and it is not sold separately. Strip the first-party access away and it becomes the overlay you already tried. The integration depth is the product.
HOW A SUGGESTION BECOMES A RECORD
The Model Proposes. The Human Decides. The Loop Repeats.
A wrong suggestion never reaches the chart, the bill, or the patient. The worst case is a few seconds spent reading and rejecting it, and the rejection writes to the audit log with the model, the version, the timestamp, and the user who acted. That is the receipt your compliance officer needs.
WHAT THE REVENUE AI DELIVERS
Suggestions a Human Approves, Numbers a Practice Feels
95%+
First-pass claim adjudication, with the scrubber suggestions a biller reviews before submission.
99%+
Collection rate of payer-allowed amounts*, with denial and underpayment AI working alongside the team.
$0
Separate per-feature or per-output charge. GlaceIQ is bundled, so the substrate runs by default.
*Of payer-allowed amounts. AI does not get a relaxed standard inside Glenwood: LUKS full-disk encryption at rest in production, single-tenant database per practice, and a BAA chain through every provider that touches PHI.
SWITCHING TO BUILT-IN AI
Retire the Overlay Stack at Your Own Pace
GlaceIQ comes on as you adopt GlaceEMR or GlaceRCM, with a recommended 30-to-90-day overlap so you can validate it on real encounters before the old AI contracts turn off. Implementation, setup, training, and data migration are $0, and the broader move onto GlaceEMR or GlaceRCM is detailed on the switching plan.
WEEK 1
Documentation and coding first
Most practices turn on documentation and coding suggestions first. The substrate runs inline in the encounter; the physician reviews, edits, and signs. Your standalone scribe and coding contracts keep running in parallel while you compare on real charts.
DAY 30
Patient drafts and revenue AI
As the team gets comfortable with the review discipline, add the patient-facing drafts and the revenue AI: scrubbing, denial appeals, underpayment detection, A/R prioritization. Most practices reach parity with their prior AI vendor stack inside the first month.
DAY 90
One vendor, one audit trail
The standalone AI contracts are canceled, the integration tickets are gone, and every suggestion lives in one queryable audit trail. Your named account manager runs a quarterly AI feature review: what your team uses, what could help next, what the substrate flagged in your data.
COMPLIANCE AND SECURITY
The Same Posture That Protects the Rest of the Platform
ONC, HIPAA, PCI-DSS
GlaceEMR is ONC Health IT certified, the platform is HIPAA aligned, and the payment surfaces are PCI-DSS compliant. Certifications and audit dates sit on the certifications page, ready for payers, auditors, and ACO partners.
Encryption and single-tenant data
LUKS full-disk encryption at rest in production, TLS in transit, and a single-tenant database per practice. Your data lives in a database you can point at, which shrinks incident blast radius and simplifies audit and discovery.
RBAC and immutable audit log
Role-based access governs who can see and act on which suggestions. Every accepted and rejected suggestion writes to an immutable audit log queryable by encounter, user, feature, model, and time range. The substrate is observable, which makes it defensible.
SPECIALTY ADAPTATION
Tuned for 35+ Specialties, Not Averaged Across Them
A model trained on a generic complaint visit fails the moment it sees an oncology infusion or a podiatry wound encounter. GlaceIQ carries prompts, templates, code preferences, and decision-support rules tuned per specialty. When a new specialty signs on, that tuning ships back into the substrate for every other practice in it.
Differentials per specialty
The decision-support prompts inside cardiology are different from those inside behavioral health, because the substrate knows the difference between the two encounters and surfaces the right suggestions for each.
Coding that respects the schedule
Coding suggestions inside pediatrics respect the well-child schedule and the vaccine catalog the way pediatricians actually use them, rather than a one-size-fits-all code list.
A named account team
The same person who runs your migration sits down with you for a quarterly AI feature review: what your team uses, what could help next, what is shipping, and what the substrate flagged in your data this quarter.
FREQUENTLY ASKED
Questions Practices Ask About GlaceIQ
Short answers below; the longer conversation happens on the demo. Tap any question to expand.
Does GlaceIQ ever finalize a clinical action on its own?
No. Every clinical, revenue, patient, and administrative output is a suggestion the human reviews. The substrate does not sign notes, finalize claim codes, place orders without physician signature, or send patient communications without a clinician approving the send. The discipline is enforced in the product, not promised in marketing copy, and the audit log records every accepted and every rejected suggestion with the model, the version, the timestamp, and the user who acted.
Which AI providers are running under the hood?
Built on HIPAA-compliant AI infrastructure from Amazon, Google, Anthropic, Deepgram, ElevenLabs, and other enterprise AI providers. We name the providers, not the specific products underneath each one, because the BAA protections and contractual safeguards sit at the provider level and the product layer changes as the market evolves. Glenwood holds a business associate agreement with every provider that touches PHI, and your contract is with Glenwood, so you do not sign separate BAAs with the underlying providers.
Do you train AI on our patient data?
No. The BAAs with every provider explicitly prohibit using your patient data for training, fine-tuning, evaluation, or any downstream use beyond serving the inference your practice requested. The clause is in writing, audited at renewal, and shareable. The practice-context tuning that improves your suggestions over time happens inside your tenant; the learning does not leave your tenant and is not shared with other Glenwood customers.
Is GlaceIQ a chatbot we talk to?
No. GlaceIQ is the AI substrate woven through every surface of the platform, not a conversational interface you query separately. Suggestions appear inline where the work is already happening: inside the encounter, the worklist, the inbox, the dashboard. There is no open-the-AI step. The substrate is always running, the suggestions are always available, and the reviewer always decides what to act on.
Can we turn specific AI features off?
Yes. Every GlaceIQ feature can be turned on or off per practice and per role. If you want to start with documentation suggestions and add the coding suggestions later, that is the configuration. If a clinician wants the substrate quieter on certain encounter types, that is configurable too. The adoption pace belongs to your practice; the substrate adapts to your posture.
Can we use GlaceIQ without GlaceEMR or GlaceRCM?
No. GlaceIQ is bundled into GlaceEMR and into GlaceRCM and is not sold separately. The architectural reason is the reason it works: the substrate has first-party access to the chart, the orders, the labs, the problem list, and the billing context. Strip that access away and it becomes the AI overlay your practice already tried and stopped trusting. The integration depth is the product, and the only path to it is running GlaceIQ inside the platform that holds the data.
Physicians Running on the Substrate
“Peace of mind. Comprehensive, easy to use. I would highly recommend Glenwood.”
Aura Ardon, MD
Florida
“Technologically advanced. In-depth analytics. Great resource for solo practices.”
Suresh Madireddy, MD
Ohio
“GlaceEMR is really quite pleasant to use. Easy to reach, easy to communicate with.”
Sanjay Batra, MD
Ohio
The bottom line on GlaceIQ is the line at the top: the model proposes, the human decides, and the audit log records it. That is the discipline most horizontal AI healthcare tools quietly abandoned for a flashier demo, and it is the one Glenwood built the substrate around across 30+ years of physician-informed product work. A demo is the fastest way to see it on your own encounters, your own codes, and your own payer panel.
Nine verified physician references across six states. Every quote is from a Glenwood client willing to take a peer call.
See What Glenwood Can Do for Your Practice
A 20-minute working call. We show you the platform on your specialty’s actual workflows, not a generic demo.