LINCR

Healthcare AI scribes the chart or predicts the number. The OR needs the cause.

The current AI-in-healthcare stack splits two ways. The first wave — Abridge, Nuance DAX, Hippocratic AI, Glass — runs language models over clinical notes for scribing, billing, and summary generation. They are useful documentation products that never see the patient's physiology. The second wave — gradient-boosted and transformer time-series predictors like Edwards's HPI, the only FDA-cleared example in intraoperative care — has two structural problems. First, it only runs on Edwards's proprietary HemoSphere monitor + Acumen IQ sensor: capital purchase per OR, per-case disposables, and gated to invasive-arterial-line cases — a small fraction of US ORs and zero ambulatory surgery centers. Second, even where it does run, it pattern-matches on waveform surface signals to predict BP will drop in 15 minutes — without modeling the cardiovascular physics underneath. It can warn that something is coming, but it can't say why, and a warning without a cause doesn't change what the clinician does.

Each year roughly 30 million U.S. patients undergo surgery, and about half develop intraoperative hypotension (IOH) at clinically meaningful thresholds (mean arterial pressure, or MAP, below 65 mmHg). The acute kidney injury (AKI), myocardial injury, prolonged hospital stays, and skilled-nursing-facility (SNF) discharges that follow drive tens of billions of dollars in direct cost and a large share of anesthesia malpractice claims. The published HPI-guided RCTs have shown the same structural result: warning the clinician earlier reduces hypotension duration, but it does not consistently translate into fewer post-operative complications. Knowing the number is about to drop is not the same as knowing what to do about it.

This is a structural problem, and it is not specific to hypotension. The Stanford EPIC manual — the field-standard reference for intraoperative emergencies — catalogs roughly 30 of these events, from anaphylaxis to bronchospasm to obstructive shock to local anesthetic systemic toxicity. They share a common pattern: the same surface presentation can be driven by several different underlying causes, and each cause requires a different clinical intervention. Clinicians already reason in those causal terms, case by case, at the bedside. What hasn't existed is the systematic record across an entire group's caseload: each event reconstructed from the lead-up vital-sign changes, the interventions delivered in response, and the surrounding labs that captured the downstream impact, then attributed to the cause that drove it. That is the quality-improvement, safety, and care-optimization layer LINCR provides.

Sources: Stanford EPIC manual (Goldhaber-Fiebert et al.). HYPE Trial (Wijnberge et al., JAMA 2020) and subsequent HPI-guided RCTs. Walsh et al., Anesthesiology 2013. Bijker et al., Anesthesiology 2009.

One product: the retrospective causal audit.

Anesthesia groups upload existing case data; we return the comprehensive record of what drove every intraoperative event in their last quarter — the QI, safety, and care-optimization documentation quality committees, malpractice carriers, and value-based-care contracts actually need.

The audit relationship and the dataset it produces unlock a pre-op risk stratifier downstream — also under the FD&C Act §520(o)(1)(E) CDS carve-out, no 510(k) required. Same QI / safety / care-optimization category. Ships after the wedge wins, not before.

What the audit attributes for hypotension events — the wedge chapter, where CF2's AUROC 0.972 validation lives. Each event is assigned to one of four causes, each requiring a different clinical intervention:

The same audit also identifies other intraoperative emergencies from the Stanford EPIC manual covered in Section 02 — bronchospasm, anaphylaxis vs. vasoplegia, obstructive shock differential, LAST — with cause-attribution from the same per-subject estimator + clinical-rule triangulation pipeline.

Value-based care turned anesthesia complications into a line item on the hospital P&L.

Healthcare reimbursement is moving off volume and onto outcomes. Hospitals and the anesthesia groups that staff them now carry direct financial exposure for the post-op complications that IOH drives — AKI, myocardial infarction (MI), prolonged length of stay, SNF discharges, and 30-day readmissions. Three CMS programs already put that exposure on the books:

The audit converts that exposure into something a quality committee can act on: which cases drove which complications, which causal pattern clustered where, which protocol changes would have prevented the events that incurred the penalty. That is why anesthesia groups and hospitals will pay for the wedge product today — the audit is a budget defense, not a research tool.

Sources: CMS TEAM Final Rule (CMS-1808-F, August 2024). HAC Reduction Program (Section 3008, ACA). BPCI-A program documentation. Anesthesia Quality Institute (AQI) closed-claims analysis 2022.

We train a model that produces per-subject estimates of human intraoperative physiology — the substrate behind every clinical decision-making tool we ship.

That substrate powers the retrospective audit shipping today, the prospective stratifier downstream, and the broader QI / safety / care-optimization tools the audit relationship and dataset unlock. CF2 is the architecture: a 63,975-parameter compartmentalized neural-ODE estimator with three compartments — cardio (27 params), respiratory (8), pharmacokinetic (14) — that encode the actual physics of preload, afterload, contractility, ventilation, and drug PK. The encoder is trained once on population data; at inference, it ingests each patient's streaming signals (waveforms, vitals, drug record, labs) and produces per-subject parameter estimates that fit that patient's physiology. Those parameter estimates feed the audit's cause-attribution layer alongside clinical-rule triangulation across vitals, interventions, and labs — CF2's contractility / SVR / preload outputs combined with drug-record timing, SpO2 drift, EKG morphology, and lab markers to assign each event to its driving cause. The model learns physiology. Notes-LLMs and time-series predictors pattern-match on the output of physiology.

On retrospective benchmarks, CF2 outperforms the only FDA-cleared incumbent. HPI remains the relevant scientific benchmark — it's the only FDA-cleared AI predictor in this space — even though it failed to change practice. CF2 reads what the OR already produces — arterial waveform or NIBP cuff, ECG, pleth, capnography, drug record — with no new hardware, no proprietary sensor, and no per-case disposable. The same trained CF2 model produces per-subject estimates across both signal regimes without retraining, while HPI is structurally locked to Edwards's HemoSphere + Acumen IQ in invasive-arterial-line cases. The same CF2 model carries from inpatient hospital cases into ambulatory surgery centers — into a regime HPI cannot operate in.

Methodology disclosure: prevalence is window-level (samples around hypotension events), not patient-level — standard in the published HPI literature and the rest of the IOH-prediction field. Patient-level intraoperative hypotension base rate is roughly 10–30% per Walsh et al. and Bijker et al. Brier Skill Score >0.7 is publication-grade calibration. Patent: USPTO Provisional App #64/011,899, "Compartmentalized Neural Ordinary Differential Equation System for Dynamical State Prediction," filed 2026-03-20, 109 clauses, sole inventor Anish Joseph.

First paid pilot Q4 2026. Wedge first; growth follows the audit relationship.

Built by someone who administers the drugs the model classifies the response to.