Challenge
Discharge practice changes and the model does not
Agnotic approach
Monthly checks of prediction error and range coverage against actual stays, with a review when they shift.
AI Agents / Length of Stay Prediction
AI Length of Stay Predictor for Hospitals
Case managers plan discharges and bed managers plan admissions around a length of stay that is usually a guess made on day one. Agnotic Technologies builds length of stay models that update every day, show their range and their reasons, and open inside the EHR where case managers already work.
Updates dailyRange, not a single numberCompared with GMLOSSMART on FHIR
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What is an AI length of stay predictor?
An AI length of stay predictor is a model that estimates how many more days an inpatient is likely to stay, first at admission and then each day as results, orders and notes arrive. Agnotic Technologies builds it for case managers and bed managers, shown as remaining days with a range beside the DRG's geometric mean length of stay.
It is not a discharge task list and not a bed board. Discharge barrier tracking and hospital capacity views have their own pages. This page covers the model: what it can know at each point in the stay, what it shows and how it is kept honest.
A single number at admission is rarely useful by day three. The value is in the update: the prediction moves when the patient's course changes, and the case manager can see why.
Original prediction timeline
What does the model know at each point in the stay?
A model can only use data that exists at the moment it predicts. Agnotic Technologies trains and tests each prediction point on the data available at that time, never on facts recorded later.
| Prediction point | Data available | What the model outputs | Who acts on it |
|---|---|---|---|
| Admission order | Admission source, working diagnosis, age, prior encounters at your hospital, admitting service, first vitals and labs | Expected total days with a range, and a flag when the range is wide | Case manager starts the plan; bed management sees expected demand |
| Day 1 | First full day of labs, medications, procedures ordered, consult requests and therapy evaluations | Remaining days updated, with the main changes since admission | Case manager compares it with the GMLOS for the working DRG |
| Each later day | Trends in vitals and labs, new orders, procedures done, mobility and therapy notes, results still pending | Remaining days, the range and what moved them since yesterday | Case management and utilization review; stays running past GMLOS reviewed earlier |
| Discharge order | Discharge order time and disposition | Prediction closed; the actual stay stored against every earlier prediction | Data team uses the pairs for calibration and drift checks |
Feature lists are agreed with your case management and data teams. Anything recorded after a prediction point, such as the final coded DRG, is kept out of that point's training data.
GMLOS comparison
How is the prediction compared with GMLOS?
Medicare payment rules use a geometric mean length of stay (GMLOS) for each DRG, for example to set the per diem for transfer cases. Case managers already watch it. Showing the prediction beside it puts the model in the terms they use.
The comparison uses the working DRG, because the final DRG is not known until coding. The display says which DRG it used and where it came from, usually your clinical documentation team's working assignment.
GMLOS is a reference point, not a target for one patient. The display shows the gap and the reasons for the prediction, and leaves the judgment to the case manager.
In the EHR
How does the predictor open inside the EHR?
The model is shown where case managers already work, launched as a SMART on FHIR app from the patient or worklist.
The case manager opens the patient
From the case management worklist or the chart, the app launches without a separate login.
The EHR passes patient and encounter context
The SMART launch supplies the current patient and encounter, so the app opens on the right stay.
The app reads the stay through FHIR
Encounter, conditions, observations and orders are read with the scopes your EHR team approves.
The prediction is shown with its reasons
Remaining days, the range, the GMLOS comparison and the main factors behind today's estimate.
The case manager records agreement
Agree or disagree, with a short reason, which the data team reviews to find where the model misreads the stay.
What does the predictor show a case manager?
A small view, built to be read in seconds during rounds.
Remaining days with a range
A central estimate and a range, so a wide range is visible as uncertainty rather than hidden.
Comparison with GMLOS
Days ahead of or behind the working DRG's geometric mean length of stay.
What changed since yesterday
The new results, orders or notes that moved the estimate overnight.
Unit and service view
Expected discharges by day for a unit, for patient flow and staffing conversations.
Feedback capture
Case managers mark estimates they disagree with, and the reasons feed the next review.
Model facts on demand
Intended use, training period, known limits and tested groups, one click from the prediction.
Drift and subgroups
How do you keep a length of stay model honest?
Length of stay depends on practice as much as on illness, so the model is checked often and by group.
Challenge
Training data leaks facts from later in the stay
Agnotic approach
Features are rebuilt as they stood at each prediction point, so test results match what the model sees live.
Challenge
It works for medical patients and fails for surgical ones
Agnotic approach
Error is reported by service line, unit, age band and admission source before release and after.
Challenge
The range is too narrow to trust
Agnotic approach
We test how often actual stays fall inside the range and widen it where the model is less certain.
Challenge
Payer or social factors push predictions in ways clinicians reject
Agnotic approach
Clinical leads review which factors drive the estimate, and predictions are never used to limit care.
Challenge
Case managers stop opening it
Agnotic approach
Use and disagreement rates are tracked by unit, so a drop is seen and discussed early.
Regulatory scope
Is a length of stay display a medical device?
It depends on what it is used for and how it is shown. Your regulatory team makes the call; we design the function and keep the evidence for it.
Capacity and planning use
Section 520(o)(1)(A) of the FD&C Act excludes administrative support software, including information about admissions and patient populations.
Clinical use
If the display informs a treatment decision, it is designed to meet the four non-device CDS criteria, including a reviewable basis.
Basis shown, never hidden
Every estimate shows its main factors, so a clinician can review why rather than rely on the number.
Written intended use
Who may use the prediction and for what, recorded before release and shown in the app, under a BAA for any PHI.
Who it is for
Who uses length of stay predictions?
The same prediction, read by different teams for different decisions.
Case managers
Daily review of patients whose stay is moving past the expected range.
Utilization review
Earlier attention to stays likely to run long, ahead of formal extended stay review.
Bed management and patient flow
Expected discharges by day for each unit, to plan admissions and transfers.
Physician advisors
A list of long stays with the factors behind each, for focused case review.
Service line leaders
Trends in predicted and actual stays by service, without patient level detail.
Health tech startups
Length of stay products sold to hospitals, validated site by site.
Compliance-First Healthcare App Development Services Backed by Global Standards
01
HIPAA
Health Insurance Portability and Accountability Act
Protect PHI with privacy-first architecture, encrypted storage and transmission, strict access controls, and traceable audit logs.
02
GDPR
General Data Protection Regulation
Implement lawful consent flows, data minimization, retention controls, and secure processing for sensitive health data.
03
FHIR
Fast Healthcare Interoperability Resources
Enable standardized health data exchange across apps, care teams, and systems through standards-based FHIR APIs.
04
HL7
Health Level Seven International
Support enterprise-grade interoperability with HL7-based integrations for records, events, and clinical messaging workflows.
05
HITRUST
Health Information Trust Alliance
Align security programs to healthcare-specific control and risk management practices trusted by providers and ecosystem partners.
06
HITECH
Health Information Technology for Economic and Clinical Health Act
Design with breach notification readiness, digital record safeguards, and operational controls that support regulated care programs.
07
SaMD
FDA Software as a Medical Device
Plan software quality, traceability, and documentation pathways for products that may require SaMD review and submission.
08
MDR (EU)
Medical Device Regulation (European Union)
Prepare EU market-ready processes for risk classification, evidence tracking, and lifecycle governance under MDR expectations.
09
SAMHSA
Substance Abuse and Mental Health Services Administration
Apply confidentiality controls and consent-aware sharing models for behavioral and mental health data experiences.
Scope drivers
What drives the cost of an AI length of stay predictor?
We size the work on a scoping call. These factors move effort most.
Historical data access
Whether past encounters, orders and notes come from a data warehouse, Bulk FHIR export or HL7 v2 feeds.
Daily feature pipeline
Rebuilding features each day from live data is the largest single piece of engineering.
EHR launch
A SMART on FHIR app needs registration and scope approval with your EHR team.
Working DRG source
Reading the working DRG from your documentation system adds an integration.
Number of units
Starting with one service line keeps validation and feedback reviews small enough to act on.
How we work
How does a length of stay engagement work?
Three stages, starting with one service line.
1.
Scoping call
We agree the service line, the data sources and who reviews the model before anyone sees it.
One service line
2.
Fixed-scope first release
Admission and daily models, validation by subgroup and a silent run against actual stays.
Fixed scope
3.
Scale and support
The SMART on FHIR app for case managers, more units and monthly drift reports.
Monitored
Facts and sources
Which rules and standards shape a length of stay predictor?
Short facts we design around, each linked to its primary source.
Medicare's transfer payment rule divides the full DRG payment by the geometric mean length of stay for the case's DRG to set a per diem rate, paying twice the per diem for the first day and the per diem for each later day, up to the full DRG payment.
Source: 42 CFR 412.4(f)(1) (eCFR)In hospitals paid under the prospective payment system, the utilization review committee must review all cases reasonably assumed to be outlier cases because the extended length of stay exceeds the threshold criteria for the diagnosis.
Source: 42 CFR 482.30(e)(2) (eCFR)The FHIR R4 Encounter resource records the start and end of an encounter as a period, and a length element for the time it lasted, excluding time during leaves of absence.
Source: HL7 FHIR R4 EncounterThe HL7 SMART App Launch specification defines a launch/encounter scope, which asks the EHR to supply the current FHIR Encounter as context when an app launches.
Source: HL7 SMART App Launch, Scopes and Launch ContextSection 520(o)(1)(A) of the FD&C Act excludes from the device definition software intended for administrative support of a health care facility, including information about patient populations, admissions, and practice and inventory management.
Source: 21 U.S.C. 360j(o)(1)(A) (govinfo, U.S. Code 2023 edition)
Frequently Asked Questions
That depends on your patients, your data and your discharge practice, so we do not quote a figure in advance. Before release we report error and range coverage on your own past stays, by service line and patient group.
Readmission models estimate whether a patient returns after discharge. A length of stay predictor estimates how long the current stay will last and updates it daily. They use some of the same data and are often shown side by side.
No. Utilization review decisions stay with the committee and physician advisors. The predictor highlights stays likely to run long earlier, so reviewers can look at them sooner.
Encounters, diagnoses, orders, results, vitals and, where available, therapy and mobility notes, for enough past stays in the service line. We check what exists in your warehouse or EHR exports on the scoping call.
Yes, where your EHR supports SMART App Launch, the app opens from the chart or worklist with patient and encounter context. Your EHR team approves the app registration and its FHIR scopes.
Yes. A first release usually covers one service line: the admission and daily models, a subgroup report and a silent run. The case manager app follows once the silent run results are reviewed.
Engineers use AI tools for drafts and tests and review every change. AI coding tools never see patient data, and models are trained only in environments your compliance team approves under a BAA.