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Research & Real-World Evidence

Publication-ready cohorts
from routine care.

Every clinical interaction your ILM touches is indexed, longitudinal, and computable. Define an outcome, pull every matching patient, and publish, with a complete audit trail and zero manual chart review.

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The research pipeline

From clinical question to published cohort, in four steps.

01

Define an outcome

Specify the clinical question, a diagnosis, a risk threshold, a care pathway outcome. The ILM maps it to computable phenotypes already indexed in your institutional data.

02

Pull every matching patient

Longitudinal cohorts assemble automatically from routine clinical documentation. No manual chart review. No retrospective extraction. No data wrangling.

03

Query and compare

Interrogate the cohort across time, intervention, subgroup, and outcome. The ILM keeps the cohort live, new patients matching your criteria accrue prospectively.

04

Publish with confidence

Data lineage is built in. Every patient record has an audit trail of how it was captured, de-identified, and included. Grant applications and journal submissions start with the audit trail, not after it.

Capabilities

Built into every ILM deployment.

Computable phenotyping

Clinical concepts, diagnoses, lab thresholds, medication patterns, are automatically translated into computable criteria from your own EHR, not a generic ontology.

Prospective by design

Define your cohort today. The ILM continues adding patients who match your criteria as they present, turning any study into a living prospective dataset without protocol amendments.

De-identification at the boundary

De-identification happens at the ILM layer, before cohort data is ever surfaced for research queries. Patient identity never enters the research pipeline.

Re-queryable follow-up

As your cohort accrues follow-up, re-run the query. Outcomes that weren't visible at baseline become accessible without re-extracting data or revisiting records.

Pharma collaboration framework

When external collaborators want access to your cohort, AIIM provides a governed query interface, no data leaves your institution. The query runs inside your walls; only the result is shared.

Multi-site federation

For studies that require population scale, ILM-generated cohort gradients can federate across consenting institutions, without raw patient data crossing any boundary.

On data sovereignty

Your research asset. Your institution's IP.

When pharma or external collaborators want real-world insight from your patient population, you hold the asset. Cohort queries run inside your walls. Results are shared under your governance. Your patients' data never leaves your institution, and neither does your intellectual property.

Every AI output is a suggestion with a complete reasoning trace. Clinicians retain override authority at every step. Regulators and IRBs can inspect the full data lineage, from raw clinical event to published cohort.

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