Real-World Data

Guideline-grounded, interactive dashboards that turn fragmented EHR data into actionable insights across the entire care continuum.

The Vizlitics RWD platform delivers disease-specific clinical intelligence dashboards built on structured real-world patient data. Each dashboard spans a guideline-grounded module architecture from screening through outcomes, embedding guideline-directed metrics (NCCN, ASCO, ACC/AHA, GOLD, ISHLT), AI-powered natural-language queries, and data dictionaries aligned to CDISC/NCI/LOINC standards.

Powered by Record Retrieval — dashboards draw on the full retrieved corpus, faxes and scans included, so cohorts reflect the complete record.

Vizlitics RWD Lung Academic Medical Center · n=52

Lung Cancer — Overview

All histologies · 2014–2026 · n=52

Total patients

Active cohort

Median age

70 yrs

Range 36–86

Stage IV at Dx

17%

n=9

Median OS — all

25.4 mo

95% CI

Enrollment trend · patients / year

Stage distribution · AJCC 8th Ed

Stage I (n=21)40%

Stage II (n=7)13%

Stage III (n=7)13%

Stage IV (n=9)17%

65%LDCT completion

118 dSymptom → Dx

33%NGS testing

51%IO utilization (1L)

Lung Cancer — Screening

USPSTF 2021 · NCCN 2024 · Low-Dose CT

Screening-eligible

USPSTF 2021

LDCT completion

65%

↑63pp since 2018

Cancer detection

10%

per 100 screens

Stage I/II — screened

100%

vs 45% unscreened

USPSTF 2021 eligibility · all three required

Age 50–80 yearsExpanded from 55–80 in 2013 guidelines

≥ 20 pack-years(cigarettes/day ÷ 20) × years smoked

Current or quit ≤ 15 years agoFormer >15 years ago do not qualify

NCCN 2024: Age ≥50 + ≥20 pack-years + ≥1 additional risk factor (COPD, radon, occupational, family Hx) may qualify even without all USPSTF criteria.

Lung-RADS distribution · ACR v2022 (n=7)

2 – Benign n=48%

4B – Very suspicious n=24%

4X – Additional features n=12%

Lung Cancer — Diagnosis

Tumor staging · WHO 2021 histology · biomarkers

Stage IV at Dx

17%

n=9; poorest prognosis

Stage I/II combined

53%

n=28; surgical candidates

NGS testing rate

33%

of eligible patients

Actionable mutation

of NGS-tested

40%

Stage I · n=21

13%

Stage II · n=7

13%

Stage III · n=7

17%

Stage IV · n=9

TNM staging reference · AJCC 8th Ed

T classification

T1a/b/c≤1 / 1–2 / 2–3 cm; within visceral pleura

T2a/b3–4 / 4–5 cm; main bronchus or visceral pleura

T35–7 cm; chest wall, phrenic nerve, pericardium

T4>7 cm; mediastinum, heart, great vessels, trachea, carina

N classification

N0No regional lymph node metastasis

N1Ipsilateral peribronchial / hilar nodes

N2Ipsilateral mediastinal / subcarinal nodes

N3Contralateral mediastinal, hilar, supraclavicular

M classification

M0No distant metastasis

M1aMalignant pleural/pericardial effusion; contralateral nodule

M1bSingle extrathoracic metastasis

M1cMultiple extrathoracic metastases

Registry staging: 68% clinical, 32% pathologic (post-surgical). Brain MRI performed in 71% of Stage III–IV at diagnosis.

NSCLC Non-Small Cell Lung Cancer67%

Squamous cell carcinomaKeratinizing · Non-keratinizing · Basaloid · 8070/313%

AdenocarcinomaLepidic · Acinar · Papillary · Micropapillary · Solid · Mucinous · 8140/354%

NET Neuroendocrine Tumours10%

Small cell lung carcinoma (SCLC)4%

Carcinoid tumours6%

Other Salivary gland-type & rare4%

MALT lymphomaExtranodal marginal zone B-cell2%

Granulomatous lymphocytic ILD2%

WHO Classification of Tumours · Thoracic Tumours, 5th ed (2021). IHC panel: TTF-1, p40/p63, Napsin A, Synaptophysin, CgA, CD56.

25%PD-L1 tested

6%PD-L1 ≥ 50%

~8%TMB high

Biomarker prevalence

ALK4%

BRAF2%

EGFR8%

HER22%

KRAS15%

MET8%

PD-L125%

RET4%

ROS16%

TMB8%

KRAS mutation subtypes · n=9 · actionable with TKI

G12C — 33% G12D — 33% G12V (c.35G>T) — 11% Other — 22%

Lung Cancer — Treatment

Treatment patterns · line-of-therapy attribution

Median time to Tx

18 days

from diagnosis

IO utilization (1L)

51%

Stage IV NSCLC

Targeted therapy (1L)

16%

biomarker-driven

Trial enrollment

14%

242 patients

First-line treatment distribution

1L Chemotherapy23%

1L Chemo, Immunotherapy4%

1L Other4%

1L Chemo, Combination, Immuno2%

1L Chemo, Immuno, Targeted2%

1L Chemo, Targeted2%

1L Immunotherapy2%

1L Targeted therapy2%

Treatment by biomarker status

ALK17%

BRAF10%

EGFR15%

HER215%

KRAS13%

MET12%

NGS panel19%

NTRK19%

Lung Cancer — Survival

Stage IV · First-line therapy · Kaplan-Meier estimate

mOS — IO + chemo

19.4 mo

n=78

mOS — Platinum doublet

11.8 mo

n=74

Hazard ratio

0.62

95% CI 0.42–0.91

Log-rank test

p=0.014

Significant

Overall survival · 36-month follow-up

Pembrolizumab + chemo Platinum doublet

Number at risk

	0	6	12	18	24	30	36
IO + chemo	78	60	45	34	26	18	13
Platinum	74	49	32	20	13	8	5

Real-world evidence: Cox proportional-hazards model adjusted for age, ECOG PS, and stage at diagnosis. HR <1 favors Pembrolizumab + chemo — curves and at-risk counts are drawn from structured therapy and survival-status fields, not a trial endpoint.

Guideline-grounded cohorts — NCCN · ASCO · AJCC 8th Ed · USPSTF · WHO 2021.

7Dashboards built

400+Data variables defined

49Interactive modules

100%Guideline-grounded

Real-world data intelligence

Unlock the data hidden in every record.

Transform faxes, scans, and free-text notes into research-grade, analyzable cohorts with AI-driven extraction — powering quality reporting, precision trial matching, and confident point-of-care decisions. Anti-hallucination rules and value-level traceability keep every value accurate and audit-ready.

Quality governanceStructured cohorts feed real-time dashboards and analytics, so you can monitor compliance, surface care gaps, and standardize best practices across your network.

Automated quality reportingExtracted, standards-mapped data streamlines ASCO/QOPI-certified reporting workflows, cutting the manual abstraction burden on your clinical staff.

Clinical decision supportNormalized records deliver data-driven, NCCN-aligned insights back at the point of care, grounding confident treatment decisions in the full patient picture.

Accelerated trial matchingAnalyzable cohorts identify eligible patients faster, boosting clinical trial enrollment while reducing manual chart review.

Validation-first: human abstraction is subject to interpretation and drift. A standards-based, AI-enabled, domain-tuned pipeline with built-in validation makes every cohort research-grade and repeatable.

Challenges addressed

Why population-level insight is so hard today.

Fragmented clinical data

Patient data is scattered across EHR modules, registries, and tumor boards — making population-level analysis nearly impossible without structured RWD.

Guideline adherence gaps

Biomarker testing rates, NCCN-concordant therapy, GDMT optimization, and quality benchmarks stay invisible without continuous measurement against evidence-based standards.

Trial-to-RWD outcome gap

Real-world outcomes consistently trail clinical-trial benchmarks by 10–20% — understanding why requires structured, disease-specific analytics.

7-module architecture

Every dashboard follows the same care-continuum structure.

From prevention to long-term outcomes, each disease dashboard is built on a consistent, guideline-grounded module architecture — so metrics stay comparable across diseases and sites.

Screening & Risk

Early detection

LDCT (Lung-RADS), mammography (BI-RADS), Pap/HPV, ASCVD risk, and ISHLT candidacy — with risk calculators, screening-adherence trends, and risk-factor prevalence.

Symptom to Diagnosis

Patient journey

Milestone-level timeline with cumulative days, referral-source analysis, and presenting symptoms — plus bottleneck identification with root cause, intervention, and estimated impact.

Diagnosis

Staging & classification

AJCC/FIGO/GOLD/GINA/NYHA staging, WHO histology with ICD-O-3 codes, molecular classification, biomarker panels, genomic alteration tables, and metastatic-site distribution.

Treatment

Therapy & GDMT

Treatment patterns by subtype, line-attribution logic with edge cases, GDMT optimization, AE profiles by drug class, dose modifications, and guideline-directed therapy gap analysis.

Outcomes

Survival & benchmarks

KM survival curves by subtype/stage/biomarker, RWD-vs-clinical-trial benchmark tables, MACE, readmission, recurrence, PFS, OS, pCR, CLAD-free survival, and quality-gap scoring.

AI Query

Gen-AI powered

Natural-language analytics over full registry data with disease-specific system prompts, across five query categories — Clinical, Quality, Research, Molecular, and Prevention.

Data Dictionary

Standards & definitions

Every variable defined and standards-aligned — CDISC, NCI Thesaurus, LOINC, ICD-O-3, MedDRA, and AJCC 8th Ed — so cohorts are auditable and interoperable.

Research-grade oncology cohorts from unstructured records.

Real-World Data extracts structured, longitudinal oncology cohorts across cancer types and criteria categories — diagnosis, staging, biomarker, radiation, surgery, systemic therapy, and risk factors — with anti-hallucination guardrails and traceability to source. The same extraction engine also builds cohorts for nephrology, hepatology, pulmonology, and cardiology.

Longitudinal oncology cohorts

Patient journeys assembled from first diagnosis through each line of therapy.

Coverage across major cancer types
Seven criteria categories per patient
Every value traceable to source

Treatment & outcomes capture

Therapy, surgery, radiation, and response structured for analysis.

Lines of therapy with dates
Surgical and radiation events
Documented response and progression

Genomic & biomarker structuring

Molecular results normalized into analyzable tuples.

Gene · variant · result tuples
IHC and companion markers
mCODE-aligned representation

Registry-ready exports

Outputs shaped for registries and downstream research systems.

NAACCR-aligned data elements
OMOP-ready structuring
De-identified delivery options

Standards & frameworks

NAACCRICD-O-3mCODEOMOPSNOMED CTLOINC

Bring Real-World Data to your service line.

We start from complete records — retrieval and curation first, then Real-World Data on top.

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