# OncoPath: Intelligent Clinical Pathway Decision Support Tool for Pre-Authorization Documentation in Non-Small Cell Lung Cancer Treatment

> **NIH NIH R43** · VIZLITICS INC. · 2021 · $398,721

## Abstract

Project Abstract
In a real-world clinical setting, busy oncologists lack the time for investigative case analysis across all feasible
treatment options, frequently updated treatment guidelines and payor specific requirements. This results in sub-
optimal decision making, incomplete pre-authorization (pre-auth) documentation, and problems with
reimbursement. Overall, this increases medical costs and payment deficits within oncology. For example, pre-
auth inefficiencies are estimated to add $83,000 a year per physician to healthcare costs, which is $1.1 billion
annually in oncology alone. To devise a treatment plan, oncologists reference National Comprehensive Cancer
Network Guidelines® (NCCN), other clinical society standards, and payor specific requirements in the context
of a patient’s medical history, tumor characteristics, and phase of treatment. One of the most used oncology
treatment guidelines referenced by oncologists and adhered to by most payors is the National Comprehensive
Cancer Network (NCCN) Guidelines®. These guidelines are presented as a schema that span 100s of pages.
 A technology driven clinical decision support (CDS) system could be employed to address the need to
streamline treatment guideline analysis, payor rules review, and treatment decision documentation for
reduced overall cost to oncology practices. This proposal focuses on developing an innovative and first-of-
a-kind technology for CDS using non-small cell lung cancer (NSCLC) as the initial test case and incorporating
NCCN Guidelines, general payor specific requirements and patient data overlaid to compute feasible treatment
pathways. The team proposes the following Phase I Specific Aims:
Aim 1: Develop a graph-based mathematical model and visual presentation of NSCLC NCCN treatment
guidelines with generalized payor specific requirements. Develop a visually interactive graph-based
representation of NCCN Guidelines®. Modeling guidelines as a visual graph (nodes and arcs) will enable
oncologists to identify the optimal treatment pathway for their patients.
Aim 2: Build an analytics engine that highlights the NCCN graph model with feasible treatment options
given the patient’s case details and common payor requirements. Use an opensource tool to create
synthetic patient data and common payor constraints with an oncologist and health plan experts. Develop a
library of graph traversal algorithms to overlay and visualize the patient data in a visual user interface.
Aim 3: Execute, validate and test a proof-of-concept CDS workflow using OncoPath. Run the complete
end-to-end CDS workflow with documentation of patient details, NCCN guideline, payor requirements and
treatment decision using synthetic patient data and payor constraints generated in Aim 2.
 OncoPath will enable an efficient, oncologist-friendly approach to treatment decisions and documentation,
subsequently benefitting the patient and decreasing oncology cost. Phase II will deploy a real-time instance of
OncoPa...

## Key facts

- **NIH application ID:** 10325551
- **Project number:** 1R43CA261312-01A1
- **Recipient organization:** VIZLITICS INC.
- **Principal Investigator:** Sharon Hensley Alford
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $398,721
- **Award type:** 1
- **Project period:** 2021-09-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10325551

## Citation

> US National Institutes of Health, RePORTER application 10325551, OncoPath: Intelligent Clinical Pathway Decision Support Tool for Pre-Authorization Documentation in Non-Small Cell Lung Cancer Treatment (1R43CA261312-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10325551. Licensed CC0.

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