Oncopath: intelligent clinical pathway decision… (R43CA261312) – Project Grant to Vizlitics Inc.

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 oncopath in a single thoracic oncology practice for integrated workflow and time savings validation.

Action	Federal	Non-federal	Date
Amendment	$0	$0	Jul 26, 2023
Amendment	$398,721	$0	Aug 31, 2021
Original award	$398,721	$398,721	Aug 31, 2021

Oncopath: intelligent clinical pathway decision support tool for pre-authorization documentation in non-small cell lung cancer treatment

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