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

OncoPath：用于非小细胞肺癌治疗预授权文档的智能临床路径决策支持工具

• 批准号: 10325551
• 负责人: Sharon Hensley Alford
• 金额: $ 39.87万
• 依托单位: VIZLITICS INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10325551
• 关键词: Address; Algorithms; Authorization documentation; Automobile Driving; Businesses; Cancer Patient; Characteristics; Chest; Clinical; Clinical Decision Support Systems; Clinical Pathways; Code; Communication; Complex; Data; Decision Making; Development; Documentation; Goals; Graph; Guidelines; Health Care Costs; Health system; Intelligence; Intuition; Libraries; Location; Malignant Neoplasms; Manuals; Maps; Medical Care Costs; Medical History; Metadata; Modeling; National Comprehensive Cancer Network; New York; Non-Small-Cell Lung Carcinoma; Oncologist; Oncology; Paper; Pathway interactions; Patient-Focused Outcomes; Patients; Phase; Physicians; Process; Production; Running; Savings; Selection for Treatments; Small Business Innovation Research Grant; Societies; Technology; Testing; Thoracic Oncology; Time; Treatment Efficacy; Update; Validation; Visual; Visualization; base; cancer therapy; clinical decision support; cloud based; cost; digital; efficacy outcomes; health plan; improved; innovation; insight; mathematical methods; mathematical model; open source; optimal treatments; payment; prototype; software as a service; support tools; tool; treatment guidelines; treatment planning; tumor

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

项目摘要 在现实世界中的临床环境中，繁忙的肿瘤学家缺乏所有可行的调查案例分析的时间 治疗方案，经常更新的治疗指南和付款人特定要求。这导致子 最佳决策制定，不完整的预授权（事前）文档以及有关 报销。总体而言，这增加了肿瘤学内的医疗费用和付款定义。例如， 估计效率低下的效率低下将为医疗保健费用增加83,000美元，为11亿美元 仅在肿瘤学上单程。为了制定治疗计划，肿瘤学家参考国家综合癌症 网络指南®（NCCN），其他临床社会标准和付款人在上下文中的特定要求 患者的病史，肿瘤特征和治疗阶段。最常用的肿瘤学之一 肿瘤学家提到并遵守大多数付款人的治疗指南是国家综合 癌症网络（NCCN）指南®。这些准则作为跨越100页的模式呈现。 可以采用技术驱动的临床决策支持（CD）系统来满足 简化治疗指南分析，付款人规则审查和治疗决策文件 肿瘤学实践的总体成本降低。该提案着重于开发创新和首个 使用非小细胞肺癌（NSCLC）作为初始测试用例和编码的CD的A型技术 NCCN指南，一般付款人特定要求和覆盖的患者数据以计算可行治疗 途径。团队提出以下I阶段特定目标： 目标1：开发基于图的数学模型和NSCLC NCCN处理的视觉呈现 具有广义付款人特定要求的准则。开发基于视觉互动图的 NCCNGuidelines®的代表。建模指南作为视觉图（节点和弧）将启用 肿瘤学家确定患者的最佳治疗途径。 AIM 2：构建一个分析引擎，该引擎可突出使用可行的治疗选项的NCCN图模型 鉴于患者的案例详细信息和普通付款人要求。使用OpenSource工具创建 肿瘤学家和健康计划专家的合成患者数据和常见的付款人约束。开发 图库的库遍历算法，以覆盖和可视化视觉用户界面中的患者数据。 AIM 3：使用OnCopath执行，验证和测试概念验证CDS工作流程。运行完整 端到端CD工作流，其中包含患者详细信息，NCCN指南，付款人要求和 使用AIM 2中产生的合成患者数据和付款人约束的治疗决策。 Oncopath将使对治疗决策和文件的有效，对肿瘤学家的方法有效， 随后使患者受益并降低肿瘤学成本。第二阶段将部署一个实例 单个胸部肿瘤学实践中的Oncopath用于集成工作流程和时间节省验证。