Developing and Evaluating a Machine-Learning Opioid Prediction & Risk-Stratification E-Platform (DEMONSTRATE)

开发和评估机器学习阿片类药物预测

基本信息

批准号：
10597698
负责人：
Wei-Hsuan Lo-Ciganic
金额：
$ 63.48万
依托单位：
UNIVERSITY OF FLORIDA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2026-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10597698
关键词：
Adopted Algorithms Ambulatory Care Facilities American Classification Clinic Clinical Clinical Data Clinical Research Computer software Criminal Justice Data Data Set Data Sources Development Diagnosis Dose Electronic Health Record Ensure Feedback Florida Focus Groups Funding Guidelines Health Health Care Costs Health system Healthcare Systems Homelessness Individual Intervention Interview Letters Link Machine Learning Measures Medicaid Medicare Methods Naloxone National Institute of Drug Abuse Natural Language Processing Nurse Practitioners Opioid Outcome Overdose Patients Pennsylvania Performance Persons Pharmacy facility Physician Assistants Physicians Policy Maker Process Proctor framework Productivity Provider Public Health Reporting Research Resources Risk Safety Social Behavior System Time Translating Translations United States Centers for Medicare and Medicaid Services Universities Visit Visualization Work acceptability and feasibility adverse outcome clinical decision support clinical practice cohort cost deep neural network design electronic health record system high risk implementation outcomes improved innovation insurance claims machine learning algorithm machine learning prediction algorithm model development neural network neural network algorithm novel strategies opioid overdose opioid use opioid use disorder overdose risk pilot test post implementation prediction algorithm predictive modeling predictive tools prescription opioid prescription opioid misuse prevent primary care clinic primary care provider programs prototype recurrent neural network response risk mitigation risk prediction risk prediction model risk stratification structured data success support tools tool usability user centered design user-friendly

项目摘要

Project Summary/Abstract An unprecedented rise in opioid overdose and opioid use disorder (OUD) has become a public health crisis in the US. In response, health systems, payers, and policy makers have developed or adopted measures and programs to target individuals at high-risk for overdose or OUD. However, significant gaps exist in the current approaches to identify individuals at high-risk for overdose or OUD. First, the definition of ‘high-risk’ currently used by payers and health systems varies widely (ranging from high opioid dose to the number of pharmacies or prescribers a patient has visited). Second, little is known about how accurately these measures truly identify patients with overdose or OUD, and there is some evidence showing they perform poorly, missing 70% to 90% of individuals with an actual OUD diagnosis or overdose. Third, our NIDA-funded work (R01DA044985) using national Medicare and Pennsylvania Medicaid claims data has shown that machine-learning algorithms can achieve better performance for risk prediction for opioid overdose and OUD. Thus, the immediate next step is to expand our algorithms to other data sources (e.g., electronic health records [EHR]), as well as to apply state-of- the-art longitudinal neural networks and natural language processing (NLP) to further improve prediction accuracy. In addition, we aim to translate these risk scores into a clinical decision tool to be used by health care systems to automatically analyze and visualize the relevant information regarding risk prediction and stratification for opioid overdose or OUD, using either claims data, EHR data, or both in real time. Leveraging our NIDA-funded work on developing machine-learning algorithms to predict opioid overdose and OUD, we propose to “develop and evaluate a machine-learning opioid prediction & risk-stratification e- platform (DEMONSTRATE)” that can be used by health care systems to identify patients at high risk for opioid overdose and OUD. We have 3 specific aims. Aim 1 will refine and validate prediction algorithms to identify patients at risk for opioid overdose/OUD using 3 different datasets (i.e., 2011-2020 Florida all-payer EHR, Florida Medicaid claims, and Florida Medicaid claims linked with EHR data) from the OneFlorida Clinical Research Consortium. We will expand our current algorithms by applying state-of-the-art methods (e.g., NLP) to improve prediction. In Aim 2, we will design and prototype a DEMONSTRATE clinical decision support tool to incorporate the best prediction algorithms to provide automatic warnings to primary care providers of patients at high risk of overdose/OUD. An iterative user-centered design approach will be used to enhance DEMONSTRATE’s functionality and usability. In Aim 3, we will integrate DEMONSTRATE into the University of Florida Health’s EHR system, and deploy and pilot test DEMONSTRATE in three primary care clinics. We will assess DEMONSTRATE’s usability, acceptability, and feasibility. Our proposed research is highly innovative in its expansion, translation, and application of a promising NIDA-funded machine-learning opioid prediction and risk stratification tool into a software platform to better inform clinical practice for improving safety of opioid use.

项目概要/摘要阿片类药物过量和阿片类药物使用障碍 (OUD) 的空前增加已成为公共卫生危机作为回应，卫生系统、付款人和政策制定者已经制定或采取了措施并然而，目前的计划还存在重大差距。识别药物过量或 OUD 高风险人群的方法首先，目前“高风险”的定义。付款人和卫生系统使用的药物差异很大（从高阿片类药物剂量到药房数量）其次，人们对这些措施的识别准确度知之甚少。服药过量或 OUD 的患者，有一些证据表明他们表现不佳，缺失 70% 至 90% 第三，我们的 NIDA 资助的工作 (R01DA044985) 使用。国家医疗保险和宾夕法尼亚州医疗补助索赔数据表明，机器学习算法可以在阿片类药物过量和 OUD 的风险预测方面取得更好的性能，因此，下一步是将我们的算法扩展到其他数据源（例如电子健康记录 [EHR]），以及应用现状最先进的纵向神经网络和自然语言处理 (NLP) 进一步改进预测此外，我们的目标是将这些风险评分转化为医疗保健所使用的临床决策工具。自动分析和可视化有关风险预测和分层的相关信息的系统对于阿片类药物过量或 OUD，使用实时索赔数据、EHR 数据或两者。利用 NIDA 资助的机器学习算法开发工作来预测阿片类药物过量和 OUD，我们建议“开发和评估机器学习阿片类药物预测和风险分层电子- 平台（DEMONSTRATE）”，医疗保健系统可以使用该平台来识别高风险患者阿片类药物过量和 OUD 目标 1 将完善和验证预测算法。使用 3 个不同的数据集（即 2011-2020 年佛罗里达州全付费 EHR、佛罗里达州医疗补助索赔以及与 EHR 数据相关的佛罗里达州医疗补助索赔）来自 OneFlorida Clinical 研究联盟。我们将通过应用最先进的方法（例如 NLP）来扩展我们当前的算法。在目标 2 中，我们将设计并制作一个演示临床决策支持工具的原型。结合最佳预测算法，向患者的初级保健提供者提供自动警告过量/OUD 的高风险将采用以用户为中心的迭代设计方法来增强。 DEMONSTRATE 的功能和可用性在目标 3 中，我们将把 DEMONSTRATE 集成到大学中。佛罗里达卫生局的 EHR 系统，并在三个初级保健诊所进行部署和试点测试。评估 DEMONSTRATE 的可用性、可接受性和可行性。我们提出的研究在以下方面具有高度创新性。其对 NIDA 资助的有前景的机器学习阿片类药物预测的扩展、翻译和应用将风险分层工具集成到软件平台中，以更好地为临床实践提供信息，从而提高阿片类药物使用的安全性。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Detection of Personal and Family History of Suicidal Thoughts and Behaviors using Deep Learning and Natural Language Processing: A Multi-Site Study.

使用深度学习和自然语言处理检测个人和家族的自杀想法和行为史：一项多站点研究。

DOI：
发表时间：
2024-03-11
期刊：
影响因子：
0
作者：
Adekkanattu, Prakash;Furmanchuk, Al'ona;Wu, Yonghui;Pathak, Aman;Patra, Braja Gopal;Bost, Sarah;Morrow, Destinee;Wang, Grace Hsin;Yang, Yuyang;Forrest, Noah James;Luo, Yuan;Walunas, Theresa L;Jenny, Wei;Gelad, Walid;Bian
通讯作者：
Bian

Developing and validating a machine-learning algorithm to predict opioid overdose in Medicaid beneficiaries in two US states: a prognostic modelling study.

开发和验证机器学习算法来预测美国两个州医疗补助受益人的阿片类药物过量：预后建模研究。

DOI：
发表时间：
2022-06
期刊：
影响因子：
0
作者：
Lo;Donohue, Julie M;Yang, Qingnan;Huang, James L;Chang, Ching;Weiss, Jeremy C;Guo, Jingchuan;Zhang, Hao H;Cochran, Gerald;Gordon, Adam J;Malone, Daniel C;Kwoh, Chian K;Wilson, Debbie L;Kuza, Courtney C;Gellad, Walid F
通讯作者：
Gellad, Walid F

Deprescribing Strategies for Opioids and Benzodiazepines with Emphasis on Concurrent Use: A Scoping Review.

取消阿片类药物和苯二氮卓类药物的处方策略，强调同时使用：范围界定审查。

DOI：
发表时间：
2023-02-23
期刊：
影响因子：
0
作者：
Wang, Yanning;Wilson, Debbie L;Fernandes, Deanna;Adkins, Lauren E;Bantad, Ashley;Copacia, Clint;Dharma, Nilay;Huang, Pei;Joseph, Amanda;Park, Tae Woo;Budd, Jeffrey;Meenrajan, Senthil;Orlando, Frank A;Pennington, John;Schmidt, Siegfried
通讯作者：
Schmidt, Siegfried

Changes in predicted opioid overdose risk over time in a state Medicaid program: a group-based trajectory modeling analysis.

州医疗补助计划中预测的阿片类药物过量风险随时间的变化：基于群体的轨迹建模分析。

DOI：
发表时间：
2022-08
期刊：
影响因子：
0
作者：
Guo, Jingchuan;Gellad, Walid F;Yang, Qingnan;Weiss, Jeremy C;Donohue, Julie M;Cochran, Gerald;Gordon, Adam J;Malone, Daniel C;Kwoh, C Kent;Kuza, Courtney C;Wilson, Debbie L;Lo
通讯作者：
Lo

Association between first-line antidepressant use and risk of dementia in older adults: a retrospective cohort study.

一线抗抑郁药的使用与老年人痴呆风险之间的关联：一项回顾性队列研究。