Jayson S. Marwaha , Maren Downing , John Halamka , Amy Abernethy , Joseph B. Franklin , Brian Anderson , Isaac Kohane , Kavishwar Wagholikar , John Brownstein , Melissa Haendel , Gabriel A. Brat
{"title":"在危机期间调动数据:利用多机构真实世界数据建立快速证据管道","authors":"Jayson S. Marwaha , Maren Downing , John Halamka , Amy Abernethy , Joseph B. Franklin , Brian Anderson , Isaac Kohane , Kavishwar Wagholikar , John Brownstein , Melissa Haendel , Gabriel A. Brat","doi":"10.1016/j.hjdsi.2024.100738","DOIUrl":null,"url":null,"abstract":"<div><p>The COVID-19 pandemic generated tremendous interest in using real world data (RWD). Many consortia across the public and private sectors formed in 2020 with the goal of rapidly producing high-quality evidence from RWD to guide medical decision-making, public health priorities, and more. Experiences were gathered from five large consortia on rapid multi-institutional evidence generation during the COVID-19 pandemic. Insights have been compiled across five dimensions: consortium composition, governance structure and alignment of priorities, data sharing, data analysis, and evidence dissemination. The purpose of this piece is to offer guidance on building large-scale multi-institutional RWD analysis pipelines for future public health issues.</p><p>The composition of each consortium was largely influenced by existing collaborations. A central set of priorities for evidence generation guided each consortium, however different approaches to governance emerged. Challenges surrounding limited access to clinical data due to various contributors were overcome in unique ways. While all consortia used different methods to construct and analyze patient cohorts ranging from centralized to federated approaches, all proved effective for generating meaningful real-world evidence. Actionable recommendations for clinical practice and public health agencies were made from translating insights from consortium analyses.</p><p>Each consortium was successful in rapidly answering questions about COVID-19 diagnosis and treatment despite all taking slightly different approaches to data sharing and analysis. Leveraging RWD, leveraged in a manner that applies scientific rigor and transparency, can complement higher-level evidence and serve as an important adjunct to clinical trials to quickly guide policy and critical care, especially for a pandemic response.</p></div>","PeriodicalId":29963,"journal":{"name":"Healthcare-The Journal of Delivery Science and Innovation","volume":"12 2","pages":"Article 100738"},"PeriodicalIF":2.0000,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Mobilizing data during a crisis: Building rapid evidence pipelines using multi-institutional real world data\",\"authors\":\"Jayson S. Marwaha , Maren Downing , John Halamka , Amy Abernethy , Joseph B. Franklin , Brian Anderson , Isaac Kohane , Kavishwar Wagholikar , John Brownstein , Melissa Haendel , Gabriel A. Brat\",\"doi\":\"10.1016/j.hjdsi.2024.100738\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The COVID-19 pandemic generated tremendous interest in using real world data (RWD). Many consortia across the public and private sectors formed in 2020 with the goal of rapidly producing high-quality evidence from RWD to guide medical decision-making, public health priorities, and more. Experiences were gathered from five large consortia on rapid multi-institutional evidence generation during the COVID-19 pandemic. Insights have been compiled across five dimensions: consortium composition, governance structure and alignment of priorities, data sharing, data analysis, and evidence dissemination. The purpose of this piece is to offer guidance on building large-scale multi-institutional RWD analysis pipelines for future public health issues.</p><p>The composition of each consortium was largely influenced by existing collaborations. A central set of priorities for evidence generation guided each consortium, however different approaches to governance emerged. Challenges surrounding limited access to clinical data due to various contributors were overcome in unique ways. While all consortia used different methods to construct and analyze patient cohorts ranging from centralized to federated approaches, all proved effective for generating meaningful real-world evidence. Actionable recommendations for clinical practice and public health agencies were made from translating insights from consortium analyses.</p><p>Each consortium was successful in rapidly answering questions about COVID-19 diagnosis and treatment despite all taking slightly different approaches to data sharing and analysis. Leveraging RWD, leveraged in a manner that applies scientific rigor and transparency, can complement higher-level evidence and serve as an important adjunct to clinical trials to quickly guide policy and critical care, especially for a pandemic response.</p></div>\",\"PeriodicalId\":29963,\"journal\":{\"name\":\"Healthcare-The Journal of Delivery Science and Innovation\",\"volume\":\"12 2\",\"pages\":\"Article 100738\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-03-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Healthcare-The Journal of Delivery Science and Innovation\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2213076424000058\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"HEALTH POLICY & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Healthcare-The Journal of Delivery Science and Innovation","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2213076424000058","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"HEALTH POLICY & SERVICES","Score":null,"Total":0}
Mobilizing data during a crisis: Building rapid evidence pipelines using multi-institutional real world data
The COVID-19 pandemic generated tremendous interest in using real world data (RWD). Many consortia across the public and private sectors formed in 2020 with the goal of rapidly producing high-quality evidence from RWD to guide medical decision-making, public health priorities, and more. Experiences were gathered from five large consortia on rapid multi-institutional evidence generation during the COVID-19 pandemic. Insights have been compiled across five dimensions: consortium composition, governance structure and alignment of priorities, data sharing, data analysis, and evidence dissemination. The purpose of this piece is to offer guidance on building large-scale multi-institutional RWD analysis pipelines for future public health issues.
The composition of each consortium was largely influenced by existing collaborations. A central set of priorities for evidence generation guided each consortium, however different approaches to governance emerged. Challenges surrounding limited access to clinical data due to various contributors were overcome in unique ways. While all consortia used different methods to construct and analyze patient cohorts ranging from centralized to federated approaches, all proved effective for generating meaningful real-world evidence. Actionable recommendations for clinical practice and public health agencies were made from translating insights from consortium analyses.
Each consortium was successful in rapidly answering questions about COVID-19 diagnosis and treatment despite all taking slightly different approaches to data sharing and analysis. Leveraging RWD, leveraged in a manner that applies scientific rigor and transparency, can complement higher-level evidence and serve as an important adjunct to clinical trials to quickly guide policy and critical care, especially for a pandemic response.
期刊介绍:
HealthCare: The Journal of Delivery Science and Innovation is a quarterly journal. The journal promotes cutting edge research on innovation in healthcare delivery, including improvements in systems, processes, management, and applied information technology.
The journal welcomes submissions of original research articles, case studies capturing "policy to practice" or "implementation of best practices", commentaries, and critical reviews of relevant novel programs and products. The scope of the journal includes topics directly related to delivering healthcare, such as:
● Care redesign
● Applied health IT
● Payment innovation
● Managerial innovation
● Quality improvement (QI) research
● New training and education models
● Comparative delivery innovation