医疗大数据在学习型健康医疗系统中的应用

柴扬帆; 孔桂兰; 张路霞

doi:10.11959/j.issn.2096-0271.2020042

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医疗大数据在学习型健康医疗系统中的应用

专题：医学大数据 | 更新时间：2024-06-03

- 医疗大数据在学习型健康医疗系统中的应用
- Application of medical big data in learning health system
- 大数据 2020年6卷第5期页码：2020042-1
- 作者机构：
  
  1. 北京大学健康医疗大数据国家研究院，北京 100191
  2. 北京大学公共卫生学院，北京 100191
- 作者简介：
  
  [ "柴扬帆（1996- ），女，北京大学公共卫生学院硕士生，主要研究方向为医疗大数据挖掘与医学决策" ]
  [ "孔桂兰（1975- ），女，博士，北京大学健康医疗大数据国家研究院副研究员，主要研究方向为临床决策支持系统、医学大数据挖掘、医学知识管理、医疗质量综合评估等" ]
  [ "张路霞（1976- ），女，博士，北京大学健康医疗大数据国家研究院教授、院长助理，主要研究方向为重大慢性疾病的变化趋势、疾病负担及防治" ]
- 基金信息：
  
  国家自然科学基金项目资助项目;The National Natural Science Foundation of China(91846101);国家自然科学基金项目资助项目;The National Natural Science Foundation of China(81771938);科技创新2030“新一代人工智能”重大项目;Chinese Scientific and Technical Innovation Project 2030(2018AAA0102100);北京大学医学部-密歇根大学医学院转化医学与临床研究联合研究所项目;UMHS-PUHSC Joint Institute for Translational and Clinical Research Project(BMU2020JI011)
- DOI：10.11959/j.issn.2096-0271.2020042
  中图分类号： R-1
- 网络首发：2020-09，
  
  纸质出版：2020-09-15
- 稿件说明：
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柴扬帆, 孔桂兰, 张路霞. 医疗大数据在学习型健康医疗系统中的应用[J]. 大数据, 2020,6(5):2020042-1.

Yangfan CHAI, Guilan KONG, Luxia ZHANG. Application of medical big data in learning health system[J]. Big Data Research, 2020, 6(5): 2020042-1.
柴扬帆, 孔桂兰, 张路霞. 医疗大数据在学习型健康医疗系统中的应用[J]. 大数据, 2020,6(5):2020042-1. DOI： 10.11959/j.issn.2096-0271.2020042.

Yangfan CHAI, Guilan KONG, Luxia ZHANG. Application of medical big data in learning health system[J]. Big Data Research, 2020, 6(5): 2020042-1. DOI： 10.11959/j.issn.2096-0271.2020042.

摘要

将医疗大数据应用于旨在加快知识生成和临床转化应用的学习型健康医疗系统（LHS）中，满足患者和医疗决策者的知识需求，有助于推动精准医学的发展。在系统阐述医疗大数据与LHS发展现状的基础上，结合LHS的典型应用案例，重点分析医疗大数据在LHS中的应用特点及面临的挑战。最后总结了我国发展LHS面临的挑战，并对未来进行了展望。

Abstract

The learning health system (LHS) aims at accelerating the process of knowledge generation

transformation and application in clinical practice.Applying medical big data in LHS to meet the knowledge needs of patients and healthcare decision makers would help to promote the development of precision medicine.Firstly

the current status of medical big data and LHS were reviewed

then the characteristics and challenges of applying medical big data in LHS were analyzed by refering to some typical application cases.Finally

the challenges faced by LHS in China were addressed and the prospect of applying medical big data to LHS in the future was provided.

关键词

Keywords

references

ETHEREDGE L M . A rapid-learning health system [J ] . Health Affairs , 2007 , 26 ( 2 ): 107 - 118 .

OLSEN L , AISNER D , MCGINNIS J M . The national academies collection:reports funded by National Institutes of Health [C ] // The Learning Healthcare System:Workshop Summary . Pittsburgh:National Academies Press , 2007 .

ELSON S L , HIATT R A , ANTONCULVER H , et al . The athena breast health network:developing a rapid learning system in breast cancer prevention,screening,treatment,and care [J ] . Breast Cancer Research and Treatment , 2013 , 140 ( 2 ): 417 - 425 .

FIORE L D , BROPHY M , FERGUSON R E , et al . A point-of-care clinical trial comparing insulin administered using a sliding scale versus a weight-based regimen [J ] . Clinical Trials , 2011 , 8 ( 2 ): 183 - 195 .

STEELS S , VAN STAA T . Evaluation protocol of the implementation of a learning healthcare system in clinical practice:the connected health cities programme in the north of England [J ] . BMJ Open , 2019 , 9 ( 6 ):e025484.

DELANEY B C , CURCIN V , ANDREASSON A , et al . Translational medicine and patient safety in Europe:TRANSFoRm-architecture for the learning health system in Europe [J ] . BioMed Research International , 2015 :961526.

BOES S , MANTWILL S , KAUFMANN C , et al . Swiss learning health system:a national initiative to establish learning cycles for continuous health system improvement [J ] . Learning Health Systems , 2018 2 ( 3 ):e10059.

许培海 , 黄匡时 . 我国健康医疗大数据的现状、问题及对策 [J ] . 中国数字医学 , 2017 , 12 ( 5 ): 24 - 26 .

XU P H , HUANG K S . The status,problems and countermeasures of the big data of health care in China [J ] . China Digital Medicine , 2017 , 12 ( 5 ): 24 - 26 .

戴明锋 , 孟群 . 医疗健康大数据挖掘和分析面临的机遇与挑战 [J ] . 中国卫生信息管理杂志 , 2017 , 14 ( 2 ): 126 - 130 .

DAI M F , MENG Q . Opportunities and challenges in data mining and data analysis of health care big data [J ] . Chinese Journal of Health Informatics and Management , 2017 , 14 ( 2 ): 126 - 130 .

MOONEY S J , PEJAVER V . Big data in public health:terminology,machine learning,and privacy [J ] . Annual Review of Public Health , 2018 , 39 : 95 - 112 .

杨朝晖 , 王心 , 徐香兰 . 医疗健康大数据分类及问题探讨 [J ] . 卫生经济研究 , 2019 , 36 ( 3 ): 29 - 31 .

YANG Z H , WANG X , XU X L . Discussion and classification of medical health big data [J ] . Health Economics Research , 2019 , 36 ( 3 ): 29 - 31 .

KO K D , EL-GHAZAWI T , KIM D , et al . Predicting the severity of motor neuron disease progression using electronic health record data with a cloud computing big data approach [C ] // 2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology . Piscataway:IEEE Press , 2014 .

QIANG X L , XU P , FANG G , et al . Using the spike protein feature to predict infection risk and monitor the evolutionary dynamic of coronavirus [J ] . Infectious Diseases of Poverty , 2020 , 9 ( 1 ):33.

韩冬 , 李其花 , 蔡巍 , 等 . 人工智能在医学影像中的研究与应用 [J ] . 大数据 , 2019 ( 1 ): 39 - 67 .

HAN D , LI Q H , CAI W , et al . Research and application of artifical intelligence in medical imaging [J ] . Big Data Research , 2019 ( 1 ): 39 - 67 .

许德泉 . 大数据在医疗个性化服务中的应用 [J ] . 中国卫生信息管理杂志 , 2013 ( 4 ): 301 - 304 .

XU D Q . The application of big data on healthcare personalized service [J ] . Chinese Journal of Health Informatics and Management , 2013 ( 4 ): 301 - 304 .

EKINS S , CLARK A M , DOLE K , et al . Data mining and computational modeling of high-throughput screening datasets [J ] . Methods in Molecular Biology , 2018 , 1755 : 197 - 221 .

DEO R C . Machine learning in medicine [J ] . Circulation , 2015 , 132 ( 20 ): 1920 - 1930 .

MORRIS Z S , WOODING S , GRANT J . The answer is 17 years,what is the question:understanding time lags in translational research [J ] . Journal of the Royal Society of Medicine , 2011 , 104 ( 12 ): 510 - 520 .

FRIEDMAN C , RUBIN J , BROWN J , et al . Toward a science of learning systems:a research agenda for the high-functioning learning health system [J ] . Journal of the American Medical Informatics Association , 2015 , 22 ( 1 ): 43 - 50 .

FLYNN A J , SHI W , FISCHER R , et al . Digital knowledge objects and digital knowledge object clusters:unit holdings in a learning health system knowledge repository [C ] // The 2016 49th Hawaii International Conference on System Sciences . New York:ACM Press , 2016 : 3308 - 3317 .

FLYNN A J , FRIEDMAN C P , BOISVERT P , et al . The knowledge object reference ontology (KORO):a formalism to support management and sharing of computable biomedical knowledge for learning health systems [J ] . Learning Health Systems , 2018 , 2 ( 2 ):e10054.

TAMMEMÄGI M C , KATKI H A , HOCKING W G , et al . Selection criteria for lung-cancer screening [J ] . New England Journal of Medicine , 2013 , 368 ( 8 ): 728 - 736 .

FORREST C B , MARGOLIS P , BAILEY C , et al . PEDSnet:a national pediatric learning health system [J ] . Journal of the American Medical Informatics Association , 2014 , 21 ( 4 ): 602 - 606 .

OHNO-MACHADO L , AGHA Z , BELL DS , et al . pSCANNER:patient-centered scalable national network for effectiveness research [J ] . Journal of the American Medical Informatics Association , 2014 , 21 ( 4 ): 621 - 626 .

SHAH A , STEWART A K , KOLACEVSKI A , et al . Building a rapid learning health care system for oncology:why CancerLinQ collects identifiable health information to achieve its vision [J ] . Journal of Clinical Oncology , 2016 , 34 ( 7 ): 756 - 763 .

WU C Y , HUANG C W , YANG H C , et al . Opportunities and challenges in Taiwan for implementing the learning health system [J ] . International Journal for Quality in Health Care , 2019 , 31 ( 9 ): 721 - 724 .

MCGLYNN E A , LIEU T A , DURHAM M L , et al . Developing a data infrastructure for a learning health system:the PORTAL network [J ] . Journal of the American Medical Informatics Association , 2014 , 21 ( 4 ): 596 - 601 .

MURPHY S N , WEBER G , MENDIS M , et al . Serving the enterprise and beyond with informatics for integrating biology and the bedside (I2B2) [J ] . Journal of the American Medical Informatics Association , 2010 , 17 ( 2 ): 124 - 130 .

MANDL K D , KOHANE I S , MCFADDEN D , et al . Scalable collaborative infrastructure for a learning healthcare system(SCILHS):architecture [J ] . Journal of the American Medical Informatics Association , 2014 , 21 ( 4 ): 615 - 620 .

KWON S , FLORENCE M , GRIGAS P , et al . Creating a learning healthcare system in surgery:Washington State’s surgical care and outcomes assessment program (SCOAP) at 5 years [J ] . Surgery , 2012 , 151 ( 2 ): 146 - 152 .

FRIEDMAN C P , ALLEE N J , DELANEY B , et al . The science of learning health systems:foundations for a new journal [J ] . Learning Health Systems , 2017 , 1 ( 1 ):e10020.

MILSTEIN A . Code red and blue-safely limiting health care’s GDP footprint [J ] . New England Journal of Medicine , 2013 , 368 ( 1 ): 1 - 3 .

JAMES B C , SAVITZ L A . How Intermountain trimmed health care costs through robust quality improvement efforts [J ] . Health Affairs , 2011 , 30 ( 6 ): 1185 - 1191 .

WILSON J F . Making electronic health records meaningful [J ] . Annals of Internal Medicine , 2009 , 151 ( 4 ): 293 - 296 .

GRINSPAN Z , BANERJEE S , KAUSHAL R , et al . Physician specialty and variations in adoption of electronic health records [J ] . Applied Clinical Informatics , 2013 , 4 ( 2 ): 225 - 240 .

ADLER-MILSTEIN J , DESROCHES C M , KRALOVEC P , et al . Electronic health record adoption in US hospitals:progress continues,but challenges persist [J ] . Health Affairs , 2015 , 34 ( 12 ): 2174 - 2180 .

WEINER M G , EMBI P J . Toward reuse of clinical data for research and quality improvement:the end of the beginning? [J ] . Annals of Internal Medicine , 2009 , 151 ( 5 ): 359 - 360 .

BATES D W , SARIA S , OHNOMACHADO L , et al . Big data in health care:using analytics to identify and manage high-risk and high-cost patients [J ] . Health Affairs , 2014 , 33 ( 7 ): 1123 - 1131 .

AMARASINGHAM R , VELASCO F , XIE B , et al . Electronic medical record-based multi-condition models to predict the risk of 30 day readmission or death among adult medicine patients:validation and comparison to existing models [J ] . BMC Medical Informatics and Decision Making , 2015 ,15.

AMARASINGHAM R , MOORE B J , TABAK Y P , et al . An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data [J ] . Medical Care , 2010 , 48 ( 11 ): 981 - 988 .

AMARASINGHAM R , PATZER R E , HUESCH M , et al . Implementing electronic health care predictive analytics:considerations and challenges [J ] . Health Affairs , 2014 , 33 ( 7 ): 1148 - 1154 .

FIHN S D , FRANCIS J , CLANCY C , et al . Insights from advanced analytics at the veterans health administration [J ] . Health Affairs , 2014 , 33 ( 7 ): 1203 - 1211 .

BRINDIS R G , FITZGERALD S , ANDERSON H V , et al . The American College of Cardiology-National Cardiovascular Data Registry (ACC-NCDR):building a national clinical data repository [J ] . Journal of the American College of Cardiology , 2001 , 37 ( 8 ): 2240 - 2245 .

MESSENGER J C , HO K K , YOUNG C H , et al . The National Cardiovascular Data Registry (NCDR) data quality brief:the NCDR data quality program in 2012 [J ] . Journal of the American College of Cardiology , 2012 , 60 ( 16 ): 1484 - 1488 .

MASOUDI F A , PONIRAKIS A , YEH R W , et al . Cardiovascular care facts:a report from the national cardiovascular data registry:2011 [J ] . Journal of the American College of Cardiology , 2013 , 62 ( 21 ): 1931 - 1947 .

CURTIS L H , BROWN J , PLATT R . Four health data networks illustrate the potential for a shared national multipurpose big-data network [J ] . Health Affairs , 2014 , 33 ( 7 ): 1178 - 1186 .

GO A S , MAGID D J , WELLS B , et al . The cardiovascular research network:a new paradigm for cardiovascular quality and outcomes research [J ] . CirculationCardiovascular Quality and Outcomes , 2008 , 1 ( 2 ): 138 - 147 .

WALLACE P J , SHAH N D , DENNEN T , et al . Optum labs:building a novel node in the learning health care system [J ] . Health Affairs , 2014 , 33 ( 7 ): 1187 - 1194 .

FORREST C B , MARGOLIS P , SEID M , et al . PEDSnet:how a prototype pediatric learning health system is being expanded into a national network [J ] . Health Affairs , 2014 , 33 ( 7 ): 1171 - 1177 .

BUFALINO V J , MASOUDI F A , STRANNE S K , et al . The American Heart Association’s recommendations for expanding the applications of existing and future clinical registries:a policy statement from the American Heart Association [J ] . Circulation , 2011 , 123 ( 19 ): 2167 - 2179 .

DUAN R , BOLAND M R , MOORE J H , et al . ODAL:a one-shot distributed algorithm to perform logistic regressions on electronic health records data from multiple clinical sites [C ] // The Pacific Symposium.[S.l.:s.n . ] , 2019 : 30 - 41 .

MADDOX T M , ALBERT N M , BORDEN W B , et al . The learning healthcare system and cardiovascular care:a scientific statement from the American Heart Association [J ] . Circulation , 2017 , 135 ( 14 ): 826 - 857 .

FRIEDMAN C P , RUBIN J C , SULLIVAN K J . Toward an information infrastructure for global health improvement [J ] . Yearbook of Medical Informatics , 2017 , 26 ( 1 ): 16 - 23 .

SPECTOR-BAGDADY K , JAGSI R . Big data,ethics,and regulations:implications for consent in the learning health system [J ] . Medical Physics , 2018 , 45 ( 10 ): 845 - 847 .

MYERS S R , CARR B G , BRANAS C . Uniting big health data for a national learning health system in the United States [J ] . JAMA Pediatrics , 2016 , 170 ( 12 ): 1133 - 1134 .

SPECTOR-BAGDADY K , LOMBARDO P A . Something of an adventure:postwar NIH research ethos and the Guatemala STD experiments [J ] . The Journal of Law Medicine ＆ Ethics , 2013 , 41 ( 3 ): 697 - 710 .

JAGSI R , GRIFFITH K A , SABOLCH A , et al . Perspectives of patients with cancer on the ethics of rapid-learning health systems [J ] . Journal of Clinical Oncology , 2017 , 35 ( 20 ): 2315 - 2323 .

武轶群 , 胡永华 , 陈大方 . 健康大数据在精准健康风险评估中的应用 [J ] . 中国慢性病预防与控制 , 2020 , 28 ( 3 ): 226 - 229 .

WU Y Q , HU Y H , CHEN D F . Application of medical big data in precision health risk assessment [J ] . Chinese Journal of Prevention and Control of Chronic , 2020 , 28 ( 3 ): 226 - 229 .

李岳峰 , 胡建平 , 周光华 , 等 . 我国卫生信息化建设:现状与发展 [J ] . 中国卫生信息管理杂志 , 2012 , 9 ( 5 ): 7 - 10 .

LI Y F , HU J P , ZHOU G H , et al . Health informationization of China:status and development [J ] . Chinese Journal of Health Informatics and Management , 2012 , 9 ( 5 ): 7 - 10 .

LIN H B , TANG X , SHEN P , et al . Using big data to improve cardiovascular care and outcomes in China:a protocol for the CHinese Electronic health Records Research in Yinzhou (CHERRY) study [J ] . BMJ Open , 2018 , 8 ( 2 ):e019698.

葛小玲 , 叶成杰 , 郭建峰 , 等 . 基于互联网+的儿联体远程医学平台设计与实践 [J ] . 中国数字医学 , 2016 , 11 ( 7 ): 20 - 23 .

GE X L , YE C J , GUO J F , et al . The design and practice of pediatric medical alliance remote medical platform based on Internet+ [J ] . China Digital Medicine , 2016 , 11 ( 7 ): 20 - 23 .

YUAN S W , LIU W W , WEI F Q , et al . Impacts of hospital payment based on diagnosis related groups (DRGs) with global budget on resource use and quality of care:a case study in China [J ] . Iranian Journal of Public Health , 2019 , 48 ( 2 ): 238 - 246 .

张旭东 , 孙圣力 , 王洪超 . 基于数据挖掘的触诊成像乳腺癌智能诊断模型和方法 [J ] . 大数据 , 2019 ( 1 ): 68 - 76 .

ZHANG X D , SUN S L , WANG H C . Intelligent diagnosis model and method of palpation imaging breast cancer based on data mining [J ] . Big Data Research , 2019 ( 1 ): 68 - 76 .

汪泽川 . 医疗大数据及其面临的机遇与挑战 [J ] . 信息记录材料 , 2018 , 19 ( 4 ): 222 - 223 .

WANG Z C . Medical big data and its opportunities and challenges [J ] . Information Recording Materials , 2018 , 19 ( 4 ): 222 - 223

刘文先 , 胡建平 , 肖大华 , 等 . 全国省级全民健康信息平台建设情况分析 [J ] . 中国卫生信息管理杂志 , 2018 , 15 ( 1 ): 20 - 23 .

LIU W X , HU J P , XIAO D H , et al . Investigation and analysis of construction situation of provincial health information platform [J ] . Journal of Health Informatics and Management , 2018 , 15 ( 1 ): 20 - 23 .

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