Challenges and Opportunities for Using Big Health Care Data to Advance Medical Science and Public Health
被引:45
|
作者:
Shortreed, Susan M.
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机构:
Kaiser Permanente, Washington Hlth Res Inst, Biostat Unit, Seattle, WA USA
Univ Washington, Sch Publ Hlth, Dept Biostat, Seattle, WA 98195 USAKaiser Permanente, Washington Hlth Res Inst, Biostat Unit, Seattle, WA USA
Shortreed, Susan M.
[1
,2
]
Cook, Andrea J.
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机构:
Kaiser Permanente, Washington Hlth Res Inst, Biostat Unit, Seattle, WA USA
Univ Washington, Sch Publ Hlth, Dept Biostat, Seattle, WA 98195 USAKaiser Permanente, Washington Hlth Res Inst, Biostat Unit, Seattle, WA USA
Cook, Andrea J.
[1
,2
]
Coley, R. Yates
论文数: 0引用数: 0
h-index: 0
机构:
Kaiser Permanente, Washington Hlth Res Inst, Biostat Unit, Seattle, WA USA
Univ Washington, Sch Publ Hlth, Dept Biostat, Seattle, WA 98195 USAKaiser Permanente, Washington Hlth Res Inst, Biostat Unit, Seattle, WA USA
Coley, R. Yates
[1
,2
]
Bobb, Jennifer F.
论文数: 0引用数: 0
h-index: 0
机构:
Kaiser Permanente, Washington Hlth Res Inst, Biostat Unit, Seattle, WA USA
Univ Washington, Sch Publ Hlth, Dept Biostat, Seattle, WA 98195 USAKaiser Permanente, Washington Hlth Res Inst, Biostat Unit, Seattle, WA USA
Bobb, Jennifer F.
[1
,2
]
Nelson, Jennifer C.
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机构:
Kaiser Permanente, Washington Hlth Res Inst, Biostat Unit, Seattle, WA USA
Univ Washington, Sch Publ Hlth, Dept Biostat, Seattle, WA 98195 USAKaiser Permanente, Washington Hlth Res Inst, Biostat Unit, Seattle, WA USA
Nelson, Jennifer C.
[1
,2
]
机构:
[1] Kaiser Permanente, Washington Hlth Res Inst, Biostat Unit, Seattle, WA USA
[2] Univ Washington, Sch Publ Hlth, Dept Biostat, Seattle, WA 98195 USA
big data;
data privacy;
data quality;
electronic health records;
pragmatic clinical trials;
predictive modeling;
privacy-protecting analyses;
retrospective cohort studies;
VACCINE SAFETY DATALINK;
DOUBLY ROBUST ESTIMATION;
PANEL COUNT DATA;
INFORMATIVE OBSERVATION TIMES;
PROPENSITY SCORE ESTIMATION;
MARGINAL STRUCTURAL MODELS;
PROBLEM OPIOID USE;
VALIDATED METHODS;
LONGITUDINAL DATA;
REGRESSION-ANALYSIS;
D O I:
10.1093/aje/kwy292
中图分类号:
R1 [预防医学、卫生学];
学科分类号:
1004 ;
120402 ;
摘要:
Methodological advancements in epidemiology, biostatistics, and data science have strengthened the research world's ability to use data captured from electronic health records (EHRs) to address pressing medical questions, but gaps remain. We describe methods investments that are needed to curate EHR data toward research quality and to integrate complementary data sources when EHR data alone are insufficient for research goals. We highlight new methods and directions for improving the integrity of medical evidence generated from pragmatic trials, observational studies, and predictive modeling. We also discuss needed methods contributions to further ease data sharing across multisite EHR data networks. Throughout, we identify opportunities for training and for bolstering collaboration among subject matter experts, methodologists, practicing clinicians, and health system leaders to help ensure that methods problems are identified and resulting advances are translated into mainstream research practice more quickly.
机构:
Tufts Univ, Sch Med, Dept Family Med & Community Hlth, Boston, MA 02111 USATufts Univ, Sch Med, Dept Family Med & Community Hlth, Boston, MA 02111 USA
Robbins, A
Freeman, P
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机构:
Tufts Univ, Sch Med, Dept Family Med & Community Hlth, Boston, MA 02111 USATufts Univ, Sch Med, Dept Family Med & Community Hlth, Boston, MA 02111 USA