ArticleJMIR medical informatics2017
Promoting Secondary Analysis of Electronic Medical Records in China: Summary of the PLAGH-MIT Critical Data Conference and Health Datathon.
Article in JMIR medical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Echo Chambers: Bias and Representation in Cardiac Imaging Datasets for Artificial Intelligence.Current cardiology reports · 2026Review
- Innovating Challenges and Experiences in Emory Health AI Bias Datathon: Experience Report.Journal of imaging informatics in medicine · 2025Article
- Development Of the VAMPCT Score for Predicting Mortality in CKD Patients with COVID-19.International journal of medical sciences · 2025Article
- Article
- Acute kidney injury in hospitalized patients with nonmalignant pleural effusions: a retrospective cohort study.BMC nephrology · 2024Article
- Data sharing and reuse in clinical research: Are we there yet? A cross-sectional study on progress, challenges and opportunities in LMICs.PLOS global public health · 2024Article
- How to organise a datathon for bridging between data science and healthcare? Insights from the Technion-Rambam machine learning in healthcare datathon event.BMJ health & care informatics · 2023Article
- [Design and implementation of Internet of Things for emergency medical devices based on cloud-edge-device architecture].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Big Data Health Care Platform With Multisource Heterogeneous Data Integration and Massive High-Dimensional Data Governance for Large Hospitals: Design, Development, and Application.JMIR medical informatics · 2022Article
- Transformation and Evaluation of the MIMIC Database in the OMOP Common Data Model: Development and Usability Study.JMIR medical informatics · 2021Article
- Artificial Intelligence Algorithm with ICD Coding Technology Guided by the Embedded Electronic Medical Record System in Medical Record Information Management.Journal of healthcare engineering · 2021Article
- MIT COVID-19 Datathon: data without boundaries.BMJ innovations · 2021Article
- Adoption of an Electronic Patient Record Sharing Pilot Project: Cross-Sectional Survey.Journal of medical Internet research · 2020Article
- Comparing the Performance of Charlson and Elixhauser Comorbidity Indices to Predict In-Hospital Mortality Among a Chinese Population.Clinical epidemiology · 2020Article
- Barriers and facilitators to data quality of electronic health records used for clinical research in China: a qualitative study.BMJ open · 2019Article
- Critical Care, Critical Data.Biomedical engineering and computational biology · 2019Review
- Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse.Journal of medical Internet research · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
Electronic health records (EHRs) have been widely adopted among modern hospitals to collect and track clinical data. Secondary analysis of EHRs could complement the traditional randomized control trial (RCT) research model. However, most researchers in China lack either the technical expertise or the resources needed to utilize EHRs as a resource. In addition, a climate of cross-disciplinary collaboration to gain insights from EHRs, a crucial component of a learning healthcare system, is not prevalent. To address these issues, members from the Massachusetts Institute of Technology (MIT) and the People's Liberation Army General Hospital (PLAGH) organized the first clinical data conference and health datathon in China, which provided a platform for clinicians, statisticians, and data scientists to team up and address information gaps in the intensive care unit (ICU).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.