Evidence map›Paper›PMID 28269833›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2016

Designing a Clinical Data Warehouse Architecture to Support Quality Improvement Initiatives.

John D Chelico, Adam B Wilcox, David K Vawdrey, Gilad J Kuperman

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Clinical Data Warehousing: A Scoping Review.Journal of the Society for Clinical Data Management · 2024
    Article
  5. Clinical Informatics in Critical Care Medicine.The Yale journal of biology and medicine · 2023
    Review
  6. Review
  7. A dimensional warehouse for integrating operational data from clinical trials.Database : the journal of biological databases and curation · 2019
    Article
  8. Article
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

John D ChelicoNorthwell Health, Manhasset, NY.
Adam B WilcoxUniversity of Washington, Seattle, WA.
David K VawdreyNewYork Presbyterian Hospital, New York, NY.
Gilad J KupermanNewYork Presbyterian Hospital, New York, NY.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clinical data warehouses, initially directed towards clinical research or financial analyses, are evolving to support quality improvement efforts, and must now address the quality improvement life cycle. In addition, data that are needed for quality improvement often do not reside in a single database, requiring easier methods to query data across multiple disparate sources. We created a virtual data warehouse at NewYork Presbyterian Hospital that allowed us to bring together data from several source systems throughout the organization. We also created a framework to match the maturity of a data request in the quality improvement life cycle to proper tools needed for each request. As projects progress in the Define, Measure, Analyze, Improve, Control stages of quality improvement, there is a proper matching of resources the data needs at each step. We describe the analysis and design creating a robust model for applying clinical data warehousing to quality improvement.

Indexed as

Hospital Information SystemsQuality ImprovementDatabase Management SystemsDatabases as TopicHospitals, UniversityMedical Records Systems, ComputerizedNew York CitySystems Integration

Identifiers

PMID28269833
PMCPMC5333328

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.