Evidence map›Paper›PMID 20351858›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2009

Automated mapping of pharmacy orders from two electronic health record systems to RxNorm within the STRIDE clinical data warehouse.

Penni Hernandez, Tanya Podchiyska, Susan Weber, Todd Ferris, Henry Lowe

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–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

23 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Leveraging Digital Data to Inform and Improve Quality Cancer Care.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020
    Article
  5. Neural Networks for Clinical Order Decision Support.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019
    Article
  6. Article
  7. Article
  8. Inpatient Clinical Order Patterns Machine-Learned From Teaching Versus Attending-Only Medical Services.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018
    Article
  9. Article
  10. Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets.Journal of the American Medical Informatics Association : JAMIA · 2017
    Article
  11. Article
  12. DYNAMICALLY EVOLVING CLINICAL PRACTICES AND IMPLICATIONS FOR PREDICTING MEDICAL DECISIONS.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2016
    Article
  13. Data-Mining Electronic Medical Records for Clinical Order Recommendations: Wisdom of the Crowd or Tyranny of the Mob?AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2015
    Article
  14. Article
  15. Review
  16. Article
  17. Mining for clinical expertise in (undocumented) order sets to power an order suggestion system.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2013
    Article
  18. Article
  19. Article
  20. Clinical use of an enterprise data warehouse.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2012
    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

5 authors.

Penni HernandezCenter for Clinical Informatics, Stanford University, Stanford, CA, USA.
Tanya Podchiyska
Susan Weber
Todd Ferris
Henry Lowe

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Stanford Translational Research Integrated Database Environment (STRIDE) clinical data warehouse integrates medication information from two Stanford hospitals that use different drug representation systems. To merge this pharmacy data into a single, standards-based model supporting research we developed an algorithm to map HL7 pharmacy orders to RxNorm concepts. A formal evaluation of this algorithm on 1.5 million pharmacy orders showed that the system could accurately assign pharmacy orders in over 96% of cases. This paper describes the algorithm and discusses some of the causes of failures in mapping to RxNorm.

Indexed as

AlgorithmsClinical Pharmacy Information SystemsMedical Order Entry SystemsMedical Records Systems, ComputerizedNatural Language ProcessingVocabulary, ControlledCaliforniaElectronic Data ProcessingElectronic PrescribingPharmacy Service, HospitalSystems Integration

Identifiers

PMID20351858
PMCPMC2815471

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.