Evidence map›Paper›PMID 28495350›Full record

ArticleInternational journal of medical informatics2017

Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets.

Jonathan H Chen, Muthuraman Alagappan, Mary K Goldstein, Steven M Asch, Russ B Altman

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of medical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing 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

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

  1. Pooled it
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  11. Bladder Cancer and Artificial Intelligence: Emerging Applications.The Urologic clinics of North America · 2024
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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.

Jonathan H ChenDepartment of Medicine, Stanford University, Stanford, CA, USA. Electronic address: jonc101@stanford.edu.
Muthuraman AlagappanInternal Medicine Residency Program, Beth Israel Deaconess Medical Center, Boston, MA USA.
Mary K GoldsteinGeriatrics Research Education and Clinical Center, Veteran Affairs Palo Alto Health Care System, Palo Alto, CA, USA; Primary Care and Outcomes Research (PCOR), Stanford University, Stanford, CA, USA.
Steven M AschDepartment of Medicine, Stanford University, Stanford, CA, USA; Center for Innovation to Implementation (Ci2i), Veteran Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
Russ B AltmanDepartment of Medicine, Stanford University, Stanford, CA, USA; Departments of Bioengineering and Genetics, Stanford University, Stanford, CA, USA.

Funding

PharmGKB: pharmacogenomics knowledge for precision medicineR24GM061374 · NIGMS · STANFORD UNIVERSITY · PI ALTMAN, RUSS BIAGIO, KLEIN, TERI ELLEN · 2010 to 2018
$25.2M
Stanford Center for Clinical and Translational Education and Research (SCCTER)UL1UL1RR025744 · NCRR · STANFORD UNIVERSITY · PI GREENBERG, HARRY BERNARD · 2008 to 2011
$22.4M
Text Mining for High-fidelity Curation and Discovery of Gene-drug-phenotype RelationshipsR01LM005652 · NLM · STANFORD UNIVERSITY · PI ALTMAN, RUSS BIAGIO · 2000 to 2018
$6.2M
Undergraduate Summer Research Experiences Support for Combining systems biology and structural biology to find new therapeuticsR01GM102365 · NIGMS · STANFORD UNIVERSITY · PI ALTMAN, RUSS BIAGIO · 2012 to 2021
$3.0M
Data-Mining Clinical Decision Support from Electronic Health RecordsK01ES026837 · NIEHS · STANFORD UNIVERSITY · PI CHEN, JONATHAN H. · 2015 to 2019
$893k
NCRR NIH HHS UL1 RR025744NIEHS NIH HHS K01 ES026837NIGMS NIH HHS R01 GM102365NIGMS NIH HHS R24 GM061374NLM NIH HHS R01 LM005652
6 · The paper itself

Abstract

objectiveDetermine how varying longitudinal historical training data can impact prediction of future clinical decisions. Estimate the "decay rate" of clinical data source relevance. MATERIALS AND

methodsWe trained a clinical order recommender system, analogous to Netflix or Amazon's "Customers who bought A also bought B..." product recommenders, based on a tertiary academic hospital's structured electronic health record data. We used this system to predict future (2013) admission orders based on different subsets of historical training data (2009 through 2012), relative to existing human-authored order sets.

resultsPredicting future (2013) inpatient orders is more accurate with models trained on just one month of recent (2012) data than with 12 months of older (2009) data (ROC AUC 0.91 vs. 0.88, precision 27% vs. 22%, recall 52% vs. 43%, all P<10 DISCUSSION: Clinical practice patterns (automatically) learned from electronic health record data can vary substantially across years. Gold standards for clinical decision support are elusive moving targets, reinforcing the need for automated methods that can adapt to evolving information. CONCLUSIONS AND RELEVANCM: Prioritizing small amounts of recent data is more effective than using larger amounts of older data towards future clinical predictions.

Indexed as

Decision Support Systems, ClinicalElectronic Health RecordsHospitalizationHumansInformation Storage and RetrievalInpatientsMedical Order Entry SystemsPatient CarePractice Patterns, Physicians'Collaborative filteringData miningElectronic health recordsPractice variabilityPrediction models

Identifiers

PMID28495350
PMCPMC5459355

What OpenQuestion holds

Textmetadata
LicenceTDM
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.