Evidence map›Paper›PMID 29446193›Full record

ArticleJournal of evaluation in clinical practice2018

Impact of nonintrusive clinical decision support systems on laboratory test utilization in a large academic centre.

Kevin P Eaton, Natasha Chida, Ariella Apfel, Leonard Feldman, Adena Greenbaum, Susan Tuddenham, Emily A Kendall, Amit Pahwa

Abstract read
In one paragraph

Article in Journal of evaluation in clinical practice, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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

  1. Pooled it
  2. Article
  3. 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

8 authors.

Kevin P EatonJohns Hopkins University School of Medicine, Baltimore, MD, USA.ORCID 0000-0001-9962-0805
Natasha ChidaJohns Hopkins University School of Medicine, Baltimore, MD, USA.
Ariella ApfelJohns Hopkins University School of Medicine, Baltimore, MD, USA.
Leonard FeldmanJohns Hopkins University School of Medicine, Baltimore, MD, USA.
Adena GreenbaumJohns Hopkins University School of Medicine, Baltimore, MD, USA.
Susan TuddenhamJohns Hopkins University School of Medicine, Baltimore, MD, USA.
Emily A KendallJohns Hopkins University School of Medicine, Baltimore, MD, USA.
Amit PahwaJohns Hopkins University School of Medicine, Baltimore, MD, USA.

Funding

RESEARCH TRAINING IN MICROBIAL DISEASEST32AI007291 · NIAID · JOHNS HOPKINS UNIVERSITY · PI JOEL N BLANKSON, Sara Elizabeth Cosgrove · 1986 to 2026
$9.1M
NIAID NIH HHS T32 AI007291
6 · The paper itself

Abstract

backgroundThe near-universal prevalence of electronic health records (EHRs) has made the utilization of clinical decision support systems (CDSS) an integral strategy for improving the value of laboratory ordering. Few studies have examined the effectiveness of nonintrusive CDSS on inpatient laboratory utilization in large academic centres.

methodsRed blood cell folate, hepatitis C virus viral loads and genotypes, and type and screens were selected for study. We incorporated the appropriate indications for these labs into text that accompanied the laboratory orders in our hospital's EHR. Providers could proceed with the order without additional clicks. An interrupted time-series analysis was performed, and the primary outcome was the rate of tests ordered on all inpatient medicine floors.

resultsThe rate of folate tests ordered per monthly admissions showed no significant level change at the time of the intervention with only a slight decrease in rate of 0.0109 (P = .07). There was a 43% decrease in the rate of hepatitis C virus tests per monthly admissions immediately after the intervention with a decrease of 0.0135 tests per monthly admissions (P = .02). The rate of type and screens orders per patient days each month had a significant downward trend by 0.114 before the intervention (P = .04) but no significant level change at the time of the intervention or significant change in rate after the intervention. DISCUSSION: Our study suggests that nonintrusive CDSS should be evaluated for individual laboratory tests to ensure only effective alerts continue to be used so as to avoid increasing EHR fatigue.

Indexed as

Academic Medical CentersDecision Support Systems, ClinicalAdolescentAdultAgedAged, 80 and overDiagnostic Tests, RoutineElectronic Health RecordsFemaleHumansMaleMedical Order Entry SystemsMiddle AgedPractice Patterns, Physicians'Young Adultcomputer-decision support toolshigh-value carelaboratory testing

Identifiers

PMID29446193
PMCPMC6050580

What OpenQuestion holds

Textmetadata
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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.