Evidence mapPaperPMID 25560319Full record

Trial reportJournal of general internal medicine2015

Randomized trial of a health IT tool to support between-visit-based laboratory monitoring for chronic disease medication prescriptions.

Richard W Grant, Jeffrey M Ashburner, Michael C Jernigan, Jaime Chang, Leila H Borowsky, Yuchiao Chang, Steven J Atlas

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of general internal medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 3 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

4 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. 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

7 authors.

Richard W GrantDivision of Research, Kaiser Permanente Northern California, Oakland, CA, USA, Richard.W.Grant@KP.org.
Jeffrey M Ashburner
Michael C Jernigan
Jaime Chang
Leila H Borowsky
Yuchiao Chang
Steven J Atlas

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLack of timely medication intensification and inadequate medication safety monitoring are two prevalent and potentially modifiable barriers to effective and safe chronic care. Innovative applications of health information technology tools may help support chronic disease management.

objectiveTo examine the clinical impact of a novel health IT tool designed to facilitate between-visit ordering and tracking of future laboratory testing. DESIGN AND

participantsClinical trial randomized at the provider level (n = 44 primary care physicians); patient-level outcomes among 3,655 primary care patients prescribed 5,454 oral medicines for hyperlipidemia, diabetes, and/or hypertension management over a 12-month period. MAIN MEASURES: Time from prescription to corresponding follow-up laboratory testing; proportion of follow-up time that patients achieved corresponding risk factor control (A1c, LDL); adverse event laboratory monitoring 4 weeks after medicine prescription. KEY

resultsPatients whose physicians were allocated to the intervention (n = 1,143) had earlier LDL laboratory assessment compared to similar patients (n = 703) of control physicians [adjusted hazard ratio (aHR): 1.15 (1.01-1.32), p = 0.04]. Among patients with elevated LDL (486 intervention, 324 control), there was decreased time to LDL goal in the intervention group [aHR 1.26 (0.99-1.62)]. However, overall there were no significant differences between study arms in time spent at LDL or HbA1c goal. Follow-up safety monitoring (e.g., creatinine, potassium, or transaminases) was relatively infrequent (ranging from 7 % to 29 % at 4 weeks) and not statistically different between arms. Intervention physicians indicated that lack of reimbursement for non-visit-based care was a barrier to use of the tool.

conclusionsA health IT tool to support between-visit laboratory monitoring improved the LDL testing interval but not LDL or HbA1c control, and it did not alter safety monitoring. Adoption of innovative tools to support physicians in non-visit-based chronic disease management may be limited by current visit-based financial and productivity incentives.

Indexed as

InternetAgedAged, 80 and overChronic DiseaseCluster AnalysisDiabetes MellitusDrug PrescriptionsFemaleHumansHyperlipidemiasHypertensionLaboratories, HospitalMaleMiddle AgedMonitoring, PhysiologicPhysicians, Primary Care

Identifiers

PMID25560319
PMCPMC4395618

What Socratic 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 Socratic graph.