Evidence map›Paper›PMID 27757740›Full record

ArticleJournal of clinical monitoring and computing2017

Design and α-testing of an electronic rounding tool (CERTAINp) to improve process of care in pediatric intensive care unit.

Manasi Hulyalkar, Stephen J Gleich, Rahul Kashyap, Amelia Barwise, Harsheen Kaur, Yue Dong, Lei Fan, Srinivas Murthy, Grace M Arteaga, Sandeep Tripathi

Abstract read
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In one paragraph

Article in Journal of clinical monitoring and computing, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 13% of its field
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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Clinician-Driven Design ofIEEE journal of translational engineering in health and medicine · 2018
    Article
  5. Article
  6. 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

10 authors at 4 institutions in 2 countries.

Manasi HulyalkarDepartment of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN, USA.
Stephen J GleichDepartment of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN, USA.
Rahul KashyapMETRIC-Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN, USA.
Amelia BarwiseMETRIC-Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN, USA.
Harsheen KaurDepartment of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN, USA.
Yue DongMETRIC-Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN, USA.
Lei FanMETRIC-Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN, USA.
Srinivas MurthyDivision of Critical Care, Department of Pediatrics, University of British Columbia, Vancouver, Canada.
Grace M ArteagaDepartment of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN, USA.
Sandeep TripathiDepartment of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN, USA. Sandeep.tripathi@osfhealthcare.org.
Mayo Clinic · USMayo Clinic in Florida · USIllinois College · USUniversity of British Columbia · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Increasing process complexity in the pediatric intensive care unit (PICU) can lead to information overload resulting in missing pertinent information and potential errors during morning rounds. An efficient model using a novel electronic rounding tool was designed as part of a broader critical care decision support system-checklist for early recognition and treatment of acute illness and injury in pediatrics (CERTAINp). We aimed to evaluate its impact on improving the process of care during rounding. Prospective pre- and post-interventional data included: team performance baseline assessment, patient safety discussion, guideline adherence, rounding time, and a survey of Residents' and Nurses' perception using a Likert scale. Attending physicians were blinded to the components of the assessment. A total of 113 pre-intervention and 114 post-intervention roundings were recorded by direct observation. Pre-intervention (108) and post-intervention staff surveys (80) were obtained. Adherence to standard of care guidelines improved to >97 % in all data points, with maximum increase seen in discussions of ulcer prophylaxis, bowel protocol, DVT prophylaxis, skin care, glucose control and head of bed elevation (2-28 % pre-vs. 100 % for all post-intervention, p < 0.01). Significant improvement was noticed in spontaneous breathing trials, sedation breaks and need for devices (45-57 % pre- vs. 100 % for all post-intervention, p < 0.01). Rounding time (mean ± SD) increased by 2 min/patient (8.0 ± 5.8 min pre-intervention vs. 9.9 ± 5.7 min post-intervention, p = 0.002). Staff reported improved perception of all aspects of rounding. Utilization of the CERTAINp rounding tool led to perfect compliance to the discussion of best practice guidelines; had minimal impact on rounding time and improved PICU staff satisfaction.

Indexed as

Intensive Care Units, PediatricSignal Processing, Computer-AssistedAttitude of Health PersonnelChildCritical CareEquipment DesignHumansMonitoring, PhysiologicPatient ComplianceProspective StudiesSoftwareTeaching RoundsUser-Computer InterfaceElectronic checklistICU roundsIntensive care unit (ICU)Pediatrics

Identifiers

PMID27757740
OpenAlexW2539246308

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.