Evidence map›Paper›PMID 27158494›Full record

ArticleBMJ quality improvement reports2016

Referral Finder: Saving Time and Improving The Quality of In-hospital Referrals.

Jennifer Cathcart, Neil Cowan, Vicki Tully

Abstract read
In one paragraph

Article in BMJ quality improvement reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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

3 authors.

Jennifer CathcartNinewells Hospital, Dundee, Scotland.
Neil CowanNinewells Hospital, Dundee, Scotland.
Vicki TullyNinewells Hospital, Dundee, Scotland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Making referrals to other hospital specialties is one of the key duties of the foundation doctor, which can be difficult and time consuming. In Ninewells hospital, Scotland, in our experience the effectiveness of referrals is limited by contact details not being readily accessible and foundation doctors not knowing what information is relevant to each specialty. We surveyed foundation doctors on their experience of the existing referral process to identify where we needed to focus to improve the process. The doctors reported significant delays in obtaining contact details from the operator, and found they did not know the specific information needed in each referral. To increase the information available to foundation doctors, we set up a page on the staff intranet called 'Referral Finder'. This page includes contact details, guidelines for referral, and links to relevant protocols for each specialty. By making this information readily accessible our objective was to increase the speed and quality of referrals. When surveyed two months after the web page was established, foundation doctors reported a reduction in calls to operator from baseline and reported achieving more effective referrals. When asked to comment, many doctors asked if the page could include details for other hospitals in our health board and provide more specialty specific information. This feedback prompted us to extend the scope of the page to include the district general hospital in our region, and update many of the existing details. Doctors were then surveyed after the updates, 100% agreed that the website saved time and there was a 49.3% reduction in doctors who reported not knowing the specific information needed for a referral. Having adequate information improved referrals and resulted in time saved. This would allow more time for patient care. The quality improvement project was praised among doctors as a useful, innovative and replicable project.

Identifiers

PMID27158494
PMCPMC4850864

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Registered trials

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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.