Evidence map›Paper›PMID 27766500›Full record

ArticleInternational journal of clinical pharmacy2016

A quantitative comparison of ward-based clinical pharmacy activities in 7 acute UK hospitals.

Raliat Onatade, Gavin Miller, Inderjit Sanghera

Abstract readComparative StudyMulticenter Study
PubMed Publisher
In one paragraph

Article in International journal of clinical pharmacy, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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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. Article
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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.

Raliat OnatadeInstitute of Pharmaceutical Sciences, King's College London, London, UK. raliat.onatade@kcl.ac.uk.ORCID http://orcid.org/0000-0002-9206-9755
Gavin MillerPharmacy Department, Lewisham and Greenwich NHS Trust, London, UK.
Inderjit SangheraPharmacy Department, London North West Healthcare NHS Trust, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Several clinical pharmacy activities are common to UK hospitals. It is not clear whether these are provided at similar levels, and whether they take similar amounts of time to carry out. Objective To quantify and compare clinical pharmacist ward activities between different UK hospitals. Setting Seven acute hospitals in the Greater London area (UK). Methods A list of common ward activities was developed. On five consecutive days, pharmacists visiting hospital wards documented total time spent and how many of each activity they undertook. Results were analysed by hospital. The range and number of activities per 100 occupied bed days, and per 24 beds were compared. Main outcome measure Time spent on wards and numbers of each activity undertaken. Results Pharmacists logged a total of 2291 h carrying out 40,000 activities. 4250 changes to prescriptions were made or recommended. 5901 individual medication orders were annotated for clarity or safety. For every 24 beds visited, mean time spent was 230 min-seeing 6.2 new patients, carrying out 3.9 calculations and 1.3 patient consultations, checking and authorising 1.8 discharge prescriptions, and providing staff with information twice. Other activities varied significantly, not all could be explained by differences in hospital specialties or Information Technology systems. Conclusion This is the first detailed comparison of clinical pharmacy ward activities between different hospitals. There are some typical levels of activities carried out. Wide variations in other activities could not always be explained. Despite a large number of contacts, pharmacists reported very few consultation sessions with patients.

Indexed as

PharmacistsProfessional RoleFemaleHospitalsHumansMaleMedical Order Entry SystemsPharmacy Service, HospitalUnited KingdomBenchmarkingClinical pharmacyHospitalPharmacistsSecondary careUnited Kingdom

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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.