Evidence map›Paper›PMID 35750350›Full record

ArticleBMJ open quality2022

Recovering staff, recovering services: massive-online support for recovering a paediatric service using Lean and compassionate communication.

Iain M Smith, Elaine Bayliss

Abstract read
In one paragraph

Article in BMJ open quality, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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

3 citing papers in PubMed.

  1. Article
  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

2 authors.

Iain M SmithImprovement Capability Building, NHS England, Newcastle upon Tyne, UK iain.smith@newcastle.ac.uk.ORCID 0000-0003-1138-2166
Elaine BaylissImprovement Capability Building, NHS England, Coventry, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic has infected tens of millions worldwide. Healthcare systems have been stretched caring for the most seriously ill and healthcare workers have struggled to maintain non-COVID services leading to backlogs.Strategies proposed to support the recovery of backlogs include additional administration support; waiting list data validation; enhanced patient communication; and use of systematic improvement methods to make rapid incremental improvements.As part of COVID-19 recovery, a hospital trust in northern England used the Lean systematic improvement approach to recover the waiting list of a paediatric service to pre-COVID levels. The intervention strategy used a massive-open-online-course (Lean Fundamentals) to support the improvement project lead to follow a structured improvement routine to apply Lean improvement techniques.By acknowledging that staff were overburdened by the requirements of COVID-19 and that patients were stuck in a system of disconnected processes, administrative activities were redesigned around an ethos of compassionate communication that put patients first.Over a period of 8 weeks, the project reduced the waiting list from 1109 to 212. Waiting times were reduced from a maximum of 36 months to a 70-day average.Lean is often described in terms of increasing process efficiency and productivity. It is not often associated with staff benefits. However, when seen in the context of unburdening staff to deliver patient care, Lean has potential to support the recovery of both staff and services. Lean Fundamentals, with its accessible massive-online design, may provide a means of supporting such improvement at scale.

Indexed as

COVID-19Efficiency, OrganizationalChildCommunicationEmpathyHumansPandemicsCOVID-19Lean managementPaediatricsQuality improvementWaiting Lists

Identifiers

PMID35750350
PMCPMC9234906

What OpenQuestion holds

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LicenceCC BY-NC
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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.