Evidence map›Paper›PMID 34244178›Full record

ArticleBMJ health & care informatics2021

Development of a customised data management system for a COVID-19-adapted colorectal cancer pathway.

Janice Miller, Frances Gunn, Malcolm G Dunlop, Farhat Vn Din, Yasuko Maeda

Abstract read
In one paragraph

Article in BMJ health & care informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

5 authors.

Janice MillerDepartment of Colorectal Surgery, Western General Hospital, Edinburgh, UK.ORCID http://orcid.org/0000-0002-0119-7736
Frances GunnDepartment of Colorectal Surgery, Western General Hospital, Edinburgh, UK.
Malcolm G DunlopDepartment of Colorectal Surgery, Western General Hospital, Edinburgh, UK.
Farhat Vn DinDepartment of Colorectal Surgery, Western General Hospital, Edinburgh, UK.
Yasuko MaedaDepartment of Colorectal Surgery, Western General Hospital, Edinburgh, UK Yasuko.maeda@ed.ac.uk.

Funding

Cancer Research UK 12076Cancer Research UK 18927Medical Research Council MC_PC_U127527198Medical Research Council MC_U127527198Medical Research Council MC_UU_00007/1
6 · The paper itself

Abstract

objectivesA customised data management system was required for a rapidly implemented COVID-19-adapted colorectal cancer pathway in order to mitigate the risks of delayed and missed diagnoses during the pandemic. We assessed its performance and robustness.

methodsA system was developed using Microsoft Excel (2007) to retain the spreadsheets' intuitiveness of direct data entry. Visual Basic for Applications (VBA) was used to construct a user-friendly interface to enhance efficiency of data entry and segregate the data for operational tasks.

resultsLarge data segregation was possible using VBA macros. Data validation and conditional formatting minimised data entry errors. Computation by the COUNT function facilitated live data monitoring.

conclusionIt is possible to rapidly implement a makeshift database system with clinicians' regular input. Large-volume data management using a spreadsheet system is possible with appropriate data definition and VBA-programmed data segregation. The described concept is applicable to any data management system construction requiring speed and flexibility in a resource-limited situation.

Indexed as

Colorectal NeoplasmsCOVID-19Critical PathwaysData ManagementDatabases, FactualEarly Detection of CancerHumansSoftwareTime FactorsUser-Computer InterfaceCOVID-19

Identifiers

PMID34244178
PMCPMC8275356

What OpenQuestion holds

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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.