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Dataset . 2020
Data sources: ZENODO
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Synthetic Patient Appointment Dataset

Authors: Kureshi, Ibad;

Synthetic Patient Appointment Dataset

Abstract

A synthetic dataset of patient appointments, referrals, and journeys to a fictional service in the North East of England. The code can be adjusted to incorporate any area on mainland Great Britain. NI or the islands can be integrated too, however the structure of postcode, GP and OSA public data is different, and data input handlers will need to be adjusted. The behaviour of the patients (visiting their nearby GP followed by attending a specialist clinic), appointments (clinic appointments within 7day-6weeks of the referral (gp appointment)), and facilities (one major facility taking the load, along with minor facilities) is meant to mirror the real data used under Pilot 2 of the Track & Know Project. Real postcodes, from Royal Mail, are used to generate the appointment population, real facilities are used based on the British Lung Foundations study of Obstructive Sleep Apnoea, and real GP's are used based on public data from the NHS.

All data is randomly generated and changes with every run of the generation code. Any resemblance to actual events or locales or persons, living or dead, is entirely coincidental. This is synthetic artificial data meant for research and tool validation purposes. It cannot and should not be used to make business decisions.

Keywords

patient mobility data, obstructive sleep apnoea, Virology, Information Systems not elsewhere classified, patient appointment data, health service provisioning, Medicine, Biotechnology, Cancer

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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