Evidence map›Paper›PMID 37926834›Full record

ArticleBMC health services research2023

Discovering patterns in outpatient neurology appointments using state sequence analysis.

Fran Biggin, Quinta Ashcroft, Timothy Howcroft, Jo Knight, Hedley Emsley

Abstract read
In one paragraph

Article in BMC health services research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

1 citing paper in PubMed.

  1. 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

5 authors.

Fran BigginLancaster Medical School, Lancaster University, Bailrigg, Lancaster, LA1 4YG, UK. f.biggin@lancaster.ac.uk.
Quinta AshcroftLancashire Teaching Hospitals NHS Foundation Trust, Royal Preston Hospital, Sharoe Green Lane, Fulwood, Preston, PR2 9HT, UK.
Timothy HowcroftLancashire Teaching Hospitals NHS Foundation Trust, Royal Preston Hospital, Sharoe Green Lane, Fulwood, Preston, PR2 9HT, UK.
Jo Knight *Lancaster Medical School, Lancaster University, Bailrigg, Lancaster, LA1 4YG, UK.
Hedley Emsley *Lancaster Medical School, Lancaster University, Bailrigg, Lancaster, LA1 4YG, UK.

Funding

EPSRC EP/R513076/1
6 · The paper itself

Abstract

backgroundOutpatient services in the UK, and in particular outpatient neurology services, are under considerable pressure with an ever-increasing gap between capacity and demand. To improve services, we first need to understand the current situation. This study aims to explore the patterns of appointment type seen in outpatient neurology, in order to identify potential opportunities for change.

methodsWe use State Sequence Analysis (SSA) on routinely collected data from a single neurology outpatient clinic. SSA is an exploratory methodology which allows patterns within sequences of appointments to be discovered. We analyse sequences of appointments for the 18 months following a new appointment. Using SSA we create groups of similar appointment sequence patterns, and then analyse these clusters to determine if there are particular sequences common to different diagnostic categories.

resultsOf 1315 patients 887 patients had only one appointment. Among the 428 patients who had more than one appointment a 6 monthly cycle of appointments was apparent. SSA revealed that there were 11 distinct clusters of appointment sequence patterns. Further analysis showed that there are 3 diagnosis categories which have significant influence over which cluster a patient falls into: seizure/epilepsy, movement disorders, and headache.

conclusionsNeurology outpatient appointment sequences show great diversity, but there are some patterns which are common to specific diagnostic categories. Information about these common patterns could be used to inform the structure of future outpatient appointments.

Indexed as

NeurologyOutpatientsAmbulatory CareAmbulatory Care FacilitiesAppointments and SchedulesHumansNeurologyOutpatient appointmentsRoutinely collected dataState sequence analysis

Identifiers

PMID37926834
PMCPMC10626691

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