Lanes, clusters, lines of Sight: Modelling diagnostic eyecare clinics to improve patient flow

Lengthy waiting times for ophthalmology appointments in the UK National Health Service (NHS) increased further in the immediate aftermath of the Covid-19 pandemic, necessitating a different approach to triaging patients safely and at speed. Moorfields Eye Hospital NHS Trust therefore opened an additional diagnostic hub designed with a linear spatial layout and patient flow system, which is analyzed in this paper in comparison to an existing clinic. We integrate direct observations of patient flows, and an architectural layout analysis based on space syntax methods with queuing simulations from operations research and show that the two clinics operate differently and that both clinics have their advantages and disadvantages. The newly opened clinic with a lane system supports flows and coordination by line of sight between stations, which contrasts with a lack of sightlines in the existing clinic. The latter layout with clusters of stations compensates by enabling a more organic flow, especially in conjunction with experienced technicians, which is beneficial when the clinic gets busy. When high patient load is simulated in the queuing models, the lane system results in slightly bigger bottlenecks and longer clinic durations. An ideal allocation of the number of stations to diagnostic activities based on clusters is suggested. This work stands in the tradition of combining architectural and operations research. By reflecting on the variability of diagnostic processes found in our observations, we contribute to the understanding of routines as performative. We also add insight to the growing field of evidence-based design, particularly by highlighting the importance of line-of-sight relationships in ophthalmology.

Methods
Fieldwork was undertaken in June and July 2021 in two outpatient clinics of Moorfield Eye Hospital NHS Trust, the Cayton Street clinic and the Hoxton Hub. Two main data sets were collected for each clinic: 1) an up-to-date floor plan of each clinic including the locations and types of all diagnostic equipment marked up, and 2) direct observations of glaucoma patient flows, recorded on tablets, including exact time stamps of entry and exit of the clinic as well as start and end times for all diagnostic tests.

For the observations, patients received a sticker with a study ID number at the reception desk, which was recorded by observers as an identifier. Over the course of ten days of observations, participant observers captured nine 4-hour shifts in the period from 8:30 to 17:00 in Hoxton, and six 4-hour day shifts and seven 3-hour evening shifts between 8:30 and 20:00 in Cayton Street. 14 patients at Cayton Street and 11 patients at Hoxton were shadowed for the entirety of their journey through the clinics with an average duration of 36 minutes (range 19-70min) and 37 minutes (range 26-79min) respectively. The majority of the data, however, was captured by additional, so called ‘zonal’ observations, where each observer was placed in a position to monitor a discrete space as well as all start and end times of diagnostic tests completed in this area, resulting in a total of 152 and 83 unique patients observed in the clinics respectively. From these data, full patient journeys were reconstructed using the patient identifiers. Aggregating data from both observation methods resulted in a sample size of 621 single data points of activity durations in Cayton Street and 485 at Hoxton (which includes observed activities of standing and waiting), hence n=1106. This full data set contained a subsample of actual examinations at stations (excluding standing and waiting) of n=1007.

Each observer was asked to record an identifier for each technician looking after a patient, i.e., the first two letters of the technician’s first name and the first two letters of their last name. Each technician was wearing a name tag which was visible to the observers. This was used in the analysis stage to understand technician workflows, for example, whether they guide a single patient through every stage of the diagnostic process, or whether they mainly stick to a particular diagnostic activity. Afterwards, all identifiers were anonymized.

The observers were also asked to assess and record the fluency in English of each patient where they could choose from four levels: ‘fluent’, ‘few issues’, ‘many issues’, and ‘translator’. Using the patient ID numbers, which were cross-referenced by hospital staff to their patient database, additional information was obtained from the hospital including age, gender, and if the patient was a first-time or a follow-up patient. The observers also recorded anything they felt worth noting down in the form of a qualitative note. This included reasons for occurring delays, causes for waiting, patients with mobility difficulties, etc.

In summary, for each diagnostic activity, the following data was collected: 1) patient ID, 2) date, 2) exact time stamps for the moment a patient sat down at each station (start time), and got up again (end time), 3) location (corridor, waiting area or station number), 4) activity (exam, wait while sitting, wait while standing), 5) technician ID, 6) English proficiency, 7) qualitative notes (see supplemental material S1 on the observation protocol).

The dataset has been processed in the following ways:

Floor plans have been analysed based on space syntax techniques to understand differences in architectural layout between the two clinics.
The patient flow data has been analysed statistically using Jupyter Notebooks to understand how the two clinics operated.
Queuing simulations have been run in R to understand how the diagnostic stations can be arranged differently, and what impact this would have on clinic effectiveness.