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Kam et al. BMC Health Services Research(2021) 21:38early (in which case appointment time was used) orlate (in which case check-in time was used) to theirappointments had on variability of in-clinic time.Page 3 of 9(Excel). Difference in mean ages of patients with validversus invalid in-clinic time data was assessed using Student’s t-test while differences in proportions of genderwere assessed using chi-squared test (SPSS).Data collectionCerner Scheduling Appointment Book (Cerner, NorthKansas City, USA), was used to schedule patient appointments. This program allowed creation of a timetable with specific appointment times and types (e.g.new, follow-up, emergency etc.) for patients to bebooked into. When patients attended appointments, itrecorded the time patients were checked-in andchecked-out by administrative staff. Waiting time beforecheck-in or after check-out (e.g. waiting for transport)was not captured.Two five-month data audits of all attended appointments were conducted to determine the efficacy of theLean Six Sigma process. A baseline audit (“Measure” and“Analyse” phases) was retrospectively conducted fromFebruary 1st to June 30th 2018. A post-implementationaudit (“Control” phase) was conducted from February1st to June 30th 2019.Data analysisPatient age, gender, appointment time, appointmenttype, check-in time and check-out time were captured.Appointments with incomplete time data or coding errors (i.e. visits with no end time or total duration of 0 orgreater than 480 minutes) were included in the count ofpatients seen but excluded from analysis of duration andvariability of patient in-clinic time.Difference in duration of patient in-clinic times preand post-implementation was assessed using MannWhitney-U test on SPSS (v24, IBM Corporation,Armonk, USA). Difference in variability of patient inclinic times was assessed using Brown-Forsythe test onExcel (Microsoft, Redmond, USA) [9]. Difference innumber of patients seen per session was assessed usingStudent’s test (SPSS). Differences in the proportions ofpatient appointment types seen were assessed using chisquared tests, with Z-tests (with Bonferonni correction)used to compare pairwise differences between pre- andpost-implementation proportions of appointment typeProcess flow maps and time-motion analysisTwo patient process flows fit most patient journeysthrough the clinic; one where investigations were performed, and one without investigations. Process flowmaps outlining steps in these journeys were created(Fig. 1).A two-week time-in-motion study was conducted fromJune 11th to June 24th 2018 to determine proportions oftotal in-clinic time spent in each step along the patientjourney. In this time-in-motion study, staff membersnoted the times they commenced and ended their rolesin the patient journey on a dedicated audit document.Time between each staff member’s contact time wastreated as waiting time.The time-in-motion study data was analysed in Excel.Visits with coding errors (i.e. no time entered, timeswith inconsistent patient flow) were excluded. Proportions of total in-clinic time were determined and superimposed on patient process flow maps to identifybottlenecks in the patient journey (Fig. 1).Root cause analysisStaff interviews, workshops, and review of patient complaint data were used to identify issues causing prolonged duration and increased variability of patient inclinic time and clinic inefficiency. Following this, rootcause analysis of issues was undertaken using the “FiveWhys Technique” [10]. Resulting root causes weregrouped and the most common root causes targeted forsolution development.ResultsBaseline audit (“Measure” and “Analyse” phases)During the baseline audit period there were 3624 visitsover 187 240-minute sessions (average 19.3 patients/session). Of these visits, 2241 had valid time data for analysis. Median patient in-clinic time was 131 minutes andFig. 1 Patient flow through the Eye Clinic and the associated proportion of time spent. In both pathway one and two, over 70% of patient inclinic time was spent waiting. Note: numbers do not sum to 100% due to rounding

Kam et al. BMC Health Services Research(2021) 21:38the interquartile range 133 minutes (84–217, quartile 1quartile 3). Of visits with invalid data, 13 had invalid inclinic times (due to patients arriving, being seen and discharged before their appointment time), while theremaining 1370 had invalid check-out times (checkedout the following day). Comparing invalid to valid datacohorts, there were no significant differences in age (invalid: 58.4 23.2 years; valid 58.0 23.4 years, p 0.568)or gender (invalid: female 49.2%; valid: female 48.8%,p 0.743), and only minimal differences in proportionsof appointment types (Table 1).There were 329 visits during the two-week time-inmotion study. Of these, 195 had valid data for analysis.Two bottlenecks within the clinic were identified. Thefirst, between patient check-in and screening, accountedfor 33–39% of total in-clinic time depending on the carepathway. The second, before seeing the ophthalmologist,accounted for 35% of total in-clinic time. Overall, over70% of patient in-clinic time was spent waiting in bothcare pathways (Fig. 1).Through ten patient interviews, ten staff interviews,two staff workshops (including all staff working in theclinic), and an audit of patient complaint data, 100unique issues causing prolonged patient in-clinic timeand clinic inefficiencies were identified. Ten commonroot causes emerged from root cause analysis, with fourcontributing to 77% of issues encountered (Fig. 2).Scheduling was the most commonly occurring rootcause identified in root cause analysis (32% of identifiedissues). Therefore, further exploration of scheduling datawas undertaken. As seen in Fig. 3a, most patients werescheduled to arrive in the middle of clinics. This wasdue to the clinic schedule design, and ad hoc addition ofinpatient and emergency patients into already fullybooked sessions through the clinic’s ‘on-call’ service. Patient influxes at these times were the primary contributor to the bottleneck at the start of the care pathwaybetween check-in and screening.Process improvements (“Improve” phase)Four main root causes: scheduling, staffing, patient communication, and clinic processes, were responsible for77% of issues encountered (Fig. 2). Although fundingwas not available to address staffing, several other targeted negligible cost interventions were implemented toaddress the remaining three main root causes.To address poor patient scheduling, the clinic schedulewas revised to control patients’ arrival times. This involved: moving the start time of screening staff and patient appointments to 7:30am so patients could bescreened and ready to see the ophthalmologist at 8am;revising appointment slot time lengths to better alignwith the needs of each appointment type; creating dedicated ‘on call’ emergency and inpatient appointmentPage 4 of 9placeholders to reduce ad hoc scheduling of these patients; and providing the ‘on-call’ registrar with a ‘live’scheduling app to allow easier identification of availableappointment slots for ad hoc bookings. Furthermore, adedicated postoperative clinic was introduced for 1-weekand 4-week postoperative follow up visits as these hadlow variation care pathways amenable to optimisationthrough grouping into a dedicated clinic. The impact ofthese solutions is shown in Fig. 3b.To address inefficient clinical processes, further stafffeedback was sought on potential solutions and the following three solutions developed:MedicationsInitially, many frequently used medications (e.g. valacyclovir, timolol, brinzolamide, preservative free lubricants)were often not readily available in-clinic. This disruptedpatient flow, requiring clinicians to call the hospitalpharmacy to request the medications and patients towait for them to be delivered. To address this, imprestmedication lists were reviewed and updated to includethese medications. Daily checks were implemented toensure that adequate supplies of medications were available in-clinic.TriageInitially, there was no standard order to see patients inafter check-in, with different staff using different approaches. There was no prioritisation system for patientswith higher clinical need, e.g. inpatients, unwell persons,and no clear instruction for paper files of newlychecked-in patients to be put in appointment order inthe clinic’s ‘patients to be seen’ box. As clinicians generally picked up patient files from the top of the box, patients were therefore seen out of chronological order,disrupting patient flow and increasing variability in inclinic time. To address these issues, defined escalationcriteria were made for patients with clinical or otherspecial requirements. Clear instructions were made toput paper files of newly checked-in patients in appointment order in the ‘patients to be seen’ box. Clinicianswere instructed to see all patients in order of appointment time, unless there was an urgent clinical need.InvestigationsInitially, there was no process to clearly document investigations needed for follow up patients at their next appointment. This resulted in inefficiency as some patientsoccasionally needed to return to the investigation roomafter seeing the ophthalmologist for further tests, whilstothers underwent unnecessary non-invasive investigations. To address this, a standard clinic documentationtemplate was introduced for investigations required atthe next follow up visit. This was done with the aim of

368 (10.2%)2455 (67.7%)245 (6.8%)248 (6.8%)201 ralNewPost-opday oneTotal2241177 (7.9%)144 (6.4%)91 (4.1%)1555 (69.4%)214 (9.5%)60 (2.7%)Preimplementation(visits with validdata only) [n,%]* 0.0010.434* 0.0010.0960.3430.442Preimplementationpairwise p (allvisits vs. validonly)131, 84–21775, 49–146161, 106–252213, 123–311132, 89–210119, 79–184188, 121–2693853242 (6.3%)290 (7.5%)308 (8.0%)2442 (63.4%)442 (11.5%)129 (3.3%)Patient in-clinic Posttime (minutes) implementation(all visits) [n,%][median, Q1Q3]3490230 (6.6%)263 (7.5%)250 (7.2%)2249 (64.4%)378 (10.8%)120 (3.4%)Postimplementation(visits with validdata only) [n, pairwise p (allvisits vs. validonly)107, 71–16278, 64–104132, 102–179117, 67–220105, 71–157109, 70–159125, 84–179Patient in-clinictime (minutes)[median, Q1Q3]0.0680.119*0.006* 0.0010.0130.186Pairwise p (preimplementation vs.post-implementationtotal cohort)For all pairwise comparisons p significant at 0.008 (Bonferroni correction); * significant post-hoc testIn the pre-implementation audit period, there were some slight differences in proportions of types of patients seen between the valid data cohort and the total cohort. Comparing appointment types of all patientsseen pre- and post- implementation, there were some minor differences in proportions of follow-up patients seen due to restructuring of the clinic timetable107 (3.0%)CataractNewPreimplementation(all visits) [n, %]Table 1 Pre vs. post-implementation appointment typesKam et al. BMC Health Services Research(2021) 21:38Page 5 of 9

Kam et al. BMC Health Services Research(2021) 21:38Page 6 of 9Fig. 2 Root causes of issues in the Eye Clinic. Of issues encountered in the clinic, 77% were due to 4 root causes: scheduling, staffing, patientcommunication and inefficient clinic processes. These root causes were targeted in solution development and implementationFig. 3 Appointment times February-June 2018/2019.Note: morning clinic sessions ran from 8am to 12 pm, afternoon clinic sessions ran from12:30 pm to 4:30 pm. Prior to solution implementation (Fig. 3a), most patients were scheduled to arrive in the middle of clinics (between 9:00am10:30am for the morning clinic and 2:00 pm-3:00 pm for the afternoon clinic). After solution implementation (Fig. 3b), patient arrival times weresmoothed throughout the day

Kam et al. BMC Health Services Research(2021) 21:38prompting clinicians to consider and order appropriateinvestigations in advance (Online supplement: Documentation Template).Based on the root cause analysis finding that poor patient communication accounted for 16% of issues in theclinic, all written patient communications were reviewed.Referral acknowledgement letters were updated to provide more accurate information regarding wait times foran initial appointment. Clinic information sheets andposters were developed to inform patients what to expect during their clinic visit. Fact sheets for commonophthalmological conditions and surgical procedureswere introduced to improve and standardise patient education, while also potentially reducing the clinician facetime needed to provide this education. Consumer representatives were used to review and provide feedback onall revised patient communications.Follow up analysis (“Control” phase)During the post-implementation period there were 3853clinic visits over 183 240-minute sessions (average 21.1patients per session), a 9% increase in patients per session compared to the baseline period (p 0.016). Ofthese visits, 3490 had valid data for analysis. Median patient in-clinic time was 107 minutes and thePage 7 of 9interquartile range 91 minutes (71–162, quartile 1- quartile 3). This was a significant reduction in duration andvariability of patient in-clinic time compared to baseline(both p 0.001). (Fig. 4). Of visits with invalid data, 11cases had no check-out time, 71 cases had invalid waiting times (patients arriving, being seen and dischargedbefore their appointment time), and 281 had invalidcheck-out times (checked-out the following day). Comparing the invalid to valid data cohort, there were no significant differences in age (invalid: 56.5 23.3 years;valid 58.1 22.8 years, p 0.190), gender (invalid: female46.5%; valid: female 43.8%, p 0.321) or proportion ofappointment types (Table 1).DiscussionIn this study, application of Lean Six Sigma techniquesin a publicly-funded tertiary outpatient ophthalmologyclinic led to development of solutions that significantlyreduced duration and variability of patient in-clinic time.Median patient in-clinic time was reduced by 18% andthe interquartile range by 32%. These results wereachieved while patients seen per session increased 9%.Solutions used to achieve these results were: clinicschedule amendments to prevent sudden influxes of patients, a dedicated weekly postoperative patient clinic forFig. 4 Distribution of patient in-clinic time pre- and post-implementation. Comparing pre- (Fig. 4a) to post-implementation (Fig. 4b), patient inclinic time significantly decreased as shown through a left-shift in the distribution of in-clinic time post-implementation. IQR: interquartile range

Kam et al. BMC Health Services Research(2021) 21:38one week and four week postoperative visits, checks toensure frequently used medications were always available in the clinic, defining a standard order to see patients in, clear follow-up patient investigation planningdocumentation templates, and patient information pamphlets for common ophthalmic conditions/surgeries. Ofnote, these solutions were implemented without additional capital requirements (e.g. purchasing new devices) or ongoing staffing costs.This study adds to the growing body of literature demonstrating that techniques from business and industry,such as Lean Six Sigma, can be used in healthcare settingsto improve system efficiency. Specific to ophthalmology,one North American group who applied Lean Six Sigmatechniques to a subspecialist retina clinic (subsequentlyhiring an extra technician, creating a dedicated intravitrealinjection patient pathway, and improving clinic scheduling), reduced mean patient visit times by 18% (p 0.05)and variation in visit time by 5% [5]. A second NorthAmerican group who applied Lean thinking (decentralising their optical coherence tomography machines from acentral photography suite into technicians’ screeningrooms), reduced patient wait times by 74% (p 0.0001)and in-clinic time by 36% (p 0.0001) [11].Outside of ophthalmology, Lean Six Sigma has beenshown to be effective in a range of healthcare contexts. The Cleveland Clinic Cardiac CatheterisationLaboratory, as an example, applied Lean Six Sigmatechniques subsequently improving patient turnovertimes, the number of on-time patient and physicianarrival times and reducing physician down times [12].A further example was seen in Indiana pertaining toorthopaedic inpatient care at the Richard L. Roudebush Veterans Affairs Medical Centre in Indianapolis.Their group used Lean Six Sigma techniques to reduce length of stay of joint replacement patients by36% from 5.3 days to 3.4 days (p 0.001) [13]. Finallyon a hospital-wide basis the University Hospital “Federico II” of Naples, used Lean Six Sigma techniquesto reduce healthcare-associated infections in inpatients across multiple medical specialties includinggeneral medicine, pulmonology, oncology, nephrology,cardiology, neurology, gastroenterology, endocrinologyand rheumatology [14].Process improvement methodologies such as Lean SixSigma, present a significant opportunity to deliver bettervalue in healthcare through improved efficiency and reduced ‘waste’. More broadly, as demands on healthcareservices continue to grow across most medical specialties, a focus on service improvement will be needed tobest utilise the limited resources available. This is particularly true within publicly-funded healthcare systemswhere long waiting times for non-emergency servicesare an increasingly common feature [15].Page 8 of 9Service improvement, particularly in organisations utilising Lean Six Sigma methodology must incorporate thefeedback of all their people including patients and themultidisciplinary healthcare team. Input from the entireteam not only allows for better issue identification andsolution generation, but also has the potential to increase team cohesiveness and motivation to actively participate in service improvement [16]. In this study, broadstaff engagement through interviews and workshopsallowed a comprehensive diagnosis of issues facing theEye Clinic, identification of suitable, low/negligible costsolutions, and motivated all staff, from check-in desk toophthalmologists to contribute to the service improvement effort. Going forward, we believe it has helped facilitate the development of a continuous improvementculture not only in the Eye Clinic, but also more broadlyin our organisation, with the lessons learnt in this studynow being applied to other outpatient clinics at ourhospital.This study has several limitations. Firstly, only qualitative data (i.e. staff interviews) was used to determine inefficient clinic processes. A quantitative investigationdefining exact contributions of these issues to pre- andpost-implementation in-clinic times would have betterclarified the efficacy of each solution. Secondly, thisstudy did not formally measure the effect of our solutions on patient and staff satisfaction. Staff interviewssuggest however, staff satisfaction and engagement inimproving clinic efficiency has improved. Other studiesin outpatient clinics have demonstrated that reduced patient wait times improve patient satisfaction [3]. Thirdly,as the baseline audit was performed retrospectively,many patient visits had invalid data and were excludedfrom in-clinic time analysis (1383 of 3624 visits). Thiswas noted in the improvement process and the checkout process was subsequently standardised, resulting inless invalid data in the post-implementation audit (363of 3853 visits). Overall, most invalid data was due to administration staff oversight in checking-out patients atthe end of their appointment (these patients werechecked-out the following day). As such, it is likely theinvalid data is missing completely at random, as opposedto being missing due to patient or in-clinic time relatedfactors. This is supported by there being no differencesin age or sex between invalid and valid data cohorts, andonly minimal differences in the proportion of appointment types between the total and valid data cohorts.There are, however, many strengths to this study.Firstly, this study was conducted in a large publiclyfunded tertiary re

Cerner Scheduling Appointment Book (Cerner, North Kansas City, USA), was used to schedule patient ap-pointments. This program allowed creation of a time-table with specific appointment times and types (e.g. new, follow-up, emergency etc.) for patients to be booked into. When patients attended appointments, it