Using discrete event simulation to design a more efficient ... - Core

Health Care Manag Sci DOI 10.1007/s10729-011-9151-1

Using discrete event simulation to design a more efficient hospital pharmacy for outpatients Matthew Reynolds & Christos Vasilakis & Monsey McLeod & Nicholas Barber & Ann Mounsey & Sue Newton & Ann Jacklin & Bryony Dean Franklin

Received: 25 August 2010 / Accepted: 4 February 2011 # Springer Science+Business Media, LLC 2011

Abstract We present the findings of a discrete event simulation study of the hospital pharmacy outpatient dispensing systems at two London hospitals. Having created a model and established its face validity, we tested scenarios to estimate the likely impact of changes in prescription workload, staffing levels and skill-mix, and utilisation of the dispensaries’ automatic dispensing robots. The scenarios were compared in terms of mean prescription turnaround times and percentage of prescriptions completed within 45 min. The findings are being used to support business cases for changes in staffing levels and skill-mix in response to changes in workload.

1 Introduction

Keywords Health care . Hospitals . Pharmacy dispensary . Simulation

Hospital pharmacies are complex service systems that deal with different types of customer orders (prescriptions), employ a wide range of staff with different possible combinations of roles and incorporate many advanced technological solutions to improve the accuracy and speed of drug dispensing. For each prescribed item, a label must be created, and then the relevant product selected and checked, the correct number of dose units counted and repackaged if necessary, and the product labelled. The completed prescription is then checked again by a pharmacist, who may also have to confirm that a valid dosage regimen has been prescribed before handing out to the relevant patient, hospital ward or department. Optimising

M. Reynolds : M. McLeod : B. D. Franklin Centre for Medication Safety and Service Quality, Imperial College Healthcare NHS Trust, London, UK

M. McLeod : N. Barber : A. Jacklin : B. D. Franklin Centre for Medication Safety and Service Quality, The School of Pharmacy University of London, London, UK

C. Vasilakis Clinical Operational Research Unit, University College London, London, UK A. Mounsey Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, UK S. Newton Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, UK

A. Jacklin Pharmacy and Therapies, Imperial College Healthcare NHS Trust, London, UK B. D. Franklin (*) Pharmacy Department, Ground Floor, Charing Cross Hospital, Fulham Palace Road, London W6 8RF, UK e-mail: [email protected]

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the organisation of the dispensing process will benefit patients, staff and hospital organisations. There are many ways in which the dispensaries’ procedures could be modified to attempt to increase efficiency, such as altering workflows, introducing prioritisation systems, and changing staffing patterns. However, it is difficult to know which of these changes would be beneficial and which would be detrimental. Conducting a series of practical experiments would be possible, but this would be time-consuming, potentially dangerous, and could create havoc if changes did not work out as planned. Reversing the effect of unsuccessful changes could also be difficult and may harm staff morale. Computer simulation modelling is one way to avoid many of these potential problems. The likely impact of changes to the dispensaries’ processes could be explored without disruption or risk. A range of options can also be explored under identical “experimental” conditions, a rare situation in empirical healthcare research. Because modelling requires exposition and analysis of the key interactions and relationships involved in a complex system, it can also lead to greater understanding of the system being studied. Computer simulation in healthcare is widely reported in the literature [1–3]. However, while there have been several studies using simulation to study pharmacy systems [4–11], the only one to have specifically examined the hospital pharmacy dispensary was published in 1974 [8]. We therefore wanted to explore the use of simulation in this setting. Our objectives were to explore the impact of a range of changes to the outpatient prescription dispensing process on each of two hospital sites using discrete event simulation and to explore the utility of this approach in this setting. In the next section we describe the study setting in detail. We then set out the technical details of the simulation, including the modelling assumptions, the input parameters and the methods used to run the simulation and analyse the output. We then report on the results of the simulation experiments before discussing their implications to pharmacy practice.

2 Study setting The study was based on the pharmacy dispensary services of two large metropolitan teaching hospitals which comprise part of Imperial College Healthcare NHS Trust, London: Charing Cross Hospital (CXH) which has about 500 beds, and Hammersmith Hospital (HH) with around 400 beds. The hospitals provide secondary and tertiary care to inpatients and outpatients, and offer a wide range of clinical specialities. Both hospital pharmacy dispensaries process three different types of prescription: outpatient prescriptions

(approximately 7,000 dispensed items per pharmacy per month), inpatient medication orders (~7,000 items) and discharge prescriptions (~5,000 items); each of the pharmacies also house automatic dispensing robots. In this study we chose to focus on the outpatient prescription dispensing process as this is the area where delays are most likely to adversely affect patient satisfaction. 2.1 The process for dispensing outpatient prescription orders Seven different staff groups work in the outpatient sections of each dispensary, each with different responsibilities and priorities: pharmacists, accredited pharmacy checking technicians (ACTs), pharmacy technicians, pre-registration pharmacists, pre-registration pharmacy technicians, pharmacy assistants, and receptionists. Different combinations of staff from these groups work in the outpatient section on different days and at different times. The only restriction is that there must be at least one pharmacist present at all times, see Table 1. The staff mix is decided a week in advance and takes into account the needs of other sections of the pharmacy department as well as staff holidays and other rotas. Outpatient prescriptions are dispensed according to the processes outlined in the Unified Modelling Language (UML) activity diagram in Fig. 1 [12, 13]. At each hospital, outpatient prescriptions for medication needed urgently are written by prescribers in day clinics or in the accident and emergency (A&E) departments. Clinics are held at different times throughout the week, and thus the patient arrival patterns vary by time of day and day of week. Patients, or their representatives, present their prescriptions at the pharmacy reception. The person attending to reception takes payment for any prescription charges or confirms proof of exemption from payment. The patient is then handed an incrementally numbered ticket which dictates the order with which the prescriptions are processed (and doubles as a collection token); they then wait for their prescriptions to be dispensed, or leave to come back and collect at a later time or date. The received prescriptions are stacked in numerical order, ready for the next task, labelling. Labelling starts with a staff member (“labeller”) examining the prescription and accessing the patient’s medication record (PMR) on the computer system. If the patient does not have a PMR then a new record is created. Next, the required medication is selected on the computer system, the dosage instructions are entered and the required quantity specified. The operator confirms the order before adhesive labels are printed and the specified quantity of the particular medication item is automatically deducted from the stock level. If the items are stocked in the automatic dispensing robot an electronic request is automatically sent to the robot. The robot then picks the ordered medication and sends it via a

Health Care Manag Sci Table 1 Task preference order for each staff group

Priority

Labelling

Charing Cross Hospital Receptionist Technician Assistant ACT Technician Pharmacist Pharmacist Trainee Trainee Assistant ACT X Hammersmith Hospital Receptionist ACT Technician Technician Trainee Trainee Pharmacist Assistant Assistant Pharmacist ACT X

1st 2nd 3rd 4th 5th 6th

ACT Accredited Checking Pharmacy Technician

Receipt

1st 2nd 3rd 4th 5th 6th

series of conveyor belts to the workstation where the labeller is located. The prescription and any automatically dispensed items are then placed together in a first-in firstout (FIFO) queue ready for assembly.

Assembly

Final Check

Counselling

Resolve problem?

Pharmacist ACT Technician Trainee Assistant X

Pharmacist X X X X X

Pharmacist ACT Technician X X X

Technician Assistant Trainee ACT Pharmacist X

Technician Trainee Assistant ACT Pharmacist X

Pharmacist X X X X X

Pharmacist ACT Technician X X X

Technician Assistant Trainee ACT Pharmacist X

The assembly task involves comparing the labels with the prescription, looking for inaccuracies and checking that the labels contain all appropriate information. The assembler must collect any remaining medication items from the

Prescription arrives Receive Rx

Resolve problem

Label Rx

[Yes]

problems?

Hand-pick items

[Yes]

[No] Items picked by robot

[Yes]

Assemble Rx

robot items?

[No]

problems?

[No]

hand-pick Items left?

[No]

Final check by pharmacist Resolve problem

[Yes]

[NonPharmacist]

problems?

Resolve problem

who assembles?

[Yes]

[No] [Pharmacist] patient waiting?

Assemble & check

[Yes] Hand-pick items

problems?

[Yes]

Resolve problem

[No]

[Yes]

hand-pick Items left?

[No]

Resolve problem

Hand out Rx

[No] [Yes]

problems?

[No]

End

Fig. 1 Unified Modelling Language (UML) activity diagram of the process for dispensing outpatient prescription orders. In UML activity diagrams rectangles represent activities/tasks; arrows represent tran-

sitions; the filled-in circle represents the starting point and the bull’seye the endpoint; diamonds represent decisions mandated by the conditions stated in the brackets above the arrows. Rx: Prescription

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shelves (“hand-picking”), check expiry dates on all items, re-package where necessary, and apply a label to each container as appropriate. Once all tasks have been completed for every item on a prescription, the assembler signs the “dispensed by” box of the prescription. Next, for outpatient prescriptions, a pharmacist “final checks” the assembled prescription. The final check comprises both an accuracy check and a clinical check. The accuracy check verifies the label’s details, the contents, the expiry date of the medical product, and ensures that patient information is included as appropriate. The clinical check confirms that the prescription is legal, and that a valid and clinically appropriate dosage regimen has been prescribed, based on the information available. On completion of the final check, the pharmacist signs the prescription which can then be given to the patient or their representative, together with an explanation of how to take the medicine, and answers any questions that may arise; this practice is known as counselling. It is current practice that wherever possible, all prescriptions are processed in the order in which they are received, and at CXH, that prescriptions are given out to patients in that order, as indicated by the ticketing system. Should a mistake be identified during processing, or if the prescriber needs to be contacted, the prescription is removed from the processing queue. Once resolved, the relevant prescription is slotted back at the appropriate place in the queue, to maintain ticket order whenever possible. At HH, staff members are allowed to give out prescriptions to waiting patients once they are completed, rather than strictly adhering to the first-in first-out rule. The prescriptions are still processed in FIFO order whenever possible. Each prescription is time-stamped as it is received and again after it has been dispensed and final checked. The difference between these two times gives the prescription’s turnaround time.

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3 Methods 3.1 Model building The process of dispensing outpatient prescriptions was observed and described by a member of the project team, then discussed with and agreed on by all authors. In constructing the baseline models of both pharmacies, several simplifying assumptions were made. These were discussed and agreed upon by all authors and were as follows:

&

only pharmacists are allowed to final check prescriptions in the outpatients section of the dispensary. The staffing level was determined by observation with the following assumptions made: staff who were observed to work for at least 45 min within an observed period of an hour were represented by a full hour in the model; those who worked for between 15 and 45 min were represented by half an hour of staff time in the model; those who worked for less than 15 min were not included. As pre-registration pharmacists and pre-registration pharmacy technicians have very similar responsibilities and task priorities we combined them into a “trainee” staff group for the purposes of this study. The automatic dispensing robots are always fully operational. The proportion of patients who wait to collect their prescriptions as opposed to those who leave and return to collect at a later time is constant throughout the day and is not influenced by the number of patients already waiting to collect. Task processing times remain unchanged throughout the day. The proportion of prescriptions in which problems are encountered remains constant throughout the day. Staffing levels do not change in response to variations in workload or turnaround times. All staff members, regardless of type, work with the same speed and accuracy. Additionally they are not always available to start working on a new task immediately after completing one. This delay, representing activities such as answering the phone and tidying-up, was called non-dispensing time and was treated as an additional task for all staff types. All tasks are subject to staff non-dispensing times except for labelling, since the labeller generally remains seated at a computer terminal and labels one prescription after another. Receptionists are not subject to non-dispensing times; they normally stand at the reception and receive prescriptions one after the other, without participating in other activities which would limit their availability. The arrival rate of patients returning to collect previously dispensed prescriptions is constant throughout the day and independent of the number of patients who collect their prescription on completion.

The simulation models were created in ExtendSim OR version 7.0.5 (http://www.extendsim.com). 3.2 Input parameters

&

No prescriptions are clinically screened prior to dispensing and therefore a pharmacist must perform a final check, or assemble and self-check, each prescription;

Model inputs included staffing levels, time-dependent prescription arrival times, number of items on each prescription, processing times for each dispensing task and utilisation of the

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automatic dispensing robot (see Tables 5, 6, 7 and 8 in appendix for details). The prescription arrival time distributions were estimated by examining the retrospectively collected in and out timestamps of 1777 prescriptions at CXH and 1279 at HH, comprising 3 weeks’ data. To estimate the remaining input parameters two data collectors prospectively recorded the times taken to perform each dispensing task over a period of approximately 2 months (a total of 845 and 1,076 outpatient prescriptions were observed at CXH and HH respectively). The data collected were used to calculate the parameters of the input distributions and to calibrate the baseline models of both pharmacy sites. For distribution fitting we used BestFit (Palisade Software, 1995) and MS Excel software tools. Distributions were selected on the basis of goodness-of-fit (using BestFit), theoretical considerations (for example, an exponential distribution being inappropriate for task completion times since it will output values of zero), and practicalities (whenever we felt that it was inappropriate to fit a distribution due to inadequate numbers of observations expert opinion was used instead). 3.3 Simulation We studied two model outputs of system performance: mean prescription turnaround time and percentage completed in less than or equal to 45 min (a reasonable target in hospital pharmacies). These outputs were chosen because they are straightforward and easy to comprehend; they are of use to dispensary managers, meaningful to patients, and readily comparable with existing real service data. We used a confidence interval method to determine the number of replications (runs) for each scenario [14]. By plotting the cumulative mean turnaround time against the number of replications for the baseline models we estimated that 100 single-day terminating and independent replications would be sufficient. The output data of all replications for each scenario were used to calculate the point estimates (mean) and 95% confidence intervals for prescription turnaround times and percentage of prescriptions completed within 45 min. The simulated day ran from 08:45 in the CXH model, and from 09:00 in the HH model, in line with site opening times. Prescriptions could arrive at any time until 18:30, corresponding to the times when the outpatient departments are open to receive prescriptions. The models then ran until 20:00 to ensure that all the day’s prescriptions were processed, as they would be in practice. However, simulation observations related to prescriptions arriving after 17:00 were excluded from the final results as we wanted to focus on the impact of changes during the normal working day.

The face validity of the models and results were repeatedly examined in a number of meetings with the entire project team, which included service managers and dispensary staff. To increase our confidence in the results, we compared baseline model predictions of prescription turnaround times and percentage completed in less than or equal to 45 min to those observed in the two hospital pharmacies. 3.4 Experimentation Following consultation with the staff of both pharmacies, we set out to conduct three sets of experiments: a) Variations in prescription workload The workload experiments were designed to evaluate the impact that changes in the daily number of incoming prescriptions would have: we varied the mean number of prescriptions arriving daily in 1% increments (or decrements) between +10% and −10% of the observed daily number used in the baseline models of the two pharmacy sites. b) Changes in staffing levels and skill-mix As it was not known whether current staffing configuration was efficient or if staff skills could perhaps be better employed, this set of experiments was designed to increase understanding of how staffing levels and skill-mix influence turnaround times. In the first set of staffing scenarios 0.5 wholetime-equivalent (WTE) staff members were added to the staffing roster during three different time periods of the day: 09:00–13:00, 11:00–15:00, and 13:00– 17:00. The simulation output was compared with that of the baseline models as well as with each other as we sought to understand whether any particular staff group offered unforeseen benefits over the others. In the second category of staffing scenarios we explored the possibility of replacing one staff member by another from the next lower grade. In total, there were four possible substitutions: pharmacist substituted by ACT; ACT substituted by technician; technician substituted by trainee; and trainee by assistant. In practice, substitution was possible only when at least one member of the staff group that was the candidate for substitution was present in the model. Thus, skill-mix was altered whilst the total number of staff present remained constant, (see Table 8 for staffing levels in the baseline models). A pharmacist was only replaced with an ACT if the requirement that at least one pharmacist always be present was met. As before, we explored the impact of substitution across the three daily timeslots.

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c) Increasing robot utilisation The robot experiments were designed to provide estimates of the effect of increasing the percentage of items dispensed automatically. The aim was to shed light on the potential impact of increasing robot utilisation and to help managers decide on any future investment decisions with regard to expanding robot capacity. The proportion of automatically dispensed items was increased at 5% relative increments, up to a maximum 50% relative increase in both models.

Changes in the workloads at HH have slightly less impact on model outputs. Specifically, a 10% increase in mean daily workload is associated with an increase in mean turnaround times of 47.2%, from 30.9 min (28.8 to 33.0) to 45.5 min (42.0 to 48.9). The number of prescriptions completed within 45 min shows a 27.6% decrease, to 57.1% (51.9 to 62.3) of all prescriptions. Finally, a 10% decrease in the daily workload at HH showed a 26.4% decrease in turnaround times, from 30.9 min (28.8 to 33.0) to 22.7 min (21.4 to 24.0), and an increase of 16.9% in prescriptions completed within 45 min, from 78.9% (75.0 to 82.9) to 92.2% (90.0 to 94.5). Results are summarised in Figs. 2 and 3.

4 Results

4.3 Changes in staffing levels & skill-mix

4.1 Model validation

Overall, we found that, as expected, the addition of extra staff improved both metrics of system performance, Figs. 4 and 5. However, we found that regardless of staff type or hospital site, the largest decrease in mean daily turnaround times occurred when 0.5 WTE staff was added to the morning slots (09:00–13:00). The smallest predicted reduction in turnaround times was seen when the addition took place in the 13:00–17:00 timeslot (except in the case of adding an assistant to the HH model where the least beneficial time was between 11:00 and 15:00). The largest decrease in turnaround times overall was seen when 0.5 WTE pharmacist was added to the 09:00– 13:00 timeslot in both hospital models. The addition of ACT, technician, trainee and assistant yielded comparable reductions in turnaround times (again with the exception of adding an assistant to the 11:00–15:00 timeslot). Results from the CXH model suggested that replacing 2.5 h of pharmacist time with 2.5 h of an ACT during the 09:00–13:00 timeslot would result in a mean turnaround time increase to 51.0 min (47.2 to 52.8); replacing 3.5 h of pharmacist during the 11:00–15:00 timeslot results in the largest increase, to 54.9 min (50.8 to 58.9) while substituting 3.5 h in the final timeslot produced predicted turnaround times of 49.0 (45.3 to 52.8), Fig. 6. Changes in the latest timeslot produced a smaller increase in turnaround times than when substituting during the first

Observed mean prescription turnaround times of 41.1 min (95% confidence interval 40.2 to 42.0) at CXH and 31.0 min (30.1 to 31.9) at HH were comparable to those estimated by the baseline models: 40.4 min (37.4 to 43.5) and 30.9 min (28.8 to 33.0) respectively, Table 2. Similarly, 64.2% (61.9 to 66.4) of real-life prescriptions at CXH and 82.8% (80.7 to 84.8) at HH were completed within 45 min; corresponding figures estimated by the baseline models were 60.2% (55.1 to 65.4) and 78.9% (75.0 to 82.9) respectively. 4.2 Variation in prescription workload CXH results showed that an increase of 10% in prescriptions received per day resulted in an increase in predicted turnaround times of 52.5%, from 40.4 min (37.4 to 43.5) to 61.7 min (57.4 to 66.0), and a decrease in the percentage of prescriptions completed within 45 min of 35.7%, from 60.2% (55.1 to 65.4) to 38.7% (33.8 to 43.6). Conversely, a 10% decrease in daily prescription workload resulted in a 34.8% decrease in turnaround times, from 40.4 min (37.4 to 43.5) to 26.4 min (24.7 to 28.1), and an increase of 44.5% in prescriptions completed within 45 min, to 87.0% (83.9 to 90.2)

Table 2 Comparison of baseline models with real-life observations

Mean turnaround time, minutes (95% CI)

Observed Baseline model

a

Confidence interval calculated using the Wilson method [15]

Observed Baseline model

Charing Cross Hospital 41.1 (40.2 to 40.4 (37.4 to Hammersmith Hospital 31.0 (30.1 to 30.9 (28.8 to

% completed in ≤45 min (95% CI)

42.0) 43.5)

64.2 (61.9 to 66.4)a 60.2 (55.1 to 65.4)

31.9) 33.0)

82.8 (80.7 to 84.8)a 78.9 (75.0 to 82.9)

Fig. 2 The effect of workload variation on mean prescription turnaround times

Mean turnaround time, minutes (95% CI)

Health Care Manag Sci 70 60 50

Charing Cross Hospital 40 30

Hammersmith Hospital

20 10 0 -10%

-8%

-6%

-4%

-2%

0%

2%

4%

6%

8%

10%

% change in daily number of prescriptions, compared to baseline

Fig. 3 The effect of workload variation on mean percentage of prescriptions completed within 45 min

% of prescriptions completed within 45 minutes (95% CI)

timeslot, even though more pharmacist time was substituted. See Table 3. There was a smaller impact on mean turnaround times when staff groups other than pharmacists were substituted. The smallest increase in turnaround time was always observed when the substitution took place in the 13:00– 17:00 timeslot, with the exception of the case where a trainee is replaced by an assistant during the 11:00–15:00 timeslot. In that latter case, we estimated a decrease of 3.4% in turnaround times, to 39.0 min (36.2 to 41.9). Results obtained from the Hammersmith Hospital model suggested that the only substitution considerably affecting the performance was substituting a pharmacist for an ACT, Fig. 7. Although the number of hours substituted in each scenario differs between timeslots and models (due to baseline staffing levels), there is a trend whereby substituting for a pharmacist had the largest effect on increasing turnaround times. Overall, apart from the case of pharmacists, replacing a single member of the other staff types with the next lower grade was predicted to have little impact on turnaround times.

4.4 Increasing robot utilisation In the CXH model the proportion of items dispensed automatically was increased from a baseline value of 40% of all items to 60%. In the HH model the corresponding baseline value of 52% was increased incrementally up to 78%. The results, Table 4, show a similar percentage reduction in turnaround times as the robot utilisation is increased. For example, a 50% increase in automatically dispensed items was estimated to decrease mean turnaround times to 35.5 min (32.9 to 38.1) at CXH and 26.4 min (24.8 to 28.0) at HH.

5 Discussion We constructed discrete event simulation models of two hospital pharmacy dispensaries, calibrated with data collected in practice, and used these to explore the impact of a range of system changes. The models were used to predict

100% 90%

Hammersmith Hospital 80% 70% 60%

Charing Cross Hospital

50% 40% 30% 20% 10% 0% -10%

-8%

-6%

-4%

-2%

0%

2%

4%

6%

% change in daily number of prescriptions, compared to baseline

8%

10%

Mean turnaround time, minutes (95% CI)

Health Care Manag Sci 50

Pharmacist

40

ACT Technician

30

Trainee Assistant

20

Baseline (95% CI)

10

0

09:00-13:00

11:00-15:00

13:00-17:00

Timeslot additional staff added to

Fig. 4 The effect of additional staff on turnaround times, Charing Cross Hospital model

Mean turnaround time, minutes (95% CI)

the effects of changes in workload, which suggested that one of the dispensaries might suffer more from the same percentage increase in workload and thus may be operating nearer capacity. We also explored the effects of various changes in staffing. We found that, as expected, adding a 0.5 WTE pharmacist reduced the turnaround time by the largest amount, compared with the other staffing groups. The reductions in turnaround times predicted by adding any of ACTs, technicians, trainees, or assistants were also substantial and almost identical. Initially, we did not predict that adding an assistant would have as large an effect as adding the other staff types. As an assistant can only perform two tasks, labelling and assembling, this may indicate that there are bottlenecks at one or both of these tasks. Further examination of the model suggested that the assistant spends most of their time assembling. Although they are permitted to label, only one staff member can do so at a given time as there is only one

computer terminal in the outpatient section of the dispensary. Whenever an extra assistant is present there is also at least one other staff member with higher priority for labelling. The effects of adding an assistant therefore suggest that staff who primarily assemble may be most beneficial. Providing an assistant to help with assembling may also free other staff to perform more specialised tasks. The effect of adding an extra labelling terminal could be explored in future work; in practice, an extra computer would require substantial redesign of both dispensaries, and may result in difficulties maintaining prescription order. The staff experiments also imply that an ACT’s checking skills could be put to better effect elsewhere in the dispensaries, as their addition has an almost identical effect to the non-checking staff members. The effect of allowing ACTs to final check prescriptions that have been clinically screened by a pharmacist could be explored in future work. The relative benefits of adding extra staff earlier in the day suggests that it may be better preventing backlogs of

40

Pharmacist

30 ACT Technician Trainee

20

Assistant Baseline (95% CI)

10

0

09:00-13:00

11:00-15:00

Timeslot additional staff added to

Fig. 5 The effect of additional staff on turnaround times, Hammersmith Hospital model

13:00-17:00

Health Care Manag Sci Mean turnaround time, minutes (95% CI)

60

50 Pharmacist substituted by ACT ACT substituted by Technician

40

Technician substituted by Trainee

30

Trainee substituted by Assistant

20

Baseline (95% CI)

10

0

09:00-13:00

11:00-15:00

13:00-17:00

Substitution timeslot

Fig. 6 Impact of staff substitution in the Charing Cross Hospital pharmacy model

work forming rather than trying to tackle them once they have formed. This new information allows better planning by suggesting the most appropriate time to use a limited resource. Further work should also explore whether or not it is advantageous to move staff from later in the day to an earlier shift. The substitution experiments indicate that there may be scope to substitute 0.5 WTE staff member for a member of staff from a lower grade without adversely effecting turnaround times. In practice, consideration must be given to tasks not individually accounted for in the model, such as whether any replacement staff members would be able to deal as effectively with queries. This study was not intended to be an economic analysis of the dispensary and accurate costs were not included. It is however assumed that staff of a higher grade “cost” more than staff of a lower grade. Thus, employing lower grade staff in place of higher grade staff can be expected to be financially favourable. For example, replacing 0.5 WTE of an accredited pharmacy technician (ACT) with 0.5 WTE pharmacy technician would save the organisation around £3000 per year in salary-related costs, whereas a similar substitution of a pharmacy technician by a pharmacy Table 3 Number of staff hours substituted by substituted staff type Charing Cross Hospital

Hammersmith Hospital

Timeslots

Timeslots

9–13

11–15

13–17

9–13

11–15

13–17

Pharmacist

2.5

3.5

3.5

1.0

1.5

1.0

ACT

1.5

1.5

1.5

1.0

2.0

2.0

Technician

2.5

2.5

2.0

3.5

4.0

4.0

Trainee

1.0

1.5

1.0

4.0

3.0

2.5

assistant would save about £2000 per year (all figures estimated using organisational costs for mid-band staff members). Most staff of technician grade or above work full time. However, there is scope to move staff between pharmacy sections without changing their job role, especially with regard to trainees. It would therefore be feasible to change staffing rotas and move staff between dispensary sections in order to maximise the use of more costly staff’s skills. The estimated effect of increasing the utilisation of the automatic dispensing robots was smaller than the team expected. Substantially increasing the robot’s utilisation would be expensive as extra storage capacity would need to be added. There is also a limit to the number of different medication items that could be dispensed through the robot as not all packages are appropriately sized or proportioned for robot storage, and some medications are unsuitable due to their storage requirements (e.g. those requiring refrigerated storage). Increasing use of the robot would be expected to have other benefits however, such as better use of floor space, improved stock control, and reduced dispensing errors, and should therefore not be discounted despite the modest decreases in turnaround time predicted [16]. The models were designed and used with the aim of exploring the impact of changes on the outpatient dispensary. We also found additional benefits from the process of developing the models, which greatly increased the team’s awareness and understanding of the processes involved. There are several potential limitations in this study, mainly related to the assumptions made in model construction. We did not take into account adaptive measures adopted by dispensary staff at times of high or low workload, such as moving staff between sections of the dispensary. Such flexibility, commonly observed in practice, may decrease the turnaround times of the prescriptions

Health Care Manag Sci Mean turnaround time, minutes (95% CI)

50

40

Pharmacist substituted by ACT ACT substituted by Technician

30

Technician substituted by Trainee Trainee substituted by Assistant

20

Baseline (95% CI)

10

0

09:00-13:00

11:00-15:00

13:00-17:00

Substitution timeslot

Fig. 7 Impact of staff substitution in the Hammersmith Hospital pharmacy model

received in very busy times and increase it for those in quiet times. Such adaptive measures are unpredictable and dependant on a variety of factors; fully characterising these and incorporating them into the models was beyond the scope of the project. In the model all staff members, regardless of type, work at the same pace. It is not unreasonable to assume that people work at different rates, indeed the same staff member may work at different rates when performing the same task at different times. It may also be the case that using staff who are not fully trained slows down other staff; this was not taken into account in the model. We used only one non-dispensing time distribution. It may have been a better representation of real life practice if there was a different set of non-dispensing times for each staff type and after each task. We suspect that the

pharmacists, for example, spend much more of their time answering questions and dealing with queries than other staff groups. It is also not known whether or not the number of patients who wait for their prescriptions is proportional to the number of patients already waiting, or the estimated waiting time, at the time they arrive and hand in their prescriptions. It is possible that people are less likely to wait at busy times, thus reducing the counselling workload at these times. We assumed the robot to be fully operational throughout. Although the robots at both sites do occasionally break down, a complete breakdown is rare, as both robots consist of individual modules which are often able to function independently of one another. In circumstances when the robots stop functioning completely, the working practices

Table 4 The effect of increasing the proportion of automatically dispensed items on system performance, both models % relative increase in robot utilisation

% automatically dispensed items

Mean turnaround times, minutes (95% CI)

% change compared to baseline

% completed in