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Abstract
Intraoperative monitoring (IOM) during orthopaedic and neurosurgical operations informs surgeons about the integrity of patients’ central and peripheral nervous systems. It is provided by IOM practitioners (IOMPs), who are usually neurophysiology healthcare scientists. Increasing awareness of the benefits for patient safety and surgical outcomes, along with post-COVID-19 service recovery, has resulted in a material increase in demand for IOM provision nationally, and particularly at Salford Royal Hospital (SRH), which is a regional specialist neurosciences centre.
There is a shortage of IOMPs in the UK National Health Service (NHS). At SRH, this is exacerbated by staff capacity shortage, requiring £202 800 of supplementary private provision in 2022.
At SRH, IOMPs work in pairs. Our productive time is wasted by delays to surgical starts beyond our control and by paired working for much of a surgery session. This quality improvement (QI) project set out to release productive time by: calling the second IOMP to theatre only shortly before start time, the other IOMP returning to the office during significant delays, releasing an IOMP from theatre when appropriate and providing a laptop in theatre for other work.
We tested and refined these change ideas over two plan–do–study–act improvement cycles. Compared with complete paired working, we increased the time available for additional productive work and breaks from an average of 102 to 314 min per operating day, not quite achieving our project target of 360 min.
The new ways of working we developed are a step towards ability (when staff capacity increases) to test supporting two (simultaneous) operations with three IOMPs (rather than two pairs of IOMPs). Having significantly improved the use of staff time, we then also used our QI project data to make a successful business case for investment in two further IOMP posts with a predicted net saving of £20 000 per year along with other associated benefits.
- PDSA
- Quality improvement
- Statistical process control
- Surgery
- Patient safety
Data availability statement
All data relevant to the study are included in the article or uploaded as online supplemental information.
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
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WHAT IS ALREADY KNOWN ON THIS TOPIC
Delayed starts to surgery create significant inefficiencies and are frustrating for all involved. Previous studies have demonstrated how reducing start time delays can help to enable finishing on time.
WHAT THIS STUDY ADDS
This study has demonstrated that considerable neurophysiology clinical scientist time can be saved during neurophysiology intraoperative monitoring (IOM), and this makes it feasible to consider using 3 (rather than 4) IOM practitioners to support simultaneous pairs of operations.
The understanding and data gained through a quality improvement (QI)-type project and monitoring appropriate metrics are valuable tools to evidence the need for, and benefits of, additional capacity resource for business cases.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
This paper should encourage clinical scientists to use QI approaches as lenses through which to convert their experience and perspectives into evidence-based service delivery improvements within their scope of influence, to the benefit of patients, staff and their organisation.
Problem
The demand for intraoperative monitoring (IOM) has significantly increased over the last 10 years. At Salford Royal Hospital (SRH), this has grown from one to two cases per week to, occasionally, as many as five or more. The in-house IOM team has been struggling to deliver IOM 5 days a week (Monday–Friday) alongside other services provided by the neurophysiology department. This has led to an increased requirement for external IOM provision from private companies, costing around £1200–£2400 per case, dependent on IOM requirements and provider. Expenditure on external IOM provision for 2022 was £202 800, a considerable outlay for the Trust. Although we do not envisage it being feasible to eliminate external IOM provision completely, our view has been that appropriate investment in additional SRH neurophysiology staff would increase in-house IOM with lower net cost. Before this investment could be justified, we set out to investigate how we might use existing resources more efficiently.
IOM cases can last from 4 to over 10 hours, and practice has been to provide a pair of IOM practitioners (IOMPs). Delays in theatre sessions, beyond the control of the IOM team, are a significant waste of our productive time. Reducing the impact of these delays would release time for other work. There are parts of the process where having a pair of IOMPs is valuable for reducing operation time and provides support for trouble shooting technical issues. It is also vital there is cover for breaks during these long and high-concentration surgical operations—mistakes or lapses in concentration could be catastrophic for patients, and poor staff well-being and burn-out is associated with poorer patient safety.1 However, we believed allocating two IOMPs to the whole case might be unnecessary.
As the department has two IOM machines, the initial plan was to look at new ways of working to see whether we could increase service provision with the existing complement of staff, for example, by covering a pair of simultaneous operations with fewer than four IOMPs to increase capacity and/or reduce expenditure on external IOM provision. Unfortunately, it became rapidly apparent that long-term sickness and issues with skill mix (and therefore ongoing training) made this infeasible without impacting our other services. However, optimising IOMP time could be our first step towards developing these new ways of working, ready for when staff capacity makes it possible.
The driver diagram2 in online supplemental figure S1 shows our high-level project logic. Our ultimate aim was to improve efficiency of IOM provision, leading, eventually, to more in-house IOM capacity at lower cost. Initial engagement with the IOM team led to the development of a goal for a quality improvement (QI) project to release IOMP time currently wasted by non-value-adding (NVA) activities in our processes. From this, we developed two Specific, Measurable, Achievable, Related and Time-bound objectives3:
Supplemental material
Objective 1: To reduce time wasted during operation set-up due to delays that are beyond our control, with a target to have at least 90 min of IOMP time released per operation here by the end of the project (September 2022).
Objective 2: To reduce unproductive IOMP time in theatre, with a target to release 270 min per operation here by project end.
Together, the overall target was to release 360 min of IOMP time for other work, per operation. We hoped to also use the insights and data from this project to support a business case for investment in additional IOMP staff.
We used the model for improvement (MfI) framework, with its plan–do–study–act (PDSA) cycles to develop, test and refine change ideas.2 4 The MfI has been used successfully in QI projects across hospital clinical sciences, including neurophysiology,5 cardiac science,6 7 radiotherapy8 and life sciences.9–12 We adopted a lean-thinking mindset to focus on waste within our processes.13–15
The next section gives some general background to the service and other work in this area. The Measurement section details all the metrics, locates them in the problem and gives baseline measurements. The interventions (change ideas) are described in the Design section, and the process and detailed results of testing them are in the Strategy section together with the learning and actions decided on for the next PDSA cycle. The Results section focuses on the final state of system versus the baseline and what the main contributors to the improvement were. Lessons and limitations consider strengths and the inevitable compromises when doing QI in a live NHS context. The Conclusion section contains general reflections and returns to the cost issues of concern to higher levels of the organisation.
Background
SRH is part of the Northern Care Alliance Foundation Trust and home of the Manchester Centre for Clinical Neurosciences (MCCN).16 MCCN serves a population of 3.5 million people across Greater Manchester and beyond, with 5000 emergency admissions, 22 000 elective admissions and 130 000 outpatient appointments per year.17 Clinical neurophysiology is part of MCCN and is a busy, predominantly diagnostic, specialty. The department also provides an extensive IOM service for both orthopaedic and neurosurgical procedures. IOM provides valuable information for the surgeon to minimise the risk of iatrogenic injury and therefore improve patient safety.
QI consists of systematic and continuous actions that lead to measurable improvements in healthcare services.18 Lean thinking incorporates an overall system approach to identifying waste such as NVA activity.4 13 In many organisations, NVA steps can outnumber the value-adding steps by as much as 9:1.14 Therefore, reduction or removal of NVA can be an effective way to improve efficiency.13–15
Previous QI projects on surgery have highlighted that its multifactorial nature leads to frequent delayed starts and that this can compromise quality of care, increasing lengths of stay and hospital-acquired infections, resulting in patient and family dissatisfaction and considerable costs to the organisation.19–21 Improvements in start times and efficiency have been achieved by improved planning and communication.20–22 More specifically, QI projects involving IOM have looked at impact on late surgical finishes5 and IOM performance including adherence to protocols, communication and documentation.23
A successful QI project will aim to achieve improvements for the patients, staff and the organisation.24 25 This project attempted to achieve these ‘three wins’ by using a lean mindset to identify NVA processes and areas of inefficiency in IOM service provision.
Measurement
Following initial team engagement (described in the Design section), our IOM booking coordinator provided baseline data regarding our in-house and bought-in IOM provision (see online supplemental figure S2A). This shows the impact of the COVID-19 pandemic, long-term sickness and staff attrition on service delivery.
Considerable reliance on external IOM provision is evident, rising through 2022, with consequent costs (online supplemental figure S2B). Online supplemental figure S3 shows the number of days each month when we were unable to provide in-house IOM service provision (or would have been unable to if required—thus avoiding data censoring), as an individual (XmR) statistical process control (SPC) chart.26 The demand for IOM by day of week (online supplemental figure S2C) indicated that Tuesdays had least demand, whereas Fridays, we could not fulfil demand the most frequently (36% of cases).
To understand the existing processes involved in IOM and to identify NVA steps, we developed a process map27 28 of IOM involvement in operations (figure 1) and collected process data. During the period of the project, we removed nine cases from data analysis as they were not first on the theatre list, so prior cases running over would bias the results. Discussing the process map within the team, we identified particular areas of wasted IOMP time.
Process map showing IOMP involvement in surgical operations
The overall project outcome metric, OM, was the IOMP time in minutes released compared with complete paired-IOM working. This is the sum of time released (waste reduced) from the two areas identified in the objectives (operation set-up and during the operation), each from two different sources, leading to a set of four additive process metrics, PMs. Online supplemental figure S1 shows these in the logic of the driver diagram, and figure 2A shows them as data with the PMs stacked to sum to the OM.
Metrics over time. BM, balancing metric; OM, outcome metric; PDSA, plan–do–study–act; PM, process metric.
The in-theatre preparation period for an operation contains potential delays beyond the control of the IOM team, generating waste for the paired IOMPs waiting for the operation to begin. The first IOMP, IOMP1, would usually attend theatre by 08:00 hours to be there for the team brief, with IOMP2 arriving between 08:30 and 09:00 hours (see figure 1). We felt we could increase this delay to IOMP2’s arrival, releasing IOMP time for other work. Our first process metric, PM1, was, therefore, defined as the time difference in minutes between the arrival of IOMP1 and IOMP2 for an operation. From experience and the data in online supplemental figure S5, 120 min should be a reasonable amount of time for set-up including team brief, checking kit, electrode preparation, sending for the patient and beginning the anaesthesia. Of this, 30 min preparation should be sufficient for IOMP2. It may actually be more than needed, but any residual inefficiency here would have minimal impact on the IOM team and ensure no delays in the surgery due to IOM set-up and team readiness. The target for saving time during set-up, objective 1 and so PM1, was thus 120–30=90 min of time released when compared with both IOMPs arriving together. Our baseline history, figure 2A blue blocks and mean line, shows PM1 the arrival delay and so productive IOMP2 time being 31 min on average prior to our QI project.
PM2 is the amount of time during an operation when we have lone working, so the amount of time in minutes when the other IOMP is released. The baseline is shown in figure 2A, orange blocks, with an initial mean of 71 min. Therefore, the baseline mean of our outcome metric, the total time released for other productive work and well-being breaks compared with complete pair working, was OM=PM1+PM2=102 min, as shown in figure 2A,B.
The initial target for saving time during an operation, so PM2, IOMP lone working, was 270 min. This would include 45 min of breaks for each IOMP. Adding the PM1 and PM2 targets gave us an OM target of 90+270=360 min as we embarked on the project.
As the project progressed, we developed further change ideas, with corresponding process metrics: PM3 is the time released by IOMP1 returning to the office when a very long set-up delay becomes apparent, contributing to objective 1; PM4 is the time released by an IOMP in theatre using a laptop for other value-adding work, when appropriate, contributing to objective 2. The inclusion of these two PMs allows us to see the additional impact of these further change ideas. So our OM became PM1+PM2+PM3+PM4.
The operation set-up duration is an important balancing metric (BM) as this could confound our results. We define this as BM1, the time from the arrival of IOMP1 in theatre to electrode application on the patient (see online supplemental figure S5). The red dotted reference line is the 120 min we considered reasonable (as above). The number of long delays (beyond 120 min) is BM2, highlighted in figure 2C, and we also monitored the operation length, from application of electrodes to the end of surgery as, BM3, figure 2C, since the duration of an operation directly influences the amount of time for potential lone working.
We collected data prospectively using a proforma, see online supplemental figure S6, to capture the metrics. All metrics were analysed and monitored using SPC charts.26
Design
We involved the IOM team through an initial brainstorming session followed by a questionnaire about team engagement (see online supplemental figure S7). From this, we generated a 4N Chart and Niggle-o-gram,29 adapted to pick out the impact on the patient, team and organisation (see online supplemental figure S8). This enabled us to gather multiple perspectives on IOM service issues, leading to development of our project objectives and generation of change ideas to test and refine during PDSA cycles. The surgeons and anaesthetists were happy with our coordination and communication with them, and for us to arrange how we provide the IOM cover they require.
Processes are passive; people are the active agents, so if we are to optimise performance we should enable them to do the right things in the right way.24 Analysing staffing, it was evident that we were not using our resources effectively. A member of the IOM team had retired but returned part time to work Mondays and Tuesdays. Tuesday has the lowest IOM demand, see online supplemental figure S2C, so an obvious and quick improvement, a Just Do It (JDI)24 applying the action principle, was to switch this part-time commitment to Mondays and Fridays. Another JDI was to request our case is first on any list where possible. These changes did not materially impact our project metrics.
Considering our process map and root cause analyses, we identified four more-major change ideas worth testing:
Further delay arrival of IOMP2 for set-up.
Increase lone working during operations.
Release IOMP1 from set-up when a long delay is in prospect (an extension of A).
Provide a laptop for other work in theatre (an extension of B).
These change ideas target the waste of productive IOM time (NVA, shaded red in figure 1). Our surgical cases are long and complicated, with 1–3+ hour set-ups and 4–10 hours surgical time; periods of non-productive time for IOMPs can be protracted. The team felt better communication among ourselves could help improve coordination and team planning to reduce the impact on our capacity.
Change idea A
Change idea A aims to avoid IOMP2 also being materially impacted by the many possible factors outside IOM control that lead to delays during set-up, and so delay the start to an operation—for our purposes, this is the point when the IOMPs apply the electrodes to the patient. Improved communication would enable IOMP1 to contact IOMP2 when he/she judges electrode set-up will be due in 10 min. This would avoid IOMP2 also being idle during non-IOM set-up delays. We felt 30 min was a reasonable threshold for IOMP2 waste, saving 90 min of the mean 120 min of set-up time, so the PM1 target was 90 min.
Change idea B
Change idea B concerns IOMP duplication waste. Working in pairs has significant benefits for reducing the impact of IOM on the length of the surgery during some steps, particularly electrode set-up and removal times. However, there are long periods when we do not really need two experienced IOMPs in theatre, as highlighted in figure 1; lone working is often sufficient to monitor the patient. This would allow the other IOMP to leave theatre to undertake some value-adding work such as reporting, updating electronic patient records, audit or mandatory training, but remain available in case support was required, plus returning to cover the other IOMP’s breaks. PM2 captures lone working time—that is, the amount of IOMP time liberated during an operation. We decided on a target for lone working of 270 min, including 90 min of breaks to promote staff well-being.
This new way of working would reduce the impact of IOM on other services we provide, while promoting a mutually supportive environment, confidence-building and staff well-being. Importantly, it would also, in the future, give us the potential to increase IOM capacity by covering a pair of simultaneous operations with three IOMPs, rather than the pair per operation at present, the third IOMP ‘floating’ between theatres as needed.
Change idea C
Change idea C arose from further consideration during the project of the issue of long delays in set-ups, building on change idea A. This was to also release IOMP1 to return to the department when a long delay seems inevitable, with metric PM3 to capture this released time.
Change idea D
Change idea D also arose from further consideration, this time of in-theatre activity. Change idea B was to allow one IOMP to leave the theatre, leaving the other lone-working. However, we knew this would not always be appropriate or possible during challenging cases, or when we expect additional support to be required. During this project, there was material impact on IOM team capacity from long-term sickness, COVID-19 isolation and staff leaving. IOM service delivery became very challenging. During our project period, it became imperative to use some theatre sessions to develop the skill mix within the team to bolster sustainability. This greater support, training and assessment reduced the opportunity for lone working possible when a pair of experienced IOMPs cover a case. Thinking about this, while still trying to keep NVA time in theatre down, we came up with the idea to provide a laptop for IOMP use in theatre, to do separate value-adding work, when appropriate. This would enable further productive time release without an IOMP leaving the theatre; PM4 captures this additional contribution to objective 2 with its target of releasing 270 of time.
Strategy
We began PDSA cycle 1 on 25 April 2022. Change ideas A and C focused on reducing the impact of set-up delays, that were beyond the control of the IOM team, by delaying the arrival of IOMP2 (PM1) and releasing IOMP1 (PM3) in the event of along delay. Change idea B focused on increasing IOMP lone monitoring during the operation itself (PM2) through reducing duplication waste in theatre when appropriate.
We saw a modest improvement on set-up delay time released, PM1 and PM3, but a much more material improvement on PM2—an increase in lone working of 98 min per case, on average, see table 1 and figure 2A. Figure 2B shows the SPC analysis of the overall time released (OM) data. Following the rules for SPC analysis,26 we froze the mean and limits after the baseline (the first 11 points), which resulted in special cause (improvement) flags in the PDSA 1 data (the next 12 datapoints), this supports recalculating OM at the start of PDSA 1 as shown in figure 2B.
PDSA improvement cycles
The time release from reducing the impact of set-up delays appears modest, but was substantial from more lone working during surgery and we resolved to continue bedding-in and refining our lone-working planning and coordination. However, towards the end of the period, staffing pressures required more in-theatre training, supervision and assessment, highlighted in figure 2B. This started to limit lone working. Considering alternatives led us to a fourth change idea: providing a laptop for other IOMP work in theatre during suitable periods.
We started PDSA cycle 2 on 1 June 2022. We refined change idea B to improve planning for lone working. Change idea D was also introduced—having a laptop available during the case allowed completion of other work at appropriate times, measured by PM4, while still being able to provide required support and, in particular, training guidance and supervision.
Both these yielded further worthwhile productive-time release, though we found a limit to what could be done with the laptop away from the department. Repeating the SPC analysis for the potential PDSA 1–2 impact on our OM yields only one special cause (improvement) flag (this was a point beyond the 3 sigma limit), so the support for SPC recalculation here is less strong, but we have applied it in figure 2B.
Results
During the project, the overall time saving rose from an average of 102 min per case (operation) during the 11 baseline cases, to 238 min during the 12 cases of PDSA1, then to 314 min for the 26 cases of PDSA. Figure 2A,B shows these two-step changes. This section focuses on the final improvement over the baseline and the contributions to it.
We achieved sustained improvement in the total time released, our OM: the change from 102 to 314 min is an additional 212 min of IOM time now available for additional productive work and well-being breaks per operating day, a 208% increase.
PM1 increased from 32 to 56 min, PM2 from 71 to 211, PM3 from 0 to 13 and PM4 from 0 to 34. The purpose of the BMs was to pick up potentially confounding influences on the results; there were no material change in these.
We can see that the major contribution was 140 min additional time saved from change idea B, more lone working, as picked up by PM2. Then, in order of contribution, change idea D, laptop working, contributed 34 min (PM4); change idea A, delayed arrival of IOMP2, 24 min (PM1); and change idea D, IOMP1 return to office, 13 min (PM3). These time savings on operating days will have positive impact on other services provided by the neurophysiology department, removing tasks from the rest of the working week.
These major improvements were, however, less than our targets. There are several factors that could influence these outcomes. In our dataset, 20 cases had long delays (>120 min) in start time (BM1), so around 40% of cases encounter a delay that could materially impact IOMPs. These are the ones over the 120 min target line in online supplemental figure S5 and are highlighted as the red points in figure 2C. The counts of these (BM2) are baseline=5 cases, PDSA1=6 and PDSA2=9. So these were comparable over the different phases of the project.
Short operations, classed as less than 270 min, highlighted in blue in figure 2B, provide less opportunity for lone working. A less-experience IOMP may also feel they require more pair-working support, especially during long, complicated cases. Although experience and difficulty are hard to measure, length of operation is straightforward and a useful BM, see BM3, figure 2C. The baseline average operation length was 379 min; in PDSA1 it was 449 min; and in PDSA2, 431 min and no SPC evidence of change; so little materially confounding impact of operation length. Although, average time masks some detail, these reasonably similar durations suggest roughly similar opportunities for lone working over the project.
Cases involving training/supervision/assessment highlighted in green and yellow in figure 2B, limit the amount of lone working possible. Removing these cases from the average times released demonstrates that our changes would generally have had greater impact without training/supervision/assessment. More use could have been made of delaying IOMP2 arrival. PM1 would be baseline 28 min without training cases (vs 31 for all cases); then with our changes: PDSA1 would be 75 min (vs 56), PDSA2 76 min (vs 54). The impact of training/supervision/assessment on lone working is similar. PM2 would be baseline 68 min (vs 71); then PDSA1 199 min (vs 169), PDSA2 202 min (vs 211). Separating cases with training/supervision/assessment also emphasises the value of the in-theatre laptop: these had PM4=54 min on average during these cases, about double that of those without.
Lessons and limitations
This QI project has demonstrated how improving communication and planning within the team can help to reduce waste from inefficient and duplicated IOMP time allocation throughout a surgical case. Prior to an operation, and during it, the pairs of IOMPs had ongoing conversations to plan and coordinate forthcoming steps, including identifying opportunities for optimising their time, particularly during set-up delays and in-theatre monitoring. This reduced NVA waiting time and duplication waste.
Engagement with the team at the start of the project and throughout meant that they felt included and their perspective valued, giving them a sense of ownership. This also facilitated smooth implementation of any changes. This approach is also in tune with the Trust’s increased focus on staff well-being and mental health following the COVID-19 pandemic, which is important for staff to be able to function at their best. We conduct long theatre cases with IOMPs concentrating on evaluating multiple waveforms on complex computer displays. Their focus and attention to detail allow early identification of changes in patient condition, to which they alert the surgeon who can take steps to reduce the risk of iatrogenic injury resulting in permanent neurological deficit. It is thus critical to patient safety and the likelihood of a positive outcome for patients and their families. Regular breaks are important, and working on other tasks (with the laptop) provided another source of mental decompression for one of an IOMP pair.
We noted many serious challenges in the working environment during the period of the project that led to the unexpected need to bolster service resilience through building wider skill mix, however, this required greater in-theatre training and supervision, and so limited the gains we had hoped for from lone working. The consequent addition of in-theatre laptop working limited the impact of this.
Although we did not reach our target time savings, we made very material improvements. After introducing changes to set up and lone working in PDSA1 (change ideas A and B), in PDSA2, we then saw further improvements on their metrics (PM1 and PM2). This may have been due to IOMPs becoming more consistent with their planning and communication, therefore, getting more used to the changes in processes and having those conversations more instinctively. Towards the end of PDSA2, there was more variability seen in impacts of delays. Although conducting IOMP training/supervision/assessment can account for some of this, there may also be an element of reverting to previous practices. Therefore, reemphasising the benefits of these changes, and of the end goal, may help increase engagement to deliver more consistent improvements over time as better IOM planning, communication and coordination become routine.
Root cause analysis of start-time delays was beyond the scope and influence of this QI project as it would involve much-wider-scale engagement. However, with 40% of cases delayed, this could be a valuable future exercise to deliver further efficiency. A major benefit of this project is that the approach could be applied to other services involving multidisciplinary theatre teams, helping to deliver improvements in other specialties.
The biggest limitation we experienced was IOMP capacity. This restricted the extent to which we could test more radical ways of working, such as covering two simultaneous cases with three IOMPs rather than two pairs. This is something to consider in future.
On the technical side, a longer baseline would have been desirable, though the number of datapoints in each PDSA was good. The baseline took a month of intense data collection, and there is a strong motivation in healthcare to get on with trying change ideas once they are agreed.
Conclusion
Outsourcing services such as IOM may help to meet demand on the service in the short term. However, this is more expensive than in-house provision. It also reduces training opportunities for our own staff, and so impacts on depth and breadth of experience, which could potentially lead in the future to the Trust losing our capability to deliver this specialist IOM service. Large-scale outsourcing of core, high-skill competencies is a known risk to an organisation’s sustainability and costs.30
As shown on the driver diagram (online supplemental figure S1), the ultimate, long-term, aim was to reduce net spend by increasing in-house capacity, reducing external IOM service provision and improving the sustainability of the Trust’s service. The improvements made during this project and the data produced were important steps in this direction. It enabled us to demonstrate to senior management that we are now making efficient use of the staff we have, and to gather data for a what-if scenario (online supplemental figure S4) of the impact on capacity of two additional IOMP staff at NHS Band 7, with a cost of around £105 500 a saving of nearly £20 000 per year compared with buying in external staff to cover this workload at an estimated cost of £125 000. When not in theatre, these additional staff would also support increased provision of our other neurophysiology services, reducing waiting lists and the requirement for waiting list initiative clinics, so yielding further cost savings. Additional in-house IOM service provision would build further continuity within the multidisciplinary teams, facilitating communication and, therefore, safer care for patients—better care at a lower cost. We noted, though, that we would not be able to stop using external IOM provision completely, since some days we require monitoring of more than two cases. This business case was successful.
This QI project demonstrates how engagement with teams on the front line of NHS services can help to identify areas for improvment and develop change ideas. Identification and measurement of appropriate metrics enabled clear demonstration of the existing situation. Collaboration within the team, and as part of larger multidisciplinary teams, paved the way for testing of these change ideas. Ongoing use of the metrics captured evidence of the impact that each change idea had on service provision, allowing review, adaptation and reinforcement. We were able to adapt quickly to challenges such as staff attrition, long-term sickness and consequent training and development needs to ensure service sustainability. The current project demonstrated improved use of IOMP time. It emphasises the importance of good communication, planning and use of technology to minimise the impact of delays and reduce duplication waste. This will be valuable for the future when we have appropriate staff resources to develop and test new ways of working while ensuring the highest levels of patient safety along with appropriate levels of staff well-being support.
Data availability statement
All data relevant to the study are included in the article or uploaded as online supplemental information.
Ethics statements
Patient consent for publication
Acknowledgments
With thanks to IOM team at Salford Care Organisation including Emma Walton-Lowe, Justine Fairbrother, Suzanne Crampton, Deborah Mercer, David Cross, Ellyn Payton, Megan Cooper, Hayley Kennedy, Niamh Collins. In addition: Robert Stoker (QI advice), Jennifer Redshaw (support manager supply of in-house/external IOM provision data), Debbie Whittle (Clinical Service Manager) and Gregory Lodwick (design support for process mapping). SJL led this work as part of the QI unit of the Higher Specialist Scientist Training Programme (HSST), run by Alliance Manchester Business School of the University of Manchester and the Manchester Academy for Healthcare Scientist Education (MAHSE), on behalf of the National School of Healthcare Science, part of NHS Health Education England.
Supplementary materials
Supplementary Data
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Footnotes
Contributors SJL designed the work and collected the data, with the assistance of the IOM team. SJL conducted analysis and interpretation of data and wrote the first draft of paper. GA and NP advised and helped revise drafts. SJL is the guarantor of the manuscript.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.