North Cumbria Integrated Care NHS FT (NCIC) is currently preparing to operationalise an integrated elective hub at their existing hospital site. These surgical hubs are ringfenced environments for performing high volume, low complexity (HVLC) surgical procedures in a streamlined fashion. By separating these procedures out from other services the hospital provides, disruption to elective procedure capacity due to competition over resources (such as beds and theatre space) can be avoided. These hubs lead to 11% fewer cancellation rates which improve patient experience and operational capacity. Additionally, by having together skills and expertise of staff under one roof, hospitals increase HVLC procedure volume by 21.9% [1], saving money for hospitals, and reducing elective backlogs. This avoids long patient waiting times which are negatively associated with patient outcomes.
Configurating a hub correctly is key to unlocking these operational and clinical benefits. To do this, providers need to understand their service specific requirements and implement best practices as outlined by the Getting It Right First Time (GIRFT) programme.
Building a flexible demand and capacity model to configure NCIC’s elective hub
To support NCIC in configuring their elective hub, our team developed a comprehensive demand and capacity model using waitlist, theatre and bed data. The model quantified HVLC demand by specialty, and the associated theatre and bed requirement to operate the hub. Our model was flexible, allowing senior management at NCIC to adjust underlying assumptions to analyse various scenarios.
How data driven decision making unlocked the full potential of the hub.
Using the outputs of the demand and capacity analysis, NCIC now has a clear understanding of how best to configure the elective hub and the benefits that would be unlocked in doing so. Driven by our approach and the model’s ability to process various proposed scenarios, it provides a detailed view of the capacity required to handle HVLC procedures in these specialties, enabling the Trust to plan resources, staffing, and the allocation of theatre space and beds more effectively.
We also partnered closely with their internal analytics team that is now able to take this work forward, which will allow the Trust to hit the ground running and realise the clinical, operational and financial benefits of their hub.
Enhancing Theatre Capacity Management at Cambridge University Hospitals
As a result of the COVID-19 crisis, Cambridge University Hospitals (CUH) had to quickly adapt their operating theatre schedules to address a significant increase in demand. This rebalancing was necessary to ensure that both emergency procedures and routine care could continue smoothly throughout this challenging period. As the immediate impacts of the pandemic began to stabilise, CUH shifted focus to developing a more sustainable approach to theatre allocation. The objective was to analyse the current demands in comparison to capacity to identify the most effective scheduling strategy that would optimise theatre allocation by specialty and division, thereby reducing waiting lists and effectively balancing emergency procedures with routine care.
By assessing elective and emergency data, as well as the changing patterns in patient needs, CUH wanted to evaluate whether a return to pre-COVID scheduling was viable or if a new, evidence-based theatre allocation model might better serve their goals of efficiency and high-quality patient care.
Developing a Data-Informed Strategy for Optimising Theatre Use
To assist the trust in effectively managing their resources, our team developed a model that utilised patient-level theatre and waitlist data to quantify demand by specialty and urgency. Concurrently, we evaluated capacity data detailing the number of theatres allotted to each specialty—taking into account all aspects of operations, such as equipment and staffing. This model, refined with qualitative insights from clinical leads, offered a comprehensive view of the demand and the theatre capacity available to meet them.
Data Insights Guide a Forward-Thinking Allocation Approach
Our in-depth look at CUH’s theatre usage and waiting lists gave us a clear idea of how they could match the theatre resources with patient needs more closely. We kept track of how emergency care demands were influencing planned surgery schedules, identifying areas where our capacities might fall short. We also considered factors such as each specialty’s out-of-hours workload and changes in specialty-specific demand since COVID-19 began, adding important context to capacity planning.
The project was key in supporting us to transition to a stable but optimal theatre schedule once our theatre capacity was restored. The team provided externality to affirm our own data modelling, and were considerate to the views of stakeholders. It was a positive experience to work with colleagues at Edge Health.
Linda Clarke, Director of Planned Care
The insights from our analysis equipped CUH with a stronger foundation for decision-making around theatre allocations. Our findings highlighted areas with potential shortages and surpluses, enabling CUH to anticipate and manage patient wait lists more strategically. With this knowledge, CUH was set to refine their planning for elective and emergency care, directing theatre resources to where they would make the most impact. The result was a targeted allocation of new theatre spaces to specialties most in need, striving to enhance patient care and experience within the hospital.
Engineering Showcase: Transforming Surgical Data Management for Enhanced Patient Care
Introduction: Comprehensive data collection of surgical data
A healthcare organisation has been undertaking a widespread data collection initiative, gathering information on surgery activities in numerous hospitals. This data is collected routinely, analysed, and benchmarked against various indicators to facilitate improvements in surgical care and efficiency in healthcare facilities.
Facing the challenge of increasing data volume and the complexity of analyses, the organisation identified the need to upgrade their data management infrastructure. They sought Edge Health‘s expertise to transition to a modern cloud-based solution that would accommodate large-scale data collection and analysis while ensuring security and accessibility.
Methodology: Engagement, modern tools, light touch process
We worked closely with the client’s team to co-develop a robust data pipeline and a versatile metric engine capable of dynamically constructing metrics, such as utilisation rates, based on all collected data, updated bi-weekly. These metrics were then securely made accessible to authorised users across the organisation.
The data pipeline was built using Azure and Databricks, leveraging a unified data access layer to ensure seamless integration and efficient data management.
The data management workflow begins with healthcare providers submitting anonymised data to a centralised system. This data undergoes rigorous quality checks in managed databases. Our custom-built pipelines then restructure and segment the data, streamlining the analysis process. A specially designed metric engine calculates various performance indicators, which are then visualised on dashboards for a wide user base.
The result: A modern infrastructure that is continuously operating to provide insight
The new infrastructure now continuously processes theatre data from operations across all submitting organisations and displays it back to enable benchmarking and improvement on the ground. The transition to this new infrastructure was smooth, with significant improvements in processing power, robustness, and automated data handling.
The updated system has markedly increased processing speed and improved data access. It supports a larger number of concurrent users and offers quicker response times for data queries. These advancements have greatly enhanced the ability of analysts to access and utilise information, leading to better-informed strategies for healthcare improvement.
Automation of data operations has further optimised the system’s efficiency. Routine updates and tasks are now managed automatically, minimising errors and saving valuable time.
SPaedIT: Bridging the Gap in Paediatric Health Data Post-Pandemic
Children’s Care in the Post-Pandemic Era: backlogs and reporting gap
The COVID pandemic placed significant pressures on the health system – the NHS has been since faced with a large backlog after countless routine procedures were cancelled. Children and young people (CYP) have been particularly disadvantaged by growing waiting lists, as they wait for procedures that can have huge impacts on their entire lifespan but are not often a matter of life or death. As a result, paediatric waiting lists have been a lower priority, and have seen a 58% growth since 2021 (NHS Digital, RTT Waiting times).
In addition to increasing waitlists, there is a large data gap when it comes to children. Collected and reported data is often not detailed enough to highlight pressures on CYP services. A large proportion of existing insight excludes children and young people under the age of 17, and age is often an optional field in existing data collections. Therefore, managers and commissioners are often left in the dark when it comes to assessing the needs of this group.
The solution: a paediatric-focused reporting tool
Edge Health has partnered with the Getting It Right First Time (GIRFT) programme to address the information gap in paediatric care. In collaboration with the national director for Paediatrics, Prof Simon Kenny (OBE), we have developed a new analytical tool – Summary Paediatric Indicator Table (SPaedIT), which not only sheds light on the magnitude of unmet need for CYP, but also offers insights into actions that providers could take to address the issue.
SPaedIT covers eight key paediatric surgical specialties and serves 180 providers who provide treatment for children, including both Specialist Providers and District General Hospitals.
The tool uses eight distinct data sources that are updated monthly and offers over 35 indicators to give a deep understanding of paediatric healthcare services.
Bringing all leaders together
SPaedIT is designed with all provider leadership in mind, from clinical to operational. By reviewing metrics across four key domains of service provision – demand, capacity, flow and outcomes – they can assess and plan services more efficiently.
SPaedIT makes it easy for the users to gauge the level of pressure their provider is experiencing (relative to other providers), diagnose the causes, and enable effective discussions and planning for solutions.
The Development Process – some technical detail
We embarked on the SPaedIT development journey in January 2023 with a vision of improving the transparency in data reporting for children and empowering providers to make better informed decisions. We launched the first version of the tool in just 6 months.
From blank slate to a data tool accessible to all NHS staff across the country – we took the following steps to get us there:
Define a vision. We kicked off the project with a series of workshops to brainstorm the vision of the tool. What are we trying to achieve? Who is our primary audience? How are we going to work with the future users to build a tool that is fit for purpose?
The starting point. Once we answered the questions above, we worked to create a static mock-up of the future tool. This allowed us to visualise and refine the output we were aiming for. We created a preliminary list of metrics we wanted to track and designed the user interface for the tool.
Deep dive into data. Knowing what we are looking to achieve, we have dived into the data. We reviewed and analysed the data from 8 distinct collections (such as Hospital Episode Statistics, Waiting List Minimum Dataset, National Theatres Data Collection, and others) aiming to assess its quality and identify the most salient and useful metrics.
Operationalise and automate. We worked to set up a maintainable infrastructure to automate metric production. We leveraged the capabilities of Databricks and Azure Data Factory, a unified data analytics platform, to organise our pipeline. This platform offered the crucial ability to orchestrate complex workflows and provide a collaborative environment for managing our notebooks and pipelines, which were instrumental in processing and transforming raw SQL tables.
A picture worth a thousand words. We built an interactive dashboard using Tableau to present data in a way that was engaging and easy to understand.
User at the forefront. Throughout the development process, we engaged with users, sharing progress updates and getting feedback on the ongoing developments. In doing so, we made sure the final product was fit for purpose and won the users’ trust.
A Journey Not a Destination
SPaedIT has been live for the past 6 months and accessible to users across the NHS, with an ever-growing user audience consisting of clinicians, operational managers, theatre managers, regional and national teams. We are continuing to collect user feedback, constantly refining and updating the list of metrics, visualisations, and methodology to ensure the data within the dashboard is useful and accurately reflects the situation with children’s services on the ground.
Routine orthopaedic procedures are more complex than ever before in the NHS
A decade of Staffing shortages, low bed capacity and a devastating 2-year pandemic has culminated in an unprecedented backlog of elective procedures for the NHS with over 7 million patients currently waiting for care in England1.
As a response to these growing waitlists, the NHS conceived the national high-volume low-complexity (HVLC) programme during the COVID-19 pandemic. This programme has worked to standardise pathways, introduce surgical hubs, and improve theatre productivity to increase the throughput of trusts performing routine procedures. It has long been suspected however, that routine procedures in the NHS are not as low complexity as they were before the pandemic2. This is in part due to increasing prevalence of long-term illnesses3, an ageing population4, and the degradation of patient health whilst waiting for surgery2.
As part of our work supporting the GIRFT HVLC programme, we have worked with surgeons to identify patient characteristics that have statistical relationships with the cost of high-volume orthopaedic surgery procedures. These include clinical diagnoses, such as cancer or diabetes in patient records, procedural features, such as the emergency admissions prior to surgery, or patient demographics, such as age and deprivation. Using Machine Learning approaches, we can quantify the impact of these features and develop an indicator of clinical complexity in routine procedures. Our work brings light on the poorly understood impact of increasing patient complexity and is the first step towards mitigating and tackling the increased burden being felt by surgical specialties in England.
Methodology
To quantify patient complexity, 2 key data sources have been used.
Hospital Episode Statistics (HES), a detailed dataset containing clinical, demographic, and patient information.
Patient Level Information and Costing Systems (PLICS), a dataset relaying the cost of hospital admissions in England.
By linking these two sources, we have been able to create statistical models that uncover the relationship between clinically relevant patient features and the cost of a procedure. Specifically, we have worked with Orthopaedic surgeons to select 22 drivers of operation cost which are shown in Figure 1.
Figure 1. Features used to estimate clinical cost from HES data.
HES/PLICS data from 2018-19 was used to extract these features and train procedure specific linear regression models that estimate procedure cost. Using these models, we can track the estimated cost that is driven by the clinical characteristics of the patient over time which is a pertinent indicator of patient complexity.
Findings
The expected costs have been calculated for 3 major HVLC orthopaedic procedures in Figure 2. They clearly show that since the COVID pandemic, patients have been more complex and resource intensive than ever before. Analysis of patients has revealed this increase is primarily driven by increased frailty, as there is a 30% increase in patients with a severe frailty score, as well as a 10% increase in the average number of significant ICD-10 codes. Worryingly, this increase shows no sign of reversing as of March 2023, suggesting that this trend is potentially here to stay.
This work reveals several far-reaching implications for the NHS, most notably that routine procedures are likely to drain resources more rapidly than ever before. Unless hospitals are paid accurately to reflect these changes, there will be a reduction on how much can be spent on staffing and other resources which further damages patient care. We have compiled a set of key recommendations that aim to mitigate the knock-on effects of complexity increase.
Increased cost and resourcing requirements should be reflected when creating activity plans. This will affect trust, care system and specialty managers with limited budget.
Tariffs should be regularly updated to reflect the ever-changing patient case mix that is seen by hospitals. The tariffs should also be sensitive to demographic features of patients, such as age and deprivation, as we have found that these are important drivers of surgery cost.
Programmes should focus on increasing the general health of patients before elective admission. We have shown that the increased expected costs of hip replacements alone amount to over £13 million pounds per year for the NHS. If programmes, such as the PREP-WELL project by the health foundation5, can demonstrate that they are able to reduce clinical complexity, there is large potential for savings.
National programmes that track surgical outcomes, such as Model Hospital and the National Consultant Information Programme, should adjust performance metrics to account for changing patient case mixes. This will enable increased buy in from clinicians who have been most directly affected by increased complexity.
Engineering and insights to support elective recovery and hub-based working with GIRFT and NHSE
One of the ways in which the NHS is trying to reduce the list of patients waiting for surgery is by enabling increased theatre throughput. A method of achieving this is bundling high-volume, low-complexity surgeries together and thus operating on these more efficiently. To understand the impact of this nationally as well as monitor implementation over time, GIRFT and the NHS more generally needed to collect theatre data on a national scale and refresh it regularly.
Several teams in NHSE and GIRFT were stood up to work on this. Edge Health was asked to assist this work by supporting part of the data collection, the engineering once the data was available, help assess the data quality and analyse the data to provide insights into the effects of theatre efficiency, both generally and at hub-based level.
Our Process
Data Collection: We ensured consistent data collection of all theatre systems
We supported the data collection by working with key stakeholders to identify what data was available and required to answer the question at hand. We then designed a data request for a one-off collection and collaborated with the national data collection teams to obtain data from their regular collection.
Engineering: We designed a system that could be robust, interpretable and updated regularly
Theatre data across all trusts, updated every two weeks requires significant streamlined plumbing under the hood to ensure accuracy, replicability and ability to use the data across teams. We therefore engineered a solution that fit into a Microsoft Azure based data platform, utilising Storage Accounts for reference and input files, Azure Data Factory to orchestrate and carry out the processing and Azure Synapse as a data warehouse from which the data could be consumed from.
Data quality: we put a process in place that flags and automatically improves data quality
As part of the process, we created a virtuous loop of data quality improvement. For every cycle update (i.e., trusts submitting data, engineering pipeline refreshing it and insight being generated), we produce automatic flags and updates that enable feedback to trusts about data quality issues.
Insights: We generated insights that are used around the country
Using the cleaned, processed data, we worked with clinical leads, theatre experts and Trusts to develop analysis to demonstrate opportunity across all theatres in the country. Particularly, we were interested to model what would happen if Trusts were able to schedule and operate their high-volume, low-complexity cases in a routine way, thus hitting the activity levels suggested by early hubs and clinicians trailing the concept.
The analysis suggested that Trusts could significantly increase throughput and reduce the waiting list quickly. That work has since fed into new funding models for hubs and advanced roll-out of the measures across the country. Our robust system ensures the NHS is able to update and track improvements over time across 110 providers and 39 systems as it moves forward towards elective recovery.
Elective Recovery: understanding targets and finding opportunities to increase activity
The first quarter of 2022 saw the launch of NHSE’s elective recovery plan, setting out a trajectory to carry out 30% more elective activity by 2024/25 than before the pandemic. The first goalpost was set at achieving a 10% increase in completed pathways during 2022/23, with additional funding available for providers delivering 4% more valued-based activity compared to 2019/20. However, as of October 2022 the targets were unmet by the majority of Trusts.
A specialist provider in London reached out to Edge Health to gain a deeper understanding of targets in practical terms and scope potential solutions.
Deep insights to model solutions
Our strategy had two tangible targets:
Understand the issue deeply through multiple on-the-ground interviews, capturing staff’s key concerns, and evaluate current and past activity to reflect NHSE’s value focus.
Analyse past and present valued activity to produce a user-friendly model to assess how much activity by point of care and team would be required to meet NHSE’s targets.
Clarifying the meaning of valued activity and the 104% vs 110% targets was central to the success of our work.
Essentially, a 104% valued activity target was set to avoid an achievement of the 110% target exclusively through reducing outpatient follow-ups and completing more referrals in primary care. Tariffs are applied to obtain valued activity, so that inpatient elective pathways are far more valuable than day cases and outpatients. In our client’s case, despite excelling at completing outpatient pathways, they were unable to meet the targets due to the value-weighting placing more importance on inpatient activity.
The technical and clinical expertise of our team was key to uncover the key reasons behind our client’s difficulty in meeting elective recovery targets. Particularly, we identified a significant increase in patient complexity leading to prolonged case duration in theatre, contributing to a reduced throughput.
Conversations with staff highlighted concerns over staff changeover with a shift towards less senior team members, as well as low morale following COVID-19 and NHS pension concerns affecting the appetite for overtime work. Our analysis also revealed significant losses in theatre staffing, mirrored nationwide, since 2019/20.
We proposed actionable and achievable recommendations spanning various opportunity areas and evidenced their resulting effect on activity recovery, to support prioritisation and a focus on the solution, not just the problem.
A local team empowered to tackle elective recovery
Our client gained clarity on elective recovery target requirements and the reasons contributing to the activity gap. This empowered the local team to look for solutions such as team expansion where needed and tackle elective activity through addressing theatre scheduling, fallow activity and utilisation.
Driving data quality improvements to support elective recovery
As the NHS continues its progression towards the era of data-driven healthcare provision, the importance of high-quality data continues to grow. Commissioners and providers, now more than ever, rely on data to signal under- and overprovision of services, so that limited resources can be reallocated where they are most needed, and more patients can receive the care they need, quicker.
With data lying at the heart of this process, it is crucial to maintain and improve its quality. This can present a challenge to operational and business intelligence teams that are already strained under the ever-growing administrative burden of numerous internal and national data collections and reporting.
Edge Health worked with a large NHS trust, supporting them with improving their waiting list data quality. The details of this work are presented below. Off the back of this work, all records on the inpatient and outpatient waiting lists were validated by the operational teams, with more than 370 erroneous records corrected within the first 2 months of use. Internal processes were also put in place to minimise the chances of further errors occurring and to correct those that do fall through the cracks.
The importance of high-quality accurate data in the NHS is growing
Data forms the backbone of healthcare decision making infrastructure. “How many patients are waiting?”, and “What are they waiting for?” are the key questions that determine the focus of short- and long-term service planning efforts. In the light of exploding waiting lists, answering these questions is of paramount importance – knowing the answers allows Trusts to put their efforts into the aspects of delivery that would make the biggest difference to patient outcomes.
Waiting list data is prone to error, resulting in delayed treatment
In order to answer these questions, one would need to dive into what is known as the PTL. PTL stands for Patient Tracking List which is exactly what it says on the tin – a list of patients waiting for elective care. In the past to find an answer to this question, one would physically go through stacks of paper files stored in a hospital. Unsurprisingly, this was a very error-prone process, with patient files falling through the cracks (potentially quite literally). Luckily, now that the NHS is moving digital, the days of sifting through the physical paper files are over (fingers crossed…). However, digital data can still be (and often is) full of errors.
We have worked with many datasets across all care settings and have seldom found a dataset without errors. It is easy to get desensitised to the concept of data quality issues, but we should always remember that in the context of healthcare, there will be a personal story behind every number. Data quality issues can lead to patients going missing and not being called in for their procedure, valuable resources mis-allocated or misinformed staffing decisions made. Knowing the cost of inaccurate data, a large Trust decided to partner with Edge Health to tackle the data quality issues in their PTL.
Edge partnered with a large Trust to support them with improving their waiting list data
We worked closely with the Trust’s internal team to identify and fix their data quality issues. We did this as follows:
1. Identify criteria for erroneous records
Firstly, we held a series of workshops with the operational leads to establish the criteria for erroneous records and to come up with a way of identifying those through manual inspection of the records held in the database. For example, we discovered that lots of patients with pre-op activities were disappearing from the waiting list before completing their treatment – pre-op activities often closed the referral to treatment pathway. As a result, patient records were falling through the digital cracks of the PTL.
2. Fix existing errors
Once the approach was signed off and we knew what to look out for, we extracted erroneous records from the internal databases and provided these to the operational leads, who were able to take action to correct them. For example, when a patient was lost and not invited for treatment on time, the team were able to reach out to them and call them in. Many errors (such as typos) were fixed simply by correcting the data stored in the database.
3. Establish source of errors and review policies
We then set off to identify the source of the errors so that a process could be developed to prevent these from occurring in the future. We discovered that data quality errors tend to fall into two categories:
Data entry (e.g., typos or mistakes made by the user when inputting the data)
Data processing (e.g., loss or duplication of data, misattribution, and other issues occurring when the data is being processed and manipulated)
Standard measures to counteract data entry errors such as logic checks at the point of entry, user training and BI/engineering team training were then put in place to minimise the chances of these errors occurring.
4. Automate process for error discovery
Once the approach was signed off, we worked with the data engineering team to automate the process of error discovery so that the quality of data can be monitored and the trends can be reviewed over time. To facilitate that, we developed an automated data pipeline that would run every day, detect erroneous records and write them into a table in the Trust’s database.
5. Build a dashboard
We then built an interactive PowerBI dashboard that allows operational leads to access this information, review existing erroneous records directly, and correct them. In addition to that, it provides high level oversight of the state of data quality in the Trust, as well as data quality trends, which is helping the executive team steer the Trust in the direction of complete, high quality PTL data.
6. Work with the team to develop internal processes
Upon the completion of the project, internal processes were put in place for dashboard maintenance and operational use. Every day, the data in the dashboard would get refreshed so that the operational teams can have the most up-to-date information and use it to correct the erroneous records. The teams would also meet every week to review trends and identify priority areas for data quality improvement
Demonstrating the value of elective orthopaedic hubs at the NOA awards
With over 750,000[i] people currently waiting for elective orthopaedic operations in England, there has never been a greater need for highly productive units working in partnership. Edge Health was therefore honoured to sponsor and present the Partnerships and Integration Initiative awardat the recent National Orthopaedic Alliance Awards event.
The winner of the award, South West Ambulatory Orthopaedic Centre (SWAOC) is the elective orthopaedic hub set up at the renovated Nightingale hospital in Exeter to deliver high-volume and low-complexity (HVLC) activity.
The SWAOC hub was formed from the national initiative to develop 91 hubs across the country and help drive down the backlog in elective orthopaedics. SWAOC won the award for its exceptional results, which included delivering over half (56%) of their activity as same day procedures – a rare, but critical achievement for managing limited inpatient bed capacity.
This outcome was driven by a clinical redesign of the facilities to best align with the hub’s goals and a multidisciplinary team that reviewed international best practice. To do this, the SWAOC team visited exemplar units, engaged with regional clinicians, and developed standardisation pathways of care to deliver better outcomes. As a result, fewer beds are taken up by patients recovering from surgery, reducing the burden on NHS resources more generally.
Edge health presenting the excellence in orthopaedics award in the ‘partnerships and integration initiative’ category to the south west ambulatory orthopaedic centre for a revolutionary collaborative approach to delivering orthopaedic surgery and tackling the backlog (download project poster)
Jonathan Howell, Consultant Orthopaedic Surgeon, Royal Devon University Healthcare said:
“This award and recognition by our peers of the fantastic results achieved by our multi-disciplinary team, has delighted all of us at SWAOC, and the many people in the wider Devon community who have contributed to this project. It is particularly fulfilling for us to have been recognised for our innovative approach to elective orthopaedics so soon after opening this year, and it is testament to the tremendous efforts of all those people who have given their precious time to work on this venture”
Jonathan Howell, Consultant Orthopaedic Surgeon, Royal Devon University Healthcare
The elective hub and HVLC programme
Since the start of the COVID-19 pandemic, there has been increasing focus on the elective care backlog that has continued to grow. A solution that emerged from GIRFT in 2021 was the prospect of establishing elective activity hubs, like SWAOC, to rapidly deliver HVLC activity.
Work undertaken by Edge Health to support this programme looked at the opportunity for these hubs, and the capacity required to meet the potential demand. As a result, the policy received over £1.5 billion of funding for the next 3 years and in the last 9 months, over 90 hubs have been set up and are starting to deliver results.
Some of the hubs, like SWAOC, have demonstrated rapid results and now provide a template for others to learn from and follow – both nationally, in the NHS, but also internationally.
What can other orthopaedic departments learn from SWAOC
It is clear that system working, collaboration, and the development of new pathways have been core to the success of SWAOC.
Mary Stocker, Consultant Anaesthetist, Torbay and South Devon NHS FT said:
“We are delighted to receive this NOA Excellence in Orthopaedics Award in recognition of the exceptional patient outcomes and innovative pathways delivered by the SWAOC multidisciplinary team. The volume of outstanding feedback we receive daily from our patients is a tribute to our team and the outstanding care they provide.
Mary Stocker, Consultant Anaesthetist, Torbay and South Devon NHS FT
This and SWAOC’s ability to deliver over half of its activity as “same day”, and with only 1% staying longer than 24 hours, has enabled the unit to achieve patient flow. This patient flow has enabled the unit to deliver highly productive results with minimal cost and, arguably more importantly, less demand on people.
Edge has already worked with GIRFT to create a dashboard that enables Trusts to track their HVLC activity. As a result, the 63 orthopaedic hubs already set up in England can identify key areas of improvement in real time that will bring their surgical outcomes in line with the high-performing SWOAC hub.
Learn more
If you are interested to learn more about Edge Health’s work on HVLC pathways, and the NOA award, please do contact us directly at [email protected].