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.
AI in Dermatology: a White Paper by Edge Health for NHSE
The NHS is currently grappling with a growing demand for Dermatology. Waiting lists have grown by 82% since 2021, and the rate of GP referrals for skin cancer having nearly doubled in the last decade. This is compounded by a national shortfall in dermatologists as vacancies in 2021 amounted to 159 WTE. Our report, published in July 2024, has shown that AI holds considerable promise for skin cancer pathways including improving effectiveness and reducing wait times.
AI is currently in use for diagnosis within NHS skin cancer pathways with all lesions second-read by a clinician. Greater efficiency, speed of diagnosis and clinician time could be released if AI as a Medical Device (AIaMD) functioned autonomously. NHS England commissioned Edge health to conduct an independent review of the safety and effectiveness of AI in Dermatology and assessment of its performance against accepted standards of accuracy.
Methods and Key Findings
We examined real-world data from over 33,000 lesions assessed by DERM – an AIaMD developed by Skin Analytics, the only product that currently meets regulatory standards for autonomous use. We conducted a semi-systematic meta-analysis reviewing 153 studies and interviewed eight members of staff across three providers currently adopting the AIaMD. This enabled a grounded perspective on its application in skin cancer detection.
Our findings indicate that DERM’s diagnostic accuracy in ruling out melanomas is at least as good as in-person consultations with dermatologists. This suggests that AI could play a crucial role in distinguishing benign from concerning lesions, streamlining referrals, and ensuring those in need of urgent care are seen promptly. We also identified potential system-level efficiencies, finding that each pound spent could return up to £2.3 in savings. In this context, our report highlights AIaMD’s potential to refine the triage process, thereby addressing the rising demand for services and reducing waiting times for assessments.
While our economic analysis suggests potential savings, the primary focus of the report is on the clinical and operational implications of AIaMD, and what steps should be taken to monitor its use in Dermatology through post-market surveillance (PMS). Clear PMS plans and agreements need to be in place, with responsibilities lying with both deployment sites and manufacturers. Our report condenses PMS recommendations from several literature sources and offers an example of how PMS could be implemented in practice.
The report makes clear that the use of AI holds considerable promise for improving the efficiency and effectiveness of skin cancer pathways. Evidence of its deployment in the NHS has demonstrated that whilst the tool could be used autonomously to exclude benign skin, adequate safeguards, will need to be in place. This provides the potential to free up specialists to focus their expertise on the most urgent and complex cases.
Julia Schofield, Clinical Lead for Dermatology for the National Outpatient Recovery and Transformation programme
Read Our Report
The report concludes that thoughtful deployment of AI in Dermatology has the potential to enhance patient pathways and alleviate system pressures. With appropriate safeguards and continuous evaluation, AI can support the NHS in upholding its commitment to innovative, high-quality patient care.
Finding cancer early is the single biggest step we can make to improving patient outcomes and saving lives. In 2023 just 57.6% of staged cancers[1] were diagnosed at an early stage[2]. This is well below the NHS Long Term Plan ambition of 75% by 2028.
With just 1 in 15 cancers diagnosed via screening, improving early diagnosis is heavily reliant on symptomatic pathways. In practice, this means supporting patients to present early in primary care and supporting primary care to make appropriate referrals through Urgent Suspected Cancer (USC) pathways.
We have partnered with RM Partners, the Cancer Alliance serving North and South West London, to identify practical steps which can be taken in primary care to improve rates of diagnosis. The research is based on analysis of 46 interviews with GPs across West London alongside data including referral behaviour, workforce and population demographics.
The research identified six actions for general practice to increase early diagnosis:
Reviewing practice performance and operation: Understanding and reviewing cancer performance data, participating in cancer audits, internal case review and knowledge sharing.
Adopting quality systems: Use of best practice decision support and safety netting tools, underpinned by a culture of quality improvement.
Addressing systemic inequity: Increasing awareness of systemic inequity and the impact on cancer through training and actively implementing best-practice process.
Workforce stability: Retaining staff without high reliance on locums, whilst ensuring clear orientation of locums when required.
System awareness and participation: Awareness and use of direct access and Vague Symptom pathways, building relationships within PCNs and with secondary care
Training and clinical improvement: Accessing cancer-specific training to support the appropriate use of cancer pathways.
These findings are underpinning the support being provided to primary care teams within RMP. More detail on the research findings and recommendations as well as the methodology can be found below, and both reports are available to download.
Since the pandemic, SACT activity has grown rapidly
The number of patients receiving Systemic Anti-Cancer Therapy (SACT) in Thames Valley grew by 7% between 2021 and 2022. This rise in activity, combined with increasing treatment complexity, length, and national shortages in SACT staff, has put pressure on departments and workforce.
Thames Valley Cancer Alliance serves a population of 2.3 million people, spanning two ICSs, four Acute Trusts and nine Hospitals. SACT is delivered within the Alliance by Oxford University Hospitals (OUH), Buckinghamshire Healthcare (BHT), Royal Berkshire (RBH) and Great Western Hospitals (GWH).
Starting in September 2023, Edge Health worked closely with TVCA and its constituent trusts to provide demand and capacity analysis, identify system pressures and develop innovative solutions to meet this need over the next five years.
As an Alliance, we were aware of the national and regional SACT pressures and wanted to review our demand, capacity and workforce to support service development and raise the awareness of the significant increase in activity we will expect in the coming years. Edge Health worked closely with our clinical teams to understand the pressures and activity in SACT services and to provide recommendations to be delivered across the system including a demand and capacity tool for our biannual SACT assessments.
Edge Health are knowledgeable, professional and a very approachable team supporting the requirements of our service.
Lyndel Moore – TVCA Cancer Clinical Lead for Nursing and AHPs
Our approach involved working closely with key stakeholders across the Alliance to draw out insights from provider data and workforce interviews
After gathering data on patient volumes, complexity and treatment types and combining this with workforce data from all four acute trusts, we interviewed key clinicians and stakeholders to understand and articulate the pressures and problems that they were facing on a day-to-day basis.
From data and interview insights, we developed a set of scenarios for the future growth of SACT treatment within the alliance. These were:
Population growth scenario: demographics-driven model
Core growth scenario: based on growth trends observed locally and nationally
High growth scenario: accounting for additional pressure exerted by factors such as high growth in demand for oral SACT treatments
Note: Chart figures hidden for confidentiality
We wanted to provide actionable solutions to pressures faced by SACT units
Engagement with trusts and thorough analysis led to the identification of four wide areas for opportunity:
Capacity
Efficiency
Workforce
Operations
We developed creative and practical opportunities that Trusts could utilise to help tackle these growing problems, such as increasing self-administration of subcutaneous SACT to manage capacity pressures or developing non-administrative and managerial professional growth avenues to help increase the retention of workforce.
To aid Trusts with their future demand and capacity planning needs, we produced an interactive demand model. This allows Trusts to use pre-generated demand projections or input their own figures for future workforce planning and has already been used as part of TVCA’s bi-annual demand and capacity planning.
Levelling-Up Cancer Patient Management with Data Engineering
Integrated Care Boards (ICBs) and cancer alliances both provide operational and strategic support for Trusts in delivering cancer care. However, supporting Trusts can be challenging for system partners who often only have access to scattered data that updates infrequently from isolated systems. This means that identifying capacity bottlenecks often occurs after treatment standards have already been breached, which adversely affects critical 28-day diagnostic standard and 62-day treatment standards.
The CanCollaborate tool, developed by Edge Health in partnership with the Northern Cancer Alliance (NCA), The North Tees and Hartlepool NHS Foundation Trust, and The South Tees Hospitals NHS Foundation Trust, was named to reflect its purpose of enhancing collaboration between Trusts and system partners. CanCollaborate is a system-wide cancer patient management tool, featuring patient tracking and demand forecasting, that helps users proactively identify and act to mitigate capacity bottlenecks.
What is CanCollaborate?
CanCollaborate is a cloud-based solution featuring seamless data integration, real-time updates, and a secure, user-friendly web application interface for monitoring and managing cancer waiting lists.
Seamless Data Integration: At the core of CanCollaborate is its robust data integration capability. We established a secure, automated data transfer process with Trusts, with encrypted data uploaded to our system every hour. Our methodology enables us to link patient pathways from disparate systems (e.g. Infoflex/Somerset), enabling an integrated view of complex pathways across both Trusts, while eliminating manual intervention.
Real-time updates: CanCollaborate takes a proactive rather than a reactive approach to cancer pathway management. Our system ensures that North Tees, South Tees and NCA always have access to the same live information. The ability to monitor the PTL dynamically allows for responses to emerging issues, helping NCA to deliver shared solutions.
Leveraging cloud technologies for data processing: We developed robust pipelines in Azure, integrated with Databricks, to ensure efficient and secure data handling and timely output of the processed data to a SQL database.
Web application interface: Leveraging our established data pipeline, CanCollaborate provides AI-enabled predictions of a patient’s risk of missing cancer waiting times standards (e.g. 62-day target), which users can interact with through a variety of tabs, including:
‘Waitlist Summary’ tab which provides a snapshot of the current state of the cancer waiting list across both Trusts, helping users quickly grasp the overall state of the patient pathways in their Trust as well as patients that are being tracked by both Trusts.‘Current Cancer PTL’ tab which allows users to identify specific pathway issues and mitigations in real-time. The tool presents patients’ risk of breaching, helping to prioritise the patients that require immediate actions.
‘Demand & Capacity’ tab utilises patient-level data to forecast demand and capacity 12 weeks into the future, facilitating provider planning and system-wide discussions around mutual aid.
Figure 1. Example of CanCollaborate workflow for system and Trust users, for lung cancer
Based on early testing, if our recommendations are actioned by Trusts, CanCollaborate has the potential to increase the percentage of patients meeting targets by an average of 15% across tumour sites (e.g. from 60% to 75%).
Lessons learned and next steps
Information governance: Secure and compliant management of data across North Tees and South Tees was crucial in the development of CanCollaborate. Before undertaking this work, we supported a joint information governance process between North Tees, South Tees and the NCA.
Linking data: At the outset of this work, data reconciliation was one of the main challenges faced by the Trusts due to their data being in separate, unlinked systems. To protect patient-level data we established advanced encryption techniques, ensuring data security and confidentiality throughout the integration process. Finally, we implemented novel linking techniques to integrate the data from the different sources.
Fostering user engagement: CanCollaborate is a tool aimed at meeting the various needs of different types of users (e.g. cancer managers, system partners, BI teams, clinicians). As CanCollaborate continues to be operationalised, we are continuing to work with the NCA and Trusts to identify the user groups that would most benefit from the tool.
Waiting List Modelling: Effectively Achieve Elective Targets
Meeting national targets for elective treatment in the wake of COVID-19 has been challenging for Trusts across England
The impact of Covid-19 on the delivery of elective services has been substantial: waiting lists have built up, with a sizeable backlog of patients waiting longer for treatment than ever before, and referral volumes have returned to levels which exceed the pre-pandemic norm.
Trusts must deliver NHSE expectations around waits to treatments and sizes of waiting lists, and to do this need a robust understanding of required capacity and efficiency levels, as well as the impact of future known interventions and referral volumes.
Staring in November 2022, Edge Health worked with Leicester, Leicestershire and Rutland (LLR) Integrated Care Board to support with operational decision-making and elective care planning to meet these targets.
Our approach involved working closely with system leadership in LLR to draw out insights from acute, community and primary care provider data
Our objective was to support stakeholders to clearly articulate the gap between current capacity, demand and delivering NHSE targets, to a range of audiences and forums. This allowed the system to proactively identify areas of pressure, develop a plan and set of scenarios and track progress relative to expectations.
Using simulation modelling and demand forecasting techniques, we built a robust understanding of the impact of system-wide interventions on waiting list projections. We proactively identified areas of pressure and followed up with demand & capacity deep-dives for 4 specialties and 9 diagnostic modalities.
The outcome was drastically improved waiting times to elective treatment, following informed, evidence-based system and Trust-level leadership and investment decisions
The sole acute provider in LLR improved from being 3rd worst-performing Trust for 92nd percentile waiting times and proportion of patients waiting over 65 weeks for elective care, into the top 50% of Trusts for both measures[1]. Total waiting list size has plummeted, with LLR on-track (again in the top 50% of Trusts) to meet the March 2025 target of no patient waiting over 1 year for treatment.
Our analysis provided the evidence base to support LLR system and Trust-level leadership with cohesive decision-making and strategic planning for the future. We helped a range of stakeholders to understand risks and focus on the most likely scenarios, and the scale of intervention and investment required to proactively reduce pressure on elective services.
Our client, the imaging analytics department of an Integrated Care Board (ICB), struggled with real time analytics regarding imaging reporting which kept them from getting on top of their backlog. Their users did not engage with their reporting tools (how many patients were waiting for reports? How many needed to be reported this week?) and all analytics was only ever discussed retrospectively rather to take action now.
It is a surprisingly common problem where ICBs often face challenges in accessing real-time, integrated data for various purposes such as benchmarking, reporting, and patient alerts across Trusts.
The solution: A modern data system that is linked up and gives proactive alerts
Building a cutting edge Slack messaging bot with access to data across Trusts in a modern cloud environment enabled us to build real time alerts for the ICB in question.
The ICB had carried out an initial discovery phase internally and commissioned us to develop an minimally viable product (MVP) using Azure and Slack. This tool, which connects to commonly used NHS services like Microsoft SharePoint, provides real-time scheduled and on-demand updates on key diagnostic metrics.
We customised the Slack messages to individual users, enabling them to access the information most relevant to them. This data included urgent patients awaiting appointments, the count of scans that breached mandated standards, scans pending a report, and average scan waiting times.
The technical detail: We used Azure, slack and modern CI/CD processes
We used the functionalities of Azure Databricks to clean and process data, making it available for important announcements through the Slack bot. Anyone with access to the clean data on Azure would also be able to use it for presentation purposes, and we created a demo using Microsoft PowerBI to showcase the potential of developing seamless dashboards.
We built the infrastructure and data pipelines using industry best practices, including Infrastructure as Code and CI/CD through Azure DevOps. This approach made the platform easily reproducible, scalable, and reduced human error.
User Personas and Testing
We initiated alert testing as early as possible, making the process interactive and tailoring the outcome to the user’s needs. The result was alerts customised to a variety of users, from technical teams needing notifications of pipeline runs and failures to operational leaders requiring actionable insights on activity and targets.
This MVP demonstrated the potential benefits of rolling out the tool on a wider scale, and that data connectivity across the whole ICB is within reach.
Back to News & Insights
