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.
AI-Teledermatology: Innovating Skin Cancer Diagnostics
The healthcare system in England and Wales is experiencing unprecedented pressure due to the sharp rise in demand for dermatology services. With one in four individuals seeking consultation for skin, hair, or nail conditions each year, the need for innovative solutions has never been greater. The COVID-19 pandemic exacerbated this strain, causing a 30% drop in dermatology appointments during 2020/21 and a subsequent surge in patient referrals post-pandemic, with suspected cancer referrals rising 13% nationally compared to 2018. Rising volumes of urgent suspected cancer referrals have significant impacts for system sustainability – under a strained system, they correlate with higher volumes of patients breaching care standards, such as the 62-day treatment standard, as explored in a previous piece of work.
The potential of teledermatology, particularly AI-powered teledermatology, has been recognised as a promising solution to expand service capacity and ensure equitable patient access to specialist care. The Skin Analytics AI-Powered teledermatology for Skin Cancer 2-week-wait (2WW) Pathway was pilot tested across University Hospitals of Leicester (UHL) sites starting from March 2022. This collaborative project was designed to respond to the local need for improved patient access to dermatology diagnostics and the achievement of 2WW cancer targets.
Our evaluation underscored the potential of AI-powered teledermatology. Despite being in its pilot phase, the AI tool demonstrated its capability to enhance patient access to dermatology services. While the initial benefit-cost ratio stood at 1.05, this figure doesn’t fully encapsulate the unquantified benefits, such as a reduction in biopsies, long-term care costs, and WLI clinics. Workforce costs were also front-loaded prior to capacity being fully utilised, leaving room for a higher benefit-cost ratio.
The current pathway model relies on second-reads to be performed on all AI-screened scans, with a further reduction in the potential benefit-cost ratio as well as increased pressure on clinical teams. In our evaluation, the AI outperformed documented clinical diagnostic standards[1], but our staff survey highlighted current reservations from consultants in dispensing of the second-reads altogether.
The evaluation also supported the health system through highlighting potential administrative challenges that scaled expansion would need to monitor for. These included timely booking of appointments for patients on the novel pathway, as well as ensuring that commissioning arrangements reflect the true costs of providing an innovative service – and are aware of the prospected savings.
Scenario Modelling for Future Savings
Looking ahead, we conducted scenario modelling to explore the potential for greater savings in the future. These scenarios hinge on reducing or removing the cost associated with the second read of dermoscopy images, leading to a benefit-cost ratio ranging from 1.3 to 1.9.
Our evaluation indicates that this novel pathway could be cost-effective in the long term. It could also offer considerable benefits to the wider Dermatology cohort, healthcare staff, and the health system if implemented at scale, with potential yearly savings across the Midlands ranging between £2.1M and £5.7M, depending on who performs the second read.
Recommendations for Enhancements
As part of our commitment to continuous improvement, we proposed several recommendations. These include streamlining administrative processes, evaluating the best option for lesion second reads and conducting further evaluations as the AI versions improve and more data becomes available.
Our work with Health Innovation East Midlands, UHL and Skin Analytics demonstrates Edge Health’s commitment to pioneering innovative healthcare solutions. Evaluating the effectiveness of new technologies such as AI-powered teledermatology is a fundamental step in improving services so that they are accessible, efficient, and patient-centred.
Our overall experience of working with Edge was very positive, and their analysis and evaluation process was robust and innovative. They handled challenges well and always sought a balanced solution with cross-stakeholder agreement. The Final Report was delivered on track and met the expectations outlined in the original scope and MOU.
Michael Ellis – Senior Innovation Lead, Health Innovation East Midlands
Key Successes
Conducted a comprehensive independent evaluation of the AI-powered teledermatology pilot initiative.
Identified potential for significant future savings through scenario modelling.
Proposed actionable recommendations to enhance the programme’s benefits and ensure long-term cost-effectiveness.
Highlighted the importance of considering administrative implications of implementing novel technologies.
Provided insights to guide future evaluations as AI technology evolves and more data becomes available.
This project was carried out in partnership with Health Innovation East Midlands
Full Report
The evaluation report can be accessed on the Health Innovation East Midlands website, as well as previewed below.
[1] Chuchu N, Dinnes J, Takwoingi Y, Matin RN, Bayliss SE, Davenport C, Moreau JF, Bassett O, Godfrey K, O’Sullivan C, Walter FM, Motley R, Deeks JJ, Williams HC. Teledermatology for diagnosing skin cancer in adults. Cochrane Database of Systematic Reviews 2018, Issue 12. Art. No.: CD013193.
Imaging Productivity: Harnessing RIS data to meet reporting targets
The focus for efficiency gains has traditionally been on theatres, inpatient and outpatient activity and more recently the growing elective backlog. Diagnostics, however, have acquired a new emphasis since COVID. This is because significant backlog in diagnostics is causing delays in finding cancers (you can read more about this on our blog).
As a response, NHSE has included diagnostics activity targets in the NHS Constitution, stating that all tests must be performed within 6 weeks from request. This puts pressure on trusts to understand their testing activity, capacity and bottlenecks in imaging reporting that cause downstream delays in the 18-week referral to treatment (RTT) target.
Trusts have not historically analysed diagnostics data in depth
Gathering these insights requires analysis and data flows which are not yet set-up well across Trusts. Data from radiology information systems (RIS) has not been used as extensively in the past and existed in lower quality than, for example, theatre data. In this context, Trusts now struggle making sense of their imaging data to action NHSE targets.
We were recently asked to support a large specialist NHS Trust in helping with this.
The ask: time-sensitive solutions to guide management
We did this in three stages:
Analyse national data to get a high-level picture.
Engage with team on the ground to understand what actionable insights are needed where and why they are not provided.
Deploying expert clinicians and analysts to provide the insight in a repeatable way.
We outline each of the below in turn and give some detail on the issues under the hood.
First: Public data
First, we explored publicly available data on the Trust’s diagnostics to form the start of meaningful conversation and gain a high-level understanding of some of their challenges. The two charts below showed that our client (Comparator 1 in the chart on the left) had above average waiting times for a scan to be reported, following testing. The time between testing and reporting had also seen a significant increase in 2022/23 compared to 2019/20, particularly for MRI, Nuclear Medicine and Single Photon Emission CT.
Second: Working with team on the ground to understand the problems
We then sat with stakeholders to map the imaging data journey at the Trust and uncovered the key issues within it (outlined below).
Issues we found were the below:
Complicated set-up: Before an end-user could reach any insight, four pieces of software needed to interact: a requesting software like ICE, a RIS software, a PACS software and finally a BI software, QlikView in the client’s case. All insights needed to wait till the analytics team had curated the data.
No quick way around it: Managers trying to extract data from RIS directly were faced with a complex interface that was both hard to work with and at risk of producing unreliable metrics. This step was incredibly time-consuming for managers, adding to undue stress.
Lack of resources: There were no dedicated imaging analysts at the Trust, which meant the imaging team had to compete with other teams to get the insights they needed from the busy BI department.
We supported the trust outlining a variety of solutions, including:
Increasing workforce, such as a specialist PACS or analytics team member.
Changing work practices, such as setting a cap on highly time-consuming MDT requests to focus on internal workload, and upskilling radiographers to report imaging.
Upgrading software to one that included basic analytics, timed with a contract soon to expire.
In order to tie the department over with an urgent need for insights, we also delivered a fast turn-around reporting solution ourselves, embedded in their current BI environment.
Third: Delivering a trusted solution
We were onboarded on the Trust’s system to work within their environment and built a relationship with the BI and analytics team to ensure seamless knowledge transfer and accuracy of outputs.
Engaging with stakeholders also helped build trust in the outputs and ensure that it was truly useful to the team and met expectations.
Early draft of one of our reporting interfaces showing high-detail overview of reporting activity by radiologist and imaging modality, and reporting waiting times at a glance.
By the time the tool was ready to be shared Trust-wide, it had received the seal of approval from the imaging manager, the clinical director, and the BI lead. The final product was fully handed over to the Trust’s BI team to use as a starting point for more analysis and maintain as required in the future.
Specifically, it provided a high-detail overview of reporting activity by radiologist and imaging modality, and reporting waiting times at a glance.
This is enabling the Trust to:
understand pressures on the imaging department
manage workflows more effectively
back department investments and business cases with evidence
improve their performance against NHS diagnostic targets.
A Proactive Solution to Bed Configurations: Using Simulation to Challenge Narratives
Being able to plan proactively to devise flexible bed configurations is a major challenge for Trusts that strive to improve the services they provide to patients and meet their changing needs.
Edge Health was recently approached by a large specialist Trust that wished to better understand their bed requirements. The Trust wanted to challenge the existing narrative within the organisation on the number of beds required and gain the ability to simulate initiatives such as building new theatres, adjusting case-mix, or achieving national or peer benchmarks for length of stay.
Our expert solution
To meet these needs, Edge followed a tried and tested three-step bed modelling approach, now implemented at several Trusts, which balances efficiencies from using existing frameworks together with bespoke tailoring to ensure a good fit for local circumstances.
Here are the key steps of our approach:
We collected detailed hour-by-hour patient data by ward, team and procedure to gain a deep understanding of the Trust’s current bed usage.
We engaged with individuals in the wards and across the Trust to understand the wards structure, usage, capacity and any future changes, and collect requirements for the bed model.
We built an interactive simulation tool to reflect the Trust’s risk preference and occupancy rates. We built several iterations of the tool to ensure it fully fitted the Trust’s needs.
Screenshots from our bed modelling dashboard
A product built to last
The model is now used within the Trust to scope the building of new theatres, change the booking of patients to achieve more constant bed occupancy, and plan for next year. We received excellent feedback from our client, who praised the interactivity and the built-in ability for lateral thinking.
Finally a bed model that you can play with, use to challenge narrative and look at things from different angles. Not just a report. I really like it
– The trust’s COO
We look forward to continuing to help healthcare organizations make data-driven decisions. If you have any questions or would like to know more about our approach, please don’t hesitate to get in touch.
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.
Quantifying the private market: an opportunity for NHS trusts
For many NHS Trusts, care for private patients is a critical part of their business model, with the income generated directly funding front-line NHS care. As budgets continue to come under pressure, there is an opportunity to expand the private care offer, delivering greater benefits for both private and NHS patients. However, deciding if as well as where to invest requires a detailed understanding of patient flow and the size of the private market.
Edge Health was commissioned by a leading NHS Trust to identify and size market investment opportunities for growth on one of their sites. Through analysis of trust activity, finance and cost data, 3 themes of the greatest market growth opportunity were identified:
Market growth opportunity top areas:
Supporting patients withprivate medical insurance (PMI) to receive privately-funded care;
Acquiring patients from competitors; and
Relocating patients between sites to provide care closer to home
Options for investment within these 3 core themes were then developed, with a minimum, medium and maximum option investments for each theme. Each of these options was presented with projected demand growth, associated costs and the potential income generated through the investment.
The conclusions of this work led to further analysis to identify where and when investments in additional capacity would be required to meet higher demand. This assessment considered the implication of doing nothing, as well as providing a year-on-year breakdown of how much additional resource (beds, theatres, radiology etc.) would be required to reach demand.
Taken together, these pieces of analysis provide evidence on the scale of the market opportunity for private treatment and provide operational support to aid in strategic decisions ahead of any investment. This analysis can be used to support proposals and business cases for investment.
Solving the Emergency: Improving Ambulance Response Times through Strategic Planning
After what seemed to be a potential recovery of ambulance response times in November 2022, the latest data release from NHS Digital shows that response times have taken a significant downturn, hitting the highest on record.
Since 2020 there has been a large increase in mean response times across all incident categories. C2 incidents (serious conditions that are not immediately life threatening, such a strokes and chest pain) have suffered the most with mean response times reaching 93 minutes in December 2022, 5 times higher than 18 minutes pledge time. Waiting this long for transport to care will have drastic impacts on a patient’s outcome, not just for life threatening issues, but also for urgent conditions needing acute care, such as C3 incidents that have seen their pledge time exceeded by 455%.
When breaking down these figures into regions in England, significant differences emerge, with C1 calls in the South West waiting an average of 13.2 minutes, 32% higher than North West and the Midlands’ average response time of 10 minutes. Despite diverting 68% of calls out of 999 (compared to 19% in November 2022) and allocating an extra 9,000 ambulances (a 45% increase) to attend C1 calls, only 2/3 of them arrived on site, meaning the other third was stuck elsewhere.
Ambulances mean response times by region, December 2022
What is behind this huge disparity across regions?
Calls to ambulances have seen a significant increase since the start of 2021, growing by 20% nationally. And although the total number of calls resulting in an ambulance being dispatched (an “incident”) have decreased, the proportion of incidents attributed to C1 calls has grown to 18% (from 9% in 2019) the total number of C1 incidents has increased significantly, up by 23% compared to last year (Dec 2021), and the number of C1 incidents has seen a sharp rise. This is particularly significant in the South West, where C1 incidents have nearly doubled since 2021, suggesting that patients are becoming sicker, not just more willing to pick up the phone.
The situation in the South West should not be seen in isolation, but rather as a premonition of what might be coming for other regions if resources are not planned adequately. The remoteness of locations in the South West should not be the main culprit in the rising ambulance times – pre-pandemic they were performing in line with other regions -, but rather evidence of the strain that population health factors place on acute and community services, and the need to plan accordingly.
On the one hand, the population of the South West is amongst the oldest in England, which naturally leads to higher levels of demand across the entire health as well as the social care spectrum. Our recent work with NHSE/I on demand for secondary care shows that significant planning is required to deal with the demand associated with ageing.
Issues with capacity and bed utilisation are on the other side: 6 out of 8 ICBs in the South West have average G&A bed occupancy of above 92% (the recommended maximum), well above the national average of 88%. Last week, 20% of their entire G&A bed capacity was taken up by patients who are medically fit for discharge.
Being unable to shift patients out of hospital results in A&E departments too busy to take handovers from ambulances. In December, the average time lost to ambulances due to delays in handover more than doubled – in fact, time lost due to delays in handover was the equivalent of 40% of the total time spent dealing with incidents.
C2 ambulance response times and G&A bed occupancy, England
A new NHSE delivery plan for recovering urgent and emergency care sets specific funding to both increasing capacity of beds, ambulances and same-day emergency care services (£1bn), speed up discharge (£1.6bn), with further mentions for growing workforce, expanding community services and tackling unwarranted variation that did not receive specific funding mentions.
This is a step in the right direction, though it will now be up to ICBs, once the funding has been streamlined, to figure out how this can be used most effectively. Too narrow a focus risks creating bottlenecks downstream, rather than solving the issue, and solutions will need to both address patient flow while targeting the whole pathway, spanning from community care to addressing workforce.
At Edge Health we are experts in using forensic data analysis to target new capacity to solve the flow problem, not just move it. In our experience, full-spectrum capacity planning is what enables effective use and distribution of resources, and we have supported trust-wide planning and reconfigurations that have enabled trusts to recover the 4-hour A&E target. To find out how we can help you, get in touch.
NHS Planning Guidance ICB dashboard: six metrics, one glance
The NHS planning guidance released at the end of last year has placed particular emphasis on acute care, elective recovery, primary care and mental health.
In order to support ICBs in responding effectively to the targets, Edge Health has created an interactive dashboard that summarises six key metrics covering community and secondary care making use of data publicly shared through NHS Digital, that allow a quick overview across systems and targets.
We welcome thoughts and feedback on our beta version that you can access through this link: https://tinyurl.com/edgehealthICB, so that we can create a tool that is as useful as possible.
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
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