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.
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.
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.
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.
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.
Analytical support to the Getting It Right First Time programme
The Getting it Right First Time (GIRFT) programme was made possible with funding from the Department of Health announced by the then Secretary of State for Health and Social Care, Jeremy Hunt in November 2016. Building on the conclusions of the Lord Carter Review, GIRFT developed and established a methodology for evidencing unwarranted variation and disseminating this knowledge across the system through the use of data and peer-to-peer conversations to drive improvement and best practice.
GIRFT improvement approach
The improvement approach draws on data packs for Trusts which present key evidence using data from a wide variety of sources – including Hospital Episode Statistics (HES), relevant registry and professional body data, and questionnaires issued to all Trusts. Analysts partner with clinical leads to collect and analyse this evidence into specialty-level data reviews. Clinical leads, who are appointed with the support of their specialty bodies, then take the data to all Trusts and identify improvement opportunities in peer-to-peer conversations. The results are written up into a national report and findings disseminated nationwide. Particularly relevant metrics are published on the Model Health System platform and continuously refreshed for Trusts to see their position against other Trusts and monitor progress.
GIRFT Vision: To provide equality of access to high quality care across the whole of England. GIRFT Mission: To identify and eliminate unwarranted variation in clinical standards and outcomes, and support the adoption of validated, efficient and cost effective best in class services across the whole of the NHS to support NHSEI in delivering the Long Term Plan.
The role of Edge
Edge has partnered with GIRFT since 2018 and as a trusted analytics partner has formed multidisciplinary teams at specialty level with several clinical leads to develop benchmarking reports that enable the unique improvement approach of GIRFT.
Key to the success of this work has been the ability by the Edge team to translate clinical experience into the language of analytics and bridge the highly technical vocabulary of HES data, clinical coding and SQL with the on-the-ground experience of non-technical clinicians.
In this way Edge have supported the development of Orthopaedics, Spinal, Neonatal Intensive Care, Paediatric Trauma, Gynaecology and Urology reviews across the NHS. Edge have also managed the process of identifying key metrics to add to the Model Health System and successfully supported GIRFT and NHSIE in developing content for a variety of specialties on MHS.
GIRFT has established its methodology across 40 workstreams. An independent evaluation of GIRFT value identified £1.2bn-worth of efficiency gains achieved by early 2020. Edge directly contributed to this through delivery of c. 1,000 data packs across a variety of specialties that enabled senior clinician-to-clinician analysis and interpretation of hospital performance data.
GIRFT improvement process
Focus on outcomes: piloting the collection of outcome data for Occupational Health
Occupational health (OH) teams keep people well at work – physically and mentally.
Research shows that good health is good for business and better workplaces have better financial results. With over 130 million days lost to sickness absence every year, at an estimated cost to the economy of £100 billion, there is huge opportunity for improving wellbeing at work. Research shows that the longer people are off sick, the less likely they are to make a successful return to work: after six months of absence from work, there is only a 50% chance of making a successful return. Despite this, a minority of the workforce has access to OH services.
During a feasibility study that we conducted in early 2021, exploring the opportunity for using outcome metrics in the OH industry, we found a need and appetite for improved outcome data collection. OH providers felt that meaningful outcome data would enable them to drive service improvement, as well as demonstrate the impact of their services to employers more effectively (see our case study on this work). Through this study we recommended a phased approach to achieving widespread outcome data collection in the industry, beginning with a pilot study to design a methodology and test the concept of collecting outcome data with a small number of OH providers.
Recognising the importance of improving access to OH, and the role that outcome data could play, the Department for Work and Pensions and Department of Health and Social Care commissioned a collaboration between Edge Health and the Getting It Right First Time Projects Directorate @RNOH to conduct this pilot study, and explore the impact that outcome data could have on the industry.
We worked closely with a group of OH providers and subject matter experts to first develop a data collection methodology, consisting of follow-up surveys for employees, their managers, and OH providers around 8 weeks post-consultation. We then worked with providers to implement this methodology, collecting data from a small number of their clients and gathering insight and learnings throughout the process.
Several key conclusions arose from this work, which we used to feed into recommendations for next steps. Capacity pressures were a common issue across providers, without dedicated resource to collect the outcome data, highlighting the importance of a digital solution in future work to reduce burden on providers and enable scaling of the data collection methodology. The value of widespread data collection in the OH industry was also explored: in particular, a wider roll-out of this approach could provide an aggregated national dataset that would enable deeper understanding of the impacts of OH services. Scaling of outcome data collection across the industry could ultimately drive quality improvement and promote uptake of OH services by employers, supporting employees to stay well at work.
Developing Metrics to Support the Growth of the Occupational Health Market
Workplace ill-health has a significant impact on both employees and businesses, with more than 130 million days lost to sickness absence every year in Great Britain, at a potential cost to the economy of £100 billion per year. Despite this, only 45% of the workforce, and 18% of small employers, have access to Occupational Health (OH) services, which have been shown to reduce sickness absence and support employee return to work.
A key barrier to increasing uptake of OH is that employers, and in particular smaller employers, often have limited understanding of its benefits. As such, there is great potential for the use of outcome data in the OH market, to demonstrate the impact and value of an OH service and give employers access to better information to support and encourage OH purchasing.
Realising the importance of being data led, Edge Health were asked by the Getting It Right First Time Projects Directorate @RNOH to provide expert support for their work with the DHSC. Between December 2020 and March 2021, we led a feasibility study to explore the opportunity for using outcome metrics in the OH market. During this time, we conducted more than 30 interviews with OH providers, employers, business groups, industry leaders and NHS OH.
Several key conclusions arose from this work. Importantly, it demonstrated a need and appetite to support improved outcome metric collection, and highlighted important challenges in this direction of travel. Following on from this work, we were commissioned to run a pilot data collection study, aiming to define a best-practice methodology for the collection of a small number of outcome metrics. This will provide a basis for wider roll-out of this approach, which could ultimately transform the OH market.