Advancements in machine learning have led to the rise of pre-trained Large-Language Models (LLMs) that can be used to interpret, understand, and process medical data. As a demonstration of this technology, we have built a proof-of-concept (POC) tool that transforms unstructured and doctors’ notes and chest x-rays into a structured database.
What is the opportunity for healthcare providers?
According to some studies, up to 97% of medical data sits unused within hospital premises. This is a huge untapped resource that could help providers optimise pathways, improve patient care and unlock financial gains through better coding and saving clinical time.
The main downside, however, is that much of this data sits in unstructured formats, such as images. Extracting meaningful data from these can be time consuming and expensive, but our proof of concept demonstrates how the latest AI developments could overcome these barriers, allowing greater utilisation of data for hospitals.
Proof of concept using chest X-rays
Our tool harnesses a state-of-the-art open-source pretrained language model (Zephyr-7B) and an image to text model that extracts information from DICOM images (llava-1.5-7b). We have tested the feasibility of our approach by applying it to the MIMIC-CXR public dataset of chest X-rays and doctors’ notes.
Our POC is able to reliably take this complex data source, which contains rich medical information, and distil the details into predetermined fields of a table, leading to a simplified version of the original dataset.
Processing pipeline for X-Ray images and doctor’s notes.
Why are structured tables, such as knowledge graphs, more useful than their unstructured counterparts?
By compressing rich clinical information into salient indicators and storing it in tables, we can create structured datasets that relay important details about patient care, such as their diagnosis, procedures and outcomes.
In turn, researchers, auditors, and clinicians can easily draw insights from large numbers of patients using simple data analysis approaches, allowing them to understand how interventions impact patient outcomes and determine methods to improve patient care.
Transformed knowledge graph output from the discharge notes and multiple X rays of a single patient.
Plenty of use cases
A tool like this could be employed to validate secondary-use datasets which are currently manually created by clinical coding experts, such as hospital episode statistics (HES), a nationally mandated dataset in English hospitals. This approach is error prone and is subject to variation in coding methodology at different hospitals and countries. By cross-referencing the data with the structured datasets generated by this tool, we can improve the accuracy and completeness of the information, unlocking deeper insights into patient care.
A more operational use case could involve the scheduling of further scans or treatment. The structured output produced by the tool could be fed into another model to automatically triage patients on hospital waitlists based on the severity of their conditions. This would ensure patients most at need of urgent care or at risk of deterioration are seen quickly.
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.
There is a recurrent emphasis on the need for Integrated Care Systems (ICSs) to address health inequalities, as highlighted by media outlets, conferences, and reports.
Addressing these disparities is a monumental task, especially for the newly established ICSs, which have been tasked with not only establishing new governance and strategies but also tackling an elective backlog and long-standing health concerns like health inequalities.
One crucial area of focus, particularly in relation to the Core20Plus5 mandates, is cancer, as we are aware of inequalities in access impacting diagnosis and survival rates. The ambitious objective set out by the NHS Long Term Plan is to diagnose 75% of cancers at Stage 1 or 2 by 2028.
Lung cancer, where 64% of patients receive a diagnosis at stage 3 or 4, is an excellent example that underscores both challenges and opportunities for ICSs.
Why avoiding emergency diagnoses is key
We examined publicly available data on lung cancer care. Patients referred by a GP are more likely to be diagnosed at an early stage than those in emergency settings.
This relationship can be seen by comparing NHS clinical commissioning groups (CCGs), where a 10% increase in GP diagnoses is associated with a 3% increase in early diagnoses (stages 1 and 2), when adjusting for confounding factors, as shown in Figure 1.
Figure 1. Source PHE 2018
Since patients are over 3 times more likely to survive more than 5 years when diagnosed at stage 1 compared to stage 3[i], detecting cases via referrals from primary care has a direct impact on lowering lung cancer mortality rates associated with a late-stage diagnosis.
Diagnosis Route
Stage 1
Stage 2
Stage 3
Stage 4
Primary Care
21%
10%
25%
44%
Emergency Department
10%
5%
14%
72%
Ohter
28%
10%
20%
42%
Table 1. Source NCRAS 2015-16
Reducing variation in primary care referral rates
The challenge, however, lies in the unequal volumes of lung cancer referrals made in primary care, which vary dramatically across NHS regions. We have used Public Health England data and machine learning approaches to uncover the relationship between the number of GP referrals and the percentage of all lung cancer cases diagnosed from these referrals.
As seen in Figure 2, there is a clear positive relationship between the volume of referrals from primary care and early diagnosis, which is particularly strong for areas with low referral volumes.
It follows that if GPs with low referring rates could be supported increase referral volumes, there will be a high impact in driving earlier diagnosis and improving survival rates. This means that increasing referral rates would be very material for the NHS and its patients. In fact, if all ICSs were able to bring all the lowest referring GP services in line with the bottom quartile, as shown in Figure 3, we would expect 700 extra early diagnoses and 100 lives saved per year across the country.
Figure 2. Source PHE, 2018
Figure 3. Source PHE, 2016
How these findings turn into practical implications for ICSs
Variation in urgent suspected cancer referrals and early diagnosis rates is likely a combination of both GP organisation/behaviour and broader patient behaviour. For the former, it is well known that there are pressures on GP numbers and overall workload, which will impact access locally.
Nonetheless, there will be opportunities for ICSs to surface data on variation in cancer referral rates and work with practices to understand variation and support where necessary.
ICSs can also lead improvements by understanding how their local population, demographic and health system factors are influencing access. Although this highlights the complexity of the challenge, it also offers multiple sources of opportunity for systems.
In our experience, some of the key actions for ICSs to address the above are:
Involve primary care networks (PCNs) and cancer alliances early into conversations about improving cancer detection – we are currently working with a cancer alliance on data-driven research to better understand the drivers of variation in the detection rate and the most effective interventions for addressing them.
Provide practices and PCNs with tools to better understand their local population and their health needs (see here for a population health management dashboard we developed for Surrey Heartlands).
Plan adequately for workforce, particularly in primary care, to make sure there is enough capacity to boost referrals and avoid workforce overwhelm. Given the falling numbers of full-time equivalent GPs, this is a priority area for ICSs and nationally.
Assess secondary care diagnostic capacity, including modelling demand and capacity and promote system-wide initiatives such as new community diagnostic centres, implementing rapid diagnostic services and supporting mutual aid between trust, as we have discussed previously.
As the landscape of healthcare continues to evolve, ICSs have a crucial role to play. The responsibility lies with them to implement innovative strategies, utilise data-driven research, and ensure a robust primary care workforce. With a concerted effort towards these goals, ICSs have the potential to significantly influence early cancer detection rates and, ultimately, patient survival.
[i] Characteristics of patients with missing information on stage: a population-based study of patients diagnosed with a colon, lung or breast cancer in England in 2013. C Girolam and others BMC Cancer (2018). Volume 18, Page 492
Why We Need to Start Unlocking Change Using Cancer Data Now
We can all agree that cancer does not go on holiday. Yet during the winter months, referrals for suspected skin cancer in England decline steeply.
The National Health Service (NHS) collects extensive data that presents a golden opportunity for improvement and targeted interventions. For instance, by examining the data on skin cancer referrals, we can identify a long-standing pattern that presents a significant opportunity and exemplifies the potential of routinely collected data to enhance outcomes.
In the case of skin cancer, this opportunity translates to 170 livesa year that may have been saved through earlier cancer detection.
Skin cancer referrals: unlocking opportunities through a 10-year trend
The NHS Digital Cancer Waiting Times records provide valuable insights into skin cancer referrals. A simple glance at the time trend reveals a stark cyclical pattern.
The difference between the lowest point in referrals in January and the peak in August is astonishing, with August witnessing a 60% increase compared to January.
If we were to expect referral volumes to be relatively constant across the year, 36,000 patients might have presented sooner than they did in 2022. Given the skin cancer conversion rate of 8%, that’s nearly 3,000 potentially delayed diagnoses.
Moreover, the number of 2WW referrals aligns rather consistently with the number of patients awaiting cancer treatment. Therefore, fewer referrals are a result of fewer cancers presenting and being detected, rather than an artificial summer spike in referrals.
It is not news either – the pattern was present in 2016 and is just as prevalent today as it was then. This is an area that would benefit from targeted intervention.
A Chance to Improve Patient Outcomes
The decline in winter referrals for skin cancer likely occurs because people tend to notice skin changes less often during this season, and others may also point them out less frequently (for instance, as suggested by Walter et al. ). This effect has been observed for several years, not only in the UK but also in countries like Italy and France.
What is notable about the UK, however, is that it lags behind much of Europe for cancer survival rates – this includes skin cancer, though to a lesser extent, given the generally higher survival rates compared to others.
Early detection represents the greatest opportunity for addressing this disparity. Patients who delay their presentation to a GP until the summer may be diagnosed with cancer at a stage later than otherwise.
While skin cancer survival rates are relatively high, late-stage detection significantly reduces chances of survival. For patients diagnosed at stage 1 cancer (55.5% of diagnoses), the 5-year survival rate is 100%. This, however, drops 84% for stage 2 (21% of diagnoses) and 73% for stage 3 (8% of diagnoses). Detecting melanoma at stage 1 versus later stages could save 560 lives for every 10,000 patients. For 2022, that means an extra 170 lives could have been saved out of the 3,000 potentially delayed diagnoses.
Addressing Service Challenges
The surge in summer referrals for skin cancer also strains the capacity of healthcare providers, leading to delays in diagnosis and treatment for all dermatology patients. This issue has become even more significant in the aftermath of the pandemic, as healthcare services struggle to cope with mounting waiting lists.
The following chart highlights this challenge – as the volume of 2WW referrals increases, so do the 62-day target to treatment breaches. Although the time lag between the two is not substantial, it has widened since the pandemic, likely due to the extra burden posed on services by the elective backlog.
Patients referred just before the peak experience the most significant impact, as the capacity for 2WW referrals competes with the capacity for treatment. This indicates a healthcare system under pressure, where there simply isn’t enough capacity to handle a sudden rise in referrals. To address this issue, targeted solutions are needed.
What can we learn from this?
There is a strong case for gaining more insights across various cancer types, examining inequalities, geographies, pathways, and populations to align them with the Core20Plus5 priorities.
This knowledge can help to focus on a broader range of interventions and measure their impact.
For skin cancer, two clear opportunities emerge:
Implementing winter skin check campaigns, akin to successful breast cancer awareness efforts. Targeted outreach could encourage earlier presentation, improving outcomes.
Provider strategies to manage summer surges, such as Teledermatology and cross-site collaborations. New approaches may expand capacity for minor procedures and biopsies, speeding up diagnosis and care.
Data-driven insights can focus and maximize initiatives by revealing where needs and inequities lie. They also offer opportunities to monitor progress and assess effectiveness.
Learning to harness information will be crucial to the future of the NHS. The skin cancer example serves as a testament to how data can uncover life-saving possibilities and point the way toward realising them.
Data Engineering as the Backbone of Modern Healthcare Analytics: Experience from Our Work in the NHS
As a healthcare technology firm, we are known for our expertise in analytical work for the NHS. This includes operational improvement or benchmarking delivered visually via dashboards, and digital tools to improve scheduling, bed planning or empower better decision-making on the ground.
But people are realising that analytics need good engineering
Until recently, we were seldomly asked about how it all works under the hood. However, we are noticing that as trusts and national bodies have developed more and more dashboards, analytical tools and data collection systems, they are noticing errors, outages, delays that are due to back-end processes not being scaled and maintained in line with the increased demands of the analytics teams. In turn, questions that we are asked are pivoting from “can you help us build a dashboard?” to “can you help us make sure our dashboard works and is usable?”.
Example of a data engineering failure
New tools make it easier than ever to do it well
At the same time, the tools that are available for data engineering, especially in the cloud with Azure, AWS or Gcloud, are making it easier and easier to get it right. And when data engineering is done well, it can ensure that analysts have the right data, refreshed without delay, displaying the right things in the right place.
Data engineering is like plumbing: data flowing to where you need it at the right time.
But without reliable and scalable data pipelines, analytical work can be compromised by poor data quality, delays, and errors. Just like how poor plumbing will lead to nightmares down the line…
To deliver high-quality analytics to our clients we have invested heavily in data engineering, both in terms of tools and talent. Our team of data engineers is skilled in building efficient and robust data pipelines using cloud technologies such as Azure, which allows us to process, store, and analyse vast amounts of data in real-time and get our data plumbing right first time.
Example: Work with a National Programme on processing a national data collection
We recently worked with a national programme that collected theatre data from every Trust in the country. The data needed to be organised, flow together into a coherent schema and ultimately be validated, checked and organised into templates and tables. All of this required to be done in a stable, scalable and reproducible fashion. We achieved this using Azure tools, which allowed the data to power high quality analytics and feed into performance improvements for the entire country.
For a detailed case study of this work, click here.
Data engineering is the foundation of analytics
As data and dashboards proliferate, data engineering is becoming increasingly important and more senior leaders are noticing that problems with dashboards may lie under the hood. At Edge, we believe that getting the data right requires even more than engagement on the ground and high quality analysts. It needs the right plumbing to ensure that integrity, scale and reliability of any data products are guaranteed. Talk to us about our data engineers, and rest reassured that any data products we deliver have the highest quality plumbing underneath.
The NHS’ Balancing Act: Prioritising Urgent Cases While Tackling the Elective Backlog
Over a year ago (February 2022) NHSE set out clear targets for maximum wait times for elective care, as part of the plan for dealing with the pandemic-induced backlog. The aim has been to achieve zero 78-week waits (78WW) by the end of last month, zero 65WW by March 2024, and zero 52WW by March 2025.
Let’s explore this operational challenge
A look at the data will show that around 5% of the RTT waiting list is waiting over 52 weeks, and only 0.8% over 78 weeks.
This seems like a fraction of the total waiting list – why can’t we just treat all of those patients straight away?
The amount of long waits varies across the country
There is significant variation in the waiting lists between systems. In some ICSs, 0 patients are waiting over 78 weeks already. In others, almost 3.5% of patients have been waiting over a year and a half. Crucially, the challenges vary between specialties. An example of this is the ENT operation for septorhinoplasty, or “nose job”, a procedure whose high complexity and relatively low clinical urgency means that patients waiting for this surgery often must wait over 1.5 years.
The question remains – why can’t existing capacity be diverted to those long waiters, to clear the backlog in 1-2 months?
Systems are actively targeting long waits – though there is no “quick fix”
When we compare activity in December with the size of the 78WW cohort across ICSs, it emerges that many systems could clear their 78WW backlog using a fraction of monthly capacity. In fact, on average across all Trusts, 3.3% of one month’s capacity (December 2022) could have been used to clear all 78-week waiters.
However, for other ICSs, around one in every four patients treated would have to be from the long-waiters cohort to reduce 78WW to zero.
Here are two reasons this would not work.
–
The Patient Tracking List (PTL) for elective surgery is a “live” document
Much like a Google doc, the PTL is constantly changing.
One hospital could have 100 patients waiting over 78 weeks on February 1st. Even if all of those particular patients were treated this month, several more will cross the 78+ week threshold as the month progresses. This means that the activity required to reduce 78WW to zero is actually much higher than just number of 78WW that we have right now. It includes patients who are also about to approach the 78WW mark.
–
We cannot forget about risk
Typically, Trusts will apportion capacity based on risk – the most clinically urgent cases (emergency admissions, cancer patients) need to be seen fastest and will be bumped to the top of the queue.
A score often used to allocate priority – the “P” priority score, developed by the Royal College of Surgeons -, is a time-based measure of risk that focuses on mortality. Patients within the 78WW cohort were assigned low mortality risk scores on triaging, which has contributed to their growing wait as more clinically urgent patients have been seen first. While there is evidence that waiting for care for prolonged periods can carry substantial morbidity, the issue of mortality risk remains.
Juggling finite resources remains a conundrum
Prioritising 78WW patients may take away capacity from the most urgent patients. On the other hand, not addressing the backlog causes longer and longer waiting lists, and increases risk that some of those patients will need urgent care. Operational and clinical teams handle this balancing act on a daily basis – it is essential that they have access to timely, accurate and insightful data that support their decisions, ensuring that they can safely and effectively managing waiting lists.
In the next blog, we will explore innovative methods that operational teams are using to tackle the elective backlog, and highlight the tremendous work by specialty teams to overcome their unique, specialty-specific challenges
The Data-Driven Approach: Strategies for Understanding Inequality in Paediatric A&E attendances
Half of all paediatrics A&E attendances are from children and young people from the most deprived areas
Worrying parallels are seen in the lack of primary care provision, and the mistrust of people from deprived areas in their GPs, partially mitigated by higher numbers of doctors in A&E
To address inequalities in healthcare provision, we need to understand the context – this is where data can help
Four key areas to investigate are: causes for utilisation, complexity of patients, the wider context and the potential benefit of novel initiatives.
Unrelenting trends
Trends in persistent health inequalities remain a key policy issue. The COVID-19 pandemic laid bare the drastic health inequalities that exist among populations served by the NHS. Here we want to focus on inequalities among children, specifically how to identify them and take practical steps to address them.
A&E attendances among children for the most deprived population are higher compared to the least deprived in the UK. Although measures stratified by IMD quintiles show disparities across Trusts, insights are limited as the data is not available at the patient-level.[1] Even pre-pandemic, in 2015/2016, the most deprived Children and Young People (CYP) overall were 58% more likely to go to A&E than the least deprived (Nuffield Trust). In our data, almost half of all paediatric A&E attendance are accounted for by the two most deprived quintiles across children aged 0 to 17 years.
What is behind the disparity?
The British Social Attitudes Survey from 2019 found that parents with children under the age of 5 living in the most deprived areas were the most frequent users of A&E in the preceding year, and they perceived it most difficult to obtain a GP appointment compared to families living in less deprived areas. Additionally, they expressed less trust in their GP but tend to utilise the internet more often to self-diagnose (BSA).
The inequality in primary care provision across IMD quintiles is evidenced by the above chart, where the most deprived areas have one whole less GP FTE per 10,000 patients compared to the least deprived. Sadly, this gap has been widening, rather than closing, as equitable workforce distribution remains a major challenge. Perhaps as a way to mitigate this and respond to the increased A&E utilisation, there are considerably higher numbers of A&E doctors across all grades in more deprived areas. Although this intervention responds to the observed differences in A&E utilisation by deprivation, workforce restructuring strategies are not enough without addressing the underlying issues causing greater inequalities (King’s Fund).
Why is this problematic?
A&E is often not the right place to provide care to children who may have more complex needs that span school settings, as well as community and secondary care (asthma, diabetes, epilepsy). The busier a paediatric A&E department, the less well suited to fully understand parents’ concerns and provide education to prevent re-admission.
The higher utilisation in A&E services reveals that the current efforts to move care in the community are failing children in most deprived areas, as A&Es respond to a lack of primary care and community investment. The increased demand for emergency services in more deprived areas is likely due to a combination of differences in need and issues with adequate primary care provision and utilisation.
Uncovering what drives higher A&E demand is paramount, especially if it is matched by lower primary care utilisation. This is because the long-term risk is fragmented children care, as A&E does not provide continuity and a holistic assessment of the wider context which is often needed in CYP.
How data can help uncover the root cause.
Data is a powerful tool that can support this quest. Here we propose four areas for investigation that are accessible to all providers, to start building solutions:
Identify the causes for utilisation: Devise a clear picture of the main causes driving A&E utilisation to target and streamline services. Asthma is often cited as a primary cause of healthcare utilisation among children. This opens the opportunity to provide services in schools, such as the My Asthma in School (MAIS) intervention which conducted educational and self-management workshop to children. The intervention was successful: 91.4% of participants (n=1814) reported the workshop changed their perspective on asthma (PFS).
Determine the complexity of patients: Are patients in more or less deprived areas presenting with more severe conditions requiring more treatment? Use admission rates from A&E, extent of treatment provided and attendance rates in minor/majors to find out. Parents of children living in areas of higher deprivation may wait longer to seek healthcare as other commitments take priority, such as working. On the other hand, a substantial proportion of ED attendances are non-urgent, especially in younger children (EMJ). Education in school, communities and GPs can have vast effects in both reducing unnecessary attendances to A&E and improving help-seeking behaviours.
Understand the wider context: We have demonstrated that GP shortages in deprived areas may be a barrier for patient access. Other services such as pharmacies and other community services (e.g. health visitors, community and school nursing) may be equally affected and should be investigated. In Greater London, Child Health GP Hubs have been set up to address a shortage of GPs. These hubs are specific to paediatric children and provide more streamlined care by following a joined up care model (Imperial).
Evaluate the benefit of novel initiatives: Could there be a benefit to integrating on-site primary care in children’s A&E? Combining community and trust data may reveal benefits in developing paediatric-specific ambulatory care centres sited in A&E. These could see children with less-acute conditions that present to A&E and provide more holistic care. Similarly, more specific paediatric services in primary care, and targeted education for primary GPs and nurses could support parents’ confidence in primary care providers, and combat the perception that paediatric A&E may provide a better service.
[1] The findings are also limited to latest available data resulting in temporal inconsistencies across analyses.
Happy International Women’s Day! This year’s theme “DigitALL: Innovation and technology for gender equality” is especially close to our heart. At Edge Health, we are lucky to boast a team of 13 incredible women, all bringing invaluable contribution to innovation and technology in healthcare analytics and consulting.
We have asked them for their thoughts on today’s celebration, and share with everyone their nuggets of wisdom.
How can we build on our past successes to create a better future for all women? 1. Advocate for equal access to education and training for women in tech. 2. Work towards equal pay. 3. Create a more supportive and inclusive work environment for women in tech. 4. Recognise and celebrate the achievements of women in tech and their contributions to the industry.
How can we ensure that women of all backgrounds and experiences are included and represented in conversations about women in technology and innovation? 1. Create inclusive environments that recognise and value diversity. 2. Providing opportunities for these women to participate in and lead conversations and initiatives related to technology and innovation. 3. Include and consider the perspective of women from all backgrounds and groups.
Marta Berglund, Analyst
How do you think healthcare consulting and data analytics can help improve healthcare outcomes for women and girls specifically? 1. Impartially shining a light on health inequalities, using data. 2. Building business cases to equitable allocate funding for services where it will optimise health outcomes
What changes do you hope to see in the industry over the next 5-10 years, and how do you see yourself contributing to those changes? I would love to see more women in leadership roles – specifically in the entrepreneurial space. I hope that I could help normalise this for others. At a policy level, I think things like stronger government policies for parental leave even in SMEs could help.
Jennifer Connolly, Senior Consultant
Why do you think IWD is important? IWD sheds light on all the important work that women have done that may not have been recognised in the past, and acts as a conversation starter on how we ought to be supporting women around the world in every industry going forwards.
Can you share a moment where you felt empowered as a woman, and what made that moment so meaningful to you? Growing up my mom was a strong female presence in my life. She started her own business when pregnant with me, and still runs that business today on her own. I feel both empowered and inspired when hearing stories of her working with other women from my home town to create a network of strong female entrepreneurs. As well, I’ve had the opportunity to work with her all-female team on projects in the past and was empowered through receiving recognition from clients on our great work as women in the industry.
Kate Cooper, Analyst
What are ways men can be good allies? What do we need to do to engage and inspire male advocates? I believe men can be good allies by recognising their privilege and using it to empower and support women. I think what needs to change for this to happen is the narrative around gender equality. The fight for equality concerns all genders and ultimately will benefit all genders. This needs to be taught to children in schools starting when they’re very young.
What would you change about the world for women if you could? I would love for all women across the world to have access to healthcare and education. Health is the essential building block, while education provides you with the knowledge and skills to pursue your goals and dreams.
Virginia Dall’Ó, Senior Analyst
How do you think we can encourage employment of women in consulting and data analytics? Show them that it’s the new normal, and women excel at this kind of work – events like IWD are the perfect occasion for this. In our day to day, we should show that we value and respect their views – include them in decision-making, place on them the same level of trust and responsibility than on men, and be careful to avoid discriminating remarks in the workplace (a colleague – not at Edge! – once kept calling me “young lady”; which demeans the individual both personally and in the eyes of others).
How do you balance your personal and professional life, especially in a field that can be demanding and high-pressure? I feel very lucky to live in a society where I am no longer pressured into giving up my career to look after family & home. Women, however, still do most of the caring (and may wish to do so). My main advice would be to take pride and joy in your career, find what excites you about it and then ditch perfectionism and unrealistic expectations. Use work as a way to express yourself, rather than a way to prove or define yourself. Don’t be afraid to ask for help, and be kind to yourself.
Lucia De Santis, Analyst and MD
What would you say to young girls to inspire them to look towards data as a career? Show them what women have achieved in the field and emphasize that careers in data can cover nearly every interest one has. You do not need a degree in computer science or mathematics to excel in data careers but instead can apply different background knowledge to help solve problems.
What challenges have you faced, as a woman, entering this industry? 1. Lack of female role models and mentors in the field 2. Perception of it being a male-dominated field with a gender bias 3. Lack of educational institutions encouraging girls to pursue a career in the field
Laura Dell’Antonio, Analyst
What is the most satisfying aspect of your work as an Analyst/Consultant? In general having the opportunity to improve healthcare for everyone! This includes women’s health, but also reducing health inequalities for other patient groups and striving to ensure everyone has access to the same quality of healthcare.
If you could have dinner with three inspirational women, dead or alive, who would they be and why? 1. Rosalind Franklin – her research was central to the discovery of the molecular structure of DNA, although credit for the discovery was originally given to Francis Crick and James Watson, who were awarded a Nobel Prize in 1962. It would be amazing to hear her perspective of working in such a male-dominated field at the time, and also appreciate how far we have come since then. 2. Emma Watson – partly as a Harry Potter fan, but also she has been a great advocate for women’s rights and has been actively involved in promoting gender equality and women’s health for many years. In her role as UN Women Goodwill ambassador she ran a campaign aiming to engage men and boys in the fight for gender equality, which I think is really important. 3. Michelle Obama – an obvious choice, but definitely an inspirational woman!
Catriona Mackay, Senior Analyst
What do you think will help combat gender stereotypes? I think that education is the most important tool to combat gender stereotypes as they are often present from childhood. By treating all children in school the same regardless of their gender and encouraging girls to pursue careers than are stereotypically chosen by men, children will be less likely to pick up gender stereotypes.
This year’s IWD theme is “DigitALL: Innovation and technology for gender equality”, what does this mean to you? Women are often left out from the design of innovative or technological tool. This leads to gender inequalities being rooted at the very base of such tools. They often don’t take into account how a women’s experience could be different from that of a man. Innovation and technology can help reduce gender inequalities if it gives similar opportunities to everyone.
Julia Mayer, Analyst
Can you share an example of a project or initiative you have worked on that has had a positive impact on healthcare outcomes for women? I am currently working on a regional audit of breast pain clinics. These clinics aim to improve the experience of patients (most commonly women) presenting to primary care with breast pain as their only symptom as well as patients entering urgent breast cancer pathways. Research has indicated that the risk of breast cancer in patients with breast pain as their only symptom is very low and, even where the patient is found to have cancer, is only coincidental. However, breast pain remains a frequent reason for referral from Primary to Secondary Care, often on an urgent (2-week wait) pathway. The referral often causes significant patient anxiety and subjects them to numerous scans, including ultrasounds and mammograms. It also adds pressure to already strained 2WW pathways, delaying treatment for those most in need. Therefore, using data to prove these breast pain clinics offer patient, staff and health system benefits, will be a step towards improving the health outcomes and care experiences of women across the country.
What perspective can a woman bring to the data world? Unfortunately women in data analytics and data science remain underrepresented. This is concerning as female perspectives in health data will help to ensure that women’s health issues get the attention they deserve. There is a growing understanding of the historic biases within health research which has limited the understanding of women’s health and led to reduced health outcomes for women. We need to do more to help change this and one key way that’s possible is to get women into the data field and bringing their perspectives and lived experiences to the job.
Lucy Pirkle, Senior Analyst
What will be the biggest challenge for the generation of women after you? I think it is important that women don’t become complacent, and maintain an international vigilance to ensure equality for every woman. I personally have witnessed many positive changes in equality- but am also living through misjustice targeted at women- we must make sure that the energy and passion to make positive change is consistent.
What can we do to ensure that the accomplishments of women throughout history are not forgotten? Keep talking! Learn and share- it is important we keep the stories alive and remember to share the new accomplishments too. We need to search for, and share the stories of women’s achievements and ensure they are not mis/under represented.
Sarah Shelley, Office Manager
How have you seen gender equality evolve throughout your life, and what changes do you hope to see in the future? Having spent my childhood in India, I witnessed a shift in the role women played in society: moving from traditional domestic roles to professional roles especially in the information technology industry. This has not only increased representation of women in the professional sector but has also led to women becoming financially independent and be heard when raising concerns about their rights. Even though the UK is more liberal, I would still like to see even more women in STEM roles (especially women of colour).
When you were 8, what/who did you want to be as a grown up? I wanted to become an architect. I loved building structures with my LEGO so I pictured myself as an architect which as an 8 year old seemed like a glorified version of a LEGO builder!
Aditi Shetty, Analyst
What do you hope to achieve in your career in healthcare consulting and data analytics, and how do you see yourself making a difference in the lives of women and girls? As cliché as it sounds, working in healthcare I would hope that I can make a difference to the lives of those around me, through using data to improve access to and quality of healthcare. On a more personal level, I am hoping that I can inspire girls and young women to step into the roles and fields that are not necessarily characterised by high representation of women. The work we do is important, valuable, exciting, and fun! It is important for girls and women to know that they can thrive in a field like this, should they choose to give it a try.
What is your International Women’s Day message? I would encourage all of us to take stock of the part women played in history, how far we have come, and how far further there is yet to go. In 1928, women and men were given equal voting rights for the first time. A lot has happened in less than a hundred years that followed. And so much more will happen in the next hundred years. The realisation that it is up to us to make it happen now takes a second to sink in.
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