The value of healthcare data is being increasingly recognised by healthcare systems worldwide. From creating improved care pathways to increasing cost savings through data analytics, arguably the true value of healthcare data lies in patient outcomes.
Good data can drive better, safer and more effective models of care for patients. As technology unlocks new data sets and more sophisticated tools of analysis, our ability to utilise information will be critical to powering improved health outcomes and more sustainable models of care.
The value of ‘longitudinal, real-world data’ is largely recognised by healthcare organisations, policy advisors, research & academia, and pharma. However, Samir Dhalla, Head of THIN and Richard Ballerand, Co-Chair of THIN’s Patient Advisory Committee insist that more could be carried out to make patients both aware of this data and how they can contribute to their own improved outcomes and also those of other patients.
The Impact of Covid-19
The COVID-19 pandemic has undoubtedly increased patient awareness of the importance of public health data. From the initial use of NHS data to locate those 2.2 million individuals who were advised to shield, to the on-going tracking and analysis to better comprehend the different COVID-19 patient experiences – such as the risks associated with obesity and Type 2 diabetes – UK citizens are becoming more aware of the significance of real-world health records.
Collaboration on a national and international level has also intensified in response to COVID-19. The UK government recognised the importance of data in managing the pandemic and has created the NHS COVID-19 Data Store. The European Health Data & Evidence Network’s (EHDEN) took part in the OHDSI COVID-19 Virtual Study-a-thon which rallied 330 researchers with very dissimilar backgrounds from thirty different countries. It also launched its COVID-19 Rapid Collaboration Call in an attempt to fill the gaps in broken, siloed and poorly interoperable real-world health data in order to characterise patients, manage their care and assess treatment safety.
Population health data for identifying and managing such pandemics is vitally important. Analysis of 16.2 million anonymised longitudinal patient records revealed a notable increase in the proportion of lower respiratory tract infections diagnosed by late 2019 compared to previous flu seasons, raising the odds of UK COVID-19 cases earlier than previously thought. By utilising the information in electronic health records, such findings visibly open a path to near real-time tracking of large-scale population health events that can aid public health bodies to detect, investigate, and respond quickly and effectively to them.
The Broader Value
The conversation surrounding patient data is changing, however it remains critical to recognise larger trust issues impacting the use of all data. Numerous studies have found that people are generally comfortable with anonymised data from medical records being used for improving health, care and services, for example through research. However, this is reliant upon a perception of public benefit; people are concerned about health data being accessed by commercial organisations. It is vital to demonstrate the broader value of patient data.
One of the most visible aspects of effective use of patient data is speed of diagnosis. The ability to overlay anonymised patient-level data onto NICE guidance, as well as local and national patient pathways, offers further insight into what treatment may be more successful for groups of patients based on subsections of similarities, such as existing conditions and co-morbidities. Exploring deeper into this data to examine the influence of age, sex, weight, faith, ethnicity and lifestyle choice, is helping clinicians get closer to the best treatment first time.
Pancreatic cancer is a prime example of a condition that is frequently diagnosed late and progresses quickly. The deadliest common cancer, by the time a diagnosis is reached, many individuals are at Stage three or Stage four and 75% do not survive a year after diagnosis. The symptoms – including unexplained weight lost, jaundice, abdominal pain and indigestion- can be vague, and easily confused with other conditions such as pancreatis, gallstones, irritable bowel syndrome (IBS) or hepatitis. For both GP and patients, embedding this information within clinical systems would be incredibly beneficial, supporting earlier diagnosis and hospital referral. The GP would spend far less of the budget on one specific patient and that patient would have faster referral to the right clinician and has received the best treatment possible.
Predictive Modelling
Real-world data can also assist clinicians to better understand patients and their responses to treatment and surgery. Amalgamating data with predictive modelling to explore outcomes, pharma can work with healthcare services to get nearer to ‘the right medicine, to the right patient, first time’. By adapting and marketing specific medicines to cohorts of patients, the potentially arduous ‘trial and error’ process to find the appropriate treatment can be minimised.
For example, analysis of cardiac surgery patients has evaluated the risks for certain types of heart surgery as well as post-surgery, taking into consideration a number of factors, including age and ethnicity to improve recovery.
This can be expanded to provide individual patients with details about their health conditions and risks to empower and encourage them to make changes to lifestyle or behaviours. This Predict, Preventive and Personalised Medicine (PPPM) explores a complicated mix of personal and population health data to avoid future health deterioration or detect problems before they appear.
Proactive Support
Crucial to this process is early identification of potential health issues, which requires proactive collection of patient health data – from weight to cholesterol and blood pressure readings. Understanding the course of this information over the past year, combined with predictive modelling as to the potential result if no preventative measures are taken, will help clinicians to offer personalised patient advice.
Ultimately, PPM will support patients to take control of their own health issues. Retaining individuals within primary care will both save money by limiting the pressure on secondary care and provide opportunities for investment in other areas of high demand healthcare. On a broader level, this detailed predictive model will allow the healthcare system to potentially predict population health issues over the next three to four years, helping to assign resources to the correct specialties at the right time.
Key to the success of these models is data volume: a large volume of anonymised, reliable real-world data from patients is essential to support in depth analysis and informed decisions. While there is a global awareness of the value of this data, many researchers are forced to rely on synthetic datasets which mirror real-world data, to support the development of medical technologies – such as those being used in the response to COVID-19. Such data simply fails to deliver the depth of insight delivered by real-world data from patients.
Utilising Real-world Data for Patients
The conversation surrounding patient data has without any doubt transformed over the past few months but it is critical to reinforce the value of patient data in supporting both individual and population health. Primacy Care data is being harnessed to inform patient pathways across a range of disease areas and enable improved understanding of local health economies. GPs have an opportunity to inform life-changing medical research, supporting research vital to receiving insights and developing policies, and helping to highlight trends in clinical effectiveness within the NHS. The opportunity to enable extraordinary and important changes in diagnosis and preventative care is there and patients need to utilise this chance.
