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Why Real-World Data Matters to Patients
Samir Dhalla

By Samir Dhalla
On Jan 21, 2021

Read time
4 minutes

Why Real-World Data Matters to Patients

Patients have become far more tuned in to the importance of public health data during the COVID-19 pandemic. NHS data enabled the identification of the 2.2 million individuals advised to shield and is now at the heart of the tier based mass vaccination roll out. Data has reinforced the risks associated with obesity and Type 2 diabetes – and is increasingly revealing the implications of long-COVID. UK citizens are learning the value of real world health records.

Few, however, realise the extraordinary power of this longitudinal, real-world data to improve health care outcomes – especially when so many researchers globally have to rely on synthetic datasets which mirror real-world data, to support the development of medical technologies. Such data simply fails to deliver the depth of insight delivered by real-world patient data.

The Health Improvement Network (THIN®), a Cegedim database, is an unobtrusive medical data collection scheme that contains anonymised longitudinal patient records for approximately 6% of the UK population. It is the key driving force behind enabling advancements in patient care and outcomes, with one of the most respected and reliable data sources for anonymised primary care records.

Managing Population Health

It is impossible to overstate the importance of population health data in both identifying and managing such pandemics. Analysis of 16.2 million anonymised longitudinal patient records within THIN® revealed a significant 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.

Harnessing the information in electronic health records can enable near real-time tracking of large-scale population health events and support public health bodies to detect, investigate, and quickly respond.

And you only have to look at the global efforts to plug the gaps in real-world health data to realise the value of these datasets. The European Health Data & Evidence Network’s (EHDEN) took part in the OHDSI COVID-19 Virtual Study-a-thon which pulled together hundreds of researchers from thirty different countries and also launched its COVID-19 Rapid Collaboration Call to gain the insights required to characterise patients, manage their care and assess treatment safety.

Reinforcing Value

The pandemic has accelerated the changing dialogue surrounding patient data, but it is still important to acknowledge data trust concerns. People are generally comfortable with anonymised data from medical records being used for improving health, care and services, for example through research.

However, people want to see a public benefit and they are concerned about health data being accessed by commercial organisations. It is important to emphasise the broader value of patient data – through speed of diagnosis for example.

Overlaying anonymised patient-level data from THIN® onto NICE guidance, as well as local and national patient pathways, provides more insight into what treatment may work better for cohorts of patients based on subsections of similarities, such as existing conditions and co-morbidities. Delving deeper into this data to examine the influence of age, sex, weight, faith, ethnicity and lifestyle choice, is helping clinicians to get closer to the best treatment first time.

Pancreatic cancer is a prime example of a disease that is often diagnosed late and progresses quickly. The deadliest common cancer, by the time a diagnosis is achieved, 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 pancreatitis, gallstones, irritable bowel syndrome (IBS) or hepatitis. For both GP and patients, embedding this information within clinical systems would be hugely beneficial, enabling far earlier diagnosis and hospital referral. The GP would spend far less of the budget on one specific patient and that patient has received the best treatment possible for that diagnosis and faster referral to the right clinician.

Predictive Modelling

Real-world data can also help to better understand patient responses to treatment and surgery. Combining data with predictive modelling to explore outcomes, pharma can work with healthcare services to get closer to ‘the right medicine, to the right patient, first time’. By tailoring and marketing specific medicines to cohorts of patients, the potentially arduous ‘trial and error’ process to find the appropriate treatment can be reduced.

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 extended to provide individual patients with information 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 complex mix of personal and population health data to avoid future health deterioration or detect problems before they arise.

We need proactive collection of patient health data, including weight, cholesterol and blood pressure readings - to support the early identification of potential health issues. Understanding the trajectory of these readings over time , combined with predictive modelling that shows the possible outcome without preventative measures, will help clinicians to present personalised patient advice.

Conclusion

Ultimately, PPM will help patients to take control of their own health issues. Keeping individuals in primary care will save money by reducing the pressure on secondary care and release 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 allocate resources to the correct specialties at the right time.

Large volumes of anonymised, reliable patient data are powering 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 harness information will be key to driving better health outcomes and more sustainable models of care.

Patients are increasingly aware of the value of their data - but it is important to reinforce the impact of that data on both individual and population health. Primary Care data is being utilised to inform patient pathways across a range of disease areas and enabling better understanding of local health economies. GPs have a chance to inform life-changing medical research, supporting research crucial to gaining insights and developing policies, and helping to highlight trends in clinical effectiveness within the NHS. Patients can enable extraordinary changes in diagnosis and preventative care.

To learn more about The Health Improvement Network (THIN®) and how you could benefit from registering your practice as a panel member, visit here.

More information on THIN

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