Welcome to the data age. Since the digital revolution kicked into gear in the latter half of the 20th century, humankind has been generating data at a truly staggering rate.
“The amount of data produced in just the last few years surpasses the amount of data generated in our entire human history.” – Foresee Medical
What’s more, the rate of health data accretion is actually accelerating over time – with the amount of data created, captured, copied, and consumed worldwide expected to triple in the years between 2020 and 2025. That’s an almost unimaginable quantity of information.
Data is everywhere, and it’s powerful. In life science, reliable, accurate data can be used to empower decision-making, reduce costs, improve patient outcomes, and more. In this article, we’re going to explore the importance of data collection in healthcare and examine how life science companies can turn different types of healthcare data into actionable insights.
Why is data collection in healthcare important?
“Data collection has far-reaching implications for all involved in the delivery of care. This includes healthcare organizations, individual providers, and even the patients themselves.” – Smart Data Collective
The word ‘data’ can sound kind of nebulous, or a bit techy and off-putting. But what we mean by ‘data’ is really information. Without accurate, reliable information, public healthcare and life science companies lack the tools they need to compete effectively in the market, respond to patient needs and trends, develop accurate diagnoses and treatment plans, map the spread of diseases and epidemics, and a lot more. The right information – captured from the right sources, and displayed in the right way – can become an extremely powerful tool for life science.
There’s more to consider when it comes to data collection, too. Compliance and data security are huge factors in healthcare and life science, where much of the data captured is of a sensitive nature. Likewise, it’s impossible to separate medical data collection from data integrity in pharma and availability. Accurate data collection can help to ensure diversity and inclusion among patient populations, HCPs, and advisors. And effective data collection is vital in the clinical trial process, too.
The big data explosion
By now, you’ve almost certainly encountered the term ‘big data’. The phrase is used to describe data sets that are so large they can’t be stored or processed by traditional data processing software. Unfortunately for the life science sector, it produces more big data sets than practically any other. There’s a tremendous opportunity here for life science companies, but there are significant challenges, too.
“The use of big data in medicine is motivated by the necessity to solve both local organizational issues – such as reducing workloads and increasing profits of a medical agency – and the global problems of humanity, such as forecasting epidemics and combating existing diseases more efficiently.” – SaM Solutions
Common sources of healthcare data
Healthcare data collection stems from a growing number of sources. These include but are by no means limited to:
- Patient data: The last decade has seen a push to digitize patient data. Sources including electronic health records, admin data, claims data, patient registries, health surveys, and clinical trial data are immensely valuable to life science organizations. Coupled with emerging data sets including the information captured by wearable technologies, telehealth services, and sentiment analysis of the patient experience, life science companies have a wealth of patient data to capture, process, and analyze.
- Customer data: Life science companies generate a huge amount of quantitative and qualitative customer data, too – including transaction data, social media data, sentiment, and trend data. By collecting this data, storing, and analyzing it appropriately, life science companies can develop more effective marketing strategies and improve decision-making.
- Expert data: Data from field team interactions, advisory boards, steering committees, and other interactions with key opinion leaders can be invaluable for life science and healthcare companies. This data quality is crucial in informing go-to-market decisions, identifying opportunities, and addressing the needs and concerns of patients and HCPs alike.
Turning data into insights
Data collection aids data analysis, but also supports conducting more clinical trials, improving healthcare analytics, more meticulous public health surveillance, developing health information technology, and overall improving the healthcare industry. Life science and healthcare providers are collecting a lot of data. But the challenge is to transform that ocean of information into something you can actually put to use.
Modern insights management platforms use AI-powered natural language processing and sentiment analysis applications to turn field team observations, HCP insights and interactions, and social media conversations into a 360-degree view of trending topics and sentiments. And by conducting steering committee meetings, advisory boards, and even clinical trials on a virtual engagement platform, businesses can quickly and easily drill down into conversations with HCPs, experts, and patients, capture their insights and share them with the wider team.
Curious about the role of data in life science? Explore the use of patient-centric data.