Big Data technologies can derive value from complex datasets in order to generate meaningful information that can improve healthcare for everyone
Information is the basis of better organisations and workflows. The more information we have, the better we can organize ourselves to achieve optimal results. Therefor the potential of Big Data to improve healthcare is enormous. But what exactly means big data in the context of healthcare?
The authors of “Ethics and Big Data in health” define Big Data in the context of healthcare as “encompassing high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points”
The health care sector grew tremendously in last few decades. By doing so the industry has generated huge amounts of data that has big volume, enormous velocity and a vast variety. With the implementation of the electronic health record system (EHR) there are also relevant sources to draw data from. Traditional database management systems can no longer cope with this amount of data. It is therefore crucial to work with Big Data solutions in order to harness the massive amount of complex data sets in order to then interpret them to generate meaningful information.With the aim to improve the health industry not only for patients but also for doctors by giving them relevant digital tools to work with.
Information is key — but which information help transform healthcare?
What exactly are meaningful information in the context of digital healthcare? The following chart shows the areas in which Big Data can help transform the healthcare sector and improve disease prevention and treatment of patients.
Preventive Healthcare: Big Data helps doctors to capture, analyze and compare patient symptoms in order to prescribe treatment earlier before the illness even breaks out.
Decrease Healthcare costs: By analysing data we get insights into health care providers to determine populations at risk for illness. By doing so, proactive steps can be taken initially. With Big data we can more accurately point out where education and prevention are needed to produce healthier populations while keeping costs low. Also medical decision making is faster and more accurate using Big Data Analytics.
Studying Drug Efficacy: While working on a cardiovascular study the University of Medicine in Pennsylvania has observed that using available Data from Electronic Health Record (EHR) is less expensive and just as good than using expensive randomized controlled trials.
Virtual Care and Wearables: More and more people are using wearable technologies to track their medical status or their fitness level. At the same time, digital therapeutic treatments are also becoming increasingly popular. While using these digital devices and services a huge amount of medical data is being produced that can be used to gain insights on the health status of the population.
Personalised Medicine: With the help of Big Data doctors will be able to personalise tests and treatments for each patients in a very early stage of the sickness. By eliminating possible blind spots thanks to hollistic data sets, treatments for patients can be applied earlier. Therefor costs will be saved that would have usually been spent on unnecessary tests and treatment methods.
Enablement of patients by information: Patients can take ownership of their own health status by getting access to their own medical data. By sharing information between patients and doctors productivity will be increased and and the overlapping of data will be reduced.
Identification & Tracking: They key to managing a patient population is to determine the patient health and financial risk and to identify the most effective care patterns. If a patient is starting to exhibit patterns that are indicating some sort of disease care takers need to be alerted. In this case an intervention plan can be put in place to avert or delay a spread of the disease. To track these kind of outbreaks doctors can identify distinct subpopulations of patients who have very similar interactions and care patterns.
Health Trend Analysis: With Big Data huge patient data-sets and data cohorts can be managed and interpreted. In addition we need comprehensive Big Data solutions in order to analyse health trends and to handle patient data efficiently. By doing so we can find patterns in the population all over the planet and be able to predict possible disease outbreaks.
We’ve seen that there are multiple opportunities when it comes to Big Data in Healthcare. The challenge now is how to effciently make use of these opportunities. Healtchcare providers already have big data assets in the form of electronic health records (EHR) and financial billing systems. Integrating these separate sources together in patient-centered datasets is necessary in order to use the data in the best way possible. Of course with the rise of Big Data in Health Care there occur numerous other challenges which we will point out in our next article.
In the meantime if you want to read more compelling stories in the field of digital health head over here: https://medium.com/medudoc
Sources:
https://www.researchgate.net/publication/301277064_A_Big_Data_Revolution_in_Health_Care_Sector_Opportunities_Challenges_and_Technological_Advancements
https://www.sciencedirect.com/science/article/pii/S2452310017300264
https://link.springer.com/article/10.1186/s40537-019-0217-0