How the Applications of Big Data Can Improve the Quality of Healthcare
Big data is able to revolutionize the healthcare system to deliver more accurate, personalized treatment, improve the quality of patient care, reduces costs, and even cure or prevent the onset of diseases. The adoption of this technology became especially important during the COVID-19 pandemic when health-care systems around the world began facing incredible challenges.
What is big data in healthcare and how can health professionals benefit from this technology?
Big data in healthcare refers to massive volumes of information collected from single individuals to large cohorts in digital format. There's a variety of sources of big data including electronic health records (EHRs), medical imaging, genomic sequencing, payer records, pharmaceutical research, wearables, and medical devices.
Beyond this definition, big data also refers to the computational techniques for analyzing data and gleaning meaningful insights and actionable knowledge from that analysis. Healthcare providers can use this knowledge to make better decisions relying on evidence-based research and clinical data rather than on their schooling and professional opinion.
Experts believe that big data has the potential to develop entirely new approaches to improve the healthcare system while reducing costs.
Let's take a look at the real-world applications of big data in health outlined by the Canadian Medical Association:
According to the Precision Medicine Initiative, precision medicine is “an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person.”
Researchers must consider that treatment and prevention strategies developed for the average patient may not work well for everyone. Access to large databases that contain combined genetic, clinical, pharmaceutical, and socioeconomic information will allow researchers to predict more accurately which treatment and prevention strategies for a particular disease will work in a certain patient group.
For example, National Cancer Institute Molecular Analysis for Therapy Choice (NCI-MATCH) trial is a precision medicine initiative that was designed to match specific types of underexplored tumors to drug-based treatments. Nearly 6000 patients were assigned targeted drugs based on genetic abnormalities found in their tumor specimens. This resulted in the database allowing physicians and patients to select the best treatments for corresponding tumors.
Machine learning is a component of artificial intelligence focused on the ability of systems to learn from data and make corresponding decisions or predictions. Machine learning algorithms are able to improve their performance with minimal human intervention. It has various applications in medicine starting from improving diagnostic accuracy and efficiency in medical imaging to predicting which patient is at high risk of suffering from a disease.
Using machine learning algorithms, researchers from New York University were able to train a model to determine patterns of COVID-19 in patients and forecast its severity. John McDevitt and his team utilized data from 160 hospitalized patients in Wuhan, China, to identify biomarkers measured in blood tests that were significantly elevated in patients who died versus those who recovered. The model estimates a COVID-19 severity score ranging from zero to 100 for each patient individually based on their biomarkers and risk factors. Clinicians can significantly benefit from this invention by detecting which patients are most likely to have severe cases of COVID-19.
Public health / Population health
Population and public health both deal with the health patterns in large groups of people, rather than in individuals. Big data plays an important role in the design and implementation of community-wide health initiatives to identify disease trends and promote good health practices. In addition, big data tools and analytics have advanced the way clinicians and epidemiologists evaluate the impact of health policies, allocate resources to maintain healthy populations, and inform authorities about the state of public health.
One of the curious applications of big data in public health is the use of social media and internet-based data to predict disease outbreaks based on the timing and location of search engine queries. Several digital surveillance systems, such as Google FluTrends, have been initiated to track influenza incidence in local areas but ended up making inaccurate predictions. However, experts believe that digital surveillance is likely to make a positive impact on public health when integrated with traditional surveillance systems, such as laboratory data, case reports, and electronic health records.
Big data is slowly revolutionizing healthcare and move the industry forward on many levels. The adoption of big data approaches has enabled clinicians to make better-informed decisions in providing more accurate patient care and enhancing operations. With the rapid and widespread development and use of big data applications, the healthcare industry and health organizations will be able to take full advantage of the insights stored in these rich streams of information.