DrKumo Best RPM
Connected Health Technology
Doctor analyzing big data in RPM

Big Data and Big Data Analytics in Modern Healthcare

Even before the Covid-19 pandemic, the healthcare industry knew that big data would improve healthcare services. In fact, the healthcare data volume increased to more than 700 exabytes in 2017 from 153 million in 2013 and is expected to rise even more in the coming years.

Big Data

Big data, also known as the three Vs (variety, volume, and velocity), is a large set of complex data. These data are so voluminous that they became too complex for traditional data processing applications to handle. However, today, big data analytics offer a solution to manage and take advantage of these large sets of data.

The Three Vs

Variety refers to the types or kinds of data being collected, stored, and processed. Volume refers to the amount of data. And velocity is the speed rate of data being received and processed. 

Brief History of Big Data

In the 1960s, the US government established the first data center that stored 175 million fingerprints and 742 million tax returns. They called it business analytics. Big data was referred to as business analytics until 2007 when the term big data was publicly introduced. 

Today, big data is no longer jargon. In fact, in a survey, the majority of the respondent companies use big data to monitor their equipment and assets to identify operating issues and enable proactive maintenance. 66 percent of the executives believed that they could lose their market position in the next three years if they do not adopt big data. Additionally, 88 percent of the executives stated that big data analytics is a top priority for their company.

Big Data Analytics

Big data analytics refers to the approach of using advanced analytic techniques to process large volumes, high velocity, and a wide variety of data that include structured, semi-structured, and unstructured data, from different sources and in sizes ranging from terabytes to zettabytes.

With big data analytics, businesses, including the healthcare industry, are ultimately fueled with better and faster decision-making, modeling and predicting future outcomes, and enhanced business intelligence. 

Big Data and Big Data Analytics in the Healthcare Industry

Today, it is believed that the healthcare industry has benefited the most from big data and big data analytics. Big data, in this industry, refers to the large volume of patients’ data collected from different sources and of various kinds.

It is undeniable that big data comes with numerous applications that are very beneficial to the healthcare industry. However, it also comes with challenges. This article will name five significant contributions of big data analytics and five challenges of big data. 

Five Significant Contributions of Big Data and Big Data Analytics in Healthcare

  1. Reduced Medication Errors. In the traditional settings, patients’ health data will only be based on what the healthcare provider gathers during their regular check-ups, their stay in medical facilities, and basically on what the patients tell their healthcare providers. As such, having limited health data may result in a wrong diagnosis and, eventually an inappropriate medication. In fact, according to NCBI, the overall misdiagnosis rate is approximately 10 to 15 percent resulting in 40,000 80,000 deaths or injuries annually.

Continuously collecting health data via IoMT will result in a massive volume of patient data, which may result in a more accurate diagnosis, allowing healthcare providers to provide more precise medication.

RELATED ARTICLE: The Internet of Medical Things (IoMT)

  • Reduce Frequent Flyers. NCBI defined ‘frequent flyers’ as patients admitted at least four times in 12 months. They are responsible for 12 to 28 percent of hospital admission. 

With the use of big data, healthcare providers can identify these frequent flyers and offer them maintenance or preventive healthcare plans, which they can adjust as needed.

  • Better Patient Engagement. To have big data,patients must play an active role in managing their healthcare. Studies show that patients who are educated about their health conditions are more motivated to captain their healthcare management, resulting in a higher recovery rate.

While patients will have increased control over their healthcare management, their healthcare providers will be able to provide better treatment and health maintenance plans as a result of the large amount of data collected from them. Additionally, it enables healthcare providers to maintain a much stronger connection to patients’ daily progress and identify potential interventions before they become high-risk.

  • Precise Number of Staff. Overstaffing will mean more expenditure to the healthcare providers, while understaffing will result in long waiting times for patients and exhaustion to healthcare staff. With predictive analysis, big data could help hospitals and clinics estimate future admission rates. This will help healthcare providers allocate the correct number of staff to reduce unnecessary expenditure and reduce long waiting times. 
  • Reduced Healthcare Cost. According to the CDC,US health care spending increased 4.6 percent to reach $3.8 trillion in 2019, compared to the increased rate of 4.7 percent in 2018. In fact, 90 percent of the $3.8 trillion of healthcare expenditures are spent on patients with mental and chronic health conditions.

However, with big data analytics, that may reduce medical errors and frequent flyers and predict the precise number of staffing in hospitals and clinics depending on the number of patients, the healthcare expenditures are expected to decrease in the coming years. 

Challenges of Big Data

Even with outstanding contributions, big data has its weaknesses or challenges. Here are some challenges of big data.

  1. Collecting Relevant Data. With so many sources and kinds of data, it may be hard to distinguish the relevant ones from those that are not. 

In the healthcare industry, the increasing use of wearable medical devices, remote patient monitoring, and mobile health applications further contribute to the already large pool of health data. As such, this already large pool of health data is expected to increase even more.

  • Security and Privacy. Patients’ health data are very sensitive and are prone to cybercriminals. As such, these data should be collected and used only by an authorized person and only for legitimate purposes. It should also be securely collected, transmitted, and stored in a secure database. 
  • Storage. One of the main concerns of big data is its volume. With the fast collection of patients’ data from different sources, healthcare providers should be very quick in analyzing these data; otherwise, their storage will be full in no time. Recently, some healthcare providers started using the cloud, while others considered using the cloud as their storage.
  • Cleaning. Duplicated, outdated, and unimportant data are also prevalent in big data. Cleaning to retain the most important and up-to-date data for analysis and further study may be too complex and, if done manually, is close to impossible. 
  • Data Management. Inadequate governance leads to duplicated and missing data that will further lead to operational inefficiencies. Without proper analytics, these data will not help the healthcare industry achieve quality and safe patient care.

Takeaway

The availability and analysis of voluminous health data have expanded the possibilities for providing high-quality and safe healthcare. With the help of big data analytics, healthcare providers may reduce medical errors, frequent flyers, and healthcare costs, predict accurate staffing, and allow better patient engagement. However, big data in healthcare is growing extremely fast, leading to a shortage of storage and data security and privacy issues. 

Nonetheless, the future of high-quality and safe healthcare lies in big data and big data analytics.

Visit our website for more information: www.drkumo.com

Share This Post On

Share on Facebook
Share on Twitter
Share on LinkedIn

Related Posts

physiotherapist works with patient, rpm for stroke patients

2 Ways Remote Patient Monitoring Prevent Occurrence of Stroke

Remote patient monitoring has features which are appropriate for stroke prevention and management strategies. When compared to traditional outpatient management, the advantages of remote patient monitoring include continuous availability and real-time monitoring at a cheaper cost.

Free Initial Consultation

Get a free 30-45 minutes consultation with one of our DrKumo RPM experts to learn everything you need to know about Remote Patient Monitoring and how you can make your RPM program successful.

To start please fill out the form and we will get in touch with you shortly.

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
;