Healthcare data is growing fast. RBC Capital Markets said that by 2025, the compound annual growth rate of data for the healthcare industry will reach 36%. More information is collected through electronic records, insurance claims, tests, health-tracking wearables and apps, etc. The COVID-19 pandemic has made this trend even stronger. All this data can and should be used to improve the quality of medical services. But only big data analytics can cope with such a volume of information.
What Is Big Data Analytics?
Big data analytics processes large and complex data sets to find valuable insights. It reveals patterns and trends and helps analysts make informed decisions. The big data is too massive for standard data analytics software to handle. The widespread use of mobile devices, the Internet of Things (IoT), and artificial intelligence (with its ability to quickly check data and generate new info) drives data growth.
Forms and Characteristics of Big Data
There are three forms of big data:
- structured (file formats in the form of rows and columns – for example, barcodes, databases, statistics, etc.);
- unstructured (the type and format of the file are known, but the content can be anything – for example, presentations, publications on social networks, texts, etc.);
- semi-structured (the file does not have a predefined structure but has structural attributes – for example, NoSQL databases or personal data presented in an XML file).
Experts also describe big data through the five V’s: velocity, veracity, volume, variety, and value.
Volume
According to BMC Health Services Research, by 2025 the healthcare big data market is expected to grow by 568% over 10 years. Data already collected is constantly updated, and new data is added.
Velocity
Every day, people create more than 8.5 billion Google queries, including queries on health care. Big data analytics handles the enormous speed at which data is created and analyzes it in real time.
Variety
Data requires different variants of analysis and appropriate tools. For example, social media analytics can help healthcare brand owners understand customer sentiment.
Veracity
Any analysis is useless if the data is unreliable. The simplest example is patient contacts with false names and inaccurate contact information.
Value
Healthcare providers can use big data reports as the basis for valuable insights. Early identification of patients at increased risk of a heart attack will allow the medical provider to offer them some measures to reduce risk (e.g., planned hospitalization).
What Are the Types of Big Data Analytics in Healthcare?
Descriptive Analytics
Descriptive analytics answers “What happened?” and analyzes real-time and historical data. It allows the medical provider to find the trends that indicate current goals or key performance indicators. An example of descriptive analytics in healthcare: a provider wants to find out how many nurses care for each patient in each hospital unit, or how many patients have safely completed treatment and gone home.
Diagnostic Analytics
This type of analytics answers “Why did this happen?” Data trends are compared to identify cause-and-effect relationships between them. An example of diagnostic analytics is stats prepared by the National Center for Health Statistics. It found that several years ago in the United States, the most common diagnoses for emergency department visits were heart failure, diabetes, pneumonia, osteoarthritis, and sepsis. With the reports, the healthcare organization can explore potential similarities between patients in each group. For example, they are less likely to seek health care because of a reluctance to adhere to treatment or dissatisfaction with social determinants of health (SDOH).
Predictive Analytics
This type of analytics helps predict the most likely development of events based on available data. Predictive analytics helps a healthcare provider, for example, study the reactions of different patient groups to medications and their dosages. Also, with the help of such analytics, a provider can anticipate service demand and proactively allocate healthcare resources.
Dmitry Baraishuk, an expert at the software development company Belitsoft with 19 years of HealthTech expertise, emphasizes that modern healthcare does not use all the capabilities of predictive analytics. They say speed and ease of data transfer through wearable devices today satisfy customers much more than a few years ago. However, according to the expert, clinicians consider that there are still several challenges: to convert these data into valuable insights and act on them immediately. These data remain underutilized assets in the healthcare data analytics market today.
Prescriptive Analytics
This type of analytics uses clinical data to determine specific actions needed to achieve a goal. For example, a solution analyses the data of a patient’s current and any pre-existing conditions and previous treatment strategy and suggests a more personalized treatment strategy. In addition, prescriptive analytics can help plan preventive care and comprehensive clinical decisions that improve patient outcomes.
Advantages of Big Data Analytics in Healthcare
Big data analytics in healthcare benefits different organizations, from small clinics to large pharmaceutical companies. Thanks to it, the healthcare provider can:
Reduce Ineffective Expenses
Big data analytics prepares reports for healthcare providers based on data from patients’ smartphones, their chips, wearables, and other IoT devices, as well as from EHRs, laboratory tests, etc. Analytics checks both historical and real-time data. Medical personnel receive reports with treatment strategies for individual patients. Based on these reports, medical personnel can order certain drugs from pharmaceutical companies and create a tailored treatment plan for each patient. It helps to avoid bulk orders of ineffective medicines, which will become unclaimed and therefore expired, and, as a result, avoid financial losses.
Expand of Studies Based on EHRs
Modern medicine uses electronic health records (EHR) to collect all available data about the patient. Using data in EHRs under the concept of big data allows the medical provider to expand the list of sources of patient data. The attending physician may request information about the patient from:
- laboratories where the patient was tested;
- hospitals where the patient was previously treated;
- nursing homes (if the patient lives there);
- clouds of wearable devices (if the patient wears a smartwatch, e-patch, etc.)
EHRs with big data make it possible to study health and disease profiles in more detail both at the level of an individual patient and at the population level. Big data analytics makes it possible to survey a specific disease and the genetic influences that lead to the risk factors for that disease.
Reduce Medical Mistakes
With access to EHR’s big data, analytics solutions can identify data potentially in error. Thus, a team at Johns Hopkins University has developed a method to assess and monitor diagnostic mistakes. The method compares the current symptoms of people who seek medical help with symptoms of diseases that can be misdiagnosed due to a wide range of atypical symptoms (for example, dizziness can be a symptom of both inner ear inflammation and stroke).
Improve the Quality of Treatment in Real Time
One of the strengths of big data analytics is the analysis of information in real time. Any new information about a patient from a healthcare provider immediately appears in the database. Based on this data, artificial intelligence (AI) algorithms can suggest conclusions about the patient’s diagnosis and, if necessary, additional tests to confirm the diagnosis. Also, big data analytics can identify patients at risk for unfavorable treatment outcomes.
Increase Patient Loyalty
If the ability to collect and analyze large volumes of healthcare information in real time is the “hard skill” of big data analytics, then “understanding” the sentiments and needs of patients can be considered a sought-after “soft skill”. Via these tools, big data analytics provides healthcare providers with patient experience data. Providers can use this data to generate valuable insights into customer centricity and optimize the company’s marketing strategy.
Bottom Line
Healthcare analytics can lower treatment costs, predict epidemic outbreaks, prevent diseases, and enhance overall quality of life. In addition, the growing volumes of global data, including healthcare, require structuring and analysis. Application of the obtained big data analytics results will help healthcare business owners turn their organization into a market leader.
