Currently, the healthcare sector produces almost 30% of the total amount of data worldwide stated by RBC Capital Markets. But, generating a lot of data is not enough; the data must be processed so it can make sense. Hence, the healthcare industry uses data mining to analyze raw data and draw meaningful conclusions. Appropriate healthcare data mining can lower treatment costs, foresee epidemic breakouts, help people avoid serious diseases, and enhance their quality of life. However, now that global data generation trends are dramatically shifting towards big data, traditional mining techniques and advantages are also facing big changes. With the many benefits of data mining in healthcare, professionals are collecting big data and looking for the best strategies to use it.
In this article, we’ll discuss important real-life big data mining applications in healthcare organizations. Additionally, read how healthcare centers are overcoming big data mining challenges by outsourcing healthcare data mining services.
- 1 How Does Big Data Mining Work?
- 2 Application of Big Data Mining In Healthcare
- 3 Challenges in Implementing Big Data Mining in Healthcare Firms
How Does Big Data Mining Work?
The phrase “big data” in the context of healthcare refers to enormous amounts of data produced and collected by advancements in medical technology. Such data volumes are too huge and complicated to be managed through conventional technology. This includes patient medical records, hospital records, medical exam results, data gathered by medical testing equipment, and many more. When processed, this data can help manage organizational performance.
Data mining is the process of analyzing huge datasets to find patterns and then using those patterns to estimate the probability of upcoming events and trends. For instance, consider analyzing thousands of MRI scans to look for patterns that might affect how diagnoses are made or therapies are developed.
Application of Big Data Mining In Healthcare
Medical data mining techniques can enhance daily healthcare in a variety of ways. Here are some of the applications.
Healthcare firms can detect patients with high-risk health conditions using big medical data, reliable mining techniques, and model-building solutions. Doctors and medical personnel can use this information to diagnose the patient’s condition and then take action to enhance the standard of care and avoid further health issues. For instance, AI or machine learning interprets tests, X-rays, and MRI pictures and creates a pattern about the healthy human body. The same data can then be used to trace any defect, virus, or bacteria in the human body. This helps doctors quickly decide on the perfect medicine, diagnostic procedure, and other medical-related decisions.
As we explained, healthcare centers train machine learning algorithms. Now healthcare firms can use these machines and sensors near the patient’s bed to continuously monitor patients’ pulse, blood pressure, and breathing rate. Any change is instantly noticed, and the responsible personnel is informed. This helps doctors to take care of their patients and relieves the workload of nurses and staff at the same time. Moreover, such AI-based systems can monitor patients’ conditions using modern wearable technology (like smartwatches), saving time and effort on routine tasks.
Avoiding Drug Mix-ups
A patient may get many courses of treatment at once, making them susceptible to drug interactions and potential negative effects. Sometimes, they even develop allergies or intolerances to certain compounds produced by medication combinations. This can be risky for the patients. Using digital medication interaction modeling, patient health considerations, and computer-assisted data mining, doctors and pharmacists may make informed prescription decisions and avoid giving patients potentially harmful drug combinations.
Fraud & Abuse
The Coalition Against Insurance Fraud estimates that erroneous and fraudulent claims totaled a staggering $3.1 billion. To protect themselves from such threats, healthcare centers can use data mining techniques. Data mining helps in spotting irregularities and warning signs in documents. The healthcare staff can then identify and charge the concerned person for such fraudulent activity.
Informed Strategic Planning
When treating patients with serious illnesses, doctors have to make quick and wise decisions. Big data mining helps in this situation. It provides data that allows doctors to make informed decisions and improve the overall medicinal process. For instance, a patient had a heart attack. Before treating them, doctors have to know about the diseases he is suffering from. With the analysis power of data mining, the modern medical machine helps to predict the other diseases a patient’s mind suffers. Doctors can treat patients who are at risk for cardiovascular disease or diabetes.
Electronic Health Records (EHRs)
It is the most widely used big data in medicine. Every individual has a digital profile that includes information about their background, health history, allergies, lab test results, etc. Records are accessible to providers from the public and private sectors and are exchanged via secure information systems. Since each record is made up of a single editable file, clinicians may make changes over time without having to complete additional paperwork or worry about data replication. EHRs may also provide alerts and reminders when a patient needs a fresh round of lab work, or they can monitor medications to check on compliance with physicians’ orders.
Measuring Treatment Effectiveness
Data mining can help assess the efficacy of medical interventions. It can be used to provide an analysis of the course of action that is most effective by contrasting and comparing causes, signs, and methods of care. For instance, hundreds of people with the same disease are given different medicines. After a while, the doctors analyze each group to understand which medicine works better. The data collected from such studies can be put through data mining techniques to measure the effectiveness of the treatment and the impact of the medicine given to the patients.
Challenges in Implementing Big Data Mining in Healthcare Firms
When data is collected by healthcare centers, it is present in both; structured and unstructured forms. However, an average of 80% of data is unstructured with no particular format and is dynamic in nature. It may be found as voicemails, photos, PDF documents, medical records, X-rays, graphics, video, and other types of files that cannot be saved row-by-row as structured data. A significant problem in big data mining is converting this data to a structured format for subsequent examination. To deal with this, hospitals have to check every bit of incoming data, categorize it, and enter it into a central database in a unified, standardized format. However, doing that poses another challenge- that of arranging the time and appropriate personnel for this task.
Any enterprise database uses various data sources. The reliability of each data source needs to be confirmed, and methods for spotting mistakes in the data have to be investigated. That’s why when a vast amount of data is collected, examined, and mined for useful patterns, security becomes the biggest challenge. However, security issues can be solved by authentication, authorization, encryption, and audit trails. Additionally, security breaches due to unexpected, unauthorized, or improper access by privileged people are always a risk. That’s why healthcare centers should limit their database access to authorized personnel only.
Data analysis will take longer as the volume of data keeps growing. However, in many cases, healthcare firms expect (and require) immediate results. For instance, if an insurance claim is fraudulent, it is ideal to flag it before it is completed by preventing the transaction from occurring at all. Obviously, it is unlikely that a complete analysis of a user’s purchasing history could be done in real-time. So, in order to quickly reach a conclusion, hospitals prefer to examine partial findings in advance. Now, imagine doing that for hundreds of patients and thousands of documents. Extracting and analyzing that entire data in-house can be problematic.
How to Overcome These Challenges?
Big data (by its very nature) demands a lot of time, a deep-seated understanding of data handling and data mining at such a large scale, and patience. Hospitals can either choose to establish an internal team dedicated to data mining, with specialized resources and advanced infra setup. But that can turn out to be an expensive affair. Another option is to choose healthcare data mining services.
An experienced third-party data company can manage a lot of tasks at once, including data collection, data mining, reporting, and analysis. You can also look for medical data entry services, data conversion, or formatting support. Outsourcing is usually cost-effective, flexible in terms of scaling, and with a lot of options for varying requirements.
The Future of Big Data Mining In Healthcare
Due to the growth of social networking sites, search engines, media sharing websites, stock trading websites, news sources, and other online resources, the volume of data is increasing dramatically on a global scale. Healthcare centers are using big data mining to draw meaningful conclusions from such expansive datasets. This analysis helps healthcare centers make better decisions, anticipate challenges, simplify operations, and discover new treatment possibilities. In coming years, data mining on Big Data for the healthcare industry is only expected to grow with the rapidly advancing technology.