Many healthcare companies rely on data analytics for financial, clinical, and operational improvement. Studies have shown that data analytics use in the healthcare industry will increase to $28 billion by 2025. Data analytics has many advantages, including improving patient care and reducing the cost of healthcare.
Here is a look at the different use cases of data analytics in healthcare:
Studies have shown that many healthcare organizations have experienced data breaches since 2016. These data breaches are projected to cost more than $4 billion. Organizations are now using data analytics to minimize security threats by detecting susceptive cyber-attack behavior, monitoring network traffics, and calculating the real-time risk scores for specific interactions.
Predicting Patient Loads
Analytics tools are now being used to predict the number of patients who visit a department at a specific time. This allows health facilities to reduce unnecessary labor costs, manage resources, including supporting staff and physicians, and allocate the right number of workers according to the workload.
For instance, a public university in Paris used data analytics to predict the number of patients likely to visit the emergency department. This allowed the hospital to staff the department based on its needs.
Detecting Fraud Risk
Healthcare fraud is currently a multi-billion expense in healthcare spending in the United States, with annual spending of $68 billion. It can be attributed to organized criminal schemes, deceitful practitioners, and practitioners who make unintended mistakes in the billing process.
Data analytics can help healthcare companies detect fraud by analyzing all claim patterns from different insurers and insurance policies, detecting upcoding like unnecessary services based on a patient’s diagnosis, and discovering phantom and duplicate billing. Phantom billing is a claim submitted by a doctor to the government for services they did not provide. Data analytics help unveil phantom billing by comparing patient claims against their previous medical history.
Monitoring Real-Time Data
Real-time healthcare data analysis allows healthcare organizations to prevent adverse events by constantly analyzing patient data using in-home and in-hospital patient devices, such as detecting alarms from beside the bed monitors. It allows resource planning by providing real-time data based on the available staff, the number of visitors and patients, and the number of beds and operating rooms available.
Preventing Opioid Abuse
Data analytics is now being used to help combat opioid abuse in the United States. Studies have shown that more deaths in the US are caused by overdosing on opioids than road accidents cause accidental deaths.
The situation has worsened with some countries, such as Canada, declaring opioid abuse a national health crisis. Data analytics has enabled professionals to identify the risk factors of opioid abuse with a high degree of accuracy. This way, doctors can better predict if a patient is at risk of abusing the opioids prescribed.
Oncology Clinical Studies
Health professionals now use data analytics in cancer research. Medical researchers can use a large amount of data available from cancer patients to find their treatment trends and the highest success rates. Data analytics often leads to unexpected benefits, such as discovering that Desipramine, an antidepressant, can cure certain types of lung cancer.
Therefore, there is a need to link up different databases from different institutions such as nonprofits, hospitals, and universities. This will offer medical researchers reliable samples of data to work from.
Medical imaging is vital in providing accurate and efficient healthcare. Every year the US performs 600 million procedures. Analyzing and storing the images manually often costs money and time when radiologists have to examine every image individually.
Data analytics have changed the way medical images are read. Algorithms have been developed to analyze hundreds of images to help identify certain patterns in the pixels and convert them into numbers to help doctors diagnose.
Learning and Development
Data analytics have played a huge role in how medical staff view information. They can now process and learn from large volumes of data to provide their patients with the best care. It also allows them to easily access medical information from specialists in a certain field.