What is Big Data in Healthcare?
What is big data in healthcare? It is rapidly growing in the medical field as well as in other fields due to its ability to provide timely and accurate information. Medical professionals such as physicians, nurses, and other clinical support professionals are able to make more effective decisions for patient care and reduce costs by identifying patterns and clusters of data that can help guide them. For example, a physician may acquire information from X-rays or other information in order to make a referral for a specific test or procedure.
There are a few key areas where this data is used effectively. One is through strategic planning. This involves aligning future decisions with the present ones. Strategic planning also helps healthcare organizations improve the efficiency by reducing the number of preventable adverse events and improves quality by eliminating wasteful practices and increasing collaboration between services and the entire organization. When large amounts of health data are available for analysis, strategies can be developed quickly that minimize potential errors and maximize results.
Another application is what is called real-time alerting. This is useful for emergency rooms as well as physicians in intensive care units and rehabilitation facilities. The primary goal of real-time alerting is to reduce delays in providing treatment to patients. In some instances, data analytics can be combined with manual actuation of machines such as IV poles. This enables quick access to staff members in critical condition. Such applications can also integrate with various computerized systems such as electronic patient records (EPRS) in a hospital and various EHR systems in long term care facilities, nursing homes, and hospices.
A third application is predictive maintenance therapy. This refers to the use of analytics to determine what preventive measures will be taken in order to keep patients healthy over time. For instance, if a patient visits his doctor two times per year for a particular disease but is also diagnosed with breast cancer one time, the chances of being diagnosed again two years later are small. By using predictive maintenance therapy, the physician can make a more informed decision and preventative action that are more likely to be effective in the long run. Studies have shown that up to 75 percent of the improvement in survival rates is due to predictive maintenance therapy.
Data mining is the process of using predictive analytics to mine information from a large database, such as the Electronic Health Record (EHR), to search for clinical information that has been previously stored or submitted by physicians, nurses, or hospitals. Some of the benefits of this are improvements in workflow, increased productivity, reduction in cost, and better quality of care. According to estimates, up to one third of healthcare improvement is due to predictive analytics. Other applications in the business intelligence field include business intelligence tools for decision support, financial service technology, and information management.
As healthcare costs continue to rise, healthcare data analytics and other technology are playing a major role in managing and controlling these costs. According to the Center for Medicare Services, annual out-of-pocket spending for patients with private health insurance accounts was $65 billion in 2021. Only a fraction of this amount was spent on actual treatment. Healthcare providers are using sophisticated medical records and software to monitor patients, manage medications, and prevent over-use or abuse of medications. Studies show that patients who are provided access to timely and relevant information about their health-care history are significantly more likely to take medications as directed and to undergo proper treatment for common conditions. Likewise, medical professionals are finding that access to accurate and current health data and medical diagnosis and treatment plans to improve the quality of care received by patients.
Another application of predictive analytics in the field of healthcare institutions is self-help and community health initiatives. Using sophisticated sensors to detect and monitor many different behaviors that might lead to increased healthcare exposure, including visiting the emergency room, visiting a physician, or visiting a hospital is not only preventing illness but preventing potential threats in the community. For example, by monitoring and diagnosing the behavior of patients prior to their need for healthcare, a patient care provider can determine whether that person is at risk for developing certain diseases and disorders, such as obesity, high blood pressure, and diabetes. This reduces unnecessary exposure to preventable health risks and improves the quality of patient care. This is also known as community wellness and is becoming an important component of primary care.
Big data analytics is also being used to determine the success rate and overall effectiveness of different healthcare services. Increasingly, hospitals are using mathematical algorithms and probability distributions to determine patient outcome and overall success rate and then using those figures to improve patient care. The results of these studies can help hospitals design better methods of delivering care, such as more efficient emergency departments, more effective medication dosing strategies, and the ability to recommend more effective rehabilitation services for patients who have been injured or rendered invalid through injury or illness. Other areas where large amounts of patient data are being analyzed are in the area of geriatrics. As people live longer, more of them are being placed in geriatric care, and researchers are looking for ways to understand how people’s age and what influences this aging process.