Big data in the healthcare industry is about to get even bigger thanks to the move toward electronic health records. Electronic medical records are getting a boost due to the implementation of the Affordable Care Act. As a result, medical researchers can expect a massive influx of healthcare data to analyze.
The scientific community is abuzz about the potential for big data in the medical research arena. According to Science 2.0, a science blog, some of the clearest opportunities recently identified in this area revolve around reducing costs in several key areas:
High-cost patients - Did you know that just 5 percent of patients account for roughly half of all US healthcare costs? By targeting these high-cost patients, big data has the potential to make a huge impact on total healthcare spending in the United States. This is a good example of the Pareto principle at work.
Readmissions - With nearly one-third of readmissions deemed to be preventable, using big data to predict which patients are at a high risk of readmission could lead to better interventions and reduced re-admissions.
Triage - Big data could also be used to improve the triage process by applying algorithms to send patients to the correct unit for care and ensuring that everyone involved in providing that care is promptly informed throughout the process.
Decompensation - Decompensation refers to a patient's worsening health condition. Patient monitoring tools such as heart rate and blood pressure monitors are used to measure a patient's current condition. Using big data, researchers may be better able to determine the risk of decompensation, allowing healthcare providers to intervene before the patient's condition worsens.
Adverse events - No one wants to suffer from an adverse health event such as infection, a drug reaction, or renal failure. These events often result in death, yet are often preventable. Big data could make huge gains in both preventing adverse events and slashing their associated costs.
Diseases affecting multiple organ systems - Systemic diseases that affect multiple organ systems are among the costliest to treat and manage. Using big data, medical researchers may be better able to predict the likely progression of a disease which, in turn, would help healthcare providers develop a more effective, and more cost-effective, treatment plan.
While these areas all represent significant opportunities for medical researchers and the healthcare industry at large, how can researchers possibly make sense of all that data? According to Dolphin, "Big Data relates to the fact that today's business intelligence systems are experiencing record levels of data growth from terabytes to petabytes and beyond. The challenge is in maximizing the opportunity for real-time business intelligence while minimizing the impact of exploding data volume on productivity and total cost of ownership (TCO)."
This is done through the use of business intelligence and data archiving software. With the right tools in hand, medical researchers have the ability to make sense of the sheer volumes of healthcare data from the past, present, and future.