A very basic definition of data mining is the searching through large amounts of computerized data to find useful trends or patterns. At one point this might have been plotted on a simple Excel sheet or graph, but in the realm of today’s standards, the amount of data and nuances involved far exceed minimal plotting. Especially in an industry as large and data-rich as healthcare, the discovery of patterns requires a lot more processing power.
Clinical analytics means examining vast amounts of information in order to uncover recurring trends, which in turn produce predictive or future forecast data. Just as when watching a weather forecast, the computers that make the predictions for five or ten days out take into account the patterns in the highs and lows currently happening, and then find previous models that held similar flows and calculates what should happen. Though this is quite simplified, this example shows the value of data mining from years of previously stored and analyzed information.
Being able to predict the future of patients and their healthcare opens the doors to being able to identify and deal with high-risk patients more appropriately, rather than continually admitting them into costly hospital beds. Also, being able to ascertain the patients with severe or chronic illnesses who might be susceptible to readmissions can be headed off with other preventative measures instead of another hospital stay. Armed with the ability to distinguish when patients fall into certain patterns, doctors and other health professionals would be able to manage staff, space, resources and the overall costs expended due to effective and efficient planning.
Healthcare and the many different fields of medicine are one of the larger industries creating a majority of data generated each day. This data is compiled together and shared as part of the information exchange that takes into account demographics, such as age, gender and zip code, along with patient’s needs, and plots out general and diverse patterns, especially within communities or populations. These patterns are then applied by professionals when treatment is sought in order to categorize the patient into a more personalized course of healing.
Data mining in healthcare doesn’t benefit just one group of people, but works to help everyone that is involved at the many different levels of treatments; doctors and other staff have more information at their fingertips to correctly and quickly diagnose a patient. The patients may not be subjected to unnecessary testing or periods of unknowing because of the actionable data. Hospitals can run more efficiently due to the fine-tuned productivity found within the patterns. Even the prevention of fraud and fraudulent behavior can be identified from previously recognized trends.
The most unfortunate aspect in all of this is the healthcare industry is playing a game of catch up within the realm of data mining. Most other businesses and industries have fewer points for which they are trying to model. The sheer complexity of data that is kept in healthcare has been a hindrance to making or finding the right software to implement. Additionally, the privacy that needs to be upheld on medical files has slowed the adaptation of data mining technology. But, persistence and a better working knowledge of restrictions and requirements that needed to be respected, a breakthrough for the healthcare industry.
The basic definition of data mining helps to describe the minimal possibilities for any business that utilizes it. However, the enormous possibilities available when implemented to its fullest potential, there is almost limitless growth. This isn’t just within healthcare but for anyone, yet healthcare may be the spheres in which data mining might have the most significant ability. At one time or another all of us will be a patient, whether for that yearly physical or for something much more dire. If you are like me, I want all those who are helping me to have as much knowledge available to them, and if that includes previous patterns determined from data collected over many years and patients, I am happy to a beneficiary. Even if we think we are absolutely unique, there are some features that make us ordinary under the hood (so to speak).