A Breakdown of Healthcare Data Mining
Data mining is the process of examining a large pre-existing pool of data in order to get new information from it. Data mining is the computing process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. It is an interdisciplinary subfield of computer science. Many industries successfully use data mining such as the business world, the education industry, government organizations such as transportation among many other including of course the healthcare industry. It helps the retail industry model customer response. It helps banks predict customer profitability. It serves similar use cases in telecom, manufacturing, the automotive industry, higher education, life sciences, and more. Healthcare Data Mining is of great importance in the healthcare industry as it enables health systems to systematically use data and analytics to identify inefficiencies and best practices that improve care and reduce cost. Some medical and research experts in the healthcare industry believe the opportunities to improve care and reduce costs concurrently could apply to as much as 30% of overall healthcare spending. This could be a win/win overall for the entire industry. However, due to the complexity of healthcare and a slower rate of technology adoption, the healthcare industry lags behind other industries in implementing effective data mining and analytic strategies. In the healthcare industry, data mining has for the most part, been just an academic exercise with only a few pragmatic and real life success stories. Academicians and researchers are using data-mining approaches like decision trees, clusters, neural networks, and time series to publish research. The Healthcare industry, however, has always been slow to incorporate the latest research in Healthcare Data Mining into everyday practice.
Some healthcare organizations choose to use the Enterprise data approach as an attempt to slowly ease into data mining. This approach is explained briefly below
Enterprise Data Model Approach
The enterprise data model approach uses the top-down method in the handling of data. The goal of this approach is to model the database to perfection from the onset. It determines in advance everything that needs to be analyzed in order to improve outcomes and patient satisfaction.
This approach involves building a secondary system that receives data from systems that already exist. Extracting data from existing systems and making it all play well together in a net-new system can get very very complicated. However, with patience, the right skills, and a bit of magic, it’s possible but it is incredibly time-consuming and expensive. The complexity of this model and how long it takes to actually get value is one setback and significant downside of this model. Other downsides are: This model binds data very early, and once data is bound, it becomes very difficult and time-consuming to make changes. In healthcare, business rules, use cases, and vocabularies change rapidly. Hence you can get stuck with outdated terminology. Another setback is that this model tends to disregard the realities of the data in an organization.
However, the most effective strategy for taking data mining beyond the realm of academic research is the three systems approach. Implementing all three systems is the key to driving real-world improvement with Healthcare Data Mining. Unfortunately, very few healthcare organizations implement all three of these systems.
These three systems are:
The analytics system: This system includes the technology and the expertise to gather data, make sense of it and standardize measurements.
The best practice system: The best practice system involves standardizing knowledge work—systematically applying evidence-based best practices to care delivery. A strong best practice system enables organizations to put the latest medical evidence into practice quickly.
The adoption system: This system involves driving change management through new organizational structures. In particular, it involves implementing team structures that will enable consistent, enterprise-wide adoption of best practices.