Data Mining and Healthcare Industry

Data Mining, defined as the process of finding the previously unknown patterns and trends. It is an analytic process which is designed to explore the unknown relationships. Data mining alsorefers to the activity of going through big data sets to look for relevant or pertinent information. 

Since the early 90s, this practice has been used to help with fraud detection, credit scoring and maintenance scheduling but it’s finally being utilized in healthcare programs around the country.

With the growth of information and communication technologies, the healthcare Industry is also producing extensively large data. Data mining holds great potential for the healthcare industry to enable health systems to systematically use data and analytics to identify inefficiencies and best practices that improve care and reduce costs.

While other solutions might favor healthcare providers or insurance companies, data mining benefits everyone concerned, from healthcare organizations to insurers to patients.

·         Patients can receive more affordable and better healthcare services. This happens when healthcare officials use data mining programs to identify and observe high-risk patients and chronic diseases and design the right interventions needed. These programs also reduce the number of claims and hospital admissions, further streamlining the process.

·         Healthcare providers can use data mining and data analysis to find best practices and the most effective treatments. These tools compare symptoms, causes, treatments and negative effects and then proceed to analyze which action will prove most effective for a group of patients. This is also a way for providers to develop the best standards of care and clinical best practices.

·         Insurers will be able to better detect medical insurance abuse and fraud because of data mining. Unusual claims patterns are easier to spot with this tool and it can identify inappropriate referrals and fraudulent medical and insurance claims. When insurers reduce their losses due to fraud, the cost of health care also decreases.

Healthcare facilities and groups use data mining tools to reach better patient-related decisions. Patient satisfaction is improved because data mining provides information that will help staff with patient interactions by recognizing usage patterns, current and future needs, and patient preferences

The Three Systems Approach

Implementing all three systems is the key to driving real-world improvement with any analytics initiative in healthcare.The three systems are:

1.      The analytics system. This system includes the technology and the expertise to gather data, make sense of it and standardize measurements. Aggregating clinical, financial, patient satisfaction, and other data into an enterprise data warehouse (EDW) is the foundational piece of this system.

2.      The best practice system. The best practice system involves standardizing knowledge work—systematically applying evidence-based best practices to care delivery. Researchers make significant findings each year about clinical best practices, but, as I mentioned previously, it takes years for these findings to be incorporated into clinical practice. 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. This system is by no means easy to implement. It requires real organizational change to drive adoption of best practices throughout an organization

 Implementing all three enables a healthcare organization to pragmatically apply data mining to everyday clinical practice.

Nivedita Parashar

Assistant Professor(BCA)

JEMTEC, Gr. Noida