According to the Merriam- Webster dictionary, an analysis is “a detailed examination of anything complex in order to understand its nature or to determine its essential features: a thorough study.” Data analytics is a method of analyzing, manipulating, and processing of complex data in a more defined and logical way. In brief, it can be defined as “a method of logical analysis.” In data analytics method, complex data are drilled down at various levels in order to arrive or conclude in a meaningful way. It is mostly used to make organizational decisions, implement any new processes, changing the existing processes, etc.
The healthcare industry generates and collects a huge amount of complex data daily. Using data analytics technique, these large set of data can be broken down in a meaningful way to know the trends and make more accurate decisions for many major quality outcome projects. Through analyzing trends and forecasting the data, the healthcare providers will know whether implementing or changing any project would have a positive or negative impact on an organization. For example, if an organization wants to increase the overall mental health of their patient population, the provider should first know what percentage of patients are not undergoing their depression screening during an annual health checkup, says Thamarai. To identify the percentage of patients who did not have their depression screening done, the data analyst will collect data from the organization’s database for a year. Once the organization identifies the base population, the experts look at the data and analyze the gap by drilling down the data to find why a depression screening was not done. Finally, using the same data analytics method, an organization can decide whether this quality project needed to be implemented after performing management level decision such resource needed and financial status. According to Thamarai, however, though data analytics aids and helps the leaders to decide, it is utmost important to include clinical folk’s knowledge as there might be something beyond this analysis. In the above example, the patients who did not have depression screening may voluntary avoid (needed to be excluded from the base population) or there is no such information captured in the medical record altogether. In these scenarios, the doctors will come with ideas to close the gaps such as using a phrase or a flag in the medical record for the patients who voluntarily declined the screening. Hence, a combination of Small and medium enterprises (SMEs) and data analytics team is required to obtain a positive outcome for any healthcare quality project.
“Can this data analytics method be feasible in the Indian Healthcare System?”. Major Indian healthcare organizations still use surveys and administrative medical records data to analyze the quality of care. These resources of data are less reliable and collecting this type of data is always a challenge for the healthcare organizations because of lack of Electronic Health Records (EHR), says Thamarai. Though EHR implementation is slow in India, it is predicted to grow in years to come because of many government-initiated policies such as ‘The Digital India Healthcare Policy’, increase in EHR implementation in Major hospitals like AIIMS, etc.
