Data analytics in healthcare is the process of examining data sets in order to draw conclusions about the information and data with the aid of specialized systems and software. Data analytics is a source of technologies and techniques that are widely used in industries such as the healthcare industry. Data analytics helps organizations or make more informed decisions and verify or disprove scientific models, theories, and hypothesis.
Data analytics in healthcare is important because healthcare organizations and healthcare leaders constantly seek to optimize the value and effectiveness of their data through analysis. When data analysts have access to the right tools they can be their most efficient and effective selves. Managing data should not be mistaken for analyzing data. This is a common mistake made by organizations that cause data analysts to spend most of their time managing data instead of analyzing it to produce useful information. Managing data involves; gathering data, validating data acquisition methods, reformatting, ensuring appropriate data types, trimming, cleaning, scrubbing, and confirming data in preparation for analysis and reporting. These activities don’t put the analytical skillset of data analysts to use. Data analysts are supposed to help organizations solve problems and improve their processes using information gotten from data.
The root of this problem was not having the right tools to analyze data and discover insights that would drive care and process improvement initiatives. It is spending too much time collecting data and not enough time transforming it into meaningful analytics. In order for data analysts to maximize their potential and provide full value, there are some steps that can be taken by the health organizations.
Build a Data Warehouse
A first step towards helping analysts be more effective is to provide them with a data warehouse. Building a data warehouse empowers analysts and aides in the identification of improvement opportunities that add value. A data warehouse which is called an Enterprise Data Warehouse (EDW) serves as a hub for all data and aggregates data. An enterprise data warehouse is necessary because analysts can access all the data from any device using the same login and it is secure.
Provide A Conducive Testing Environment
Analysts should be provided with a testing environment accompanied with full access and no restrictions (within reason). Data analysts should be able to test the data to build solutions and test ideas. They should also be able to store data, break down ideas and data sets and rebuild as they see fit. There should, however, be a central communication channel for all analysts so there’s a harmonious understanding of the happenings in the data warehouse. This is important so there is no clash or errors like one analyst deleting data that’s relevant to another analyst.
Provide Tools for Data Discovery
There are data discovery tools that are highly rated such as Business Intelligence tool. These data discovery tools make it easier for analysts to explore the available data and identify trends and areas of improvement and also do in-depth analysis. These tools feature charts, graphs etc that help the data analysts relay information in the form of insightful and visual reports that are easily understandable for other members of the organization and those the data analysts report to. This would lead to system improvements and cause the organization to run more smoothly.
Provided Direction and Clear Goals to Analysts
Healthcare data analysts need direction and clear goals set of what the healthcare organization wants and needs. This goal might differ from department to department. However, care has to be taken not to give step-by-step instructions resulting in micro-management. Just giving them a clear need or goal and letting the analysts apply their skill set will lead to more colorful results and solutions to problems.
Healthcare Data Model Analytics
In an industry as complex and far-reaching as healthcare, making sure that you know or at least have access to as much knowledge as possible is essential. This knowledge is gained in more conventional ways so as to be more accurate and overall more efficient. The knowledge is stored in digital form, which is nothing new to any of us, but what happens to all that stored data is being used in fashions not many of us could ever imagine. However, the end result doesn’t come by some magical data fairy that organizes everything and spits out a report or graph that explains current or future patterns; there is a lot of work that went into planning for and producing a healthcare data model that combines information, searches out and almost seamlessly provides answers you were looking for.