Data Analysis in Healthcare: 3 Things Every Analyst Should Know
Hope is not a plan, and intuition is not a substitute for information. Businesses in the medical field simply cannot afford to function in an inefficient manner. Not only do problems with internal processes cost healthcare companies valuable time, money, and resources, but they can also lead to negative patient outcomes. While having a healthcare data analysis plan in place is a great way to deal with these issues, it’s equally important for professional healthcare analysts to understand how to optimize data collection, architecture, transfers, analysis, and application. Here, we’ll highlight three aspects of data analysis in healthcare that everyone on your team should know:
Data Architecture & Warehousing
A successful analytics model relies on quality data. As such, data architecture and data warehousing are key concepts that all healthcare data analysts need to understand fully. In many instances, healthcare companies collect data from many different sources and for many different purposes. For example, a person’s medical history, credit card information, and previous prescriptions all may exist in different “silos” of data. It’s the job of data analysis in healthcare to bring all of these disparate pieces of information together under one unified system. To accomplish this, they must understand:
- Structured Query Language (SQL).
- Data transference –– loading, export, etc.
- Data modeling.
- Clean data accumulation.
Data analysts must be able to create and use a data system that manages to combine real-time metrics with historical data. Incomplete data, “unclean data,” irrelevant data, and plain old incorrect data will significantly hamper any organization’s attempt to use it in a meaningful way.
Once a company has managed to create a bottom-up system that ensures quality data accumulation and warehousing, the next step in data analysis in healthcare is to implement action plans with that information. Analysts in the healthcare industry must be familiar with using business intelligence techniques to create specific queries and tangible solutions. For example, say a healthcare company wants to set up an automated appointment check-in system for their patients. Analysts in such a situation should appreciate the difference between Artificial Intelligence measures and Robotic Process Automation in order to create a system that will facilitate this upgrade. Remember, business intelligence is all about using data effectively, which means that healthcare analysts must be familiar with the tools required to put data to good use.
Some healthcare companies attempt to use cookie-cutter analytics solutions that businesses in other fields have pioneered. While healthcare companies have some things in common with other industries, they also present analysts numerous unique challenges. Factors like patient care, clinical outcomes, and HIPAA compliance are considerations that healthcare data analysts need to make when accruing and using data. The goals of an urgent care facility are much different from the objectives of a software development company. So healthcare analysts must be able to utilize data to benefit medical professionals and their patients.
At Amitech, we have years of experience working with medical organizations to manage data, analyze key metrics, and deliver automated upgrades. We offer unique solutions tailored to companies in the healthcare and medical field that will directly benefit your staff and your clientele. Contact us here to learn more or to get started with us today!