Healthcare analytics is likely the future of the healthcare industry. Having a way to efficiently and accurately handle healthcare big data can help save money and improve patient care. Unfortunately, there can be issues that occur all too commonly when approaching EHR data and analytics systems. If there is an understanding of what the most common pitfalls are, then there can be a better chance of them being fixed so that the software runs as smoothly and effectively as it is supposed to.
1. Limits of Point Solutions
Point solutions can be used in many healthcare databases, but just because they are often chosen doesn’t mean they are necessarily suited for the job. Point solutions’ weakness can come from the fact that they only gather information from one source of data. While this can bring relevant information forward, it is generally limited and insufficient, failing to capture the bigger picture. For example, they may only diagnose a patient’s individual pains instead of looking at the underlying cause of the ailment as a whole. Since the system fetches only one piece of data at a time, looking for a lot of information can easily cause it to run slowly, causing further frustration and inefficiency.
2. Disorganization of Data in EHR
Issues with point solutions can reflect issues with entire EHR systems. The weakness with most EHR systems can be that they are unable to focus data into a single source of accurate information or truth. Rather than giving a complete view of situations, they can offer only limited, fragmented views. They often store data into separate silos, or categories, that can prevent inquirers from seeing connections in various situations.
3. Time-Consumption of Independent Data Marts
At the root of many healthcare analytics systems can be many smaller data marts, or information storehouses. These data marts can be independent of one another, which can be a big issue. Healthcare should primarily be focused on the individual as a whole, but due to information having to be pulled in from separate sources, it can be impossible to see the larger scope of things. Data marts can cause the system to run slowly and poorly, delivering mediocre results in a less than prompt amount of time.
Ability of EDW to Prevent Common Issues
Luckily for the healthcare industry, there can be a solution to the common failings of analytics systems. An EDW, or enterprise data warehouse, can be implemented to help better organize data. Unlike point solutions, an EDW isn’t restricted to individual data sources. It can be equipped to give enquirers an all-encompassing view without bogging down the system. An EDW can be faster and more informative than other varieties of healthcare analytics systems. It can be an up-to-date and efficient way to get the most from healthcare big data.
The Big Picture of Healthcare Analytics
As the future rushes onwards, healthcare analytics will likely continue to grow as an integral part of the industry. Currently healthcare systems can have many issues, such as being slow and giving only a limited picture of patient information and needs, but by implementing up to date programs, such as EDW, databases can become more efficient and faster, giving caregivers the information they need to offer patients the best treatment possible.