What are some key mistakes companies tend to make when it comes to implementing and using big data analytics?
For more than a decade, healthcare organizations have invested millions of dollars building data warehouses and armies of data analysts with the sole purpose of making better decisions with data to improve patient outcomes. The historical problem has been that these warehouses and analytics alone aren’t enough because the analytics, reporting and dashboard insights they provide are not actionable. They simply report what is happening, but the insights cannot explain why it’s happening and what can be done to either 1) prevent it from happening in the future if its impact on operations is negative, or 2) encourage the desired positive outcomes.
Now, instead of just understanding “what is going on,” the infrastructure and technology have come of age to figure out “why” and “what to do about it.” At LeanTaaS, first, we mine reams of historical electronic health record (EHR) data and use sophisticated algorithms to spot trends and patterns — both positive and negative. Then we provide prescriptive guidance to address operational issues to improve access to constrained resources, reduce patient wait times in hospital or infusion center settings, increase staff satisfaction, and lower the overall cost of healthcare delivery.
Unfortunately, the majority of big data analytics companies focus only on their dashboards and reporting tools, complete with vast amounts of data. But it’s time to expect more from analytics companies than the mere presentation of data. The data needs to tell a story and make recommendations that result in meaningful process change. The solution must be able to develop accurate predictions and generate recommendations that are specific enough for the front line to make hundreds of tangible decisions each day — not just “admire the problem.”
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- Big Data
- Big Data Analytics
- Data Warehouse
- Data Analyst
- Electronic Health Record
- Information Assurance
- Database Column
- Data Modeling
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