Late Binding Data Warehouse

The rate at which data flows in a hospital or any healthcare facility changes depending on different factors such as the outbreak of a disease like Ebola, an accident occurring and even effects of climatic change. The nature of data is also very different.  It could be a child, an adult, elderly person, or even from another animal species. To coordinate such complex and ever-changing data, hospitals need a late binding data warehouse.

Data binding refers to the process of mapping raw data with vocabularies and rules that govern medical operations. In other words, binding is the process of taking data coming from the sensor, doctors notes, patients behavior or physicians payments,  and organizing it using the right terminologies while observing the rules of the medical field.

It is thus very important for healthcare facilities to adopt the new technological methods of managing data. Some call it the big data science of managing and interpreting data. One of the models used in most industries is the Enterprise Data Warehouse (EDW). While it is easy to understand and interpret data with this tool in other sectors, the healthcare sector needs a better approach since the data here is bound to change at any minute.

The other reason why EDW is difficult to use in healthcare is the fact that the data in the medical field is much unstructured. Data coming from the doctors or a nurse’s observation notebook is amorphous and is subject to different interpretation by different people. It is thus important to use a binding technique that will bring out the best results out of the information when need be.

There are three approaches that have been used to bind data namely the;

  • Enterprise data binding approach,
  • Data mart model, and
  • Late binding data warehouse model.

Enterprise data binding model

This is a top-down model that takes into account the parameters to measure before moving to an analysis of the data. This model is simple, structured and comprehensive but not an appropriate model when it comes to handling changing data. It can only be used by healthcare departments whose data set is more predictable and steady over a period of time. But in real life, healthcare analytics change rapidly in all sectors of medical profession thus rendering this approach unproductive when applied in healthcare analytics. Binding here is done early.

Data Mart model

This is an early binding model too but with a bottom-up approach, that deals with data from different departments. Data marts are collected from individual hospital departments and analysis was done to come up with actionable conclusions. The advantage of this model is that it makes it easier for developers to come up with conclusions. Since this method is also an early binding data model, there is no flexibility of data.

Late binding data model

This model leaves data in a more actionable form because the data is open for changes and thus more useful in different fields. The model takes into account the fact that the healthcare data is dynamic. Data binding is only done when the actual need arises. This could be a medical or a financial need. You do not need to make conclusions upfront while things may change along the way.

Some of the advantages of a late binding data warehouse include;

  • Hospitals save a lot of money from daily operations due to the reliable information they gather using the enterprise data warehouse tools.
  • Easy to work with insurance companies since the cost and the future analysis can be done effectively and thus present reliable information to the medical insurers.
  • The data helps determine how ready a hospital is in the case of an emergency.
  • Easy to carry out financial data comparisons making it easy to standardize prices for patients across hospitals.

Information in hospitals will always be diverse and come in at different speeds, but with late binding data models, managing the data and enjoying the full benefits is possible.