Healthcare Big Data

 

Part 1: Introduction
When it comes to big data, healthcare is an excellent place to find it.
Data is structured and captured in different manners, there’s lots of data, and it is hard to get any sensible information out of this data put together.
In healthcare, traditionally, data (and models) are split by department; data needed for a department were modelled according to the needs of that department. Most of this data is not shared. But data that represent patient or workflow information could be shared: patient demographics, patient location/status, current attending physician, allergies, wound documentation, which user accesses the patient record...
The need for cross-department (and cross-institution, and cross domain) data is emerging as important e.g. for decision making. This data is important for clinical reasons (informed decision making), and operational reasons (e.g. Meaningful Use in the US, and the growing interest in Europe for operational/efficiency improvements).
Even breaking the boundaries between healthcare domains will be insufficient: Healthcare data is not a silo. Institutions and patients collaborate on health data, but also other institutions can play a role. Work and social environment, demographics information, etc. can be potentially relevant.

Should a person that lives in the mountains and takes many intercontinental flights mention that to the physician that is evaluating the possibility of an ionizing radiation procedure?

Should a patient’s demographics be taken into account in deciding the sensitivity of a procedure?

Should a patient’s social habits considered when recommending a continued treatment?

When prescribing a drug, should we consider the habits of the patient and his surroundings?

Coming down to spaceship earth, within a healthcare organization, information can be captured anywhere, and this information is getting more stretched to be viewed under different perspectives.
Data Interfaces (messages) are usually designed to provide just enough data for continuation of a workflow. And these data are modelled for the specific needs of the originating system. If we want to know a patient’s full history, there will be some collage work, especially if we want the information to be in a usable form.
Here I propose a look at the problem, the possibilities and the goals, and I present a way of jumping across these water lilies. The keyword is: Iterate.

 

Next:

• Part 2: The approach
• Part 3: The delicious analogy