Overview of Analytics

The Five Ws

The Analytics section has been organized around the Five Ws (Who, What, Where, When and Why). Additionally, all the filters that you can apply are designed to adjust the Five Ws so that you can narrow down the data into only what you want.

Patients (Who)

The Patients section is all about who your patients are. This section tells you: what your most prevalent patients are by common name, how many patients have been in your care, how many species have been admitted, and a whole lot more. There are pages summarizing your patients by many different taxa categories such as common name, taxonomic class, biological group, and endemic status. There are also pages summarizing your patients by many demographic and health indicators such as age, sex, attitude, body condition, dehydration, and mucus membranes. The details of each category can be viewed to see their changes over time. There is also a taxonomy treemap to visualize your patients grouped by their taxonomic ranks.

Origin (Where)

The Origin section is all about where your patients are found. You can see the most prevalent cities and states you receive patients from and see a map that plots where your patients are found. The map will cluster patients together within a close geographic area to help you visualize groups.

Circumstances of Admission (Why)

The Circumstances of Admission section tells you why your patients are brought to your hospital. In this section, there is an overview of your patient’s most frequent circumstances of admission and all your circumstances of admission grouped by their root circumstance. You can also see the totals of each circumstance of admission as well as the percentage of each circumstance of admission compared to each other as well as circumstances of admission over time to illustrate their fluctuations seasonally. There is also a survival rate for each circumstance of admission including their first 24 hours of care compared to after the first 24 hours.

Disposition (What)

The disposition section is about what happened to your patients at the end of their care. There is an overview page with the totals of each disposition as well as graphs illustrating when each disposition occurred. There are also specific graphs for released and transferred patients, including the type of release (or transfer) and the release (or transfer) rate of your survived patients. You can see the survival rate of your patients and the percentage of each disposition. There is also a map that plots where your patients are released (or transferred to).

Dates (When)

This section expands your ability to modify the date range to render the analytics within. Not only can you change the date range, but you can also compare to previous date ranges and group your data by different time frequencies, ie Day, Week, Month, Quarter, Year.

Segment the Data

By default the graphs show data for all your patients, but what if you want to see analytics for a unique segment of your patients? For example, what if you only want to see data for your birds, or raptors, or perhaps all patients from a particular city, or even only patients that were poisoned? No problem!

Just click on the box labeled All Patients and choose which segment of your patients you want to see.

Want to compare multiple segments of patients? Again, no problem. You can choose up to three segments to create your graphs with.

Future Plans

It is challenging to describe all the features and functionality of the Analytics section simply because there is so much you can do with it.

We are continually working on improving the analytics, so we are very interested in any charts that you may generate at your own hospital. If there are any useful charts that you think we are missing, PLEASE TELL US and share your charts with us (support@wildneighborsdp.org).

Additionally, if you are not able to “discover answers to your own questions” PLEASE TELL US. There may be some tweaking that we need to do.

A Word of Caution

Looking at your analytics can be extremely fun and interesting; however, it is also very easy to read too far into what the analytics are showing you. In other words, don’t let the analytics force you to jump to conclusions. Allow the analytics to confirm, deny, or improve your questions. If you see anomalies in the analytics, it may indicate something important, but it also could be the result of bad data that needs correcting.