Data Capture: The Beginning Of Analytics In Healthcare

Analytics can help improve outcomes in healthcare, and capturing data through EHRs is a vital step in the process

Analytics, and the big data which analytics rely on, have been used with success in many industries varying from retail to insurance. Yet in healthcare, the ability to use analytics to improve outcomes and increase both staff productivity and the financial position of the health system has not had nearly as much traction.

There is a key IT initiative in healthcare that is already underway that has the potential to be a major factor in bringing analytics to healthcare. Since the High Tech Act of 2009, implementation of electronic health records (EHRs) in U.S. hospitals has seen a significant boom. According to the latest HIMSS EHR implementation data, over three-quarters of all US hospitals have exceeded stage three on the HIMSS adoption chart. This means hospitals have the ability to utilize their EHR at the center of a new analytics ecosystem.

To understand where we are in terms of analytics, consider the following model:

One of the greatest challenges of realizing the potential of analytics in healthcare is the first step in this model of an analytics eco-system – data capture – was notoriously difficult for healthcare providers. In other words, how do healthcare providers get to big data without a process for capturing the pieces to get to robust analytics? This is where the potential of the EHR can help because so much data is now being captured electronically and stored in the EHR.

Of course, part of the process in capturing the data entails getting the information from the patient, clinician, or asset into the EHR. As with the implementation of the EHR, there are already processes in place including RTLS technology, biometrics, and barcoding to serve as that crucial connection between the data we want to capture and the EHR.

So what are some of the data available for capture today using existing technologies? Here is a sampling by department.

In admissions – both within and without the four walls of the facility – biometrics and health system ID in the form of wristbands enables immediate and positive identification of the patient prior to and after admissions. Not just how long patients stayed, but where they were admitted and the length of the admissions process itself.

In the Pharmacy, data such as the number of medications processed, which medications, and the number processed by pharmacist by shift and across multiple locations is now available. Additionally, healthcare providers can build hospital pharmacy benchmarks that can be compared to pharmacies managed by clinics or even retail pharmacists. With in-house script printers, the number of actual medications provided at discharge, as well as which medications, can be analyzed.

At patient bedside, captured data can tell us about sample collection including which clinician performed the draw and the number of samples processed by shift, clinician or facility.

In the lab, the sample types that are most prevalent, the number of samples processed by technician, shift, lab, or facility, and the total time between the sample draw and processing in the lab are some of the data points that can be analyzed.

Hospitals have a lot invested in their EHR implementations. The ability to use the EHR as part of a new, robust analytics ecosystem is yet another way the EHR can add value and eventually drive additional positive outcomes.

About the author
As healthcare practice lead – Americas, Andy Tippet oversees healthcare practice for North America and South America Regions of Zebra Technologies. Tippet provides planning and execution on marketing initiatives to drive revenues.