Data-Driven Health Care Delivery

Big data has big potential, but despite the value of electronic health record (EHR) systems in digitizing care at hospitals, data-driven improvements are commonly hindered by an inability to measure and analyze this data in a timely manner.

An electronic data warehouse (EDW), however, can effectively leverage this data for specific patient populations, which is something that Charles G. Macias, MD, knows about extensively.

Dr. Macias, who is Chief Clinical Systems Integration Officer, an attending physician and director of the Evidence-Based Outcomes Center and the Center for Clinical Effectiveness at Texas Children’s Hospital (TCH), has developed a niche in population health management.

“In order for any health care system to contextualize the science of health care delivery to their own health care systems, it is necessary to understand the structure, process and outcomes of their own populations,” explains Dr. Macias, who is also an associate professor of Pediatrics at Baylor College of Medicine. “This requires a very nimble access to big data around a health care system’s population, and that can’t happen without an investment in analytics – an investment beyond a simple pooling of local data.”

According to Dr. Macias, this investment involves a collection of data on the clinical, operational and financial structure of an organization’s population.

This is exactly what TCH did, using an integrated approach involving a health care EDW, advanced analytics and cross-functional workgroups – or clinical improvement teams – consisting of physicians and nurses, as well as experts in patient safety, quality improvement, finance and IT. Through their combined efforts, raw clinical and financial data were transformed into meaningful information that the hospital then applied in improving the quality and efficiency of care for pediatric patients within a disease cohort, such as asthma or diabetes.

As an example, for part of the asthma clinical quality improvement project, the clinical improvement team was assigned to measure and manage acute asthma from the time of arrival in the ED to discharge. With support from TCH’s Evidence-Based Outcomes Center and the use of an advanced analytics application to examine the EDW data, the team was able to pinpoint variations in practice and areas of high resource consumption throughout the hospital.

“The weaning of beta agonist therapies for inpatients was widely variable,” says Dr. Macias of the findings from the project. “Some patients would be weaned to treatments every four hours for several treatments, while others would be on treatments every three hours for only a couple treatments before discharge. By standardizing the weaning process, we were able to save almost half a day across the board for children admitted to the hospital with asthma, with no increase in days of work missed by the parents or days of school missed by the children.”

Additionally, notes Dr. Macias, through consistent education, improved rates for influenza immunizations and better access to clinics and primary care physicians, TCH was able to decrease six-month readmissions rates by more than half of what they were prior to the clinical quality improvement project for asthma patients.

These results cement the notion that an EHR system alone is not enough when pursuing advances in the data-driven delivery of care for patients, although it is a necessary first step in the process. An EDW allows for improved measurement by providing a clearer, more comprehensive visualization of care delivery, as well as advanced analyses of the data to standardize clinical processes.

Reference:

Macias CG. Data Analytics in Modern Healthcare. Pediatric Grand Rounds, Nationwide Children’s Hospital. 2015 Apr 30.