While many of the major gains promised by electronic health records (EHRs) and big data remain elusive, Geisinger Health’s Unified Data Architecture demonstrates how big data might help healthcare providers and clinical laboratories optimize care, improve outcomes, and control costs as the technology evolves
Use of big data in healthcare gets plenty of hoopla these days. Many experts predict great things as clinical laboratory test data is pooled with other patient information and demographic data. But there are many technical problems to be overcome before the full potential of healthcare big data can be translated into ways that improve the health of individuals.
Big data in healthcare is essential to the success of both precision medicine and population health management. However, without the ability to consolidate other data sources and provide intuitive ways for healthcare providers to access, analyze, and utilize the data coming from the various sources, such as clinical laboratory and anatomic pathology test results, much of the data can be underutilized or overlooked.
Medical laboratories continue to generate increased amounts of data, much of which often finds its way into electronic health record (EHR) systems and other data silos. A Harvard Business Review (HBR) report from doctors at Geisinger Health in Pennsylvania shows how this data might be used.
Consolidating Data to Create Cohesive Snapshots of Patient Health
The HBR report attributes Geisinger’s ability to utilize big healthcare data to its Unified Data Architecture (UDA). According to a Healthcare Informatics article, Geisinger’s UDA was based on Hadoop and other open source software. According to the doctors who wrote the HBR report, “… pulling meaningful data aggregated from many sources back out of EHRs has historically been vexingly complex. The potential insight from these data are limited in practice by the shortcomings of traditional data repositories.”
Geisinger’s UDA addresses two key issues the Healthcare Informatics authors see as obstacles to the expanded, easier use of big healthcare data:
- Lack of ways to deal with unstructured patient notes that do not adhere to traditional database organizational structures; and
- Data silos created when multiple departments collect data but use separate storage systems.
Using natural language processing (NLP), the UDA system can pull critical information from long-form written reports or analyses.
Geisinger’s system connects nurses on the floor, medical technologists in the clinical laboratory, and surgeons in operating rooms to the same pools of data. However, it also pulls in data from external sources, such as pathology groups, other reference or medical laboratories, and even patient-worn mobile medical devices. The HBR report states, “The integration of data from Health Information Exchanges, clinical departmental systems (such as radiology and cardiology), patient satisfaction surveys, and health and wellness apps provides us with a detailed, longitudinal view of the patient.”
Big Data Helps Healthcare Professionals Spot Future Worries
Geisinger’s Abdominal Aortic Aneurysm (AAA) Close the Loop Program—named semi-finalists in Healthcare Informatics’ 2016 Innovator Awards Program—is an example of how NLP and data collation offers benefits often overlooked with traditional approaches.
Geisinger doctors found that AAAs typically are discovered during care for another condition. Often, the conditions for which the patient seeks care are more serious than the small AAA and it isn’t mentioned. While AAAs might be noted in patient records, healthcare providers typically do not look for the data. Thus, left untreated, a AAA can develop into a serious condition that could have been prevented.
NLP enables Geisinger doctors to analyze UDA data for warning signs of AAA and create follow-up and treatment plans that might otherwise remain overlooked. According to the HBR report, this program has led to 12 lifesaving operations to date that might otherwise have been missed.
Real-Time, Comprehensive Updates Offer Big Gains in Combating Sepsis
Big healthcare data shows potential for treating many life-threatening conditions, such as sepsis. Prompt treatment is essential to positive outcomes in sepsis cases. Physicians at Geisinger use the company’s UDA data to both pinpoint when sepsis indicators appeared, as well as to consolidate data from across a patient’s care continuum to optimize treatment.
Instead of sorting through disparate streams of data from various operational areas and reports, data is combined into a consolidated dashboard featuring real-time physiologic metrics, such as:
- Blood pressure measurements;
- Blood culture results; and
- Antibiotic administration.
The HBR report notes, “By tracking, aggregating, and synthesizing all sepsis-patient data, we expect we will be able to both reduce the incidence of hospital-acquired sepsis and improve its management.”
Using Big Data to Track Surgical Supply Chains and Waste
With the unique cost and outcome aspects of each surgical case, and the differences in payouts from payers, creating big data for tracking the efficiency and waste of surgeries is difficult without a big picture view of the factors. Using their UDA, Geisinger can track the exact supplies used in an operation along with the outcome, recovery, cost, and follow-up data related to the procedure.
“This gives surgeons and administrators an important new view of how they perform comparatively from both a cost and outcome perspective,” noted the HBR report’s authors.
Big data is still a developing technology. Nevertheless, programs such as at Geisinger Health offer useful lessons into how data streaming from clinical laboratories, pathology assays, operating rooms, intensive care units, and even personal health-tracking devices might be combined to provide a unified patient record. That would make it possible for caregivers to use analytical tools to tailor each patient’s care and treatment to his or her specific conditions and physiology.