Researchers at the University of California, San Francisco, used previously untapped data buried in EHRs to pinpoint the source of a particularly nagging hospital-acquired infection.
The culprit? A CT scanner in the emergency department.
The UCSF researchers used time and location stamps in the system’s EHR to track the movements of more than 86,000 hospitalized patients over a three-year period. The results, published in JAMA Internal Medicine, showed patients who passed through the emergency department’s CT scanner within 24 hours after a patient with Clostridium difficile (C. diff), were twice as likely to become infected. Nearly 4% of patients who were exposed in the scanner became infected within two months.
Although previous studies tracking C. diff transmission have focused on a single hospital floor or even a hospital bed recently vacated by an infected patient, the UCSF study tracked nearly 435,000 patient movements throughout the entire hospital, which helped them build a comprehensive map of potential hotspots.
“If we just look at transmission in their room, we’re missing potential opportunities for disease transmission,” Russ Cucina, M.D., senior author on the study and chief health information officer at UCSF, said in a release. The researchers plan to continue exploring EHR data to track patient movements.
Time stamps in EHRs have shown to be increasingly valuable to researchers. A recent study out of Oregon Health and Science University found that time-stamp data can be helpful in evaluating physician workflows.
Providers have also leaned on EHRs to prevent infections like sepsis, although some experts have voiced concern that relying on electronic screenings could actually increase resistant superbugs by leading to increased use of broad-spectrum antibiotics.Betty Rockendorf, MS, RHIA, CHPS, CHTS-IM
The world of healthcare and health information management (HIM) is quickly moving to meet the demand of analyzing and making sense of all the data that is collected—and, ultimately, turning it into useful information.
Data is defined as “facts and statistics collected together for reference or analysis.” Information is “facts provided or learned about something or someone, ‘a vital piece of information.’” The question of how information governance and data governance differ from each other is addressed in the Information Governance FAQs on AHIMA’s website:
Data governance “is primarily concerned with policies and strategies that address the creation and use of granular data as inputs into a system,” i.e., master data management, metadata management, data models and architecture.
Information Governance is concerned with “lifecycle management of this data and information, including its use, protection, and preservation,” i.e. health information exchange, compliance audits, e-discovery and retention of records.
So the two are related and data governance is actually a domain within Information Governance.
HIM professionals are tasked with improving the consistency, reliability, and usability of data assets while optimizing electronic health record (EHR) interfaces. This is necessary to eliminate duplicate records, to address problematic workarounds, and to maintain patient safety. If the data is incorrect (or missing completely) in the case of allergies, current medications, past procedures, and health conditions of a patient, it can be detrimental to the course of treatment and care of the patient.
Additionally, providers are now trying to pull useful information out of the data in their EHRs to support the goals of healthier patients, lower costs, improved performance, and increased staff and patient satisfaction rates.
Merida Johns defines Big Data as “the concept of large volumes of complex and diverse data.” We must utilize our Big Data assets and extract business and clinical value from them. Strong information governance and data governance practices will allow healthcare organizations to maximize the value of their data and information to use in order to meet strategic goals and other requirements. Some of the top challenges facing organizations—and thus opportunities for HIM professionals to step up and demonstrate their value to the organizations—who wish to begin Big Data analytics, according to an article by Jennifer Bresnick in HealthITAnalytics, include:
- Capture of data
- Cleansing of data
- Storage of data
- Security of data
- Stewardship of data
- Querying of data
- Reporting of data
- Visualization of data
- Updating of datasets
- Sharing data
It’s a big list of challenges and opportunities, but “In order to develop a big data exchange ecosystem that connects all members of the care continuum with trustworthy, timely, and meaningful information, providers will need to overcome every challenge on this list,” according to Bresnick.
What are some first steps that we can take? We need to gain the support and buy-in of our organizational leaders. HIM professionals should be strongly advocating within their organizations for a data governance strategy. Get the C-suite involved and make sure that everyone on the corporate ladder understands the importance. As Bresnick writes in another HealthITAnalytics article: “ignoring the role of data governance in the big data environment may be penny wise, pound foolish. Without robust, accurate, timely, clean, and complete data, healthcare organizations will not be able to move beyond the basics of record keeping and develop the analytics competencies that will become vital survival skills in the emerging world of value-based care.”
In a 2013 report from Kaiser Permanente, the University of Pennsylvania, and several public health institutes researchers strongly recommended the creation of “a set of guiding data governance principles that fit the mission, vision, and values of the particular provider,” according to Bresnick. It was further recommended to start with specific policies and procedures about data collection, paying particular attention to:
- How people work together
- Management of cross-functional conflicts
- Decision-making and rights
- Management of change
- Resolution of issues
- Making and enforcing rules
- Management of cost and complexity
- Creating value
Next, communicate to everyone in the organization, being sure to explain, give details, answer questions, and get buy-in for improvement activities that are planned. After the data governance leadership team has established a strong data governance vision and has gotten everyone on board, start with prioritizing projects that need to be improved on the data level. Take the time to train staff within the organization who are charged with creating, using, and sharing data. This could include areas such as clinical documentation improvement or patient registration.
The benefits for the healthcare organization will be seen on the financial side as well as quality side. And the goal, of course, would be improved patient outcomes. This project is not a one-and-done endeavor. Developing and sustaining the program is an ongoing process. To be sure, continued monitoring needs to exist, as well as continued improvements and reassessments. The organization and the data governance leadership team will need to continue training end-users, identifying roles as it relates to data governance activities, providing reminders about data integrity, and maintaining sound data entry practices. Audits should also be conducted within the organization to maintain high data quality.
As Bresnick writes, “These activities will ensure that healthcare providers are prepared to utilize their growing big data resources for generating actionable insights, and that they are being mindful of patient safety and care quality as they optimize their assets for the future of value-based care.” We need to be sure our information at the data level is accurate, reliable, and timely as we use it to make important business and clinical decisions. Data governance best practices are imperative for a successful information governance program and keeping up with the current in the Big Data era.