Can the importance of data management in healthcare be overstated today? It saves lives. And it’s how professionals do their best when organizing all of the information that’s relevant to care and healing.
What kinds of information can be involved? There is Clinical Data Management that consists of the collection, integration, and validation of the data obtained from any clinical trial. There are all the details from each electronic medical record (EMR)—a patient’s digital chart.
There are details of disease conditions, forms of care delivery, data on readmissions, and cost accounting. Analysis must be applied to account for outcomes, as directed under current federal law. Providers must also share data. Providers that save money while achieving quality goals keep part of those savings.
Analysts must examine, review, and report on healthcare claims as they look for eligibility, accuracy, and completeness. They examine employee shift records. Analysts in a variety of positions, and often from different locations, work with all of this data. Ultimately, data analysis supports the best possible health care in every kind of institution.
Medical schools manage data to improve the outcomes of their work. Healthcare companies focus on quality improvement. And a children’s hospital develops state-of-the-art care for its young patients—all through advanced analytics.
Case Study: A Children’s Hospital
To bring the importance of data management in healthcare to life, take a closer look at the third of the above three scenarios—focusing on children suffering from appendicitis. In the United States, a quarter of a million appendicitis patients seek help every year. These cases involve a million hospital days per year.
Of course, patients’ length of stays in the hospital will vary. This depends on the complexity of the appendicitis, how soon an operating room is ready, the surgeon’s availability, nursing policies, and so forth. Within those variables are numerous opportunities for analytics technology to improve care for both the business and the patients—including lifesaving care for severe appendicitis.
Gains Waiting to Be Made
In old-fashioned data-gathering, residents and fellows pull results from charts to analyze them. It can take weeks. But a patient’s need is now.
So the hospital adopted data warehousing. This move could be replicated in most any healthcare organization. In a common scenario, some 80% of data analysts’ time goes into searching for and compiling data, rather than the vital work of actually interpreting the data and setting forth ways to improve the outcomes. Clearly, great gains stand to be made through effective data management.
With their new analytics platform, teams—clinicians and technical staff together—developed the capacity to actively assess the effectiveness and outcomes of care improvement. They brought evidence-based practices into clinician training, nursing plans including antibiotic treatments, and patient education materials. The highest levels of the organization supported the new, data-driven paradigm. Working in near real time with dashboards, they’re standardizing excellence in their care delivery.
Trust the Data
Teams that develop the platform will trust the data—which they can delve into themselves without calling the IT department. They now compare data across several hospital branches. They’ve improved outcomes and lowered costs. Among the specific successes: they shortened the children’s hospital stays by 36%; sped up diagnosis for simple appendicitis; and reduced time from diagnosis to the operating room.
Now, the team plans to publish clinical outcomes online—including complication rates. Families will be able to see what to expect. New bedside “outcome rounds” include nurses, surgeons, and administrators, all helping answer families’ questions about their child’s progress.
This hospital exemplifies how any health care organization can benefit and grow. Understanding data management empowers professionals, as the Health Resources and Services Administration (HRSA) puts it, “to identify where systems are falling short, to make corrective adjustments, and to track outcomes.”
Using a Data Model
A data model is the bare bones structure of how data is saved, related, extracted and so much more. Many other business industries were able to more easily create data models that made best practices and more efficient work of their incoming information. Yet, healthcare information systems weren’t ever all that easy to structure, due to factors including how many people needed access to information, the protection of that information, unstructured and outside data being saved, etc. This was a major factor in why healthcare is not as advanced in data collection, storage, and analysis.
Once programs were being developed to specifically take on the challenges of the diverse fields within medical practices, the utilization of analytics to provide better treatment, cut costs for both patient and professional, and ultimately be able to forecast when and where needs might be required became a motivating factor in fine-tuning software programs.