Like many acute care facilities around the country, the Parkland Health and Hospital System in Dallas, Texas sees a lot of patients with heart-related ailments. Following a cardiac event, the practices of follow-up monitoring, ensuring medication adherence, and identifying high-risk patient populations are all instrumental in reducing readmission risk.
A study from December 2013 examined the use of analytics as a predictive model for flagging patients likely to be readmitted. Parkland Health patients deemed higher risk received more unsolicited intervention, including phone calls, primary follow-up appointments and other measures to mitigate the chances of another hospital visit. The use of analytics to single out patients in need of more aggressive attention, researchers reported, reduced readmissions at Parkland by 26 percent.
In an increasingly digital landscape, physicians and healthcare providers find themselves trying to parse data with the analog world of clinical care. Elaborate and expensive software systems and analytic filters can gauge everything from vaccine clearances to wait times to the need for antibiotic intervention. It’s a landslide of information, one that some practitioners might feel overwhelmed in exploring. But with proper integration, such data doesn’t need to feel extraneous. Interpreted properly, it can provide actionable insights that can bolster financial and clinical outcomes for practices.
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“One of the advantages of electronic health records is being able to generate real-time analytics,” says Ravi Parikh, a fellow in hematology and oncology at the University of Pennsylvania who has studied the use of data in improving patient care. “It’s a decision support tool.”
As an example, Parikh cites analytics that can examine the efficacy of using antibiotics in patients with upper-respiratory infections. At a glance, a physician can determine which antibiotic has had success with a specific strain of bacteria, whether a patient relapsed and what the success rate has been in a patient pool with similar symptoms. For patients with chronic issues like diabetes, analytics can assess treatment options using data within a healthcare practice or region, weighing treatment options trialed by other physicians. While none of it can replace a diagnostic evaluation, providers can get a snapshot of potential outcomes at a glance.
While analytics can also be used to manage office financials, it’s better not to think of the two as separate. “Physicians are inherently more excited about doing clinical work,” says Walter Morrissey, MD, managing director at Kaufman Hall, a healthcare advisory firm based in Skokie, Illinois. “But I would urge them to pursue a dual path around both care quality and the financial impact. Physicians are often held accountable for financial cost, care quality and patient satisfaction.”
Morrissey says that data is often extrapolated from insurance claims, which document what a physician has seen and how the issue is treated. If the physician is part of a healthcare system, that data might be processed by a third-party vendor. If they’re in private practice, they might choose to align themselves with a regional system like a local hospital to access data. “Claims provide what happened, what the physician provided, what the outcome was and if there were complications,” he says. Claims can also make determinations on office processing of CPT codes to gauge what gets paid, what gets rejected, and whether any patterns exist to determine reimbursement complications.
Increasingly, practices are also turning to patient surveys to gather information. “Engaging patients following surgery and asking how they’re doing is a way of tracking functional status,” Morrissey said. “Can they walk three blocks? Can they walk up the stairs? By tracking their response to therapy, you’re getting more of the picture than just looking at readmissions.”
Analytics can also provide care models — not, as some physicians assume, to dictate the time they spend with patients, but to determine which patients are in need of additional attention. “They may not need more physician time, but more caregiver time,” Morrissey says. “You can’t apply a one-size-fits-all approach to a healthy 45-year-old versus a chronically ill 65-year-old.” Knowing when and how to direct office resources leads to completed patient encounters — beneficial for both the patient and the office’s bottom line.
For virtually any physician, having access to integrated analytics via area healthcare systems or vendors can offer data on potential outcomes relating to chronic disease, commonly seen illness, surgical intervention and physical therapy, as well as a multitude of other repeatable situations. Data can also bolster claims processing to streamline reimbursement. Patients might not be able to quantify the difference, but Parikh says that practices with access to algorithms will observe measurable success.
“You might not see an increase in patient satisfaction,” he says, “but you will see improvement in patient outcomes.”