Given its complexity, medical coding often seems like a “necessary evil” for health care organizations. Sure, coding is something that must be done before providers can be paid, but the data generated in the process can also be leveraged to improve business processes and patient care.
Some call it “health care analytics” and others call it “data mining.” Either way, it’s about scouring the vast quantity of information locked within medical codes, electronic medical records, laboratory results, billing systems, and other places to discover trends and opportunities.
Essentially, you can combine data from various IT sources to form a large data warehouse, which can then be queried. Let’s look at two simplistic examples that show the promise of health care analytics.
Patient referral analysis: Maximizing efficiency and revenue
An endocrinology patient may end up at an endocrinologist by a variety of paths: self-referred, referred by a primary care physician at the same medical center, referred by a nephrologist at the same medical center, referred by a primary care physician at a different medical center, and on and on.
An analytical software tool could examine (using ICD-9-CM and CPT codes, and billing information) the treatment pattern of each type of patient. For example, a retrospective analysis of the past two years of endocrinology patients might show that self-referred patients — those who simply call up and make an appointment — had minor ailments that could’ve been treated by their primary care physician. Such “one and done” patients take appointment slots away from — and generate less revenue than — patients who truly need long-term endocrinology care.
Lesson learned: The endocrinology department can use these findings to better prioritize how it fills its appointment slots, perhaps by more closely screening self-referred patients to determine whether they really need to see an endocrinologist.
Outcomes research: Improving quality of care
There’s no doubt that the “art” of medicine is aggressively moving toward the “science” of medicine. Federal research dollars are pouring into areas like comparative effectiveness research, where researchers analyze outcomes over time to determine which treatments are statistically better than others for different patient populations.
For instance, from the aforementioned data warehouse, a medical center could select all male patients between the ages of 25 and 44 diagnosed with headaches in the past year. Presumably, different patients will be prescribed different treatments, such as over-the-counter medication (ibuprofen, acetaminophen, etc.), massage, or acupuncture.
This group of patients could then be analyzed over time. In the six months following initial treatment, perhaps the data show that patients prescribed acupuncture sessions are statistically less likely (controlling, of course, for other variables) to return to the physician with continued headaches.
Lesson learned: If acupuncture is statistically more effective, the organization could build a clinical decision support tool to alert physicians at the point-of-care that their headache patient might benefit most from acupuncture.
Robust analytics are the future
Medical coding is here to stay, but it shouldn’t be viewed solely as a means to get paid for services rendered. Rather, health care organizations ought to look at medical coding as one of several crucial sources of data that can be combined and analyzed to help optimize future revenue, improve patient care, and more.
Products such as DataScout, recently released by our friends at CodeRyte, are leading the way in the growing health care analytics software market. pmFAQtory stands ready to help your health care organization implement and make use of these valuable tools.
What secrets are locked within all the data you’re generating each day? Get in touch with pmFAQtory and let one of our health care consulting experts show you the way.