Enhancing problem list reconciliation with Natural Language Processing (NLP): Improve patient care quality with population health text mining analytics
The shift from volume to value-based compensation is driving provider demand for better insights into the health of patient populations. Providers recognize that access to more complete patient data can enhance their ability to deliver cost-effective care and high quality outcomes. This is especially true for patients with multiple chronic conditions, who typically have more complicated care needs and higher hospital utilization rates.
Figure 1 High risk patients are frequently suffering from complex comorbidities
Typically, physicians refer to problem lists when assessing a patient’s health and evaluating treatment alternatives. Problem lists rely on coded disease states and offer a concise view of a patient’s medical issues. Unfortunately, these lists are often incomplete or out of date. Consider, for example, a patient who is referred to an orthopedic surgeon for a broken wrist. If the problem list only includes details of the wrist injury, the physician may not be immediately aware of underlying chronic conditions, such as diabetes, that could impact the best course of treatment and outcomes.