One Dataset a Day Won’t Keep the Doctor Away
The healthcare industry is currently suffering from a host of issues. Knowledge sharing between hospitals, determination of patient adherence to medications, and the efficient management of surgical procedures are just three topics in a long list of areas that need improvement. All of these issues have the same thing in common: the healthcare industry has a data problem.
The fact is that there is an abundance of raw data and no one really knows what to do with it. From patient records to heart-rate monitors, hospitals produce reams of raw data that, after an initial reference, is usually forgotten. The good news is that all of this data can be used to solve a multitude of common, day-to-day problems using predictive analytics.
The No-Show Problem
The unfortunate reality is that no-shows have become extremely common — one study reported that the no-show rate in U.S. primary care practice can vary from as little as 5% to as much as 55% 1.Appointment cancellation rates are also a systemic problem in addiction treatment centers where 29% — 42% of patients fail to begin treatment 2 and 15% — 50% of patients do not even return for a second visit 3.
Dealing effectively with patient no-shows has been a challenge in the healthcare industry, especially now that reimbursement is more closely tied to performance measures surrounding physical appointments.
The long-term effect of this phenomenon is lowered reimbursement for providers and, more importantly, the health welfare impact on adherence, quality, and clinical outcome measures on patients. For patients, spotty appearances with healthcare providers results in less coordinated care, particularly in cases of chronic diseases and preventive encounters. Patients suffering from chronic conditions may require very regimented treatment plans — missing even one treatment may have debilitating consequences.
Missing preventive care treatments leads to longer and more expensive care as potential issues become real health problems. No-shows also have a direct financial effect on healthcare providers as expected revenue targets fall short, labor hours are wasted, and inefficiencies are created.
Dealing effectively with patient no-shows has been a challenge in the healthcare industry, especially now that reimbursement is more closely tied to performance measures surrounding physical appointments. Many providers are simply overwhelmed with the problem and resort to traditional stopgap policies, such as reminding patients the day before their appointments. The effect is marginal and, ultimately, is short-sighted because it does not directly address the problem itself.
What’s inside?
This ebook aims at providing healthcare professionals with a clear view of how efficiency gains could be realized at little cost via the integration of data analytics. We will start by having a look at what is wrong with the current implementation of data analytics in the healthcare ecosystem and how it applies to the no-show issue. We will then offer an alternative approach to addressing no-show appointments that makes use of predictive analytics. Lastly, we will discuss how this method could be applied to the healthcare industry.
Enjoy your read and let me know what you think!
