Machine Learning Distills Point-of-Prescribing Data for a Better Patient Experience

By Nick Anderson, Director of Product Management, CoverMyMeds

The healthcare industry has long outgrown pen-and-paper processing in favor of automated tools and electronic record keeping. Speed to therapy is paramount for patients’ health and smart technology’s assistance is all but necessary to keep up with the volumes of data and demand for instant results.

Providers need more than just a faster way to process the data they already have. The next frontier in healthcare technology is procuring information to tell the story of the unknown—predictive data through machine learning.

Machine learning is a sector of artificial intelligence (AI) that automates the analysis of data. As healthcare becomes more patient-centric, machine learning technology can help answer questions earlier in the health journey than ever before—many at the point of prescription. Patients can even get medication access answers from their smartphones.

Analysis and Aggregation

Foremost, machine learning of any kind begins with analyzing data—usually huge sets beyond human comprehension. AI is looking for data trends based on algorithms to inform a particular goal or outcome. For prescription decision support, this can mean drug pricing data, prior authorization, history of certain drugs and pharmacy location data.

One of the biggest advantages of machine learning for healthcare is the wide span of data it can analyze, including doctor’s notes, demographic data and lab findings. These data can then be applied to prognoses and diagnoses, and even disease risk calculations.

Language Comprehension

Health record data is nothing without integrity. Attempting to surface a name, drug or symptom that’s entered incorrectly can drag down clinical workflows and become an issue of patient safety if health history is missing or the wrong drug is ordered. Through machine learning, data systems can help mitigate human error in health systems and records by resolving common language errors or suggesting related data—the name Charles instead of Chuck, for example.

Inferring Missing Data

Inferring missing data is another machine learning benefit to ensure language integrity. Real-time benefit-check solutions can surface prescription and patient details automatically. This reduces errors and shifts more of the appointment time away from the computer screen and toward the patient, allowing for meaningful conversations around health and medication access.

Predictive Indicators

Perhaps the most time-saving boon of machine learning in healthcare is the ability to predict provider and patient needs based on key indicators. It happens through simple conditional statements—If P, then Q—applied over massive data sets.

Where is this useful? Ask the 84% of physicians in a recent AMA study who cited prior authorization as a high or extremely high burden in their practice. Machine learning can indicate to prescribers which medications will require a prior authorization for a particular patient or plan. This allows them to initiate the prior authorization request process prospectively, saving time and headaches down the road for their staff, patients and pharmacists.

With more electronic healthcare information being exchanged than ever before, efficient interoperability is key to maintaining workflows. To make the right point-of-prescription decision quickly, providers need robust data at their fingertips, and they need to know it’s accurate. Machine learning can accelerate the data gathering process, culling prescription decision factors such as price, location and prior authorization requirements into a distinct data set, specific to the medication and patient.

Through intelligent data procurement, technology refines the data available to get providers only the information they need—and eliminates what they don’t. By making the point-of-prescribing stop in the healthcare journey more efficient and accurate, machine learning bridges data gaps to assist the patient’s entire care team.

 

Sponsored content. The views and opinions expressed in this blog or by commenters are those of the author and do not necessarily reflect the official policy or position of HIMSS or its affiliates.

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