Health Information Management (HIM) is the practice of acquiring, analyzing, and protecting digital and traditional medical information vital to providing quality patient care. HIM Patient Deficiency Analysis is primarily used to manage the identification and completion of patient chart deficiencies. We are in the process of designing a machine learning system to automate the addition and completion of patient deficiency requests to improve the quality and timeliness of the deficiency process. One example of a patient deficiency request is document deficiencies.

There are two methods we developed for predicting document deficiencies:
1. Via configurable rules that were created based on common historical sets of documents.
2. A machine learning model, which would be able to assist the HIM Analyst by predicting and eventually auto assigning a list of document deficiencies in the system.

In this talk, we will walk through this real-life data science and machine learning example and explain association rules and One vs Rest Classifier machine learning techniques that were applied to Document References data for predicting document deficiencies using HealtheDataLab. This will add value to our clients through improved HIM Analyst productivity and efficiency. This will modernize our new deficiency analysis product, which is a SaaS offering built on Orion and Alva technologies.

Target Audience:
Engineers, Data Analysts, Data Scientists, Data Strategists, Data Architects - This talk is beneficial for anyone interested in learning about machine learning and its practical applications. The goal of this talk is to help technical attendees to get familiar with various machine learning techniques and start thinking about different ways how machine learning can be applied in their fields.