HIM Patient Deficiency Management is primarily used to manage the identification and completion of patient chart deficiencies. Currently, we are in the process of designing a machine learning framework to automate the addition and completion of patient deficiency requests. This will enhance our new deficiency management suite, which is our SaaS offering built on cloud technologies.

The primary goals of this framework are:
1. To provide recommendations and capabilities with the current product line for improved deficiency assignment via configurable rules.
2. To train a machine learning model, which would be able to assist the user by predicting and eventually auto assigning deficiencies in the system, only available in the SaaS solution.

We will walk through real-life data science and machine learning algorithms being applied to Document References Data from HealtheDataLab to make predictions and find patterns that add value to our solutions and our clients. In addition, we will demonstrate visualization techniques that were instrumental in computing relationships within Document References Data. We will conclude with the impact of these relationships on the healthcare industry.

We will dissect the plethora of various machine learning strategies such as decision tree, clustering, principal component analysis, recommendation, association rules, random forest, resampling strategies, etc. The audience will get a thorough understanding of data cleaning, data retrieval, and predictive modeling strategies in HealtheDataLab.

Target Audience:
Data Analysts, Data Scientists, Data Strategists, Data Architects - This talk is beneficial for anyone interested in learning about machine learning and its practical applications. Technical attendees will learn about various machine learning algorithms, tools, and techniques to make those concepts a reality. Applied correctly, artificial intelligence and machine learning will transform the healthcare industry and help achieve the quadruple aim.