## 2 Background  
Given the newly increasing support for electronic medical and clinical data in recent times, the need for data evaluation within the medical fields has also seen increased importance [3]. Throughout many different clinical facilities that utilize electronic health records, a widespread issue occurs in data entry; much of the data falls incomplete or blank compared to the rest of the data entered [3]. Data quality measures throughout EHR and EMR studies consist of data extraction from multiple sources and providing a measure of the data usage/effectiveness [4; 5]. However, studies into the completeness of data have not been widely conducted throughout the clinical research world. Many Clinical Data Research Networks (CDRN) and Distributed Clinical Data Networks (DCDN) have gone through an individual process of checking their available data and processes for quality assurance and effectiveness; however, the process can prove tedious or unproductive when results vary widely between analyses. Furthermore, many of the developed algorithms used to check data in a medical system typically cast on data quality checks rather than data completeness checks [6]. CDNRs and DCDNs are both complex data systems, varying between various modules and departments and requiring many different processes to give optimal patient care and output complete solutions to clients (as both patients and researchers) [7]. As a result, many of these clients or shareholders have vastly different purposes or resources at hand, requiring the data attained to be as versatile and complete as possible [8]. In this current day and age where \"useful data\" can provide a subtle look into how the pandemic and ongoing COVID-19 viral strains affect the US population, the issue of data incompleteness and a full understanding of each patient demographic and background becomes all the more important [9]. Medical providers and different health systems should be looking into filling in the information gaps seen in their medical systems and find where they can improve in their collection of the background of their patients [10; 11]. As seen during the pandemic, when information is not complete, the comprehension of a systematic issue may take longer to come to mind than usual. When providers and medical systems neglect to fill in all patient information, the result can be major pharmaceutical errors and ills that come from misunderstandings. A common example is seen in the psychiatric world, where many of the possible medications can compound symptoms and side effects among other medications. These chemical reactions can have direct or indirect interactions; however, both are unacceptable in terms of clinical safety. When clinical providers read back into their records and expect a complete and comprehensive record of their current patient, they may be placing their trust in digital systems higher than possible and misdiagnosing or accidentally prescribing something that can harm a patient more than benefit. These systems only provide the benefit as used, and only to the extent of the data that is received during patient intake. Networking solutions in the past algorithms usually take a top-down technique, requiring an extensive algorithmic search through the data, and can cause a strain on resources and time. However, with a quick, statistical measure, medical professionals can save time and effort on analyses, and turn their attention to improving data completeness within their own electronic records systems [12]. This holistic approach allows for an interdisciplinary understanding of how well medical facilities manage their data and provides them with a method to turn missing data into fully pieced-together records. Statistical usage for data optimization and quality checks exist throughout the history of electronic health records systems. Historically, many different models exist; however, some of the main models include deep learning, concordance statistics, natural language processing, and linear regression models [13].