Enhancing Healthcare Claims and Membership Data Quality: SPSS Modeler Predictive Analysis

Dhivya Sudeep ()

International Journal of Health Sciences, 2024, vol. 7, issue 8, 51 - 63

Abstract: Purpose: This paper explores the use of SPSS Modeler predictive analysis to enhance healthcare claims and membership data quality. The analysis uses advanced analytical techniques and algorithms to identify discrepancies and improve data accuracy, improving decision-making and operational efficiencies within healthcare organizations. The paper also provides insights to optimize data integrity, streamline claims processing, and ultimately improve patient care outcomes by ensuring that accurate and reliable data can sustain all healthcare operations. Methodology: This paper explores the use of advanced data mining and predictive analytics techniques to improve the identification of claims and membership data quality. The study aims to leverage supervised learning methods, including Neural Networks and the Auto Data Prep Modeling Option, and unsupervised learning methods, utilizing cutting-edge machine learning algorithms to train models capable of detecting and addressing data quality issues. Findings: Data Quality of an application affects various factors of an organization including operations, decision making and Planning. It therefore becomes very important to make sure that the data being stored and used is of high quality. Data must be regularly monitored and cleaned to support more informed and effective healthcare decision-making. As per a research study published by MIT Sloan, poor data quality has made companies lose around 15% to 25% of their revenues [1]. Another study found that data scientist spends around 80% of their time cleaning and correcting data leaving them with only 20%of time to perform the actual analysis [2]. Unique Contributions to Theory, Practice, and Policy: By incorporating advanced predictive analytics techniques like supervised and unsupervised learning models within SPSS Modeler, the study enhances the ability to proactively address data quality issues, streamline operations, and ensure regulatory compliance. It can also help healthcare organizations by offering. Innovative perspectives on how data mining and predictive analysis can help reshare healthcare data governance, policy development and industry-wide standards for data quality.

Keywords: SPSS Modeler; Predictive Analysis; Healthcare Data Quality; Neural Networks; Auto Data Prep Modeling (search for similar items in EconPapers)
Date: 2024
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