In this healthcare analytics project, I present a comprehensive analysis of hospital data to enhance healthcare management and improve patient outcomes. Leveraging advanced tools and technologies, including IBM Cognos Analytics, DB2 Database, Excel, Python, Google Colaboratory, and Github, I delve into data-driven insights and recommendations for optimizing resource allocation and patient care.
The primary objective of this project was to leverage data analytics techniques to improve healthcare management efficiency in hospitals, specifically focusing on predicting and optimizing patient length of stay. Through insightful analysis, the aim was to empower hospitals to make data-driven decisions for resource allocation and enhance overall functioning.
To accomplish this project, we utilized cutting-edge tools and technologies including IBM Cognos Analytics, DB2 Database, and Excel, Github, Google collaboratory. These robust platforms provided us with the capabilities to perform comprehensive healthcare data analysis and derive meaningful insights.
Our analysis revealed noteworthy findings that shed light on crucial aspects of hospital operations and patient care, including:
Trauma admissions accounted for the highest percentage (30.4%) of patients. Additionally, a small fraction (2.3%) of trauma admissions required stays exceeding 100 days. Prolonged stays were also observed in emergency and urgent admissions, while gynecology cases exhibited a moderate volume with generally moderate severity.
Patients with moderate illnesses constituted a larger proportion compared to those with extreme or minor conditions.
Trauma admissions were frequent, whereas urgent admissions were relatively lower.
Among the five wards, Ward R recorded the highest number of cases, highlighting the need for targeted resource allocation.
Hospital code 26 stands out with a notable surplus of available rooms, surpassing other hospital types. This observation presents valuable insights into the potential for optimizing capacity and enhancing resource utilization effectively.
As the team leader for this project, I effectively coordinated and guided our team through the comprehensive analysis of hospital admissions data. Through collaborative efforts, we generated significant insights into patient demographics, illness severity, admission types, ward distribution, and hospital utilization patterns. The culmination of our work resulted in the development of a robust predictive model capable of estimating the length of stay for each patient on a case-by-case basis. This valuable information equips hospitals with the ability to optimize resource allocation and enhance overall operational efficiency.
