Analytics for Hospitals Health-Care Data

Project Overview

Introduction: 

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.

Objective: 

The objective of this project was to employ data analytics techniques to enhance the efficiency of healthcare management in hospitals, with a primary focus on predicting and optimizing patient length of stay. By leveraging insightful analysis, our aim was to empower hospitals to make data-driven decisions for resource allocation and improve overall functioning. 

Tools and Technologies: 

For this project, we utilized cutting-edge tools and technologies including IBM Cognos Analytics, IBM Db2 Database, Excel, Python, Google Collaboratory, GitHub. These robust platforms provided us with the capabilities to perform comprehensive healthcare data analysis and derive meaningful insights.

Key Findings: 

Our analysis uncovered several noteworthy findings that shed light on important aspects of hospital operations and patient care. These findings include: 

1. Patient Admissions: 

• Trauma admissions accounted for the highest percentage of patients (30.4%). 

• A small fraction (2.3%) of trauma admissions required stays exceeding 100 days. 

• Prolonged stays were also observed in emergency and urgent admissions. 

• Gynecology cases exhibited a moderate volume with generally moderate severity. 

2. Illness Severity:

• Patients with moderate illnesses constituted a larger proportion compared to those with extreme or minor conditions. 

3. Admission Types: 

• Trauma admissions were frequent, while urgent admissions were relatively lower. 

4. Ward Distribution: 

• Among the five wards, Ward R recorded the highest number of cases, indicating a need for targeted resource allocation. 

5. Hospital Utilization: 

• 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.

Outcome: 

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.

Significance: 

This project not only demonstrated my adeptness in leading and managing a team but also showcased my proficiency in data analytics. The derived outcomes present valuable insights for healthcare resource allocation and improved patient care. By leveraging data-driven strategies, our project contributed to the transformation of healthcare management practices, establishing a solid foundation for enhanced efficiency and superior patient outcomes. Overall, this project serves as a testament to my capabilities as skilled data analytics professional, underscoring my unwavering commitment to utilizing data for meaningful insights and driving positive impact within the healthcare industry.

I take great pride in receiving a certificate of completion for successfully conducting a comprehensive analysis of hospital admissions. This recognition validates my strong analytical skills and ability to derive actionable insights from complex healthcare data.