DEPARTMENT OF COMPUTER SCIENCE
AND APPLICATIONS
IBM PROJECT REPORT
Healthcare Data Analysis for Patient outcomes
Submited By
Anamika.I.S(24BAI046)
Jhonsi.M(24BAI007)
Blessy.G(24BAI006)
Epsi Evanglin.M(24BAI011)
Varsha Sree.P(24BAI031)
BACHELOR OF SCIENCE IN COMPUTER SCIENCE
WITH ARTIFICIAL INTELLIGENCE
Under the Guidance of
Mr. S. Ananda Kumar-Corporate trainer
2024-2025
ABSTRACT:
Healthcare data plays a pivotal role in improving patient outcomes by enabling
evidence-based decision- making, personalized care, and predictive analytics. With the
increasing digitization of medical records and the advent of advanced technologies, healthcare
providers now have access to vast amounts of data, including clinical records, diagnostic
results, genomic information, and patient-generated data from wearable devices and mobile
applications.
These data sources allow for a deeper understanding of individual health conditions and
population health trends, facilitating more accurate diagnoses, targeted treatments, and better
disease management strategies.
One of the most significant advancements in this domain is the integration of artificial
intelligence (AI) and machine learning (ML) technologies. These tools analyze complex
datasets to uncover patterns, predict patient risks, and recommend treatment options tailored to
each individual. For example, predictive analytics can identify patients at risk of readmission or
complications, enabling timely interventions that prevent adverse outcomes. Similarly, real-
time analytics from connected devices allow healthcare professionals to monitor patients
remotely, improving the management of chronic diseases and reducing the need for in-person
visits.
However, leveraging healthcare data effectively comes with challenges. Ensuring data privacy
and security is paramount, as sensitive patient information must be protected against breaches
and unauthorized access. Interoperability is another critical issue, as healthcare systems often
use different data formats and platforms, making integration and unified analysis difficult.
Despite these challenges, the future of healthcare data is promising, with advancements in big
data platforms, blockchain technology, and genomics paving the way for more equitable and
efficient care delivery. By addressing these barriers, healthcare systems can fully realize the
potential of data-driven innovation to enhance patient outcomes and optimize healthcare
delivery worldwide.
BUSINESS INTEGLLIGENCE:
Business Intelligence (BI) is a set of strategies and technologies that
organizations use to analyze business data and transform it into actionable insights. By
leveraging data from various sources such as databases, spreadsheets, and cloud applications BI
tools help companies understand their operations, market trends, customer behaviors, and
financial performance. This process includes data collection, analysis, reporting, and
visualization. BI enables businesses to make informed decisions based on data-driven evidence,
rather than intuition or guesswork.
At its core, BI empowers organizations to improve efficiency, identify new opportunities,
optimize processes, and reduce risks. Through tools like dashboards, predictive analytics, and
interactive reports, decision-makers can quickly grasp complex data and respond faster to
changing business conditions. Whether it's monitoring key performance indicators (KPIs),
spotting emerging market trends, or identifying potential areas for cost-saving, BI is essential
for maintaining a competitive edge in today’s fast-paced business environment. By making data
accessible and understandable, BI fosters a culture of informed decision-making across all
levels of an organization.
IBM Cognos:
IBM Cognos is a comprehensive suite of business intelligence and performance
management tools that empower organizations to make data-driven decisions and optimize their
performance. With its robust capabilities for reporting, analytics, and dashboard creation,
Cognos allows businesses to transform raw data into actionable insights. Cognos offers features
like real-time data access, intuitive user interfaces, efficient data modeling, and advanced OLAP
capabilities, ensuring organizations of all sizes can efficiently analyze data across multiple
dimensions. From retail to finance, healthcare to manufacturing, IBM Cognos supports a wide
range of industries by streamlining data-driven processes and providing a unified platform for
deep-dive analysis. Its scalability and versatility make it an invaluable tool for businesses
seeking to stay ahead in a competitive environment, leveraging predictive analytics to anticipate
future trends and make informed decisions.
INTRODUCTION:
In this project, we collected datasets containing
information on patient Names, gender,Age, Bill amount, Medical conditions, Blood
group, hospital name, doctor,Room number and states from across all states
in India using Kaggle. For Business Intelligence purposes, we utilized a tool called
IBM Cognos, through which we aligned data with lists, cross-tabulations,
and various visualizations such as pie charts, bubble chart , bar charts,map and local
on charts
HEALTHCARE DATABASE:
DATABASE OUPUT:
CROSSTAB:
CROSSTAB OUTPUT:
LINE BAR:
PIE CHART:
LOCAL CHART:
BUBBLE CHART:
CONCLUSION:
Our project utilized IBM COGNOS Analytics to analyze healthcare data
from multiple medical institutions in India. We analyzed 10 datasets from Kaggle using various
tools such as lists, cross tabulation and visualizations such as pie chart, bar chart, bubble chart
and local chart. We have included data from a major hospital's dashboard, which provides
insights into patient outcomes, treatment effectiveness, and healthcare professionals'
performance, including factors like number of surgeries performed, patient satisfaction scores,
and clinical research publications. By analyzing operational efficiency, we can improve patient
care, optimize resource allocation, and enhance overall healthcare delivery and organizational
effectiveness.
