Healthcare Data Analysis Using SAS Training Course

Course Introduction

The Healthcare Data Analysis Using SAS Training Course is designed to equip participants with comprehensive knowledge and practical skills in healthcare data management, statistical analysis, epidemiological research, predictive analytics, and evidence-based healthcare decision-making using Statistical Analysis System (SAS) software. In today's rapidly evolving healthcare environment, hospitals, public health institutions, research organizations, pharmaceutical companies, and development agencies increasingly rely on advanced healthcare analytics to improve patient outcomes, optimize healthcare delivery systems, monitor disease patterns, and support strategic health planning. This course provides participants with practical competencies in healthcare data management, statistical programming, epidemiological analysis, healthcare reporting, and predictive modeling using SAS for improved healthcare performance and policy development.

The course focuses on the principles and practical applications of SAS in healthcare analytics, including healthcare data management, data cleaning and validation, descriptive and inferential statistics, epidemiological analysis, regression modeling, survival analysis, healthcare performance measurement, and reporting techniques. Participants will gain practical experience in managing healthcare datasets, conducting advanced analyses, generating professional reports, and developing evidence-based recommendations for healthcare planning and decision-making. The course emphasizes practical applications of SAS in clinical research, public health surveillance, hospital performance management, healthcare monitoring and evaluation, pharmaceutical research, and health systems strengthening initiatives.

As healthcare organizations increasingly adopt digital health technologies, electronic medical records, big data analytics, and evidence-based healthcare management systems, competencies in healthcare data analysis and SAS programming have become indispensable for healthcare researchers, epidemiologists, biostatisticians, public health specialists, monitoring and evaluation professionals, policy analysts, and healthcare administrators. This training emphasizes analytical reasoning, statistical rigor, quantitative problem-solving, and data-driven healthcare management approaches that improve research quality, strengthen analytical capabilities, and facilitate informed and strategic healthcare decision-making.

Through presentations, practical exercises, computer-based applications, collaborative group work, programming assignments, and real-world case studies, participants will develop competencies necessary to manage healthcare datasets, perform advanced analyses, interpret analytical findings, and communicate results effectively. Upon completion of this course, participants will be capable of applying SAS healthcare analytics techniques to solve complex healthcare challenges, improve research and evaluation capabilities, strengthen healthcare information systems, and contribute to innovation and evidence-based healthcare management practices.

Course Objectives

Upon completion of this course, participants will be able to:

1.     Understand the principles and applications of healthcare data analysis using SAS.

2.     Import, organize, and manage healthcare datasets effectively.

3.     Apply data cleaning and validation techniques to healthcare databases.

4.     Conduct descriptive and inferential statistical analyses using SAS.

5.     Perform epidemiological and biostatistical analyses on healthcare data.

6.     Develop regression and predictive models for healthcare decision-making.

7.     Conduct survival and time-to-event analyses using healthcare datasets.

8.     Generate healthcare reports, dashboards, and data visualizations.

9.     Interpret analytical findings and formulate evidence-based recommendations.

10.  Utilize SAS analytical techniques to improve healthcare planning and policy development.

Organizational Benefits

Organizations that invest in this training will benefit by:

1.     Strengthening evidence-based healthcare planning and decision-making capabilities.

2.     Improving healthcare research quality and analytical rigor.

3.     Enhancing disease surveillance and epidemiological monitoring systems.

4.     Building staff competencies in healthcare data management and analytics.

5.     Improving healthcare reporting and performance measurement systems.

6.     Supporting policy development and healthcare resource allocation decisions.

7.     Strengthening health information and knowledge management systems.

8.     Improving monitoring, evaluation, and impact assessment processes.

9.     Promoting innovation and data-driven healthcare management practices.

10.  Enhancing accountability, operational efficiency, and continuous improvement in healthcare services.

Target Participants

This course is designed for healthcare researchers, epidemiologists, biostatisticians, public health professionals, healthcare administrators, data analysts, monitoring and evaluation specialists, medical researchers, health information officers, policy analysts, statisticians, consultants, government officials, academicians, postgraduate students, development practitioners, hospital managers, pharmaceutical researchers, and professionals involved in healthcare research, statistical analysis, health information management, and evidence-based healthcare decision-making.

Course Outline

Module 1: Introduction to Healthcare Data Analysis and SAS Environment

1.     Principles and applications of healthcare data analytics

2.     Introduction to SAS analytical environment and programming concepts

3.     Healthcare information systems and data structures

4.     Types and sources of healthcare datasets

5.     Healthcare analytics workflows and methodologies

6.     General Case Study: Designing analytical frameworks for hospital performance assessment

Module 2: Healthcare Data Importation and Management

1.     Importing healthcare datasets from multiple data sources

2.     Managing electronic medical records and healthcare databases

3.     Variable definitions and metadata management techniques

4.     Organizing and documenting healthcare datasets

5.     Data security and confidentiality considerations

6.     General Case Study: Developing integrated patient information databases

Module 3: Data Cleaning and Validation Techniques

1.     Principles of healthcare data quality management

2.     Identifying errors and inconsistencies in healthcare records

3.     Managing missing values and incomplete observations

4.     Detecting duplicate records and validation procedures

5.     Data transformation and recoding techniques

6.     General Case Study: Cleaning and validating hospital patient datasets

Module 4: Descriptive Statistics and Exploratory Analysis

1.     Principles of descriptive statistical analysis

2.     Frequency distributions and summary statistics

3.     Measures of central tendency and variability

4.     Cross-tabulation and comparative analysis techniques

5.     Exploratory healthcare data analysis and interpretation

6.     General Case Study: Analyzing disease prevalence and patient demographic patterns

Module 5: Inferential Statistics and Hypothesis Testing

1.     Principles of inferential statistical analysis

2.     Parametric and non-parametric testing procedures

3.     Confidence intervals and significance testing techniques

4.     Comparative analytical methods and group analyses

5.     Interpretation and reporting of inferential findings

6.     General Case Study: Evaluating the effectiveness of healthcare interventions

Module 6: Epidemiological Analysis Using SAS

1.     Principles of epidemiological study designs

2.     Measures of disease occurrence and association

3.     Risk analysis and exposure assessment techniques

4.     Disease surveillance and outbreak investigations

5.     Epidemiological reporting and interpretation methods

6.     General Case Study: Investigating determinants of infectious disease outbreaks

Module 7: Regression Analysis and Predictive Modeling

1.     Principles of regression analysis techniques

2.     Linear and logistic regression methods

3.     Predictive modeling and risk stratification procedures

4.     Model diagnostics and assumption testing

5.     Interpretation of predictive analytical outputs

6.     General Case Study: Predicting patient readmission and treatment outcomes

Module 8: Survival Analysis and Time-to-Event Modeling

1.     Principles of survival and time-to-event analysis

2.     Kaplan-Meier estimation techniques

3.     Cox proportional hazards modeling procedures

4.     Model diagnostics and validation methods

5.     Interpretation and reporting of survival analytical findings

6.     General Case Study: Analyzing patient survival and treatment effectiveness

Module 9: Healthcare Performance Measurement and Monitoring

1.     Principles of healthcare performance management

2.     Development of healthcare indicators and metrics

3.     Monitoring and evaluation analytical frameworks

4.     Healthcare quality assessment methodologies

5.     Results-based management systems in healthcare

6.     General Case Study: Measuring healthcare service delivery performance

Module 10: Data Visualization and Healthcare Reporting

1.     Principles of healthcare data visualization

2.     Development of charts and graphical presentations

3.     Dashboard design and reporting techniques

4.     Interpretation and communication of analytical findings

5.     Preparation of evidence-based healthcare reports

6.     General Case Study: Developing healthcare performance dashboards and executive reports

Module 11: Applications of Healthcare Analytics in Policy and Research

1.     Healthcare analytics for policy development

2.     Public health research and analytical methodologies

3.     Pharmaceutical and clinical research applications

4.     Health systems strengthening analytical techniques

5.     Strategic planning and evidence-based healthcare management

6.     General Case Study: Developing policy recommendations using healthcare analytics

Module 12: Emerging Trends in Healthcare Analytics and SAS Applications

1.     Big data analytics in healthcare systems

2.     Artificial intelligence and machine learning applications

3.     Digital health technologies and predictive analytics

4.     Cloud-based healthcare analytical environments

5.     Future trends in healthcare analytics and evidence systems

6.     General Case Study: Designing integrated healthcare analytics systems for organizational transformation and strategic health planning

General Information

1.     Customized Training: All our courses can be tailored to meet the specific needs of participants.

2.     Language Proficiency: Participants should have a good command of the English language.

3.     Comprehensive Learning: Our training includes well-structured presentations, practical exercises, web-based tutorials, and collaborative group work. Our facilitators are seasoned experts with over a decade of experience.

4.     Certification: Upon successful completion of training, participants will receive a certificate from Foscore Development Center (FDC-K).

5.     Training Locations: Training sessions are conducted at Foscore Development Center (FDC-K) centers. We also offer options for in-house and online training, customized to the client's schedule.

6.     Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of days.

7.     Onsite Training Inclusions: The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a Certificate of Successful Completion. Participants are responsible for their travel expenses, airport transfers, visa applications, dinners, health/accident insurance, and personal expenses.

8.     Additional Services: Accommodation, pickup services, freight booking, and visa processing arrangements are available upon request at discounted rates.

9.     Equipment: Tablets and laptops can be provided to participants at an additional cost.

10.  Post-Training Support: We offer one year of free consultation and coaching after the course.

11.  Group Discounts: Register as a group of more than two and enjoy a discount ranging from 10% to 50%.

12.  Payment Terms: Payment should be made before the commencement of the training or as mutually agreed upon, to the Foscore Development Center account. This ensures better preparation for your training.

13.  Contact Us: For any inquiries, please reach out to us at training@fdc-k.org or call us at +254712260031.

14.  Website: Visit our website at www.fdc-k.org for more information.