Program at-a-Glance
15 months or less
5 online classes
$6,125

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Improving Health Care Through Analytics

The increasing availability of electronic health data creates an incredible opportunity to apply large-scale, clinical analytics to improve health care, manage risks and improve patient outcomes. UC Davis Division of Continuing and Professional Education’s online Healthcare Analytics Certificate Program gives you the knowledge and practical skills to become a leader in this high-demand niche of the healthcare industry.

What You’ll Learn

This career-oriented program gives you the knowledge to succeed as a clinical and operational analyst in health care. Providing a strong foundation in the structure of healthcare data, it dives into hands-on coding and exposes you to leading industry applications such as SQL, SAS and Tableau. You’ll graduate from the program with a comprehensive understanding of the use and implementation of healthcare analytics, including:

  • The changing context of healthcare services, the trend from volume to value-based purchasing and the role of data in promoting improved outcomes
  • How to construct data files using advanced statistical and data programming techniques
  • How to design data models that integrate patient data from multiple sources to create comprehensive, patient-centered views of data
  • Analytic strategy to frame a potential issue and solution relevant to health improvement of patient populations
  • Integrating clinical and business data to assess or compare the cost effectiveness of clinical interventions or processes
  • Analysis of the distribution of disease and health outcomes in relevant populations of interest
  • The application of clinical analytics to various contexts of quality improvement

How You’ll Benefit

The program features:

  • Online convenience
  • World-class curriculum developed by experts across several health systems, including UC Davis
  • Access to industry-leading instructors, including UC Davis faculty, who have significant experience in healthcare analytics
  • Hands-on access to analytics software and live data sets
  • Practical knowledge you can use immediately in the workplace
  • Small class sizes with exceptional networking opportunities
  • $80,236 average salary for clinical analyst (Glassdoor)
  • 9% projected growth in all computer analyst jobs from 2018-2028* (Bureau of Labor Statistics)
  • $94,500 average salary for medical and health services managers (Bureau of Labor Statistics) 
  • 18% projected growth for medical and health services managers jobs from 2018-2028 (Bureau of Labor Statistics) 

*analysts working in health care not separated out 

Required Prerequisite: Completion of an introductory statistics course (Statistics 1 or equivalent) is required for Applied Healthcare Statistics. Students should understand and be able to apply the following concepts: variables and distributions, correlations, Null Hypothesis, regressions, group comparisons (t-tests and ANOVAs), generalized linear models. Students not comfortable with the preceding should take a refresher course. If you are unsure if a refresher course is necessary, review the Self-Exam for Applied Healthcare Statistics.

Recommended Prerequisite: Prior professional experience in health care or a previous background working with data and understanding of relational databases is strongly recommended but not required. Students with little or no relational database experience should include the SAS Primer course as part of their curricular plan.

Program Requirements: Courses may be taken individually or as part of the certificate program. The certificate is awarded upon the successful completion of five required courses (15 units), with a minimum grade of ‘C’ in each course and an overall average of ‘B’ or higher.

About the Online Format: Course modules are released weekly, where lessons typically load on Wednesdays and are due the following Tuesdays. Students can log in and work on courses at any time within that week to view lectures and complete assignments. Courses consist of video lectures, reading assignments, written assignments and discussion forums.

For more details please review our FAQs page.

Courses must be taken in sequential order: Introduction to Healthcare Analytics, Healthcare Data Acquisition and Management, Applied Healthcare Statistics, Data Mining for Healthcare Analytics, and Quantitative Methods of Decision Analysis.

Required Courses

Introduction to Healthcare Analytics
Quarter Academic Credit
3

With the increasing adoption of electronic health record systems, new forms of data are becoming available that can be used to measure healthcare delivery and improve patient outcomes. In this introductory course designed for healthcare analysts and IT professionals, participants explore the value proposition for clinical intelligence and the role of analytics in supporting a data-driven healthcare system. Through video lectures, reading assignments, written assignments and discussion forums, students learn key concepts in measuring health system performance and leveraging analytics for health improvement. 

Learning Outcomes

  • Demonstrate an understanding of how the health care industry is changing, the drivers of change and the role of data analytics in supporting the transition from fee for service to value-based care
  • Assess clinical data structures supported by electronic health records, clinical equipment and other data sets
  • Describe how healthcare performance is measured according to existing quality frameworks
  • Discuss and contrast the various methods for comparing healthcare delivery across populations of patients
  • Analyze examples of clinical improvement projects and the impact of health analytics to decision-making and systems improvement

Skills You Will Gain

  • Clinical care processes
  • Health data analysis
  • Business intelligence
  • Methods of healthcare delivery
  • Decision making and problem solving

Download the syllabus for this course.

Course Code
397120
Healthcare Data Acquisition and Management
Quarter Academic Credit
3

Learn to navigate complex data structures and efficiently retrieve the data needed to answer a question or solve a problem. Explore the types and sources of healthcare data, along with methods for selecting, preparing, querying and transforming healthcare data. Through video lectures, assignments and discussions, clinical operations analysts will examine the range of data sources and the strategies, tools and methods for data preparation and optimizing data quality. By the end of this course, students will understand the new models of healthcare data organization and analytics, such as clinical registries and query health. In addition, you will learn the basics of SQL programming or improve your SQL skills, within the concepts of other course topics.

Learning Outcomes

  • Analyze the various types and sources of healthcare data, including clinical, operational and patient-generated data
  • Compare and contrast common data models used in healthcare data systems
  • Assess the quality of healthcare data and make appropriate interpretations of meaning according to existing quality frameworks and standards
  • Design data models that integrate patient data from multiple sources to create comprehensive, patient-centered views of individual and population data
  • Harmonize data from multiple sources and prepare integrated data files for analysis

Skills You Will Gain

  • Healthcare data models and representations
  • Data selection, preprocessing and querying
  • Data harmonization
  • Data quality assessment
  • SQL Programming

Note: For students starting the Healthcare Analytics Certificate Program, you may take Healthcare Data and Acquisition and Management as your first course if Introduction to Healthcare Analytics is not offered in the quarter you begin the program.

Course Code
396690
Applied Healthcare Statistics
Quarter Academic Credit
3

Successful healthcare analysts require a solid grasp of statistical inference as a foundation of their work. Designed for clinical and operational analysts, this course introduces concepts in healthcare epidemiology, outcomes research and experimental studies. The course focuses on four domains: 1) fundamentals of data and statistics 2) techniques to assess relationships between exposure and outcomes 3) study design and 4) measuring main effects and error. Through online lectures and hands-on practice, students will explore methods to analyze healthcare data, contrast efficacy and effectiveness trials, and learn strategies to adjust for patient, provider and hospital characteristics in a statistical model. All topics will be covered in the context of their direct application to health care. By the end of this course students will be able to design a healthcare study; assess confounding, biases, internal and external validity; and understand variance.

Learning Outcomes

  • Demonstrate an understanding of statistical techniques widely used to conduct healthcare research
  • Develop a study design to appropriately address a research question
  • Differentiate between the various components in a statistical model (variables, parameters, error)
  • Calculate performance characteristics of a clinical assay (sensitivity, specificity, predictive value)
  • Conduct a simple analysis of [at least] three variables (predictor, outcome and other) using statistical software

Skills You Will Gain

  • Applied statistics
  • Epidemiology and applications to healthcare
  • Analysis and reporting
  • Statistics theory
  • Measurements of precision and accuracy in models
Course Code
500052
Data Mining for Healthcare Analytics
Quarter Academic Credit
3

The proliferation of data in the post-EHR era creates opportunities for large-scale data analysis to discover meaningful patterns and trends. Through interactive, online learning, explore the application of data mining techniques for purposes of big data analytics using administrative and clinical systems data. Gain an overview of the data mining process, data mining standards and output protocols, and common techniques used in mining healthcare data. By the end of this course, students will be familiar with data mining techniques and visual representation methods that increase understanding of complex data.

Learning Outcomes

  • Define and examine the objectives of data mining
  • Illustrate the ability to observe, manipulate and chart data
  • Explain when to use each contract type
  • Apply appropriate data mining methods to data sets
  • Execute a data mining project  

Skills You Will Gain

  • Data mining techniques and standards
  • Data mining preparation
  • Data visualization and reporting

*In order to utilize all course tools, students need access to a PC. Students with Mac computers can take and complete the course but will not gain as much hands-on experience.

Course Code
500006
Quantitative Methods and Decision Analysis
Quarter Academic Credit
3

Health analytics practitioners use an array of quantitative methods to evaluate questions of efficiency and effectiveness in health care. This advanced course for healthcare analysts and other professionals integrates and builds on prior coursework in statistics and data mining and provides additional exposure to advanced methodologies, such as event sequencing, analytical groupers, simulation and predictive modeling. Heavy emphasis is given to the application of methods to contemporary analytic challenges in health care, including demand/utilization forecasting, risk stratification, population health management, quality measurement, fraud detection, and cost containment from readmissions. Throughout this course, students will complete a series of hands-on assignments in the use of specific analytic approaches, as well as a final course project.

Learning Outcomes

  • Understand/compare the array of quantitative methods available from diverse fields of statistics, data mining and domain-specific fields
  • Utilize a variety of clinical and administrative data sets to identify cohorts for research, quality measurement or interventions
  • Evaluate a simulation model, interpret the findings and assess model performance
  • Utilize appropriate methods to determine which interventions are most likely to lead to positive outcomes
  • Identify the best approaches to profile the highest-cost patients of members and evaluate mechanisms to lower costs

Skills You Will Gain

  • Decision analysis
  • Predictive modeling and visualization
  • Risk assessment and stratification
  • Cost containment
  • Fraud detection
Course Code
500064

Courses must be taken in sequential order as noted below:

Required Courses

Required Courses Units Fall Winter Spring Summer
Introduction to Healthcare Analytics 3 Online Online
Healthcare Data Acquisition and Management 3 Online Online
Applied Healthcare Statistics 3 Online Online
Data Mining for Healthcare Analytics 3 Online Online
Quantitative Methods and Decision Analysis 3 Online Online
  • Program cost: $6,000 (or $1,200 per course)
  • See each course offering for discount opportunities
  • A one-time, nonrefundable registration fee of $125 must be paid before completing this certificate program
  • For more details, please review our FAQs page

For information about financing your education, please click here.

There are three ways to start earning your certificate. Choose the option that best suits your needs:

  • Register – Complete this brief certificate registration form and pay the registration fee. By doing so, you declare your intent to complete the program and lock in program requirements. Once you register, program staff will be available to answer any questions you may have and assist you with your course plan.
  • Enroll in an individual course – Check out individual courses that are currently open for enrollment. Courses will be applied to a certificate program if you later decide to complete your certificate.
  • Sign up for an information session – Learn more about the Healthcare Analytics program by registering for a free information session. If an information session for this program is not currently open for enrollment, click on “notify me,” and we’ll contact you when the next one becomes available.

Questions? We’re here to help. If you’ve got a question, email us or call (530) 757-8899.

Student Reviews

Kristin Seidl, Ph.D., RN

“This program was exactly what I needed to advance my healthcare quality expertise, and I look forward to using all of my new skills.”

Meet our Faculty