Healthcare Analytics Certificate Program | UC Davis Extension

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Healthcare Analytics Certificate Program

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 Extension’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.

Designed for working clinicians and IT professionals, the program features:

  • Online Convenience – Engaging online courses let you schedule your learning around your work and lifestyle
  • Industry-leading Instructors – Learn from working professionals and acknowledged experts, including UC Davis faculty, who have significant experience in healthcare analytics
  • Immediate ROI – Career-based program offers practical knowledge you can immediately use in the workplace
  • World-Class Education – Developed in partnership with the nationally regarded UC Davis School of Medicine, these courses will put you on the cutting edge of the industry
  • Small Classes with Exceptional Networking Opportunities – Small class sizes allow you to connect with instructors and an interdisciplinary group of peers who can help advance your career
  • “Real-World” Learning –  Interactive class assignments give you hands-on access to analytics software and live data sets

About the Program:

This five-course, career-oriented program gives you the knowledge to succeed as a clinical and operational analyst in health care. The Healthcare Analytics Certificate Program dives into healthcare data and hands-on coding, leaning more toward a data scientist, while the Health Informatics Certificate Program focuses on the foundational management of patient information in health care, without the hands-on coding aspect. In the Healthcare Analytics Program, you'll learn to select, prepare, analyze, interpret, evaluate and present data related to health system performance and clinical effectiveness. 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 (e.g., general population, health system members, patient sub-groups).
  • The application of clinical analytics to various contexts of quality improvement (e.g., chronic disease, patient utilization, population health, public health).

Online Course Logistics

The courses follow a weekly model with lessons loading on Wednesdays, discussion forums during the week and work generally due on Tuesdays. The courses consist of online course materials, reading assignments, written assignments and discussion forums. The time it takes you to complete each lesson depends on your individual approach and learning style. Lesson presentations vary in length, and you will spend time working on your assignments, reading, and interacting with classmates in the discussion forums. As a general rule, plan on spending about 2-4 hours online each week, plus 3-4 hours of work outside of class on readings and assignments.

How to Apply:

You must submit an application and pay the nonrefundable application fee. 

Once you complete the last course in the program, please submit a Notice of Completion. This action prompts our offices to review your file and take the necessary steps to get your certificate mailed in a timely manner.

Prerequisites:

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.

Prior coursework in statistics (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 have questions, please consult a program representative.

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.

Course content is frequently revised to ensure that the program is up-to-date with the latest industry standards. For this reason, you must complete all of the course requirements of your certificate within five years from the day you enroll in the first course. A certificate will not be awarded if the requirements are not completed and your application for candidacy is not received within this timeframe.

Quarterly schedule of courses

CourseUnitsFWSPSU
Required
Courses
Introduction to Healthcare Analytics3OO
Healthcare Data Acquisition and Management3.0OO
Applied Healthcare Statistics3OO
Data Mining for Healthcare Analytics3.0OO
Quantitative Methods and Decision Analysis3OO
F=Fall W=Winter SP=Spring SU=Summer; Schedules subject to change.
O = Online C = Classroom OC = Online and Classroom H = Hybrid


Required Courses

3 quarter units academic credit X426.5

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, participants explore the value proposition for clinical intelligence and the role of analytics in supporting a data-driven learning healthcare system. Key topics include:

  • The value-driven healthcare system
  • Measuring health system performance
  • Existing quality/performance measurement frameworks (NQF, HEDIS)
  • Comparing healthcare delivery
  • Attributes of high-performing healthcare systems
  • Definition and scope of business and clinical intelligence
  • Key components of healthcare analytics
  • The IT infrastructure and human capital needed to leverage analytics for health improvement

This is not a self-paced course.  Students will progress through the course together.  Lessons will be posted one week at a time.  The previous week's lesson will remain available for the duration of the course.  Students who enroll after the start date of the course will have to contact the instructor regarding missed lessons. 

Sections of this course open for enrollment:
3.0 quarter units academic credit X423.2

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.

Examine the range of data sources, including:

  • Administrative, clinical, patient-reported and external data (e.g., CCDs, HL-7 messages)
  • Common representations of data in health information systems (ICD, CPT)
  • Strategies for optimizing data quality
  • Querying tools and methods
  • Data preparation/transformation
  • Ethics, data ownership and privacy

Also covered are new models of healthcare data organization and analytics, such as clinical registries and query health.  

This is not a self-paced course. Students will progress through the course together. Lessons will be posted one week at a time. The previous week's lesson will remain available for the duration of the course. Students who enroll after the start date of the course will have to contact the instructor regarding missed lessons. 

Students will learn the basics of SQL programming, or improve their SQL skills, within the concepts of other course topics.

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.

This course is not currently open for enrollment.
3 quarter units academic credit X423.3

Examine epidemiological concepts in health outcomes research and their use in evaluating the patterns, causes and effects of health in patient populations. Topics include study design; generalized linear models; internal and external validity; and methods to minimize bias, adjusting for confounding and measuring effect modification. Participants will learn to develop a testable hypothesis, build and refine analytic models and interpret results relevant to health outcomes.  

This is not a self-paced course. Students will progress through the course together. Lessons will be posted one week at a time. The previous week's lesson will remain available for the duration of the course. Students who enroll after the start date of the course will have to contact the instructor regarding missed lessons. 

Sections of this course open for enrollment:
3.0 quarter units academic credit X423.5

The proliferation of data in the post-EHR era creates opportunities for large-scale data analysis to discover meaningful patterns and trends. Explore the application of data mining techniques for purposes of big data analytics using administrative and clinical systems data. Topics include an overview of the data mining process, data mining standards and output protocols, and common techniques used in mining healthcare data. Also covered are visual representation methods that increase understanding of complex data.

This is not a self-paced course.  Students will progress through the course together.  Lessons will be posted one week at a time.  The previous week's lesson will remain available for the duration of the course.  Students who enroll after the start date of the course will have to contact the instructor regarding missed lessons.  This class will conduct one-mandatory synchronous Adobe Connect session towards the end of the course, please plan accordingly.

This class will conduct one-mandatory synchronous Adobe Connect session towards the end of the course, please plan accordingly.

This course is not currently open for enrollment.
3 quarter units academic credit X423.4

Examine an array of quantitative methods used by health analytics practitioners to evaluate questions of efficiency and effectiveness in healthcare. This advanced course 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 healthcare, including demand/utilization forecasting, risk stratification, population health management, quality measurement, fraud detection, and cost containment from readmissions.  Participants complete a series of hands-on assignments in the use of specific analytic approaches, as well as a final course project. 

This is not a self-paced course.  Students will progress through the course together.  Lessons will be posted one week at a time.  The previous week's lesson will remain available for the duration of the course.  Students who enroll after the start date of the course will have to contact the instructor regarding missed lessons. 

Sections of this course open for enrollment:

STUDENT REVIEWS

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

Kristin Seidl, Ph.D., RN, clinical data scientist, University of Maryland Medical Center, and assistant professor, University of Maryland School of Nursing

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FREE INFORMATION SESSION

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FREE SAMPLE LESSON

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FAQs

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Certificate Programs as Stepping Stones to Career Advancement

MEET OUR FACULTY

Instructor Bios

“Looking at the quality of data and analyzing it carefully can save patient lives.”

Brian Panciotti, Healthercare Analytics instructor

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RESOURCES & DOWNLOADS

Healthcare Analytics Factsheet

ADDITIONAL INFORMATION

Declare Your Candidacy

Notice of Completion

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