Real World Evidence in Healthcare and Life Sciences (MS)

Program Overview

With the Master of Science in Real World Evidence (RWE) you will gain all the knowledge and skills to produce important scientific and business results from real-world health data, such as electronic medical records and insurance claims. Experts will teach you how epidemiologists, health services researchers, and health policymakers use real-world data to produce reliable evidence. You will explore hands-on how this research discipline creates comprehensive understanding of disease, and you will learn about the complex methods and software to build that competence.

The skills you will obtain are immediately deployable to highly sought-after positions at pharmaceutical companies, regulatory authorities, health systems, technology companies and consulting groups specializing in life sciences and healthcare. This program is designed for healthcare and other professionals who are passionate about creating the evidence that will change medicine and the lives of people.

Full-time/Part-time

Boston Campus/Online/Hybrid

GRE not required

Application Deadlines
Aug 1, 2022
Dec 1, 2022

Curriculum

To graduate students must complete 31 total semester hours (approximately 3 full-time semesters). These include:

  • All Core courses
  • 6 to 12 credits of Selective courses
  • Up to 6 credits of Elective course

Core Requirements

Course Code Course Title Credit Hours
HSCI 5130 Introduction to Real World Evidence 2
HSCI 5140 Foundations of Data Models 2
HSCI 5150 Methods for Observational Research 1 3
HSCI 5160 Standardization of Real World Data 2
HSCI 5170 Data Model Transformation 2
HSCI 5151 Methods for Observational Research 2 3
PHSC 5212 Research Skills and Ethics 2
HSCI 6980 Capstone 3
Total 19

Selectives

Course Code Course Title Credit Hours
HSCI 5180 Phenotyping 2
HSCI 5190 Cohort Building 2
HSCI 6110 Advanced Population Characterization 2
HSCI 6120 Advanced Population Estimation 3
HSCI 6130 Advanced Patient Prediction 3
Total 12

Electives

Electives are selected in consultation with the Program Director. Concentration courses may not be double counted as elective courses.

  • HINF 6355 — Interoperability Key Standards in Health Informatics
  • HINF 5300 — Personal Health Interface Design and Development
  • HINF 6205 — Creation and Application of Medical Knowledge
  • HINF 6220 — Database Design, Access, Modeling, and Security

Experiential Learning

The Master of Science in Real World Evidence in Healthcare and Life Sciences program draws on strong industry partnerships to provide experiential learning opportunities for students enrolled in this program. During the program, you will work with the Experiential Network (XN) in projects. The capstone course will consist of experiential learning that may be offered in concert with Northeastern partners in the life sciences and healthcare industry. Additional experiential learning opportunities will be rolled out during this program.

Program Learning Outcomes

Students will be able to:

  • Describe the value and process of the ethical use of observational health data to answer clinical questions.
  • Illustrate how different forms of observational health data are collected, organized, and standardized to generate accurate, reproducible, and well-calibrated evidence.
  • Use state-of-the-art statistical software and methods to combine and analyze large-scale federated health data from diverse sources (e.g. EHRs) while preserving privacy.
  • Construct and take part in a team to conceptualize, analyze, and communicate the results of a study using observational health data to answer a clinical question.
  • Evaluate strengths and weaknesses of an observational health analysis.

Admissions

Fall Deadline: Aug 1
Spring Deadline: Dec 1

Admissions Requirements

A minimum GPA of 3.000
GRE not required

Admissions Checklist

Click each required application item for more information.

Completed application
Official or unofficial transcripts
2 letters of recommendation
Personal goal statement

Frequently Asked Questions

Why study Real World Evidence?

Real World Evidence is the clinical evidence regarding the usage and potential benefits, or risks of a medical product derived from analysis of real-world data. It can be generated by different study designs or analyses such as randomized trials, pragmatic trials, and observational studies. Real-world data (RWD) are the data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources, like electronic health records, claims and billing activities.

Together, real world data and real world evidence are playing an increasing role in health care decisions. The US Food & Drug Administration uses RWD and RWE to monitor post-market safety and to make regulatory decisions. The health care community uses these data to support coverage decisions and to develop guidelines and decision support tools for clinical practice. Medical product developers use RWD and RWE to support clinical trial designs and observational studies to generate innovative, new treatment approaches.

What can I do with this degree?

The hiring market for RWE-related skills is strong, particularly in the United States, where there were over 85,000 job postings related to RWE from December 2020 to November 2021 (Source: Emsi Knowledge Base, 2021).

What kind of jobs could I apply for?
  • Regulatory professionals (e.g. Director of Global Regulatory Affairs, Global Regulatory Affairs Managers, Global Regulatory Leads)
  • Medical and research scientists (e.g. Biostatistics Managers, Biostatisticians, Medical Science Liaisons, Clinical Scientists)
  • Data Scientists and Analysts (e.g. Data Scientists, Statistical Programmers, Associate Director of Analytics)
  • Data Strategy
  • Data Governance

Contact and About Our Real World Evidence Community

Learn more about Northeastern’s Real World Evidence research community at the OHDSI Center at Northeastern’s Roux Institute.

Kristin Kostka Northeastern University

Kristin Kostka
Acting Program Director
Director of the OHDSI Center

[email protected]