Health Informatics Courses

Harold Lehmann, MD, PhD
1st quarter, 3 quarter credits *online (1.5 semester credits)

Virtual Live Talks (Wednesday 5:30 - 7:00 pm ET)

Introduces students to the core principles of informatics as applied to the entire range of health, from prevention, through illness, to population and public health. Focuses on frameworks within which to describe and explain health information systems. Provides to non-clinicians basic exposure to the terminology and concepts of clinical care and public health. Provides to technical novices basic exposure to IT terminology. Provides all students entry-level concepts and skills for later courses in the informatics sequences.

JHU students, faculty, and staff not matriculated in our formal degree or certificate programs must seek the instructor's permission.

Paul Nagy, PhD & Matthew Robinson, MD
1st quarter, 3 quarter credits *online (1.5 semester credits)

Virtual Live Talks (Tuesday 5:00 - 6:30 pm ET)

This course will introduce students to the rapidly evolving field of precision medicine and the role of big data analytics in improving patient care, clinical decision making, and population health management. Students will be provided access to the Johns Hopkins Precision Medicine Analytics Platform (PMAP) and learn how the infrastructure is built to support clinical research by integrating data from multiple research and clinical information systems such as the enterprise wide electronic medical record (EMR). The class will provide students access to a de-identified EMR curated dataset of 60k patients with a diagnosis of Asthma. The class will utilize Python and Jupyter notebooks to learn how to analyze EMR data. The PMAP cookbook of Jupyter notebook recipes and Datacamp accounts will be provided for students. After completing the course, students will have an overview of the full lifecycle of a learning health system, from understanding the data elements needed to address their problem, to figuring out the right analysis tools, and finishing with how to take their algorithm and deploy it in the clinical setting as a clinical decision support application.

This class is supported by DataCamp, the most intuitive learning platform for data science and analytics. Learn any time, anywhere and become an expert in R, Python, SQL, and more. DataCamp’s learn-by-doing methodology combines short expert videos and hands-on-the-keyboard exercises to help learners retain knowledge. DataCamp offers 350+ courses by expert instructors on topics such as importing data, data visualization, and machine learning. They’re constantly expanding their curriculum to keep up with the latest technology trends and to provide the best learning experience for all skill levels. Join over 6 million learners around the world and close your skills gap.

JHU students, faculty, and staff not matriculated in our formal degree or certificate programs must seek the instructor's permission.

ME.250.959 Digital Health Laws and Regulations

Adler Archer, JD & Tamra Moore, JD
1st quarter, 3 quarter credits  *online (1.5 semester credits)

Virtual Live Talks (Thursday 5:00 - 6:30 pm ET)

From smartwatch apps and telehealth to artificial intelligence and machine learning, digital health is revolutionizing the practice of medicine. In some instances, medical software is not only increasing access to data but also diagnosing and treating diseases. For all its potential, digital health is not without risks, though. This seminar is for students who want to explore the promise that digital health devices offer and investigate the legal, quality, and safety protections in place to help ensure responsible and high-quality innovation. Students will explore key digital health terminology and trends and examine the regulatory pathways to usher medical software devices from bench to bedside.

JHU students, faculty, and staff not matriculated in our formal degree or certificate programs must seek the instructor's permission.

Richard Schreiber, MD & Peter Greene, MD
2nd quarter, 3 quarter credits  *online (1.5 semester credits)

Virtual Live Talks (Wednesday 7:00 - 8:30 pm ET)

Prepares learners to lead organizations implementing new IT systems. Covers the knowledge and skills that enable clinical and public health informaticians to lead and manage changes associated with implementation, adoption, and evaluation of effective use of health information systems.​ The course covers the following topics: Leadership & Governance in Health IT, Effective Teams in Health IT, Project Management, Strategic Planning for Health Information Systems, Workflow Re-eingineering, and Change Management.

JHU students, faculty, and staff not matriculated in our formal degree or certificate programs must seek the instructor's permission.

ME 250.770 Clinical Data Analysis with Python

Jules Bergmann, MD & Brad Genereaux, HL7 v3 RIM Specialist, PMC-III
2nd quarter, 3 quarter credits *online (1.5 semester credits)

Virtual Live Talks (Mondays 5:00 - 6:30 pm ET)

Pre-requisite ME 250.771 Introduction to Precision Medicine

Through class discussion and interactive Python data exercises, this course provides practical experience working with electronic medical record data.  The class will provide students access to the Johns Hopkins Precision Medicine Analytics Platform (PMAP) to conduct analysis on a de-identified EMR curated dataset of 60k patients with a diagnosis of Asthma.

The class will introduce Python and Jupyter notebooks to learn how to analyze EMR data. The topics will include exploratory data analysis, data cleaning, feature extraction, model construction, and evaluation. The class will have access to the PMAP cookbook of Juptyer notebook recipes and Datacamp accounts will be provided for students to master working with major python libraries.

JHU students, faculty, and staff not matriculated in our formal degree or certificate programs must seek the instructor's permission.

ME 250.750 Design Discovery for Health Care 

* formally Health Information Systems: Design to Deployment

Jasmine McNeil, MBA, MA & Andrea Luxenberg, B.A.
3rd quarter, 3 quarter credits *online (1.5 semester credits)

Virtual Live Talks (Wednesday 5:30 - 7:00 pm ET)

This course is the first of the design for healthcare series (it is strongly recommended that prototyping for healthcare design is taken just after this course). Design discovery for healthcare applies design thinking techniques to the beginning stages of digital health app ideation. Working as part of a team, participants will explore methods for mapping stakeholders, and plan for and execute user interviews to gain insights about user needs for a digital health app. They will also choose design research methods, practice synthesizing results, and facilitate ideation sessions, with a goal of creating a design research brief. Teams will explore design software tools in this course but will not be required to code.

All students must seek the instructor's permission.

ME 250.777  Clinical Decision Analysis

Harold Lehmann, MD, PhD & Robert Koski, DMD 
3rd quarter, 3 quarter credits *online (1.5 semester credits)

Virtual Live Talks (Wednesday 7:00 - 9:00 pm ET)

This advanced elective introduces students to the basic theory and practice of decision analysis as applied to the clinical context, with an eye towards clinical decision support and the place of decision modeling in the informatics context. Topics include articulating and structuring a decision problem, creating a decision model, skill building in decision trees, and if time permits, exposure to Markov models and discrete event simulation.

JHU students, faculty, and staff not matriculated in our formal degree or certificate programs must seek the instructor's permission.

Krishnaj Gourab, MD & Carrie Stein, MSN, MBA
3rd quarter, 3 quarter credits *online (1.5 semester credits)

Virtual Live Talks (Tuesdays 5:00 - 6:30 pm ET)

This course introduces students to the field of Applied Clinical Informatics, which is focused on leveraging clinical information systems and technology to improve patient and family-centered care. Students will be exposed to a range of clinical workflows as well as patient/caregiver needs and how these may be supported by health information technology. Topics include: workflow analysis, clinical decision support (CDS), electronic health record (EHR) and patient portal best practices, health information exchange (HIE), integrated laboratory, imaging and pharmacy information, telehealth and digital health strategies, and evaluation. Each of these topics will be examined across the care continuum and within the appropriate context of clinical care transitions, patient safety and care quality, regulatory requirements, information security, organizational governance and project management.

JHU students, faculty, and staff not matriculated in our formal degree or certificate programs must seek the instructor's permission.

ME.250.788 Observational Research Methods in R

Paul Nagy, PhD, Adam Black, MS, & Erik Westlund, PhD
3rd quarter, 3 quarter credits *online (1.5 semester credits)

Virtual Live Talks (Thursdays 12:00 - 1:30 pm ET)

Pre-requisite: ME.250.782 Observational Health Research Methods on Medical Records (OMOP)

This course will teach students how to conduct data characterization, time at risk analysis, and causal inference testing on EHR based observational research data.      

This course will be a hands-on course leveraging the open-source R based Health Analytics Data Evidence Suite. Students will get access to de-identified real world EHR data to perform and create computational patient population cohorts and conduct statistical analysis.

JHU students, faculty, and staff not matriculated in our formal degree or certificate programs must seek the instructor's permission.

Thomas Grader Beck, MD & Harold Lehmann, MD, PhD
4th quarter, 3 quarter credits *online (1.5 semester credits)

Virtual Live Talks (Wednesday 7:00-8:30 pm ET)

This course provides a framework for understanding decision support in the workflow of the health sciences. The focus is on the types of support needed by different decision makers, and the features associated with those types of support. A variety of decision support algorithms is discussed, examining advantages and disadvantages of each, with a strong emphasis on decision analysis as the basic science of decision making. Students are expected to demonstrate facility with one algorithm in particular through the creation of a working prototype, and to articulate the evidence for efficacy and effectiveness of various types of decision support in health sciences and practice, in general.

JHU students, faculty, and staff not matriculated in our formal degree or certificate programs must seek the instructor's permission.

Masoud Rouhizadeh, PhD & Nic Dobbins, PhD
4th quarter, 3 quarter credits *online (1.5 semester credits)

Virtual Live Talks (Monday 7:00 - 8:30 pm ET)

Pre-requisites: ME 250.771 Introduction to Precision Medicine Data Analytics and ME 250.770 Clinical Data Analysis with Python

There is significant demand in both academia, research and industry for informatics professionals who are well versed in natural language processing (NLP). In this course, students will be oriented to the various applications of NLP in biomedicine, healthcare and public health. The course will emphasize the importance of clearly defining what problem needs to be solved or what questions one seeks to get answered via the use of NLP. Approaches to data mining of free text from the biomedical literature, clinical narratives, and other novel data sources will be covered. There will be opportunities for students to develop NLP and machine learning algorithms. Applications of these tools in epidemiologic surveillance, clinical decision support, and other relevant use cases will be covered.

All students must seek the instructor's permission.

ME.250.783 Imaging Informatics and Deep Learning

Paul Nagy, PhD & Bradley Genereaux, HL7 v3 RIM Specialist, PMC-III
4th quarter, 3 quarter credits *online (1.5 semester credits)

Virtual Live Talks (Tuesday 5:00 - 6:30 pm ET)

Pre-requisite: ME 250.770 Clinical Data Analysis with Python

This class will describe how to leverage deep learning models for classification and segmentation of clinical medical imaging data. Students will get hands-on experience in working with medical images and learn how to integrate AI models in a clinical setting using the DICOM (Digital Imaging Communication in Medicine) interoperability standard. 

JHU students, faculty, and staff not matriculated in our formal degree or certificate programs must seek the instructor's permission.

Krishnaj Gourab, MD & Terri Sippel Schmidt, MS
4th quarter, 3 quarter credits *online (1.5 semester credits)

Virtual Live Talks (Monday 5:00 - 6:30 pm ET)

Pre-requisite: ME.250.778 Implementation of Fast Healthcare Interoperable Resources (FHIR)  

The ultimate goal of informatics and data science is to drive patient care.  This class discusses the implementation of Clinical Decision Support applications integrated with the practice of medicine.
  
By the completion of this course, students should be able to:
1.)    Describe the current state of CDS implementation in EHRs
2.)    List the advantages of implementing interoperable  CDS algorithms in EHRs
3.)    Implement basic interoperability of CDS algorithms using CDShooks and HL7 FHIR
4.)    Implement basic HL7 Clinical Query Language (CQL) queries for EHR data to support CDS algorithms

JHU students, faculty, and staff not matriculated in our formal degree or certificate programs must seek the instructor's permission.

ME 250.963 Health Information Technology Startup Generator / Accelerator

Paul Nagy, PhD & Jasmine McNeil, MBA, MA
4th quarter, 3 quarter credits; In person (1.5 semester credits)

NOTE: This is a two-part course. Students must also register for Quarter 3 ME 250.963 Health Information Technology Startup Generator / Accelerator.

Hexcite (Excited for Healthcare) is an early-stage medical software accelerator program for entrepreneurs hosted by the Johns Hopkins Medicine Technology Innovation Center in collaboration with Johns Hopkins Technology Ventures.

Weekly, expert-led sessions help teams navigate the first steps of business and technical design using the Lean Start-up methodology which focuses on growing a business with maximum acceleration. Teams must go through customer discovery (interviewing to test assumptions), a design thinking process to prioritize technology requirements, and build a pitch that includes market research and storytelling components.  

All students must seek the instructor's permission. 

ME 250.958 Digital Health Innovation & Regulatory Science

Adler Archer, JD
1 quarter credit *online (.5 semester credits)

Virtual Live Talks (Thursday 5:00 - 6:00 pm ET)

From smartwatch apps and tele-health to the use of artificial intelligence (AI) and machine learning (ML) on big data, digital health is shaking up the health care industry. These tools promise to revolutionize how patients and healthcare providers access health data. In some cases, digital health tools will even diagnose and treat diseases. For all its potential, digital health is not without risks, though. There must be adequate legal, quality, and safety protections to ensure responsible and high-quality innovation. This course will introduce students to the rapidly evolving field of digital health regulation and the role of the FDA, FTC, OCR, and other legal and regulatory bodies in this space.

At the end of this course, students will be able to:
1.    Define key terminology relevant to the fields of digital health innovation and medical device regulation.
2.    Discuss the relationships between regulators, technology developers, healthcare providers, and patients. 
3.    Describe the requirements for digital health technology to be considered Software as a Medical Device (SaMD) by the FDA
4.    Identify the various regulatory pathways for SaMD and the main considerations.

JHU students, faculty, and staff not matriculated in our formal degree or certificate programs must seek the instructor's permission.

Paul Nagy PhD, Harold Lehmann, MD, PhD, & Nic Dobbins, PhD
1st, 2nd, 3rd, and 4th quarter
1 quarter credit *online (.5 semester credits)

Student Seminar Virtual Live Talks (1st & 3rd Thursdays, 7:00 - 8:30 pm ET)

Grand Rounds (2nd Wednesday of each month, 12:00 -1:00 pm ET)

Weekly combined seminar and Grand Rounds during term. 1 credit per quarter provided students attend both seminar and Grand Rounds. Students not matriculated in our formal degree or certificate programs must seek the instructor's permission. Grand Rounds is open to all for those not seeking course credit for attending. Details on speakers and remote access to the lecture may be found here on the Grand Rounds page.

Hadi Kharrazi, MD, PhD & Harold Lehmann MD, PhD

Location: 2024 East Monument Street, Room 1-207

(Thursday, 8:45 – 10:15 am ET)

Please note attendance must be in person.

This course number applies to MS Applied, Research Masters students and both lab rotations for PhD students and to continuing research for PhD students. The informatics research is precepted by a faculty member in the Division or approved by the Training Program Director. The research may originate with the preceptor or with the student, and may be at different phases of development. In the case of the lab rotation, most of the activity is supervised by the preceptor. In the case of ongoing research, there is supervision by the Training Program Director as well as the research committee assembled by the student. Milestones are set for each quarter. Please note that a comprehensive research plan must be submitted to the program director for approval no later than September 15 of Year 2. Failure to do so will result in probation for the student.

Edward Bunker, MS, MPH

On campus students 4th quarter and summer
Online students the last year of degree completion

The purpose of the Capstone is to provide students an opportunity to:

1. Demonstrate integration of skills and knowledge learned
2. Develop a significant component of their portfolio
3. Contribute to the field.

The Capstone Project will generally last 2 quarters. Students will join an active work group, supervised directly or indirectly by the capstone preceptor. They will also have a faculty advisor. The student will be responsible for spending time at the Capstone site, with specific timing to be negotiated with the capstone preceptor. Attendance may include participating in project and staff meetings, as well as front-line activity, such as working with clients.

The final report shall document attendance, how (or whether) the learning objectives were met, and shall include the report generated for the preceptor. A presentation will be made of the final report at a Capstone Presentation Seminar, with students, faculty, and capstone preceptors in attendance.

ME 250.856 Independent Study

Staff

Independent Study courses must be approved by the Program Director. Please note that it is important to follow the steps outlined below in order to comply with SOM registration and grading policies. Students submit a course description to the Training Program Director, Course Instructor and Program Coordinator. The description will include the length of Independent Study (up to 2 quarters or 1 semester), the time commitment (given in hours per week or quarter), the student’s goals and what the deliverable will be. On approval by the Program Director, the Coordinator will supply you with the appropriate course number for registration. It is important that the course instructor be prepared to submit a letter grade on their departmental letterhead to the Program Coordinator.

Students not matriculated in our formal degree or certificate programs must seek the instructor’s permission.

The rules governing independent study are as follows:

1. Gain the commitment of a Hopkins faculty member to work with you. Independent studies must be completed with a Hopkins faculty member.
2. Work out your learning objectives.
   "By the end of the course, I will have demonstrated my ability to identify threats to validity of conclusions based on analysis of health system data." You are allowed more than one learning objective.
3. In advance of starting, work out with the faculty member a "deliverable" that would reflect your having achieved your learning objective(s).
4. Work out a schedule. This will include attending informatics seminars, mentor sessions, reading, and working on the deliverable.

ME 250.855 Health Sciences Informatics Technology Practicum

Staff
3 quarter credits (1.5 semester credits), for Certificate program students

This course applies to the Post Baccalaureate Certificate program students and is a practical experience supervised by Hopkins faculty that enables students to showcase and develop skills gained during the didactic curriculum. With a preceptor and an academic advisor, students articulate a concrete deliverable and work with the preceptor and their team to accomplish the deliverable. Example activities include, but are not limited to, literature review, systems analysis, systems evaluations, data analysis, or plans for any of these.