Courses

Virtual Live Talks (Wednesdays, 5:30 – 7:00 PM EST)

Introduces students to the core principles of informatics as applied to the entire range of health, from prevention, through illness, to the wider population. Focuses on frameworks within which to describe and explain health information systems.  

Topics: 

• terminology and concepts of clinical care and general health care
• IT terminology 
• entry-level concepts and skills for later courses in the informatics sequences 

Virtual Live Talks (Tuesdays, 5:00 – 6:30 pm EST)

Introduces 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. Provides 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).  Allows students access to a de-identified EMR curated dataset of 60k patients with a diagnosis of Asthma. Utilizes Python and Jupyter notebooks for analyzing EMR data. The PMAP cookbook of Jupyter notebook recipes and Datacamp accounts will be provided for students.  

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. 

Topics: 

• overview of the full lifecycle of a learning health system 
• data elements needed to address problems 
• how to determine the right analysis tools 
• how to take algorithms and deploy in the clinical setting as a clinical decision support application 

Virtual Live Talks (Thursdays, 5:00 – 6:30 PM EST)

Introduces students to the ways digital health is revolutionizing the practice of medicine, increasing access to data, and diagnosing and treating diseases. For all its potential, digital health is not without risks. Students will 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. 

Topics: 

• key digital health terminology 
• digital health trends 
• regulatory pathways for medical software devices 

Virtual Live Talks Tuesdays, 7:00 – 8:30 PM EST

Students gain practical experience working with the OMOP common data model (CDM) from the Observational Health Data Science and Informatics (OHDSI) community. The class will provide students with an understanding of the research challenges posed by traditional healthcare data sources and will highlight the importance of the standardized data model in addressing these challenges, specifically how the CDM can maximize the value of observational health data through facilitation of large-scale analytics. The class will explore the use of the CDM in facilitating reproducible and interoperable observational studies that are becoming the industry standards in emerging healthcare research.  

Topics: 

• data quality 

• data characterization 

• major clinical terminologies 

• research cohort definitions 

• how to frame an observational research question 

• tools for cohort discovery such as Athena and Atlas 

In Person Date / Time TBD

Location: 2024 East Monument Street, Room 1-207

Please note: In-person attendance required.

This course will introduce students to a range of skills for successful entry into the biomedical informatics field. Students engage in educational and professional goals analysis and career coaching, such as refining resumes. Students will engage in self-discovery exercises to support technical training on basic Agile software project management, a range of software carpentry skills, healthcare domain specific concepts, software engineering, and cloud computing, to ensure workforce readiness and competitive positioning in data-driven healthcare careers. This course will also foster partnerships with alumni and industry mentors to provide networking and job pathways. 

Goals:

By the end of this course, you will be able to:

• Describe goals and a career path
• Design a well-constructed resume and curriculum vitae (CV)
• Communicate your own skill sets and why they are relevant and important
• Hone interviewing skills
• Receive and act upon feedback and mentoring from program alumni and/or industry
• Directly engage in academic, research, and industry activities
• Engage in conferences, hackathons, and other industry-led consortiums
• Participate in mock argumentation and negotiation skill encounters
• Develop practical skills in software development, cloud computing, and project management

Virtual Live Talks (Wednesdays, 7:00 – 8:30 PM EST)

Students learn how 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.​  

Topics: 

• leadership and governance in Health IT 
• effective teams in Health IT 
• project management 
• strategic planning for health information systems
• workflow re-engineering 
• change management 

Virtual Live Talks (Tuesdays, 5:00 – 6:30 PM EST)

This course introduces core concepts of relational databases using SQL. Students will utilize the Precision Medicine Analytics Platform (PMAP) with access to de-identified medical records of 60,000 patients with asthma with over 100 million data elements including labs, medications, encounters, procedures, symptoms, and vitals. 

Topics: 

• answering key questions on data originating from electronic medical records using SQL 
• special issues related to databases used in health information systems  

Virtual Live Talks Mondays, 5:00 - 6:30 pm EST

Pre-requisite ME 250.771 Introduction to Precision Medicine 

Students gain practical experience working with electronic medical record data through class discussion and interactive Python data exercises. Using the Johns Hopkins Precision Medicine Analytics Platform (PMAP), students will conduct analyses on a de-identified electronic medical record dataset of 60k patients with a diagnosis of asthma. The class will introduce Python and Jupyter notebooks to learn how to analyze electronic medical record data. 

Topics: 

• exploratory data analysis
• data cleaning 
• feature extraction 
• model construction and evaluation  

Virtual Live Talks Tuesdays, 12:00 - 1:30 pm

Students will examine the adoption of digital health innovation through the lens of health care providers and entrepreneurs and will be matched with C-suite executives for mentorship. This course begins by looking at how problems are identified and solutions are sourced by the spectrum of health care provider types. Simultaneously, each student will look at this process from the entrepreneur’s perspective, better understanding how solutions should position themselves in the market, target key stakeholders, and successfully navigate the adoption and implementation process. Mentors will be available for guidance throughout the class, and students will be expected to adopt the mentors’ companies as an avatar through which they will examine this process.   

Topics: 

• procurement process, integration across the buyer’s organization, identification of pitfalls 
• breadth of the health care provider landscape
• key elements of a pitch needed to successfully engage a health care provider  
• how health care providers can productively work with early-stage innovations 
• effective strategies and common mistakes that past digital health solutions have made 

Virtual Live Talks Wednesdays, 5:00 - 6:30 pm EST

This project-oriented class equips clinical investigators with the team, essential knowledge, and skills to effectively leverage the observational medical outcomes partnership (OMOP) common data model (CDM) to engage and conduct network studies for their research endeavors. Students will form investigation-based teams. By the end of the program, participants will have a solid foundation in these crucial aspects, enabling them to conduct robust network studies using the OHDSI community.   

Topics: 

• use case selection
• study design 
• IRB considerations
• protocol development 
• preliminary phenotypes 

All students must seek the instructor’s permission. 

Virtual Live Talks Tuesdays, 7:00 - 8:30 PM EST

Pre-requisites: ME 250.770 Clinical Data Analysis with Python; ME 250.953 Introduction to Biomedical Informatics (Note: Pre-requisites may be waived if a student receives permission from the instructor.)

This course is designed to bridge the gap between engineering and medicine by exploring the application of generative artificial intelligence (AI) technologies in healthcare settings. Through a combination of lectures, case studies, and hands-on projects, students will gain a comprehensive understanding of how generative AI can be leveraged to solve complex health-related problems, while also navigating the ethical, legal, and social implications of these technologies. The course will cover a range of topics, including but not limited to, the fundamentals of generative AI, its current and potential applications in healthcare, data privacy and security, ethical considerations in AI deployment, regulatory frameworks, and the impact of AI on patient care and healthcare systems.

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

In Person Date / Time TBD

Location: 2024 East Monument Street, Room 1-207

Please note: In-person attendance required.

This course will introduce students to a range of skills for successful entry into the biomedical informatics field. Students engage in educational and professional goals analysis and career coaching, such as refining resumes. Students will engage in self-discovery exercises to support technical training on basic Agile software project management, a range of software carpentry skills, healthcare domain specific concepts, software engineering, and cloud computing, to ensure workforce readiness and competitive positioning in data-driven healthcare careers. This course will also foster partnerships with alumni and industry mentors to provide networking and job pathways. 

Goals:

By the end of this course, you will be able to:

• Describe goals and a career path
• Design a well-constructed resume and curriculum vitae (CV)
• Communicate your own skill sets and why they are relevant and important
• Hone interviewing skills
• Receive and act upon feedback and mentoring from program alumni and/or industry
• Directly engage in academic, research, and industry activities
• Engage in conferences, hackathons, and other industry-led consortiums
• Participate in mock argumentation and negotiation skill encounters
• Develop practical skills in software development, cloud computing, and project management

Virtual Live Talks Tuesdays, 5:00 -6:30 PM EST

Introduces students to the field of Applied Clinical Informatics with focus on leveraging clinical information systems and technology to improve patient- and family-centered care. Exposes students to a range of clinical workflows as well as patient/caregiver needs and how these may be supported by health information technology. Allows students to examine each topic 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. 

 Topics:  

• 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 

Virtual Live Talks Wednesdays, 5:30 - 7:00 PM EST

* formerly Health Information Systems: Design to Deployment 

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.  

Topics: 

• methods for mapping stakeholders
• user interviews to gain insights about user needs for a digital health app 
• design research methods 
• creating a design research brief 
• design software tools (will not be required to code) 

Virtual Live Talks Wednesdays, 7:00 - 9:00 PM EST

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:

• articulating and structuring a decision problem
• creating a decision model
• skill building in decision trees
• exposure to Markov models and discrete event simulation (if time permits) 

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

Students will learn how Fast Healthcare Interoperability Resources (FHIR) are transforming healthcare with an open-web services’ standards approach to clinical integration.  

Topics: 

• integrating digital health and clinical interoperability 

In Person

NOTE: This is a two-part course. Students must also register for Quarter 4 ME 250.963 Health Information Technology Startup Generator / Accelerator 

Hexcite (Excited for Healthcare) is a medical software start-up generator 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.  

Topics: 

• customer discovery (interviewing to test assumptions) 

• design thinking process to prioritize technology requirements 

• building a pitch that includes market research and storytelling components   

All students must seek the instructor’s permission. 

Note: Interested students must first apply to the Hexcite program directly here. There is an application deadline, generally early November. Only students whose applications have been accepted by Hexcite are permitted to register for this course.

Virtual Live Talks Tuesdays, 7:00 - 9:00 PM EST

Pre-requisite: ME 250.770 Clinical Data Analysis with Python 

Students will get hands-on experience in working with medical images and learn how to process images in a clinical setting using the DICOM (Digital Imaging Communication in Medicine) interoperability standard. 

Goals

By the end of this course, you will be able to: 

• Describe the DICOM standard data model used in the medical imaging industry
• Demonstrate the use of DICOMweb REST APIs for querying, retrieving, and storing medical images
• Manipulate basic pixel-level transformations to medical images to extract structured information
• Apply common imaging processing techniques in a research context
• Design imaging research pipelines to manage and integrate multimodal data
• Implement a standardized multimodal data representation using radiomics and EHR data within the medical imaging OMOP Common Data Model (MI-CDM)

Virtual Live Talks Thursdays, 3:30 - 5:00 PM EST

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

Goals:

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.

Prerequisite: ME.250.782 Observational Health Research Methods on Medical Records (OMOP)

Virtual Live Talks Fridays, 12:00 - 1:30 PM EST

Pre-requisite: ME.250.770 Clinical Data Analysis with Python

This course provides a comprehensive introduction to machine learning with Python, with a strong emphasis on supervised learning techniques and model evaluation.

Goals:
• Students will learn fundamental concepts and standard algorithms including regression, classification, cross-validation, tree-based models, support vector machines, and basic neural networks with applications focusing on diabetes and obesity-related use cases.
• Best practices will be discussed and students will reproduce analyses from a range of publications from the biomedical informatics literature.
• Considerations around model explainability and actionability in the context of real-world clinical scenarios will be discussed.
• The course includes hands-on experience using libraries such as pandas, scikit-learn, and pytorch for real-world biomedical data analysis.

In Person Date / Time TBD

Location: 2024 East Monument Street, Room 1-207

Please note: In-person attendance required.

This course will introduce students to a range of skills for successful entry into the biomedical informatics field. Students engage in educational and professional goals analysis and career coaching, such as refining resumes. Students will engage in self-discovery exercises to support technical training on basic Agile software project management, a range of software carpentry skills, healthcare domain specific concepts, software engineering, and cloud computing, to ensure workforce readiness and competitive positioning in data-driven healthcare careers. This course will also foster partnerships with alumni and industry mentors to provide networking and job pathways. 

Goals:

By the end of this course, you will be able to:

• Describe goals and a career path
• Design a well-constructed resume and curriculum vitae (CV)
• Communicate your own skill sets and why they are relevant and important
• Hone interviewing skills
• Receive and act upon feedback and mentoring from program alumni and/or industry
• Directly engage in academic, research, and industry activities
• Engage in conferences, hackathons, and other industry-led consortiums
• Participate in mock argumentation and negotiation skill encounters
• Develop practical skills in software development, cloud computing, and project management

Virtual Live Talks TBD: Date / Time

Note: Students must register for both quarters, Q3 & Q4. Students not matriculated in the HSI formal degree or certificate programs must seek the instructor’s permission.

Students will learn to leverage Large Language Models (LLMs) to accelerate Electronic Health Record (EHR) research. The course focuses on two overlapping applications: coding agents that rapidly build analytic code, and LLM-based extraction of clinical features from unstructured clinical notes. These approaches transform painstaking manual phenotyping into scalable, efficient workflows and unlock the full clinical picture of patients by converting narrative documentation into discrete, research-ready data.

Students work with both served and open-weight LLM architectures using Databricks on Azure and the Discovery HPC environment, with access to REACH, a de-identified dataset encompassing 8 million Johns Hopkins Medicine patients seen over a 10-year period. Students will work in teams partnered with clinical faculty-mentored research projects.

Goals:

• Identify how to work with unstructured EHR clinical data to extract structured data
• Learn how to assess Large Language Models (LLM) architecture performance evaluating models
• Illustrate how to use high performance computing (HPC) environments using the Johns Hopkins Discovery HPC environment

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

Students will gain understanding of decision support in the health sciences workflow. The focus is on the types of support needed by different decision makers, and the features associated with those types of support.  

Topics: 

• various decision support algorithms, examining advantages and disadvantages of each 

• strong emphasis on decision analysis as the basic science of decision making 

• facility with one algorithm in particular through the creation of a working prototype 

• evidence for efficacy and effectiveness of various types of decision support in health sciences and practice 

Virtual Live Talks Fridays, 5:00 – 6:30 pm EST

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

Students will be oriented to the various applications of natural language processing (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.  

Topics: 

• approaches to data mining of free text from the biomedical literature, clinical narratives, and other novel data sources  

• NLP and machine learning algorithms 

• applications of these tools in epidemiologic surveillance, clinical decision support, and other relevant use cases  

All students must seek the instructor’s permission. 

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

Pre-requisite ME 250.750 Design Discovery for Health Care; All students must seek the instructor’s permission.

This course is the second part of the Design for Healthcare series (to directly follow Design Discovery for Healthcare). Participants will build from prior design research and will learn to recognize common patterns and language to promote a seamless user experience and prepare a design plan for hand off. 

 Topics:

• wireframing and prototyping a software application with a team 

• prototype testing with users 

• using feedback for iterative improvements  

• design software tools (will not be required to code)

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

Pre-requisite: Implementation of Fast Healthcare Interoperable Resources (FHIR) (ME.250.778 Q3)    

Students will learn about informatics and data science driving patient care and how the implementation of Clinical Decision Support (CDS) applications integrate with the practice of medicine. 
   
Topics:  

• current state of CDS implementation in electronic health records 
• advantages of implementing interoperable CDS algorithms in EHRs
• basic interoperability of CDS algorithms using CDShooks and HL7 FHIR
• basic HL7 Clinical Query Language (CQL) queries for HER data to support CDS algorithms 

In Person

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 a medical software start-up generator 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. 

Topics: 

• Lean Start-up methodology which focuses on growing a business with maximum acceleration 
• customer discovery (interviewing to test assumptions) 
• design thinking process to prioritize technology requirements 
• pitch building that includes market research and storytelling components   

All students must seek the instructor’s permission. 

Note: Interested students must first apply to the Hexcite program directly here. There is an application deadline, generally early November. Only students whose applications have been accepted by Hexcite are permitted to register for this course.

Virtual Live Talks Tuesdays, 5:00 - 6:30 PM EST

Not offered in AY2025-2026.

Pre-requisite: ME.250.783 Imaging Informatics

This class will provide a background on deep learning theory and describe how to leverage deep learning models for imaging research. This includes classification and segmentation of clinical medical imaging data, as well as multimodal data analysis. Students will get hands-on experience in working with medical images and learn how to integrate AI models in clinical research.  

Goals:

Be able to identify the components of building a deep neural network; Understand how to use neural networks for medical image and multimodal analysis; Understand how data affects model training and how to create a well-defined dataset and/or utilize pre-trained models; Experiment with creating deep learning models and learn how to evaluate their performance. 

In Person Date / Time TBD

Location: 2024 East Monument Street, Room 1-207

Please note: In-person attendance required.

This course will introduce students to a range of skills for successful entry into the biomedical informatics field. Students engage in educational and professional goals analysis and career coaching, such as refining resumes. Students will engage in self-discovery exercises to support technical training on basic Agile software project management, a range of software carpentry skills, healthcare domain specific concepts, software engineering, and cloud computing, to ensure workforce readiness and competitive positioning in data-driven healthcare careers. This course will also foster partnerships with alumni and industry mentors to provide networking and job pathways. 

Goals:

By the end of this course, you will be able to:

• Describe goals and a career path
• Design a well-constructed resume and curriculum vitae (CV)
• Communicate your own skill sets and why they are relevant and important
• Hone interviewing skills
• Receive and act upon feedback and mentoring from program alumni and/or industry
• Directly engage in academic, research, and industry activities
• Engage in conferences, hackathons, and other industry-led consortiums
• Participate in mock argumentation and negotiation skill encounters
• Develop practical skills in software development, cloud computing, and project management

Virtual Live Talks TBD: Date / Time

Note: Students must register for both quarters, Q3 & Q4. Students not matriculated in the HSI formal degree or certificate programs must seek the instructor’s permission.

Students will learn to leverage Large Language Models (LLMs) to accelerate Electronic Health Record (EHR) research. The course focuses on two overlapping applications: coding agents that rapidly build analytic code, and LLM-based extraction of clinical features from unstructured clinical notes. These approaches transform painstaking manual phenotyping into scalable, efficient workflows and unlock the full clinical picture of patients by converting narrative documentation into discrete, research-ready data.

Students work with both served and open-weight LLM architectures using Databricks on Azure and the Discovery HPC environment, with access to REACH, a de-identified dataset encompassing 8 million Johns Hopkins Medicine patients seen over a 10-year period. Students will work in teams partnered with clinical faculty-mentored research projects.

Goals:

• Identify how to work with unstructured EHR clinical data to extract structured data
• Learn how to assess Large Language Models (LLM) architecture performance evaluating models
• Illustrate how to use high performance computing (HPC) environments using the Johns Hopkins Discovery HPC environment

Virtual Live Talks Date / Time TBD

Prerequisite: Q3 ME.250.788 Observational Research Methods in R

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

This hands-on course introduces students to the art and science of data visualization using R and ggplot2, beginning with foundational concepts like perception, visual channels, and graphic integrity. Rather than treating visualization as a set of discrete chart types, this course emphasizes why good graphics work and how effective visual thinking can deepen insight, clarify patterns, and communicate results clearly. Students will learn to turn raw data into compelling visual stories by applying visual perception principles, leveraging tidy data workflows, and constructing plots layer by layer. Students will develop proficiency in creating a variety of plot types, while learning to refine and tailor visualizations for different audiences and purposes. This course emphasizes reproducibility and literate programming (via RMarkdown); students will build workflows that produce figures that are reproducible, transparent, and ready for publication or presentation.

Goals:

• Explain key concepts in data perception and graph design, including how visual encoding choices influence interpretation
• Use R with the ggplot2 grammar of graphics to create a broad range of plots, beginning with simple scatterplots and progressing to advanced layered graphics
• Prepare and transform data for visualization using tidy data principles so that plots are meaningful, efficient, and reproducible.
• Apply RMarkdown and reproducible code workflows to document analytic decisions and produce visualizations that can be updated or shared reliably.
• Evaluate other people’s graphics for effectiveness and potential misrepresentation and justify visualization choices in terms of audience and task

Virtual Live Talks Wednesdays, 10:00 - 11:00 am EST

Introduces students to becoming health informatics professionals and the need to stay current on key topics, how to find evidence to solve informatics problems that cross the disciplinary boundaries of health, computing, and human factors, and contribute publishable papers to the body of informatics scholarship. Students will gain the necessary foundation and skills to engage in these research endeavors.  

Topics: 

• available biomedical sources and how to search them efficiently and effectively
• techniques for evaluating what you find from these sources 
• what tools to use for storing and managing this information 
• issues in the research field including how open access impacts your work as a scholar and consumer of research 
• tools for establishing yourself as a professional and staying current in your field 

Only offered to students in the School of Medicine. Instructor permission required. 

Virtual Live Talks (Thursdays, 5:00 – 6:00 pm ET)

Introduces students to the ways digital health is revolutionizing the practice of medicine, increasing access to data, and diagnosing and treating diseases. For all its potential, digital health is not without risks. Students will 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. This course will also 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. 

Topics: 

• key terminology relevant to the fields of digital health innovation and medical device regulation 
• relationships between regulators, technology developers, healthcare providers, and patients 
• requirements for digital health technology to be considered Software as a Medical Device (SaMD) by the FDA 
• various regulatory pathways for SaMD and the main considerations 

Virtual Live Talks Student Seminar (1st & 3rd Thursdays, 7:00 – 8:30 pm EST) Grand Rounds (2nd Wednesday of each month, 12:00 – 1:00 pm EST)

Weekly combined seminar and Grand Rounds during term. Only matriculated students in a BIDS formal degree or certificate program can take this course. Details on the Grand Rounds speakers and remote access to the lecture may be found here on the  Grand Rounds page. 

In Person Thursdays, 8:45 – 10:15 am EST

Location: 2024 East Monument Street, Room 1-207

Please note: In-person attendance required. 

This course 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 guided by a faculty member in BIDS or approved by the program director. The research may originate with the preceptor or with the student and may be at different phases of development. In lab rotation, most of the activity is supervised by the preceptor. In ongoing research, there is supervision by the 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. 

Students gain the opportunity to demonstrate integration of skills and knowledge, develop a significant component of their portfolio, and 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. A presentation will be made of the final report at a Capstone Presentation Seminar, with students, faculty, and capstone preceptors in attendance. 

Independent Study courses must be approved by the Program Director and students must follow the steps outlined below in order to comply with SOM registration and grading policies. Students submit a course description to the Course Instructor and Program Coordinator which includes 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.

Students must: 

• gain the commitment of a Hopkins faculty member to work with
• define learning objectives 
• determine with the faculty member a “deliverable” that would reflect having achieved learning objective(s) 
• develop a schedule which includes attending informatics seminars, mentor sessions, reading, and working on the deliverable 

This course applies to the Post Baccalaureate Certificate program students and is a practical experience supervised by Johns Hopkins faculty that enables students to showcase and develop skills gained during the didactic curriculum.  

Students work with a preceptor and an academic advisor to 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. 

In Person Tuesdays & Thursdays, 12:00 pm - 3:00 pm EST

Location: 2024 East Monument Street, Room 1-207

Please note: This course requires in-person attendance.

This course introduces students to the principles of health informatics research design and methods. Topics covered in this course include identifying health informatics research domains, designing informatics research, selecting appropriate informatics methods, integrating data science in informatics research, and, conducting literature and systematic reviews for health sciences informatics research.

This course is restricted to students enrolled in the Health Sciences Informatics MS Research program. It is a full year course offered in four quarters (ME.250.861; ME.250.862; ME.250.863; ME.250.864).