Current courses in HILS learning sciences curriculum are described in detail below.
Many of the courses in the program are open to any enrolled University of Michigan student with graduate-level standing.
LHS 610: Exploratory Data Analysis for Health
Students in this course will learn foundational topics in data science focused on healthcare data. The course is based on two large themes: (a) understanding and becoming familiar with healthcare data, and (b) making inferences based on data. Students will develop a working understanding of R, one of the most widely used languages for data science, and an introductory understanding of several other tools used in analyzing healthcare data. Students will participate in a longitudinal group project spanning the principles learnt during the course using real-life healthcare data sets.
Contact: Karandeep Singh, MD, MMSc
LHS 611: Knowledge Representation and Management in Health
This course provides an intensive introduction to methods and topics for knowledge representation and management, with an emphasis on symbolic methods and the social context of knowledge use in learning health systems. Students who complete the course will become familiar with important knowledge engineering technologies and standards. They will also have mastered foundational methods necessary for more advanced study and mentored research in collaborative knowledge representation and management.
Contact: Zach Landis-Lewis, PhD, MLIS
LHS 621: Implementation Science in Health 1
Many evidence-based health care interventions fail to produce successful outcomes when implemented into practice. Implementation and dissemination sciences comprise a multidisciplinary set of theories and methods to improve and expedite translating research evidence to everyday health-related practices. Both disciplines are systematic approaches to understanding how healthcare interventions can be better integrated into diverse practice settings, and emphasize direct engagement with institutions and communities where health interventions take place. In order to optimize public health, it is essential to not only understand how to create the best interventions, but how to best ensure that they are effectively delivered within clinical and community practice.
LHS 622: Learning Cycle Capstone
This course includes completion of a learning cycle project accompanied by development of products describing the process and findings from the project. Project content is negotiated on an individual basis with faculty. This course will be offered Spring/Summer term.
Contact: Caren Stalburg, MD, MA
LHS 641: Quality Improvement in Healthcare Systems
This course addresses QI in healthcare using a multi-level systems perspective. The course addresses both conceptual foundations of QI and direct application of QI tools and processes. Course materials will include examples and application at Michigan Medicine. The course will help participants perform successful QI activities in healthcare settings.
LHS 650: Health Infrastructures Pro Seminar 1
This course provides theoretical and practical perspectives on the evolution of major infrastructures, focusing in particular on health and information infrastructures. The course begins by examining how infrastructures emerge, evolve, and decay in the context of social systems. Students gain fluency in the language of infrastructure as a technological and social phenomenon linking people, processes, policy, and technology. The course focuses on the organization of health as a problem of infrastructure and considers how this perspective might inform research, practice, and the capacity for change.
Contacts: Timothy A. Pletcher, DHA (online course)
LHS 660: Evaluation and Research Methods for Health Informatics and Learning Systems
This course provides a foundational introduction to empirical methods, both quantitative and qualitative, that will be applicable to the study of health infrastructures and learning systems. As such, it offers a broad overview that will enable students in the PhD program to begin formulating their interests into researchable problems, and make informed choices of the more advanced research methods courses they will need to pursue their research agenda.
Contact: Charles P. Friedman, PhD
LHS 671: Ethics and Policy Issues for Learning Health Systems
Bioethics is an enterprise in ascendance. In the early 1960s, there were individuals concerned with moral questions occasioned by medicine and medical research, but they were not known as bioethicists, nor did they have the institutional support of centers for bioethics, professional journals, government commissions, or graduate programs and professorships. Today bioethics is part of the landscape of the life sciences: “ethics committees” are now mandatory in American hospitals; all federally funded research that involves human beings or animals must be reviewed by a board constituted to protect the subjects of research; a plethora of seminars offer training in bioethics for those who need, or wish, to offer ethical advice; bioethics courses are now a regular part of the curriculum at universities, colleges and medical schools. Students in this course will learn about the social sources of morality; the organization of professions; the politics of science, medicine and biotechnology; the interface between law and ethics; the place of religion in pluralist societies; the sociology of science; and the social uses of bioethics in the complex context of learning health systems.
LHS 678: Learning Cycle Informatics
This introductory course in informatics teaches students about informatics pertaining to the three main phases of the full 360-degree learning cycle for learning health systems. These phases are the performance-to-data (P2D) or data collection phase, the data-to-knowledge (D2K) or data aggregation and analysis phase, and the knowledge-to-practice (K2P) or intervention towards improvement phase
Contact: Patricia Abbott, RN, PhD
LHS 680: Teamwork for Healthcare
The past, present, and future of medicine are inherently team-based care. We know that good teamwork saves lives. However, teams can be filled with exceptionally smart and highly-skilled individuals, but still fail to achieve their objectives and improve performance. Critically, many examples of preventable medical errors have been caused by breakdowns in team collaboration. In this course, we will explore the following questions: What are the characteristics of effective teams in health care? What are the most powerful factors that allow interprofessional teams to build up their power as a collective entity? Besides unpacking why some teams thrive and others struggle, what are some evidence-based strategies and technology solutions for achieving behavioral changes? Using simulations, real-world data, and case analyses, you will learn to apply data science tools and techniques to analyze team processes and outcomes. You will also participate in a semester-long project on addressing the specific challenge(s) of a real-world healthcare team.
Contact: Vitaliy Popov, PhD
For more information about how these courses fit together, read about the Health Infrastructures and Learning Systems curriculum.