Clinical Informatics Courses

This listing serves as the course catalog for the graduate programs that lead to the MS degree in Clinical Informatics, and Certificate in Applied Clinical Informatics. In addition, the graduate student curriculum management system (CLEARvue) provides a full description of each course, course learning objectives, and course directors with their contact information in the introductory material in CLEARvue for each course. Required and recommended textbooks are listed on the internet and intranet, and updated annually. Information about other learning resources (both electronic and print) is provided to graduate students at the beginning of each year and beginning of each course. Methods of learner assessment and course grading are described in the Graduate Student Handbook.

BINF 7210 Applied Clinical Informatics I: Fundamentals of Biomedical Informatics; Data Acquisition and Management; Clinical Decision Support
Credits:
7
Directors:
Brown, Walsh
Grading:
Pass/Fail
Description:

This course presents an overview of biomedical informatics theories, methods, and techniques. The main features of each division of the field of biomedical informatics (bioinformatics, translational informatics, imaging informatics, clinical informatics and public health informatics) are described and analyzed. Social, economic, ethical, cultural, environmental, historical, and other factors driving the development and implementation of clinical informatics are described and discussed. The student is then introduced to important structural and technical concepts of health care data. Students get hands-on experience on how to analyze a healthcare problem and model its data effectively using appropriate work flow and data modeling techniques. The third major component of this course covers clinical decision support as a technology-mediated process by which patient information and characteristics are captured, matched to an algorithm, and used to guide patient care. Students learn the basic principles and advanced concepts of clinical decision support, benefits as well as the drawbacks of these systems, and how these are used support the practice of evidence based medicine. Important design principles such as signal-to-noise ratios, alert fatigue, and usability are also covered.

Objectives:

Upon completion of this course, each student will be able to:

Ethics, Privacy, Legal and Regulatory Issues

  1. Explain the roles of the main accreditation and regulatory bodies, and professional associations in healthcare in the United States.
  2. Describe the rulemaking process employed in the US for promulgating rules that affect CI.
  3. Describe the main provisions of the Patient Protection and Affordable Care Act with respect to CI.
  4. Describe key components of the Health Insurance Portability and Accountability Act (HIPAA) and current issues of patient privacy and security in the United States.
  5. Describe the provisions of the ARRA HITECH Act relative to meaningful use and regional extension centers.
  6. Apply the HIPAA Privacy and Security Rules to a variety of clinical situations and scenarios.
  7. Describe meaningful use (MU) under the HITECH Act and identify the different stages of MU.
  8. List and explain some of the main criticisms of MU

 History and Current State of Informatics

  1. Define the terminology describing informatics and related fields.
  2. Identify and define the major domains of Biomedical Informatics (BI).
  3. Describe major milestones in informatics.
  4. Describe the major milestones in the evolution of the medical record (e.g. SOAP, EHR, PHR).
  5. Identify major figures and organizations in informatics
  6. Identify and outline international codes of practice and ethical codes relevant to Clinical Informatics (CI).

The Health System

  1. Explain the basic characteristics and organization of the US healthcare delivery system.
  2. Describe the roles of and customers served by various types of healthcare organizations.
  3. Describe the administrative and functional organization of entities that deliver healthcare in the United States in both inpatient and outpatient settings.
  4. Explain how healthcare organizations interact with each other and with patients to provide appropriate levels of care.
  5. Describe the services provided to unique populations, including underserved populations.
  6. Define public health and how it has improved healthcare.
  7. Identify significant problems in the US health care system and their contributing factors.
  8. Define “complex adaptive system” and why this model can be applied to US the health care system.
  9. Describe the “Six Aims for Improvement” and the associated structural and process changes needed to accomplish these aims as recommended by the IOM in 2001.
  10. Describe how US health care compares to health care in other developed countries in terms of cost, efficiency and health care outcomes
  11. Describe the main features of a learning health care system.

Knowledge Acquisition and Use for Clinical Support

  1. Define clinical data.
  2. Identify the five elements of a clinical datum.
  3. Identify the major types of clinical data.
  4. Identify the main sources of clinical data.
  5. Describe the main uses of clinical data.
  6. Describe current and historical methods of collecting and storing clinical data.
  7. Identify strengths and weaknesses of widely used methods of collecting, storing and sharing clinical data.
  8. Describe the “data-to-knowledge” continuum.

Clinical Data and Clinical Decision Making

  1. Describe the Hypothetico-Deductive approach for clinical decision making.
  2. Describe everyday techniques of decision-making and potential biases.
  3. Understand the relevance of "choice under uncertainty" to medical decisions.
  4. Demonstrate how decision analysis can be used to model complex decisions.
  5. Understand how definitions of utility and patient preference impact the value of an outcome.
  6. Understand how cost effectiveness analysis can be used to make decisions about allocation of constrained healthcare resources.
  7. Define cognitive heuristics and describe its relevance to medical decision making.
  8. Define sensitivity, specificity, PPV, and NPV using the syntax "the probability of X given Y".
  9. Describe the applicability and limitations of sensitivity, specificity, PPV, and NPV to clinical decision-making, disease screening, and diagnostic testing.
  10. Identify different types of bias that can occur in diagnostic testing and test interpretation.
  11. Be able to apply Bayes theorem to estimate the probability of the presence of a disease.
  12. Use Markov models to determine treatment threshold probabilities
  13. Compare and contrast the various approaches to representing knowledge in clinical decision support systems from the past and present.
  14. Describe known problems of safety with health IT systems and how they can be minimized.
  15. Understand the current legal and regulatory framework for clinical decision support.

Applied Clinical Decision Support

  1. Describe the difference between interruptive/modal and non-interruptive /modeless alerts with possible. applications to Clinical Decision Support (CDS) systems
  2. Classify CDS interventions by area of clinical care (prevention, diagnosis, treatment, follow-up, care planning).
  3. Classify CDS interventions by intervention intent (reminder; information; recommendation; corrective action; alerting).
  4. Classify CDS interventions by intended audience.
  5. Describe the "five rights" of an effective CDS intervention.
  6. Understand common limitations of evaluations of CDS interventions and ways to overcome these limitations.
  7. Explain how interoperability, clinical terminology, and guideline representation standards could be used to facilitate broader adoption of CDS tools.
  8. Describe common strategies for maintaining and updating decision support tools, and the risks of not having these strategies in place.

 

 

BINF 7220 Applied Clinical Informatics II: Computer Science Fundamentals and Data Analytics; Challenges in Informatics Quality and Safety
Credits:
8
Directors:
Brown, Walsh
Grading:
Pass/Fail
Description:

In this course students learn database concepts, design, development, implementation, and administration that is specifically targeted towards healthcare environments. Healthcare data integrity, data quality, and data security are emphasized. Management of data structure and content for compliance with standards, regulations (including HIPAA and HITECH), and accrediting agencies are detailed. Students examine strategies and technologies for data storage, controlling access, protecting confidentiality, archiving and backing up, and restoring massive amounts of healthcare data. This course provides students the opportunity to analyze the various types of healthcare data and explore the challenges related to modeling, collecting, using and analyzing each main type of healthcare data. This course explores different strategies for representing data, information and knowledge, including required and emerging standards for coding, nomenclature, and their associated taxonomies and ontologies. It also examines how these standards are used to create tools for mining, analyzing, interpreting and sharing information for a variety of clinical and administrative purposes throughout the healthcare system.

Objectives:

Upon completion of this course, each student will be able to:

Computer Programming and Software Development

  1. Give examples of common data structures; use the example of date representations to illustrate how choice of data structure influences its use.
  2. Using pseudo-code, be able to define a clinical rule using each of the following control structures: “IF-THEN-ELSE","CASE", "FOR”, “LOOP", and "WHILE”.
  3. Describe the main different software development methodologies and how they differ in their approaches to requirement gathering, scope definition, and risk mitigation.
  4. Describe at a high level how software systems may be integrated through interfaces, messaging standards, and web services.
  5. Compare "black-box" and "white-box" software testing.
  6. Define software verification and software validation.
  7. Give clinical examples of software testing strategies such as beta testing, testing, and regression testing following system enhancement.

Information Retrieval and Analysis

  1. Identify the major search systems used by clinicians and be able to use advanced features within them to retrieve the most relevant content.
  2. Identify the major search systems used by patients and be able to provide resources for their most effective use.
  3. Help clinicians and patients find the highest quality information possible for application in health and clinical decisions.

Systems, Databases and Networks

  1. Distinguish between hierarchical, relational, and object-oriented databases; list one advantage and disadvantage of each.
  2. Use Unified Modeling Language (UML) Entity Relationship (ER) diagrams to describe the logical schema of a database.
  3. Describe how a suite of UML diagrams are used to model a process and assist in software development and maintenance.
  4. Describe how update, insert, and deletion anomalies in databases are prevented through database normalization.
  5. Describe how de-normalization of a database can be used to optimize certain queries, for example, in a clinical datamart.
  6. Describe some of the common network topologies, such as star, tree, and bus networks.
  7. Provide the names and uses of common telecommunications standards.

Challenges and Strategies

  1. Describe at a high level the flow of data in clinical systems from collection to storage to analysis.
  2. Describe the uses and challenges for identification and anonymization of patient data.
  3. Describe the general phases of the user-centered design process.
  4. Describe key principles of good user interface design.
  5. Describe important usability heuristics and usability evaluation methods.

Health Information Systems and Applications

  1. Describe the architecture, technical and computing infrastructure underlying important health information systems (HIS).
  2. Describe the key role the EHRs play in supporting a learning health system.
  3. Describe the breadth of HIS functionality and features and that have been challenging for physicians.
  4. Identify telemedicine application areas and types.

Healthcare Data Re-Use

  1. Describe the use and limitations of clinical data for patient care and other uses.
  2. Define and describe the 3 “V’s” of biomedical big data.
  3. Describe the challenges in data retrieval when dealing with structured and unstructured healthcare data.
  4. Describe Structured Query Language (SQL) and perform simple database queries using SQL.
  5. Explain the differences between supervised and unsupervised learning.
  6. Describe at a high level some approaches used for unsupervised learning and supervised learning.
  7. Describe Application Programmer Interfaces (APIs) and how APIs can be used for data integration.

Clinical Workflow Analysis and Re-Design

  1. Identify the components of a workflow analysis (WA) effort, and key questions that should be considered in the design of a workflow study.
  2. Describe the difference between quantitative and qualitative data collection methods in WA.
  3. Identify different methods of mapping and recording workflow data in the healthcare setting, and recognize what type of data each method is best suited to record.
  4. Identify the practical considerations and limitations of conducting observational fieldwork.
  5. Identify common types of questions that may be answered via analysis of workflow data.
  6. Compare the impact that the design of a system, versus the people who work in a system, has on system performance (e.g., patient safety).
  7. Describe the contributions of timeliness and high reliability to the success of Workflow Re-engineering/ Process Redesign in the Healthcare Setting.
  8. Describe some of the various models of Workflow Re-engineering, and identify the benefits of applying a consistent model within and across a particular healthcare system.
  9. Identify key components of a Workflow Re-engineering effort.

Healthcare Quality Improvement

  1. Define healthcare quality from the standpoint of a patient, a healthcare provider, a society/community, and a payer; explain how these definitions are sometimes challenging to reconcile.
  2. Distinguish between healthcare quality indicators that measure structure, process, and outcomes.
  3. Identify the major well-established quality improvement (QI) frameworks in use in healthcare such as: Toyota Lean, Six Sigma, Associates for Process Improvement (API), and Total Quality Management (TQM); describe high-level concepts associated with each.
  4. Describe how lshikawa/fishbone diagrams and Pareto charts can be used to identify targets for Ql efforts in healthcare.
  5. Describe the Plan-Do-Study-Act cycle.
  6. Explain the applicability of control charts to evaluation of healthcare Ql efforts.
  7. Compare control charts and evaluation methods based on hypothesis testing, such as randomized trials.

Security

  1. Describe at a high level key elements of the HIPAA Security Rule.
  2. Define key terms in the Security Rule such as: “protected health information (PHI)”, “covered entity”, “business associate”, “technical” vs “administrative” standards, “required” vs “addressable” standards, etc.
  3. Identify required policy, and technical measures to protect the security of PHI.
  4. Describe in detail 3 of each of these measures (e.g., firewalls, VPNs, encryption, user training, sanction policies, etc.) and how they are used to protect PH
BINF 7230 Applied Clinical Informatics III: Big Data and Advanced Analytics, Interoperability; Current Research in Clinical Informatics: Other Biomedical Informatic Domains
Credits:
7
Directors:
Brown, Walsh
Grading:
Pass/Fail
Description:

This course has four modules and provides hands on training in the use of advanced data analytic tools to generate actionable information and knowledge for use in clinical settings. The course covers theories and methodologies for analyzing, measuring, and predicting health outcomes as well as reducing errors and inefficiencies, and controlling costs in health care. Students read and critique current research articles in publications including Journal of the American Medical Informatics Association, Applied Clinical Informatics, and Journal of Biomedical Informatics. The course also provides students with an overview of the main topics of study in the other primary biomedical informatics including: bioinformatics, translational informatics, imaging informatics, public health informatics, and consumer informatics.

Objectives:

Upon completion of this course, each student will be able to:

Computer Programming and Methods of Software Development

  1. Describe some of the new “disruptive” technologies that are being introduced into HIT (Blockchain, SMART, FHIR).
  2. Describe and demonstrate simple applications of Application Programmer Interfaces (APIs) for the integration of clinical data.
  3. Use analytics technologies to create reports and “apps” that leverage data from EHRs and other data sources.

Human-Computer Interaction

  1. Give examples of clinical errors that can be prevented through the application of human factors engineering principles.
  2. Compare and contrast usability inspection and usability testing.
  3. Describe the three components of discount usability engineering: scenario/mockups, simplified think-aloud exercise, and heuristic evaluation.
  4. Enumerate and describe commonly accepted standards of good interface design.

Clinical Data Standards

  1. Describe the importance and limitations of standards in clinical information systems.
  2. Discuss the major types of standards and their roles in clinical information systems.
  3. Define identifier standards and the major standards used for them.
  4. Define transaction standards and the major standards used for them.
  5. Define messaging standards and the major standards used for them.       
  6. Describe the major terminology standards in biomedicine, their uses and their limitations.

Domains of Biomedical Informatics

  1. List and explain the key domains of biomedical informatics.
  2. Compare and contrast health informatics, clinical informatics, medical informatics, and public health informatics.
  3. Define and describe imaging informatics and clinical research informatics.
  4. Compare and contrast bioinformatics and translational bioinformatics.

Current Informatics Research

  1. Describe the key components of an informatics research paper.
  2. Define the key steps for providing a formal evaluation, assessment, and critique of an informatics research paper.
  3. Discuss the strengths and weaknesses of open access vs. closed access publishing.
  4. Define and describe impact factor and other measures of journal importance.
BINF 7240 Applied Clinical Informatics IV: Computer Information System Implementation and Planning; Capstone project
Credits:
8
Directors:
Brown, Walsh
Grading:
Pass/Fail
Description:

This course has three modules and focuses on the design, analysis, selection, and management of health information systems through hands-on experience and conceptual modeling of healthcare applications (eg, electronic health records, clinical decision support systems, ancillary systems, analytic systems, and practice management systems). Students gain an understanding of how health information technologies are used to support health information processing, services delivery, and administration. This course covers system building blocks, systems integration, work flow redesign, and business process integration for health data exchange and resource sharing among health care stakeholders. Fundamental subjects such as system analysis concepts, life cycle modeling, interface design, system evaluation, and management of health care applications within and across health care organizations are covered. The course also covers important concepts in strategic planning, project leadership, team building, and change management. The course culminates with a capstone project that offers students the opportunity to gain real-world experience by working on informatics projects in clinical settings. Students may work independently or as part of a team on various applied projects to facilitate selection, implementation, and optimal use of health information technologies in a health care organization. Students participate in the design of their individual projects and are required to develop project plans that leverage the academic training they have received in the degree program.

Objectives:

Upon completion of this course, each student will be able to:

Implementation and Operation of Clinical Information Systems

  1. Define several institutional governance models for clinical information systems.
  2. List formal and informal methods to define and specify system requirements and solicit vendor proposals.
  3. Describe system conversion strategies and their relative merits.
  4. Describe the key elements of a system implementation plan.
  5. Describe key elements of a clinical systems operation and maintenance plan.
  6. Identify the critical components of a good disaster recovery plan.
  7. Identify at a high level the main, physical, technical and administrative ways to mitigate risk.
  8. Enumerate the important features of a good user support system.

Evaluation of Clinical Information Systems

  1. Describe the measurement of outcomes and quality from use of clinical information systems.
  2. Design an evaluation study of a clinical information system.

Leadership Models, Processes and Practices

  1. Identify dimensions of effective leadership and their relationship to successful management of technological change in the healthcare setting.
  2. Identify elements of good organizational governance that support effective technological change in healthcare settings.
  3. Enumerate and describe effective techniques in negotiation, conflict management, decision making for technological change in healthcare organizations.

Building Effective Healthcare IT Teams

  1. Describe the different types of human expertise required for a team to be successful with a clinical information systems implementation plan.
  2. List human resource factors that should be considered ahead of time when planning to recruit internally or externally for positions on a healthcare IT team.
  3. List factors that are critical to a team's ability to work together effectively, and to be successful in turning out product.
  4. Identify the characteristics of team goals that are likely to promote team effectiveness.
  5. Identify and describe three processes commonly employed in group management.
  6. Describe elements for successful management of team meetings, and techniques for management of group deliberations.

Communication Strategies

  1. Distinguish between rich and lean types of communication.
  2. Identify the differences among three change concepts: Roger's Diffusion of Innovations concept, Lewin's Change Theory, and Bridge's Transition Theory, and discuss how each may apply to one-on-one or group communication during the execution of a clinical implementation.
  3. Identify 2 “new" modes or channels of communication that have been promoted by the use of electronic health records in the healthcare setting.
  4. Give an example of when written communication would be most effective in a clinical information systems implementation. State how that written communication may need to differ with respect to Informing clinical staff, versus informing patients, versus informing administrators, etc.
  5. State the effect of too much information on human performance and on communication effectiveness.
  6. Describe the pivotal role of a comprehensive communication plan in any information management project plan.

Project Management

  1. State the basic principles of project management
  2. Define the "triple constraint" in project management planning.
  3. Identify five major process groups in the project management lifecycle.
  4. Identify four major components of an effective project plan.
  5. Describe strategies in project planning that help to avoid scope creep.
  6. List tools useful in project management.

Strategic Planning for Clinical Information Systems

  1. Explain the critical importance of aligning strategic and financial planning for clinical information systems, including mission statement and objectives, with the healthcare organization's overall strategic plan.
  2. Outline the basic tenets of three models of strategic planning for health IT (pull model, push model, component alignment model), and how they may be used to guide strategy formulation.
  3. Explain the benefit of performing a rigorous internal and external environmental scan of IT and CIS resources prior to formulating a long range strategic plan.

Financial Planning for Clinical Information Systems

  1. Identify general principles of capital and operating budgeting as they pertain to clinical information systems.
  2. Describe general principles of managerial accounting.
  3. Explain key financial concepts used in financial planning for clinical information systems.

Change Management

  1. Explain why change management is an ongoing organizational process, rather than a means to a single event.
  2. Describe the relevance of "people" and "process" factors with respect to organizational readiness and willingness to participate in change.
  3. Outline how the different change theories can help us to understand specific organizational behavior, and guide development of a successful change management strategy
  4. Identify key components of a change management strategy, and discuss the features of each component.
  5. Describe how a change management strategy can be tailored to promote adoption and effective use of clinical information systems in a particular setting.

Capstone Project

  1. Design and propose a capstone project focused on applied clinical informatics in collaboration with internal and external mentors.
  2. Formulate and execute project plan that leverages concepts and skills acquired during the degree program.
  3. Write up and present key findings and takeaways in final presentation.