Master of Science in Health Informatics

Pre-Requisite: None

Description: 

This on-line, interactive course is an introductory survey of the discipline of biomedical informatics.  This course will introduce the student to the use of computers for processing, organizing, retrieving and utilizing biomedical information at the molecular, biological system, clinical and healthcare organization levels through substantial, but not overwhelming, reading assignments.  The course is targeted at individuals with varied backgrounds including medical, nursing, pharmacy, administration, and computer science.  The course will describe essential concepts in biomedical informatics that are derived from medicine, computer science and the social sciences.

Learning Objectives:

1. Demonstrate in writing and verbally a basic understanding of the learned concepts of biomedical informatics and their direct application to healthcare.
2. Demonstrate the ability to compare, select, apply and integrate multiple technologies in and across a healthcare organization via leadership, clinical, administrative, or other staff positions.
3. Discuss key legal and ethical issues that must be considered when implementing biomedical technology and supporting information systems to include initiatives such as the Electronic Health Record.
4. Differentiate multiple methods to evaluate the costs versus benefits of implementing biomedical information systems.
5. Produce evidence of a forward thinking ability to stay current in biomedical informatics.

Pre-Requisite: None

Description: 

Learning Objectives:

1. Acquire the knowledge and attitudes needed to understand the principles of data analysis. It includes ability to classify data types (e.g., qualitative vs. quantitative), relationships of input and outcome data types, understand how to frame questions for which data will be needed to answer the questions.
2. Acquire the knowledge, skills, and attitudes needed to use Excel with basic understanding of Excel including menus and tools available, setting up a data structure within Excel in order to organize data for analysis, use of pivot tables, statistical tools, and other basic analysis tools, and using Excel to import or extract data from other sources (e.g., Internet, Wolfram) and explore various analyses.
3. Acquire the knowledge, skills, and attitudes needed to conduct descriptive statistical analyses including Shape, Center, spread of quantitative data types, qualitative data analysis including categorization / Chi-Square analysis of pairs of qualitative data types, etc.
   

Pre-Requisite: None

Description: 

This course introduces the fundamental principles of project management from an information technology (IT) perspective as it applies to healthcare organizations (HCOs). Critical features of core project management are covered including: integration management, scope management, time management, cost management, quality management, human resource management, communication management, risk management, and procurement management.  Also covered is information technology management related to project management:  user requirements management, infrastructure management, conversion management, software configuration, workflow management, security management, interface management, test management, customer management, and support management.  The following areas of change management related to project management will also be covered:  realization management, sponsorship management, transformation management, training management, and optimization management.  Students will explore and learn hands-on skills with project management software assignments, and participate in a healthcare systems implementation course-long group project intended to apply these newly developed knowledge and skills in a controlled environment.

Learning Objectives:

1. Explain the genesis of project management, information technology management, change management and their importance to improving successful HIT projects in the healthcare setting
2. Apply project management concepts by working on a group project as project management or active team member
3. Demonstrate how to use Microsoft Project 2013 software to help plan and manage a project
4. Demonstrate knowledge of project management terms and methods
5. Demonstrate knowledge of information technology terms and methods as they relate to project management
6. Demonstrate knowledge of change management terms and methods as they relate to project management

Pre-Requisite: None

Description: 

This course covers basic to intermediate knowledge of the concept, the design, and the implementation of database applications in healthcare. Students will study tools and data models for designing databases such as ER Model and SQL. The course also covers Relational DBMS systems such as SQL Server, Access, Oracle and MySQL. In addition, database connectivity design (essential in data-driven web development) and database administration will also be introduced.   Students will practice designing, developing and implementing a test relational online health IT database application through a comprehensive project that contains the above topics.

Learning Objectives:

1. Identify the key elements of database management system and applications in healthcare.
2. Plan, document, and design a medical informatics database application.
3. Identify and model healthcare database application using ER Model and query against the database with SQL.
4. Identify the key concepts and process in order to SQL server, Access, Oracle or MySQL DBMS systems to build up a data-driven web application.
5. Identify the basic concepts of database administration and data warehouse for decision support system (DSS).

Pre-Requisite: None

Description: 

The course will cover concepts, applications and techniques of data security in healthcare system. Topics include healthcare industry, regulatory environment, decision making, policy assurance, information management, access control, risks and vulnerabilities management, database security, web security, personnel and physical security issues, and issues of law and privacy. Areas of particular focus include secure healthcare system design, implementation, data encryption and decryption, attacks, and techniques for responding to security breaches.

Learning Objectives:

1. Identify healthcare organizations and third party affiliates.
2. Prioritize threats to healthcare information resources.
3. Define an information security strategy and architecture.
4. Plan for and respond to intruders in a healthcare information system.
5. Identify the practical application of risk management and decision making.
6. Identify the practical application of risk assessment.

Pre-Requisite: None

Description: 

The need to create effective, new solutions and innovative interventions to deliver quality patient care outside of the traditional medical setting is at the forefront of society today. The basis of this course will be providing a solid educational foundation for systems design & analysis, as it relates to current and future healthcare systems. In addition, this course will build upon the fundamental systems design & analysis principles to explore current and future healthcare systems that will include integration of disparate clinical healthcare systems, mobile technologies, as well as a combination of remote-monitoring technology, sensors, and online communications and intelligence to improve patient adherence, engagement and clinical outcomes.

Learning Objectives:

1. In the role of a systems analyst, investigate and demonstrate the foundations of systems analysis & design theory and applications as it relates to healthcare systems
2. Demonstrate skills needed to successfully function as a healthcare systems analyst by Identify key stakeholders, discover / document requirements, convert requirements into vendor specifications and evaluate vendor’s proposals, document Service Level Agreement (SLA), document / coordinate testing, training, and implementation including SLA.
3. Determine appropriate measurement methods to evaluate and compare mobile technologies based on user requirements, available / upgradable infrastructure, and application specific software / hardware in the healthcare marketplace.
4. Define role of stakeholders as users / customers of healthcare information systems, and give examples of each.
5. Manage multiple roles of business drivers and technology drivers, as they relate to the healthcare industry.
6. Distinguish between knowledge, process, and communications goals for healthcare systems.
7. Employ the essential phases of systems development that includes purpose, inputs, and outputs of a healthcare system.
8. Objectively judge and evaluate wireless networking in healthcare systems from a systems analysis & design perspective.
9. Incorporate 10 basic principles of systems development as it relates to healthcare systems.
10. Use a variety of automated tools for systems development in creation of state of the art healthcare systems.

Pre-Requisite: None

Description:

Lean Six Sigma for Health Care (Yellow Belt) participants will learn the basic philosophy, tools, and techniques to deliver breakthrough business improvements that will reduce waiting times, improve quality, and reduce costs in a health care environment. More specifically, they will learn to apply a comprehensive set of 15-20 Lean Six Sigma process improvement tools by using the PDCA (Plan, Do, Check, Act) problem solving model. They will learn techniques for both quantitative and qualitative analysis, as well as methods and tools for waste reduction and process enhancement and acceleration. The course also covers how to map out processes and identify sources of variation, as well as to gain a basic understanding of descriptive statistical analysis. Finally, they will learn how to perform basic pilot studies and analyze the results, in order to determine the most effective way to improve and stabilize processes.  Candidates work on either an integrated health care case study or on an actual business project, and will apply classroom techniques to the project. 

Learning Objectives:

1. Summarize Lean Six Sigma history and philosophy and describe how it applies to modern health care organizations.
2. Identify opportunities for system and process improvement in health care settings.
3. Use basic problem solving and critical thinking skills and apply systems thinking to quality improvement projects in hospitals and other clinical settings.
4. Apply techniques to decrease health care costs, increase patient safety, improve treatment outcomes, and increase customer satisfaction.
5. Identify valid and critical to quality customer and business requirements and related measures and then turn the data into actionable information to manage and improve organizational processes.
6. Map out work flow processes using Excel/Visio to identify sources of waste.
7. Apply the PDCA/DMAIC model in accordance with Lean Six Sigma principles.
8. Conduct beginning-level descriptive statistical analyses to determine baselines and identify improvements
9. Learn different improvements designs to most effectively improve and stabilize processes.
10. Analyze measurement patterns and results of biomedical information utilizing basic statistical concepts in conjunction with Lean Six Sigma-specific software (e.g.,SigmaXL) to synthesize pertinent data.
11. Identify risks and basic root causes for typical process challenges.

Pre-Requisite: None

Description: 

This course will provide an introductory, hands-on experience for life science researchers in bioinformatics using R and Bioconductor. Emphasis will be placed on accessing, formatting, and visualizing genomics data. Most analyses will deal with “little” data (no mapping or assembly of short reads), but some techniques to work with “big” data (e.g. BAM files) will be covered. Lecture and lab will both be held in a computer lab, so lecture will be “hands-on”. Working in small groups is encouraged.

Learning Objectives:

1. Students will learn the fundamentals of bioinformatics analyses of genomics data using R and Bioconductor.
2. Students will gain a greater appreciation for bioinformatics and the parallels with “wet bench” experiments.
3. Students will be introduced to the concept of “literate programming” and how it can be applied to document their work are write legible reports.
4. Students will be prepared for more advanced courses in R or bioinformatics, or for continued self-learning.

Pre-Requisite: MI 5200, and HIPAA modules are prerequisites for MI7000. In addition, CITI certification is required for research projects. Students should complete HPD Medicine Module #13. The course director may also require specific electives to be completed depending on the nature of the project that the student chooses to perform.

Please note that students must have a GPA of at least 3.00 to be eligible to register for or participate in practicum work.

Description: 

This is a required course for all MSBI students. The practicum allows the student to select an area of interest in which to apply the theories, concepts, knowledge, and skills gained during the didactic courses in a real-world setting. The student will work under the supervision of a site-based preceptor and an NSU-based faculty advisor.

The student is expected to acquire skills and experiences in the application of basic biomedical informatics concepts and specialty knowledge to the solution of health information technology (HIT) problems. Students will be actively involved in the development, implementation, or evaluation of an informatics-based application or project.

A specific set of measurable learning objectives and deliverables will be determined by the student, the site preceptor, and the NSU-based faculty advisor. These learning objectives must be approved by the course director. The student’s area of interest would be determined at an earlier point in the program or by the needs of the precepting organization.

The practicum is evaluated by completion of an ePortfolio. The ePortfolio is an evidence based digital format method to assess the quality and quantity of learning gained from a student practicum experience. The ePortfolio is standardized in its structure and format yet individualized in its content for each student. Overall, the ePortfolio is a goal-driven documentation of professional growth and achieved competencies during the practicum. The ePortfolio combines self-reflection, instructor assessments, and documentation supplied by students (evidence/samples) to document what they learned/produced, and is used to help students prepare for career transition/development.

Students are responsible for finding their own practicum site. Once a site is located, the Program Office will facilitate a legal affiliation agreement between the site and the Program. Some practicum sites may require background checks, drug screening, and immunization records. Students are responsible for any associated costs.

Learning Objectives:

Individualized

No. of Credits: 3

Pre-Requisite: None

Description: 

This course covers major concepts, systems and methodology in managing healthcare information systems. Topics will include concepts in: system implementation and support, information architecture, IT governance in health care, information systems standards, organizing IT services, strategic planning, IT alignment with the healthcare facility, and management’s role in major IT initiatives.

Learning Objectives:

Upon completion of the course the student will be able to:

1. Design strategies for management in acquiring, planning, and implementing major healthcare IT initiatives;
2. Implement sound project management methodologies in healthcare IT systems, which can be critical to the strategic plan of the facility;
3. Evaluate technologies such as electronic medical records (EMRs), enterprise resource planning, or enterprise collaboration systems, which can facilitate a healthcare facility’s business processes;
4. Integrate the roles of stakeholders, IT staff, and management in designing and implementing health information technology (HIT) projects;
5. Analyze legal compliance requirements that organizations must comply with while implementing and supporting healthcare information systems (i.e. HIPAA regulations and JCAHO standards);
6. Evaluate HIT systems, projects, and provider requirements.

Pre-Requisite: None

Description: 

The dynamics of human-computer interaction (HCI) directly impacts health care. This course will introduce the student to usable interfaces and the study of social consequences associated with the changing environment due to technology innovation.

Learning Objectives:

1. Examine the need for evaluating health care information system user interfaces
2. Analyze the roles of stakeholders in designing health information user interfaces
3. Determine appropriate measurement methods to evaluate interfaces and interaction from the prospective of individual end users isolating your known knowledge from the evaluation.
4. Appraise ongoing evaluation barriers and facilitators at various phases of design and provide the necessary insight regarding interface integration.
5. Determine appropriate HCI measurement methods for various evaluation projects.
6. Examine the need for evaluating health care information technology systems and the impact of HCI in error prevention and real time applications.

Pre-Requisite: MI 5120, MI 5130, MI 5200

Description: 

This course introduces students to theoretical, statistical, and practical concepts underlying modern medical decision making. Students will be provided a review of the multiple methods of knowledge generation for clinical decision support systems (CDSS) and create their own prototype of CDSS. Current implementations of stand-alone and integrated CDSS will be evaluated. Techniques for planning, management, and evaluation of CDSS implementations will be reviewed. Human factors, including work-flow integration, and the ethical, legal and regulatory aspects of CDSS use will be explored, as applicable to commercial implementations in patient care settings. Future models of healthcare, supported by CDSS and evidence-based medicine, will be discussed and reviewed.

Learning Objectives:

1. Describe the scope and kinds of clinical decision support systems; analyze CDSS effectiveness in terms of implementing for diagnostic and therapeutic purposes.
2. Evaluate the linkage of CDSS to the basic concepts of evidence-based medicine.
3. Apply practice guidelines for clinical decision support, including commonly-used formalisms and authoring tools for computer-interpretable guidelines.
4. Describe the social and political forces driving implementations of CDSS in the clinical field.
5. Compare and contrast the types of CDSS available in commercial and research implementations.
6. Apply statistical methods and logic concepts, such as probability, regression, Boolean logic, set theory, and inference, to underlying medical decision making.
7. Evaluate at least three methods of knowledge generation for CDSS, including decision trees, neural networks, and Bayesian analysis.
8. Compare the advantages and disadvantages of supervised vs. unsupervised learning methods in data-mining applications.
9. Evaluate how CDSS fold into the overall hospital and/or medical office health information technology environment.
10. Analyze technology and business characteristics of successful CDSS implementations using recent industry cases as guidelines and input to build student’s own attributes of an effective CDSS implementation.
11. Recognize business and clinical implementation and maintenance challenges in commercial CDSS projects, as well as possible resolutions to these challenges.
12. Assess risks involved with poor CDSS implementations from the following standpoints: health outcomes, quality of care, medical error rates, and patient and provider satisfaction standpoints.
13. Discuss ethical and regulatory issues involved in design and implementation of CDSS systems.
14. Identify opportunities for use of CDSS in personal health records and shared decision-making.

Pre-Requisite: MI 5120, MI 5130, MI 5200

Description: 

This interactive course will introduce students to various evaluation methods for healthcare informatics systems, projects and proposals. Students will consider both quantitative and qualitative methods of evaluation as they examine the design and implementation processes.

Learning Objectives:

1. Examine the need for evaluating health care information technology systems and projects.
2. Analyze the roles of stakeholders in designing health information technology evaluation projects.
3. Compare and contrast quantitative and qualitative evaluation methods and their application to healthcare informatics evaluation studies.
4. Create an evaluation proposal for a health care information technology system or project.
5. Appraise ongoing evaluation barriers and facilitators at various phases of an evaluation process in health care informatics.
6. Explain the importance of usability assessment and describes techniques for completing usability assessment on information systems.
7. Determine appropriate measurement methods for various evaluation projects.
8. Explain the difference between cost-effectiveness and cost-benefit analyses in the design and implementation of an evaluation process.

Pre-Requisite: None

Description: 

This course focuses on the principles and reasoning underlying modern biostatistics and on inferential techniques commonly used in public health research. Students will be able to apply basic inferential methods in research endeavors and improve their abilities to understand the data analysis of health-related research articles.

Learning Objectives:

1. For a given study the students will be able to formulate the research question(s) and the corresponding statistical hypotheses.
2. Describe the roles biostatistics serves in the discipline of public health.
3. Describe basic concepts of probability, random variation and commonly used statistical probability distributions.
4. Describe preferred methodological alternatives to commonly used statistical methods when assumptions are not met.
5. Distinguish among the different measurement scales and the implications for selection of statistical methods to be used based on these distinctions.
6. Apply descriptive techniques commonly used to summarize public health data.
7. Apply common statistical methods for inference.
8. Apply descriptive and inferential methodologies according to the type of study design for answering a particular research question.
9. Apply basic informatics techniques with vital statistics and public health records in the description of public health characteristics and in public health research and evaluation.
10. Apply sample size and power calculation techniques.
11. Interpret results of statistical analyses found in public health studies.
12. Develop written and oral presentations based on statistical analyses for both public health professionals and educated lay audiences.
13. Learn the rules of research with human subjects by taking the Citi course.
14. Using computing statistical packages such as JMP, SAS and EpiInfo, the students will be able to apply the biostatistical methods producing results and interpreting the computer output in an appropriate.

Pre-Requisite: None

Description: 

Examines basic principles and methods of modern epidemiology used to assess disease causation and distribution. Students develop conceptual and analytical skills to measure association and risk, conduct epidemiological surveillance, evaluate screening and diagnostic test, as well as investigate disease outbreaks and epidemics.

Learning Objectives

1. Identify and define the core concepts and terminology of Epidemiology
2. Define and calculate measures of disease frequency and mortality including incidence, prevalence and mortality rates (crude and adjusted)
3. Contrast the concepts of association and causality and explain the “criteria for causality.”
4. Understand and apply the basic measures of the exposure-disease association: absolute, relative and attributable risk
5. Identify the descriptive study designs and discuss their applications and limitations
6. Compare and contrast the designs of case-control and cohort studies, including relative strengths and weaknesses
7. Describe the design of interventional studies including clinical trials.
8. Become familiar with data sources used in epidemiology and describe the utility and components of disease surveillance systems
9. Understand the principles of disease screening and define the objective performance measures associated with screening tests (including sensitivity, specificity, positive and negative predictive value)
10. Identify the types of, and demonstrate methods to control for non-random error in investigations (bias and confounding)
11. List the steps in an investigation of a disease outbreak
12. Utilize the information learned to critically evaluate an epidemiologic study

Pre-Requisite: None

Description: 

MI-6404 is an elective course designed as a student/self-directed course. In consultation with the chosen advisor/mentor and the course director, the student will determine a focused topic of quasi-independent study, research, or other appropriate learning activity. A final paper or other appropriate document(s) will serve as documentation of having met the mutually agreed upon objectives.

Learning Objectives:

• Individualized

Pre-Requisite: None

Description: 

Public health informatics is the systematic application of information and computer science and technology to public health practice, research and learning. This course focuses on developing the knowledge and skills of systemic application of information, computer science, and technology to public health practice. Students will acquire a basic understanding of informatics in public health practice, and be able to apply the skills of using some informatics tools in public health practices.

Learning Objectives:

1. Develop a true understanding your personal strengths and talents.
2. Articulate how you have used your strengths in your daily work and personal performance.
3. Analyze which strengths you can apply best to various tasks required in the use of health technology.
4. Assess the strengths of your class and determine which person performs best in different situations.
5. Evaluate which of your strengths are best suited for various positions in the Health IT industry including healthcare organizations.
6. Recognize the importance of results driven organizations using individual talents to increase effectiveness.
7. Conclude which direction HIT should move in to best manage patient care.
8. Conclude which HIT applications are best suited for evaluating a patient care program.
9. Conclude which talents are best suited for being in a leadership position in HIT.
10. Conclude which talents are best suited for developing a strong HIT communication program.

Pre-Requisite: None

Description: 

This course provides an introduction to the skills of grant writing in biomedical informatics. Each student will submit a completed grant application as a culminating experience. This course introduces students to grant development and preparation so that they can participate in the process of obtaining public or private funds to support research, education and/or service projects.

Learning Objectives:

1. Describe the elements of successful and unsuccessful grant applications
2. Prepare a complete grant application for research, education, and/or service projects
3. in public health
4. Evaluate a grant proposal and identify its strengths and weaknesses.
5. Interpret guidelines listed in a Request for Proposal (RFP)
6. Identify sources of grant funds

Pre-Requisite: None

Description: 

Consumer Health Informatics is a relatively new application of information technologies in the field of health care that aims to engage and empower consumers to become involved in their health care. This course provides an introduction to, and overview of, consumer health informatics, mobile health (mHealth), and social media applications used in healthcare. It explores the development of consumers as ePatients and tools such as personal health records (PHRs), as well as the fluid nature of social media in medicine and the emerging area of mobile health (mHealth). Students will learn from a combination of lectures and a hands-on approach of interacting directly with the tools and technologies discussed.

Learning Objectives:

1. Provide definitions for core terminology including consumer health informatics (CHI), mobile health (mHealth), digital health, and social media.
2. Understand the drivers of consumer health informatics
3. Explore opportunities and challenges in consumer health informatics technologies
4. Outline elements of participatory medicine and health literacy associated with online health information seeking behaviors.
5. Identify patient tools such as personal health records and assess their utility and impact.
6. Recognize health-related social media roles for consumers and health care professionals
7. Demonstrate an understanding of the role of social media as marketing strategy
8. Manipulate social media tools (e.g., Twitter) for maintaining current awareness and professional development in informatics.
9. Identify strengths and weaknesses of mHealth applications (apps), tools, and devices (e.g., wearables) for consumers and health care professionals.
10. Appraise the utility and value of mHealth or social media tools employed for social good via direct use.
11. Delineate risks inherent to patients with consumer health informatics, social media, and mHealth.
12. Construct a mobile app or device framework to address a problem or opportunity in health care.
13. Apply principles of design and user interface (UI) and user experience (UX) creation to evaluate a digital health tool.

Pre-Requisite: None

Description: 

This class will provide students with introductory understanding of clinical analysts’ daily responsibilities and functions within hospitals. Students will be introduced to daily operations of clinical software systems and lead to understand how such systems are used by health care organizations to provide quality care services. 

Learning Objectives:

1. Analyze the management and support of clinical users’ HIT business needs.
2. Evaluate how clinical information systems are used to improve quality of care.
3. Illustrate and apply commonly used HIT terminologies.
4. Take the CPHIMSS examination for certification.

Pre-Requisite: None

Description: 

Telemedicine is the exchange of health information from one side to another utilizing electronic communications. This course introduces the student to fundamental concepts and knowledge of telemedicine technologies, its application and usage including: essential aspects of communication networks and services; wired and wireless infrastructures; safeguarding medical data including health information privacy; systems deployment; patient monitoring and care; information processing; and future trends in telemedicine will be studied. Discussions areas include telemedicine: technical perspectives; scalability to support future growth; integration with legacy infrastructures and interoperability; history; trauma; emergencies and disasters; clinical applications; and other critical components of telemedicine technologies.

Learning Objectives:

1. Define the capabilities, challenges and limitations of current information technologies utilized in healthcare information communication systems in telemedicine;
2. Describe the technical components used in medical information processing;
3. Illustrate a wireless telemedicine systems deployment plan;
4. Compare information communication technologies used in patient monitoring and disaster response situations;
5. Identify the information communication technologies utilized for safeguarding medical data and privacy.

Pre-Requisite: MI 6413

Description:

Lean Six Sigma for Health Care (Green Belt) participants will learn intermediate level tools, and techniques to deliver breakthrough business improvements that will reduce waiting times, improve quality, and reduce costs in a health care environment. More specifically, they will learn to apply a comprehensive set of 15-20 Lean Six Sigma process improvement tools by using the DMAIC (define, Measure, Analyze, Improve, and Control) problem solving model. They will learn techniques for both quantitative and qualitative analysis, as well as methods and tools for work flow enhancement and acceleration. The course also covers how to map out processes and identify sources of variation, as well as to gain a basic understanding of inferential statistical analysis. Finally, they will learn how to perform how to implement lean management tools and philosophy, in order to improve and stabilize processes.  Candidates work on either an integrated health care case study or on an actual business project, and will apply class techniques to the project. There will be additional practice with basic tools to help promote mastery.

Learning Objectives:

1. Understand Lean Six Sigma implementation strategies for modern health care organizations
2. Identify opportunities for system and process improvement in health care settings by using project selection and solution selection matrices.
3. Use intermediate level problem solving and critical thinking skills on quality improvement projects in hospitals and other clinical settings.
4. Identify valid and critical to quality customer and business requirements and related measures and then turn the data into actionable information to manage and improve organizational processes.
5. Use break-through equations and cause and effect analysis to identify the important X and Y measures in processes and systems.
6. Apply the DMAIC model in accordance with Lean Six Sigma principles.
7. Map out health care value streams and other high level processes to identify sources of variation, and to acquire a beginning-level understanding of inferential statistical analysis, as well as learn to perform basic experiments and analyze data to determine the most effective way to improve and stabilize processes.
8. Conduct measurement system analysis to determine measurement reliability and validity.
9.  Analyze measurement patterns and results of biomedical information utilizing basic statistical concepts in conjunction with Lean Six Sigma-specific software (e.g.,SigmaXL) to synthesize pertinent data.
10. Conduct basic risk analysis and contingency planning.
11. Develop a lean management program to identify and sustain improvements.

Pre-Requisite: MI 5120, MI 5130, MI 5200

Description: 

This course will provide students with the opportunity to learn the fundamentals of set-up and using the applications of one of the most commonly used electronic health record systems in the US, NextGen, in clinical settings.  Students will be required to complete the NextGen e-learning modules before the on campus hands on training sessions. 

This course is required for the competitive internship opportunity in the NSU clinics (more details to follow).  

Learning Objectives:

1. Demonstrate the ability to use and set-up NextGen EHR and ExpressRx applications.
2. Use the Knowledge Base Model (KBM) templates and workflows
3. Complete at least one demonstration of Stage 1 Meaningful use with NexGen solutions.
4. Evaluate the current use of clinical application of NextGen at NSU clinics
5. Identify ways to improve the functionality and workflow for NSU clinics

Pre-Requisite: None

Description: 

The course will introduce the clinical workflow analysis as a method of choice to improve clinical processes in healthcare delivery systems. Students will review the primary objectives for process improvement in clinical healthcare: outcome quality (including patient safety) and the development of health information technology (HIT) to support the Electronic Health Record (EHR) with initiatives showing a significant impact on clinical workflows (e.g. meaningful use). Students will define the functional components of the healthcare activities and learn to map on a flowchart the standard symbols used to represent all tasks and steps, decision points, resources, and outcomes of the clinical workflow. Students will apply the tools of workflow analysis by assessing a workflow in a healthcare setting using graphical representations of 0the workflow phases (current state, desired state), and process defects identification and classification. The course will introduce the quantitative measures of workflow improvement used in Lean Six-Sigma. Students will formalize a proposal for an intervention aimed at the modification and optimization of a clinical workflow. 

Learning Objectives:

1. Define business processes and process improvement in healthcare from the patient, clinician, and analyst perspective.
2. Identify two major challenging objectives for improvement in healthcare: 1) quality and patient safety and 2) meaningful use of EHR.
3. Map the components of a clinical workflow chronologically into a flowchart, using the standard symbolic shapes for tasks and steps, decisions and processes, resources, inputs and outputs, connectors, and outcomes.
4. Assess clinical workflow efficiency with workflow analysis by identifying differences with the desired state, identification of process defects, and root-cause analysis.
5. Define the quantitative measures used to assess workflow improvement in Lean Six Sigma.
6. Create the workflow representation of a clinical process in a healthcare setting and present a strategy for improvement.

Pre-Requisite: None

Description: 

This advanced cognitive engineering systems course expands upon introductory topics presented as parts of the clinical decision support and analytics courses to take a deeper dive into data science and artificial intelligence algorithms, with application to such medical specialties as oncology, cardiology, radiology, and neurology. It provides students with skills necessary to undertake programmatic analysis of patient information data sets, apply unsupervised learning techniques to enhance outcomes of the predictive and prescriptive analytics methods, use supervised learning methods to represent evidence based guidelines and detect medical fraud, compare and analyze graphs and images, and apply natural language processing techniques to ingest and analyze text information.

Learning Objectives:

1. Determine the need for and basic concepts of artificial intelligence in healthcare.
2. Gain structural understanding of the latest HL7 clinical data exchange standards.
3. Evaluate medical care quality models through the prism of data analysis by acquiring practical clinical data measuring and monitoring skills.
4. Apply the latest longitudinal data analysis methods to perform real world evidence based studies of drug performance surveillance.
5. Apply supervised, unsupervised, fuzzy, and wavelet-based knowledge management techniques to generate predictive and prescriptive analytics models in the patient care and medical reimbursement management settings.
6. Investigate advanced graph and image analysis algorithms to implement several methods of automated visual medical information processing.
7. Acquire and apply statistical programming skills to analyze complex patient data sets using open-source R language.
8. Conceptualize natural language generation and processing methods to work with unstructured medical knowledge and data.
9. Apply theoretical natural language processing and training skills to hands-on implementations using OpenNLP open-source command line programming language.
10. Integrate theoretical and practical concepts through hands-on writing, programming, and data analysis exercises.
11. Apply AI knowledge and skills in various disease management situations to enhance practical application of the subject and adopt innovative approaches to linking disparate pieces of information into cohesive medical data science strategy.
12. Integrate understanding of the clinical decision support, database, basic programming, and decision tree building knowledge into ability to solve complex problems through systematic approaches and step-by-step implementations of building applied solutions to medical problems.
13. Convert knowledge about medical information, cognitive processing, and statistical analysis to complex application in the emerging AI fields of medical fraud detection, pharmacological surveillance, and real-world evidence of drug performance

Pre-Requisite: None

Description: 

This course would introduce students to a variety of mathematical techniques that are commonly used in healthcare analytics and biomedical informatics.  The emphasis would be on developing an understanding of the methods, their uses, and their limitations. Mathematical rigor would not be emphasized, but an understanding of the meaning and uses of the techniques.  The instruction would also include inculcating a mathematical mindset in the students which would allow them to extend their knowledge and understanding to further areas as needed in their future endeavors.  

 Learning Objectives:

1. Examine mathematical concepts and techniques used commonly in healthcare analytics and biomedical informatics
2. Discover the meaning of the techniques covered
3. Demonstrate the uses and limitations of the techniques covered
4. Solve assigned quantitative problems in these areas
5. Develop the mindset to extend knowledge and skills in quantitative methods beyond what is presented in the course