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Our program was among the first to partner with the American Medical Informatics Association (AMIA) to provide their 10x10 certification.
MSBI Program graduates were among the first group of physicians in the nation to become board certified in clinical informatics.
MSBI Program students have completed internship and practicum experiences at many sites including Cleveland Clinic Florida, Palmetto General Hospital, National Institutes of Health, and Mayo Clinic.
The Biomedical Informatics Program is growing with faculty positions currently available.
Our students have won the South Florida Chapter Health Information Management Systems Society (HIMSS) Scholarship three times.
As a part of the Biomedical Informatics Program’s Academic Organizational Affiliate status with the Health Information Management Systems Society (HIMSS), all full time students are entitled to a free HIMSS membership!
The Biomedical Informatics Program was ranked No. 6 out of 25 on BestMedicalDegrees.com’s 2015 list of Best Value Online Master’s in Health Informatics and Health Information Management.
NSU's Biomedical Informatics Program was was the first graduate program in health informatics in the state of Florida.
"NSU's M.S.B.I. program gave me the tools and knowledge to navigate the health informatics industry with confidence. It prepared me to provide meaningful contributions to seasoned professionals and organizations. Two years after graduation my relationship with the program is still going strong."
Teresa Blanco, M.S.B.I., (’12), Charge Services Support Analyst at Cerner Corporation
"Tons of work to reach this simple goal: 'The right information, to the right person, at the right time.' The healthcare industry is not there yet, but with programs such as NSU’s Biomedical Informatics program, it will be soon."
April M. Green, M.S.B.I., ('13), Clinical Integration Specialist at Holy Cross Hospital
"This program allowed me to gain the experience that I needed in order to follow the career path that I wanted to take."
Anthony Meglino, N.C.P., M.S.B.I.,(’14), NextGen EHR Template Developer Consultant at Dell
"The program helped me turn an adversary into an opportunity. Tenacity and a strong drive led to my success in the M.S.B.I. program and in my world."
Stephen Amoah, M.S.B.I. (’14), Ph.D. Student
"The faculty and staff of the NSU MS in Biomedical Informatics program are amazing! I was nervous to enroll in this program because I had little knowledge of the healthcare industry. With preparation from the pre-requisite courses, I was able to obtain an entry level position in healthcare. Approaching the end of the program, I was able to obtain a promising healthcare IT position, where I could effectively contribute my knowledge and skills at a large healthcare organization."
Kenneth Simpson, Current Student, Application System Analyst II at University of Maryland Medical System
"Certainly, I had such an amazing experience[with my practicum],where I could put what I've learned into practice and also I've explored other learning possibilities and gained some new skills. "
Eswald Fertil, Current Student
Congratulations to alum Tracy Eckerle for passing the ANCC Certification for Informatics Nursing!
Congratulations to student Tanny Schertzer for winning the 2016 South Florida Chapter HIMSS Foundation Scholarship!
Congratulations to student Mark Carnemolla for winning the 2011 South Florida Chapter HIMSS Foundation Scholarship!
Congratulations to alum Monty Islam for winning the 2012 South Florida Chapter HIMSS Scholarship!
Congratulations to student Jacques Orces, D.O. for passing the clinical informatics subspecialty board exam administered by the American Board of Preventive Medicine!
Congratulations to alum Danielle Oryn, D.O. for passing the clinical informatics subspecialty board exam administered by the American Board of Preventive Medicine!
Congratulations to alum James Seltzer, D.O. for passing the clinical informatics subspecialty board exam administered by the American Board of Preventive Medicine!
Congratulations to student Monica Terrazas on winning a Broward Women's Alliance scholarship!
Congratulations to alum Darnell Smith for passing the ANCC Certification for Informatics Nursing!
Congratulations to student Monica Terrazas for winning the 2017 South Florida Chapter HIMSS Foundation Scholarship!
The NSU COM Biomedical Informatics Program is designed to prepare students to meet the challenges and opportunities of a career in the health information technology sector. The three major focus areas of the NSU COM Biomedical Informatics Program's curriculum are: computer science with a medical informatics focus, clinical informatics with a concentration in the areas of applications and evaluation, and business and management of health information technologies.
The NSU COM Biomedical Informatics Programs can be completed entirely online allowing working professionals to obtain their degree or certificate without career disruption. The skills-based curriculum includes courses leading to Lean Six Sigma Green Belt, CPHIMSS, and NextGen certifications. A paid internship at NSU's clinics is also available, in addition to a number of practicum experience opportunities in the surrounding community and beyond.
The innovative skills-based curriculum leading to a Master of Science in Biomedical Informatics degree consists of the following didactic courses offered predominantly in an online fashion via NSU's state-of-the-art web-based, distance-learning technology. Students are required to complete a practicum project consisting of hands-on practical work within a health information technology or other appropriate environment.
No. of Credits: 3
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.
Upon completion of the course the student will be able to:
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.
After completion of the course, will be able to:
5. Demonstrate knowledge of information technology terms and methods as they relate to project management such as:
6. Demonstrate knowledge of change management terms and methods as they relate to project management such as:
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.
At the end of the course, student will be able to:
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).
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.
Upon completion of this course, students will have gained knowledge of information security and healthcare information security. Students will be able to use security tools and devices to encrypt data, to enhance access control and to increase application and system security.
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.
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.
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.
Upon completion of this course, the student will comprehend the following issues and 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.
15. Identify a basic clinical problem or an operational situation with the purpose of simulating an expert system to assist clinicians with problem resolution process.
16. Present a full implementation of CDSS with commercially applicable attributes, aimed at solving specific clinical problem or improving clinical workflow.
17. Integrate theoretical and practical knowledge of current and future CDSS learned in class, to apply in healthcare settings.
Pre-Requisite: MI 5120, MI 5130, MI 5200
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.
Description: This on-line course is an introduction to the management of employees in healthcare organizations (HCO’s). Students will gain a working knowledge of how to manage personal, interpersonal, and group processes by having the interpersonal skills to assume responsibility for leading and promoting teamwork among diverse stakeholders. Students will learn to manage individual and group behaviors in improving organizational productivity and performance. Students will be able to apply newly learned organizational skills, developed through experiential and application based learning scenarios in the form of case studies as well as from their home, work, and educational observations and experiences. It is anticipated that this practical learning experience can be transferred to their day to day managerial responsibilities.
Upon completion of this course, the student will be able to:
1. Evaluate basic concepts of organizational behavior and organizational development
2. Critique the organizational behavior theories of McGregor, Maslow, Herzberg, McClelland, Blake and Mouton, Fiedler, Vroom, Skinner and Alderfer
3. Assess basic behavioral models of communication, motivation, performance organizational learning and development, and leadership
4. Analyze his/her leadership/management style
5. Synthesize the structure and dynamics of the small group process
6. Appraise the fundamentals and strategies of organizational change
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.
Elective Courses - A total of 12 credits (4 courses) must be taken:
Description: The understanding of telecommunications and networking is imperative for adequate functioning of healthcare organizations. This is due to the convergence of computing, data management, telecommunications, and the growing applications of information technology in the healthcare arena and medical facilities. The knowledge of these key areas of information systems also becomes essential for competitive advantage. This course combines the basic technical concepts of data communications, telecommunications and networking with the healthcare IT management aspects and practical applications.
At the end of the course, the students should be able to:
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.
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.
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.
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.
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.
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.
Topics will include:
Discusses principles and logic involved in health policy, planning and management. Address history, political and environmental contexts, and their incorporation into population research.
This course is an in-depth review of basic planning & evaluation techniques for the implementation of community health care program. The course is designed & will be taught employing comparative methodology. The material will be taught using examples & experiences from multiple international examples. The course covers the interdependence between policy and planning and management. It will consist of policy analysis techniques as well as the conceptual framework for the planning and management of health care programs. The course also reviews essential methods for effective planning & evaluation considering the economic, political epidemiological, demographic, and other components that contribute to the assessment of health needs and resource allocation.
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.
Upon completing MI 6410, the student will be able to:
Pre-Requisite: MI 5120
This course immerses students in the technical, business, cultural and organizational dynamics typically encountered during HIT systems selection and contract negotiation process. Real world case studies, replete with dynamic political, financial and technical roadblocks and opportunities, will be used to introduce the student to skills required to make the best cultural decisions and negotiate a viable contract.
This course provides the conceptual and technical skills needed in leading health information technology. It is designed to create a profound understanding of leadership at the cognitive and action levels to enable health information leaders to optimize decision-making in the workplace. Students review remarkable leaders, organizations, and teams in order to hone their own observation, sense-making, and innovating skills in a health information setting. This leadership course reviews and builds upon the basic knowledge of leadership provided in the organizational behavior course by expanding the scope and depth of the student's knowledge of leadership theories, conflict management techniques, and by developing the student's self-knowledge of his or her preferred leadership styles.
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.
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.
At the end of the course, students will be able to:
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.
Students will apply newly learned telemedicine technology knowledge and skills to course assignments, thus after completion of this course, students will be able to:
Pre-Requisite: MI 6413
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.
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).
After Completion of the course students will be able to:
This course provides an introduction to iOS Applications (apps) development with an emphasis on health information technology projects. Topics cover iOS development environment setup, the Swift language syntax, Model-View-Controller design patterns, iOS apps lifecycle, GUI implementation, multi-touch handling, graphics processing, file handling, SQLite database handling, audio and video processing, multi-platform support for iPhone and iPad, maps displaying, and web service interfacing.
After completing this course, a student will be able to:
This course will provide students with a preliminary understanding of the theory and practice of medical image processing and analysis in healthcare. Basic concepts and fundamentals of medical image processing and analysis will be described in the course. The application of medical image processing and analysis in biomedical information systems will be discussed. Students will be introduced to the fundamentals and methodology of medical image processing, image analysis, image compression, and molecular imaging.
After completion of the course, students will be able to:
This course will introduce students to geographic information systems (GIS) to map and spatially analyze public health and demographic data. Students will learn the fundamentals of the ArcMap software system and ways to integrate cartography into biomedical informatics practice. Beyond use of GIS for cartography, this course will also examine ethical issues and methods of analyzing demographic and spatial health patterns using GIS and demography analysis methods. The versatility of GIS in a public health setting will be examined and will include exercises involving GIS applications in health marketing, demography, epidemiology, and health care systems. For example, we will look at how different socioeconomic groups use urban spaces differently in terms of transportation and how these differences in navigation impact contact points for health marketing. Other issues covered in the class will be the ethics of GIS, manipulation of data, sources of data, and understanding some commonly used public health datasets such as the YRBS, BRFSS, and US Census.
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.
In the ever changing world of information and global economic competition it is crucial that individuals and organizations understand their personal as well as group talents. Today’s educational, healthcare and institutional structures lack leadership and cutting edge thinking. By applying strength based leadership practices one comes to understand their own as well as the group’s strengths and talents and is able to apply these practices in their daily work as well as in leadership roles.
The course will produce a personal understanding of individual as well as group personality/strengths and how these evolve and affect performance in individuals. Students will develop a better self-awareness of what strengths they possess and how this affects personal as well as work performance. It demonstrates how leaders if it is a chosen career path can continue to grow and how to develop each of your group’s talents to maximize the performance of your team and organization. The Affordable Care Act will be incorporated and students will discover what individual as well as organizational talents must be utilized to improve patient care in the future utilizing technology.
The course will expose students to healthcare “big data” focused on current needs such as population health, outcome reporting, clinical decision support, physician quality measurement, and various other measures including CMS initiatives such as meaningful use and Medicare and payer quality reporting requirements. The course will use current real world problem scenario’s where data analytics and visualization can be applied to successfully report on and solve various problem prevalent in today’s value based payer model. Students will learn how to do large scale data mining and the infrastructures needed to support the various system designs such as Hadoop ecosystems and Hadoop based tools. The student will be exposed to the application of predictive analytics specific to healthcare with an understanding of using data to help deliver quality and safe patient care as well as data driven methods of improving care. The course will expose students to real time data analytics where data is collected and reported on around the clock. The course will also expose student to mobile data acquisition and analysis coming from various local and remote devices. This course will introduce students to data visualization methods which will teach them how to communicate analytical insights to both technical and non-technical audiences.
This course is a continuation of MI 6424 (Introduction to Healthcare Analytics and Data Visualization I). The course will expose students to healthcare “big data” focused on current needs such as population health, outcome reporting, clinical decision support, physician quality measurement, and various other measures including CMS initiatives such as meaningful use and Medicare and payer quality reporting requirements. The course will use current real world problem scenarios where data analytics and visualization can be applied to successfully report on and solve various problem prevalent in today’s value based payer model. Students will learn how to do large-scale data mining and the infrastructures needed to support the various system designs such as Hadoop ecosystems and Hadoop based tools. The student will be exposed to the application of predictive analytics specific to healthcare with an understanding of using data to help deliver quality and safe patient care as well as data driven methods of improving care. The course will expose students to real time data analytics where data is collected and reported on around the clock. The course will also expose student to mobile data acquisition and analysis coming from various local and remote devices. This course will introduce students to data visualization methods, which will teach them how to communicate analytical insights to both technical and non-technical audiences.
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.
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.
This course provides a comprehensive and rigorous introduction to big data analytics in healthcare. It will describe the hardware/software infrastructures that are used today for big data (e.g., Hadoop, Hive) and the implications of these infrastructures for the accurate and efficient analysis of big data for healthcare applications. Students will learn the mathematical, statistical, artificial intelligence, and modeling techniques that have been developed for analysis of big data, especially for healthcare applications. Also, it will describe the visualization techniques which are useful for displaying big data analysis results for meaningful interpretation of the results by humans. It will use current real world problems involving big data analytics in healthcare, including the Big Data to Knowledge (BD2K) initiative of the National Institutes of Health (NIH). Students will gain experience in applying the techniques of big data analytics to healthcare problems.