Training Program

BIOINF 2204: Introduction to Health Information Technology in Dentistry

Course Description

Dental offices are high-tech places. Many dentists use electronic dental records, digital radiology and other IT tools daily. But, have you ever wondered who makes all those tools work in a busy dental practice? Yes, it is the Chief Information or Chief Technology Officer … who is, in most cases, the dentist. Yet, most dentists are ill prepared to take on the complexities and challenges of managing health information technology (HIT) in their office. This course is designed to help change that situation. Our premise is that participants are not simply interested in becoming consumers of a fully implemented information system, but intend to acquire a deeper background for the “why” and “how” of HIT implementation in dentistry. By doing so, participants will become “educated consumers” of HIT and will be able to optimize how HIT contributes to achieving their goals. So, how do we plan to do that? At the end of the course, participants will be able to:

1. use information systems for managing dental data and supporting clinical decision making in the context of the dental care and office workflow

2. apply principles of technology evaluation to identify and select appropriate information technology products and services to achieve specific goals

3. plan, administer and manage information technology implementations in dentistry

 

A world-class team of instructors will teach the course. Dr. Titus Schleyer is an internationally renowned expert on dental informatics and the application of computer technology in dentistry. Dr. Thankam Thyvalikakath has conducted groundbreaking research on functions and usability of electronic dental records (EDR). Dr. Heiko Spallek is an expert on dental Internet applications, and information retrieval and evaluation. Last, Dr. Richard Oldham and Corey Stein are two Masters students in our dental informatics graduate program. Together, we look forward to taking you on an exciting tour of health information technology applications for dentistry!

 

 

 

Course Director

Dr. Titus Schleyer:

Titus Schleyer, DMD, PhD

Assoc. Professor and Director, Center for Dental Informatics

School of Dental Medicine, University of Pittsburgh, 3501 Terrace Street, Pittsburgh, PA 15261

Skype: titus.schleyer, Ph: (412) 648-8886, Fax: (412) 648-9960, E-mail: titus@pitt.edu

 

Profile: http://digitalvita.pitt.edu/dvprofiles/titus

Web: http://di.dental.pitt.edu/

Twitter: http://twitter.com/titusschleyer

Facebook: http://www.facebook.com/titusschleyer

Scribd: http://scribd.com/titusschleyer

 

Other Course Faculty

Dr. Thankam Thyvalikakath

Thankam P. Thyvalikakath, DMD, MDS, MS

Assistant Professor, Center for Dental Informatics

334 Salk Hall, School of Dental Medicine

University of Pittsburgh

3501 Terrace Street, Pittsburgh, PA 15261

Ph: (412) 648-9196, Fax: (412) 648-9960

E-mail: tpt1@pitt.edu

 

Dr. Heiko Spallek

Heiko Spallek, DMD, PhD, MSBA

Associate Dean, Office of Faculty Development and Information Management

Associate Professor, Dental Public Health, Center for Dental Informatics

School of Dental Medicine, University of Pittsburgh, 3501 Terrace Street, Pittsburgh, PA 15261

Skype: hspallek, Ph: (412) 648-8886, Fax: (412) 648-9960

E-mail: hspallek@pitt.edu

http://www.dental.pitt.edu/

Profile: http://researchgateway.ctsi.pitt.edu/dvprofiles/hspallek

 

Dr. Richard Oldham

Richard A. Oldham, DDS

Graduate Student, Department of Biomedical Informatics

University of Pittsburgh, 5607 Baum Blvd, Pittsburgh, PA 15232

Skype: dick.oldham, Ph: (412) 642-8942

E-mail: rio6@pitt.edu

 

Mr. Corey Stein

Corey Stein, BSc

Graduate Student, Center for Dental Informatics

University of Pittsburgh, 5607 Baum Blvd, Pittsburgh, PA 15232

Ph: (412) 624-8902

E-mail: cds51@pitt.edu

Online Courses: 

BIOINF 2011: Foundations of Health Informatics (3 credits)

A key goal of health informatics is to represent biomedical data and knowledge in computable form and thus enable the storage, retrieval, analysis and interpretation of biomedical data using computers. This course is designed to provide the foundational concepts in health informatics. The course will cover topics including biomedical data, symbolic biomedical knowledge, probabilistic biomedical knowledge, clinical decision support, electronic health records and biomedical terminologies. Read More>>

BIOINF 2024: Project Design and Data Analysis (3 credits)

This course will introduce students to the planning, execution and evaluation of a project that involves data collection and analysis. It will cover basic concepts in data acquisition, experimental methodology and statistical evaluation. Students will also be required to pick a topic upon which they will build the experimental design and analysis. In this hands-on course they will learn how to plan and develop such a project, and how to evaluate the data in order to determine whether the goals of the project have been met. These experimental design tasks will be shared online and a facilitated discussion will mirror the regular classroom discussion.

BIOINF 2110: Software Project Engineering Concepts in Health Care (3 credits)

This course will examine how health care organizations manage the software development process. This course will study how technology, people, and economics of software projects interact and the impact these elements have on managing software projects.  Software development projects underway at the University of Pittsburgh will serve as the primary case studies for practical examples of system management and operation. The student will have the opportunity to learn about industry data standards, and the changing landscape of electronic medical record (EMR) deployment in the era of Accountable Care Organizations and Meaningful Use. The course is designed to prepare students for a software project leadership role to build quality software. Read More>>

BIOINF 2117: Applied Medical Informatics (3 credits)

This course is designed to provide an overview of the field of Applied Medical Informatics. Students will learn about the myriad issues that arise when deploying information technology into clinical environments. Practical, real world solutions to the challenges of Healthcare IT will be addressed by experts involved in the day-to-day operations of these types of systems and help prepare the student for applied informatics roles. Online teaching will follow a constructivist approach requiring students to develop a concept map of a large-scale deployment task using a wiki. Read More>>

BIOINF 2122: Critical Reflections on Biomedical Informatics (3 credits)

This course will showcase presentations from DBMI researchers and invited speakers from across campus and beyond. Session will be videotaped and presented as weekly one-hour recording. The on-site Q/A session afterwards will be substituted by a facilitated asynchronous online discussion in Blackboard. Special emphasis will be put on peer- and self-assessment of the contributions to the online discussion which will promote higher-level thinking among the students. Read More>>

BIOINF 2123: Terminology and Coding (3 credits)

This course will cover standardized terminologies (including ontologies) and classification systems with an emphasis on how they relate to the information infrastructure that supports health care delivery and biomedical research. Consideration will be given to both theoretical and practical issues in terminologies and classification systems as well as demonstration of the application of these methods within biomedical informatics systems. A variety of vocabularies, terminologies, standards, and classification systems will be studied including: SNOMED, MeSH, ICD, HL7, the Foundational Model of Anatomy and the Gene Ontology. Later in the course, we will explore the relationship of terminologies, information models, messages, and document and record structures. Selected advanced topics such as semantic interoperability will also be addressed. Online students will engage in authentic learning assignments ranging from the creation of concept maps to the participation in asynchronous panel discussions which will assign students to teams with opposing viewpoints.  

BIOINF 2124: Principles of Global Health Informatics

While health information systems continue to improve the efficacy of healthcare delivery and public health interventions in the West, they have yet to be widely used in the developing world, where most of the global disease burden exists. If appropriately introduced, such systems could have the same effect in a developing-world setting. However, transplanting systems developed in and for the West may be a poor fit based on cultural, geographic and socio-economic differences.

This course explores challenges and opportunities in developing and supporting health information systems in developing-world settings by examining differences, and ways to both integrate and sustain systems in an appropriate way in low-resource settings. The course will review the current "state-of-the-art" in this field by looking at examples of systems currently deployed in the developing world, and explore opportunities for advancing this work through a series of case studies and hands-on exercises based on real-world scenarios. Read More>>

BIOINF 2127: Fundamentals of Pathology Informatics (3 credits)

Fundamentals of Pathology Informatics is an intensive introduction to informatics and information technology concepts as they apply to pathology. The course is intended both for those with a limited background in informatics as well as those wishing to enhance their foundation and understanding of core pathology informatics principles.  Participants will learn basic concepts upon which they can build a better understanding of the role for informatics in pathology. The topics covered include basic computing for pathology, databases in pathology, laboratory information systems  (overview, implementation & operations), Pathology workstations, Lab automation, Regulations, Coding, digital imaging including whole slide imaging and electronic medical record from a lab perspective. The overall goal of this course will be for the participant to gain an understanding of how multiple information systems work together to support the delivery of healthcare as it relates to pathology.

BIOINF 2128: Advanced Topics in Pathology Informatics (3 credits)

Pathology informatics has become critical to help pathology laboratories meet current and future challenges. Some of these challenges include providing synoptic reporting, patient safety, subspecialty centralization, and personalized medicine. Many of these challenges can be met by leveraging existing and advancing technologies, such as specimen tracking and telepathology. However, without current standards and easy guidelines to follow the selection, implementation and actual use of these technologies in the laboratory today can be overwhelming. This course will provide the participants with current information on advanced topics in pathology informatics including laboratory information selection, middleware, specialty lab systems, patient and specimen identification systems, synoptic reporting, whole slide imaging, telepathology, image analysis, tissue banking and error reduction and quality management.

BIOINF 2129: Summer Internship in Global Health Informatics

The Summer Internship in Global Health Informatics will be expanded to accommodate 5 students from the US. Students will travel to Malawi to study Global Health Informatics in low-resource settings alongside Malawian health and technology professionals. Students will have an opportunity to propose, design and develop a product or intervention relevant to solving a particular problem the group has identified. Read More>>

BIOINF 2202: Dental Informatics Seminar (3 credits)

This course has three objectives: (1) provide an introduction to current and/or seminal dental informatics, as well as selected dental and craniofacial, research topics; expose students to selected biomedical informatics research methods; and introduce students to the process of conducting research studies and publishing them. At the end of the course, participants will be able to recognize, identify and formulate research questions for dental informatics; select appropriate research methodologies for specific projects; and critique research questions and methods. In addition, participants will have basic knowledge about the logistics of conducting research projects, presenting results, and preparing research papers and reports. Scholarly discussions will follow each session which will be evaluated using an online discussion rubric. The instructors have significant experience in online teaching through various dental continuing education courses taught online.

BIOINF 2204: Introduction to Health Information Technology in Dentistry (3 credits)

An introduction to health information technology (HIT) for dentists, dental team members and others involved in dentistry with three objectives: (1) understand how HIT can support the activities and processes of clinical dental care; (2) select and evaluate HIT applications; and (3) plan, administer and manage HIT implementations. Course covers topics such as dental care workflow and analysis; electronic dental records; dental data and their representation; controlled vocabularies and terminologies; human computer interaction; information design; computer-based decision support; strategic planning; requirements analysis; evaluating technology; managing human resources for IT; planning and implementing HIT; basics of hardware and software; and privacy, confidentiality and security of patient information. Read More>>

All Courses: 

BIOINF 2011

Foundations of Clinical and Public Health Informatics (3 credits)

A survey of fundamental concepts and activities on information technology applied to health care. Topics include computer-based medical records, knowledge-based systems, telehealth, decision theory and decision support, human-computer interfaces, systems integration, the digital library, bioinformatics, and educational applications. Department-specific applications such as pathology, radiology, psychiatry and intensive care are also discussed.

Instructor: Rich Tsui, Ph.D.

Days/Times: Mondays and Wednesdays, 10:00 a.m. to 11:25 p.m.

Location: 407A BAUM, 5607 Baum Blvd.

Prerequisites: None

Recitations: None

Expected class size: 20-25

This course is usually offered in the fall term.

Available Online- Spring 2013

 

BIOINF 2012

Problem-Oriented Programming(ISSP 2062) (3 credits)

This course is designed to extend students' programming abilities through review of current program design and coding techniques, including fourth-generation languages, the Unified Modeling Language (UML), Object-oriented Programming and Extreme Programming. The course includes a strong practical programming component based on the Python language that includes in-class laboratories, weekly practical programming problems, and midterm and final programming projects. Programming assignments are drawn from areas relevant to medical informatics such as structured text and image processing, network communications, database management, natural language processing, expert systems, etc. Through the course, students learn to understand the programming process at a practical level and gain the ability to independently create useful software tools.

Instructor: Roger Day, Sc.D.

Days/Times: Tuesdays and Thursdays, 9:00 a.m. to 10:25 a.m.

Location: 407A BAUM, 5607 Baum Blvd.

Prerequisites: One course in introductory programming, or equivalent experience.

Recitations: None

Expected class size:8-16

This course is usually offered in the fall term.

 

BIOINF 2013

Introduction to Patient Care and Clinical Environments(3 credits; optional for U.S. trained clinicians)

This three credit course is designed for students who have no significant clinical experience with the U.S. healthcare system. The course is divided into two main sections. In the first section, we will cover medical and health care concepts and terms, and discuss observational techniques derived from the Toyota Production System. In the second section of the course, students will shadow physicians in a variety of clinical settingsand report back to the class on their observations using the skills learned in the first half of the course. No previous clinical experience is assumed. Students will be expected to attend lectures and will spend a significant portion of their time observing and reporting on different clinical settings throughout the semester.

Instructor: Steven Handler, M.D.

Days/Times: Thursdays from 1:00 p.m.to 4:00 p.m.

Location: 407A BAUM, 5607 Baum Blvd. and various clinical areas

Prerequisites: None

Recitations: None

Expected class size: 10-12

This course is offered in the fall term.

 

BIOINF 2014

Biomedical Informatics Project Course(3 credits)

This course provides an opportunity for students to apply concepts that they learned in BIOINF 2011 to carry out a one-term research project. They will be asked to identify, plan, develop, carry out, and report on such a project. This hands-on course will encourage students to think more deeply and concretely about the concepts and methods presented in BIOINF 2011 and in doing so to develop a better understanding of that material. This course will also serve as an early, mentored introduction to performing biomedical informatics research.

Instructor: Gregory F. Cooper, M.D.

Days/Times: Tuesdays and Thursdays from 1:00 p.m. to 2:30 p.m.

Location: 407A BAUM, 5607 Baum Blvd.

Prerequisites: BIOINF 2011 – Introduction to Biomedical Informatics.

Recitations: None

This course is offered during the spring term.

 

BIOINF 2015

Mathematics for Biomedical Informatics (3 credits)

The purpose of this class is to review mathematical techniques that underly biomedical informatics. Knowledge of these mathematical subjects will be assumed in many subsequent biomedical informatics courses (e.g. statistics and machine learning). The course is will emphasize conceptual understanding and applications rather than formal proofs. Each mathematical subject will be illustrated with problems from within biomedical informatics.

Instructor: Claudia Mello-Thoms, Ph.D.

Days/Times: Tuesday and Thursdays, 1:00 p.m. to 2:25 p.m.

Location: M407A BAUM, 5607 Baum Blvd.

Prerequisites: None

Recitations: None

Expected class size:10-16

This course is usually offered in the fall term.

 

BIOINF 2016

Foundations of Translational Bioinformatics (3 credits)

The course goals are to gain familiarity of data produced with current biotechnologies, such as DNA arrays (e.g. SNP data), microarrays (transcriptional profiles), proteomics (mass spectrometry data), epigenomics (methylation profiles). Understand what can be done with such data to infer relations between genome, epigenome, and phenome, in order to discover molecular mechanisms of diseases, or identify biomarkers, or discover novel therapies for diseases.

Instructor: Xinghua Lu, M.D., Ph.D. and Madhavi Ganapathiraju, Ph.D.

Days/Times: Wednesdays, 9:00 a.m. to 11:55 a.m.

Location: 407A BAUM, 5607 Baum Blvd.

Prerequisites: None

Recitations: None

Expected class size:10-16

This course is usually offered in the spring term every even year.

 

BIOINF 2017

Clinical Research Informatics (3 credits)

This course is an introduction to the emerging field of clinical research informatics (CRI). It involves informatics solutions in support of scientists who conduct clinical research, as well as those who translate evidence in biological, clinical, and epidemiological studies. CRI endeavors to improve clinical research information systems, recruitment of participants to clinical trials, mining of electronic health records for comparative effectiveness research, scientific collaboration, data sharing, re-analysis of extant data, patient registries, results databases, optimization of research workflows, semantic harmonization, and more.

Instructor: Rebecca Crowley, M.D., M.S. and Tanja Bekhuis, Ph.D.

Term:  Spring

Days/Times: Monday /Wednesdays, 10:00 a.m. to 11:25 a.m.

Location: 407A BAUM, 5607 Baum Blvd.

Expected class size: 10-16

 

Biomedical Informatics Colloquium (Lecture Series)(This is not a formal course.)

This course meets once each week for one hour. The current research of Biomedical Informatics faculty and senior fellows will be presented.

Instructor: Various speakers

Days/Times: Fridays, 11:00 a.m. to 12:00 noon

Location: 407A BAUM, 5607 Baum Blvd.Prerequisites: None

Recitations: None

Expected class size: 35

This course is offered in both fall and spring terms.

 

BIOINF 2032

Biomedical Informatics Journal Club(ISSP 2083) (1 credit)

Biomedical informatics is a broad field encompassing many different research domains. What all of the domains have in common is the need to review and publish scientific papers and to give talks that present research to different audiences. The aim of this journal club is to expose students to recent research in various topics of biomedical informatics and to teach students how to critique a research article, present research from a research study; and critique a verbal presentation of research.

Instructor: Richard Boyce, Ph.D.

Days/Times: Fridays, 10:00 a.m. to 11:00 a.m.

Location: 536B BAUM, 5607 Baum Blvd.

Prerequisites: None

Recitations: None

Expected class size: 35

This course is offered in the spring term.

 

BIOINF 2051

Foundations of Bioinformatics (ISSP 2081) (3 credits)

Provides an introduction to selected topics of bioinformatics also known as computational biology. In this course, the difficult computational problems involving different types of biological information are identified using case studies from current literature. Emphasis is on genomic aspects of computational biology with some overview of proteomics and structural aspects. The course is structured as a seminar course intending to draw students into participating in discussions related to both problems and existing solutions.

Instructor: Vanathi Gopalakrishnan, Ph.D.

Days/Times: Mondays and Wednesdays 12:30 p.m. to 2:00 p.m.

Location: 407A BAUM, 5607 Baum Blvd.

Prerequisites: An introductory biology course and an undergraduate mathematics course.

Recitations: none

Expected class size: 10

This course is offered in the fall term.

 

BIOINF 2052

Introduction to Computational Structural Biology(CMPBIO 2030 / MSBIO 2030) (3 credits)

This course is a general introduction to current theories and methods used in computational structural biology. Fundamental concepts of probability, statistics, statistical thermodynamics and polymer physics will be considered as well as a general description of our current knowledge of biomolecular structure and dynamics for modeling and simulations of biological interactions and function. The Protein Data Bank and software commonly used in computational structural biology will be used for modeling and simulations of structure and dynamics.

Instructor: Ivet Bahar, Ph.D.

Days/Times: Tuesdays and Thursdays, 9:30 a.m. to 10:45 a.m.

Location: BST-3, Room 3073

Prerequisites: An introductory biology course and an undergraduate mathematics course.

Recitations: none

Expected class size: 15

This course is offered in the spring term, every odd year.

 

BIOINF 2054

Statistical Foundations for Bioinformatics Data Mining (BIOST 2018) (3 credits)

This course introduces data analysis methods which are widely used or rapidly gaining use in bioinformatics. Methods deal with prediction, classification, optimization, and clustering. Methods covered include classification trees, flexible varieties of discriminant analysis including support vector machines, EM algorithm and Monte Carlo Markov chain, the bootstrap and bagging, boosting, and self-organizing maps. The methods are placed into the context of principles and models of statistical science, with emphasis on Bayesian methods. Examples are drawn from microarrays, analysis of genetic networks, proteomics, computational pharmacology, and research text mining.

Instructor: Roger S. Day, Sc.D.

Days/Times: Wednesdays and Fridays, 3:00 to 4:30 p.m.

Location: 407A BAUM, 5607 Baum Blvd.

Prerequisites: An introductory statistics/biostatistics course.

Recitations: none

Expected class size: 6-10

This course is offered in the spring term, every odd year. Special permission from instructor is required for this course.

 

BIOINF 2057

Elements of Statistical Learning (BIOST 2015) (3 credits)

The purpose of the course is to present the theory and practice of statistical learning algorithms, placing “statistical learning” or “data mining” techniques in the proper context with regard to their origins in simple classical methods like linear regression, to clarify the strengths and weaknesses from theoretical and practical sides. “Supervised learning” techniques studied include using regularization and Bayesian methods, kernel methods, basis function methods, neural networks, support vector machines, additive trees, boosting, bootstrap-based methods. Unsupervised learning techniques studied include cluster analysis, self-organizing maps, independent component analysis and projection pursuit.

Instructor: Roger S. Day, Sc.D.

Days/Times: Wednesdays and Fridays, 3:00 p.m. to 4:25 p.m.

Location: TBA

Prerequisites: BIOST 2041, 2042, 2043, 2044 or permission of the instructor

Recitations: none

Expected class size: 6-10

This course is offered in the spring term, every even year.

 

BIOINF 2058

Bayesian & Empirical Bayes Computational Methods (BIOST 2064) (3 credits)

This course provides the students with an understanding of both the theory and practice with regard to the EM algorithm, Markov-chain, sampling techniques, importance sampling, and the solution of decision trees. Students gain hands-on experience programming with S-Plus.

Instructor: Roger S. Day, Sc.D.

Days/Times: Tuesdays and Thursdays, 11:30 a.m. to 12:55 p.m.

Location: TBA

Prerequisites: BIOST 2063

Recitations: none

Expected class size: 6-10

This course is offered in the fall term, every even year.

 

BIOINF 2059

Bayesian & Empirical Bayes Statistical Methods (BIOST 2063) (3 credits)

The theoretical foundations of Bayesian and empirical Bayes statistical methods will be presented. The use of these methods in data analysis will be illustrated with specific examples and with discussions of common data analysis issues contrasts and similarities between Bayesian, empirical Bayesian, and classical methods will be evaluated.

Instructor: Roger S. Day, Sc.D.

Days/Times: Tuesdays and Thursdays, 11:30 a.m. – 12:55 p.m.

Location: TBA

Prerequisites: BIOST 2042, BIOST 2044

Recitations: none

Expected class size: 6-10

This course is offered in the fall term, every odd year.

 

BIOINF 2060

Computational Genomics (MSCBIO 2070) (3 credits)

In this course, we will discuss classical approaches and latest methodological advances in the context of the following biological problems: 1) Computational genomics, focusing on gene finding, motif detection and sequence evolution. 2) Analysis of high throughput biological data, such as gene expression data,

focusing on issues ranging from data acquisition to pattern recognition and classification. 3)Molecular

and regulatory evolution, focusing on phylogenetic inference and regulatory network evolution, and 4)

Systems biology, concerning how to combine sequence, expression and other biological data sources to

infer the structure and function of different systems in the cell. From the computational side this course

focuses on modem machine learning methodologies for computational problems in molecular biology

and genetics, including probabilistic modeling, inference and learning algorithms, pattern recognition,

data integration, time series analysis, active learning, etc.

Instructor: Ziv Bar-Joseph and Takis Benos

Days/Times: TBA

Location: TBA

Prerequisites: Students are expected to have successfully completed Machine Learning, or an equivalent class

Recitations: None

Expected class size: 35

This course is offered in the spring term.

 

BIOINF 2101

Probabilistic Methods for Computer-Based Decision Support (ISSP 2070) (3 credits)

This course is now being offered as a graduate-student seminar. It covers more advanced computational approaches for probabilistic modeling and inference than the previous version of the course. A particular focus is placed on Bayesian networks, although other probabilistic models are studied. Healthcare applications are emphasized, however, the principles are general and no medical knowledge is needed to take the seminar.

Instructor: Gregory F. Cooper, M.D., Ph.D.

Term: Fall term 2010

Days/Times: Tuesdays and Thursdays, 2:30 to 4:00 p.m.

Location: 407A BAUM, 5607 Baum Blvd.

Prerequisites: Students should have either taken Introduction to Health Informatics (BIOINF 2011) or have a basic understanding of probability theory and Bayesian networks.

Recitations: None

Expected class size: 10

This course is usually offered in the fall term, every even year.

 

BIOINF 2110

Concepts of Software Project Engineering in Health Care (3 credits)

This course examines how health care organization implement both clinical and financial information systems. The course will study the implementation process and how to integrate systems to create the computerized patient record (CPR). Students will also have the opportunity to learn about the industry-wide implementation data standards and how to manage them.

Instructor: Melissa Saul, M.S.

Days/Times: Mondays and Wednesdays, 5:00-7:55 p.m.

Location: 6048 Forbes Tower.

Prerequisites: No prerequisites.

Recitations: none

Expected class size: 30

This course is offered in the summer term. Special permission from instructor is required for this course. (e-mail mis18@pitt.edu, obtain permission)

Available Online- Spring 2013

 

BIOINF 2111

Cognitive Studies for Health Informatics(3 credits)

This course is intended to serve as an intensive introduction to Human Information Processing and a survey of its applications to Health Care Informatics. The first four weeks present an overview of the basic architecture of the human information processing system. For each of the last twelve weeks of the course, we alternate classes concentrating on underlying basic cognitive science issues and principles, with classes focusing on how these principles and issues apply in medical informatics domains, such as medical decision support, design of information systems, and computer-based education for health professionals. Students will learn and apply methods for studying cognitive tasks, such as verbal protocol analysis and cognitive modeling.

Instructor: Claudia Mello-Thoms, Ph.D.

Days/Times: Mondays, 9:00-12:00 noon

Location: 407A BAUM, 5607 Baum Blvd.

Prerequisites: No prerequisites.

Recitations: none

Expected class size: 10-12

This course is offered in the fall term, every odd year.

 

BIOINF 2113

Realtime Outbreak and Disease Surveillance(3 credits)

Many countries are constructing real-time public health surveillance systems. This work--which is proceeding in an accelerated manner due to the threats of emerging diseases, bioterrorism, and common infectious diseases--can benefit greatly from the expertise of the medical informatics community.

This course on the theory and practice of outbreak detection will present up-to-the minute information about the theory and practice of real-time public health surveillance. This course will cover key topics ranging from the network level to the application level to the organizational level. Specific topics will include functional requirements (e.g., for data, for analysis, for performance), terminology standards, data models, and messaging standards. We will cover algorithms for the automatic detection of epidemics including natural language processing techniques with an emphasis on methods for validation. The experience gained from field deployments of real-time detection systems in Utah, Ohio, Taiwan, New Jersey, Georgia, the Commonwealth of Pennsylvania and other locations will be presented. There will be demonstrations of a surveillance system in operation.

Instructor: Michael M. Wagner, M.D., Ph.D.

Days/Times: Tuesdays and Thursdays, 3:30 to 5:00 p.m.

Location: 407A BAUM, 5607 Baum Blvd.

Prerequisites: Introductory statistics course. The course can be followed by anyone with medical, medical informatics, or public health background. Ideally, the student will already understand the basic concepts of ROC curve analysis, sensitivity, specificity, positive predictive value, statistical significance testing.

Recitations: none

Expected class size: 12-25

This course is offered in the spring term, every even year.

  

BIOINF 2117

Applied Medical Informatics(2 credits)

This course is designed to provide an overview of the field of Applied Medical Informatics. Students will learn about the myriad issues that arise when deploying information technology into clinical environments. Various clinical, social, organizational, legal, and technical challenges make deployment a challenge. Learning how others have addressed these challenges will equip the student for applied informatics roles.

Instructor: TBA

Days/Times: Tuesdays, 1:00 p.m. to 3:00 p.m.

Location: 536B BAUM, 5607 Baum Blvd.

Prerequisites: There are no prerequisites.

Recitations: none

Expected class size: 10-15

This course will be offered in the spring term, every odd year.

Available Online- Fall 2013

 

BIOINF 2118

Statistical Foundations of Biomedical Informatics(3 credits)

This is an introductory probability and statistics course intended primarily for biomedical informatics students. The first part of the course covers probability, including basic probability, random variables, univariate and multivariate distributions, transformations, expectation, numerical integration, and approximations. The second part of the course covers statistics, including study design, classical parametric inference, hypothesis testing, Bayesian inference, non-parametric methods, classification, ANOVA, and regression. We will use R for statistical computing and applications. Examples and applications will focus on biomedical informatics and related discipline.

Instructor: Roger Day, Sc.D.

Days/Times: Tuesdays/Thursdays, 2:30 p.m. to 4:00 p.m.

Location: 407B BAUM, 5607 Baum Blvd.

Prerequisites: There are no prerequisites.

Recitations: none

Expected class size: 10-15

This course will be offered in the spring term.

 

BIOINF 2119

Probabilistic Methods in Artificial Intelligence (3 credits)

This course is designed for students who do not necessarily have a background in computer science and want to learn and apply methods in artificial intelligence to problems in biomedicine. The course will introduce and provide the foundations artificial intelligence methods in search, probabilistic knowledge representation and reasoning, and machine learning with applications to biomedical informatics. Prerequisites for this course include introductory mathematics and programming.

Instructor:Shyam Visweswaran, MD, PhD plus guest lecturers

Days/Times:Monday/Wednesday from 12:30 p.m. -2:00 p.m.

Location:407A BAUM, 5607 Baum Blvd.

Prerequisites: There are no prerequisites.

Recitations: none

Expected class size: 15-20

This course will be offered in the spring term.

 

BIOINF 2120

Symbolic Methods in Artificial Intelligence(3 credits)

This course is designed for students who do not necessarily have a background in computer science and want to learn and apply methods in artificial intelligence to problems in biomedicine. The course will introduce and provide the foundations of artificial intelligence methods in logical knowledge representation and reasoning, biomedical ontologies and terminologies and information retrieval. Prerequisites for this course include introductory mathematics and programming.

Instructor:Rebecca Crowley, MD, plus guest lecturers

Days/Times:Tuesday/Thursdays 9:00 a.m.-10:30 a.m.

Location:407B BAUM, 5607 Baum Blvd.

Prerequisites: BIOINF 2119

Recitations: none

Expected class size: 15-20

This course will be offered in the fall term.

 

BIOINF 2121

Human Computer Interaction and Evaluation Methods(3 credits)

This course is designed to provide informatics students with the knowledge necessary to take an applied role in the design, implementation and evaluation of healthcare information systems.In this course, students will apply principles of usability and evaluation theory to informatics projects. Topics include: critical success factors, test plan development and user interface design.

Instructor:Harry Hochheiser, PhD

Days/Times:Monday and Wednesday from 2:00-3:25 p.m.

Location:407B BAUM, 5607 Baum Blvd.

Prerequisites: There are no prerequisites.

Recitations: none

Expected class size: 15-20

This course will be offered in the fall term.

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BIOINF 2122

Critical Reflections on Biomedical Informatics(3 credits)

This course will showcase presentation from DBMI researchers and invited speakers from across the campus and beyond. Sessions will be videotaped and presented as weekly one-hour recording. The on-site question and answer session afterwards will be substituted by a facilitated asynchronous online discussion in Blackboard.

Instructor:Various Speakers

Days/Times:TBA

Location:TBA

Prerequisites: There are no prerequisites.

Recitations: none

Expected class size: 15-20

Under development for Fall term 2012 for the on-line certificate program.

 

BIOINF 2123

Terminology and Coding(3 credits)

Under development for Fall term 2011 for the on-line certificate program.

Instructor:TBA

Days/Times:TBA

Location:TBA

Prerequisites: There are no prerequisites.

Recitations: none

Expected class size: 15-20

Under development for Fall term 2012 for the on-line certificate program.

 

BIOINF 2124

Principles of Global Health Informatics(3 credits)

This course explores challenges and opportunities in developing and supporting health information systems in developing-world settings by examining differences, and ways to both integrate and sustain systems in an appropriate way in low-resource settings. The course will review the current "state-of-the-art" in this field by looking at examples of systems currently deployed in the developing world, and explore opportunities for advancing this work through a series of case studies and hands-on exercises based on real-world scenarios.

Instructor:Gerald Douglas, PhD

Days/Times:Monday/Wednesday from 2:30 p.m.-4:00 p.m.

Location:407A BAUM, 5607 Baum Blvd.

Prerequisites: BIOINF 2011 or permission from instructor

Recitations: none

Expected class size: 15-20

This course will be offered in the spring term, every even year.

Available Online- Summer 2013

 

BIOINF 2125

Informatics and Industry(1 credits)

This class will be held once a week, for 1 hour. The focus of the class is to provide an opportunity for students to interact with leading industry representatives and to learn techniques/tools that would enable them to market their skills in non-academic environments. We will invite speakers from various local, regional, national, and international industry relationships that we have established

Instructor:Steven Handler, M.D., Ph.D. and Richard Boyce, Ph.D.

Days/Times:Thursdays, 12:00 p.m. to 12:55 p.m.

Location:407A BAUM, 5607 Baum Blvd.

Prerequisites: BIOINF 2011

Recitations: none

Expected class size: 15-20

This course will be offered in the spring every even year.

 

BIOINF 2126

Clinical Research Informatics(3 credits)

Under development.

Instructor:Rebecca Crowley, M.D., M.S.

Days/Times:TBA

Location:TBA

Prerequisites: BIOINF 2011

Recitations: none

Expected class size: 15-20

This course will be offered in the spring term.

 

BIOINF 2127

Dental Informatics(3 credits)

Under development for Fall term 2011 for the on-line certificate program.

Instructor:TBA

Days/Times:TBA

Location:TBA

Prerequisites: There are no prerequisites.

Recitations: none

Expected class size: 15-20

Under development for Fall term 2012 for the on-line certificate program.

 

BIOINF 2128

Pathology Informatics(3 credits)

Under development for Fall term 2011 for the on-line certificate program.

Instructor:TBA

Days/Times:TBA

Location:TBA

Prerequisites: There are no prerequisites.

Recitations: none

Expected class size: 15-20

Under development for Fall term 2012 for the on-line certificate program.

 

BIOINF 2131

Practicum in Advanced Biomedical Information Technology(ISSP 2090) (1-6 credits)

This course is designed for people who want a practical experience in working with advanced information technology in the Department of Biomedical Informatics.

Instructor: Department of Biomedical Informatics Faculty and Staff

Days/Times: TBA

Location: 407A BAUM, 5607 Baum Blvd.

Prerequisites: Discuss with Instructor

Recitations:None

Expected class size: 20

This course could be offered in any given term -- check with Toni Porterfield (tls18@pitt.edu).

 

BIOINF 2132

Special Topic Seminar in Medical Informatics(3 credits)

This course is designed for faculty to offer small groups of students a study course on a topic of mutual interest and concern in the faculty member’s area of expertise.

Instructor: Department of Biomedical Informatics Faculty (will vary)

Days/Times: TBA

Location: 407A BAUM, 5607 Baum Blvd.

Prerequisites: Discuss with Instructor

Recitations:None

Expected class size: 20

This course could be offered in any given term -- check with Toni Porterfield (tls18@pitt.edu).

 

BIOINF 2133

Practicum in Advanced Infectious Disease and Public Health Surveillance (Biosurveillance) Technology(1-6 credits)

This course is designed for people who want a practical experience in working with advanced biosurveillance technology in the realtime outbreak and disease surveillance (RODS) laboratory.

Instructor: Department of Biomedical Informatics Faculty (will vary)

Days/Times: TBA

Location: 407A BAUM, 5607 Baum Blvd.

Prerequisites: Discuss with Instructor

Recitations:None

Expected class size: 20

This course could be offered in any given term – check with Toni Porterfield (tls18@pitt.edu).

 

BIOINF 2134

Publication & Presentation in Biomedical Informatics(3 credits)

This course provides a practical overview of how to write a research manuscript and how to give a scientific talk. It is usually taken after completing the Project Course (BIOINF 2014). Students taking this course must have a completed research project that can be used to complete the course exercises. Each week, we will target a specific section of the manuscript or scientific talk. Didactic sessions describing common problems and approaches will alternate with student presentation and peer critique. The course also covers the details of the publication process. At the end of the course, a special presentation workshop gives students the opportunity to improve their talks using videotaping and debriefing methods. By the end of the course, students will have completed a research paper and a finalized colloquium presentation.

Instructor: Rebecca Crowley, MD, MS

Days/Times:Mondays from 12:00 noon – 2:55 p.m.

Location: 407B BAUM, 5607 Baum Blvd.

Prerequisite: Completed data collection for study in research project with approval of both research advisor and course instructor.

Recitations: None

Expected Class Size: 5

This course will be offered during the fall term.

 

BIOINF 2200

Introduction to Dental Informatics Research(3 credits)

This course is intended to provide trainees with a rich practical experience in conceptualizing, formulating, conducting and publishing short-term (3-6 months) research projects in dental informatics. Practical experience with research projects is a crucial component of the dental informatics training program. In this course, students will begin by identifying ideas for short term research projects in cooperation with the course faculty. The group will then jointly formulate the research question(s) to be addressed and conduct a thorough review of the literature. It will then develop the research methodology using state-of-the-art methodological approaches. Students will also prepare the submission of the research protocol to the Institutional Review Board if required. As appropriate, students will participate in the actual conduct of the research project itself, as well as in the analysis and publication of the results. Through this course, we expect trainees to develop several ideas for their Master's Thesis or other research projects.

Instructor: Titus K.L. Schleyer, D.M.D., Ph.D., Heiko Spallek, Ph.D. and Thankam Thyvalikakath, M.D.S, M.S.

Days/Times: TBA

Location: Salk Hall

Prerequisites: None

Recitations:None

Expected class size: 2-4

This course is offered in fall or spring term (as per instructor decision).

 

BIOINF 2201

Dental Information Systems Infrastructures (3 credits)

Graduates of dental informatics programs often are asked to develop, establish or direct organizational units to support information technology and/or informatics. Most dental schools do not have informatics departments and/or faculty. Thus, dental informaticians are faced with numerous challenges in establishing an organizational presence. Often, they are asked to set up and/or direct support for the computing infrastructure, teach dental informatics courses, and engage in research. As IT implementations grow in scale (e.g. the number of users they support) and scope (e.g. the number of different applications used), managing the infrastructure presents a significant challenge. This course is designed to equip students with the basic skills necessary to meet those challenges. The course also covers several other topics necessary for survival in a new academic discipline.

Instructor: Titus K.L. Schleyer, D.M.D., Ph.D., Heiko Spallek, Ph.D. and Thankam Thyvalikakath, M.D.S, M.S.

Days/Times: TBA

Location: Salk Hall

Prerequisites: None

Recitations:None

Expected class size: 2-4

This course is offered in fall or spring term (as per instructor decision).

 

BIOINF 2202

Dental Informatics Seminar(3 credits)

This course has two primary objectives. The first one is to expose participants to current research questions and issues in dental informatics. To that end, the course will review several different dental informatics research projects in-depth, and also provide an opportunity to explore research questions that should be addressed in the future. The second objective is to prepare participants for teaching in informatics and information technology, both at the predoctoral and continuing education level. The course focuses on providing the concepts and methods for teaching these subjects, rather than developing participants into full-fledged content experts. Participants will begin with conceiving an informatics course, continue to the development of a full course proposal, and explore implementation and evaluation issues.

Instructor: Titus K.L. Schleyer, D.M.D., Ph.D., Heiko Spallek, Ph.D. and Thankam Thyvalikakath, M.D.S, M.S.

Days/Times: TBA

Location: Salk Hall

Prerequisites: None

Recitations:None

Expected class size: 2-4

This course is offered in fall or spring term.

 

BIOINF 2203

Dental Informatics Masters Thesis Research (3 credits)

Dental informatics trainees will be expected to register for this mentored research experience with dental informatics faculty while they are working on their research project/thesis. This course emphasizes interdisciplinary projects that integrate several domains. Research topics may include information needs and retrieval, decision support, intelligent agents, computer-based patient records and educational applications. Special emphasis is placed on applying informatics research methods to ongoing research projects at the School of Dental Medicine.

 

BIOINF 2480 (1-6 credits)

Masters Thesis/Project Research

BIOINF 2990 (1-14 credits)

Masters Independent Study

BIOINF 2993 (1-9 credits)

Masters Directed Study

BIOINF 3990 (1-14 credits)

Doctoral Independent Study

BIOINF 3995 (1-9 credits)

Doctoral Directed Study

BIOINF 3998 (3 credits)

Doctoral Teaching Practicum

BIOINF 3999 (1-9 credits)

Doctoral Dissertation Research

NOTE:Students registering for Full-time Dissertation Studymust register under the School of Medicine’s Course Number:FTDS 0000 (0 credits)All Courses