Harry Hochheiser, PhD

Room 519
5607 Baum Boulevard
Pittsburgh, PA 15206
Admin Support: 

Assistant Professor in the Department of Biomedical Informatics
Associate Director of the Biomedical Informatics Training Program

Primary Appointment: 
Assistant Professor, Biomedical Informatics, Medicine, University of Pittsburgh
Peer-reviewed Publications: 
My research has covered a range of topics, including human-computer interaction, information visualization, bioinformatics, universal usability, security, privacy, and public policy implications of computing systems. I have published more than 25 peer-reviewed journal and conference papers and two book chapters. I am currently working on the development of highly-interactive, user-centered systems for finding and exploring biomedical datasets, with specific applications ranging from basic research data to electronic health records. Earlier efforts included NSF-funded projects in computer security in introductory computer science classes and computational thinking. In addition to my research experience in computer science education, I have taught and developed several courses at both undergraduate and graduate levels, including Introductory Computer Science, Introduction to Algorithms, Information Visualization, Advanced Web Development, and Human-Computer Interaction. I am a reviewer for several journals, including Information Visualization, ACM Transactions on Human Computer Interaction, Interacting with Computers, Risk Analysis, and Advances in Bioinformatics. I have also served on program committees for several conferences, including the IEEE Information Visualization Symposium (2007-2009), the ACM Symposium on Usable Privacy and Security (2009-2010), Advanced Visual Interfaces (2010), and the Security and Privacy in Medical and Home-Care Systems Workshop (2009-2010). I have been a member of the Executive Committee of the Association of Computing Machinery's US Public Policy Committee (USACM) since 2004, and I am co-author of Research Methods in Human-Computer Interaction (Wiley, 2010).
Research/Scholarship Interests: 
biomedical informatics; bioinformatics; clinical informatics; collaborative science; human-computer interaction; information visualization; computer-supported cooperative work; Attitude of Health Personnel; Humans; Workflow; Young Adult; Aged; Adult; Nursing Methodology Research; Qualitative Research; Nursing Staff, Hospital; Health Insurance Portability and Accountability Act; Nursing Evaluation Research; Point-of-Care Systems; United States; Male; Electronic Health Records; Nursing Records; Middle Aged
(Legend: Current Major/ Current Minor, Non-current Major/ Non-current Minor)
Education & Training: 
Post-Doctoral Computational Biology National Institute on Aging in 2006
PhD Computer Science University of Maryland in 2003
MS Electrical Engineering and Computer Science Massachusetts Institute of Technology in 1991
BS Computer Science and Engineering Massachusetts Institute of Technology in 1991
Academic Appointments: 
2012 - present
Assistant Professor, Intelligent Systems Program, Kenneth P. Dietrich School of Arts and Sciences, University of Pittsburgh
2009 - present
Assistant Professor, Biomedical Informatics, Medicine, University of Pittsburgh
Grants & Contracts: 
09/15/2014 - 08/31/2018
NHGRI U54HG008540
Center for Causal Modeling and Discovery of Biomedical Knowledge from Big Data
Role: Co-Investigator
08/1/2014 - 07/31/2015
NIH R24OD011883
Supplement to Supplement to Semantic LAMHDI: Linking diseases to model organism resources
Role: Co-Investigator
05/1/2014 - 04/30/2019
NIH/NCI 1 U24 CA184407-01
Cancer Deep Phenotype Extraction from Electronic Medical Records
Role: Co-Investigator
05/1/2014 - 04/30/2019
NIH 1U01DE024425-01
Human Genomics Analysis Interface for FaceBase 2
Role: Co-Investigator
02/1/2014 - 02/1/2018
Baobab Health Trust (CDC) U2GGH00729
Improving the delivery & management of HIV /AIDS care in Malawi through Appropriate Medical Informatics
Role: Co-Investigator
02/1/2014 - 01/31/2017
NIH/NLM 1 R01 LM011838-01
Addressing gaps in clinically useful evidence on drug-drug interactions
Role: Co-Investigator
09/1/2012 - 06/30/2016
Oregon Health Sciences University (NIH) 1R24 OD011883
Semantic LAMHDI: Linking diseases to model organism resources
Role: Co-Investigator
07/1/2012 - 06/30/2016
AHRQ 5R01HS021290
Quantifying Electronic Medical Records Usability to Improve Clinical Workflow
Role: Co-Investigator
04/1/2012 - 03/31/2015
NIH 1U01HL112707
Sarcoidosis and A1AT Genomics & Informatics Center
Role: Co-Investigator
07/1/2011 - 08/31/2015
NIH 1R01LM010964
Interactive Search and Review of Clinical Records with Multi-layered Semantic Annotation
Role: Co-Investigator
Selected Peer-Reviewed Publications: 
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- Landis-Lewis Z, Douglas GP, Hochheiser H, Kam M, Gadabu O, Bwanali M, Crowley RS. Computer-Supported Feedback Message Tailoring for Healthcare Providers in Malawi: Proof-of-Concept. In: Proceedings of the American Medical Informatics Association (AMIA). AMIA 2015 Annual Symposium; 2015 Nov 14 - 18; San Francicso CA.
- King A, Cooper GF, Hochheiser H, Clermont G, Visweswaran S. Development and Preliminary Evaluation of a Prototype of a Learning Electronic Medical Record System. 2015 AMIA Annual Symposium; 2015 Nov 14 - 18; San Francisco, California. 1967-1975.
- Hochheiser H, Ning Y, Hernandez AM, Horn JR, Jacobson RS, Boyce R. Using Nonexperts for Annotating Pharmacokinetic Drug-Drug Interaction Mentions in Product Labeling: A Feasibility Study. JMIR Research Protocols.
Stein CD, Xiao X, Levine S, Schleyer TK, Hochheiser H, Thyvalikakath TP. A prototype mobile application for triaging dental emergencies. Journal of The American Dental Association (1939). 2016 May 17. PMID:27206728.
Abstract: Evidence suggests that dental emergencies are likely to occur when preferred care is less accessible. Communication barriers often exist that cause patients to receive suboptimal treatment or experience discomfort for extended lengths of time. Furthermore, limitations in the conventional approach for managing dental emergencies prevent dentists from receiving critical information before patient visits.
- Lazar J, Abascal J, Barbosa S, Barksdale J, Grossklags J, Gulliksen J, Johnson J, McEwan T, Martínez-Normand L, Michalk W, Tsai J, van der Veer G, von Axelson H, Walldius A, Whitney G, Winckler M, Sabatier P, Wulf V, Churchill EF, Cranor L, Davis J, Hedge A, Hochheiser H, Hourcade J, Lewis C, Nathan L, Paterno F, Reid B, Quesenbery W, Selker T, Wentz B. Human-Computer Interaction and International Public Policymaking: A Framework for Understanding and Taking Future Actions. Foundations and Trends(r) Human-Computer Interaction. 2016 May 2;9(2):65-148.
Romagnoli KM, Boyce R, Empey PE, Adams S, Hochheiser H. Bringing clinical pharmacogenomics information to pharmacists: a qualitative study of information needs and resource requirements. International Journal of Medical Informatics. 2016;86:54-61. PMID:26725696.
Abstract: As key experts in supporting medication-decision making, pharmacists are well-positioned to support the incorporation of pharmacogenomics into clinical care. However, there has been little study to date of pharmacists' information needs regarding pharmacogenomics. Understanding those needs is critical to design information resources that help pharmacists effectively apply pharmacogenomics information.
Cooper GF, Bahar I, Becich MJ, Benos PV, Berg J, Espino JU, Glymour C, Jacobson RC, Kienholz M, Lee AV, Lu X, Scheines R, Center for Causal Discovery team. The center for causal discovery of biomedical knowledge from big data. Journal of The American Medical Informatics Association : JAMIA. 2015 Nov;22(6):1132-6. PMID:26138794.
Abstract: The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools. Collaborative activities with the BD2K Consortium further advance causal discovery tools and integrate tools and resources developed by other centers.
Mungall CJ, Washington NL, Nguyen-Xuan J, Condit C, Smedley D, Köhler S, Groza T, Shefchek K, Hochheiser H, Robinson PN, Lewis SE, Haendel MA. Use of model organism and disease databases to support matchmaking for human disease gene discovery. Human Mutation. 2015 Oct;36(10):979-84. PMID:26269093.
Abstract: The Matchmaker Exchange application programming interface (API) allows searching a patient's genotypic or phenotypic profiles across clinical sites, for the purposes of cohort discovery and variant disease causal validation. This API can be used not only to search for matching patients, but also to match against public disease and model organism data. This public disease data enable matching known diseases and variant-phenotype associations using phenotype semantic similarity algorithms developed by the Monarch Initiative. The model data can provide additional evidence to aid diagnosis, suggest relevant models for disease mechanism and treatment exploration, and identify collaborators across the translational divide. The Monarch Initiative provides an implementation of this API for searching multiple integrated sources of data that contextualize the knowledge about any given patient or patient family into the greater biomedical knowledge landscape. While this corpus of data can aid diagnosis, it is also the beginning of research to improve understanding of rare human diseases.
Moller DR, Koth LL, Maier LA, Morris A, Drake W, Rossman M, Leader JK, Collman RG, Hamzeh N, Sweiss NJ, Zhang Y, O'Neal S, Senior RM, Becich MJ, Hochheiser H, Kaminski N, Wisniewski SR, Gibson KF, Study Group G. Rationale and Design of the Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis Study (GRADS): Sarcoidosis Protocol. Ann Am Thorac Soc. 2015 Jul 20. PMID:26193069.
Abstract: Sarcoidosis is a systemic disease characterized by noncaseating granulomatous inflammation with tremendous clinical heterogeneity and uncertain pathobiology and lacking in clinically useful biomarkers. The Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS) study is an observational cohort study designed to explore the role of the lung microbiome and genome in these two diseases. This article describes the design and rationale for the GRADS study sarcoidosis protocol. The study addresses the hypothesis that distinct patterns in the lung microbiome are characteristic of sarcoidosis phenotypes and are reflected in changes in systemic inflammatory responses as measured by peripheral blood changes in gene transcription. The goal is to enroll 400 participants, with a minimum of 35 in each of 9 clinical phenotype subgroups prioritized by their clinical relevance to understanding of the pathobiology and clinical heterogeneity of sarcoidosis. Participants with a confirmed diagnosis of sarcoidosis undergo a baseline visit with self-administered questionnaires, chest computed tomography, pulmonary function tests, and blood and urine testing. A research or clinical bronchoscopy with a research bronchoalveolar lavage will be performed to obtain samples for genomic and microbiome analyses. Comparisons will be made by blood genomic analysis and with clinical phenotypic variables. A 6-month follow-up visit is planned to assess each participant's clinical course. By the use of an integrative approach to the analysis of the microbiome and genome in selected clinical phenotypes, the GRADS study is powerfully positioned to inform and direct studies on the pathobiology of sarcoidosis, identify diagnostic or prognostic biomarkers, and provide novel molecular phenotypes that could lead to improved personalized approaches to therapy for sarcoidosis.
Strange C, Senior RM, Sciurba FC, O'Neal S, Morris A, Wisniewski SR, Bowler R, Hochheiser H, Becich MJ, Zhang Y, Leader JK, Methe BA, Kaminski N, Sandhaus RA, Study Group G. Rationale and Design of the Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis Study: Alpha-1 Protocol. Ann Am Thorac Soc. 2015 Jul 8;1-35. PMID:26153726.
Abstract: Severe deficiency of alpha-1 antitrypsin has a highly variable clinical presentation. The Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis α1 Study is a prospective, multicenter, cross-sectional study of adults older than age 35 years with PiZZ or PiMZ alpha-1 antitrypsin genotypes. It is designed to better understand if microbial factors influence this heterogeneity. Clinical symptoms, pulmonary function testing, computed chest tomography, exercise capacity, and bronchoalveolar lavage (BAL) will be used to define chronic obstructive pulmonary disease (COPD) phenotypes that can be studied with an integrated systems biology approach that includes plasma proteomics; mouth, BAL, and stool microbiome and virome analysis; and blood microRNA and blood mononuclear cell RNA and DNA profiling. We will rely on global genome, transcriptome, proteome, and metabolome datasets. Matched cohorts of PiZZ participants on or off alpha-1 antitrypsin augmentation therapy, PiMZ participants not on augmentation therapy, and control participants from the Subpopulations and Intermediate Outcome Measures in COPD Study who match on FEV1 and age will be compared. In the primary analysis, we will determine if the PiZZ individuals on augmentation therapy have a difference in lower respiratory tract microbes identified compared with matched PiZZ individuals who are not on augmentation therapy. By characterizing the microbiome in alpha-1 antitrypsin deficiency (AATD), we hope to define new phenotypes of COPD that explain some of the diversity of clinical presentations. As a unique genetic cause of COPD, AATD may inform typical COPD pathogenesis, and better understanding of it may illuminate the complex interplay between environment and genetics. Although the biologic approaches are hypothesis generating, the results may lead to development of novel biomarkers, better understanding of COPD phenotypes, and development of novel diagnostic and therapeutic trials in AATD and COPD. Clinical trial registered with www.clinicaltrials.gov (NCT01832220).