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Jeffrey Gordon Klann, Ph.D.

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Biography
MIT, Cambridge, MABS2001Computer Science and Engineering
MIT, Cambridge, MAMEng2004Electrical Engineering and Computer Science
Indiana University, Indianapolis, INPhD2012Health Informatics
Regenstrief Institute, Indianapolis, INfellowship2012Medical Informatics
2002
Sigma Xi Research Honor Society
2007
Outstanding incoming student
2009
Student Paper Competition: Finalist
2010
Student Paper Competition: 2nd Place
2011
Top 15% of Graduate Students at my University
2014
Editor's Choice Award
2015
Best Paper Award in Computerized Clinical Decision Support Systems
2016
Most Influential Data Interoperability Paper of 2015

Overview
I believe that technology can and should have the same positive impact in the medical world it has in the consumer world, and that the nation's massive investment in Health Information Technology should translate into accelerated innovation and discovery and improved quality, efficiency, and safety of healthcare. I feel that computers are a paradigm shift in medicine, rather than an evolution of paper-based processes. To that end, I am interested in fundamentally new ways of using medical data that were not possible in paper records.

Knowledge discovery for clinical decision support: How can we learn from the mass of EMR data, and what can we learn from it to help clinicians perform their work more accurately and efficiently? In the world-wide-web, we believe the crowd. We trust them to help us select movies (Netflix), to shop (Amazon), and to filter our spam (Gmail). However, clinical decision support systems and health standards are generally developed by experts carefully studying small cohorts of patients. In healthcare, how much is the crowd (of highly trained physicians) to be trusted? How can we leverage data mining techniques developed over the last decade to extract crowd wisdom from the masses of data? I have developed and continue to refine methods to transform medical data into drafts of decision support content.

Sharing medical data to improve population health: I am currently engaged in two projects in this area. One, I am the lead researcher in enabling i2b2 for distributed cross-platform measurement of population health (the Query Health initiative). i2b2 is a clinical data analytics platform in use at over 80 sites nationwide. My contributions are currently showcased at two national pilots, with more coming. Two, I lead a project to improve patient safety by making IHI Trigger Tool methodologies for adverse event detection more accessible across institutions.

Revolutionizing user interfaces: Services like Google use a single interface to perform search, calculations, and many other functions. EMRs, on the other hand, are often rigid point-and-click interfaces prevalent before the web. Many clinicans find the amount of clicking and the awkward drop-down-menus to be very aggravating, which is unacceptable in a time-constrained environment. Additionally, EMRs need to display very complex historical and anatomical data in easy-to-digest ways. How can we summarize complex data in a rapidly digestable format? I have published work in: summarizing medical records through a problem-oriented view, visualizing the patient record as a timeline, filtering irrelevant problems from the problem list, searching the patient record, and automatically listening to and interpreting doctor-patient conversations through microphones in the office. Several of these projects are ongoing, such as producing a predictive problem-oriented view for dermatology and continued development of a substitutable technology platform to enable innovate medical apps to run on multiple systems.

Making PHRs viable: Personal Health Records (PHRs) have appeared over the last several years with the promise of helping people manage their health information more easily. Yet most existing PHRs are only slightly more useful than an unstructured collaborative document. Few have adopted PHRs, except for those with massive information-cataloging needs (e.g., caregivers of elderly adults). However, there may be a use-case for the PHR. Patients are aggravated they have no access to their health information. Physicians, also, express frustration at having limited access to patient-gathered information (e.g., blood glucose logs). Patient portals have been successful in providing secure communication and some medical information exchange between patients and single hospital systems. Services such as PatientsLikeMe and CureTogether have been successful in using collective patient information to build community and collaborative disease profiles. A useful PHR must integrate these features. Essentially, the PHR should be part of the social web, allowing us to share medical information (in a controlled way) in the same vein that we share our activities with Facebook friends. It is not a disconnected application, but a tool in our increasingly information-hungry society. Of course, this raises many issues of security, privacy, liability, and data ownership.

Bibliographic
Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Faculty can login to make corrections and additions.
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PMC Citations indicate the number of times the publication was cited by articles in PubMed Central, and the Altmetric score represents citations in news articles and social media. (Note that publications are often cited in additional ways that are not shown here.) Fields are based on how the National Library of Medicine (NLM) classifies the publication's journal and might not represent the specific topic of the publication. Translation tags are based on the publication type and the MeSH terms NLM assigns to the publication. Some publications (especially newer ones and publications not in PubMed) might not yet be assigned Field or Translation tags.) Click a Field or Translation tag to filter the publications.
  1. Estiri H, Klann JG, Weiler SR, Alema-Mensah E, Joseph Applegate R, Lozinski G, Patibandla N, Wei K, Adams WG, Natter MD, Ofili EO, Ostasiewski B, Quarshie A, Rosenthal GE, Bernstam EV, Mandl KD, Murphy SN. A federated EHR network data completeness tracking system. J Am Med Inform Assoc. 2019 Mar 29. PMID: 30925587.
    Citations:    
  2. Klann JG, Joss MAH, Embree K, Murphy SN. Data model harmonization for the All Of Us Research Program: Transforming i2b2 data into the OMOP common data model. PLoS One. 2019; 14(2):e0212463. PMID: 30779778.
    Citations:    
  3. Klann JG, Phillips LC, Herrick C, Joss MAH, Wagholikar KB, Murphy SN. Web services for data warehouses: OMOP and PCORnet on i2b2. J Am Med Inform Assoc. 2018 Oct 01; 25(10):1331-1338. PMID: 30085008.
    Citations:    Fields:    
  4. Klann JG, Joss M, Shirali R, Natter M, Schneeweiss S, Mandl KD, Murphy SN. The Ad-Hoc Uncertainty Principle of Patient Privacy. AMIA Jt Summits Transl Sci Proc. 2018; 2017:132-138. PMID: 29888058.
    Citations:    
  5. Raisaro JL, Klann JG, Wagholikar KB, Estiri H, Hubaux JP, Murphy SN. Feasibility of Homomorphic Encryption for Sharing I2B2 Aggregate-Level Data in the Cloud. AMIA Jt Summits Transl Sci Proc. 2018; 2017:176-185. PMID: 29888067.
    Citations:    
  6. Estiri H, Stephens KA, Klann JG, Murphy SN. Exploring completeness in clinical data research networks with DQe-c. J Am Med Inform Assoc. 2018 01 01; 25(1):17-24. PMID: 29069394.
    Citations:    Fields:    Translation:Humans
  7. Culbertson A, Goel S, Madden MB, Safaeinili N, Jackson KL, Carton T, Waitman R, Liu M, Krishnamurthy A, Hall L, Cappella N, Visweswaran S, Becich MJ, Applegate R, Bernstam E, Rothman R, Matheny M, Lipori G, Bian J, Hogan W, Bell D, Martin A, Grannis S, Klann J, Sutphen R, O'Hara AB, Kho A. The Building Blocks of Interoperability. A Multisite Analysis of Patient Demographic Attributes Available for Matching. Appl Clin Inform. 2017 04 05; 8(2):322-336. PMID: 28378025.
    Citations:    Fields:    Translation:Humans
  8. Klann JG, Abend A, Raghavan VA, Mandl KD, Murphy SN. Data interchange using i2b2. J Am Med Inform Assoc. 2016 09; 23(5):909-15. PMID: 26911824.
    Citations: 8     Fields:    Translation:Humans
  9. Klann JG, Phillips LC, Turchin A, Weiler S, Mandl KD, Murphy SN. A numerical similarity approach for using retired Current Procedural Terminology (CPT) codes for electronic phenotyping in the Scalable Collaborative Infrastructure for a Learning Health System (SCILHS). BMC Med Inform Decis Mak. 2015 Dec 11; 15:104. PMID: 26655696.
    Citations:    Fields:    Translation:Humans
  10. Klann JG, Pfiffner PB, Natter MD, Conner E, Blazejewski P, Murphy SN, Mandl KD. Supporting Multi-sourced Medication Information in i2b2. AMIA Annu Symp Proc. 2015; 2015:747-55. PMID: 26958210.
    Citations:    Fields:    Translation:Humans
  11. Klann JG, Mendis M, Phillips LC, Goodson AP, Rocha BH, Goldberg HS, Wattanasin N, Murphy SN. Taking advantage of continuity of care documents to populate a research repository. J Am Med Inform Assoc. 2015 Mar; 22(2):370-9. PMID: 25352566.
    Citations: 7     Fields:    Translation:Humans
  12. Mandl KD, Kohane IS, McFadden D, Weber GM, Natter M, Mandel J, Schneeweiss S, Weiler S, Klann JG, Bickel J, Adams WG, Ge Y, Zhou X, Perkins J, Marsolo K, Bernstam E, Showalter J, Quarshie A, Ofili E, Hripcsak G, Murphy SN. Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS): architecture. J Am Med Inform Assoc. 2014 Jul-Aug; 21(4):615-20. PMID: 24821734.
    Citations: 22     Fields:    Translation:Humans
  13. Klann JG, Buck MD, Brown J, Hadley M, Elmore R, Weber GM, Murphy SN. Query Health: standards-based, cross-platform population health surveillance. J Am Med Inform Assoc. 2014 Jul-Aug; 21(4):650-6. PMID: 24699371.
    Citations: 14     Fields:    Translation:HumansPHPublic Health
  14. Klann JG, Szolovits P, Downs SM, Schadow G. Decision support from local data: creating adaptive order menus from past clinician behavior. J Biomed Inform. 2014 Apr; 48:84-93. PMID: 24355978.
    Citations: 10     Fields:    Translation:Humans
  15. Klann JG, Anand V, Downs SM. Patient-tailored prioritization for a pediatric care decision support system through machine learning. J Am Med Inform Assoc. 2013 Dec; 20(e2):e267-74. PMID: 23886921.
    Citations: 2     Fields:    Translation:Humans
  16. Klann JG, McCoy AB, Wright A, Wattanasin N, Sittig DF, Murphy SN. Health care transformation through collaboration on open-source informatics projects: integrating a medical applications platform, research data repository, and patient summarization. Interact J Med Res. 2013 May 30; 2(1):e11. PMID: 23722634.
    Citations: 9     
  17. Klann JG, Murphy SN. Computing health quality measures using Informatics for Integrating Biology and the Bedside. J Med Internet Res. 2013 Apr 19; 15(4):e75. PMID: 23603227.
    Citations: 12     Fields:    Translation:Humans
  18. Klann JG, Murphy SN. Supporting the Health Quality Measures Format in i2b2. AMIA Jt Summits Transl Sci Proc. 2013; 2013:124. PMID: 24303250.
    Citations:    
  19. Klann J, Schadow G, Downs SM. A method to compute treatment suggestions from local order entry data. AMIA Annu Symp Proc. 2010 Nov 13; 2010:387-91. PMID: 21347006.
    Citations: 8     Fields:    Translation:Humans
  20. Klann J, Schadow G. Proceedings of the 1st ACM International Health Informatics Symposium. Modeling the information-value decay of medical problems for problem list maintainance. 2010; 2010:371-375. View Publication.
  21. Klann J, Schadow G, McCoy JM. A recommendation algorithm for automating corollary order generation. AMIA Annu Symp Proc. 2009 Nov 14; 2009:333-7. PMID: 20351875.
    Citations: 12     Fields:    
  22. Klann JG, Szolovits P. An intelligent listening framework for capturing encounter notes from a doctor-patient dialog. BMC Med Inform Decis Mak. 2009 Nov 03; 9 Suppl 1:S3. PMID: 19891797.
    Citations:    Fields:    Translation:Humans
  23. Klann J, McCoy JM. Patient record 2.0: using structured clinical documents to provide ranked, relevant display of patient records. AMIA Annu Symp Proc. 2008 Nov 06; 1012. PMID: 18999098.
    Citations:    Fields:    
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Funded by the NIH/NCATS Clinical and Translational Science Award (CTSA) program, grant number UL1TR001102, and through institutional support from Harvard University, Harvard Medical School, Harvard T.H. Chan School of Public Health, Beth Israel Deaconess Medical Center, Boston Children's Hospital, Brigham and Women's Hospital, Massachusetts General Hospital and the Dana Farber Cancer Institute.