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profileJeffrey Gordon Klann, Ph.D.

TitleInstructor in Medicine
InstitutionMassachusetts General Hospital
DepartmentMedicine
AddressMassachusetts General Hospital
One Constitution Center
Charlestown MA 02129
Phone617/643-5879
Fax617/643-5280
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Collapse Biography 
Collapse awards and honors
2002Sigma Xi Research Honor Society
2007Outstanding incoming student
2009Student Paper Competition: Finalist
2010Student Paper Competition: 2nd Place
2011Top 15% of Graduate Students at my University
2012Ad-hoc Motorcycle Guy Harley Award

Collapse Overview 
Collapse 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.


Collapse Bibliographic 
Collapse selected publications
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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  1. 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 Apr 05; 8(2):322-336. PMID: 28378025.
    View in: PubMed
  2. Klann JG, Abend A, Raghavan VA, Mandl KD, Murphy SN. Data interchange using i2b2. J Am Med Inform Assoc. 2016 Sep; 23(5):909-15. PMID: 26911824; PMCID: PMC4997035 [Available on 09/01/17].
  3. 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.
    View in: PubMed
  4. 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; PMCID: PMC4765563.
  5. 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.
    View in: PubMed
  6. 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; PMCID: PMC4078286.
  7. 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; PMCID: PMC4078284.
  8. 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; PMCID: PMC4004673.
  9. 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; PMCID: PMC3861915.
  10. 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; PMCID: PMC3668611.
  11. 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; PMCID: PMC3636801.
  12. Klann JG, Murphy SN. Supporting the Health Quality Measures Format in i2b2. AMIA Jt Summits Transl Sci Proc. 2013; 2013:124. PMID: 24303250.
    View in: PubMed
  13. 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; PMCID: PMC3041352.
  14. 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.
  15. 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; PMCID: PMC2815486.
  16. 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; PMCID: PMC2773918.
  17. 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.
    View in: PubMed
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