or blood pressure looks like. Hence, we have been deﬁning a simple set of abstractions, which we are reﬁning over time. We believe that the SMART standards and a modern web- based app platform are strong initial steps toward a universal exchange language suggested by the Presidential Council of Advisors on Science and Technology in their report to the Pres- ident on Health Information Technology.26 The report recog- nizes lack of interoperability as a major barrier to health IT adoption and recommends abandoning traditional health data standards and allowing the market to drive semantic harmoni- zation. SMART puts forth a clear model for common health data, starting with medications and fulﬁllments, problems, allergies, and simple blood laboratory results. The model will continue to evolve, especially over the next year, as the apps market requirements become clear. We have not abandoned current standards, and rely heavily on SNOMED, RxNorm, and LOINC. Our modeling approach allows expression of atomic data points such as single allergies, problems, or a single blood laboratory value, exactly as recommended by the Presidential Council of Advisors on Science and Technology. Further, it allows for the addition of additional semantics over time, without the existing data being affected, or the existing soft- ware that relies on these data. CONCLUSION Over a period of less than 14 months, we have gone from the point of deﬁning an approach to building a working API which has been used as the basis of several EHR apps developed by groups with no relationship to the SMART team. Nonetheless, we recognize several important challenges and questions. First, where is the most effective market for further disseminating SMART? Is it with the existing market-leading EHR vendors, with the new entrants into the EHR market, or with other stakeholders such as health information exchanges and accountable care organizations? At this time it remains unclear where the major adoption is going to occur. We are currently experimenting with three models of technology diffusion: (1) integration with legacy systems (as described above in our Cerner integration); (2) deployment of apps in health informa- tion exchanges; (3) deployment of apps running on a parallel platform (i2b2) with real-time extraction, transformation, and loading from the EHR. Acknowledgments We are indebted to Dr Charles Friedman and Mr Wil Yu for their dedicated and enabling programmatic support of the project. We are grateful to Aneesh Chopra and Todd Park for their extraordinary commitment to promoting the SMART Platforms project and concept. Contributors KDM wrote the ﬁrst and ﬁnal draft of the manuscript and is the co-PI of SMART. JCM co-wrote the manuscript and is lead architect of SMART. SNM provided valuable sections of the manuscript and is a core member of the SMART leadership team. EVB provided valuable comments on the manuscript and is a core member of the SMART leadership team. RBR provided valuable comments on the manuscript and is the Executive Director of SMART. DAK is a core member of the SMART leadership team, and contributed substantially to the SMART architecture. JMM is a core member of the SMART leadership team, contributed substantially to the SMART architecture and provided substantial input to the manuscript. BA is a core member of the SMART leadership team, contributed substantially to the SMART architecture, and provided valuable input to the manuscript. ISK co-wrote the manuscript and is the PI of SMART. R e s e a r c h a n d a p p l i c a t i o n s Funding This work was funded by the Strategic Health IT Advanced Research Projects Award 90TR000101 from the Ofﬁce of the National Coordinator of Health Information Technology. Competing interests None. Provenance and peer review Not commissioned; externally peer reviewed. REFERENCES 1. Mandl KD, Kohane IS. No small change for the health information economy. N Engl J Med 2009;360:1278e81. 2. Ofﬁce of the National Coordinator for Health Information Technology. Strategic Health IT Advanced Research Projects (SHARP) Program. http://healthit.hhs. gov/portal/server.pt/community/healthit_hhs_gov__sharp_program/ 3. U.S. National Library of Medicine. 2011AA National Drug FiledReference Terminology Source Information. 2011. http://www.nlm.nih.gov/research/umls/ sourcereleasedocs/current/NDFRT/ 4. U.S. National Library of Medicine. SNOMED Clinical Terms (SNOMED CT). http:// www.nlm.nih.gov/research/umls/Snomed/snomed_main.html 5. U.S, Food and Drug Adminstration. National Drug Code Directory. http://www.fda. gov/Drugs/InformationOnDrugs/ucm142438.htm 6. U.S. National Library of Medicine. RxNorm. http://www.nlm.nih.gov/research/ umls/rxnorm/ 7. W3C. Resource Description Framework (RDF): Primer. http://www.w3.org/TR/rdf- primer/ (accessed 2 Sep 2011). 8. W3C. OWL 2 Web Ontology Language: Primer. http://www.w3.org/TR/owl2-primer/ (accessed 2 Sep 2011). 9. SMART Platforms Team. SMART Development Site. http://www.smartplatforms. org/development 10. OAuth Community. OAuth. http://oauth.net/ 11. Challenge. Gov. SMART Apps for Health Challenge. http://challenge.gov/HHS/134- smart-apps-for-health 12. SMART Platforms Team. SMART Platforms Challenge. http://www. smartplatforms.org/challenge/ 13. Foundation for the National Institutes of Health. 2010 mHealth Summit: Meeting 21st Century Health Goals Through Mobile Technology Research & Innovation. http://www.fnih.org/events/2010-mhealth-summit 14. Chopra A. SMART Prize for Patients, Physicians, and Researchers. http://www. whitehouse.gov/blog/2011/03/10/smart-prize-patients-physicians-and-researchers 15. Mandl KD, Simons WW, Crawford WC, et al. Indivo: a personally controlled health record for health information exchange and communication. BMC Med Inform Decis Mak 2007;7:25. 16. Mandl KD, Kohane IS. Tectonic shifts in the health information economy. N Engl J Med 2008;358:1732e7. 17. Adida B, Sanyal A, Zabak S, et al. Indivo x: developing a fully substitutable personally controlled health record platform. AMIA Annu Symp Proc 2010;2010:6e10. 18. Murphy SN, Weber G, Mendis M, et al. Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). J Am Med Inform Assoc 2010;17:124e30. 19. Murphy SN, Mendis M, Hackett K, et al. Clinical research chart from informatics for integrating biology and the bedside. AMIA Annu Symp Proc 2007:548e52. 20. Wolfe BA, Mamlin BW, Biondich PG, et al. The OpenMRS system: collaborating toward an open source EMR for developing countries. AMIA Annu Symp Proc 2006:1146. 21. Mamlin BW, Biondich PG, Wolfe BA, et al. Cooking up an open source EMR for developing countries: openMRSda recipe for successful collaboration. AMIA Annu Symp Proc 2006:529e33. 22. Leckart S. The Blood Test Gets a Makeover. Wired, 2010. 23. McCandless D. Visualizing Blood Tests. Information is Beautiful: Ideas, Issues, Knowledge, DatadVisualized! http://www.informationisbeautiful.net/2010/ visualizing-bloodtests/ 24. Jonikas MA, Mandl KD. Surveillance of medication use: early identiﬁcation of poor adherence. J Am Med Inform Assoc. Published Online First: 19 November 2011. doi:10.1136/amiajnl-2011-000416 25. The Regenstrief Institute. Logical Observation Identiﬁers Names and Codes (LOINC ). http://loinc.org/ 26. Executive Ofﬁce of the President, President’s Council of Advisors on Science and technology. Report to the President: Realizing the Full Potential of Health Information Technology to Improve Healthcare for Americans: the Path Forward. 2010. http://www.whitehouse.gov/sites/default/ﬁles/microsites/ostp/pcast-health-it- report.pdf PAGE fraction trail=7 J Am Med Inform Assoc 2012;19:597e603. doi:10.1136/amiajnl-2011-000622 603