Disrupting electronic health records systems: The next generation

JMIR Medical Informatics

Volumn 3, Issue 4, 2015, Pages

  Celi, Leo Anthony ^a   Marshall, Jeffrey David ^b   Lai, Yuan ^c   Stone, David J ^d  

^a BETH ISRAEL DEACONESS MEDICAL CENTER   (United States)

^b YALE UNIVERSITY   (United States)

^c NEW YORK UNIVERSITY   (United States)

^d UNIVERSITY OF VIRGINIA   (United States)

Author keywords

Clinical decision making; Clinical decision support; Electronic health records; Electronic notes

Indexed keywords

EID: 85012869639     PISSN: None     EISSN: 22919694     Source Type: Journal    
DOI: 10.2196/medinform.4192     Document Type: Review

References (40)

1. Weed LL. Medical records, patient care, and medical education. Ir J Med Sci 1964 Jun; 462:271-282. [Medline: 14160426]
2. Celi L, Csete M, Stone D. Optimal data systems: The future of clinical predictions and decision support. Curr Opin Crit Care 2014 Oct; 20(5):573-580. [doi: 10.1097/MCC.0000000000000137] [Medline: 25137399]
3. Cook DA, Sorensen KJ, Hersh W, Berger RA, Wilkinson JM. Features of effective medical knowledge resources to support point of care learning: A focus group study. PLoS One 2013 Nov; 8(11):e80318 [FREE Full text] [doi: 10.1371/journal.pone.0080318] [Medline: 24282535]
4. Cook DA, Sorensen KJ, Wilkinson JM, Berger RA. Barriers and decisions when answering clinical questions at the point of care: A grounded theory study. JAMA Intern Med 2013 Nov 25; 173(21):1962-1969. [doi: 10.1001/jamainternmed.2013.10103] [Medline: 23979118]
5. Hsiao C, Hing E. US Department of Health and Human Services. Hyattsville, MD: US Department of Health and Human Services, CDC; 2001. Use and characteristics of electronic health record systems among office-based physician practicesURL: http://www.cdc.gov/nchs/data/databriefs/db143.pdf [accessed 2014-12-30] [WebCite Cache ID 6VDPpho29]
6. US Department of Health and Human Services. 2014 Aug 14. More physicians and hospitals are using EHRs than ever beforeURL: http://www.hhs.gov/news/press/2014pres/08/20140807a.html [accessed 2014-12-30] [WebCite Cache ID 6VDPuIvPv]
7. HealthIT.gov. 2013 Apr 03. How to implement EHRsURL: http://www.healthit.gov/providers-professionals/ehr-implementation-steps [accessed 2014-12-30] [WebCite Cache ID 6VDPy8bEb]
8. Ghassemi M, Marshall J, Singh N, Stone DJ, Celi LA. Leveraging a critical care database: Selective serotonin reuptake inhibitor use prior to ICU admission is associated with increased hospital mortality. Chest 2014 Apr; 145(4):745-752 [FREE Full text] [doi: 10.1378/chest.13-1722] [Medline: 24371841]
9. Hammond KW, Helbig ST, Benson CC, Brathwaite-Sketoe BM. Are electronic medical records trustworthy? Observations on copying, pasting and duplication. AMIA Annu Symp Proc 2003:269-273 [FREE Full text] [Medline: 14728176]
10. Tufte E. The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press; 2001.
11. Tsoukalas A, Albertson T, Tagkopoulos I. From data to optimal decision making: A data-driven, probabilistic machine learning approach to decision support for patients with sepsis. JMIR Med Inform 2015; 3(1):e11 [FREE Full text] [doi: 10.2196/medinform.3445] [Medline: 25710907]
12. Rochefort CM, Verma AD, Eguale T, Lee TC, Buckeridge DL. A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data. J Am Med Inform Assoc 2015 Jan; 22(1):155-165 [FREE Full text] [doi: 10.1136/amiajnl-2014-002768] [Medline: 25332356]
13. Amland RC, Dean BB, Yu H, Ryan H, Orsund T, Hackman JL, et al. Computerized clinical decision support to prevent venous thromboembolism among hospitalized patients: Proximal outcomes from a multiyear quality improvement project. J Healthc Qual 2015; 37(4):221-231. [doi: 10.1111/jhq.12069] [Medline: 26151096]
14. Papova M. Bloomberg Business Week. 2009. The 21 heroes of data visualizationURL: http://images.businessweek.com/ss/09/08/0812_data_visualization_heroes/1.htm [accessed 2014-12-30] [WebCite Cache ID 6VDRAE5R9]
15. HeRO by MPSC. URL: http://www.heroscore.com/[accessed 2014-12-30] [WebCite Cache ID 6VDRPJo4E]
16. Etiometry Inc. URL: http://www.etiometry.com/[accessed 2014-12-30] [WebCite Cache ID 6VDRUwD7k]
17. Drew BJ, Harris P, Zègre-Hemsey JK, Mammone T, Schindler D, Salas-Boni R, et al. Insights into the problem of alarm fatigue with physiologic monitor devices: A comprehensive observational study of consecutive intensive care unit patients. PLoS One 2014; 9(10):e110274 [FREE Full text] [doi: 10.1371/journal.pone.0110274] [Medline: 25338067]
18. Shen Y, Colloc J, Jacquet-Andrieu A, Lei K. Emerging medical informatics with case-based reasoning for aiding clinical decision in multi-agent system. J Biomed Inform 2015 Aug; 56:307-317. [doi: 10.1016/j.jbi.2015.06.012] [Medline: 26133480]
19. Celi LA, Zimolzak AJ, Stone DJ. Dynamic clinical data mining: Search engine-based decision support. JMIR Med Inform 2014; 2(1):e13 [FREE Full text] [doi: 10.2196/medinform.3110] [Medline: 25600664]
20. Gold A. Fierce Health IT. 2014. Survey: Docs increasingly use online search engines to make clinical decisions, but Googling can also harm the patient-doctor relationshipURL: http://www.fiercehealthit.com/story/survey-docs-increasingly-use-online-search-engines-make-clinical-decisions/2014-04-30 [accessed 2014-12-30] [WebCite Cache ID 6VDRc6H11]
21. Study: Google search results offer mixed-quality health information. 2014 Apr 14. iHealth BeatURL: http://www. ihealthbeat.org/articles/2014/4/14/study-google-search-results-offer-mixed-quality-health-information [accessed 2014-12-30] [WebCite Cache ID 6VDRhQtiq]
22. Sullivan D. Search Engine Land. FAQ: All about the new Google "Hummingbird" algorithmURL: http://searchengineland. com/google-hummingbird-172816 [accessed 2014-12-30] [WebCite Cache ID 6VDRmqWvR]
23. Trip Database. URL: https://www.tripdatabase.com/[accessed 2015-09-10] [WebCite Cache ID 6bR7rpHJw]
24. Up To Date Clinical Database. URL: http://www.uptodate.com/home [accessed 2015-09-10] [WebCite Cache ID 6bR7uPaPb]
25. Humphreys K, Maisel NC, Blodgett JC, Fuh IL, Finney JW. Extent and reporting of patient nonenrollment in influential randomized clinical trials, 2002 to 2010. JAMA Intern Med 2013 Jun 10; 173(11):1029-1031 [FREE Full text] [doi: 10.1001/jamainternmed.2013.496] [Medline: 23608926]
26. Selby JV, Lipstein SH. PCORI at 3 years - progress, lessons, and plans. N Engl J Med 2014 Feb 13; 370(7):592-595. [doi: 10.1056/NEJMp1313061] [Medline: 24521104]
27. Longhurst CA, Harrington RA, Shah NH. A "green button" for using aggregate patient data at the point of care. Health Aff (Millwood) 2014 Jul; 33(7):1229-1235. [doi: 10.1377/hlthaff.2014.0099] [Medline: 25006150]
28. QPID Health: Clinical Reasoning Power. URL: http://www.qpidhealth.com/[accessed 2014-12-30] [WebCite Cache ID 6VDRrrOgg]
29. American Society of Clinical Oncology. Transforming cancer care through big data: ASCO unveils CancerLinQ prototypeURL: http://www.asco.org/quality-guidelines/transforming-cancer-care-through-big-data-asco-unveils-cancerlinq-prototype [accessed 2014-12-30] [WebCite Cache ID 6VDRuCXpd]
30. UPMC Enterprises. Fluence: A breakthrough clinical platform for cliniciansURL: http://upmctdc.com/Pages/fluence.aspx [accessed 2014-12-30] [WebCite Cache ID 6VDS3jptI]
31. Grossman C, Powers B, McGinnis M. The Institute of Medicine. Washington, DC: The National Academies Press; 2011 May 23. Digital infrastructure for the learning health systemURL: http://www.iom.edu/Reports/2011/Digital-Infrastructure-for-a-Learning-Health-System.aspx [accessed 2014-12-30] [WebCite Cache ID 6VDSA0Mvl]
32. The Office of the National Coordinator for Health Information Technology. HealthIT.gov. 2014. Connecting health and care for the nation: A 10-year vision to achieve an interoperable health IT infrastructureURL: http://healthit.gov/sites/default/files/ONC10yearInteroperabilityConceptPaper.pdf [accessed 2014-12-30] [WebCite Cache ID 6VDSVW1Zn]
33. Carr N. Wall St Journal. 2014 Nov 21. Automation makes us dumbURL: http://www.wsj.com/articles/automation-makes-us-dumb-1416589342 [accessed 2014-12-30] [WebCite Cache ID 6VDSXtmh5]
34. Medicaid.gov. Affordable Care ActURL: http://medicaid.gov/affordablecareact/affordable-care-act.html [accessed 2015-03-24] [WebCite Cache ID 6XGguflhk]
35. CMS.gov - Centers for Medicare & Medicaid Services. Clinical decision support : More than just "alerts" tipsheetURL: http://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/Downloads/ClinicalDecisionSupport_Tipsheet-.pdf [accessed 2015-03-24] [WebCite Cache ID 6XGhTlwCC]
36. WhatIs.com. What is cognitive computing?URL: http://whatis.techtarget.com/definition/cognitive-computing [accessed 2015-03-24] [WebCite Cache ID 6XGh4btTm]
37. IBM Research. Cognitive computingURL: http://www.research.ibm.com/cognitive-computing/[accessed 2015-03-24] [WebCite Cache ID 6XGh8lPLq]
38. Wikipedia. Deep learningURL: http://en.wikipedia.org/wiki/Deep_learning [accessed 2015-03-24] [WebCite Cache ID 6XGkO2Zi0]
39. Witten IH, Frank E, Hall MA. Data mining. In: Data Mining: Practical Machine Learning Tools and Techniques, Third Edition. Burlington, MA: Morgan Kaufmann; 2011.
40. Wikipedia. Natural language processingURL: http://en.wikipedia.org/wiki/Natural_language_processing [accessed 2015-03-24] [WebCite Cache ID 6XGkIOyfK]