“Big Data” and the Electronic Health Record

Summary

Objectives: Implementation of Electronic Health Record (EHR) systems continues to expand. The massive number of patient encounters results in high amounts of stored data. Transforming clinical data into knowledge to improve patient care has been the goal of biomedical informatics professionals for many decades, and this work is now increasingly recognized outside our field. In reviewing the literature for the past three years, we focus on “big data” in the context of EHR systems and we report on some examples of how secondary use of data has been put into practice.

Methods: We searched PubMed database for articles from January 1, 2011 to November 1, 2013. We initiated the search with keywords related to “big data” and EHR. We identified relevant articles and additional keywords from the retrieved articles were added. Based on the new keywords, more articles were retrieved and we manually narrowed down the set utilizing predefined inclusion and exclusion criteria.

Results: Our final review includes articles categorized into the themes of data mining (pharmacovigilance, phenotyping, natural language processing), data application and integration (clinical decision support, personal monitoring, social media), and privacy and security.

Conclusion: The increasing adoption of EHR systems worldwide makes it possible to capture large amounts of clinical data. There is an increasing number of articles addressing the theme of “big data”, and the concepts associated with these articles vary. The next step is to transform healthcare big data into actionable knowledge.

Keywords

Electronic health records - data mining - natural language processing - privacy - security - quality improvement

 

^* These authors contributed equally to this manuscript

• References

• 1 Laney D. 3D Data Management: Controlling Data Volume, Velocity, and Variety. META Group; 2001
• 2 Ward JS, Barker A. Undefined By Data: A Survey of Big Data Definitions. arXiv:1309.5821 2013
• 3 Murdoch TB, Detsky AS. The inevitable application of big data to health care. Jama. 2013; 309 (13) 1351-2.
• 4 Index for Excerpts from the American Recovery and Reinvestment Act of 2009.. Health Information Technology (HITECH) Act. 2009 p. 112-64.
• 5 Charles DK J, Patel V, Furukawa M. Adoption of Electronic Health Record Systems among U.S. Non-federal Acute Care Hospitals: 2008-2012; 2013. www.healthit.gov/sites/default/files/oncdatabrief9final.pdf. Accessibility verified April 20, 2014
• 6 Shah NH. Translational bioinformatics embraces big data. Yearb Med Inform 2012; 7 (01) 130-4.
• 7 Heinze O, Birkle M, Koster L, Bergh B. Architecture of a consent management suite and integration into IHE-based Regional Health Information Networks. BMC Med Inform Decis Mak 2011; 11: 58.
• 8 Tejero A, de la Torre I. Advances and current state of the security and privacy in electronic health records: survey from a social perspective. J Med Syst 2012; 36 (05) 3019-27.
• 9 Mense A, Hoheiser-Pfortner F, Schmid M, Wahl H. Concepts for a standard based cross-organisational information security management system in the context of a nationwide EHR. Stud Health Technol Inform 2013; 192: 548-52.
• 10 Faxvaag A, Johansen TS, Heimly V, Melby L, Grimsmo A. Healthcare professionals’ experiences with EHR-system access control mechanisms. Stud Health Technol Inform 2011; 169: 601-5.
• 11 Hripcsak G, Albers DJ. Next-generation phenotyping of electronic health records. J Am Med Inform Assoc 2013; 20 (01) 117-21.
• 12 Wagholikar KB, Sundararajan V, Deshpande AW. Modeling paradigms for medical diagnostic decision support: a survey and future directions. J Med Syst 2012; 36 (05) 3029-49.
• 13 Rowley J. The wisdom hierarchy: representations of the DIKW hierarchy. J Inf Sci 2007; 33 (02) 163-80.
• 14 Sullivan P, Goldmann D. The promise of comparative effectiveness research. JAMA 2011; 305 (04) 400-1.
• 15 Fernandes L, O’Connor M, Weaver V. Big data, bigger outcomes: Healthcare is embracing the big data movement, hoping to revolutionize HIM by distilling vast collection of data for specific analysis. J AHIMA 2012; 83 (10) 38-43 quiz 44.
• 16 Harper EM. The economic value of health care data. Nurs Adm Q 2013; 37 (02) 105-8.
• 17 Colpas P. Integration, analytics key to next-generation EMRs. Industry experts discuss the year ahead in EMRs/EHRs. Health Manag Technol 2013; 34 (01) 6-8 10-1.
• 18 Weinstock M. 2013 most wired. Hosp Health Netw 2013; 87 (07) 26-37.
• 19 Hoffman S, Podgurski A. Big bad data: law, public health, and biomedical databases. J Law Med Ethics 2013; 41 (Suppl. 01) 56-60.
• 20 Chute CG, Ullman-Cullere M, Wood GM, Lin SM, He M, Pathak J. Some experiences and opportunities for big data in translational research. Genet Med 2013; 15 (10) 802-9.
• 21 Ackerman MJ. Big data. J Med Pract Manage 2012; 28 (02) 153-4.
• 22 Liyanage H, Liaw ST, de Lusignan S. Accelerating the development of an information ecosystem in health care, by stimulating the growth of safe intermediate processing of health information (IPHI). Inform Prim Care 2012; 20 (02) 81-6.
• 23 Bonney S. HIM’s role in managing big data: Turning data collected by an EHR into information. J AHIMA 2013; 84 (09) 62-4.
• 24 Leventhal R. Trend: big data. Big data analytics: from volume to value. Healthc Inform 2013; 30 (02) 12 4.
• 25 Glaser J, Overhage JM. The role of healthcare IT: becoming a learning organization. Healthc Financ Manage 2013; 67 (02) 56-62 4.
• 26 Institute of Medicine.. Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. 2012
• 27 Hood L, Flores M. A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory. N Biotechnol 2012; 29 (06) 613-24.
• 28 Phan JH, Quo CF, Cheng C, Wang MD. Multiscale integration of -omic, imaging, and clinical data in biomedical informatics. IEEE Rev Biomed Eng 2012; 5: 74-87.
• 29 Weiskopf NG, Hripcsak G, Swaminathan S, Weng C. Defining and measuring completeness of electronic health records for secondary use. J Biomed Inform 2013; 46 (05) 830-6.
• 30 Conn J. Pairing up. Early adopters of big data seek advisers, partners. Mod Healthc 2013; 43 (24) 8-9.
• 31 Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet. 2012; 13 (06) 395-405.
• 32 Barr P. Analyze this: health systems get help with big data. Hosp Health Netw 2013; 87 (06) 16.
• 33 Bellazzi R, Zupan B. Predictive data mining in clinical medicine: current issues and guidelines. Int J Med Inform 2008; 77 (02) 81-97.
• 34 Lependu P, Iyer SV, Fairon C, Shah NH. Annotation Analysis for Testing Drug Safety Signals using Unstructured Clinical Notes. J Biomed Semantics 2012 3 Suppl 1 S5
• 35 Rink B, Roberts K, Harabagiu SM. A supervised framework for resolving coreference in clinical records. J Am Med Inform Assoc. 2012; 19 (05) 875-82.
• 36 Patrick JD, Nguyen DH, Wang Y, Li M. A knowledge discovery and reuse pipeline for information extraction in clinical notes. J Am Med Inform Assoc. 2011; 18 (05) 574-9.
• 37 Hrovat G, Stiglic G, Kokol P, Ojstersek M. Contrasting temporal trend discovery for large healthcare databases. Comput Methods Programs Biomed 2014; 113 (01) 251-7.
• 38 Sun J, Hu J, Luo D, Markatou M, Wang F, Edabollahi S. et al. Combining knowledge and data driven insights for identifying risk factors using electronic health records. AMIA Annu Symp Proc 2012; 2012: 901-10.
• 39 Bates DW, Spell N, Cullen DJ, Burdick E, Laird N, Petersen LA. et al. The costs of adverse drug events in hospitalized patients. Adverse Drug Events Prevention Study Group. JAMA 1997; 277 (04) 307-11.
• 40 Warrer P, Hansen EH, Juhl-Jensen L, Aagaard L. Using text-mining techniques in electronic patient records to identify ADRs from medicine use. Br J Clin Pharmacol 2012; 73 (05) 674-84.
• 41 Coloma PM, Trifiro G, Patadia V, Sturkenboom M. Postmarketing safety surveillance : where does signal detection using electronic health-care records fit into the big picture?. Drug Saf 2013; 36 (03) 183-97.
• 42 Haerian K, Varn D, Vaidya S, Ena L, Chase HS, Friedman C. Detection of pharmacovigilance-related adverse events using electronic health records and automated methods. Clin Pharmacol Ther 2012; 92 (02) 228-34.
• 43 Chazard E, Ficheur G, Bernonville S, Luyckx M, Beuscart R. Data mining to generate adverse drug events detection rules. IEEE Trans Inf Technol Biomed 2011; 15 (06) 823-30.
• 44 Ji Y, Ying H, Dews P, Mansour A, Tran J, Miller RE. et al. A potential causal association mining algorithm for screening adverse drug reactions in postmarketing surveillance. IEEE Trans Inf Technol Biomed 2011; 15 (03) 428-37.
• 45 Sohn S, Kocher JP, Chute CG, Savova GK. Drug side effect extraction from clinical narratives of psychiatry and psychology patients. J Am Med Inform Assoc 2011; 18 (Suppl. 01) i144-9.
• 46 Vilar S, Harpaz R, Santana L, Uriarte E, Friedman C. Enhancing adverse drug event detection in electronic health records using molecular structure similarity: application to pancreatitis. PLoS One 2012; 7 (07) e41471.
• 47 Eriksson R, Jensen PB, Frankild S, Jensen LJ, Brunak S. Dictionary construction and identification of possible adverse drug events in Danish clinical narrative text. J Am Med Inform Assoc 2013; 20 (05) 947-53.
• 48 Iyer SV, Harpaz R, Lependu P, Bauer-Mehren A, Shah NH. Mining clinical text for signals of adverse drug-drug interactions. J Am Med Inform Assoc 2013
• 49 Yoon D, Park MY, Choi NK, Park BJ, Kim JH, Park RW. Detection of adverse drug reaction signals using an electronic health records database: Comparison of the Laboratory Extreme Abnormality Ratio (CLEAR) algorithm. Clin Pharmacol Ther 2012; 91 (03) 467-74.
• 50 Harpaz R, Vilar S, Dumouchel W, Salmasian H, Haerian K, Shah NH. et al. Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions. J Am Med Inform Assoc 2013; 20 (03) 413-9.
• 51 Oliveira JL, Lopes P, Nunes T, Campos D, Boyer S, Ahlberg E. et al. The EU-ADR Web Platform: delivering advanced pharmacovigilance tools. Pharmacoepidemiol Drug Saf 2013; 22 (05) 459-67.
• 52 Pendergrass SA, Brown-Gentry K, Dudek S, Frase A, Torstenson ES, Goodloe R. et al. Phenome-wide association study (PheWAS) for detection of pleiotropy within the Population Architecture using Genomics and Epidemiology (PAGE) Network. PLoS Genet 2013; 9 (01) e1003087.
• 53 Denny JC, Ritchie MD, Basford MA, Pulley JM, Bastarache L, Brown-Gentry K. et al. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics 2010; 26 (09) 1205-10.
• 54 Pathak J, Wang J, Kashyap S, Basford M, Li R, Masys DR. et al. Mapping clinical phenotype data elements to standardized metadata repositories and controlled terminologies: the eMERGE Network experience. J Am Med Inform Assoc 2011; 18 (04) 376-86.
• 55 Richesson RL, Rusincovitch SA, Wixted D, Batch BC, Feinglos MN, Miranda ML. et al. A comparison of phenotype definitions for diabetes mellitus. J Am Med Inform Assoc 2013
• 56 Roque FS, Jensen PB, Schmock H, Dalgaard M, Andreatta M, Hansen T. et al. Using electronic patient records to discover disease correlations and stratify patient cohorts. PLoS Comput Biol 2011; 7 (08) e1002141.
• 57 Lyalina S, Percha B, Lependu P, Iyer SV, Altman RB, Shah NH. Identifying phenotypic signatures of neuropsychiatric disorders from electronic medical records. J Am Med Inform Assoc 2013
• 58 Ferrandez O, South BR, Shen S, Friedlin FJ, Samore MH, Meystre SM. Evaluating current automatic de-identification methods with Veteran’s health administration clinical documents. BMC Med Res Methodol 2012; 12: 109.
• 59 Deleger L, Molnar K, Savova G, Xia F, Lingren T, Li Q. et al. Large-scale evaluation of automated clinical note de-identification and its impact on information extraction. J Am Med Inform Assoc 2013; 20 (01) 84-94.
• 60 Pantazos K, Lauesen S, Lippert S. De-identifying an EHR database - anonymity, correctness and readability of the medical record. Stud Health Technol Inform 2011; 169: 862-6.
• 61 Jiang M, Chen Y, Liu M, Rosenbloom ST, Mani S, Denny JC. et al. A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries. J Am Med Inform Assoc 2011; 18 (05) 601-6.
• 62 Xu H, Jiang M, Oetjens M, Bowton EA, Ramirez AH, Jeff JM. et al. Facilitating pharmacogenetic studies using electronic health records and natural-language processing: a case study of warfarin. J Am Med Inform Assoc 2011; 18 (04) 387-91.
• 63 Clark C, Aberdeen J, Coarr M, Tresner-Kirsch D, Wellner B, Yeh A. et al. MITRE system for clinical assertion status classification. J Am Med Inform Assoc 2011; 18 (05) 563-7.
• 64 Bejan CA, Xia F, Vanderwende L, Wurfel MM, Yetisgen-Yildiz M. Pneumonia identification using statistical feature selection. J Am Med Inform Assoc 2012; 19 (05) 817-23.
• 65 Schuemie MJ, Sen E, tJong GW, van Soest EM, Sturkenboom MC, Kors JA. Automating classification of free-text electronic health records for epidemiological studies. Pharmacoepidemiol Drug Saf 2012; 21 (06) 651-8.
• 66 Minard AL, Ligozat AL, Ben Abacha A, Bernhard D, Cartoni B, Deleger L. et al. Hybrid methods for improving information access in clinical documents: concept, assertion, and relation identification. J Am Med Inform Assoc 2011; 18 (05) 588-93.
• 67 Savova GK, Chapman WW, Zheng J, Crowley RS. Anaphoric relations in the clinical narrative: corpus creation. J Am Med Inform Assoc 2011; 18 (04) 459-65.
• 68 Dai HJ, Chen CY, Wu CY, Lai PT, Tsai RT, Hsu WL. Coreference resolution of medical concepts in discharge summaries by exploiting contextual information. J Am Med Inform Assoc 2012; 19 (05) 888-96.
• 69 Jindal P, Roth D. Using domain knowledge and domain-inspired discourse model for coreference resolution for clinical narratives. J Am Med Inform Assoc 2013; 20 (02) 356-62.
• 70 Xu Y, Liu J, Wu J, Wang Y, Tu Z, Sun JT. et al. A classification approach to coreference in discharge summaries: 2011 i2b2 challenge. J Am Med Inform Assoc 2012; 19 (05) 897-905.
• 71 Ware H, Mullett CJ, Jagannathan V, El-Rawas O. Machine learning-based coreference resolution of concepts in clinical documents. J Am Med Inform Assoc 2012; 19 (05) 883-7.
• 72 Jonnalagadda SR, Li D, Sohn S, Wu ST, Wagholikar K, Torii M. et al. Coreference analysis in clinical notes: a multi-pass sieve with alternate anaphora resolution modules. J Am Med Inform Assoc 2012; 19 (05) 867-74.
• 73 Uzuner O, Bodnari A, Shen S, Forbush T, Pestian J, South BR. Evaluating the state of the art in coreference resolution for electronic medical records. J Am Med Inform Assoc 2012; 19 (05) 786-91.
• 74 Warner JL, Zollanvari A, Ding Q, Zhang P, Snyder GM, Alterovitz G. Temporal phenome analysis of a large electronic health record cohort enables identification of hospital-acquired complications. J Am Med Inform Assoc 2013
• 75 Yadav K, Sarioglu E, Smith M, Choi HA. Automated outcome classification of emergency department computed tomography imaging reports. Acad Emerg Med 2013; 20 (08) 848-54.
• 76 Kahn MG, Batson D, Schilling LM. Data model considerations for clinical effectiveness researchers. Med Care 2012; 50 Suppl: S60-7.
• 77 Rothman B, Leonard JC, Vigoda MM. Future of electronic health records: implications for decision support. Mt Sinai J Med 2012; 79 (06) 757-68.
• 78 Memorial Sloan-Kettering Center.. Memorial Sloan-Kettering’s Collaboration with IBM Watson Featured on CBS This Morning. 2013 http://www.mskcc.org/blog/msk-s-collaboration-ibm-watson-featured-cbs-morning. Accessibility verified April 20, 2014
• 79 Mishra NK, Son RY, Arnzen JJ. Towards automatic diabetes case detection and ABCS protocol compliance assessment. Clin Med Res 2012; 10 (03) 106-21.
• 80 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
• 81 Nair BG, Newman SF, Peterson GN, Schwid HA. Automated electronic reminders to improve redosing of antibiotics during surgical cases: comparison of two approaches. Surg Infect (Larchmt) 2011; 12 (01) 57-63.
• 82 Wagholikar KB, MacLaughlin KL, Henry MR, Greenes RA, Hankey RA, Liu H. et al. Clinical decision support with automated text processing for cervical cancer screening. J Am Med Inform Assoc 2012; 19 (05) 833-9.
• 83 Murphy DR, Laxmisan A, Reis BA, Thomas EJ, Esquivel A, Forjuoh SN. et al. Electronic health record-based triggers to detect potential delays in cancer diagnosis. BMJ Qual Saf 2013
• 84 Bradley EH, Yakusheva O, Horwitz LI, Sipsma H, Fletcher J. Identifying patients at increased risk for unplanned readmission. Med Care 2013; 51 (09) 761-6.
• 85 Cholleti S, Post A, Gao J, Lin X, Bornstein W, Cantrell D. et al. Leveraging derived data elements in data analytic models for understanding and predicting hospital readmissions. AMIA Annu Symp Proc 2012; 2012: 103-11.
• 86 Mathias JS, Agrawal A, Feinglass J, Cooper AJ, Baker DW, Choudhary A. Development of a 5 year life expectancy index in older adults using predictive mining of electronic health record data. J Am Med Inform Assoc 2013; 20 e1 e118-24.
• 87 Collins SA, Cato K, Albers D, Scott K, Stetson PD, Bakken S. et al. Relationship between nursing documentation and patients’ mortality. Am J Crit Care 2013; 22 (04) 306-13.
• 88 Jackson HA, Cashy J, Frieder O, Schaeffer AJ. Data mining derived treatment algorithms from the electronic medical record improve theoretical empirical therapy for outpatient urinary tract infections. J Urol 2011; 186 (06) 2257-62.
• 89 Wagholikar KB, MacLaughlin KL, Kastner TM, Casey PM, Henry M, Greenes RA. et al. Formative evaluation of the accuracy of a clinical decision support system for cervical cancer screening. J Am Med Inform Assoc 2013; 20 (04) 749-57.
• 90 van den Branden M, Wiratunga N, Burton D, Craw S. Integrating case-based reasoning with an electronic patient record system. Artif Intell Med 2011; 51 (02) 117-23.
• 91 Gotz D, Stavropoulos H, Sun J, Wang F. ICDA: a platform for Intelligent Care Delivery Analytics. AMIA Annu Symp Proc 2012; 2012: 264-73.
• 92 Wang HQ, Li JS, Zhang YF, Suzuki M, Araki K. Creating personalised clinical pathways by semantic interoperability with electronic health records. Artif Intell Med 2013; 58 (02) 81-9.
• 93 Steinhubl SR, Muse ED, Topol EJ. Can Mobile Health Technologies Transform Health Care?. JAMA; 2013
• 94 Steinberg DM, Levine EL, Askew S, Foley P, Bennett GG. Daily text messaging for weight control among racial and ethnic minority women: randomized controlled pilot study. J Med Internet Res 2013; 15 (11) e244.
• 95 Shi HJ, Jiang XX, Yu CY, Zhang Y. Use of mobile phone text messaging to deliver an individualized smoking behaviour intervention in Chinese adolescents. J Telemed Telecare 2013; 19 (05) 282-7.
• 96 Quantified Self.. Http://quantifiedself.com. Accessibility verified April 20 2014
• 97 Dobkin BH. Wearable motion sensors to continuously measure real-world physical activities. Curr Opin Neurol 2013; 26 (06) 602-8.
• 98 de Nazelle A, Seto E, Donaire-Gonzalez D, Mendez M, Matamala J, Nieuwenhuijsen MJ. et al. Improving estimates of air pollution exposure through ubiquitous sensing technologies. Environ Pollut 2013; 176: 92-9.
• 99 Jovanov E, Milosevic M, Milenkovic A. A mobile system for assessment of physiological response to posture transitions. Conf Proc IEEE Eng Med Biol Soc 2013; 2013: 7205-8.
• 100 Dobkin BH, Dorsch A. The promise of mHealth: daily activity monitoring and outcome assessments by wearable sensors. Neurorehabil Neural Repair 2011; 25 (09) 788-98.
• 101 Guilbert TW, Arndt B, Temte J, Adams A, Buckingham W, Tandias A. et al. The theory and application of UW ehealth-PHINEX, a clinical electronic health record-public health information exchange. WMJ : Official publication of the State Medical Society of Wisconsin 2012; 111 (03) 124-33.
• 102 Lin G, Ma J, Zhang L, Qu M. Linking cancer registry and hospital discharge data for treatment surveillance. Health Informatics J 2013; 19 (02) 127-36.
• 103 Fernandez-Luque L, Karlsen R, Bonander J. Review of extracting information from the Social Web for health personalization. J Med Internet Res 2011; 13 (01) e15.
• 104 Vickey TA, Ginis KM, Dabrowski M. Twitter classification model: the ABC of two million fitness tweets. Transl Behav Med 2013; 3 (03) 304-11.
• 105 Myslin M, Zhu SH, Chapman W, Conway M. Using twitter to examine smoking behavior and perceptions of emerging tobacco products. J Med Internet Res 2013; 15 (08) e174.
• 106 Agno CF, Guo KL. Electronic health systems: challenges faced by hospital-based providers. Health Care Manag (Frederick) 2013; 32 (03) 246-52.
• 107 Nazi KM, Hogan TP, McInnes DK, Woods SS, Graham G. Evaluating patient access to Electronic Health Records: results from a survey of veterans. Med Care 2013; 51 3 Suppl 1 S52-6.
• 108 Dwork C, Pottenger R. Toward practicing privacy. J Am Med Inform Assoc 2013; 20 (01) 102-8.
• 109 Sweeney L. k-anonymity: A model for protecting privacy. Int J Uncertain Fuzz 2002; 10 (05) 557-70.
• 110 Machanavajjhala AGJ, Kifer D. I-diversity: Privacy Beyond k-anonymity. ACM Trans Knowl Discov Data 2007
• 111 Dwork C. Differential privacy. Automata, Languages and Programming, Pt 2. 2006; 4052: 1-12.
• 112 Machanavajjhala A, Kifer D, Gehrke J, Venkitasubramaniam M. l-diversity: Privacy beyond k-anonymity. ACM Trans Knowl Discov Data 2007; 1 (01) 3.
• 113 Yoo S, Shin M, Lee D. An Approach to Reducing Information Loss and Achieving Diversity of Sensitive Attributes in k-anonymity Methods. Interact J Med Res 2012; 1 (02) e14.
• 114 Gardner J, Xiong L, Xiao Y, Gao J, Post AR, Jiang X. et al. SHARE: system design and case studies for statistical health information release. J Am Med Inform Assoc 2013; 20 (01) 109-16.
• 115 Xiao XWG, Gehrke J. Differential Privacy Via Wavelet Transforms. IEEE Trans Knowl Data Eng 2011; 23 (08) 1200-14.
• 116 Meslin EM, Alpert SA, Carroll AE, Odell JD, Tierney WM, Schwartz PH. Giving patients granular control of personal health information: Using an ethics ‘Points to Consider’ to inform informatics system designers. Int J Med Inform 2013; 82 (12) 1136-43.
• 117 Malin B, Nyemba S, Paulett J. Learning relational policies from electronic health record access logs. J Biomed Inform 2011; 44 (02) 333-42.
• 118 Boxwala AA, Kim J, Grillo JM, Ohno-Machado L. Using statistical and machine learning to help institutions detect suspicious access to electronic health records. J Am Med Inform Assoc 2011; 18 (04) 498-505.
• 119 Ferrandez O, South BR, Shen S, Friedlin FJ, Samore MH, Meystre SM. BoB, a best-of-breed automated text de-identification system for VHA clinical documents. J Am Med Inform Assoc 2013; 20 (01) 77-83.
• 120 Regola N, Chawla NV. Storing and using health data in a virtual private cloud. J Med Internet Res 2013; 15 (03) e63.
• 121 Alabdulatif A, Khalil I, Mai V. Protection of electronic health records (EHRs) in cloud. Conf Proc IEEE Eng Med Biol Soc 2013; 2013: 4191-4.
• 122 Ohno-Machado L, Bafna V, Boxwala AA, Chapman BE, Chapman WW, Chaudhuri K. et al. iDASH: integrating data for analysis, anonymization, and sharing. J Am Med Inform Assoc 2012; 19 (02) 196-201.
• 123 Ohno-Machado L. To share or not to share: that is not the question. Sci Transl Med 2012 4(165).