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Yearb Med Inform 2017; 26(01): 38-52
DOI: 10.15265/IY-2017-007
Special Section: Learning from Experience: Secondary Use of Patient Data
Working Group Contributions
Georg Thieme Verlag KG Stuttgart

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

S. M. Meystre
a   Medical University of South Carolina, Charleston, SC, USA
,
C. Lovis
b   Division of Medical Information Sciences, University Hospitals of Geneva, Switzerland
,
T. Bürkle
c   University of Applied Sciences, Bern, Switzerland
,
G. Tognola
d   Institute of Electronics, Computer and Telecommunication Engineering, Italian Natl. Research Council IEIIT-CNR, Milan, Italy
,
A. Budrionis
e   Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway
,
C. U. Lehmann
f   Departments of Biomedical Informatics and Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
› Author Affiliations
Further Information

Publication History

08 May 2017

Publication Date:
11 September 2017 (online)

   Permissions and Reprints

Summary

Objective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field.

Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed.

Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems).

Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research.

Keywords

Medical informatics - electronic health records - health services research - health care evaluation mechanisms - clinical studies as topic
 

  • References

  • 1 U.S. Department of Health and Human Services. Doctors and hospitals’ use of health IT more than doubles since. 2012 Available from: http://www.hhs.gov/news/press/2013pres/05/20130522a.html
    PubMedGoogle Scholar
  • 2 Schoen C, Osborn R, Doty MM, Squires D, Peugh J, Applebaum S. A survey of primary care physicians in eleven countries, 2009: perspectives on care, costs, and experiences. Health Affairs Project HOPE - The People-to-People Health Foundation, Inc 2009; Nov; 28 (06) w1171-83.
    PubMedGoogle Scholar
  • 3 Safran C, Bloomrosen M, Hammond WE, Labkoff S, Markel-Fox S, Tang PC. et al. Toward a national framework for the secondary use of health data: an American Medical Informatics Association White Paper. J Am Med Inform Assoc 2007; Jan; 14 (01) 1-9.
    CrossrefPubMedGoogle Scholar
  • 4 National Research Council (US) Committee on A Framework for Developing a New Taxonomy of Disease. Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease. Washington (DC): National Academies Press (US); 2011
    Google Scholar
  • 5 National Committee on Vital and Health Statistics. Information for health: A strategy for building the National Health Information Infrastructure. Available from: aspe.hhs.gov/sp/nhii/documents/NHIIReport2001/toc.htm
    PubMedGoogle Scholar
  • 6 Fries JF, McShane DJ. ARAMIS (the American Rheumatism Association Medical Information System). A prototypical national chronic-disease data bank. West J Med 1986; Dec; 145 (06) 798-804.
    PubMedGoogle Scholar
  • 7 Centers for Medicare Medicaid Services. 42 CFR 424: Medicare Program; Electronic Submission of Medicare Claims. Available from: https://www.federalregister.gov/articles/2005/11/25/05-23080/medicare-program-electronic-submission-of-medicare-claims
    PubMedGoogle Scholar
  • 8 Charles D, Gabriel M, Furukawa M. Adoption of Electronic Health Record Systems among U.S. Non-federal Acute Care Hospitals: 2008-2013. Available from: http://www.healthit.gov/sites/default/files/oncdatabrief16.pdf
    PubMedGoogle Scholar
  • 9 Lehmann CU, O’Connor KG, Shorte VA, Johnson TD. Use of electronic health record systems by office-based pediatricians. Pediatrics 2015; Jan; 135 (01) e7-15.
    CrossrefPubMedGoogle Scholar
  • 10 Payne TH, Detmer DE, Wyatt JC, Buchan IE. National-scale clinical information exchange in the United Kingdom: lessons for the United States. J Am Med Inform Assoc 2011; Jan; 18 (01) 91-98.
    PubMedGoogle Scholar
  • 11 Fadly AEl, Rance B, Lucas N, Mead C, Chatellier G, Lastic P-Y. et al. Integrating clinical research with the Healthcare Enterprise: from the RE-USE project to the EHR4CR platform. J Biomed Inform 2011; Dec; 44 (Suppl. 01) S94-102.
    PubMedGoogle Scholar
  • 12 Steele SJ. Working with the CTSA Consortium: what we bring to the table. Sci Transl Med 2010; Dec 22; 02 (63) 63mr5-63mr5.
    PubMedGoogle Scholar
  • 13 Rea S, Pathak J, Savova G, Oniki TA, Westberg Les, Beebe CE. et al. Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: The SHARPn project. J Biomed Inform 2012; Aug; 45 (04) 763-71.
    PubMedGoogle Scholar
  • 14 McCarty CA, Chisholm RL, Chute CG, Kullo IJ, Jarvik GP, Larson EB. et al. The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med Genomics 2011; 04 (01) 13.
    PubMedGoogle Scholar
  • 15 Ross MK, Wei W, Ohno-Machado L. “Big data” and the electronic health record. Yearb Med Inform 2014; 09: 97-104.
    PubMedGoogle Scholar
  • 16 Gartner Inc. What Is Big Data? - Gartner IT Glossary - Big Data. 2012 Available from: http://www.gartner.com/it-glossary/big-data/
    PubMedGoogle Scholar
  • 17 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; Oct; 83 (10) 38-43 quiz 44.
    PubMedGoogle Scholar
  • 18 Safran C. Using routinely collected data for clinical research. Stat Med 1991; Apr; 10 (04) 559-64.
    PubMedGoogle Scholar
  • 19 Hersh WR. Adding value to the electronic health record through secondary use of data for quality assurance, research, and surveillance. Am J Manag Care 2007; Jun; 13 (6 Part 1): 277-8.
    PubMedGoogle Scholar
  • 20 Weiner MG, Embi PJ. Toward reuse of clinical data for research and quality improvement: the end of the beginning?. Ann Intern Med 2009; Sep; 151 (05) 359-60.
    CrossrefPubMedGoogle Scholar
  • 21 Safran C. Reuse of Clinical Data. Yearb Med Inform 2014; 09 (01) 52-4.
    PubMedGoogle Scholar
  • 22 Price Waterhouse Coopers. Transforming Healthcare Through Secondary Use of Health Data. 2009
    PubMedGoogle Scholar
  • 23 Murff HJ, FitzHenry F, Matheny ME, Gentry N, Kotter KL, Crimin K. et al. Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA 2011; Aug 24; 306 (08) 48-855.
    PubMedGoogle Scholar
  • 24 Garvin JH, DuVall SL, South BR, Bray BE, Bolton D, Heavirland J. et al. Automated extraction of ejection fraction for quality measurement using regular expressions in Unstructured Information Management Architecture (UIMA) for heart failure. J Am Med Inform Assoc 2012; Aug 9; 19 (05) 859-66.
    PubMedGoogle Scholar
  • 25 Herrmann FR, Safran C, Levkoff SE, Minaker KL. Serum albumin level on admission as a predictor of death, length of stay, and readmission. Arch Intern Med 1992; Jan; 152 (01) 125-30.
    PubMedGoogle Scholar
  • 26 McManus DD, Saczynski JS, Lessard D, Waring ME, Allison J, Parish DC. et al. Reliability of Predicting Early Hospital Readmission After Discharge for an Acute Coronary Syndrome Using Claims-Based Data. Am J Cardiol 2016; Feb 15; 117 (04) 501-7.
    PubMedGoogle Scholar
  • 27 Cai X, Perez-Concha O, Coiera E, Martin-Sanchez F, Day R, Roffe D. et al. Real-time prediction of mortality, readmission, and length of stay using electronic health record data. J Am Med Inform Assoc 2015; Sep 15; ocv110.
    PubMedGoogle Scholar
  • 28 Temple MW, Lehmann CU, Fabbri D. Predicting Discharge Dates From the NICU Using Progress Note Data. Pediatrics 2015; Aug; 136 (02) e395-405.
    CrossrefPubMedGoogle Scholar
  • 29 Evans RS, Burke JP, Classen DC, Gardner RM, Menlove RL, Goodrich KM. et al. Computerized identification of patients at high risk for hospital-acquired infection. Am J Infect Control 1992; Feb; 20 (01) 4-10.
    PubMedGoogle Scholar
  • 30 Pittet D, Safran E, Harbarth S, Borst F, Copin P, Rohner P. et al. Automatic alerts for methicillin-resistant Staphylococcus aureus surveillance and control: role of a hospital information system. Infect Control Hosp Epidemiol 1996; Aug; 17 (08) 496-502.
    PubMedGoogle Scholar
  • 31 Samore MH, Lichtenberg D, Saubermann L, Kawachi C, Carmeli Y. A clinical data repository enhances hospital infection control. Proc AMIA Annu Fall Symp 1997; 56-60.
    PubMedGoogle Scholar
  • 32 Wagner MM, Moore AW, Aryel RM. Handbook of biosurveillance. 2011
    PubMedGoogle Scholar
  • 33 Kopcke F, Prokosch H-U. Employing computers for the recruitment into clinical trials: a comprehensive systematic review. J Med Internet Res 2014; 16 (07) e161.
    CrossrefPubMedGoogle Scholar
  • 34 Weiner MG, Xie D, Tannen RL. Clinical trials in silico: rigorous assessment of treatment effect using electronic health records. AMIA Annu Symp Proc 2008; 1172.
    PubMedGoogle Scholar
  • 35 Coorevits P, Sundgren M, Klein GO, Bahr A, Claerhout B, Daniel C. et al. Electronic health records: new opportunities for clinical research. J Intern Med 2013; Oct 18; 274 (06) 547-60.
    CrossrefPubMedGoogle Scholar
  • 36 Friedman CP, Wong AK, Blumenthal D. Achieving a nationwide learning health system. Sci Transl Med 2010; Nov 10; 02 (57) 57cm29.
    PubMedGoogle Scholar
  • 37 Harper EM. The economic value of health care data. Nurs Adm Q 2013; Apr; 37 (02) 105-8.
    PubMedGoogle Scholar
  • 38 Hripcsak G, Albers DJ. Next-generation phenotyping of electronic health records. J Am Med Inform Assoc. 2012 Sep 6.
    PubMedGoogle Scholar
  • 39 Overhage JM, Overhage LM. Sensible use of observational clinical data. Stat Methods Med Res 2013; Feb; 22 (01) 7-13.
    PubMedGoogle Scholar
  • 40 Hoffman S, Podgurski A. Big bad data: law, public health, and biomedical databases. J Law Med Ethics 2013; Mar; 41 (Suppl. 01) (s1): 56-60 PMID: 23590742.
    PubMedGoogle Scholar
  • 41 Hripcsak G, Knirsch C, Zhou L, Wilcox A, Melton G. Bias associated with mining electronic health records. J Biomed Discov Collab 2011; 06: 48-52 PMID: 21647858.
    PubMedGoogle Scholar
  • 42 Ancker JS, Shih S, Singh MP, Snyder A, Edwards A, Kaushal R. Root causes underlying challenges to secondary use of data. AMIA Annu Symp Proc 2011; 2011: 57-62.
    PubMedGoogle Scholar
  • 43 de Lusignan S, Pearce C, Shaw NT, Liaw S-T, Michalakidis G, Vicente MT. et al. What are the barriers to conducting international research using routinely collected primary care data?. Stud Health Technol Inform 2011; 165: 135-40.
    PubMedGoogle Scholar
  • 44 Botsis T, Hartvigsen G, Chen F, Weng C. Secondary Use of EHR: Data Quality Issues and Informatics Opportunities. AMIA Summits Transl Sci Proc 2010; 2010: 1-5.
    PubMedGoogle Scholar
  • 45 Thiru K, Hassey A, Sullivan F. Systematic review of scope and quality of electronic patient record data in primary care. BMJ 2003; May 17; 326 (7398): 1070.
    PubMedGoogle Scholar
  • 46 Weiskopf NG, Rusanov A, Weng C. Sick patients have more data: the non-random completeness of electronic health records. AMIA Annu Symp Proc 2013; 2013: 1472-7.
    PubMedGoogle Scholar
  • 47 Kahn MG, Raebel MA, Glanz JM, Riedlinger K, Steiner JF. A pragmatic framework for single-site and multisite data quality assessment in electronic health record-based clinical research. Med Care 2012; Jul;50 Suppl: S21-9.
    PubMedGoogle Scholar
  • 48 Weiskopf NG, Hripcsak G, Swaminathan S, Weng C. Defining and measuring completeness of electronic health records for secondary use. J Biomed Inform 2013; Oct; 46 (05) 830-6.
    CrossrefPubMedGoogle Scholar
  • 49 Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc 2013; Jan; 20 (01) 144-51.
    Google Scholar
  • 50 Silversides A. Privacy concerns raised over “secondary use” of health records. CMAJ 2009; Dec; 181 (12) E287.
    PubMedGoogle Scholar
  • 51 Emam KEl, Rodgers S, Malin B. Anonymising and sharing individual patient data. BMJ 2015; 350 (mar20 1): h1139.
    PubMedGoogle Scholar
  • 52 Geissbuhler A, Safran C, Buchan I, Bellazzi R, Labkoff S, Eilenberg K. et al. Trustworthy reuse of health data: a transnational perspective. Int J Med Inform 2013; Jan; 82 (01) 1-9.
    PubMedGoogle Scholar
  • 53 Hersh WR, Cimino J, Payne PRO, Embi P, Logan J, Weiner M. et al. Recommendations for the use of operational electronic health record data in comparative effectiveness research. EGEMS (Wash DC) 2013; 01 (01) 1018.
    PubMedGoogle Scholar
  • 54 Hersh WR, Weiner MG, Embi PJ, Logan JR, Payne PRO, Bernstam EV. et al. Caveats for the use of operational electronic health record data in comparative effectiveness research. Med Care 2013; Aug; 51 (8 Suppl 3): S30-7.
    PubMedGoogle Scholar
  • 55 Health Information & the Law. Who Owns Medical Records: 50 State Comparison. Available from: http://www.healthinfolaw.org/comparative-analysis/who-owns-medical-records-50-state-comparison
    PubMedGoogle Scholar
  • 56 Shiyanbola OO, Mort JR. Patients’ perceived value of pharmacy quality measures: a mixed-methods study. BMJ Open 2015; Jan 19; 05 (01) e006086.
    PubMedGoogle Scholar
  • 57 Ganguli I. Stuck on loop: why do patients have to repeat their stories?. Available from: http://healthydebate.ca/opinions/stuck-on-loop-why-do-patients-have-to-repeat-their-stories
    PubMedGoogle Scholar
  • 58 International Medical Informatics Association. The IMIA Code of Ethics for Health Information Professionals. Available from: http://www.imia-medinfo.org/new2/pubdocs/Ethics_Eng.pdf
    PubMedGoogle Scholar
  • 59 GPO US. CFR Title 45 Subtitle A Part 164: Security and Privacy. Available from: http://www.access.gpo.gov/nara/cfr/waisidx_08/45cfr164_08.html
    PubMedGoogle Scholar
  • 60 GPO US. CFR Title 45 § 46: Protection of Human Subjects. Available from: http://www.access.gpo.gov/nara/cfr/waisidx_08/45cfr46_08.html
    PubMedGoogle Scholar
  • 61 Directive 95/46/EC of the European Parliament and of the Council. Available from: http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CEL-EX:31995L0046:en:HTML
    PubMedGoogle Scholar
  • 62 United Kingdom. Data protection act. 1998 Available from: http://legislation.gov.uk/ukpga/1998/29
    PubMedGoogle Scholar
  • 63 Iacono LLo. Multi-centric universal pseudonymisation for secondary use of the EHR. Stud Health Technol Inform 2007; 126: 239-47.
    PubMedGoogle Scholar
  • 64 Kushida CA, Nichols DA, Jadrnicek R, Miller R, Walsh JK, Griffin K. Strategies for De-identification and Anonymization of Electronic Health Record Data for Use in Multicenter Research Studies. Med Care 2012; Jul;50 Suppl: S82-S101.
    PubMedGoogle Scholar
  • 65 Somolinos R, Munoz A, Elena MHernando, Pascual M, Caceres J, Sanchez-de-Madariaga R. et al. Service for the Pseudonymization of Electronic Healthcare Records Based on ISO/EN 13606 for the Secondary Use of Information. IEEE J Biomed Health Inform 2015; Nov; 19 (06) 1937-44.
    PubMedGoogle Scholar
  • 66 Chan EM, Lam PE, Mitchell JC. Understanding the Challenges with Medical Data Segmentation for Privacy. HealthTech’13; Washington, DS: 2013: 1-10.
    Google Scholar
  • 67 Loukides G, Liagouris J, Gkoulalas-Divanis A, Terrovitis M. Disassociation for electronic health record privacy. J Biomed Inform 2014; Aug 1; 50 (C): 46-61.
    PubMedGoogle Scholar
  • 68 Meystre SM. De-identification of Unstructured Clinical Data for Patient Privacy Protection. Medical Data Privacy Handbook; 2015: 697-716.
    Google Scholar
  • 69 Dorr DA, Phillips WF, Phansalkar S, Sims SA, Hurdle JF. Assessing the difficulty and time cost of de-identification in clinical narratives. Methods Inf Med 2006; 45 (03) 246-52.
    PubMedGoogle Scholar
  • 70 Meystre SM, Friedlin FJ, South BR, Shen S, Samore MH. Automatic de-identification of textual documents in the electronic health record: a review of recent research. BMC Med Res Methodol 2010; 10: 70.
    PubMedGoogle Scholar
  • 71 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; Jul 27; 12 (01) 109.
    PubMedGoogle Scholar
  • 72 Ferrandez O, South BR, Shen S, Friedlin FJ, Samore MH, Meystre SM. Generalizability and comparison of automatic clinical text de-identification methods and resources. AMIA Annu Symp Proc 2012; 2012: 199-208.
    PubMedGoogle Scholar
  • 73 Aberdeen J, Bayer S, Yeniterzi R, Wellner B, Clark C, Hanauer D. et al. The MITRE Identification Scrubber Toolkit: design, training, and assessment. Int J Med Inform 2010; Dec; 79 (12) 849-59.
    PubMedGoogle Scholar
  • 74 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; Jan 1; 20 (01) 77-83.
    PubMedGoogle Scholar
  • 75 Zuccon G, Kotzur D, Nguyen A, Bergheim A. De-identification of health records using Anonym: Effectiveness and robustness across datasets. Artif Intell Med 2014; Jul; 61 (03) 145-51.
    PubMedGoogle Scholar
  • 76 Uzuner O, Luo Y, Szolovits P. Evaluating the state-of-the-art in automatic de-identification. J Am Med Inform Assoc 2007; Sep; 14 (05) 550-63.
    PubMedGoogle Scholar
  • 77 Stubbs A, Kotfila C, Uzuner O. Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1. J Biomed Inform 2015; Sep 26; 1-9.
    PubMedGoogle Scholar
  • 78 Meystre SM, Ferrandez O, Friedlin FJ, South BR, Shen S, Samore MH. Text de-identification for privacy protection: a study of its impact on clinical text information content. J Biomed Inform 2014; Aug; 50: 142-50.
    PubMedGoogle Scholar
  • 79 Carrell D, Malin B, Aberdeen J, Bayer S, Clark C, Wellner B. et al. Hiding in plain sight: use of realistic surrogates to reduce exposure of protected health information in clinical text. J Am Med Inform Assoc 2013; Mar; 20 (02) 342-8.
    PubMedGoogle Scholar
  • 80 McMurry AJ, Fitch B, Savova G, Kohane IS, Reis BY. Improved de-identification of physician notes through integrative modeling of both public and private medical text. BMC Med Inform Decis Mak 2013; 13 (01) 112.
    PubMedGoogle Scholar
  • 81 Chazard E, Mouret C, Ficheur G, Schaffar A, Beuscart J-B, Beuscart R. Proposal and evaluation of FASDIM, a Fast And Simple De-Identification Method for unstructured free-text clinical records. Int J Med Inform 2014; Apr; 83 (04) 303-12.
    PubMedGoogle Scholar
  • 82 Grouin C, Névéol A. De-identification of clinical notes in French: towards a protocol for reference corpus development. J Biomed Inform 2014; Aug 1; 50 (C): 151-61.
    PubMedGoogle Scholar
  • 83 Dalianis H, Velupillai S. De-identifying Swedish clinical text - refinement of a gold standard and experiments with Conditional random fields. J Biomed Sem 2010; 01 (01) 6.
    PubMedGoogle Scholar
  • 84 Sweeney L. K-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems 2002; Oct; 10 (05) 557-70.
    PubMedGoogle Scholar
  • 85 Machanavajjhala A, Gehrke J, Kifer D, Venkitasubramaniam M. l-Diversity: Privacy Beyond k-Anonymity. ICDE IEEE; 2006: 24.
    Google Scholar
  • 86 Dankar FK, Emam KEl. Practicing Differential Privacy in Health Care: A Review. TDP 2013; 06 (01) 35-67.
    PubMedGoogle Scholar
  • 87 Gkoulalas-Divanis A, Loukides G, Sun J. Publishing data from electronic health records while preserving privacy: A survey of algorithms. J Biomed Inform 2014; Aug 1; 50 (C): 4-19.
    PubMedGoogle Scholar
  • 88 Tamersoy A, Loukides G, Nergiz ME, Saygin Y, Malin B. Anonymization of longitudinal electronic medical records. IEEE Trans Inf Technol Biomed 2012; May; 16 (03) 413-23.
    PubMedGoogle Scholar
  • 89 Loukides G, Gkoulalas-Divanis A. Utility-aware anonymization of diagnosis codes. IEEE journal of biomedical and health informatics IEEE J Biomed Health Inform 2013; Jan; 17 (01) 60-70.
    PubMedGoogle Scholar
  • 90 Xia W, Heatherly R, Ding X, Li J, Malin BA. R-U policy frontiers for health data de-identification. J Am Med Inform Assoc 2015; Sep; 22 (05) 1029-41.
    PubMedGoogle Scholar
  • 91 Kohlmayer F, Prasser F, Eckert C, Kuhn KA. A flexible approach to distributed data anonymization. J Biomed Inform. 2013 Dec 12.
    PubMedGoogle Scholar
  • 92 Chakaravarthy VT, Gupta H, Roy P, Mohania MK. Efficient techniques for document sanitization. CIKM New York, New York, USA: ACM Press; 2008: 843-852.
    Google Scholar
  • 93 Jiang W, Murugesan M, Clifton C, Si L. t-Plausibility: Semantic Preserving Text Sanitization. CSE IEEE 2009; 03: 68-75.
    PubMedGoogle Scholar
  • 94 Loukides G, Denny JC, Malin B. The disclosure of diagnosis codes can breach research participants’ privacy. J Am Med Inform Assoc 2010; May; 17 (03) 322-7.
    PubMedGoogle Scholar
  • 95 Emam KEl, Jabbouri S, Sams S, Drouet Y, Power M. Evaluating common de-identification heuristics for personal health information. J Med Internet Res 2006; 08 (04) e28.
    CrossrefPubMedGoogle Scholar
  • 96 Benitez K, Malin B. Evaluating re-identification risks with respect to the HIPAA privacy rule. J Am Med Inform Assoc 2010; Feb 26; 17 (02) 169-77.
    PubMedGoogle Scholar
  • 97 Kwok PK, Lafky D. Harder Than You Think: A Case Study of Re-Identification Risk of HI-PAA-Compliant Records. Joint Statistical Meetings 2011 2011; 1-8.
    PubMedGoogle Scholar
  • 98 Emam KEl, Jonker E, Arbuckle L, Malin B. A systematic review of re-identification attacks on health data. PLoS One 2011; 06 (12) e28071.
    PubMedGoogle Scholar
  • 99 Dankar FK, Emam KEl, Neisa A, Roffey T. Estimating the re-identification risk of clinical data sets. BMC Med Inform Decis Mak 2012; Jul 9; 12 (01) 1-1.
    PubMedGoogle Scholar
  • 100 Meystre S, Shen S, Hofmann D, Gundlapalli A. Can Physicians Recognize Their Own Patients in De-identified Notes?. Stud Health Technol Inform 2014; 205: 778-2.
    PubMedGoogle Scholar
  • 101 Heitmann KU. The role of communication servers in the architecture of healthcare information systems. Stud Health Technol Inform 1997; 45: 156-62.
    PubMedGoogle Scholar
  • 102 Wentz B, Seggewies C, Bell R, Knispel S, Kraska D. The Erlangen Hospital Communication Hub: migration from proprietary to standardised communication. Stud Health Technol Inform 1997; 45: 163-67.
    PubMedGoogle Scholar
  • 103 Channin DS. Integrating the Healthcare Enterprise: a primer. Part 2. Seven brides for seven brothers: the IHE integration profiles. Radiographics 2001; Sep; 21 (05) 1343-50.
    PubMedGoogle Scholar
  • 104 Bernardini A, Alonzi M, Campioni P, Vecchioli A, Marano P. IHE: integrating the healthcare enterprise, towards complete integration of healthcare information systems. Rays 2003; Jan; 28 (01) 83-93.
    PubMedGoogle Scholar
  • 105 Bichutskiy VY, Colman R, Brachmann RK, Lathrop RH. Heterogeneous biomedical database integration using a hybrid strategy: a p53 cancer research database. Cancer Inform 2007; Feb 20; 02: 277-87.
    PubMedGoogle Scholar
  • 106 Zapletal E, Rodon N, Grabar N, Degoulet P. Methodology of integration of a clinical data warehouse with a clinical information system: the HEGP case. Stud Health Technol Inform 2010; 160 (Pt 1): 193-7.
    PubMedGoogle Scholar
  • 107 Bauer CRKD, Ganslandt T, Baum B, Christoph J, Engel I, Löbe M. et al. Integrated Data Repository Toolkit (IDRT). A Suite of Programs to Facilitate Health Analytics on Heterogeneous Medical Data. Methods Inf Med 2016; 55 (02) 125-35.
    PubMedGoogle Scholar
  • 108 Ganslandt T, Mate S, Helbing K, Sax U, Prokosch HU. Unlocking Data for Clinical Research - The German i2b2 Experience. Appl Clin Inform 2011; 02 (01) 116-27 PMID: 23616864.
    PubMedGoogle Scholar
  • 109 Fadly AEl, Rance B, Lucas N, Mead C, Chatellier G, Lastic P-Y. et al. Integrating clinical research with the Healthcare Enterprise. J Biomed Inform 2011; Dec; 44 (S1): S94-S102.
    PubMedGoogle Scholar
  • 110 Marco-Ruiz L, Moner D, Maldonado JA, Kolstrup N, Bellika JG. Archetype-based data warehouse environment to enable the reuse of electronic health record data. Int J Med Inform 2015; Sep; 84 (09) 702-14.
    PubMedGoogle Scholar
  • 111 Schweiger R, Bürkle T, Hölzer S, Tafazzoli AG, Dudeck J. Plug and play--fiction or reality?. Stud Health Technol Inform 1998; 52 Pt 2: 999-1001.
    PubMedGoogle Scholar
  • 112 Bürkle T, Schweiger R, Altmann U, Holena M, Blobel B, Dudeck J. Transferring data from one EPR to another: content--syntax--semantic. Methods Inf Med 1999; Dec; 38 (4-5): 321-5.
    PubMedGoogle Scholar
  • 113 Huser V, Cimino JJ. Desiderata for healthcare integrated data repositories based on architectural comparison of three public repositories. AMIA Annu Symp Proc 2013; 2013: 648-56.
    PubMedGoogle Scholar
  • 114 Kuchinke W, Wiegelmann S, Verplancke P, Ohmann C. Extended cooperation in clinical studies through exchange of CDISC metadata between different study software solutions. Methods Inf Med 2006; 45 (04) 441-6.
    PubMedGoogle Scholar
  • 115 Mathura VS, Rangareddy M, Gupta P, Mullan M. CliniProteus: A flexible clinical trials information management system. Bioinformation 2007; 02 (04) 163-5.
    PubMedGoogle Scholar
  • 116 Kuchinke W, Aerts J, Semler SC, Ohmann C. CDISC standard-based electronic archiving of clinical trials. Methods Inf Med 2009; 48 (05) 408-13.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 117 Ohmann C, Kuchinke W. Future developments of medical informatics from the viewpoint of networked clinical research. Interoperability and integration. Methods Inf Med 2009; 48 (01) 45-54.
    PubMedGoogle Scholar
  • 118 Hripcsak G, Duke JD, Shah NH, Reich CG, Huser V, Schuemie MJ. et al. Observational Health Data Sciences and Informatics (OHDSI) - Opportunities for Observational Researchers. Stud Health Technol Inform 2015; 216: 574-8.
    PubMedGoogle Scholar
  • 119 Gini R, Schuemie M, Brown J, Ryan P, Vacchi E, Coppola M. et al. Data Extraction and Management in Networks of Observational Health Care Databases for Scientific Research: A Comparison of EU-ADR, OMOP, Mini-Sentinel and MATRICE Strategies. EGEMS (Wash DC) 2016; 04 (01) 1189.
    PubMedGoogle Scholar
  • 120 Selby JV, Krumholz HM, Kuntz RE, Collins FS. Network news: powering clinical research. Sci Transl Med 2013; Apr 24; 182fs13.
    PubMedGoogle Scholar
  • 121 PCORnet PPRN Consortium. Daugherty SE, Wahba S, Fleurence R. Patient-powered research networks: building capacity for conducting patient-centered clinical outcomes research. J Am Med Inform Assoc 2014; Jul; 21 (04) 583-6.
    PubMedGoogle Scholar
  • 122 Kaushal R, Hripcsak G, Ascheim DD, Bloom T, Campion TR, Caplan AL. et al. Changing the research landscape: the New York City Clinical Data Research Network. J Am Med Inform Assoc 2014; Jul; 21 (04) 587-90.
    PubMedGoogle Scholar
  • 123 Cannon J, Lucci S. Transcription and EHRs: Benefits of a Blended Approach. Journal of AHIMA 2010; Feb; 81 (02) 36-40 Available from: http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_046429.hcsp?dDoc-Name=bok1_046429
    PubMedGoogle Scholar
  • 124 Meystre SM, Savova GK, Kipper-Schuler KC, Hurdle JF. Extracting information from textual documents in the electronic health record: a review of recent research. Yearb Med Inform 2008; 128-44.
    PubMedGoogle Scholar
  • 125 Carrell DS, Halgrim S, Tran D-T, Buist DSM, Chubak J, Chapman WW. et al. Using natural language processing to improve efficiency of manual chart abstraction in research: the case of breast cancer recurrence. Am J Epidemiol 2014; Mar 15; 179 (06) 749-58.
    PubMedGoogle Scholar
  • 126 Uzuner O, South BR, Shen S, DuVall SL. 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. J Am Med Inform Assoc 2011; Aug 16; 18 (05) 552-6.
    PubMedGoogle Scholar
  • 127 Xu H, Stenner SP, Doan S, Johnson KB, Waitman LR, Denny JC. MedEx: a medication information extraction system for clinical narratives. J Am Med Inform Assoc 2010; Jan; 17 (01) 19-24.
    PubMedGoogle Scholar
  • 128 Liu H, Bielinski SJ, Sohn S, Murphy S, Wagholikar KB, Jonnalagadda SR. et al. An information extraction framework for cohort identification using electronic health records. AMIA Summits Transl Sci Proc 2013; 2013: 149-53.
    PubMedGoogle Scholar
  • 129 Kreuzthaler M, Schulz S, Berghold A. Secondary use of electronic health records for building cohort studies through top-down information extraction. J Biomed Inform 2015; Feb 1; 53 (C): 188-95.
    PubMedGoogle Scholar
  • 130 Ni Y, Wright J, Perentesis J, Lingren T, Deleger L, Kaiser M. et al. Increasing the efficiency of trial-patient matching: automated clinical trial eligibility Pre-screening for pediatric oncology patients. BMC Med Inform Decis Mak 2015; 15 (01) 28.
    PubMedGoogle Scholar
  • 131 Pakhomov SSV, Hemingway H, Weston SA, Jacobsen SJ, Rodeheffer R, Roger VL. Epidemiology of angina pectoris: role of natural language processing of the medical record. Am Heart J 2007; Apr; 153 (04) 666-73.
    PubMedGoogle Scholar
  • 132 Pakhomov S, Weston SA, Jacobsen SJ, Chute CG, Meverden R, Roger VL. Electronic medical records for clinical research: application to the identification of heart failure. Am J Manag Care 2007; Jun; 13 (6 Part 1): 281-8.
    PubMedGoogle Scholar
  • 133 Weng C, Wu X, Luo Z, Boland MR, Theodoratos D, Johnson SB. EliXR: an approach to eligibility criteria extraction and representation. J Am Med Inform Assoc 2011; Dec; 18 (Suppl. 01) Supplement 1 i116-24.
    PubMedGoogle Scholar
  • 134 Boland MR, Tu SW, Carini S, Sim I, Weng C. EliXR-TIME: A Temporal Knowledge Representation for Clinical Research Eligibility Criteria. AMIA Summits Transl Sci Proc 2012; 2012: 71-80.
    PubMedGoogle Scholar
  • 135 Al-Haddad MA, Friedlin J, Kesterson J, Waters JA, Aguilar-Saavedra JR, Schmidt CM. Natural language processing for the development of a clinical registry: a validation study in intraductal papillary mucinous neoplasms. HPB (Oxford) 2010; Dec; 12 (10) 688-95.
    PubMedGoogle Scholar
  • 136 Xu H, Fu Z, Shah A, Chen Y, Peterson NB, Chen Q. et al. Extracting and integrating data from entire electronic health records for detecting colorectal cancer cases. AMIA Annu Symp Proc 2011; 2011: 1564-72.
    PubMedGoogle Scholar
  • 137 Pham A-D, Névéol A, Lavergne T, Yasunaga D, Clément O, Meyer G. et al. Natural language processing of radiology reports for the detection of thromboembolic diseases and clinically relevant incidental findings. BMC Bioinformatics 2014; 15 (01) 266.
    CrossrefPubMedGoogle Scholar
  • 138 Kontio E, Airola A, Pahikkala T, Lundgren-Laine H, Junttila K, Korvenranta H. et al. Predicting patient acuity from electronic patient records. J Biomed Inform 2014; Oct; 51: 35-40.
    PubMedGoogle Scholar
  • 139 Kullo IJ, Fan J, Pathak J, Savova GK, Ali Z, Chute CG. Leveraging informatics for genetic studies: use of the electronic medical record to enable a genome-wide association study of peripheral arterial disease. J Am Med Inform Assoc 2010; Sep; 17 (05) 568-74.
    PubMedGoogle Scholar
  • 140 Rea S, Pathak J, Savova G, Oniki TA, Westberg L, Beebe CE. et al. Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: the SHARPn project. J Biomed Inform 2012; Aug; 45 (04) 763-71.
    PubMedGoogle Scholar
  • 141 Sohn S, Kocher J-PA, Chute CG, Savova GK. Drug side effect extraction from clinical narratives of psychiatry and psychology patients. J Am Med Inform Assoc 2011; Dec; 18 (Suppl. 01) Supplement 1 i144-9.
    PubMedGoogle Scholar
  • 142 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; May 1; 20 (03) 413-9.
    PubMedGoogle Scholar
  • 143 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; 07 (07) e41471.
    PubMedGoogle Scholar
  • 144 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; Aug; 92 (02) 228-34.
    PubMedGoogle Scholar
  • 145 Wang X, Hripcsak G, Markatou M, Friedman C. Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. J Am Med Inform Assoc 2009; May; 16 (03) 328-37.
    PubMedGoogle Scholar
  • 146 Overby CL, Pathak J, Gottesman O, Haerian K, Perotte A, Murphy S. et al. A collaborative approach to developing an electronic health record phenotyping algorithm for drug-induced liver injury. J Am Med Inform Assoc 2013; Dec; 20 (e2): e243-52.
    PubMedGoogle Scholar
  • 147 Yadav K, Sarioglu E, Smith M, Choi H-A. Automated outcome classification of emergency department computed tomography imaging reports. Acad Emerg Med 2013; Aug; 20 (08) 848-54.
    PubMedGoogle Scholar
  • 148 St-Maurice J, Kuo MH. Analyzing primary care data to characterize inappropriate emergency room use. Stud Health Technol Inform 2012; 180: 990-4.
    PubMedGoogle Scholar
  • 149 Denny JC. Chapter 13: Mining Electronic Health Records in the Genomics Era. PLoS Comp Biol 2012; Dec 27; 08 (12) e1002823.
    PubMedGoogle Scholar
  • 150 Cuggia M, Bayat S, Garcelon N, Sanders L, Rouget F, Coursin A. et al. A full-text information retrieval system for an epidemiological registry. Stud Health Technol Inform 2010; 160 (Pt 1): 491-5.
    PubMedGoogle Scholar
  • 151 Ludvigsson JF, Pathak J, Murphy S, Durski M, Kirsch PS, Chute CG. et al. Use of computerized algorithm to identify individuals in need of testing for celiac disease. J Am Med Inform Assoc 2013; Dec; 20 (e2): e306-10.
    PubMedGoogle Scholar
  • 152 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 2014; Mar; 21 (02) 353-62.
    PubMedGoogle Scholar
  • 153 LePendu P, Iyer SV, Fairon C, Shah NH. Annotation Analysis for Testing Drug Safety Signals using Unstructured Clinical Notes. J Biomed Sem 2012; 03 (Suppl. 01) S5.
    PubMedGoogle Scholar
  • 154 Patel R, Lloyd T, Jackson R, Ball M, Shetty H, Broadbent M. et al. Mood instability is a common feature of mental health disorders and is associated with poor clinical outcomes. BMJ Open 2015; 05 (05) e007504.
    PubMedGoogle Scholar
  • 155 Schuemie MJ, Sen E, ‘t Jong GW, van Soest EM, Sturkenboom MC, Kors JA. Automating classification of free-text electronic health records for epidemiological studies. Pharmacoepidemiol Drug Saf 2012; Jun; 21 (06) 651-8.
    PubMedGoogle Scholar
  • 156 Campillo-Gimenez B, Garcelon N, Jarno P, Chapplain JM, Cuggia M. Full-text automated detection of surgical site infections secondary to neurosurgery in Rennes, France. Stud Health Technol Inform 2013; 192: 572-5.
    PubMedGoogle Scholar
  • 157 Craig JB, Culley JM, Tavakoli AS, Svendsen ER. Gleaning data from disaster: a hospital-based data mining method to study all-hazard triage after a chemical disaster. Am J Disaster Med 2013; 08 (02) 97-111.
    PubMedGoogle Scholar
  • 158 Weber GM, Murphy SN, McMurry AJ, Mac-Fadden D, Nigrin DJ, Churchill S. et al. The Shared Health Research Information Network (SHRINE): a prototype federated query tool for clinical data repositories. J Am Med Inform Assoc 2009; Sep; 16 (05) 624-30.
    PubMedGoogle Scholar
  • 159 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; May; 15 (03) 428-37.
    PubMedGoogle Scholar
  • 160 Aoki N, Demsar J, Zupan B, Mozina M, Pretto EA, Oda J. et al. Predictive model for estimating risk of crush syndrome: a data mining approach. J Trauma 2007; Apr; 62 (04) 940-5.
    PubMedGoogle Scholar
  • 161 Lee J, Maslove DM. Customization of a Severity of Illness Score Using Local Electronic Medical Record Data. J Intensive Care Med 2017; Jan; 32 (01) 38-47.
    PubMedGoogle Scholar
  • 162 Roth C, Shivade CP, Foraker RE, Embi PJ. Integrating population- and patient-level data for secondary use of electronic health records to study overweight and obesity. Stud Health Technol Inform 2013; 192: 1100.
    PubMedGoogle Scholar
  • 163 Schildcrout JS, Basford MA, Pulley JM, Masys DR, Roden DM, Wang D. et al. An analytical approach to characterize morbidity profile dissimilarity between distinct cohorts using electronic medical records. J Biomed Inform 2010; Dec; 43 (06) 914-23.
    PubMedGoogle Scholar
  • 164 Stifter J, Yao Y, Lodhi MK, Lopez KD, Khokhar A, Wilkie DJ. et al. Nurse Continuity and Hospital-Acquired Pressure Ulcers: A Comparative Analysis Using an Electronic Health Record “Big Data” Set. Nurs Res 2015; Oct; 64 (05) 361-71.
    PubMedGoogle Scholar
  • 165 Westra BL, Savik K, Oancea C, Choromanski L, Holmes JH, Bliss D. Predicting improvement in urinary and bowel incontinence for home health patients using electronic health record data. J Wound, Ostomy Continence Nurs 2011; Jan; 38 (01) 77-87.
    PubMedGoogle Scholar
  • 166 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. Clini Pharmacol Ther 2012; Mar; 91 (03) 467-74.
    PubMedGoogle Scholar
  • 167 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.
    PubMedGoogle Scholar
  • 168 Hrovat G, Stiglic G, Kokol P, Ojsteršek M. Contrasting temporal trend discovery for large healthcare databases. Comput Methods Programs Biomed 2014; 113 (01) 251-7.
    PubMedGoogle Scholar
  • 169 Li D, Simon G, Chute CG, Pathak J. Using association rule mining for phenotype extraction from electronic health records. AMIA Summits Transl Sci Proc 2013; 2013: 142-6.
    PubMedGoogle Scholar
  • 170 Mullins IM, Siadaty MS, Lyman J, Scully K, Garrett CT, Miller WG. et al. Data mining and clinical data repositories: Insights from a 667,000 patient data set. Comput Biol Med 2006; Dec; 36 (12) 1351-77.
    PubMedGoogle Scholar
  • 171 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; Nov; 15 (06) 823-30.
    PubMedGoogle Scholar
  • 172 Glasgow JM, Kaboli PJ. Detecting adverse drug events through data mining. Am J Health Syst Pharm 2010; Feb; 67 (04) 317-20.
    PubMedGoogle Scholar
  • 173 van den Branden M, Wiratunga N, Burton D, Craw S. Integrating case-based reasoning with an electronic patient record system. Artif Intell Med 2011; Feb; 51 (02) 117-23.
    PubMedGoogle Scholar
  • 174 Skevofilakas M, Zarkogianni K, Karamanos BG, Nikita KS. A hybrid Decision Support System for the risk assessment of retinopathy development as a long term complication of Type 1 Diabetes Mellitus. Conf Proc IEEE Eng Med Biol Soc IEEE 2010; 2010: 6713-6.
    PubMedGoogle Scholar
  • 175 Zhao D, Weng C. Combining PubMed knowledge and EHR data to develop a weighted bayesian network for pancreatic cancer prediction. J Biomed Inform 2011; Oct; 44 (05) 859-68.
    PubMedGoogle Scholar
  • 176 Ahmed MU, Funk P. Mining rare cases in post-operative pain by means of outlier detection. IEEE Computer Society; 2011: 35-41.
    Google Scholar
  • 177 Melamed RD, Khiabanian H, Rabadan R. Data-driven discovery of seasonally linked diseases from an Electronic Health Records system. BMC Bioinformatics 2014; 15 (Suppl. 06) (Suppl 6): S3.
    PubMedGoogle Scholar
  • 178 Ho JC, Ghosh J, Steinhubl SR, Stewart WF, Denny JC, Malin BA. et al. Limestone: high-throughput candidate phenotype generation via tensor factorization. J Biomed Inform 2014; Dec; 52: 199-211.
    PubMedGoogle Scholar
  • 179 Chen Y, Nyemba S, Malin B. Detecting Anomalous Insiders in Collaborative Information Systems. IEEE Trans Dependable Secure Comput 2012; May; 09 (03) 332-44.
    PubMedGoogle Scholar
  • 180 Peissig PL, Santos VCosta, Caldwell MD, Rottscheit C, Berg RL, Mendonca EA. et al. Relational machine learning for electronic health record-driven phenotyping. J Biomed Inform 2014; Dec; 52: 260-70.
    PubMedGoogle Scholar
  • 181 Norén GN, Hopstadius J, Bate A, Star K, Edwards IR. Temporal pattern discovery in longitudinal electronic patient records. Data Min Knowl Discov 2010; May; 20 (03) 361-87.
    PubMedGoogle Scholar
  • 182 Zhang H, Mehotra S, Liebovitz D, Gunter CA, Malin B. Mining Deviations from Patient Care Pathways via Electronic Medical Record System Audits. ACM Trans Manag Inf Syst 2013; Dec; 04 (04) 1-20.
    PubMedGoogle Scholar
  • 183 Chen Y, Ghosh J, Bejan CA, Gunter CA, Gupta S, Kho A. et al. Building bridges across electronic health record systems through inferred phenotypic topics. J Biomed Inform 2015; Jun; 55: 82-93.
    PubMedGoogle Scholar
  • 184 Gupta S, Hanson C, Gunter CA, Frank M, Liebovitz D, Malin B. Modeling and detecting anomalous topic access. IEEE; 2013: 100-105.
    Google Scholar
  • 185 Schuemie MJ, Coloma PM, Straatman H, Herings RMC, Trifirò G, Matthews JN. et al. Using electronic health care records for drug safety signal detection: a comparative evaluation of statistical methods. Med Care 2012; Oct; 50 (10) 890-7.
    PubMedGoogle Scholar
  • 186 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; May; 26 (09) 1205-10.
    PubMedGoogle Scholar
  • 187 Kuhn T, Basch P, Barr M, Yackel T. Medical Informatics Committee of the American College of Physicians. Clinical documentation in the 21st century: executive summary of a policy position paper from the American College of Physicians. Ann Intern Med 2015; 162 (04) 301-3.
    CrossrefPubMedGoogle Scholar
  • 188 Clynch N, Kellett J. Medical documentation: part of the solution, or part of the problem? A narrative review of the literature on the time spent on and value of medical documentation. Int J Med Inform 2015; Apr; 84 (04) 221-8.
    PubMedGoogle Scholar
  • 189 Weir CR, Hammond KW, Embi PJ, Efthimiadis EN, Thielke SM, Hedeen AN. An exploration of the impact of computerized patient documentation on clinical collaboration. Int J Med Inform 2011; Aug; 80 (08) e62-71.
    PubMedGoogle Scholar
  • 190 Jung M, Hoerbst A, Hackl WO, Kirrane F, Borbolla D, Jaspers MW. et al. Attitude of Physicians Towards Automatic Alerting in Computerized Physician Order Entry Systems. Methods Inf Med 2013; 52 (02) 99-108.
    PubMedGoogle Scholar
  • 191 Gregg EW, Li Y, Wang J, Burrows NR, Ali MK, Rolka D. et al. Changes in diabetes-related complications in the United States, 1990-2010. N Engl J Med 2014; Apr 17; 370 (16) 1514-23.
    CrossrefPubMedGoogle Scholar
  • 192 Wollersheim D, Sari A, Rahayu W. Archetype-based electronic health records: a literature review and evaluation of their applicability to health data interoperability and access. HIM J 2009; 38 (02) 7-17.
    PubMedGoogle Scholar
  • 193 Santos MR, Bax MP, Kalra D. Dealing with the archetypes development process for a regional EHR system. Appl Clin Inform 2012; 03 (03) 258-75.
    PubMedGoogle Scholar
  • 194 Christensen B, Ellingsen G. Evaluating Model-Driven Development for large-scale EHRs through the openEHR approach. Int J Med Inform 2016; May; 89: 43-54.
    PubMedGoogle Scholar
  • 195 Monsen KA, Finn RS, Fleming TE, Garner EJ, LaValla AJ, Riemer JG. Rigor in electronic health record knowledge representation: lessons learned from a SNOMED CT clinical content encoding exercise. Inform Health Soc Care 2014; Oct; 1-15.
    PubMedGoogle Scholar
  • 196 Daniel C, Ouagne D, Sadou E, Forsberg K, Gilchrist MM, Zapletal E. et al. Cross border semantic interoperability for clinical research: the EHR4CR semantic resources and services. AMIA Jt Summits Transl Sci Proc 2016; 2016: 51-59.
    PubMedGoogle Scholar
  • 197 Teodoro D, Pasche E, Gobeill J, Emonet S, Ruch P, Lovis C. Building a transnational biosurveillance network using semantic web technologies: requirements, design, and preliminary evaluation. J Med Internet Res 2012; May 29; 14 (03) e73.
    CrossrefPubMedGoogle Scholar
  • 198 Weis JM, Levy PC. Copy, paste, and cloned notes in electronic health records: prevalence, benefits, risks, and best practice recommendations. Chest 2014; Mar 1; 145 (03) 632-8.
    PubMedGoogle Scholar
  • 199 Klann JG, Abend A, Raghavan VA, Mandl KD, Murphy SN. Data interchange using i2b2. J Am Med Inform Assoc 2016; Sep; 23 (05) 909-15.
    PubMedGoogle Scholar
  • 200 De Moor G, Sundgren M, Kalra D, Schmidt A, Dugas M, Claerhout B. et al. Using electronic health records for clinical research: the case of the EHR4CR project. Biomed Inform 2015; Feb; 53: 162-73.
    PubMedGoogle Scholar
  • 201 Bolens M, Borst F, Scherrer JR. Recurrent infections in children observed throughout electronic medical record. Medinfo 1995; 8 Pt 1: 331.
    PubMedGoogle Scholar
  • 202 Lovis C, Séroussi B, Hasman A, Pape-Haugaard L, Saka O, Andersen SK. MIE2014 preface. Stud Health Technol Inform 2014; 205: v-vi.
    PubMedGoogle Scholar
  • 203 Milchak JL, Shanahan RL, Kerzee JA. Implementation of a peer review process to improve documentation consistency of care process indicators in the EMR in a primary care setting. J Manag Care Pharm 2012; Jan; 18 (01) 46-53.
    PubMedGoogle Scholar
  • 204 Smithburger PL, Buckley MS, Bejian S, Burenheide K, Kane-Gill SL. A critical evaluation of clinical decision support for the detection of drug-drug interactions. Expert Opin Drug Saf 2011; Nov; 10 (06) 871-882.
    CrossrefPubMedGoogle Scholar
  • 205 Eppenga WL, Derijks HJ, Conemans JMH, Hermens WAJJ, Wensing M, De Smet PAGM. Comparison of a basic and an advanced pharmacotherapy-related clinical decision support system in a hospital care setting in the Netherlands. J Am Med Inform Assoc 2012; Jan; 19 (01) 66-71.
    PubMedGoogle Scholar
  • 206 Suresh S. Big Data and Predictive Analytics: Applications in the Care of Children. Pediatr Clin North Am 2016; Apr; 63 (02) 357-66.
    PubMedGoogle Scholar
  • 207 Mohsen MO, Aziz HA. The Blue Button Project: Engaging Patients in Healthcare by a Click of a Button. Perspect Health Inf Manag 2015; 12 (Spring): 1d.
    PubMedGoogle Scholar
  • 208 Centers for Medicare Medicaid Services CMS. Glossary of eCQI Terms. Available from: https://ecqi.healthit.gov/content/glossary-ecqi-terms
    PubMedGoogle Scholar
  • 209 Centers for Medicare Medicaid Services CMS. CMS Quality Measures Inventory. Available from: https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityMeasures/CMS-Measures-Inventory.html
    PubMedGoogle Scholar
  • 210 AHRQ. National Quality Measures Clearinghouse (NQMC). Available from: https://www.qualitymeasures.ahrq.gov/index.aspx
    PubMed
  • 211 NQF. National Quality Forum (NQF). Available from: http://www.qualityforum.org
    PubMedGoogle Scholar
  • 212 Harkema H, Chapman WW, Saul M, Dellon ES, Schoen RE, Mehrotra A. Developing a natural language processing application for measuring the quality of colonoscopy procedures. J Am Med Inform Assoc 2011; Dec 16; 18 (Suppl. 01) i150-i156.
    PubMedGoogle Scholar
  • 213 Gawron AJ, Thompson WK, Keswani RN, Rasmussen LV, Kho AN. Anatomic and advanced adenoma detection rates as quality metrics determined via natural language processing. Am J Gastroenterol 2014; Dec; 109 (12) 1844-9.
    PubMedGoogle Scholar
  • 214 Raju GS, Lum PJ, Slack RS, Thirumurthi S, Lynch PM, Miller E. et al. Natural language processing as an alternative to manual reporting of colonoscopy quality metrics. Gastrointest Endosc 2015; Sep; 82 (03) 512-9.
    PubMedGoogle Scholar
  • 215 Meystre SM, Kim J, Garvin J. Comparing Methods for Left Ventricular Ejection Fraction Clinical Information Extraction. AMIA Summits Transl Sci Proc, CRI; 2012: 138.
    Google Scholar
  • 216 Kim Y, Garvin JH, Heavirland J, Meystre SM. Improving heart failure information extraction by domain adaptation. Medinfo 2013; 185-9.
    PubMedGoogle Scholar
  • 217 Meystre SM, Kim Y, Heavirland J, Williams J, Bray BE, Garvin JH. Heart Failure Medications Detection and Prescription Status Classification in Clinical Narrative Documents. Stud Health Technol Inform 2015; Aug 21; 216: 609-13.
    PubMedGoogle Scholar
  • 218 Meystre S, Meystre SM, Kim Y, Redd A, Garvin JH. Congestive Heart Failure Information Extraction Framework (CHIEF) Evaluation. AMIA Annu Symp Proc 2014; 86.
    PubMedGoogle Scholar
  • 219 Institute of Medicine (US) Roundtable on Evidence-Based Medicine. Washington (DC): National Academies Press; 2009
  • 220 Budrionis A, Gustav JBellika. The Learning Healthcare System: Where are we now? A systematic review. J Biomed Inform 2016; Sep 28; 64: 87-92.
    PubMedGoogle Scholar
  • 221 Institute of Medicine (US) and National Academy of Engineering (US) Roundtable on Value & Science-Driven Health Care. Washington (DC): National Academies Press (US); 2011
  • 222 IOM Roundtable on Value & Science-Driven Care and Institute of Medicine. Washington, DC: National Academies Press (US); 2015
  • 223 Patients Charting the Course: Citizen Engagement and the Learning Health System: Workshop Summary. Washington, DC: National Academies Press (US); 2011
    Google Scholar
  • 224 Kuchinke W, Karakoyun T, Ohmann C, Arvanitis TN, Taweel A, Delaney BC. et al. Extension of the primary care research object model (PCROM) as clinical research information model (CRIM) for the “learning healthcare system”. BMC Med Inform Decis Mak 2014; Dec 18; 14 (01) 118.
    PubMedGoogle Scholar
  • 225 Ethier J-F, Curcin V, Barton A, McGilchrist MM, Bastiaens H, Andreasson A. et al. Clinical data integration model. Core interoperability ontology for research using primary care data. Methods Inf Med 2015; 54 (01) 16-23.
    PubMedGoogle Scholar
  • 226 Lim SNChoi Keung. Transform: Implementing a Learning Healthcare System in Europe through Embedding Clinical Research into Clinical Practice. Hawaii International Conference on System Sciences HICSS 2015; 3176-85
    PubMedGoogle Scholar
  • 227 Abernethy AP, Ahmad A, Zafar SY, Wheeler JL, Reese JB, Lyerly HK. Electronic patient-reported data capture as a foundation of rapid learning cancer care. Med Care 2010; Jun; 48 (6 Suppl): S32-8.
    PubMedGoogle Scholar
  • 228 Ohno-Machado L, Agha Z, Bell DS, Dahm L, Day ME, Doctor JN. et al. pSCANNER: patient-centered Scalable National Network for Effectiveness Research. J Am Med Inform Assoc 2014; Jul; 21 (04) 621-626.
    PubMedGoogle Scholar
  • 229 Deeny SR, Steventon A. Making sense of the shadows: priorities for creating a learning healthcare system based on routinely collected data. BMJ Qual Saf 2015; Aug; 24 (08) 505-15.
    PubMedGoogle Scholar
  • 230 Berwick DM, Nolan TW, Whittington J. The triple aim: care, health, and cost. Health Affairs Project HOPE - The People-to-People Health Foundation, Inc 2008; May; 27 (03) 759-69.
    PubMedGoogle Scholar
  • 231 Vuokko R, Mäkelä-Bengs P, Hyppönen H, Doupi P. Secondary use of structured patient data: interim results of a systematic review. Stud Health Technol Inform 2015; 210: 291-5.
    PubMedGoogle Scholar