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Yearb Med Inform 2014; 23(01): 215-223
DOI: 10.15265/IY-2014-0009
Original Article
Georg Thieme Verlag KG Stuttgart

Clinical Research Informatics and Electronic Health Record Data

R. L. Richesson
1   Duke University School of Nursing, Durham, NC, USA
,
M. M. Horvath
2   Health Intelligence and Research Services, Duke Health Technology Solutions, Durham, NC, USA
,
S. A. Rusincovitch
3   Duke Translational Medicine Institute, Duke University, Durham, NC, USA
› Author Affiliations
Further Information

Publication History

15 August 2014

Publication Date:
05 March 2018 (online)

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Summary

Objectives: The goal of this survey is to discuss the impact of the growing availability of electronic health record (EHR) data on the evolving field of Clinical Research Informatics (CRI), which is the union of biomedical research and informatics.

Results: Major challenges for the use of EHR-derived data for research include the lack of standard methods for ensuring that data quality, completeness, and provenance are sufficient to assess the appropriateness of its use for research. Areas that need continued emphasis include methods for integrating data from heterogeneous sources, guidelines (including explicit phenotype definitions) for using these data in both pragmatic clinical trials and observational investigations, strong data governance to better understand and control quality of enterprise data, and promotion of national standards for representing and using clinical data.

Conclusions: The use of EHR data has become a priority in CRI. Awareness of underlying clinical data collection processes will be essential in order to leverage these data for clinical research and patient care, and will require multi-disciplinary teams representing clinical research, informatics, and healthcare operations. Considerations for the use of EHR data provide a starting point for practical applications and a CRI research agenda, which will be facilitated by CRI’s key role in the infrastructure of a learning healthcare system.

Keywords

Biomedical research - electronic health records - data collection - research design
 

  • References

  • 1 Embi PJ, Payne PR. Clinical research informatics: challenges, opportunities and definition for an emerging domain.. J Am Med Inform Assoc 2009; 16 (03) 316-27.
    CrossrefPubMedSearch in Google Scholar
  • 2 Embi PJ. Clinical research informatics: survey of recent advances and trends in a maturing field.. Yearb Med Inform 2013; 8 (01) 178-84.
    Thieme ConnectPubMedSearch in Google Scholar
  • 3 Murdoch TB, Detsky AS. The inevitable application of big data to health care.. JAMA 2013; 309 (13) 1351-2.
    CrossrefPubMedSearch in Google Scholar
  • 4 Jiang X, Tse K, Wang S, Doan S, Kim H, Ohno-Machado L. Recent trends in biomedical informatics: a study based on JAMIA articles.. J Am Med Inform Assoc 2013; 20 e2 e198-205.
    CrossrefPubMedSearch in Google Scholar
  • 5 Shah NH, Tenenbaum JD. The coming age of data-driven medicine: translational bioinformatics’ next frontier.. J Am Med Inform Assoc 2012; 19 e1 e2-4.
    CrossrefPubMedSearch in Google Scholar
  • 6 Bellazzi R, Zupan B. Predictive data mining in clinical medicine: current issues and guidelines.. Int J Med Inform 2008; 77 (02) 81-97.
    CrossrefPubMedSearch in Google Scholar
  • 7 Denny JC, Bastarache L, Ritchie MD, Carroll RJ, Zink R, Mosley JD. et al, Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data.. Nat Biotechnol 2013; 31 (12) 1102-11.
    CrossrefPubMedSearch in Google Scholar
  • 8 Kass NE, Faden RR, Goodman SN, Pronovost P P, Tunis S, Beauchamp TL. The research-treatment distinction: a problematic approach for determining which activities should have ethical oversight.. Hastings Cent Rep 2013; Spec No: S4-S15.
    PubMedSearch in Google Scholar
  • 9 Faden RR, Kass NE, Goodman SN, Pronovost P, Tunis S, Beauchamp TL. An ethics framework for a learning health care system: a departure from traditional research ethics and clinical ethics.. Hastings Cent Rep; 2013. Spec No S16-27.
    Search in Google Scholar
  • 10 Grossmann C, Powers B, McGinnis JM. editors. IOM, in Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care:. Workshop Series Summary, 2011;; Washington (DC);: 2011
    Search in Google Scholar
  • 11 D’Avolio L, Ferguson R, Goryachev S, Woods P, Sabin T, O’Neil J. et al. Implementation of the Department of Veterans Affairs’ first point-of-care clinical trial.. J Am Med Inform Assoc 2012; 19 e1 e170-6.
    CrossrefPubMedSearch in Google Scholar
  • 12 NIH.. The NIH Director’s Panel on Clinical Research Report to the Advisory Committee to the NIH Director, December, 1997.. 1997 [cited 2011 May 15]; Available from: http://www.oenb.at/de/img/executive_summary--nih_directors_panel_on_clinical_research_report_12_97_tcm14-48582.pdf.
    PubMedSearch in Google Scholar
  • 13 Patsopoulos NA. A pragmatic view on pragmatic trials.. Dialogues Clin Neurosci 2011; 13 (02) 217-24.
    CrossrefPubMedSearch in Google Scholar
  • 14 Richesson RL, Hammond WE, Nahm M, Wixted D, Simon GE, Robinson JG. et al. Electronic health records based phenotyping in next-generation clinical trials: a perspective from the NIH Health Care Systems Collaboratory.. J Am Med Inform Assoc 2013; 20 e2 e226-31.
    CrossrefPubMedSearch in Google Scholar
  • 15 Hennekens CH, Buring JE, Mayrent SL. editor. Epidemiology in Medicine.. Boston:: Little, Brown, and Company;; 1987
    Search in Google Scholar
  • 16 Concato J, Lawler EV, Lew RA, Gaziano JM, Aslan M, Huang GD. Observational methods in comparative effectiveness research.. Am J Med 2010; 123 12 Suppl 1 e16-23.
    CrossrefPubMedSearch in Google Scholar
  • 17 Johnson ML, Crown W, Martin BC, Dormuth CR, Siebert U. Good research practices for comparative effectiveness research: analytic methods to improve causal inference from nonrandomized studies of treatment effects using secondary data sources: the ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report--III. Value Health 2009; 12 (08) 1062-73.
    CrossrefPubMedSearch in Google Scholar
  • 18 Rawlins M. De testimonio: on the evidence for decisions about the use of therapeutic interventions.. Lancet 2008; 372 9656 2152-61.
    CrossrefPubMedSearch in Google Scholar
  • 19 Gliklich RE, Dreyer NA. editors. AHRQ, Registries for Evaluating Patient Outcomes: A User’s Guide.. Agency for Healthcare Research and Quality:; Rockville, MD;: 2010
    Search in Google Scholar
  • 20 Guilloud-Bataille M, De Crozes D, Rault G, Degioanni A, Feingold J. Cystic fibrosis mutations: report from the French Registry. The Clinical Centers of the CF.. Hum Hered 2000; 50 (02) 142-5.
    CrossrefPubMedSearch in Google Scholar
  • 21 Richesson R, Vehik K. Patient registries: utility, validity and inference.. Adv Exp Med Biol 2010; 686: 87-104.
    PubMedSearch in Google Scholar
  • 22 Metzger J. Using Computerized Registries in Chronic Disease Care.. California: HealthCare Foundation;; 2004
    Search in Google Scholar
  • 23 Arts DG, De Keizer NF, Scheffer GJ. Defining and improving data quality in medical registries: a literature review, case study, and generic framework.. J Am Med Inform Assoc 2002; 9 (06) 600-11.
    CrossrefPubMedSearch in Google Scholar
  • 24 Paxton EW, Kiley ML, Love R, Barber TC, Funahashi TT, Inacio MC. Kaiser Permanente implant registries benefit patient safety, quality improvement, cost-effectiveness.. Jt Comm J Qual Patient Saf 2013; 39 (06) 246-52.
    PubMedSearch in Google Scholar
  • 25 Molina-Ortiz EI, Vega AC, Calman NS. Patient registries in primary care: essential element for quality improvement.. Mt Sinai J Med 2012; 79 (04) 475-80.
    CrossrefPubMedSearch in Google Scholar
  • 26 McNeil JJ, Evans SM, Johnson NP, Cameron PA. Clinical-quality registries: their role in quality improvement.. Med J Aust, 2010; 192 (05) 244-5.
    PubMedSearch in Google Scholar
  • 27 Butler J, Kalogeropoulos A. Registries and health care quality improvement.. J Am Coll Cardiol 2009; 54 (14) 1290-2.
    CrossrefPubMedSearch in Google Scholar
  • 28 Franklin PD, Allison JJ, Ayers DC. Beyond joint implant registries: a patient-centered research consortium for comparative effectiveness in total joint replacement.. JAMA 2012; 308 (12) 1217-8.
    CrossrefPubMedSearch in Google Scholar
  • 29 Aghayev E, Henning J, Munting E, Diel P, Moulin P, Röder C. et al; SWISSspine and Spine Tango Registry groups. Comparative effectiveness research across two spine registries.. Eur Spine J 2012; 21 (08) 1640-7.
    CrossrefPubMedSearch in Google Scholar
  • 30 Shah BR, Drozda J, Peterson ED. Leveraging observational registries to inform comparative effectiveness research.. Am Heart J 2010; 160 (01) 8-15.
    CrossrefPubMedSearch in Google Scholar
  • 31 Psaty BM, Larson EB. Investments in infrastructure for diverse research resources and the health of the public.. JAMA 2013; 309 (18) 1895-6.
    CrossrefPubMedSearch in Google Scholar
  • 32 Methodology Committee of the Patient-Centered Outcomes Research (PCORI).. Methodological standards and patient-centeredness in comparative effectiveness research: the PCORI perspective.. JAMA 2012; 307 (15) 1636-40.
    CrossrefPubMedSearch in Google Scholar
  • 33 Hersh WR, Weiner MG, Embi PJ, Logan JR, Payne PR, Bernstam EV. et al. Recommendations for the Use of Operational Electronic Health Record Data in Comparative Effectiveness Research eGEMs (Generating Evidence & Methods to improve patient outcomes),. Med Care 2013; Aug; 51 (8 Suppl 3). S30-7.
    CrossrefPubMedSearch in Google Scholar
  • 34 Hand DJ. Data mining: statistics and more?. The American Statistician; 1998. 52(2).
    Search in Google Scholar
  • 35 Bacchieri A, della Cioppa G. Observational Studies, in Fundamentals of Clinical Research.. Bridging Medicine, Statistics, and Operations. Springer-Verlag:; Milano;: 2007. p. 28-56.
    Search in Google Scholar
  • 36 Vandenbroucke JP. When are observational studies as credible as randomised trials?. Lancet 2004; 363 9422 1728-31.
    CrossrefPubMedSearch in Google Scholar
  • 37 Green SB, Byar DP. Using observational data from registries to compare treatments: the fallacy of omnimetrics.. Stat Med 1984; 3 (04) 361-73.
    CrossrefPubMedSearch in Google Scholar
  • 38 Häyrinen K, Saranto K, Nykänen P. Definition, structure, content, use and impacts of electronic health records.. Int J Med Inform 2008; May 77 (05) 291-304.
    CrossrefPubMedSearch in Google Scholar
  • 39 Köpcke F, Trinczek B, Majeed RW, Schreiweis B, Wenk J, Leusch T. et al. Evaluation of data completeness in the electronic health record for the purpose of patient recruitment into clinical trials: a retrospective analysis of element presence.. BMC Med Inform Decis Mak 2013; 13: p. 37.
    PubMedSearch in Google Scholar
  • 40 Hammond WE, McCourt B. Making sense of standards.. J AHIMA 2007; 78: 60-61 .
    PubMedSearch in Google Scholar
  • 41 Kane RL. Understanding Health Care Outcomes Research.. Gaithersburg: Aspen; 1997
    Search in Google Scholar
  • 42 Estabrooks PA, Boyle M, Emmons KM, Glasgow RE, Hesse BW, Kaplan RM. et al. Harmonized patient-reported data elements in the electronic health record: supporting meaningful use by primary care action on health behaviors and key psychosocial factors.. J Am Med Inform Assoc 2012; Jul-Aug 19 (04) 575-8.
    CrossrefPubMedSearch in Google Scholar
  • 43 Hripcsak G, Albers DJ. Correlating electronic health record concepts with healthcare process events.. J Am Med Inform Assoc 2013; 20 e2 e311-8.
    CrossrefPubMedSearch in Google Scholar
  • 44 Kokkonen EW, Davis SA, Lin HC, Dabade TS, Feldman SR, Fleischer Jr. AB. Use of electronic medical records differs by specialty and office settings.. J Am Med Inform Assoc 2013; 20 e1 e33-8.
    CrossrefPubMedSearch in Google Scholar
  • 45 Embi PJ, Weir C, Efthimiadis EN, Thielke SM, Hedeen AN, Hammond KW. Computerized provider documentation: findings and implications of a multisite study of clinicians and administrators.. J Am Med Inform Assoc 2013; 20 (04) 718-26.
    CrossrefPubMedSearch in Google Scholar
  • 46 Hersh WR, Weiner MG, Embi PJ, Logan JR, Payne PR, Bernstam EV. et al. Caveats for the use of operational electronic health record data in comparative effectiveness research.. Med Care 2013; 51 8 Suppl 3 S30-7.
    CrossrefPubMedSearch in Google Scholar
  • 47 Curcin V, Miles S, Danger R, Chen Y, Bache R, Taweel A. Implementing interoperable provenance in biomedical research.. Future Generation Computer Systems-the International Journal of Grid Computing and Escience 2014; 34: 1-16.
    PubMedSearch in Google Scholar
  • 48 I.O.f.. Standardization, editor.. ISO,; ISO-8000-2:2012(E) Data Quality - Part 2: Vocabulary; 2012
    Search in Google Scholar
  • 49 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; 50 Suppl: S21-9.
    CrossrefPubMedSearch in Google Scholar
  • 50 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; 20 (01) 144-51.
    CrossrefPubMedSearch in Google Scholar
  • 51 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.
    CrossrefPubMedSearch in Google Scholar
  • 52 Zozus MN. Assessing Data Quality for Healthcare Systems Data Used in Clinical Research.. An HSC Research Collaboratory Core White Paper;; 2013
    Search in Google Scholar
  • 53 Madigan D, Ryan PB, Schuemie M, Stang PE, Overhage JM, Hartzema AG. et al. Evaluating the impact of database heterogeneity on observational study results.. Am J Epidemiol 2013; 178 (04) 645-51.
    CrossrefPubMedSearch in Google Scholar
  • 54 Schmiemann G. [The preliminary draft of the methodology report by the Patient-Centered Outcomes Research Institute].. Z Evid Fortbild Qual Gesundhwes 2012; 106 (07) 496-9.
    CrossrefPubMedSearch in Google Scholar
  • 55 PCORI.. PCORI Methodology Standards;. 2012
    PubMedSearch in Google Scholar
  • 56 Moher D. CONSORT: an evolving tool to help improve the quality of reports of randomized controlled trials. Consolidated Standards of Reporting Trials.. JAMA 1998; 279 (18) 1489-91.
    CrossrefPubMedSearch in Google Scholar
  • 57 Vandenbroucke JP, von Elm E, Altman DG, Gøtzsche PC, Mulrow CD, Pocock SJ. et al., STROBE Initiative. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration.. Epidemiology 2007; 18 (06) 805-35.
    CrossrefPubMedSearch in Google Scholar
  • 58 Brown JS, Kahn M, Toh S. Data quality assessment for comparative effectiveness research in distributed data networks.. Med Care 2013; 51 8 Suppl 3 S22-9.
    CrossrefPubMedSearch in Google Scholar
  • 59 Holve E, Kahn M, Nahm M, Ryan P, Weiskopf N. A comprehensive framework for data quality assessment in CER.. AMIA Summits Transl Sci Proc 2013; 2013: 86-8.
    PubMedSearch in Google Scholar
  • 60 Pathak J, Kho AN, Denny JC. Electronic health records-driven phenotyping: challenges, recent advances, and perspectives.. J Am Med Inform Assoc 2013; 20 e2 e206-11.
    CrossrefPubMedSearch in Google Scholar
  • 61 Gregg EW, Li Y, Wang J, Burrows NR, Ali MK, Rolka D, Williams DE, Geiss L. Changes in diabetes-related complications in the United States, 1990-2010.. N Engl J Med 2014; 370 (16) 1514-23.
    CrossrefPubMedSearch in Google Scholar
  • 62 Chen Y, Carroll RJ, Hinz ER, Shah A, Eyler AE, Denny JC. et al. Applying active learning to high-throughput phenotyping algorithms for electronic health records data.. J Am Med Inform Assoc, 2013; 20 e2 p. e253-9.
    CrossrefPubMedSearch in Google Scholar
  • 63 Cobb JN, Declerck G, Greenberg A, Clark R, McCouch S. Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement.. Theor Appl Genet 2013; 126 (04) 867-87.
    CrossrefPubMedSearch in Google Scholar
  • 64 Conway M, Berg RL, Carrell D, Denny JC, Kho AN, Kullo IJ. et al. Analyzing the heterogeneity and complexity of Electronic Health Record oriented phenotyping algorithms.. AMIA Annu Symp Proc 2011; 2011: 274-83.
    PubMedSearch in Google Scholar
  • 65 Newton KM, Peissig PL, Kho AN, Bielinski SJ, Berg RL, Choudhary V. et al. Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network.. J Am Med Inform Assoc 2013; Jun;20 e1 e147-54.
    CrossrefPubMedSearch in Google Scholar
  • 66 Shivade C, Raghavan P, Fosler-Lussier E, Embi PJ, Elhadad N, Johnson SB. et al. A review of approaches to identifying patient phenotype cohorts using electronic health records.. J Am Med Inform Assoc 2014; Mar-Apr 21 (02) 221-30.
    CrossrefPubMedSearch in Google Scholar
  • 67 Rosenman M, He J, Martin J, Nutakki K, Eckert G, Lane K. et al. Database queries for hospitalizations for acute congestive heart failure: flexible methods and validation based on set theory.. J Am Med Inform Assoc 2014; 21 (02) 345-52.
    CrossrefPubMedSearch in Google Scholar
  • 68 Peissig PL, Rasmussen LV, Berg RL, Linneman JG, McCarty CA, Waudby C. et al. Importance of multi-modal approaches to effectively identify cataract cases from electronic health records.. J Am Med Inform Assoc 2012; 19 (02) 225-34.
    CrossrefPubMedSearch in Google Scholar
  • 69 Boyd AD, Li JJ, Burton MD, Jonen M, Gardeux V, Achour I. et al. The discriminatory cost of ICD-10-CM transition between clinical specialties: metrics, case study, and mitigating tools.. J Am Med Inform Assoc 2013; 20 (04) 708-17.
    CrossrefPubMedSearch in Google Scholar
  • 70 Carnahan RM, Moores KG. Mini-Sentinel’s systematic reviews of validated methods for identifying health outcomes using administrative and claims data: methods and lessons learned.. Pharmacoepidemiol Drug Saf 2012; 21 Suppl (01) 82-9.
    CrossrefPubMedSearch in Google Scholar
  • 71 Fox BI, Hollingsworth JC, Gray MD, Hollingsworth ML, Gao J, Hansen RA. Developing an expert panel process to refine health outcome definitions in observational data.. J Biomed Inform 2013; 46 (05) 795-804.
    CrossrefPubMedSearch in Google Scholar
  • 72 eMERGE.. What is the Phenotype Knowledge-Base?. 2012 [cited 2013 March 25]; Available from: http://www.phekb.org/.
    PubMedSearch in Google Scholar
  • 73 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.
    CrossrefPubMedSearch in Google Scholar
  • 74 Thompson WK, Rasmussen LV, Pacheco JA, Peissig PL, Denny JC, Kho AN. et al. An evaluation of the NQF Quality Data Model for representing Electronic Health Record driven phenotyping algorithms.. AMIA Annu Symp Proc 2012; 2012: 911-20.
    PubMedSearch in Google Scholar
  • 75 Chute CG, Pathak J, Savova GK, Bailey KR, Schor MI, Hart LA. et al. The SHARPn project on secondary use of Electronic Medical Record data: progress, plans, and possibilities.. AMIA Annu Symp Proc 2011; 2011: 248-56.
    PubMedSearch in Google Scholar
  • 76 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; 45 (04) 763-71.
    CrossrefPubMedSearch in Google Scholar
  • 77 Pathak J, Bailey KR, Beebe CE, Bethard S, Carrell DC, Chen PJ. et al. Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium.. J Am Med Inform Assoc 2013; 20 e2 e341-8.
    CrossrefPubMedSearch in Google Scholar
  • 78 Pathak J, Endle C, Suesse D, Peterson K, Stancl C, Li D. et al. PhenotypePortal: An Open-Source Library and Platform for Authoring, Executing and Visualization of Electronic Health Records Driven Phenotyping Algorithms.. AMIA Summits Transl Sci Proc 2013
    PubMedSearch in Google Scholar
  • 79 Post AR, Kurc T, Cholleti S, Gao J, Lin X, Bornstein W. et al. The Analytic Information Warehouse (AIW): a platform for analytics using electronic health record data.. J Biomed Inform 2013; 46 (03) 410-24.
    CrossrefPubMedSearch in Google Scholar
  • 80 Post AR, Kure T, Willard R, Rathod H. et al. Clinical Phenotyping with the Analytic Information Warehouse.. in AMIA Summits Transl Sci Proc 2013
    PubMedSearch in Google Scholar
  • 81 Hripcsak G, Albers DJ. Next-generation pheno-typing of electronic health records.. J Am Med Inform Assoc 2013; 20 (01) 117-21.
    CrossrefPubMedSearch in Google Scholar
  • 82 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; 20 e2 e319-26.
    CrossrefPubMedSearch in Google Scholar
  • 83 Richesson RL, Rusincovitch SA, Smerek MM, Pathak J. Standardized Representation for Electronic Health Record-Driven Phenotypes, in Accepted for presentation at: AMIA Joint Summits for Translational Research, Summit on Clinical Research Informatics.. San Francisco: AMIA; 2014
    Search in Google Scholar
  • 84 Olsen L, Aisner D, McGinnis JM. editors. The Learning Healthcare System: Workshop Summary (IOM Roundtable on Evidence-Based Medicine).. The National Academies Press;; 2007
    Search in Google Scholar
  • 85 Eapen ZJ, Vavalle JP, Granger CB, Harrington RA, Peterson ED, Califf RM. Rescuing clinical trials in the United States and beyond: a call for action.. Am Heart J 2013; 165 (06) 837-47.
    CrossrefPubMedSearch in Google Scholar
  • 86 Brown JS, Holmes JH, Shah K, Hall K, Lazarus R, Platt R. Distributed health data networks: a practical and preferred approach to multi-institutional evaluations of comparative effectiveness, safety, and quality of care.. Med Care 2010; 48 (06) Suppl S45-51.
    CrossrefPubMedSearch in Google Scholar
  • 87 Platt R, Carnahan RM, Brown JS, Chrischilles E, Curtis LH, Hennessy S. et al. The US. Food and Drug Administration’s Mini-Sentinel program: status and direction.. Pharmacoepidemiol Drug Saf 2012; 21 Suppl (01) 1-8.
    PubMedSearch in Google Scholar
  • 88 Bhandari M, Giannoudis PV. Evidence-based medicine: what it is and what it is not.. Injury 2006; 37 (04) 302-6.
    CrossrefPubMedSearch in Google Scholar
  • 89 Manchikanti L, Datta S, Smith HS, Hirsch JA. Evidence-based medicine, systematic reviews, and guidelines in interventional pain management: part 6. Systematic reviews and meta-analyses of observational studies.. Pain Physician 2009; 12 (05) 819-50.
    PubMedSearch in Google Scholar
  • 90 Manchikanti L, Benyamin RM, Helm S, Hirsch JA. Evidence-based medicine, systematic reviews, and guidelines in interventional pain management: part 3: systematic reviews and meta-analyses of randomized trials.. Pain Physician 2009; 12 (01) 35-72.
    PubMedSearch in Google Scholar
  • 91 Yuan Y, Hunt RH. Systematic reviews: the good, the bad, and the ugly.. Am J Gastroenterol 2009; 104 (05) 1086-92.
    CrossrefPubMedSearch in Google Scholar
  • 92 Wells BJ, Nowacki A, Chagin SK, Kattan MW. Strategies for Handling Missing Data in Electronic Health Record Derived Data.. eGEMs (Generating Evidence & Methods to improve patient outcomes),; 2013. 1: Iss. 3, Article 7.
    Search in Google Scholar
  • 93 McCann E. EHR users unhappy, many switching.. Vendors often failing to meet provider needs, report finds. Healthcare IT News; 2013
    Search in Google Scholar
  • 94 Ohno-Machado L. Data science and informatics: when it comes to biomedical data, is there a real distinction?. J Am Med Inform Assoc 2013; 20 (06) 1009.
    CrossrefPubMedSearch in Google Scholar
  • 95 NIH.. NIH Names Dr. Philip E. Bourne First Associate Director for Data Science.. 2013 [cited 2013 December 19]; Available from: http://www.nih.gov/news/health/dec2013/od-09.htm.
    PubMedSearch in Google Scholar
  • 96 ICH.. Clinical Safety Data Management and Definitions and Standards for Expedited Reporting E2A 1994.. International Conference on Harmonisation;; 1994
    Search in Google Scholar
  • 97 Califf R.M. Attribution of causality in clinical research: an important element for learning health systems.. Am Heart J 2013; 165 (03) p. 252-3.
    CrossrefPubMedSearch in Google Scholar
  • 98 Peeples MM, Iyer AK, Cohen JL. Integration of a mobile-integrated therapy with electronic health records: lessons learned.. J Diabetes Sci Technol 2013; 7 (03) 602-11.
    CrossrefPubMedSearch in Google Scholar
  • 99 Eggleston EM, Weitzman ER. Innovative uses of electronic health records and social media for public health surveillance.. Curr Diab Rep 2014; 14 (03) 468.
    CrossrefPubMedSearch in Google Scholar
  • 100 Ansermino JM. Universal access to essential vital signs monitoring.. Anesth Analg 2013; 117 (04) 883-90.
    CrossrefPubMedSearch in Google Scholar
  • 101 Fortney JC, Burgess Jr JF, Bosworth HB, Booth BM, Kaboli PJ. A re-conceptualization of access for 21st century healthcare.. J Gen Intern Med 2011; 26 Suppl (02) 639-47.
    CrossrefPubMedSearch in Google Scholar
  • 102 IOM.. Capturing Social and Behavioral Domains in Electronic Health Records: Phase 1.. The National Academies Press;; 2014
    Search in Google Scholar
  • 103 Rojo MG, Castro AM, Goncalves L. COST Action “EuroTelepath”: digital pathology integration in electronic health record, including primary care centres.. Diagn Pathol 2011; 6 Suppl (01) S6.
    CrossrefPubMedSearch in Google Scholar
  • 104 Hammond WE, Bailey C, Boucher P, Spohr M, Whitaker P. Connecting information to improve health.. Health Aff (Millwood) 2010; 29 (02) 284-8.
    CrossrefPubMedSearch in Google Scholar
  • 105 Joffe E, Byrne MJ, Reeder P, Herskovic JR, Johnson CW, McCoy AB. et al. A benchmark comparison of deterministic and probabilistic methods for defining manual review datasets in duplicate records reconciliation.. J Am Med Inform Assoc 2014; 21 (01) 97-104.
    CrossrefPubMedSearch in Google Scholar
  • 106 Dong X, Bahroos N, Sadhu E, Jackson T, Chukhman M, Johnson R. et al. Leverage hadoop framework for large scale clinical informatics applications.. AMIA Summits Transl Sci Proc 2013; 2013: 53.
    PubMedSearch in Google Scholar
  • 107 Russom P. Integrating Hadoop into Business Intelligence and Data Warehousing.. TDWI Best Practices Report;; 2013
    Search in Google Scholar
  • 108 Embi PJ, Payne PR. Evidence generating medicine: redefining the research-practice relationship to complete the evidence cycle.. Med Care 2013; 51 8 Suppl 3 S87-91.
    CrossrefPubMedSearch in Google Scholar