REFERENCE:
1. Burykin A, Peck T, Krejci V, et al. Toward optimal display of
physiologic status in critical care: I. Recreating bedside displays from
archived physiologic data. J Crit Care. 2011;26(1):105.e1-105.e9..
2. Mathews SC. The Need for Systems Integration in Health Care.
JAMA.2011;305(9):934.
3. Celi LA, Marshall JD, Lai Y, et al. Disrupting Electronic Health
Records Systems: The Next Generation. JMIR Med Inform. 2015;3(4).
4. Celi LA, Mark RG, Stone DJ, et al. « Big data » in the intensive care
unit. Closing the data loop. Am J Respir Crit Care Med.
2013;187(11):1157‑60.
5. Celi LA, Csete M, Stone D. Optimal data systems: the future of
clinical predictions and decision support. Curr Opin Crit Care. 2014;1.
7. Brossier D, Sauthier M, Alacoque X, et al. Perpetual and Virtual
Patients for Cardiorespiratory Physiological Studies. J Pediatr
Intensive Care. 2016;5(03):122‑8.
8. Brossier D, El Taani R, Sauthier M, et al. Creating a High-Frequency
Electronic Database in the PICU: The Perpetual Patient. Pediatr Crit
Care Med. 2018;19(4):e189‑98.
9. Johnson S, Speedie S, Simon G, et al. Application of an Ontology for
Characterizing Data Quality for a Secondary Use of EHR Data. Appl Clin
Inform. 2016;07(01):69‑88.
10. Barnes J, Chambers I, Piper I, et al. Accurate data collection for
head injury monitoring studies: a data validation methodology. Acta
Neurochir Suppl. 2005;95:39‑41.
11. Brossier D, Sauthier M, Mathieu A, et al. Qualitative subjective
assessment of a high-resolution database in a paediatric intensive care
unit-Elaborating the perpetual patient’s ID card. J Eval Clin Pract.
2019; In Press.
12. Kahn MG, Brown JS, Chun AT, et al. Transparent Reporting of Data
Quality in Distributed Data Networks. EGEMs Gener Evid Methods Improve
Patient Outcomes. 2015;3(1):7.
13. Callahan T, Barnard J, Helmkamp L, et al. Reporting Data Quality
Assessment Results: Identifying Individual and Organizational Barriers
and Solutions. EGEMS Wash DC. 2017;5(1):16.
14. Arts DGT. Defining and Improving Data Quality in Medical Registries:
A Literature Review, Case Study, and Generic Framework. J Am Med Inform
Assoc. 2002;9(6):600‑11.
15. Black N, Payne M. Directory of clinical databases: improving and
promoting their use. Qual Saf Health Care. 2003;12(5):348‑52.
16. Hall GC, Sauer B, Bourke A, et al. Guidelines for good database
selection and use in pharmacoepidemiology research. Pharmacoepidemiol
Drug Saf. 2012;21(1):1‑10.
17. Black N, Barker M, Payne M. Cross sectional survey of multicentre
clinical databases in the United Kingdom. Bmj. 2004;328(7454):1478.
18. Saeed M, Villarroel M, Reisner AT, Clifford G, Lehman L-W, Moody G,
et al. Multiparameter Intelligent Monitoring in Intensive Care II: A
public-access intensive care unit database. Crit Care Med. mai
2011;39(5):952‑60.
19. Johnson AE, Pollard TJ, Shen L, et al. MIMIC-III, a freely
accessible critical care database. Sci Data. 2016;3:160035.
20. Goldstein B, McNames J, McDonald BA, et al. Physiologic data
acquisition system and database for the study of disease dynamics in the
intensive care unit*: Crit Care Med. 2003;31(2):433‑41.
21. Feder SL. Data Quality in Electronic Health Records Research:
Quality Domains and Assessment Methods. West J Nurs Res.
2018;40(5):753‑66.
22. Revelle W. psych: Procedures for Psychological, Psychometric, and
Personality Research. 2018. Available on
https://CRAN.R-project.org/package=psych.
23. Sauthier M, Brossier D. BlandAltman: Bland-Altman Analysis and Plot.
2018. Available on: http://github.com/sauthiem/BlandAltman.
24. Bland JM, Altman DG. Statistical methods for assessing agreement
between two methods of clinical measurement. Lancet Lond Engl.
1986;1(8476):307‑10.
25. Giavarina D. Understanding Bland Altman analysis. Biochem Medica.
2015;25(2):141‑51.
26. Gamer M, Lemon J, Singh IFP. irr: Various Coefficients of Interrater
Reliability and Agreement. 2019. Available on:
https://CRAN.R-project.org/package=irr.
27. Koo TK, Li MY. A Guideline of Selecting and Reporting Intraclass
Correlation Coefficients for Reliability Research. J Chiropr Med.
2016;15(2):155‑63.
28. Kahn MG, Callahan TJ, Barnard J, et al. A Harmonized Data Quality
Assessment Terminology and Framework for the Secondary Use of Electronic
Health Record Data. EGEMs Gener Evid Methods Improve Patient Outcomes.
2016;4(1):18.
29. Sukumar SR, Natarajan R, Ferrell RK. Quality of Big Data in health
care. Int J Health Care Qual Assur. 2015;28(6):621‑34.
30. Shaw M, Piper I, Chambers I, et al. The brain monitoring with
Information Technology (BrainIT) collaborative network: data validation
results. Acta Neurochir Suppl. 2008;102:217‑21.
31. Whitney CW, Lind BK, Wahl PW. Quality assurance and quality control
in longitudinal studies. Epidemiol Rev. 1998;20(1):71‑80.
32. Adelson PD, Pineda J, Bell MJ, et al. Common data elements for
pediatric traumatic brain injury: recommendations from the working group
on demographics and clinical assessment. J Neurotrauma.
2012;29(4):639‑53.
33. Stow PJ, Hart GK, Higlett T, et al. Development and implementation
of a high-quality clinical database: the Australian and New Zealand
Intensive Care Society Adult Patient Database. J Crit Care.
2006;21(2):133‑41.
34. Recher M, Bertrac C, Guillot C, et al. Enhance quality care
performance: Determination of the variables for establishing a common
database in French paediatric critical care units. J Eval Clin Pract.
2018;24(4):767‑71.
35. Siebig S, Kuhls S, Imhoff M, et al. Collection of annotated data in
a clinical validation study for alarm algorithms in intensive care—a
methodologic framework. J Crit Care. 2010;25(1):128‑35.
36. Nadkarni PM, Brandt C. Data extraction and ad hoc query of an
entity-attribute-value database. JAMIA. 1998;5(6):511‑27.
37. Murphy SN, Weber G, Mendis M, et al. Serving the enterprise and
beyond with informatics for integrating biology and the bedside. JAMIA.
2010;17(2):124‑30.