Abstract
Clinical data repositories increasingly are used for big data science; flowsheet data can extend current CDRs with rich, highly granular data documented by nursing and other healthcare professionals. Standardization of the data, however, is required for it to be useful for big data science. In this chapter, an example of one CDR funded by NIH’s CTSA demonstrates how flowsheet data can add data repositories for big data science. A specific example of pressure ulcers demonstrates the strengths of flowsheet data and also the challenges of using this data. Through standardization of this highly granular data documented by nurses, a more precise understanding about patient characteristics and tailoring of interventions provided by the health team and patient conditions and states can be achieved. Additional efforts by national workgroups to create information models from flowsheets and standardize assessment terms are described to support big data science.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bergquist-Beringer S, Dong L, He J, Dunton N. Pressure ulcers and prevention among acute care hospitals in the united states. Jt Comm J Qual Patient Saf. 2013;39(9):404–14.
Bodenheimer T, Sinsky C. From triple to quadruple aim: care of the patient requires care of the provider. Ann Fam Med. 2014;12(6):573–6. doi:10.1370/afm.1713.
Clancy TR, Delaney CW, Morrison B, Gunn JK. The benefits of standardized nursing languages in complex adaptive systems such as hospitals. J Nurs Adm. 2006;36(9):426–34. doi:00005110-200609000-00009 [pii].
Goossen WT. Detailed clinical models: representing knowledge, data and semantics in healthcare information technology. Healthc Inform Res. 2014;20(3):163–72. doi:10.4258/hir.2014.20.3.163.
Goossen WT, Goossen-Baremans A. Clinical professional governance for detailed clinical models. In: Hovenga EJS, Grain H, editors. Health information governance in a digital environment. Amsterdam: IOS Press; 2013. p. 231–59.
Goossen W, Goossen-Baremans A, van der Zel M. Detailed clinical models: a review. Healthc Inform Res. 2010;16(4):201–14. doi:10.4258/hir.2010.16.4.201.
Goossen W, Boterenbrood F, Krediet I. Exchanging nursing oncology care data with use of a clinical data ware house. ETELEMED 2013: The Fifth International Conference on eHealth, Telemedicine, and Social Medicine; 2013 Feb 5. p. 183–7.
Harman TL, Seeley RA, Oliveira IM, Sheide A, Kartchner T, Woolstenhulme RD, et al. Standardized mapping of nursing assessments across 59 U.S. military treatment facilities. AMIA Annu Symp Proc. 2012;2012:331–9.
Harris MR, Langford LH, Miller H, Hook M, Dykes PC, Matney SA. Harmonizing and extending standards from a domain-specific and bottom-up approach: an example from development through use in clinical applications. J Am Med Inform Assoc. 2015;22(3):545–52. doi:10.1093/jamia/ocu020.
Johnson SG, Byrne MD, Christie B, Delaney CW, LaFlamme A, Park JI, et al. Modeling flowsheet data for clinical research. AMIA Joint Summits on Translational Science Proceedings AMIA Summit on Translational Science; 2015. p. 77–81.
Kim H, Harris MR, Savova GK, Chute CG. The first step toward data reuse: disambiguating concept representation of the locally developed ICU nursing flowsheets. Comput Inform Nurs. 2008;26(5):282–9. doi:10.1097/01.NCN.0000304839.59831.28.
Matney SA, Settergren TT, Carrington JM, Richesson RL, Sheide A, Westra BL. Standardizing physiologic assessment data to enable big data analytics. West J Nur Research. 2016:63–77.
Montalvo I. The national database of nursing quality indicators™ (NDNQI®). OJIN. 2007;12(3):Manuscript 2. doi:10.3912/OJIN.Vol12No03Man02.
Smeltzer CH, Hines PA, Beebe H, Keller B. Streamlining documentation: an opportunity to reduce costs and increase nurse clinicians’ time with patients. J Nurs Care Qual. 1996;10(4):66–77.
Tao C, Jiang G, Oniki TA, Freimuth RR, Zhu Q, Sharma D, Pathak J, Huff SM, Chute CG. A semantic-web oriented representation of the clinical element model for secondary use of electronic health records data. J Am Med Info Association: JAMIA. 2013;20(3)554–62.
Waitman LR, Warren JJ, Manos EL, Connolly DW. Expressing observations from electronic medical record flowsheets in an i2b2 based clinical data repository to support research and quality improvement. AMIA Annu Symp Proc. 2011;2011:1454–63.
Warren JJ, Matney SA, Foster ED, Auld VA, Roy SL. Toward interoperability: a new resource to support nursing terminology standards. Comput Inform Nurs. 2015;33(12):515–9. doi:10.1097/CIN.0000000000000210.
Westra BL, Latimer GE, Matney SA, Park JI, Sensmeier J, Simpson RL, et al. A national action plan for sharable and comparable nursing data to support practice and translational research for transforming health care. J Am Med Inform Assoc. 2015;22(3):600–7. doi:10.1093/jamia/ocu011.
Westra BL, Christie B, Johnson SG, Pruinelli L, LaFlamme A, Park JI, Sherman SG, Byrne MD, Svenssen-Renallo P, Speedie S. Expanding Interprofessional EHR Data in i2b2. AMIA Jt Summits Transl Sci Proc. 2016;20:260–8. eCollection 2016.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Westra, B.L. et al. (2017). Inclusion of Flowsheets from Electronic Health Records to Extend Data for Clinical and Translational Science Awards (CTSA) Research. In: Delaney, C., Weaver, C., Warren, J., Clancy, T., Simpson, R. (eds) Big Data-Enabled Nursing. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-319-53300-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-53300-1_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-53299-8
Online ISBN: 978-3-319-53300-1
eBook Packages: MedicineMedicine (R0)