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Appl Clin Inform 2017; 08(03): 763-778
DOI: 10.4338/ACI-2017-02-RA-0033
Research Article
Schattauer GmbH

A Qualitative Exploration of Nurses’ Information-Gathering Behaviors Prior to Decision Support Tool Design

Alvin D. Jeffery
1   U.S. Department of Veterans Affairs, GRECC, Nashville, Tennessee, United States
,
Betsy Kennedy
2   Vanderbilt University School of Nursing, Nashville, Tennessee, United States
,
Mary S. Dietrich
2   Vanderbilt University School of Nursing, Nashville, Tennessee, United States
,
Lorraine C. Mion
3   Ohio State University College of Nursing, Columbus, Ohio, United States
,
Laurie L. Novak
4   Vanderbilt University Department of Biomedical Informatics, Nashville, Tennessee, United States
› Institutsangaben Funding The publication was supported by CTSA award No. UL1TR000445 from the National Center for Advancing Translational Sciences as well as resources and the use of facilities at the VA Tennessee Valley Healthcare System. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences, the National Institutes of Health, the Department of Veterans Affairs, or the United States Government.
Weitere Informationen

Publikationsverlauf

received: 21. Februar 2017

accepted in revised form: 11. Mai 2017

Publikationsdatum:
29. Dezember 2017 (online)

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Summary

Background: Large and readily-available clinical datasets combined with improved computational resources have permitted the exploration of many new research and clinical questions. Predictive analytics, especially for adverse events, has surfaced as one promising application of big data, and although statistical results can be highly accurate, little is known about how nurses perceive this new information and how they might act upon it. Objectives: Within the context of recognizing patients at risk for cardiopulmonary arrest, this study explored the possibility of incorporating predictive analytics into clinical workflows by identifying nurses’ current information gathering activities and perceptions of probability-related terms. Methods: We used a qualitative description approach for data collection and analysis in order to understand participants’ information gathering behaviors and term perceptions in their own words. We conducted one-on-one interviews and a focus group with a total of 10 direct care bedside nurses and 8 charge nurses. Results: Participants collected information from many sources that we categorized as: Patient, Other People, and Technology. The process by which they gathered information was conducted in an inconsistent order and differed by role. Major themes comprised: (a) attempts to find information from additional sources during uncertainty, (b) always being prepared for the worst-case scenario, and (c) the desire to review more detailed predictions. Use of the words probability, risk, and uncertainty were inconsistent. Conclusions: In an effort to successfully incorporate predictive analytics into clinical workflows, we have described nurses’ perceived work practices for gathering information related to clinical deterioration and nurses’ beliefs related to probability-based information. Findings from our study could guide design and implementation efforts of predictive analytics in the clinical arena.

Jeffery AD, Kennedy B, Dietrich MS, Mion LC, Novak LL. A Qualitative Exploration of Nurses’ Information-Gathering Behaviors Prior to Decision Support Tool Design. Appl Clin Inform 2017; 8: 763–778 https://doi.org/10.4338/ACI-2017-02-RA-0033

Keywords

Qualitative Research - Decision-Making - Computer-Assisted - Nursing

Human and Animal Subjects Protections

The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects and was reviewed by Vanderbilt University Institutional Review Board. Animal subjects were not included in the project.


 

  • References

  • 1 Fallik D. For big data, big questions remain. Health Aff (Millwood) 2014; 33 (07) 1111-4.
    CrossrefPubMedGoogle Scholar
  • 2 Murdoch TB, Detsky AS. The inevitable application of big data to health care. JAMA 2013; 309 (13) 1351-2.
    CrossrefPubMedGoogle Scholar
  • 3 Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff (Millwood) 2014; 33 (07) 1123-31.
    CrossrefPubMedGoogle Scholar
  • 4 Dekker S. Complexity, signal detection, and the application of ergonomics: Reflections on a healthcare case study. Appl Ergon 2012; 43 (03) 468-72.
    CrossrefPubMedGoogle Scholar
  • 5 Merchant RM, Yang L, Becker LB, Berg RA, Nadkarni V, Nichol G, Carr BG, Mitra N, Bradley SM, Abella BS, Groeneveld PW. American Heart Association Get With The Guidelines-Resuscitation I. Incidence of treated cardiac arrest in hospitalized patients in the United States. Crit Care Med 2011; 39 (11) 2401-6.
    CrossrefPubMedGoogle Scholar
  • 6 Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Blaha MJ, Dai S, Ford ES, Fox CS, Franco S, Fullerton HJ, Gillespie C, Hailpern SM, Heit JA, Howard VJ, Huffman MD, Judd SE, Kissela BM, Kittner SJ, Lackland DT, Lichtman JH, Lisabeth LD, Mackey RH, Magid DJ, Marcus GM, Marelli A, Matchar DB, McGuire DK, Mohler ER, 3rd, Moy CS, Mussolino ME, Neumar RW, Nichol G, Pandey DK, Paynter NP, Reeves MJ, Sorlie PD, Stein J, Towfighi A, Turan TN, Virani SS, Wong ND, Woo D, Turner MB, American Heart Association Statistics C, Stroke Statistics S. Heart disease and stroke statistics--2014 update: a report from the American Heart Association. Circulation 2014; 129 (03) e28-e292.
    CrossrefPubMedGoogle Scholar
  • 7 Hodgetts TJ, Kenward G, Vlackonikolis I, Payne S, Castle N, Crouch R, Ineson N, Shaikh L. Incidence, location and reasons for avoidable in-hospital cardiac arrest in a district general hospital. Resuscitation. 2002; 54 (02) 115-23.
    CrossrefPubMedGoogle Scholar
  • 8 Hazlehurst B, McMullen C. Orienting frames and private routines: the role of cultural process in critical care safety. Int J Med Inform 2007; 76 01 S129-35.
    CrossrefPubMedGoogle Scholar
  • 9 Sandelowski M. Whatever happened to qualitative description?. Res Nurs Health 2000; Aug 23 (04) 334-40.
    CrossrefPubMedGoogle Scholar
  • 10 Kim H, Sefcik JS, Bradway C. Characteristics of Qualitative Descriptive Studies: A Systematic Review. Res Nurs Health 2017; 40 (01) 23-42.
    CrossrefPubMedGoogle Scholar
  • 11 Jaspers MW. A comparison of usability methods for testing interactive health technologies: methodological aspects and empirical evidence. Int J Med Inform 2009; 78 (05) 340-53.
    CrossrefPubMedGoogle Scholar
  • 12 Reeder B, Hills RA, Turner AM, Demiris G. Participatory design of an integrated information system design to support public health nurses and nurse managers. Public Health Nurs 2014; 31 (02) 183-92.
    CrossrefPubMedGoogle Scholar
  • 13 Sonnenwald DH, Pierce LG. Information behavior in dynamic group work contexts: interwoven situational awareness, dense social networks and contested collaboration in command and control. Information Processing & Management 2000; 36 (03) 461-79.
    CrossrefPubMedGoogle Scholar
  • 14 Endsley MR. Toward a theory of situation awareness in dynamic systems. Human Factors: The Journal of the Human Factors and Ergonomics Society 1995; 37 (01) 32-64.
    CrossrefPubMedGoogle Scholar
  • 15 Reddy M, Dourish P. A finger on the pulse: temporal rhythms and information seeking in medical work. Proceedings of the 2002 ACM conference on Computer supported cooperative work; 2002: ACM; 2002: 344-53.
    PubMedGoogle Scholar
  • 16 Koch SH, Weir C, Haar M, Staggers N, Agutter J, Gorges M, Westenskow D. Intensive care unit nurses‘ information needs and recommendations for integrated displays to improve nurses‘ situation awareness. J Am Med Inform Assoc 2012; 19 (04) 583-90.
    CrossrefPubMedGoogle Scholar
  • 17 Gorman P, Ash J, Lavelle M, Lyman J, Delcambre L, Maier D, Weaver M, Bowers S. Bundles in the wild: Managing information to solve problems and maintain situation awareness. Library Trends 2000; 49 (02) 266-89.
    PubMedGoogle Scholar
  • 18 Schulz KF, Grimes DA. Case-control studies: research in reverse. Lancet 2002; 359 9304 431-4.
    CrossrefPubMedGoogle Scholar
  • 19 Krueger RA, Casey MA. Focus Groups: A Practical Guide for Applied Research. Los Angeles, CA: SAGE; 2009
    Google Scholar
  • 20 SocioCultural Research Consultants, LLC.. Dedoose Version 7.0.23, web application for managing, analyzing, and presenting qualitative and mixed method research data. 2016 [cited October 4, 2016]; Available from: http://www.dedoose.com
    PubMedGoogle Scholar
  • 21 Sandelowski M. What‘s in a name? Qualitative description revisited. Res Nurs Health 2010; 33 (01) 77-84.
    CrossrefPubMedGoogle Scholar
  • 22 Novak LL, Holden RJ, Anders SH, Hong JY, Karsh BT. Using a sociotechnical framework to understand adaptations in health IT implementation. Int J Med Inform 2013; 82 (12) e331-44.
    CrossrefPubMedGoogle Scholar
  • 23 Lipshitz R, Strauss O. Coping with uncertainty: A naturalistic decision-making analysis. Organ Behav Hum Decis Process 1997; 69 (02) 149-63.
    CrossrefPubMedGoogle Scholar
  • 24 Jeffery AD. Methodological Challenges in Examining the Impact of Healthcare Predictive Analytics on Nursing-Sensitive Patient Outcomes. Comput Inform Nurs 2015; 33 (06) 258-64.
    CrossrefPubMedGoogle Scholar
  • 25 Braaten JS. CE: Original research: hospital system barriers to rapid response team activation: a cognitive work analysis. Am J Nurs 2015; 115 (02) 22-32.
    CrossrefPubMedGoogle Scholar
  • 26 Odell M, Victor C, Oliver D. Nurses‘ role in detecting deterioration in ward patients: systematic literature review. J Adv Nurs 2009; 65 (10) 1992-2006.
    CrossrefPubMedGoogle Scholar
  • 27 Cioffi J. Nurses‘ experiences of making decisions to call emergency assistance to their patients. J Adv Nurs 2000; 32 (01) 108-14.
    CrossrefPubMedGoogle Scholar
  • 28 Parker CG. Decision-making models used by medical-surgical nurses to activate rapid response teams. Medsurg Nurs 2014; 23 (03) 159-64.
    PubMedGoogle Scholar
  • 29 Tanner CA. Thinking like a nurse: A research-based model of clinical judgment in nursing. J Nurs Educ 2006; 45 (06) 204-11.
    CrossrefPubMedGoogle Scholar
  • 30 Kahneman D. Thinking, fast and slow. New York, NY: Farrar, Straus and Giroux; 2013
    Google Scholar
  • 31 Wright MC, Dunbar S, Macpherson BC, Moretti EW, Del Fiol G, Bolte J, Taekman JM, Segall N. Toward Designing Information Display to Support Critical Care. A Qualitative Contextual Evaluation and Visioning Effort. Appl Clin Inform 2016; 7 (04) 912-29.
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 32 Weber S. A qualitative analysis of how advanced practice nurses use clinical decision support systems. J Am Acad Nurse Pract 2007; 19 (12) 652-67.
    CrossrefPubMedGoogle Scholar
  • 33 The Weather Channel. Hourly Weather Forecast. 2014 [cited 2014]; Available from: http://www.weather.com/
    PubMed
  • 34 AccuWeather. Hourly Weather Forecast. 2014 [cited 2014]; Available from: http://www.accuweather.com
    PubMed