Making Use of Diverse Data Sources in Healthcare Simulation Research

  • Jill S. SankoEmail author
  • Alexis Battista


This chapter provides readers with an introduction to some of the diverse data sources available to simulation researchers. This chapter compliments previous chapters (e.g., Chap. 4, Starting your Research Project) by drawing on research examples to highlight data generated from healthcare simulation-based encounters that may help the reader determine which data forms will best answer their research questions. In keeping with the goals of this text and the diversity of simulation in healthcare, the chapter also draws on examples from a variety of health professions domains, including nursing, medicine, and allied health, while highlighting a variety of simulation modalities, such as live simulations and augmented reality. The chapter ends with advice and suggestions as well as pointers to other complementary chapters in this book.


Simulation-based research Survey data Questionnaires Simulated person based data Biologic data Physiologic data Video data Audio data Systems-based interface data Simulator-generated data Sensor-based data 


  1. 1.
    Gaba DM. The future vision of simulation in health care. BMJ Qual Saf. 2004;13(suppl 1):i2–10.CrossRefGoogle Scholar
  2. 2.
    Dieckmann P, Phero JC, Issenberg SB, Kardong-Edgren S, Østergaard D, Ringsted C. The first research consensus summit of the society for simulation in healthcare: conduction and a synthesis of the results. Simul Healthc. 2011;6(7):S1–9.CrossRefGoogle Scholar
  3. 3.
    Lopreiato JO, Downing D, Gammon W, Lioce L, Sittner B, Slot V, Spain AE, The Terminology & Concepts Working Group. Healthcare Simulation Dictionary. 2016. Retrieved from
  4. 4.
    Cook DA, Hatala R, Brydges R, Zendejas B, Szostek JH, Wang AT, Hamstra SJ. Technology-enhanced simulation for health professions education. JAMA. 2011;306(9):978–88.CrossRefGoogle Scholar
  5. 5.
    McGaghie WC, Issenberg SB, Cohen MER, Barsuk JH, Wayne DB. Does simulation-based medical education with deliberate practice yield better results than traditional clinical education? A meta-analytic comparative review of the evidence. Acad Med J Assoc Am Med Coll. 2011;86(6):706.CrossRefGoogle Scholar
  6. 6.
    Swanson DB, van der Vleuten CP. Assessment of clinical skills with standardized patients: state of the art revisited. Teach Learn Med. 2013;25(sup1):S17–25.CrossRefGoogle Scholar
  7. 7.
    Petrusa ER. Status of standardized patient assessment: taking standardized patient-based examinations to the next level. Teach Learn Med. 2004;16(1):98–110.CrossRefGoogle Scholar
  8. 8.
    Falcone JL, Claxton RN, Marshall GT. Communication skills training in surgical residency: a needs assessment and metacognition analysis of a difficult conversation objective structured clinical examination. J Surg Educ. 2014;71(3):309–15.CrossRefGoogle Scholar
  9. 9.
    DeMaria S, Silverman ER, Lapidus KA, Williams CH, Spivack J, Levine A, Goldberg A. The impact of simulated patient death on medical students’ stress response and learning of ACLS. Med Teach. 2016;38(7):730–7.CrossRefGoogle Scholar
  10. 10.
    Bong CL, Lightdale JR, Fredette M, Weinstock P. Effects of simulation versus tutorial-based training on physiologic stress levels among clinicians: a pilot study. Simul Healthc. 2010;5(2):272–8.CrossRefGoogle Scholar
  11. 11.
    Harvey A, Nathens A, Bandiera G, LeBlanc V. Threat and challenge: cognitive appraisal and stress responses in simulated trauma resuscitations. Med Educ. 2010;44(6):587–94.CrossRefGoogle Scholar
  12. 12.
    Derry SJ, Pea RD, Barron B, Engle RA, Erickson F, Goldman R, Hall R, Koschmann T, Lemke JL, Sherin MG, Sherin BL. Conducting video research in the learning sciences: guidance on selection, analysis, technology, and ethics. J Learn Sci 2010;19(1):3–53. Derry SJ. Guidelines for video-research in education: recommendations from an expert panel. Retrieved 28 Dec 2017 from
  13. 13.
    Battista A. An activity theory perspective of how scenario-based simulations support learning: a descriptive analysis. Adv Simul. 2017;2(1):23.CrossRefGoogle Scholar
  14. 14.
    Sadideen H, Weldon SM, Saadeddin M, Loon M, Kneebone R. A video analysis of intra-and interprofessional leadership behaviors within “The Burns Suite”: identifying key leadership models. J Surg Educ. 2016;73(1):31–9.CrossRefGoogle Scholar
  15. 15.
    Sanko JS, Mckay M. Impact of simulation-enhanced pharmacology education in prelicensure nursing education. Nurse Educ. 2017;42(5S Suppl 1):S32–7.CrossRefGoogle Scholar
  16. 16.
    Kowalewski TM, White LW, Lendvay TS, Jiang IS, Sweet R, Wright A, Hannaford B, Sinanan MN. Beyond task time: automated measurement augments fundamentals of laparoscopic skills methodology. J Surg Res. 2014;192(2):329–38.CrossRefGoogle Scholar
  17. 17.
    Partin JL, Payne TA, Slemmons MF. Students’ perceptions of their learning experiences using high-fidelity simulation to teach concepts relative to obstetrics. Nurs Educ Perspect. 2011;32(3):186–8.CrossRefGoogle Scholar
  18. 18.
    Forsberg E, Ziegert K, Hult H, Fors U. Clinical reasoning in nursing, a think-aloud study using virtual patients–a base for an innovative assessment. Nurse Educ Today. 2014;34(4):538–42.CrossRefGoogle Scholar
  19. 19.
    Tschan F, Semmer NK, Gurtner A, Bizzari L, Spychiger M, Breuer M, Marsch SU. Explicit reasoning, confirmation bias, and illusory transactive memory: a simulation study of group medical decision making. Small Group Res. 2009;40(3):271–300.CrossRefGoogle Scholar
  20. 20.
    Fonteyn ME, Kuipers B, Grobe SJ. A description of think aloud method and protocol analysis. Qual Health Res. 1993;3(4):430–41.CrossRefGoogle Scholar
  21. 21.
    Derry SJ. Guidelines for video-research in education: recommendations from an expert panel. Retrieved 28 Dec 2017 from
  22. 22.
    Al-Rasheed RS, Devine J, Dunbar-Viveiros JA, Jones MS, Dannecker M, Machan JT, Jay GD, Kobayashi L. Simulation intervention with manikin-based objective metrics improves CPR instructor chest compression performance skills without improvement in chest compression assessment skills. Simul Healthc. 2013;8(4):242–52.CrossRefGoogle Scholar
  23. 23.
    Ashton A, McCluskey CL, Gwinnutt AM, Keenan AM. Effect of rescuer fatigue on performance of continuous external chest compressions over 3 min. Resuscitation. 2002;55(2):151–5.CrossRefGoogle Scholar
  24. 24.
    Bjorshol CA, Sunde K, Myklebust H, Assmus J, Soreide E. Decay in chest compression quality dues to fatigue is rate during prolonged advanced life support in a manikin model. Scand J Trauma Resusc Emerg Med. 2011;19(46):1–7.Google Scholar
  25. 25.
    Birnbach D, Fitzpatrick M, Thomas R, Ramirez J, Sanko J, Rosen L, Shekhter I. Testing a hand hygiene compliance monitoring system utilizing a depth-sensing camera in a simulated clinical environment. Presented at the 13th annual international meeting on simulation in healthcare. Jan 2013, Orlando.Google Scholar
  26. 26.
    Barrat A, Cattuto C, Tozzi AE, Vanhems P, Voirin N. Measuring contact patterns with wearable sensors: methods, data characteristics and applications to data-driven simulations of infectious diseases. Clin Microbiol Infect. 2014;20(1):10–6.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Nursing and Health StudiesUniversity of MiamiCoral GablesUSA
  2. 2.Graduate Programs in Health Professions Education, The Henry M. Jackson Foundation for the Advancement of Military MedicineBethesdaUSA

Personalised recommendations