Download PDF
Appl Clin Inform 2019; 10(04): 771-776
DOI: 10.1055/s-0039-1697594
State of the Art/Best Practice Paper
Georg Thieme Verlag KG Stuttgart · New York

Improving the Effectiveness of Health Information Technology: The Case for Situational Analytics

Laurie Lovett Novak
1   Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
,
Shilo Anders
1   Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
2   Center for Research and Innovation in Systems Safety, Vanderbilt University Medical Center, Nashville, Tennessee, United States
,
Kim M. Unertl
1   Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
,
Daniel J. France
2   Center for Research and Innovation in Systems Safety, Vanderbilt University Medical Center, Nashville, Tennessee, United States
,
Matthew B. Weinger
1   Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
2   Center for Research and Innovation in Systems Safety, Vanderbilt University Medical Center, Nashville, Tennessee, United States
› Author Affiliations Funding This study was funded by the U.S. Department of Health and Human Services, National Institutes of Health, and U.S. National Library of Medicine (grant 4 R00 LM010038–02).
Further Information

Publication History

05 March 2019

04 August 2019

Publication Date:
09 October 2019 (online)

   Permissions and Reprints

Abstract

Health information technology has contributed to improvements in quality and safety in clinical settings. However, the implementation of new technologies in health care has also been associated with the introduction of new sociotechnical hazards, produced through a range of complex interactions that vary with social, physical, temporal, and technological context. Other industries have been confronted with this problem and have developed advanced analytics to examine context-specific activities of workers and related outcomes. The skills and data exist in health care to develop similar insights through situational analytics, defined as the application of analytic methods to characterize human activity in situations and identify patterns in activity and outcomes that are influenced by contextual factors. This article describes the approach of situational analytics and potentially useful data sources, including trace data from electronic health record activity, reports from users, qualitative field data, and locational data. Key implementation requirements are discussed, including the need for collaboration among qualitative researchers and data scientists, organizational and federal level infrastructure requirements, and the need to implement a parallel research program in ethics to understand how the data are being used by organizations and policy makers.

Keywords

qualitative - methodologies - evaluation - system improvement - analytics - data access - integration - analysis

Protection of Human and Animal Subjects

This paper does not present new research data for which Human Subjects approval is required. The published research referenced in the paper was approved by the Vanderbilt University Institutional Review Board.


 

  • References

  • 1 Bates DW, Teich JM, Lee J. , et al. The impact of computerized physician order entry on medication error prevention. J Am Med Inform Assoc 1999; 6 (04) 313-321
    CrossrefPubMedGoogle Scholar
  • 2 Poon EG, Keohane CA, Yoon CS. , et al. Effect of bar-code technology on the safety of medication administration. N Engl J Med 2010; 362 (18) 1698-1707
    CrossrefPubMedGoogle Scholar
  • 3 Buntin MB, Burke MF, Hoaglin MC, Blumenthal D. The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Aff (Millwood) 2011; 30 (03) 464-471
    CrossrefPubMedGoogle Scholar
  • 4 Patterson ES, Cook RI, Render ML. Improving patient safety by identifying side effects from introducing bar coding in medication administration. J Am Med Inform Assoc 2002; 9 (05) 540-553
    CrossrefPubMedGoogle Scholar
  • 5 Coiera E, Ash J, Berg M. The Unintended Consequences of Health Information Technology Revisited. Stuttgart, Germany: IMIA Yearbook; 2016: 163-169
    Google Scholar
  • 6 Castro GM, Buczkowski L, Hafner JM. The contribution of sociotechnical factors to health information technology–related sentinel events. Jt Comm J Qual Patient Saf 2016; 42 (02) 70-76
    PubMedGoogle Scholar
  • 7 Kim MO, Coiera E, Magrabi F. Problems with health information technology and their effects on care delivery and patient outcomes: a systematic review. J Am Med Inform Assoc 2017; 24 (02) 246-250
    CrossrefPubMedGoogle Scholar
  • 8 Koppel R, Wetterneck T, Telles JL, Karsh B-T. Workarounds to barcode medication administration systems: their occurrences, causes, and threats to patient safety. J Am Med Inform Assoc 2008; 15 (04) 408-423
    CrossrefPubMedGoogle Scholar
  • 9 Holden RJ, Brown RL, Alper SJ, Scanlon MC, Patel NR, Karsh BT. That's nice, but what does IT do? Evaluating the impact of bar coded medication administration by measuring changes in the process of care. Int J Ind Ergon 2011; 41 (04) 370-379
    CrossrefPubMedGoogle Scholar
  • 10 Unertl KM, Novak LL, Johnson KB, Lorenzi NM. Traversing the many paths of workflow research: developing a conceptual framework of workflow terminology through a systematic literature review. J Am Med Inform Assoc 2010; 17 (03) 265-273
    CrossrefPubMedGoogle Scholar
  • 11 Novak LL, Holden RJ, Anders SH, Hong JY, Karsh B-T. Using a sociotechnical framework to understand adaptations in health IT implementation. Int J Med Inform 2013; 82 (12) e331-e344
    CrossrefPubMedGoogle Scholar
  • 12 Middleton B, Bloomrosen M, Dente MA. , et al; American Medical Informatics Association. Enhancing patient safety and quality of care by improving the usability of electronic health record systems: recommendations from AMIA. J Am Med Inform Assoc 2013; 20 (e1): e2-e8
    CrossrefPubMedGoogle Scholar
  • 13 Belmont E, Singh H. Identification and Prioritization of Health IT Patient Safety Measures [Internet]. National Quality Forum. Available at: http://www.qualityforum.org/Publications/2016/02/Identification_and_Prioritization_of_HIT_Patient_Safety_Measures.aspx . Accessed August 25, 2019
    PubMedGoogle Scholar
  • 14 Thayer JG, Miller JM, Fiks AG, Tague L, Grundmeier RW. Assessing the safety of custom web-based clinical decision support systems in electronic health records: a case study. Appl Clin Inform 2019; 10 (02) 237-246
    Article in Thieme ConnectPubMedGoogle Scholar
  • 15 Ash JS, Stavri PZ, Kuperman GJ. A consensus statement on considerations for a successful CPOE implementation. J Am Med Inform Assoc 2003; 10 (03) 229-234
    CrossrefPubMedGoogle Scholar
  • 16 Singh H, Sittig DF. Measuring and improving patient safety through health information technology: the Health IT Safety Framework. BMJ Qual Saf 2016; 25 (04) 226-232
    CrossrefPubMedGoogle Scholar
  • 17 Carayon P. Handbook of Human Factors and Ergonomics in Health Care and Patient Safety. Boca Raton: CRC Press; 2011
    Google Scholar
  • 18 Weinger MB, Wiklund ME, Gardner-Bonneau D. , eds. Handbook of Human Factors in Medical Device Design. Boca Raton, FL: CRC Press; 2010: 850
    Google Scholar
  • 19 Salvendy G. Handbook of Human Factors and Ergonomics. Hoboken, NJ: John Wiley & Sons; 2012
    Google Scholar
  • 20 Weinger MB, Gaba DM. Human factors engineering in patient safety. Anesthesiology 2014; 120 (04) 801-806
    CrossrefPubMedGoogle Scholar
  • 21 Sharma R, Reynolds P, Scheepers R, Seddon PB, Shanks GG. Business Analytics and Competitive Advantage: A Review and a Research Agenda. In: Respício A, Adam F, Phillips-Wren GE, Teixeira C, Telhada J. , eds. Bridging the Socio-technical Gap in Decision Support Systems. Amsterdam: IOS Press; 2010: 187-198
    Google Scholar
  • 22 Morris B. Stephen Curry Is The Revolution. Available at: http://fivethirtyeight.com/features/stephen-curry-is-the-revolution/ . Accessed August 25, 2019
    PubMedGoogle Scholar
  • 23 Meeks DW, Takian A, Sittig DF, Singh H, Barber N. Exploring the sociotechnical intersection of patient safety and electronic health record implementation. J Am Med Inform Assoc 2014; 21 (e1): e28-e34
    CrossrefPubMedGoogle Scholar
  • 24 Jylhä V, Mikkonen S, Saranto K, Bates DW. The impact of information culture on patient safety outcomes. Development of a structural equation model. Methods Inf Med 2017; 56 (Open): e30-e38
    Article in Thieme ConnectPubMedGoogle Scholar
  • 25 Koppel R. The health information technology safety framework: building great structures on vast voids. BMJ Qual Saf 2016; 25 (04) 218-220
    CrossrefPubMedGoogle Scholar
  • 26 Wachter RM. The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine's Computer Age. New York: McGraw-Hill Education; 2015
    Google Scholar
  • 27 Karsh B-T, Weinger MB, Abbott PA, Wears RL. Health information technology: fallacies and sober realities. J Am Med Inform Assoc 2010; 17 (06) 617-623
    CrossrefPubMedGoogle Scholar
  • 28 Suchman L. Plans and Situated Actions: The Problem of Human-Machine Communication. Cambridge: Cambridge University Press; 1987
    Google Scholar
  • 29 Ash JS, Sittig DF, Campbell EM, Guappone KP, Dykstra RH. An unintended consequence of CPOE implementation: shifts in power, control, and autonomy. In AMIA Annual Symposium Proceedings (Vol. 2006, p. 11). American Medical Informatics Association
    PubMedGoogle Scholar
  • 30 Østerlund CS. Documents in place: demarcating places for collaboration in healthcare settings. Comput Support Coop Work CSCW. 2008; 17 (2–3): 195-225
    PubMedGoogle Scholar
  • 31 Berg M, Toussaint P. The mantra of modeling and the forgotten powers of paper: a sociotechnical view on the development of process-oriented ICT in health care. Int J Med Inform 2003; 69 (2-3): 223-234
    CrossrefPubMedGoogle Scholar
  • 32 Reddy M, Dourish P, Pratt W. Temporality in medical work: time also matters. Comput Support Coop Work 2006; 15: 29-53
    CrossrefPubMedGoogle Scholar
  • 33 Zheng K, Hanauer DA, Weibel N, Agha Z. Computational Ethnography: Automated and Unobtrusive Means for Collecting Data In Situ for Human–Computer Interaction Evaluation Studies. In: Cognitive Informatics for Biomedicine. Cham: Springer; 2015: 111-140
    CrossrefGoogle Scholar
  • 34 Ozkaynak M, Wu DTY, Hannah K, Dayan PS, Mistry RD. Examining workflow in a pediatric emergency department to develop a clinical decision support for an antimicrobial stewardship program. Appl Clin Inform 2018; 9 (02) 248-260
    Article in Thieme ConnectPubMedGoogle Scholar
  • 35 Yoshida E, Fei S, Bavuso K, Lagor C, Maviglia S. The value of monitoring clinical decision support interventions. Appl Clin Inform 2018; 9 (01) 163-173
    Article in Thieme ConnectPubMedGoogle Scholar
  • 36 Novak L, Brooks J, Gadd C, Anders S, Lorenzi N. Mediating the intersections of organizational routines during the introduction of a health IT system. Eur J Inf Syst 2012; 21 (05) 552-569
    CrossrefPubMedGoogle Scholar
  • 37 Novak LL, Anders S, Gadd CS, Lorenzi NM. Mediation of adoption and use: a key strategy for mitigating unintended consequences of health IT implementation. J Am Med Inform Assoc 2012; 19 (06) 1043-1049
    CrossrefPubMedGoogle Scholar
  • 38 Miller A, Aswani A, Zhou M, Weinger M, Slagle J, France D. Using telephone call rates and nurse-to-patient ratios as measures of resilient performance under high patient flow conditions. Cogn Technol Work 2019; 21 (02) 225-236
    CrossrefPubMedGoogle Scholar