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Appl Clin Inform 2012; 03(04): 404-418
DOI: 10.4338/ACI-2012-05-RA-0017
Research Article
Schattauer GmbH

Impact of a Prototype Visualization Tool for New Information in EHR Clinical Documents

O. Farri
1   Institute for Health Informatics, University of Minnesota, Minneapolis
5   Philips Research North America – Briarcliff, New York
,
A. Rahman
1   Institute for Health Informatics, University of Minnesota, Minneapolis
,
K.A. Monsen
1   Institute for Health Informatics, University of Minnesota, Minneapolis
2   School of Nursing, University of Minnesota, Minneapolis
,
R. Zhang
1   Institute for Health Informatics, University of Minnesota, Minneapolis
,
S.V. Pakhomov
1   Institute for Health Informatics, University of Minnesota, Minneapolis
3   College of Pharmacy, University of Minnesota, Minneapolis
,
D.S. Pieczkiewicz
1   Institute for Health Informatics, University of Minnesota, Minneapolis
,
S.M. Speedie
1   Institute for Health Informatics, University of Minnesota, Minneapolis
,
G.B. Melton
1   Institute for Health Informatics, University of Minnesota, Minneapolis
4   Department of Surgery, University of Minnesota, Minneapolis
› Author Affiliations
Further Information

Publication History

Received 21 May 2012

Accepted 23 October 2012

Publication Date:
19 December 2017 (online)

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Summary

Background: EHR clinical document synthesis by clinicians may be time-consuming and error-prone due to the complex organization of narratives, excessive redundancy within documents, and, at times, inadvertent proliferation of data inconsistencies. Development of EHR systems that are easily adaptable to the user’s work processes requires research into visualization techniques that can optimize information synthesis at the point of care.

Objective: To evaluate the effect of a prototype visualization tool for clinically relevant new information on clinicians’ synthesis of EHR clinical documents and to understand how the tool may support future designs of clinical document user interfaces.

Methods: A mixed methods approach to analyze the impact of the visualization tool was used with a sample of eight medical interns as they synthesized EHR clinical documents to accomplish a set of four pre-formed clinical scenarios using a think-aloud protocol.

Results: Differences in the missing (unretrieved) patient information (2.3±1.2 [with the visualization tool] vs. 6.8±1.2 [without the visualization tool], p = 0.08) and accurate inferences (1.3±0.3 vs 2.3±0.3, p = 0.09) were not statistically significant but suggest some improvement with the new information visualization tool. Despite the non-significant difference in total times to task completion (43±4 mins vs 36±4 mins, p = 0.35) we observed shorter times for two scenarios with the visualization tool, suggesting that the time-saving benefits may be more evident with certain clinical processes. Other observed effects of the tool include more intuitive navigation between patient details and increased efforts towards methodical synthesis of clinical documents.

Conclusion: Our study provides some evidence that new information visualization in clinical notes may positively influence synthesis of patient information from EHR clinical documents. Our findings provide groundwork towards a more effective display of EHR clinical documents using advanced visualization applications.

Citation: Farri O, Rahman A, Monsen KA, Zhang R, Pakhomov SV, Pieckiewicz DS, Speedie SM, Melton GB. Impact of a prototype visualization tool for new information in EHR clinical documents. Appl Clin Inf 2012; 3: 404–418

http://dx.doi.org/10.4338/ACI-2012-05-RA-0017

Keywords

Electronic health records - data visualization - interfaces and usability

 

  • References

  • 1 Miller RH, Sim I. Physicians’ use of electronic medical records: barriers and solutions. Health Aff 2004; 23 (Suppl. 02) 116-126.
    CrossrefPubMedGoogle Scholar
  • 2 Bui AAT, Hsu W, Taira RK. Medical Data Visualization: Toward Integrated Clinical Workstations. Berlin: Springer-Verlag Berlin; 2009: 139-193.
    Google Scholar
  • 3 Feblowitz J, Wright A, Singh H, Samal L, Sittig D. Summarization of clinical information: A conceptual model. J Biomed Inform 2011; 44 (Suppl. 04) 688-699.
    CrossrefPubMedGoogle Scholar
  • 4 Embi PJ, Yackel TR, Logan JR, Bowen JL, Cooney TG, Gorman PN. Impacts of computerized physician documentation in a teaching hospital: perceptions of faculty and resident physicians. J Am Med Inform Assoc 2004; 11 (Suppl. 04) 300-309.
    CrossrefPubMedGoogle Scholar
  • 5 Christensen T, Grimsmo A. Instant availability of patient records, but diminished availability of patient information: a multi-method study of GP’s use of electronic patient records. BMC Med Inform Decis Mak 2008; 8: 12.
    Google Scholar
  • 6 DesRoches CM, Campbell EG, Rao SR, Donelan K, Ferris TG, Jha A, Kaushal R, Levy DE, Rosenbaum S, Shields AE. Electronic health records in ambulatory care--a national survey of physicians. N Engl J Med 2008; 359: 50-60.
    CrossrefPubMedGoogle Scholar
  • 7 Tange HJ, Schouten HC, Kester ADM, Hasman A. The Granularity of Medical Narratives and Its Effect on the Speed and Completeness of Information Retrieval. J Am Med Inform Assoc 1998; 5 (Suppl. 06) 571-582.
    CrossrefPubMedGoogle Scholar
  • 8 Payne TH, tenBroek AE, Fletcher GS, Labuguen MC. Transition from paper to electronic inpatient physician notes. J Am Med Inform Assoc 2010; 17 (Suppl. 01) 108-111.
    CrossrefPubMedGoogle Scholar
  • 9 Johnson SB, Bakken S, Dine D, Hyun S, Mendonça E, Morrison F, Bright T, Van Vleck T, Wrenn J, Stetson P. An electronic health record based on structured narrative. J Am Med Inform Assoc 2008; 15 (Suppl. 01) 54-64.
    CrossrefPubMedGoogle Scholar
  • 10 Ash JS, Berg M, Coiera E. Some Unintended Consequences of Information Technology in Health Care: The Nature of Patient Care Information System-related Errors. J Am Med Inform Assoc 2004; 11 (Suppl. 02) 104-112.
    PubMedGoogle Scholar
  • 11 Laxmisan A, Hakimzada F, Sayan OR, Green RA, Zhang J, Patel VL. The multitasking clinician: Decision-making and cognitive demand during and after team handoffs in emergency care. Int J Med Inf 2007; 12 (76) 11–12 801-811.
    PubMedGoogle Scholar
  • 12 Arocha J, Wang D, Patel V. Identifying reasoning strategies in medical decision making: A methodological guide. J Biomed Inform 2005; 38 (Suppl. 02) 154-171.
    CrossrefPubMedGoogle Scholar
  • 13 Hripcsak G, Vawdrey D, Fred M, Bostwick S. Use of electronic clinical documentation: time spent and team interactions. J Am Med Inform Assoc 2011; 18 (Suppl. 02) 112-117.
    CrossrefPubMedGoogle Scholar
  • 14 O’Donnell HC, Kaushal R, Barrón Y, Callahan MA, Adelman RD, Siegler EL. Physicians’ attitudes towards copy and pasting in electronic note writing. J Gen Intern Med 2009; 24 (Suppl. 01) 63-68.
    CrossrefPubMedGoogle Scholar
  • 15 Wrenn JO, Stein DM, Bakken S, Stetson PD. Quantifying clinical narrative redundancy in an electronic health record. J Am Med Inform Assoc 2010; 17 (Suppl. 01) 49-53.
    CrossrefPubMedGoogle Scholar
  • 16 Zhang R, Pakhomov S, McInnes BT, Melton GB. Evaluating Measures of Redundancy in Clinical Texts. AMIA Annu Symp Proc 2011
    PubMedGoogle Scholar
  • 17 Hammond KW, Helbig ST, Benson CC, Braithwaite-Sketoe BM. Are electronic medical records trustworthy? Observations on copying, pasting and duplication. AMIA Annu Symp Proc; 2003.
    PubMedGoogle Scholar
  • 18 Zhang R, Pakhomov S, Melton GB. Automated identification of relevant new information in clinical narrative. 2nd ACM SIGHIT Symp Proc 2012
    PubMedGoogle Scholar
  • 19 Cabitza F, Sarini M, Simone C, Telaro M. When once is not enough: the role of redundancy in a hospital ward setting. ACM SIGGROUP Conf Proc 2005
    PubMedGoogle Scholar
  • 20 Ahmed A, Chandra S, Herasevich V, Gajic O, Pickering BW. The effect of two different electronic health record user interfaces on intensive care provider task load, errors of cognition, and performance. Crit Care Med 2011; 39 (Suppl. 07) 1626-1634.
    CrossrefPubMedGoogle Scholar
  • 21 Tufte ER. Visual Explanations: Images and Quantities, Evidence and Narrative. Chesire, CT: Graphics Press; 1997
    Google Scholar
  • 22 Chittaro L. Information visualization and its application to medicine. Artif Intell Med 2001; 22 (Suppl. 02) 81-88.
    CrossrefPubMedGoogle Scholar
  • 23 Borycki E, Kushniruk A. Identifying and preventing technology-induced error using simulations: Application of usability engineering techniques. Healthc Q 2005; 8: 99-105.
    CrossrefPubMedGoogle Scholar
  • 24 Virzi RA. Refining the test phase of usability evaluation: How many subjects is enough?. Hum Factors 1992; 34 (Suppl. 04) 457-468.
    CrossrefPubMedGoogle Scholar
  • 25 Boehm BW. A spiral model of software development and enhancement. Computer 1988; 21 (Suppl. 05) 61-72.
    PubMedGoogle Scholar
  • 26 Brown SH, Lincoln MJ, Groen PJ, Kolodner RM. VistA--US department of veterans affairs national-scale HIS. Int J Med Inf 2003; 69 2–3 135-156.
    CrossrefPubMedGoogle Scholar
  • 27 McAlearney AS, Robbins J, Hirsch A, Jorina M, Harrop JP. Perceived efficiency impacts following electronic health record implementation: An exploratory study of an urban community health center network. Int J Med Inf 2010; 12 79 (12) 807-816.
    CrossrefPubMedGoogle Scholar
  • 28 Farri O, Pieczkiewicz DS, Rahman A, Adam TJ, Pakhomov S, Melton GB. A Qualitative Analysis of EHR Clinical Document Syntheis by Clinicians. AMIA Annu Symp Proc 2012.
    PubMedGoogle Scholar
  • 29 Fonteyn ME, Kuipers B, Grobe SJ. A Description of Think Aloud Method and Protocol Analysis. Qual. Health Res 1993; 3 (Suppl. 04) 430-441.
    PubMedGoogle Scholar
  • 30 Peute L, Jaspers M. The significance of a usability evaluation of an emerging laboratory order entry system. Int J Med Inf 2007; 76 2–3 157-168.
    CrossrefPubMedGoogle Scholar
  • 31 Levin A, Chaudhry MR, Djurdjev O, Beaulieu M, Komenda P. Diabetes, kidney disease and cardiovascular disease patients. Assessing care of complex patients using outpatient testing and visits: additional metrics by which to evaluate health care system functioning. Nephrol Dial Transplant 2009; 24 (Suppl. 09) 2714-2720.
    CrossrefPubMedGoogle Scholar
  • 32 Viera AJ, Garrett JM. Understanding interobserver agreement: the kappa statistic. Fam Med 2005; 37 (Suppl. 05) 360-363.
    PubMedGoogle Scholar
  • 33 Evans DC, Nichol WP, Perlin JB. Effect of the implementation of an enterprise-wide Electronic Health Record on productivity in the Veterans Health Administration. Health Econ Policy and Law 2006; 1 (02) 163-169.
    PubMedGoogle Scholar
  • 34 Jaspers M, Steen T, van den Bos C, Geenen M. The think aloud method: a guide to user interface design. Int J Med Inf 2004; 73 11–12 781-795.
    CrossrefPubMedGoogle Scholar
  • 35 Johnson C, Turley J. The significance of cognitive modeling in building healthcare interfaces. Int J Med Inf 2006; 75 (Suppl. 02) 163-172.
    CrossrefPubMedGoogle Scholar
  • 36 Benbunan-Fich R. Using protocol analysis to evaluate the usability of a commercial web site. Information & Management 2001; 12 39 (Suppl. 02) 151-163.
    CrossrefPubMedGoogle Scholar
  • 37 Plaisant C, Mushlin R, Snyder A, Li J, Heller D, Shneiderman B. LifeLines: using visualization to enhance navigation and analysis of patient records. AMIA Annu Symp Proc 1998
    PubMedGoogle Scholar
  • 38 Zheng K, Padman R, Johnson MP, Diamond HS. An interface-driven analysis of user interactions with an electronic health records system. J Am Med Inform Assoc 2009; 16 (Suppl. 02) 228-237.
    CrossrefPubMedGoogle Scholar
  • 39 Patel VL, Kushniruk AW. Interface design for health care environments: the role of cognitive science. AMIA Annu Symp Proc 1998
    PubMedGoogle Scholar
  • 40 Kushniruk AW, Patel VL. Cognitive and usability engineering methods for the evaluation of clinical information systems. J Biomed Inform 2004; 2 37 (Suppl. 01) 56-76.
    CrossrefPubMedGoogle Scholar
  • 41 Linder JA, Schnipper JL, Tsurikova R, Melnikas AJ, Volk LA, Middleton B. Barriers to electronic health record use during patient visits. AMIA Annu Symp Proc 2006
    PubMedGoogle Scholar
  • 42 Menachemi N. Barriers to ambulatory EHR: who are‘imminent adopters’ and how do they differ from other physicians?. Inform Prim Care 2006; 14 (Suppl. 02) 101-108.
    PubMedGoogle Scholar