Appl Clin Inform 2018; 09(02): 403-410
DOI: 10.1055/s-0038-1653967
Research Article
Schattauer GmbH Stuttgart

How Do Experienced Physicians Access and Evaluate Laboratory Test Results for the Chronic Patient? A Qualitative Analysis

Torbjørn Torsvik
1   Department of Neuroscience, Faculty of Medicine and Health Sciences, Norwegian EPR Research Centre, Norwegian University of Science and Technology, Trondheim, Norway
,
Børge Lillebo
2   Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
,
Morten Hertzum
3   Department of Information Studies, University of Copenhagen, Copenhagen, Denmark
› Author Affiliations
Funding The project is supported by The Liaison Committee for education, research, and innovation in Central Norway (Reference:2015/1459).
Further Information

Publication History

08 November 2017

07 April 2018

Publication Date:
06 June 2018 (online)

Abstract

Background Electronic health records may present laboratory test results in a variety of ways. Little is known about how the usefulness of different visualizations of laboratory test results is influenced by the complex and varied process of clinical decision making.

Objective The purpose of this study was to investigate how clinicians access and utilize laboratory test results when caring for patients with chronic illness.

Methods We interviewed 10 attending physicians about how they access and assess laboratory tests when following up patients with chronic illness. The interviews were audio-recorded, transcribed verbatim, and analyzed qualitatively.

Results Informants preferred different visualizations of laboratory test results, depending on what aspects of the data they were interested in. As chronic patients may have laboratory test results that are permanently outside standardized reference ranges, informants would often look for significant change, rather than exact values. What constituted significant change depended on contextual information (e.g., the results of other investigations, intercurrent diseases, and medical interventions) spread across multiple locations in the electronic health record. For chronic patients, the temporal relations between data could often be of special interest. Informants struggled with finding and synthesizing fragmented information into meaningful overviews.

Conclusion The presentation of laboratory test results should account for the large variety of associated contextual information needed for clinical comprehension. Future research is needed to improve the integration of the different parts of the electronic health record.

Authors' Contributions

Conception and design of the study: T.T., B.L., and M.H. Data collection: T.T. and B.L. Data analysis and interpretation: T.T., B.L., and M.H. Drafting and revising the article for important intellectual content: T.T., B.L., and M.H. Approving the final version of the submission: T.T., B.L., and M.H.


Protection of Human and Animal Subjects

The study protocol was approved by the Norwegian Center for Research Data (NSD, 45615/3/HIT). All informants gave their informed consent prior to interviews.


 
  • References

  • 1 Burtis CA, Edward RA, Bruns DE. Tietz Textbook of Clinical Chemistry and Molecular Diagnostics. St. Louis, MO: Elsevier Health Sciences; 2012
  • 2 Roy CL, Poon EG, Karson AS. , et al. Patient safety concerns arising from test results that return after hospital discharge. Ann Intern Med 2005; 143 (02) 121-128
  • 3 Edelman D. Outpatient diagnostic errors: unrecognized hyperglycemia. Eff Clin Pract 2002; 5 (01) 11-16
  • 4 Bonini P, Plebani M, Ceriotti F, Rubboli F. Errors in laboratory medicine. Clin Chem 2002; 48 (05) 691-698
  • 5 Murray CJ, Lopez AD. Measuring the global burden of disease. N Engl J Med 2013; 369 (05) 448-457
  • 6 Beasley JW, Wetterneck TB, Temte J. , et al. Information chaos in primary care: implications for physician performance and patient safety. J Am Board Fam Med 2011; 24 (06) 745-751
  • 7 Singh H, Spitzmueller C, Petersen NJ, Sawhney MK, Sittig DF. Information overload and missed test results in electronic health record-based settings. JAMA Intern Med 2013; 173 (08) 702-704
  • 8 Jha AK. The promise of electronic records: around the corner or down the road?. JAMA 2011; 306 (08) 880-881
  • 9 Poissant L, Pereira J, Tamblyn R, Kawasumi Y. The impact of electronic health records on time efficiency of physicians and nurses: a systematic review. J Am Med Inform Assoc 2005; 12 (05) 505-516
  • 10 Viitanen J, Hyppönen H, Lääveri T, Vänskä J, Reponen J, Winblad I. National questionnaire study on clinical ICT systems proofs: physicians suffer from poor usability. Int J Med Inform 2011; 80 (10) 708-725
  • 11 Lium JT, Faxvaag A. Removal of paper-based health records from Norwegian hospitals: effects on clinical workflow. Stud Health Technol Inform 2006; 124: 1031-1036
  • 12 Himmelstein DU, Wright A, Woolhandler S. Hospital computing and the costs and quality of care: a national study. Am J Med 2010; 123 (01) 40-46
  • 13 Rind A, Wang TD, Aigner W. , et al. Interactive information visualization to explore and query electronic health records. Comp Interaction 2013; 5 (03) 207-298
  • 14 West VL, Borland D, Hammond WE. Innovative information visualization of electronic health record data: a systematic review. J Am Med Inform Assoc 2015; 22 (02) 330-339
  • 15 Clynch N, Kellett J. Medical documentation: part of the solution, or part of the problem? A narrative review of the literature on the time spent on and value of medical documentation. Int J Med Inform 2015; 84 (04) 221-228
  • 16 Sittig DF, Murphy DR, Smith MW, Russo E, Wright A, Singh H. Graphical display of diagnostic test results in electronic health records: a comparison of 8 systems. J Am Med Inform Assoc 2015; 22 (04) 900-904
  • 17 Meyer J. Performance with tables and graphs: effects of training and a Visual Search Model. Ergonomics 2000; 43 (11) 1840-1865
  • 18 Schaubroeck J, Muralidhar K. A meta-analysis of the relative effects of tabular and graphic display formats on decision-making performance. Hum Perform 1991; 4 (02) 127-145
  • 19 Bauer DT, Guerlain S, Brown PJ. The design and evaluation of a graphical display for laboratory data. J Am Med Inform Assoc 2010; 17 (04) 416-424
  • 20 Torsvik T, Lillebo B, Mikkelsen G. Presentation of clinical laboratory results: an experimental comparison of four visualization techniques. J Am Med Inform Assoc 2013; 20 (02) 325-331
  • 21 Zhang J, Walji MF. TURF: toward a unified framework of EHR usability. J Biomed Inform 2011; 44 (06) 1056-1067
  • 22 Kushniruk AW, Patel VL. Cognitive and usability engineering methods for the evaluation of clinical information systems. J Biomed Inform 2004; 37 (01) 56-76
  • 23 Ratwani RM, Fairbanks RJ, Hettinger AZ, Benda NC. Electronic health record usability: analysis of the user-centered design processes of eleven electronic health record vendors. J Am Med Inform Assoc 2015; 22 (06) 1179-1182
  • 24 Merton RK, Kendall PL. The focused interview. Am J Sociol 1946; 51 (06) 541-557
  • 25 Malterud K. Systematic text condensation: a strategy for qualitative analysis. Scand J Public Health 2012; 40 (08) 795-805
  • 26 Giorgi A. The descriptive phenomenological psychological method. J Phenomenol Psychol 2012; 43 (01) 3-12
  • 27 Bossen C, Jensen LG. How physicians' achieve overview': a case-based study in a hospital ward. In Proceedings of the 17th ACM conference on Computer Supported Cooperative Work & Social Computing. ACM; 2014 February 15, pp. 257–268
  • 28 Glasziou P, Irwig L, Mant D. Monitoring in chronic disease: a rational approach. BMJ 2005; 330 (7492): 644-648
  • 29 Ash JS, Gorman PN, Lavelle M. , et al. Bundles: meeting clinical information needs. Bull Med Libr Assoc 2001; 89 (03) 294-296
  • 30 Gorman P, Ash J, Lavelle M. , et al. Bundles in the wild: managing information to solve problems and maintain situation awareness. Library Trends 2000; 49 (02) 266-289
  • 31 Bates MJ. The design of browsing and berrypicking techniques for the online search interface. Online Rev 1989; 13 (05) 407-424
  • 32 Berner ES, Moss J. Informatics challenges for the impending patient information explosion. J Am Med Inform Assoc 2005; 12 (06) 614-617
  • 33 Bath PA. Health informatics: current issues and challenges. J Inf Sci 2008; 34 (04) 501-518
  • 34 Braun LM, Wiesman F, van den Herik HJ, Hasman A, Korsten E. Towards patient-related information needs. Int J Med Inform 2007; 76 (2-3): 246-251
  • 35 Chang KC, Overhage JM, Hui SL, Were MC. Enhancing laboratory report contents to improve outpatient management of test results. J Am Med Inform Assoc 2010; 17 (01) 99-103
  • 36 Hirsch JS, Tanenbaum JS, Lipsky Gorman S. , et al. HARVEST, a longitudinal patient record summarizer. J Am Med Inform Assoc 2015; 22 (02) 263-274
  • 37 Hsu W, Taira RK, El-Saden S, Kangarloo H, Bui AA. Context-based electronic health record: toward patient specific healthcare. IEEE Trans Inf Technol Biomed 2012; 16 (02) 228-234
  • 38 Varpio L, Rashotte J, Day K, King J, Kuziemsky C, Parush A. The EHR and building the patient's story: a qualitative investigation of how EHR use obstructs a vital clinical activity. Int J Med Inform 2015; 84 (12) 1019-1028
  • 39 Shneiderman B. The eyes have it: A task by data type taxonomy for information visualizations. In The Craft of Information Visualization; 2003, pp. 364–371
  • 40 Hornbæk K, Hertzum M. The notion of overview in information visualization. Int J Hum Comput Stud 2011; 69 (7–8): 509-525
  • 41 Plaisant C, Mushlin R, Snyder A, Li J, Heller D, Shneiderman B. LifeLines: using visualization to enhance navigation and analysis of patient records. In The Craft of Information Visualization; 2003, pp. 308–312
  • 42 Samal L, Wright A, Wong BT, Linder JA, Bates DW. Leveraging electronic health records to support chronic disease management: the need for temporal data views. Clin Prob Lists Electronic Health Record 2014; 24: 279