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

Torbjørn Torsvik; Børge Lillebo; Morten Hertzum

doi:10.1055/s-0038-1653967

Applied Clinical Informatics, Inhaltsverzeichnis

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

¹Department of Neuroscience, Faculty of Medicine and Health Sciences, Norwegian EPR Research Centre, Norwegian University of Science and Technology, Trondheim, Norway

,

Børge Lillebo

²Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway

,

Morten Hertzum

³Department of Information Studies, University of Copenhagen, Copenhagen, Denmark

› Institutsangaben

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.

Keywords

laboratory information systems - electronic health records and systems - qualitative - interfaces and usability - requirements analysis and design

Volltext

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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