Presentation of clinical laboratory results: an experimental comparison of four visualization techniques

Objective To evaluate how clinical chemistry test results were assessed by volunteers when presented with four different visualization techniques. Materials and methods A total of 20 medical students reviewed quantitative test results from 4 patients using 4 different visualization techniques in a balanced, crossover experiment. The laboratory data represented relevant patient categories, including simple, emergency, chronic and complex patients. Participants answered questions about trend, overall levels and covariation of test results. Answers and assessment times were recorded and participants were interviewed on their preference of visualization technique. Results Assessment of results and the time used varied between visualization techniques. With sparklines and relative multigraphs participants made faster assessments. With relative multigraphs participants identified more covarying test results. With absolute multigraphs participants found more trends. With sparklines participants more often assessed laboratory results to be within reference ranges. Different visualization techniques were preferred for the four different patient categories. No participant preferred absolute multigraphs for any patient. Discussion Assessments of clinical chemistry test results were influenced by how they were presented. Importantly though, this association depended on the complexity of the result sets, and none of the visualization techniques appeared to be ideal in all settings. Conclusions Sparklines and relative multigraphs seem to be favorable techniques for presenting complex long-term clinical chemistry test results, while tables seem to suffice for simpler result sets.

[1]  James Agutter,et al.  Evaluation of Graphic Cardiovascular Display in a High-Fidelity Simulator , 2003, Anesthesia and analgesia.

[2]  M. Weinger,et al.  Visual Display Format Affects the Ability of Anesthesiologists to Detect Acute Physiologic Changes: A Laboratory Study Employing a Clinical Display Simulator , 1995, Anesthesiology.

[3]  E. Tufte Beautiful Evidence , 2006 .

[4]  David W. Bates,et al.  Design and implementation of a comprehensive outpatient Results Manager , 2003, J. Biomed. Informatics.

[5]  M Mayer,et al.  Unit-Independent Reporting of Laboratory Test Results , 2001, Clinical chemistry and laboratory medicine.

[6]  Gerald L. Lohse,et al.  A Cognitive Model for Understanding Graphical Perception , 1993, Hum. Comput. Interact..

[7]  Anders Larsson,et al.  Longitudinal trends in laboratory test utilization at a large tertiary care university hospital in Sweden , 2011, Upsala journal of medical sciences.

[8]  Frank Drews,et al.  Research Paper: The Evaluation of a Pulmonary Display to Detect Adverse Respiratory Events Using High Resolution Human Simulator , 2006, J. Am. Medical Informatics Assoc..

[9]  Andrew Georgiou,et al.  The safety implications of missed test results for hospitalised patients: a systematic review , 2011, Quality and Safety in Health Care.

[10]  J Meyer,et al.  Performance with tables and graphs: effects of training and a Visual Search Model , 2000, Ergonomics.

[11]  David T. Bauer,et al.  The design and evaluation of a graphical display for laboratory data , 2010, J. Am. Medical Informatics Assoc..

[12]  David Edelman,et al.  Outpatient diagnostic errors: unrecognized hyperglycemia. , 2002, Effective clinical practice : ECP.

[13]  Erik Magid,et al.  Improved Laboratory Test Selection and Enhanced Perception of Test Results as Tools for Cost-Effective Medicine , 1998, Clinical chemistry and laboratory medicine.

[14]  E. Tufte,et al.  Graphical summary of patient status , 1994, The Lancet.

[15]  Christopher L. Roy,et al.  Patient Safety Concerns Arising from Test Results That Return after Hospital Discharge , 2005, Annals of Internal Medicine.

[16]  Ben Shneiderman,et al.  LifeLines: using visualization to enhance navigation and analysis of patient records , 1998, AMIA.