Visualizing surgical quality data with treemaps.
BACKGROUND
Treemaps are space-constrained visualizations for displaying hierarchical data structures using nested rectangles. The visualization allows large amounts of data to be examined in one display. The objective of this research was to examine the effects of using treemap visualizations to help surgeons assess surgical quality data from the American College of Surgeons created the National Surgical Quality Improvement Program database in a quick and timely manner.


METHODS
A controlled human subjects experiment was conducted to assess the ability of individuals to make quick and accurate judgments on surgery data by visualizing a treemap, with data hierarchically displayed by surgeon group, surgeon, and patient. Participants were given 20 task questions to complete involving examining the treemap and comparing surgeons' patients based on outcomes (dead or alive) and length of stay days. The outcomes measured were error (incorrect or correct) and task completion time.


RESULTS
120 participants completed 20 task questions for a total of 2400 responses. The main effects of layout and node size were found to be significant for absolute error, P < 0.0505 and P < 0.0185, respectively. The average judgment time to complete a task was 24 s with an accuracy rate of approximately 68%.


CONCLUSIONS
This study served as a proof of concept to determine if treemaps could be beneficial in assessing surgical data retrospectively by allowing surgeons and healthcare administrators to make quick visual judgments. The study found that factors about the layout design affect judgment performance. Future research is needed to examine whether implementing the treemap within a dashboard system will improve on judgment accuracy for surgical quality questions.
Department of Systems & Information Engineering, University of Virginia, Charlottesville, Virginia. Electronic address: alh8p@virginia.edu.
Akilah L
AL
Hugine
Department of Systems & Information Engineering, University of Virginia, Charlottesville, Virginia.
Stephanie A
SA
Guerlain
Department of Surgery, University of Virginia Health System, Charlottesville, Virginia.
Florence E
FE
Turrentine
D000293:Adolescent; D000328:Adult; D016208:Databases, Factual / Q000592:standards; D003663:Decision Trees; D005260:Female; D013502:General Surgery / Q000458:organization & administration / Q000592:standards*; D006740:Hospital Administrators / Q000458:organization & administration / Q000592:standards*; D006801:Humans; D007600:Judgment; D007902:Length of Stay; D008297:Male; D011785:Quality Assurance, Health Care / Q000458:organization & administration; D058996:Quality Improvement / Q000458:organization & administration*; D012189:Retrospective Studies; D066231:Surgeons / Q000458:organization & administration / Q000592:standards*; D055815:Young Adult
ACS NSQIP; Data visualization; Quality improvement; Surgical quality data; Treemaps