Blood transfusion is a frequently performed medical procedure in surgical and nonsurgical contexts. Although it is often necessary or even life-saving, it has been identified as one of the most overused procedures in hospitals. Unnecessary transfusions not only waste resources but can also be detrimental to patient outcomes. Patient blood management (PBM) is the clinical practice of optimizing transfusions and associated outcomes. In this paper, we introduce Sanguine, a visual analysis tool for transfusion data and related patient medical records. Sanguine was designed with two user groups in mind: PBM experts who oversee blood management practices across an institution and clinicians performing transfusions. PBM experts use Sanguine to explore and analyze transfusion practices and their associated medical outcomes. They can compare individual surgeons, or compare outcomes or time periods, such as before and after an intervention regarding transfusion practices. PBM experts then curate and annotate views for communication with clinicians, with the goal of improving their transfusion practices. We validate the utility and effectiveness of Sanguine through case studies.
PfuntnerAWierLMStocksC. Most frequent procedures performed in U.S. hospitals, 2010: statistical brief #149. In: Healthcare cost and utilization project (HCUP) statistical briefs. Rockville (MD): Agency for Healthcare Research and Quality (US), 2013. http://www.ncbi.nlm.nih.gov/books/NBK132428/ (accessed 24 August 2020)
2.
EllingsonKDSapianoMRPHaassKA, et al. Continued decline in blood collection and transfusion in the United States 2015. Transfusion2017; 57(Suppl 2): 1588–1598.
DelaneyMWendelSBercovitzRS, et al. Transfusion reactions: prevention, diagnosis, and treatment. Lancet2016; 388(10061): 2825–2836.
5.
GrossISeifertBHofmannA, et al. Patient blood management in cardiac surgery results in fewer transfusions and better outcome. Transfusion2015; 55(5): 1075–1081.
ShanderAJavidrooziMPerelmanS, et al. From bloodless surgery to patient blood management. Mt Sinai J Med2012; 79(1): 56–65.
8.
FrankSMResarLMRothschildJA, et al. A novel method of data analysis for utilization of red blood cell transfusion: analysis of RBC Utilization. Transfusion2013; 53(12): 3052–3059.
9.
FrankSMSavageWJRothschildJA, et al. Variability in blood and blood component utilization as assessed by an anesthesia information management system:. Anesthesiology2012; 117(1): 99–106.
10.
HannonT.Waste not, want not. Am J Clin Pathol2015; 143(3): 318–319.
11.
FrankSMOleyarMJNessPM, et al. Reducing unnecessary preoperative blood orders and costs by implementing an updated institution-specific maximum surgical blood order schedule and a remote electronic blood release system. Anesthesiology2014; 121(3): 501–509.
12.
MetcalfRAPaganoMBHessJR, et al. A data-driven patient blood management strategy in liver transplantation. Vox Sang. Epub ahead of print 1May2018. DOI: 10.1111/vox.12650.
13.
KerznerEGoodwinSDykesJ, et al. A framework for creative visualization-opportunities workshops. IEEE Trans Vis Comput Graph2019; 25(1): 748–758.
14.
BrehmerMMunznerT.A multi-level typology of abstract visualization tasks. IEEE Trans Vis Comput Graph2013; 19(12): 2376–2385.
15.
GleicherM.Considerations for visualizing comparison. IEEE Trans Vis Comput Graph2018; 24(1): 413–423.
16.
RindA.Interactive information visualization to explore and query electronic health records. Found Trends Hum Comput Interact2013; 5(3): 207–298.
17.
CabanJJGotzD.Visual analytics in healthcare – opportunities and research challenges. J Am Med Inform Assoc2015; 22(2): 260–262.
18.
ShneidermanBPlaisantCHesseBW.Improving healthcare with interactive visualization. Computer2013; 46(5): 58–66.
19.
WestVLBorlandDHammondWE.Innovative information visualization of electronic health record data: a systematic review. J Am Med Inform Assoc2015; 22(2): 330–339.
20.
PlaisantCMushlinRSnyderA, et al. LifeLines: using visualization to enhance navigation and analysis of patient records. In: BedersonBBShneidermanB (eds.) The craft of information visualization. Interactive technologies. San Francisco, CA: Morgan Kaufmann, 2003, pp.308–312.
21.
BuiAATAberleDRKangarlooH.TimeLine: visualizing integrated patient records. IEEE Trans Inform Technol Biomed2007; 11(4): 462–473.
22.
FaiolaA.Medical information visualization assistant system and method. Patent US8,645,164 B2, USA. 19.
23.
GotzDStavropoulosH.DecisionFlow: visual analytics for high-dimensional temporal event sequence data. IEEE Trans Vis Comput Graph2014; 20(12): 1783–1792.
24.
WongsuphasawatKGotzD.Exploring flow, factors, and outcomes of temporal event sequences with the outflow visualization. IEEE Trans Vis Comput Graph2012; 18(12): 2659–2668.
25.
KrauseJPererAStavropoulosH.Supporting iterative cohort construction with visual temporal queries. IEEE Trans Vis Comput Graph2016; 22(1): 91–100.
26.
RogersJSpinaNNeeseA, et al. Composer—visual cohort analysis of patient outcomes. Appl Clin Inform2019; 10(2): 278–285.
27.
BernardJSesslerDMayT, et al. A visual-interactive system for prostate cancer cohort analysis. IEEE Comput Graph Appl2015; 35(3): 44–55.
28.
GlueckMGvozdikAChevalierF, et al. PhenoStacks: cross-sectional cohort phenotype comparison visualizations. IEEE Trans Vis Comput Graph2017; 23(1): 191–200.
29.
BernardJSesslerDKohlhammerJ, et al. Using dashboard networks to visualize multiple patient histories: a design study on post-operative prostate cancer. IEEE Trans Vis Comput Graph2018; 25(3): 1615–1628.
30.
LeeJRibeyEWallaceJR.A web-based data visualization tool for the MIMIC-II database. BMC Med Inform Decis Mak2016; 16(1): 15.
31.
RajwanYGKimGR. Medical information visualization conceptual model for patient-physician health communication. In: Proceedings of the 1st ACM international health informatics symposium. IHI’10, Arlington, Virginia, USA, November 2010, pp.512–516. New York, NY: Association for Computing Machinery.
32.
HakoneAHarrisonLOttleyA, et al. PROACT: iterative design of a patient-centered visualization for effective prostate cancer health risk communication. IEEE Trans Vis Comput Graph2017; 23(1): 601–610.
33.
GálvezJAAhumadaLSimpaoAF, et al. Visual analytical tool for evaluation of 10-year perioperative transfusion practice at a children’s hospital. J Am Med Inform Assoc2014; 21(3): 529–534.
34.
GratzlSLexAGehlenborgN, et al. LineUp: visual analysis of multi-attribute rankings. IEEE Trans Vis Comput Graph2013; 19(12): 2277–2286.
35.
FurmanovaKGratzlSStitzH, et al. Taggle: combining overview and details in tabular data visualizations. Inform Vis2020; 19(2): 114–136.
36.
CutlerZTGadhaveKLexA.Trrack: a library for provenance tracking in web-based visualizations. Open Science Framework preprint, 2020. DOI: 10.31219/osf.io/wnctb.
37.
BrownBReevesSSherwoodS.Into the wild: challenges and opportunities for field trial methods. In: Proceedings of the 2011 annual conference on human factors in computing systems – CHI’11. Vancouver, BC, Canada, 7–12 May 2011, pp.1657–1666. New York, NY: Association for Computing Machinery. http://dl.acm.org/citation.cfm?doid=1978942.1979185.
38.
MetcalfRAWhiteSKPotterS, et al. The association of inpatient blood utilization and diagnosis-related group weight: implications for risk-adjusted benchmarking. Transfusion2019; 59(7): 2316–2323.
39.
MccurdyNGerdesJMeyerM.A Framework for externalizing implicit error using visualization. IEEE Trans Vis Comput Graph2019; 25(1): 925–935.
40.
SedlmairMMeyerMMunznerT.Design study methodology: reflections from the trenches and the stacks. IEEE Trans Vis Comput Graph2012; 18(12): 2431–2440.
41.
RogersJPattonAHHarmonL, et al. Insights from experiments with rigor in an EvoBio design study. IEEE Trans Vis Comput Graph2021; 27(2): 1106–1116.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
