SANGUINE: VISUAL ANALYSIS FOR PATIENT BLOOD ...

SANGUINE: VISUAL ANALYSIS FOR PATIENT BLOODMANAGEMENT

Haihan Lin∗

University of UtahRyan A. Metcalf∗University of Utah

Jack WilburnUniversity of Utah

Alexander LexUniversity of Utah

[email protected]

ABSTRACT

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 overusedprocedures in hospitals. Unnecessary transfusions not only waste resources but can also be detri-mental to patient outcomes. Patient blood management (PBM) is the clinical practice of optimizingtransfusions and associated outcomes. In this paper, we introduce Sanguine, a visual analysis tool fortransfusion data and related patient medical records. Sanguine was designed with two user groups inmind: PBM experts who oversee blood management practices across an institution and cliniciansperforming transfusions. PBM experts use Sanguine to explore and analyze transfusion practices andits associated medical outcomes. They can compare individual surgeons, or compare outcomes ortime periods, such as before and after an intervention regarding transfusion practices. PBM expertsthen curate and annotate views for communication with clinicians, with the goal of improving theirtransfusion practices. We validate the utility and effectiveness of Sanguine through case studies.

Keywords Data Visualization · Patient Blood Management · Biomedical Data Visualization · Electronic HealthRecords Visualization

1 Introduction

Transfusion is the most commonly performed medical pro-cedure during hospitalizations in the United States [1],with 17.2 million blood products transfused in 2015 [2].Although transfusions are often medically necessary andeven life-saving, they have also been identified as one ofthe most overused procedures in hospitals [3]. Unnecessarytransfusions can lead to adverse reactions and complica-tions, such as volume overload, lung injury, hemolysis, andmore [4].

Management and acquisition of blood products is alsoexpensive, creating financial incentives for hospitals to op-timize their usage. To better address these problems, manyhospitals have hired dedicated experts in patient blood

*These two authors contributed equally to this work.This is the authors’ preprint version of this paper. License:CC-By Attribution 4.0 International. Please cite the follow-ing reference:Haihan Lin, Ryan A. Metcalf, Jack Wilburn, Alexander Lex.Sanguine: Visual Analysis for Patient Blood Management.Information Visualization, to appear, 2021.

management (PBM), and PBM has become an indepen-dent academic field. Through PBM, health care providersaim to reduce unnecessary transfusions and improve pa-tients’ outcomes using various methods, such as treatinganemia (lack of red blood cells) prior to surgeries, pro-viding guidelines on when transfusions are necessary, andusing cell salvage (“recycling” blood) during surgery [5].

Successful PBM involves multiple stakeholders. PBM ex-perts analyze the usage of blood products and give adviceor develop guidelines on best practices. Clinicians, in turn,make decisions about when and how much to transfuse,thus they need to be well informed about best practices,their individual performance regarding these best practices,and their performance relative to their peers.

These analysis processes, however, are difficult with thecurrent tools available in hospitals. Electronic healthrecords software may provide static charts and reportson blood product usage, but they lack the nuance and so-phistication necessary to make a holistic evaluation of thepractice of individual clinicians. For example, when onesurgeon uses significantly more blood products than theirpeers, they might rightly claim that they do so becausethey are treating the most difficult cases with many patient

LIN ET AL.; SANGUINE: VISUAL ANALYSIS FOR PATIENT BLOOD MANAGEMENT; 2021

Figure 1: An overview of Sanguine visualizing patient blood management data with multiple views. The left panel isdedicated to managing filters and selections. The workspace in the center contains visualizations that can be flexiblyarranged. A heatmap, a dot plot, and a dumbbell chart are shown. On the right, a patient-specific detail view showsattributes of a case.

comorbidities. With current tools, analysis is often staticor limited to only certain clinical contexts.

To tackle this issue, we created Sanguine, shown in Fig-ure 1, an interactive visual analytics tool for PBM analysis.Sanguine was developed as a design study using an itera-tive design process involving PBM experts, surgeons, andanesthesiologists at the University of Utah Hospital. San-guine enables analysts to view outcomes juxtaposed withblood product usage data, review patient blood manage-ment practices for different providers, and compare theeffect of patient blood management in clinical settings.Sanguine is designed with two types of stakeholders inmind: PBM experts who conduct in-depth analysis, andclinicians (primarily surgeons and anesthesiologists) whoreceive tailored and annotated interactive reports from thePBM experts. These two groups have radically differenttypes of engagement with Sanguine. The PBM expertsindependently analyze data and ask a variety of analysisquestion, creating charts in the process. The clinicians,in contrast, mostly consume the visualizations as reportsthat contains feedback on their practice. We designed San-guine to facilitate communication around patient bloodmanagement between the two types of stakeholders.

Our main contribution is the design and development ofSanguine, an open-source visual analytics tool for the anal-ysis of patient blood management data. We also contributean analysis of domain goals and a task abstraction. We

evaluate the utility of Sanguine in case studies and throughinterviews with domain experts.

2 Medical Background

Patient blood management encourages minimizing blooduse through the appropriate use of blood products, as-certained by evidence-based transfusion strategies [6, 7].However, the decision to transfuse often depends on theclinical experience of the practitioners, and blood productusage can vary significantly among individuals. Analy-sis of transfusion records [8, 9] has shown how differentsurgeons within the same department vary in their blooduse. Current electronic medical record systems do not givepractitioners the opportunity to easily review and reflecton multiple cases simultaneously, and the static charts andreports used by clinicians to review transfusions limit themfrom exploring the collected data in a comprehensive andcontext-specific fashion.

Blood banks at hospitals regularly report wasted bloodproducts, primarily because of excessive ordering of bloodproducts during preoperative preparations. A blood prod-uct issued to the operating room, if unused, must be re-turned to the blood bank in a timely manner and prop-erly stored; otherwise, it must be discarded. Estimates ofwasted blood components range from 200,000 to 1 millionin the US annually, with associated costs of 46 million to

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230 million US dollars [10]. Many hospitals adopt pro-grams using a maximum surgical blood order schedule(MSBOS). MSBOS contains a list of recommended bloodorders for commonly performed surgeries regionally. A“one size fits all” solution, however, does not account formuch of the variation in patient conditions or risk. A studyby Frank et al. [11] has shown that an institution-specificblood order schedule can significantly reduce unnecessarypreoperative blood ordering. Metcalf et al. [12] developedan example of a more personalized prediction model tofacilitate more accurate and precise ordering, but institu-tions have not widely adopted this or other novel orderingmethods.

3 Formative Process

Our main collaborator, Dr. Ryan Metcalf, also a co-authorof this paper, is the medical director of the TransfusionMedicine Service at University of Utah Hospital. As aPBM expert, he is studying how transfusion affects patientoutcomes and the overall use and effectiveness of patientblood management. Transfusion is common across special-ities, including in a nonsurgical context, but we decidedto focus on cardiac surgical cases because hemorrhage ishighly prevalent in cardiac surgeries. Cardiac surgeries areinherently complex, and numerous PBM modalities existin this clinical setting.

To get a good understanding of the domain goals, we metwith our collaborator on a weekly basis starting in May2018 about his process for evaluating patient blood man-agement practice. We also involved the data warehouse atthe University of Utah Hospital, which aided in curating adataset, acquired IRB approval for using the anonymizedpatient data, and secured funding by a university-affiliatednon-profit reference laboratory.

Next, to understand the needs of a broader set of stake-holders involved in PBM, in July of 2019, we conducted acreative visualization-opportunities (CVO) workshop [13].We recruited participants form Dr. Metcalf’s network atthe cardiac surgery department. Participants included twocardiac anesthesiologists, one cardiac surgery critical carephysician, and one IT manager. Two cardiac surgeonswere scheduled to participate but were called away forsurgery. An IT manager was included so we could learnmore about currently available tools and technology con-straints. To accommodate the schedule of the clinicians,the workshop lasted three hours, which is shorter than atypical CVO workshop. After a brief introduction, we usedthe Wishful Thinking, Visualization Analogies, and BarrierIdentification and Removal activities [13]. The participantsidentified factors that potentially lead to unnecessary trans-fusions, and brainstormed on how to improve the currentstandard of care for better patient outcomes. They agreedon the need for better PBM practices, and were hopeful thata tool showing comprehensive PBM data could achievethis goal.

After the workshop, we did an initial analysis on the resultwe collected. One key insight that emerged was that clini-cians believed that a tailored report to their practice wouldbe much more useful and likely be more widely adoptedthan a general-purpose tool. This, in turn, let us to designfor these two different user groups. To also understandwhether the results also match up with cardiac surgeons’workflows, and given that the cardiac surgeon participantshad to cancel at the last minute, we decided to presentour intermediate results and elicit feedback from a largegroup of cardiac surgeons during the surgery department’sweekly all-hands meeting. The surgeons stated that a toolthat can aid them in their blood management practice couldlead to improved outcomes but cautioned that they wouldnot want a system that would obstruct them from orderingblood or slow down their current practice.

The workshop and notes from the all-hands meeting gen-erated rich artefacts, in the form of thematically groupedsticky notes, audio-recordings, photographs, and notes bythe facilitators. To analyze, we first created a machine-readable corpus (transcribing sticky notes, etc.) and thengrouped the concepts into thematic areas. We then basedour domain goals, and subsequently, our design on theseresults.

4 Domain Goals

Based on the CVO workshops, the all-hands meeting, andthe regular meetings with our main collaborator, we identi-fied the following high-level domain goals our collabora-tors have regarding patient blood management:

• Identifying problematic transfusion practices,and thereby improving outcomes is the primarygoal that all our stakeholders share. Meeting thisgoal requires the analysis of transfusion data inthe context of patient records and evidence-basedguidelines from the literature. Also, comparisonsof various aspects (between clinicians, betweentime intervals, etc.) emerged as a way to achievethe goal.

• Decision support for when to transfuse duringsurgery was brought up as another goal duringthe workshop. Clinicians would like to have moredata and information about when it is appropriateto transfuse while in the operating room.

When considering these goals, we quickly realized thatreal-time decision support would not be possible giventhe regulatory environment and the data we have avail-able. The US FDA regulates devices (including software)that are used in the diagnosis, mitigation and treatmentof diseases. Getting FDA approval for a real-time deci-sion system would have been beyond the scope of thisproject. Also, the data used for real-time decision makingis collected during surgery using various devices and isnot stored in real time in a database. The data we can getaccess to, in contrast, is medical record data. Hence, we de-

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cided to focus on the retrospective analysis of transfusionpractices.

Based on the collected data and in working with Dr. Met-calf, we identified a typical workflow: He may start by ana-lyzing transfused units by patient preoperative hemoglobinvalue. A patient should first be treated for anemia if it ispresent, and ideally have a hemoglobin value above 13g/dLbefore surgery. If a patient has a hemorrhage during theprocedure, cell salvage should be considered as an alter-native to transfusion. Our collaborator also analyzes thepatient’s postoperative hemoglobin value: a hemoglobinvalue well above the post-transfusion hemoglobin targetfor transfused cases indicates overuse of red blood celltransfusion. The most important measures are patient out-comes, which can be influenced by transfusion practice.Analyzing transfusions within the context of preconditions,risk factors, details about the surgery, vital signs, and out-comes, is critical for an accurate analysis and can alsodemonstrate the value of PBM.

Dr. Metcalf described certain barriers to sharing insightseffectively with surgeons, which has been only partiallysuccessful in the past. Surgeons may not always havethe time to analyze data closely. At the same time, theywould like for analyses to account for variation in patientcharacteristics and other specific contexts to meaningfullycapture the complexity of a particular case or group ofcases. When advising surgeons and anesthesiologists, hewould prefer a medium that flexibly and rapidly conveysthe key aspects in the appropriate context. Because a well-designed visualization can communicate insights from thedata with ease, we decided to design and implement avisual analysis tool for analyzing and reviewing patientblood management practice with outcomes of interest.

Based on this goal, we curated a list of data items thatwe would need to address the analysis questions. In co-operation with the data warehouse at the University ofUtah Hospital, we compiled a dataset drawing from theelectronic health records of over 4000 cardiac surgery pa-tients, spanning the years 2014 to 2020, and covering 111different cardiac surgery procedures, ranging from rou-tine coronary bypass surgeries to heart transplants. Foreach patient, we included transfusion records, medicationrecords, and lab results, such as hemoglobin level recordsand thromboelastogram (ROTEM) results. We break downthe high-level goal of identifying problematic transfusionpractices into domain goals as follows:

• G1: Comparing Transfusion Practice. Weidentified comparison of transfusion practice asa central aspect of the analysis question. Specifi-cally, our collaborators want to compare transfu-sion practices among (1) surgeons, (2) anesthe-siologists, and (3) years. The former two enablea comparison between individual clinicians, thelatter enable comparison over time, to, for ex-ample judge the effectiveness of an intervention.However, as was evident from the workshops,crude measures for comparisons, such as total

units of blood transfused, do not adequately ac-count for the complexity and heterogeneity ofsurgical cases. For example, a comparison has toaccount for the types of procedures (certain proce-dures rarely require transfusion, while others canresult in significant bleeding). A comparison alsohas to account for other risk factors, such as thegeneral health of the patients. Our interviews andworkshop revealed that providing this contextualinformation is critical for practitioners to trust thevisualization.

• G2: Analyzing Adherence to Best Practicesand Standards. The fields of cardiac surgeryand patient blood management have developed asuite of evidence-based clinical guidelines andbest practices for many procedures. For ex-ample, postoperative hemoglobin values clearlyabove a post-transfusion target can indicate over-transfusion, and preoperative hemoglobin valuesbelow a threshold can indicate a failure to man-age anemia prior to elective (scheduled) surgeries.Similarly the absence or presence of methods,such as recycling the patient’s own blood or treat-ing bleeding pharmaceutically are important indi-cators when analyzing blood management. Bothpatient blood management experts and cliniciansneed to analyze whether these standards are met.

• G3: Analyzing Individual Patients. In certainsituations, aggregate or summary informationdoes not provide enough information to judge,e.g., why a specific case was an outlier, or whybest practices were not followed. To understandthe specific situation, clinicians need to accessindividual patient records.

• G4: Prepare for Surgery using a “PatientsLike Mine” Approach. Ordering an appropriateamount of blood is important for both outcomesand efficient use of resources. PBM professionalswant to make good estimates for the amount ofblood to prepare, taking into account various fac-tors such as the type of surgery, but also key vari-ables about the patient. To do this, they want toexplore similar historical cases to see the amountand variability of blood needed, and then orderunits of blood based on this information.

• G5: Communication and Sharing. Finally,since we have identified two types of stakehold-ers, our visual analysis interface needs to be tai-lored to both groups: PBM professionals, whowant to interactively explore the data, create cus-tom views, and create cohorts based on multipleattributes, whereas clinicians want to see theirperformance relative to their peers and to analyzewhether they follow evidence-based standards.Hence, we envision split roles of a “power user”analyzing the data in detail, and providing contextthrough annotations, and a “consumer” of thesevisualizations.

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These domain specific goal map to a wide variety of vi-sualization tasks. Using Brehmer and Munzner’s task ty-pology [14], G1 to G3 map to the “discover” and “lookup”tasks on the “why” dimension, and the “encode”, “select”,“filter”, and “aggregate” tasks on the “how” dimension. G1is a comparison task, for which Gleicher has developeda set of considerations [15]. The characteristics of thedata subsets to be compared (a handful of different items,such as the records of surgeons, and multiple complexdistributions as data values) suggest “juxtaposition” as anappropriate design choice for the comparison task.

G4 — looking for “patients-like-mine” is, again usingBrehmer and Munzner’s typology, of type “browse” inthe “search” category, as the characteristics are know, butthe target is not. G5 — communication and sharing — isdifferent from the other tasks, as it is mostly about “pro-ducing” (why) based on “annotations” and “recordings”(how).

The heterogeneity of the goals and tasks suggests thata flexible, multi-view visualization will be necessary toaddress all of them.

5 Related Work

As digital medical records are now ubiquitous, visualizingmedical records has been an important area of research foranalyzing medical data. We provide an overview here, butfor a more detailed survey on visualizing and exploringdigital patient records, we refer to Rind et al. [16]. Ca-ban et el. [17] describe four major types of visual analyticapplications in healthcare: clinicians analyzing patients’records, administrators making data-supported decisions,researchers working on large medical datasets, and patientsunderstanding their own data. Our work falls into the firstcategory. Shneiderman et al. [18] analyze the challenges ofimplementing interactive visualizations for healthcare pro-fessionals, which include offering busy clinicians timelyinformation in the right format. West et al. [19] encouragecreators of visualization systems to also consider the train-ing time required. In Sanguine, we address these points byenabling a workflow where a PBM professional curates anannotated visual report for the clinicians.

In many clinical contexts, a timeline of symptoms andtreatments is informative; hence, multiple works have beenfocused on visualizing medical records in a timeline for-mat. Lifeline [20], for example, shows details of individualpatient medical histories, representing events and episodesusing horizontal lines. TimeLine [21] uses a similar event-based arrangement for personal medical records, but isabundant in image and file resources for users to explore.Faiola et al.’s work [22] is specifically designed for ICUclinicians, juxtaposing event timelines over vital signs. De-cisionFlow [23] visualizes a group of patients with similarcharacteristics in temporal event sequences. Outflow [24]also provides an overview of event sequences extractedfrom groups of patient records. All these works focus onvisualizing an individual patient or a group of patients on

a timeline, and emphasize specific events happening in thesequence. While event sequences can play a role in trans-fusions, Sanguine takes a provider and practice-focusedapproach instead.

Patient cohorts analysis is another mainstay of visualizingmedical records [18]. Cohort definition and creation is acommon theme in these works. COQUITO [25] uses atreemap and visual temporal queries to help users generatecohorts. Composer [26] adopts an attribute filter methodfor cohort creation, and compares patient cohort develop-ments and changes over time and to other cohorts. Bernardet al. [27] also follow the attribute-oriented cohort creation,but visualize all patient records of the selected cohort, in-stead of comparing several cohorts through an aggregatedvisualization design. Phenostacks [28] uses a set visualiza-tion to compare various phenotypes over multiple cohortssimultaneously and a sunburst graph for subset and globalpattern discovery. Bernard et al. [29] use static dashboardsand compact data encodings to visualize groups of patients;their approach lacks the flexibility for users to curate theirown charts. Visualization studies of patient cohorts anal-ysis typically focus on cohorts that are defined by patientcharacteristics, such as shared demographics. Sanguine,instead, focuses on cohorts that relate to the practice of theproviders: cohorts are either created temporally, to makeyear-to-year comparisons, or by the provider, to comparethe performance of surgeons and anesthesiologists.

Medical record visualizations can help users navigate largedatasets. Lee et al. [30] developed a web-based visualiza-tion tool to help users querying large datasets with compar-ative visualizations; however, the tool has limited abilityfor customization.

Another research topic on visualizing health record is bridg-ing the communication gap between providers and pa-tients [31]. As an example, PROACT [32] helps patientsunderstand the complex risk of cancer and the treatmentplans available to them through visualizations. We arenot aware of existing techniques explicitly designed forprovider-to-provider communication, which is a key aspectof Sanguine.

Finally, various projects have applied statistical analysisto transfusion data. Gálvez et al. [33] developed a toolfor decision-making support on blood product orderingat a pediatric hospital, which gives patient-specific bloodorder advice. This tool allows users to filter the data basedon age groups, procedures, and conditions. After filteringthe data, the tool visualizes all the transfusion data andother attributes of interest in previous procedures using barcharts. This approach offers a concrete solution to a patient-specific blood order schedule, and shows how visualizationcan be effective for visualizing transfusion in a clinicalsetting. Although the tool shows all blood usage of casesfrom filtered results, Sanguine offers more personalizedblood ordering preparation for providers, where they canview their history of transfusions on a particular procedurewith specific conditions.

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Figure 2: Our initial design used violin plots to showdistributions. We abandoned this design due to the skewedand discrete distribution of the data.

6 Visualization Design

The design of Sanguine reflects the goals described inDomain Goals section, which, in turn, are rooted in theinterviews and creativity workshop we conducted. Thesubsequent development of Sanguine was done using aniterative, user-centered process based on the feedback fromweekly meetings between the visualization developmentteam and the PBM expert. Because we are designing thisvisualization for two user types, Sanguine needs to bepowerful enough for PBM experts to discover relevantresults, and be straightforward enough so that surgeonscan leverage the visualization with limited time.

Sanguine consists of three main components, shown in Fig-ure 1: a filtering and selection view, the main workspacewhere visualizations can be flexibly arranged, and apatient-specific detail view. A video demonstrating San-guine is available at https://youtu.be/DhTNyvCJgtM.The source code is available at https://github.com/visdesignlab/Sanguine. Due to the sensitive nature ofthe data, we cannot provide a live-demo.

6.1 Data

A large amount of data is recorded in patient records, butnot all of the data is typically necessary to answer a par-ticular research question. Hence, we designed Sanguinesuch that a PBM expert can flexibly compose visualiza-tions based on the data relevant to the task. The primarydata that PBM experts are interested in are the number ofunits transfused, cell salvage (an alternative to transfusions,measured in milliliters), and laboratory measurements ofhemoglobin level, which are the main indicators for anemiamanagement and transfusion appropriateness.

Depending on the research question, other data can pro-vide relevant context: patient outcomes (death, need forlong-term ventilation, etc.), PBM-related drug administra-tion, and information about whether a surgery was plannedor was an emergency are examples. These attributes are

extracted directly from electronic health records and anes-thesia flowsheets.

6.2 Comparing and Contextualizing

The most important functionality of Sanguine is to visual-ize the transfusion data mentioned above, while addressingthe comparison-related goals — comparing between prac-titioners and time intervals (G1), as well as dividing andcomparing using additional variables, such as outcomes.Breaking these goals into abstract tasks, we need to enablethe comparison of distributions of values. Following Gle-icher’s guidance [15], we chose a juxtaposition strategy.

In the early development stage, we used bar charts to showthe total number of transfusions, but quickly decided toabandon bar charts because they did not account for thenumber of surgeries performed, or for the number of unitstransfused per surgery. We then evaluated violin plots(Figure 2), since violin plots are generally well suitedto visualize distributions. However, as is evident fromFigure 2, the discrete nature of the data made violin plotsa poor choice for visualizing these values.

Hence, we decided to use heatmaps as the main charttype for comparison visualization, as shown in Figure 1.Addressing the practice-focused comparison (G1), theheatmap shows transfusion data aggregated by surgeons,anesthesiologists, or years. To account for the differencein total case count between these facets, we use relativescales showing the percentage of cases that had 0–4 unitsof red blood cells, or 5 or more transfusions. The thresholdvaries depending on the blood component. The decisionto aggregate values above a certain threshold into one binwas driven by the effect that rare outliers had on the overallvisualization. For cell salvage data, which are measured incontinuous values, we used bins with a dedicated bin forno usage.

As is evident from the heat map in Figure 1, a large per-centage of cases received no transfusion. This aspect ofthe data is important to include in the visualization, butthe heavily skewed distribution makes it hard to notice anydifference in the nonzero transfusion cases. To address thisproblem, we provide a toggle to remove zero transfusionfrom the color-scale in the visualization, i.e., the maximumvalue that maps to the darkest red color is taken from thecounts of cases that have received at least one transfusion.This makes differences in transfusion practice much moreapparent, as is evident in Figure 3, which is important asanalyzing cases that required transfusion is a key task. Theheatmap still shows the data on zero transfusion, but usesa separate (gray) color scale encoding the percentage ofzero transfused cases out of all cases of the row.

The resulting heatmap enables PBM experts and clini-cians to compare their relative practice of transfusion (G1).Although Sanguine can be used to compare transfusionsacross all procedures, filtering by procedures is essential,because diverse procedures such as heart transplants andbypass surgery differ in the typical need for transfusions.

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https://youtu.be/DhTNyvCJgtM

https://github.com/visdesignlab/Sanguine

https://github.com/visdesignlab/Sanguine


Figure 3: A heatmap showing transfusion data (red cells) for surgeons performing bypass procedures (CABG),subdivided by the need for long-term ventilation. Cases shown in the rows with a green indicator required ventilationfor more than 24 hours, whereas cases in blue rows were removed from ventilation before that, or did not require aventilator. For context, five attributes are visualized on the left: distribution of DRG weights (risk scores), per casetransfusion amount, B12 medicine usage, preoperative hemoglobin distributions, and ECMO usage rates. The redcolor map for red blood cell transfusion is set to exclude cases without transfusions, and the gray color map shows thepercentage of zero transfusion in the total cases of each row.

However, even when narrowing cases down to specificprocedures, there can be systematic differences in howcomplicated cases are, and hence what outcomes can beexpected and how much will be transfused.

To provide this context and show transfusion-relevant out-comes, Sanguine can visualize additional attributes to dis-play along with the heatmap, such as lab values and patientoutcomes (see Figure 3). The visual encoding used de-pends on the attribute types. For numerical/distributionvalues (such as hemoglobin values), we use a violin plotthat morphs into a dot plot when there are few observa-tions; for individual numerical values, we use bar charts(e.g., average transfusions per case) or labeled heatmapcells (e.g., mortality rates).

Finally, to enable outcome and intervention-focused com-parisons, the heatmap in Sanguine can be divided by binaryoutcome variables such as mortality, or by a time-range.The heatmap shown in Figure 3 is divided by the needfor long-term ventilation, as indicated by the green andblue fields, respectively. Every row is divided, showingthe transfusion data and contextual information in separaterows. In Figure 3, for example, we can see that long-termventilation appears to correlate with higher transfusionrates and higher risk scores.

6.3 Standards and Best Practices

Hemoglobin laboratory values are a key indicator in patientblood management. Clinical trials have led to the devel-opment of evidence-based guidelines that use hemoglobinvalue as red blood cell transfusion thresholds and cor-responding expectation for post-transfusion hemoglobintargets can thus be inferred. Hence, analyzing transfu-sion practice in this context is essential (G2). A lowpreoperative hemoglobin level indicates improper ane-mia management before surgery, whereas high postopera-

tive hemoglobin levels imply excessive transfusion duringsurgery. To facilitate the analysis, Sanguine provides adumbbell chart dedicated to hemoglobin level evaluation(see Figure 4a). We encode the recommended levels forpre- and postoperative hemoglobin values in the chart, andthey are easy to change in Sanguine through a configu-ration file. An individual dumbbell visualizes the preop-erative (green) and the postoperative (blue) hemoglobinvalues of a single case as dots. A link connecting thedots shows the gap between these values. Cases and theircorresponding dumbbells can be divided by all relevantattributes; Figure 4a shows dividing by surgeons. Withineach division, solid horizontal lines show the medians forpre- and postoperative hemoglobin level respectively, andstippled lines provide clinically recommended values forminimal preoperative hemoglobin level and transfusion-trigger hemoglobin level i.e., the level at which cliniciansshould initiate transfusion during surgery. The dumbbellswithin each division can be sorted based on preoperativevalue, postoperative value, or the gap between the two.

6.4 Filters and Brushes

In addition to dynamic comparisons, filters and brushes areessential tools to create the visualization relevant to answerspecific analysis questions. In addition to the procedurefilter view (Figure 1), Sanguine provides views specificallydesigned for filtering based on data values. The dot plot(Figure 4b) can visualize correlations and support rectanglebrushes, which can be converted into filters in the filtermanager view (Figure 1). The dot plot also visualizes meanvalues and confidence intervals.

We also integrate a LineUp [34, 35] view (Figure 4c) tovisualize all attributes of individual cases (G3) and com-plement Sanguine’s filter system with the one that comeswith LineUp. LineUp visualizes data in a tabular layout,

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(a) Dumbbell Chart (b) Dot Plot (c) LineUp

Figure 4: Visualizing bypass (CABG) procedure data: (a) A dumbbell chart that shows pre- and postoperativehemoglobin values of elective surgery cases, grouped by surgeons and sorted by preoperative hemoglobin levels.Horizontal lines in each band represent the medians. The stippled lines are for references to preoperative hemoglobinlevel (13g/dL) and transfusion-trigger hemoglobin level (7.5g/dL). (b) A series of dot plot shows the postoperativehemoglobin value grouped by units of frozen plasma transfused intraoperatively. The horizontal line in each bandrepresents the mean, and the vertical line is the 95% confidence interval. (c) LineUp, a tabular data visualizationtechnique, is available in Sanguine as a separate tab view. The filtering system in LineUp is connected to the main view.

and applies different techniques to columns based on theattribute types.

6.5 Case View

An alternative way to view information about individualcases (G3) is the detail view, shown in Figure 1 on the right.All selected cases are shown in a list; the attributes forone case are shown, including transfusion record, surgerydescription, relevant medicines administered, and out-comes. Using the detail view, analysts can study thesecases closely, and if they identify something of interest,they can add that attribute to another visualization in San-guine. Ultimately, practitioners can also cross-referencecases with the medical record system, which contains evenmore data on the patients.

6.6 Communication and Sharing

Sanguine enables communication and sharing (G5) in twocomplementary ways: first, communication is facilitatedthrough annotations: each visualization is accompaniedwith a text field, which can be used for notes or to recordinsights and conclusions.

Second, sharing of findings is enabled through provenancetracking. Each change to the visualization, including visu-alization configurations, filter settings, and annotations, issaved as a state, and recorded using the Trrack provenancetracking library [36]. Based on Trrack’s functionality, San-guine provides undo/redo, saving and loading the state ofa workspace to/from a server, and sharing the state via aURL. URL based sharing is convenient for distributingfindings made by the PBM expert to clinicians, who canthen review the visualizations and adjust their practice, ifappropriate. As these “visualization consumers” typicallydo not want to leverage the full complexity of Sanguine,we introduced a “View Mode”, which removes editingfunctionality and simplifies the interface.

6.7 Implementation and Deployment

Sanguine is developed in Typescript using the React frame-work. We use Semantic UI and D3 for the graphical inter-face and the MobX and Trrack library [36] for state control.The frontend is supported by a Django server. The serverinterfaces with an SQL database housed in the data ware-house of the University of Utah Hospital. It is deployedin a protected environment suitable for sensitive medicaldata, and uses encryption and two-factor authentication toensure security.

7 Evaluation

We evaluate Sanguine through case studies with Dr. Met-calf, the PBM expert, and through feedback from surgeons,thereby covering our two user groups of analysts who inter-act with the tool in depth and clinicians who consume cu-rated reports. As our collaborators were closely involved inthe development of Sanguine, experimental demand char-acteristics, which often arise in close collaborations [37],can bias responses, hence, we refrain from reporting sub-jective assessments and instead report factually on analysisscenarios and make comparisons of capabilities of a work-flow with and without Sanguine.

7.1 Case Study

To demonstrate the utility of Sanguine, we present casestudies of two scenarios where a PBM expert, Dr. Metcalf,uses Sanguine (1) for analysis of transfusion practices, and(2) as a “patients like mine” decision support tool whenordering blood components during preparation for surgery.The main research question for these case studies is ifSanguine can provide insights into transfusion data andcan be used to answer the domain goals. Our collaboratorhad the opportunity to use the tool in practice for severalmonths after we completed a prototype.

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7.1.1 PBM Review and Analysis

Figure 5: Heatmap of units of red blood cells transfusedfor the CABG, TAVR, Open AVR, VAD, and thoracicaortic aneurysm procedure. DRG weights and per caseunits transfused are added for peer group benchmarking.Surgeon (a) and (b) have higher medium value for DRGvalues, 6.39 and 5.26 respectively, but their per case unitstransfused are 0.67 and 0.45, whereas cases treated bysurgeon (c) have relatively low average DRG weight (4.13),but a high transfusion rate at 0.93 units per case.

Peer Group Benchmarking. Context matters whencomparing the transfusion practices of surgeons and anes-thesiologists to their peers (G1, comparing transfusionpractice). For example, if one surgeon transfuses moreblood on average than others in the peer group, the sur-geon may rightly complain that the comparison is not fairif they tend to see sicker or more complex patients. Pre-vious work [38] has shown that diagnosis related group(DRG) billing code weights are associated with transfusionvolumes in surgical populations. In other words, more com-plex patients will have a higher DRG weight and would beexpected to receive more blood on average.

To analyze differences in transfusion practice, the PBMexpert created a heat map to visualize blood utilization ofthe different surgeons, shown in Figure 5. He filtered to in-clude only the most common complex cardiac surgery pro-cedures. Blood use varies substantially, so to see whetherthe differences was due to the complexity of the cases andallow for fairer comparisons, he chose to visualize the dis-tribution of DRG weights next to the heat map and thebar chart of average transfusion per case. By adding theDRG weight distribution, it is easy to see which surgeonsrepresent a reasonable peer group comparison and whichsurgeons do not. In the example shown in Figure 5, thetwo surgeons (indicated as (a) and (b)) seeing the mostcomplex cases did not have the highest blood utilization,while one surgeon (c) mostly saw cases with low DRGweight but still transfused a lot. The expert commentedthat this identifies surgeons performing above and belowof the context-appropriate peer group, which in turn can beused as a starting point for a discussion and intervention toimprove practice.

Anemia Management. Preoperative anemia manage-ment (G2, best practices) is an essential part of patientblood management. Dr. Metcalf started his analysis bystudying whether patients who had anemia were treatedfor it before surgery, as anemia can require transfusionsthat would otherwise not be necessary. A patient witha hemoglobin level at 10g/dL or below is considered tobe anemic. To view the overall hemoglobin trend, ourcollaborator first created a dumbbell chart comparing sur-geons. Since preoperative anemia management can bedone only for elective surgeries, he then used the surgeryurgency filter in the filter manager to remove emergent andurgent cases. He also selected a common procedure coro-nary artery bypass grafting (CABG) in the filter selection,and analyzed a subset of surgeons who have performedCABG. By comparing preoperative hemoglobin values tothe stippled line for 13g/dL as the clinically recommendedpreoperative hemoglobin values, as shown in Figure 4a,he noticed that several cases should have been treated foranemia before surgery, but were not. He also noticed thatthere appeared to be some variation in patient preoperativehemoglobin levels between surgeons.

Figure 6: Transfusion Appropriateness: A dumbbell chartcomparing surgeons, sorted by postoperative hemoglobinlevels. Cases shown are with one or more units of red bloodcell transfusions. The high postoperative hemoglobin val-ues (blue line above the lower stippled line) indicate thatover-transfusion is widespread.

Transfusion Appropriateness. Using another dumbbellchart, Dr. Metcalf also reviewed transfusion appropri-ateness (G2, best practices). When a patient receives ared blood cell transfusion, the provider’s target postoper-ative hemoglobin value is between 7g/dL and 9g/dL. Toview only patients who received red blood cell transfu-sions, our collaborator added a dot plot of postoperativehemoglobin and units of red blood cells transfused intraop-eratively. Appropriate transfusion is a practice applicableto all cases, elective or urgent; hence, he removed theelective case filter, and sorted the dumbbell chart to post-operative hemoglobin levels for easier comparison of thepostoperative values. Using the brush feature of the dot

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plot, our collaborator could filter out patients who did notreceive red blood cell transfusions during the surgery, re-sulting in the dumbbell chart shown in Figure 6. Afterapplying the filter, our collaborator observed a significantamount of cases for which postoperative hemoglobin val-ues were much higher than the target value, indicatingover-transfusion in these cases. He then used this infor-mation to advise surgeons and anesthesiologists for betterhemoglobin level targeting when performing transfusions.

Cell Salvage. Another key point of patient blood man-agement is the use of cell salvage. Cell salvage is recyclinga patient’s blood when they bleed during the surgery, analternative to transfusions. Ideally, all surgeons should usecell salvage when performing transfusions (G2, best prac-tices). Dr. Metcalf started with a heatmap of cell salvage,which he aggregated by surgeons. The heatmap shows thatmost surgeons used cell salvage, but the chart does notindicate how surgeons were using cell salvage when theywere also transfusing.

Figure 7: Cell Salvage Usage: A heatmap of cell salvageusage, for cases with one or more units of red blood cellstransfused. Several surgeons do not or rarely use cellsalvage when transfusing, which violates best practices

After removing all cases without transfusions, our collab-orator observed that even though cell salvage was usedin most cases, there was still room for improvement, asshown in Figure 7. Most providers were using cell sal-vage properly; however, a few providers were performingtransfusions without using cell salvage for over 20% oftheir cases. This information is valuable to PBM experts,who can now identify the providers and inform them aboutproperly using cell salvage.

Drug Treatment. Finally, Dr. Metcalf analyzed usageof antifibrinolytic agents, in this case, aminocaproic acid.Antifibrinolytic agents help to reduce bleeding in surgeries,and overall reduce the use of transfusion (G2, best prac-tices). To visualize the use of aminocaproic acid, he addeda comparison heatmap of red blood cells transfused, ag-gregated by surgeon, divided by aminocaproic acid usage,shown in Figure 8. The chart gave him an idea of how fre-quently providers were administering aminocaproic acid,

Figure 8: AMICAR Usage: A heatmap of red blood celltransfusion, divided by the use of aminocaproic acid, adrug that can reduce bleeding. The violin plot shows DRGweights (risk scores), and the bar chart shows the unitstransfused per case. Cases using aminocaprioc acid usedless RBC units.

and how effective it was at reducing transfusions. By an-alyzing the heatmap and comparing the case counts, heconcluded that the majority of cases did appropriately re-ceive aminocaproic acid, which is indicated for patientblood management in cardiac surgeries.

Our collaborator identified a trend of fewer transfusionsassociated with the use of aminocaproic acid across in-dividual surgeons To investigate the use of aminocaproicacid in more context, he added plots of the distribution ofthe DRG weights (risk scores). He noticed that despitesimilar DRG weights, cases using aminocaproic acid usedless blood. This is consistent with what is known aboutthe benefit of this drug in cardiac surgery (reduced needfor transfusions) and further drives home the importanceof this patient blood management modality.

Dr. Metcalf remarked that none of these analyses were pos-sible before, at least not with the flexibility that Sanguineenables.

7.1.2 “Patients Like Mine” Decision Support

Dr. Metcalf explored using Sanguine as a decision sup-port tool for blood component ordering when preparing forsurgeries (G4). For example, a surgeon preparing for anopen mitral valve replacement (MVR) can view the trans-fusion data for prior open MVR cases, using Sanguine’sheatmap shown in Figure 9. To get a more specific picture,they can apply a filter using the dot plot based on theirpatient’s preoperative hemoglobin value, and even filterbased on the anesthesiologist with whom they will workin the surgery. The surgeon can view their own historictransfusion record for open MVR and order blood com-ponents based on their personal records. For example, asurgeon who performed 11 open MVR cases (first row inFigure 9) did not transfuse in 64% of cases, and used only1 unit in 15% of all their cases. Hence, they can conclude

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that they should order one unit of red blood cells beforesurgery. Surgeons can also view the outcomes of casesfrom historic data, and know what they can expect fortheir upcoming case. For example, for the same surgeon inthe first row, they can see that 55% of the cases requiredlong-term ventilation after the surgery.

Figure 9: Heatmap of units of red blood cells transfusedfor the open MVR procedure and cases with preoperativehemoglobin levels of 10-12g/dL. By narrowing the recordsdown to procedures and parameters specific to the case,surgeons can plan how many units of blood to order.

7.2 Feedback from Clinicians

Dr. Metcalf, the PBM expert, presented his findings,described in the previous section, to a cardiac surgeonand several anesthesiologists in virtual meetings, using ascreen-share of Sanguine in “View Mode” (G5, communi-cation). As we designed Sanguine to be used by cliniciansas visualization consumers, we considered the modality ofthe PBM expert presenting via screen-share and advisingon blood product usage representative of a real-world usecase.

After the presentation, the surgeon said, “this is what Ihave been looking into and could not find an answer any-where. Besides just blood transfusion, there is a lot ofother data in there too.” The anesthesiologists discussedopportunities Sanguine could bring for implementing in-terventions: [Sanguine] can guide our potential interven-tions...this is great to look at combinations of data...andfor evaluating practices. I would use this tool every day.

We asked them to compare Sanguine with their currentpractice for analyzing PBM data, which consists mostly ofreviewing the semi-annual report provided by the Societyof Thoracic Surgeons. The surgeon commented on thedifference: the STS reports we currently receive are aboutsix months delayed, and you can update [Sanguine] muchfaster... This can be used for meetings to see where we canimprove.

Speaking to a weakness, the surgeon raised a concernregarding the integrity of some of the data. First, theyquestioned the validity of the cell salvage values, because

sometimes the usage is not properly captured in the anes-thesia flowsheets, from which Sanguine derives the data.They also were surprised by and doubted the accuracyof the initial database results showing minimal usage ofaminocaproic acid, which they expected everyone to useon every case for cardiac surgery. This allowed us togo back to the database and identify an error in how theaminocaproic acid data was extracted and populated. Thisinitial error was present in the data that Sanguine is builton, but was subsequently resolved after our investigationthat spanned several month with the data warehouse. Theobservation that the data that is present in a database maynot be a perfect reflection of reality, and that domain ex-perts often have deep knowledge about such issues [39]spawned a line of future work our lab is currently pursu-ing: How can such knowledge be externalized and madeexplicit in the visualization so that it can be shared andreviewed.

8 Discussion and Limitations

Overall, Dr. Metcalf remarked that using Sanguine is amuch more personalized and precise way for surgeons toprepare, compared to the guidelines currently in place. Hecommented that in this way, Sanguine can help reducewaste, while at the same time also reducing the need foremergency blood releases during the surgery. The COVID-19 pandemic has led to unprecedented blood shortages athospitals. Each morning, our collaborator has been usingSanguine’s “patient like mine” support to predict bloodutilization for the most complex surgeries for the day. This,in turn, has enhanced preparation for individuals surgeriesand to maintain the hospital blood bank inventory to meetcritical patient needs.

Comparison to Prior Practice Prior to the implemen-tation of Sanguine, the most commonly used informationabout clinical practice quality at our institution are reportsfrom the Society of Thoracic Surgeons (STS). The STSdatabase includes quality reporting data and has many par-ticipating sites across the United States. However, thesedata are static and not focused on patient blood manage-ment (PBM), and there is typically a time lag of severalmonths to receive reports. Other institutions have pub-lished their standard PBM performance visualizations inthe clinical literature [8]. While these are useful for eval-uating broad trends, they lack detailed context and arenot flexible or interactive like Sanguine. In contrast, San-guine’s dynamic and interactive nature allows the user tointeract with the data and create highly contextualized visu-alizations. The filtering and context setting adds meaningbecause the clinical circumstances are important for un-derstanding what an outcome means or why it may haveoccurred. However, a benefit of the STS report over San-guine is that they can also be used to make comparisonsbetween different clinics. We hence believe that Sanguineand STS reports complement each other well: the formeris useful for internal analysis and benchmarking, the latter

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can be used to compare the overall performance of a sitenationally.

Evaluation. We demonstrate the utility of Sanguinethrough case studies and through feedback from cliniciansand PBM experts. Case studies with real users, tasks, anddatasets are the most common evaluation approach in de-sign study research [40], and demonstrate rich, practicaland impactful insights on the domain. Even though casestudies tend to not significantly contribute to an under-standing of the research contribution, they are considerednecessary to ensure validity and trust [41]. Nevertheless,our validation is limited to our primary collaborator and asmall group of clinicians. Alternative evaluation strategies,such as a comparative, quantitative evaluation, are difficultto achieve with specialized tools due to the lack of alter-native systems that address the same task, and the smallgroup of expert users that could meaningfully participate.We consider long-term observations on usage and adop-tion as the gold standard for evaluating design study tools.Sanguine has now been used in regular practice for abouta year by Dr. Metcalf. However, adoption by outside, un-affiliated parties takes time, as striking agreements, gettingIRB approval, and compiling the relevant databases in adifferent hospital is a complicated process. While we arecurrently discussing adoption in two unaffiliated medicalcenters, reporting on adoption in these centers is beyondthe scope of this paper.

Data Integrity. As described in previous section, theintegrity of some of the data was questioned when wepresented Sanguine to clinicians, which led to the identi-fication of a problem in how the database was populated.A tool like Sanguine can make the clinical team aware ofdeficiencies in the data collection process. However, thisproblem indicated that it will be important for Sanguineto provide a better annotation system or even more inno-vative techniques to record these disagreements with thedata, enabling PBM experts and clinicians to pinpoint andcommunicate these problems.

Data Integration. The dataset we used to develop San-guine is static and deidentified; however, the deidentifi-cation procedures are not certified and, hence, we cannotshow certain data fields in this paper. Since we querythe data directly from the data warehouse of the Univer-sity of Utah Hospital through SQL queries, and the datatypes were not altered during deidentification, we expect asmooth transition from deidentified dataset to the originaldataset for clinical use.

9 Conclusion and Future Work

In this paper we introduced Sanguine, a tool for analyz-ing patient blood management data. Sanguine is currentlydeployed and our collaborator has adopted it in his reg-ular workflow. Going forward, we plan to evaluate thelong-term use of Sanguine and also study the influence

it has on the decision-making process with independentexperts at different institutions. We are currently in negoti-ations to deploy Sanguine in two different hospital systems.However, due to the necessary customizations, as data arestored differently in each hospital, we expect that such adeployment and subsequent evaluation may take up to ayear. Nevertheless, we consider the willingness of theseoutside hospital systems to invest in deploying Sanguine asan indication for its usefulness beyond our own institution.

While we have taken first steps towards a dedicated, sim-plified interface for clinicians with the “View Mode”, weplan on ensuring compatibility of this mode with mobileinterfaces, such as tablets and phones.

We would also like to explore expanding the Sanguineannotation system, to better address the data integrity is-sue described in Discussion and Limitation. A systemthat allows analysts to annotate on any components wouldenhance our current text-based chart annotation structure,and further facilitate communication between PBM expertsand clinicians.

Even though we focus on PBM data in this design study,the techniques we used can likely be applied to other areasof medicine, where analysts want to quickly view outcomesof interest and their relationships with variables of interest.

Acknowledgements

We thank Dr. Vikas Sharma, Dr. Joseph Tonna, Dr. JoshZimmerman, and Dr. Candice Morrisey for their input andfeedback. We also thank the support from the EnterpriseData Warehouse and the Center for High PerformanceComputing at the University of Utah.

Funding

This project is funded by ARUP laboratories; the NationalScience Foundation (IIS 1751238); and the National Insti-tutes of Health (1S10OD021644-01A1).

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