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A Visual Analytics Approach to Understanding Care Process Variation and Conformance A Visual Analytics Approach to Understanding Care Process Variation and Conformance Rahul C. Basole, PhD Hyunwoo Park Mayank Gupta Georgia Tech Georgia Tech Georgia Tech School of Interactive School of Industrial & Systems School of Computer Science Computing Engineering Atlanta, GA USA Atlanta, GA USA Atlanta, GA USA [email protected] [email protected] [email protected] Mark L. Braunstein, MD Duen Horng Chau, PhD Michael Thompson Georgia Tech Georgia Tech Children’s Healthcare of School of Interactive School of Computational Atlanta Computing Science & Engineering Atlanta, GA USA Atlanta, GA USA Atlanta, GA USA [email protected] [email protected] [email protected] ABSTRACT Conceptualizing sequential healthcare activities as\careflow" With greater pressures of providing high-quality care at lower has become widespread in visualization research [14] and cost due to a changing financial and policy environment, the healthcare systems engineering research [12]. A key motiva- ability to understand variations in care delivery and associ- tion behind this body of research is to understand how care ated outcomes and act upon this understanding is of critical is delivered to an individual or a group of patients, with the importance. Building on prior work in visualizing health- aim to identify common care delivery patterns, bottlenecks, care event sequences and in collaboration with our clinical and best practices [4]. With greater pressures of providing partner, we describe our process in developing a multiple, high-quality care at lower cost due to a changing financial coordinated visualization system that helps identify and an- and policy environments, the ability to understand varia- alyze care processes and their conformance to existing care tions in care and associated outcomes and act upon this guidelines. We demonstrate our system using data of 5,784 understanding is of critical importance [21]. pediatric emergency department visits over a 13-month pe- [18] provide a comprehensive overview to different interac- riod for which asthma was the primary diagnosis. tive information visualization approaches for exploring and querying electronic health records of individual as well as collection of patients. Exemplary visualization systems at CCS Concepts the individual patient level include LifeLines [16], KHOSH- •Human-centered computing ! Visual analytics; In- PAD [7], and Midgaard [2]; at the cohort level examples formation visualization; Visualization systems and include Lifelines2 [22], Similan [25], LifeFlow [24], Outflow tools; •Social and professional topics ! Personal health [23], and VisCareTrails [11]. However, there are still many records; •Applied computing ! Health informatics; open challenges in visualizing time-oriented healthcare data, including the scalable analysis of patient cohorts and varia- tions in care [1]. Recently, visual analytics approach is being Keywords actively applied to comparison between actual care process Visual analytics, information visualization, health informat- and guideline care process for a single patient [5] or a patient ics, visual process mining, conformance, pediatric emergency cohort [8]. medicine Our research builds on and integrates many different as- pects of prior and focuses on the design and development 1. INTRODUCTION of a multiple coordinated visualization system that helps identify and analyze variation of care processes and their Visualizing healthcare event sequences derived from clini- conformance to existing care guidelines. Our use context cal and administrative claims data has been a topic of grow- is pediatric asthma care in emergency departments. This ing interest to information visualization researchers [20, 6]. paper describes our journey in designing and implementing Permission to make digital or hard copies of all or part of this work for personal or our system in collaboration with our clinical partner. We classroom use is granted without fee provided that copies are not made or distributed conclude with implications and next steps. for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission 2. DATA and/or a fee. Request permissions from [email protected]. VAHC ’15, October 25, 2015, Chicago, IL, USA Our dataset includes all pediatric ED visits over a 13- month period for which asthma was the primary diagno- c 2015 Copyright held by the owner/author(s). Publication rights licensed to ACM. ISBN 978-1-4503-3671-0/15/10. $15.00 sis. For each of these 5,784 visits we obtained informa- DOI: http://dx.doi.org/10.1145/2836034.2836040 tion regarding administrative events, clinical respiratory test to save these customized patient populations for sub- Table 1: Descriptive Summary of Patient Population sequent analysis. Data (n=5,784) Gender Male 3575 (61.8%) • Provide multiple, coordinated visualizations. Five Female 2209 (38.2%) of the participants encouraged us to develop multiple, Age 0-18 months 562 (9.7%) coordinated visualizations that provided complemen- 18-36 months 1048 (18.1%) tary insights into the same underlying dataset. As one 3-6 years 1682 (29.1%) quality improvement manager commented\it is impor- >6 years 2492 (43.1%) tant to see the data from different perspectives to gain Acuity ESI 1 3 (0.1%) triangulated insights." ESI 2 1516 (26.2%) ESI 3 2913 (50.4%) • Enable comparisons between patient popula- ESI 4 1283 (22.2%) tions. Six participants encouraged us to develop vi- ESI 5 62 (1.1%) sualizations that would compare the care processes of Unknown 7 (0.1%) patient populations. Disposition Discharge 3,995 (69.1%) Admit to Ward 1,598 (27.6%) • Provide data in table view. Interestingly, despite Admit to ICU 140 (2.4%) the perceived value of visualizations, all participants Admit to OR 47 (0.8%) also wanted to see the raw data in a sortable table for- Transfer 4 (0.1%) mat, partly because they were compatible with spread- sheets formats. events, laboratory test events and medication administra- 4. SYSTEM tion events with their date/timestamps. We also received detailed demographic, charge, and provider-related infor- Based on this user and task analysis, we developed a mation for each visit. A summary of the data is provided web-based visualization system that enabled clinicians and in Table 1. For this study we focused only on the visual- quality managers to explore care processes and their con- ization of laboratory and medication-related events for pa- formance to guidelines. The initial version of the system tients grouped based on laboratory tests or medications. We provided a single graph-based visualization using a semantic ignored administrative events since they are performed for substrate approach [3]. While most users felt the visualiza- almost all patients. The data was received as comma sep- tion was intuitive and user-friendly, it lacked the ability to arated value (csv) files split into several tables as a rela- deeply analyze and compare care processes of patient popu- tional database. The visit.csv file contained 5,785 visit ob- lations because it focused on visualizing individual careflows servations and had 143 attributes, including demographic and comparing two careflows from separate two individuals information and administrative timestamps. The medica- [10]. We thus decided to fundamentally redesign our system tions.csv and labresult.csv files contained information re- incorporating the knowledge we gained building and evalu- garding medication and lab-related date/timestamps, respec- ating the first version. tively. The system interface (see Figure 1) is divided into two regions. At the top is the navigation bar that allows the clinicians to switch between visualizations and access the 3. DESIGN REQUIREMENTS performance summary page. A menu icon at the top left We conducted in-depth field studies and interviews with allows a user to see, on-demand, what patient population seven clinicians, health informaticians, and care quality im- has been selected and what filters have been applied. The provements managers with significant work experience to bottom frame is dedicated to the display of visualizations. derive design requirements for a care process visualization Within each visualizations, there are tabs that allow switch- tool. All participants had significant decision support expe- ing between subvisualizations. rience and basic knowledge of data visualization techniques. Cumulatively, this group of practitioners provided a signif- 4.1 Visualizations and Interactions icant level of expertise needed to inform the design of our system. The results of our field study led to the identifica- 4.1.1 Summary Charts and Tables tion of a number of core requirements that drove our system The summary chart page provides three patient cohort development. descriptors and interactive histograms to represent the dis- • Provide a performance summary. All participants tribution of six key performance variables (see Figure 1). emphasized the need for a single page summary dash- The patient cohort descriptors include the total number of board of key performance metrics. One clinician noted patients, the number of providers for these patients, and that \this summary should help provide an overview to an overall disposition index. The key performance variables
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