Intensive Outpatient Program Response Among Service Members With Mild Traumatic Brain Injury: Change Between Distinct Post-Concussive Symptom Subgroups.

OBJECTIVE: Among service members (SMs) with mild traumatic brain injury (mTBI) admitted to an intensive outpatient program (IOP), we identified qualitatively distinct subgroups based on post-concussive symptoms (PCSs) and characterized changes between subgroups from admission to discharge. Further, we examined whether co-morbid posttraumatic stress disorder (PTSD) influenced changes between subgroups. DESIGN: Quasi-experimental. Latent transition analysis identified distinctive subgroups of SMs and examined transitions between subgroups from admission to discharge. Logistic regression examined the effect of PTSD on transition to the Minimal subgroup (low probability of any moderate-very severe PCS) while adjusting for admission subgroup designation. SETTING: National Intrepid Center of Excellence (NICoE) at Walter Reed National Military Medical Center. PARTICIPANTS: 1141 active duty SMs with persistent PCS despite prior treatment (N=1141). INTERVENTIONS: NICoE 4-week interdisciplinary IOP. MAIN OUTCOME MEASURE(S): Subgroups identified using Neurobehavioral Symptom Inventory items at admission and discharge. RESULTS: Model fit indices supported a 7-class solution. The 7 subgroups of SMs were distinguished by diverging patterns of probability for specific PCS. The Minimal subgroup was most prevalent at discharge (39.4%), followed by the Sleep subgroup (high probability of sleep problems, low probability of other PCS; 26.8%). 41% and 25% of SMs admitted within the Affective (ie, predominantly affective PCS) and Sleep subgroups remained within the same group at discharge, respectively. The 19% of SMs with co-morbid PTSD were less likely to transition to the Minimal subgroup (odds ratio=0.28; P<.001) and were more likely to remain in their admission subgroup at discharge (35.5% with PTSD vs 22.2% without). CONCLUSIONS: Most of SMs achieved symptom resolution after participation in the IOP, with most transitioning to subgroups characterized by reduced symptom burden. SMs admitted in the Affective and Sleep subgroups, as well as those with PTSD, were most likely to have continuing clinical needs at discharge, revealing priority targets for resource allocation and follow-up treatment.

Changes in Outpatient Healthcare Utilization and Costs Following Mild Traumatic Brain Injury Among Service Members in the Military Health System by Preexisting Behavioral Health Condition Status.

OBJECTIVE: To evaluate changes in healthcare utilization and cost following an index mild traumatic brain injury (mTBI) diagnosis among service members (SMs). We hypothesized that differences in utilization and cost will be observed by preexisting behavioral health (BH) diagnosis status. SETTING: Direct care outpatient healthcare facilities within the Military Health System. PARTICIPANTS: A total of 21 984 active-duty SMs diagnosed with an index mTBI diagnosis between 2017 and 2018. DESIGN: This retrospective study analyzed changes in healthcare utilization and cost in military treatment facilities among SMs with an index mTBI diagnosis. Encounter records 1 year before and after mTBI were assessed; preexisting BH conditions were identified in the year before mTBI. MAIN MEASURES: Ordinary least squares regressions evaluated difference in the average change of total outpatient encounters and costs among SMs with and with no preexisting BH conditions (eg, posttraumatic stress disorder, adjustment disorder). Additional regressions explored changes in utilization and cost within clinic types (eg, mental health, physical rehabilitation). RESULTS: There was a 39.5% increase in overall healthcare utilization during the following year, representing a 34.8% increase in total expenditures. Those with preexisting BH conditions exhibited smaller changes in overall utilization (ß, -4.9; [95% confidence interval (CI), -6.1 to -3.8]) and cost (ß, $-1873; [95% CI, $-2722 to $-1024]), compared with those with no BH condition. The greatest differences were observed in primary care clinics, in which those with prior BH conditions exhibited an average decreased change of 3.2 encounters (95% CI, -3.5 to -3) and reduced cost of $544 (95% CI, $-599 to $-490) compared with those with no prior BH conditions. CONCLUSION: Despite being higher utilizers of healthcare services both pre- and post-mTBI diagnosis, those with preexisting BH conditions exhibited smaller changes in overall cost and utilization. This highlights the importance of considering prior utilization and cost when evaluating the impact of mTBI and other injury events on the Military Health System.

Racial and Ethnic Differences in Deaths by Suicide, Drug Overdose, and Opioid-Related Overdose in a National Sample of Military Members With Mild Traumatic Brain Injury, 1999-2019.

OBJECTIVE: To examine racial and ethnic differences in suicide and drug and opioid-related overdose deaths among a population-based cohort of military service members who were diagnosed with a mild traumatic brain injury (mTBI) during military service. DESIGN: Retrospective cohort. SETTING: Military personnel receiving care within the Military Health System between 1999 and 2019. PARTICIPANTS: In total, 356 514 military members aged 18 to 64 years, who received an mTBI diagnosis as their index TBI between 1999 and 2019, while on active duty or activated. MAIN MEASURES: Death by suicide, death by drug overdose, and death by opioid overdose were identified using International Classification of Diseases, Tenth Revision (ICD-10) codes within the National Death Index. Race and ethnicity were captured from the Military Health System Data Repository. RESULTS: Overall crude rates were 38.67 per 100 000 person-years for suicide; 31.01 per 100 000 person-years for drug overdose death; and 20.82 per 100 000 person-years for opioid overdose death. Crude and age-specific rates for military members who self-identified as Other were higher than all other racial/ethnic groups for all 3 mortality outcomes. Adjusting for age, suicide rates for those classified as Other were up to 5 times that of other racial/ethnic groups for suicide, and up to 11 and 3.5 times that of other race/ethnicity groups for drug and opioid overdose death, respectively. CONCLUSION: Findings extend previous knowledge regarding risk for suicide and deaths by drug overdose among those with mTBI and highlight new important areas for understanding the impact of race and ethnicity on mortality. Methodological limitations regarding classification of race and ethnicity must be addressed to ensure that future research provides a better understanding of racial and ethnic disparities in suicide and drug overdose mortality among military members with TBI.

Traumatic Brain Injury Classification Variability During the Afghanistan/Iraq Conflicts: Surveillance, Clinical, Research, and Policy Implications.

OBJECTIVE: Challenges associated with case ascertainment of traumatic brain injuries (TBIs) sustained during the Afghanistan/Iraq military operations have been widespread. This study was designed to examine how the prevalence and severity of TBI among military members who served during the conflicts were impacted when a more precise classification of TBI diagnosis codes was compared with the Department of Defense Standard Surveillance Case-Definition (DoD-Case-Definition). SETTING: Identification of TBI diagnoses in the Department of Defense's Military Health System from October 7, 2001, until December 31, 2019. PARTICIPANTS: Military members with a TBI diagnosis on an encounter record during the study window. DESIGN: Descriptive observational study to evaluate the prevalence and severity of TBI with regard to each code set (ie, the DoD-Case-Definition and the more precise set of TBI diagnosis codes). The frequencies of index TBI severity were compared over time and further evaluated against policy changes. MAIN MEASURES: The more precise TBI diagnosis code set excludes the following: (1) DoD-only extender codes, which are not used in other healthcare settings; and (2) nonprecise TBI codes, which include injuries that do not necessarily meet TBI diagnostic criteria. RESULTS: When comparing the 2 TBI classifications, the DoD-Case-Definition captured a higher prevalence of TBIs; 38.5% were classified by the DoD-Case-Definition only (>164 000 military members). 73% of those identified by the DoD-Case-Definition only were diagnosed with nonprecise TBI codes only, with questionable specificity as to whether a TBI occurred. CONCLUSION: We encourage the field to reflect on decisions made pertaining to TBI case ascertainment during the height of the conflicts. Efforts focused on achieving consensus regarding TBI case ascertainment are recommended. Doing so will allow the field to be better prepared for future conflicts, and improve surveillance, screening, and diagnosis in noncombat settings, as well as our ability to understand the long-term effects of TBI.

Risk Factors Associated With the Prescription of Opioids Among Service Members Following a First Mild Traumatic Brain Injury.

OBJECTIVE: To evaluate factors impacting opioid receipt among active-duty service members (SMs) following a first mild traumatic brain injury (mTBI). SETTING: Active-duty SMs receiving care within the Military Health System. PARTICIPANTS: In total, 14 757 SMs who have sustained an initial mTBI, as documented within electronic health records (EHRs), between 2016 and 2017. DESIGN: A retrospective analysis of EHR metadata. MAIN MEASURES: Multivariable logistic regression assessed factors impacting opioid receipt and initiation. Factors include demographics, military characteristics, and preexisting clinical conditions, including prior opioid prescription. RESULTS: Of the sample population, 33.4% (n = 4927) were prescribed opioids after their initial mTBI, of which, 60.6% (n = 2985) received opioids for the first time following injury. Significant risk factors associated with the increased probability of opioid receipt included age, gender, and preexisting behavioral health and musculoskeletal conditions. Military characteristics also exhibited changes in the probability of opioid receipt, both among initiation and new prescription. No changes were observed among race, nor among those with preexisting headaches or migraines. CONCLUSION: Despite concerns about the negative impact on recovery, the prescribing of opioids is common in this population of active-duty SMs first diagnosed with an mTBI. As several demographic and preexisting health conditions are factors in the receipt of opioids post-mTBI, the entire medical history of these patients should be considered prior to prescription. Understanding these factors may further inform policy for opioid use in the Military Health System.

Findings from Structural MR Imaging in Military Traumatic Brain Injury.

PURPOSE: To describe the initial neuroradiology findings in a cohort of military service members with primarily chronic mild traumatic brain injury (TBI) from blast by using an integrated magnetic resonance (MR) imaging protocol. MATERIALS AND METHODS: This study was approved by the Walter Reed National Military Medical Center institutional review board and is compliant with HIPAA guidelines. All participants were military service members or dependents recruited between August 2009 and August 2014. There were 834 participants with a history of TBI and 42 participants in a control group without TBI (not explicitly age- and sex-matched). MR examinations were performed at 3 T primarily with three-dimensional volume imaging at smaller than 1 mm(3) voxels for the structural portion of the examination. The structural portion of this examination, including T1-weighted, T2-weighted, before and after contrast agent administrtion T2 fluid attenuation inversion recovery, and susceptibility-weighted images, was evaluated by neuroradiologists by using a modified version of the neuroradiology TBI common data elements (CDEs). Incident odds ratios (ORs) between the TBI participants and a comparison group without TBI were calculated. RESULTS: The 834 participants were diagnosed with predominantly chronic (mean, 1381 days; median, 888 days after injury) and mild (92% [768 of 834]) TBI. Of these participants, 84.2% (688 of 817) reported one or more blast-related incident and 63.0% (515 of 817) reported loss of consciousness at the time of injury. The presence of white matter T2-weighted hyperintense areas was the most common pathologic finding, observed in 51.8% (432 of 834; OR, 1.75) of TBI participants. Cerebral microhemorrhages were observed in a small percentage of participants (7.2% [60 of 834]; OR, 6.64) and showed increased incidence with TBI severity (P < .001, moderate and severe vs mild). T2-weighted hyperintense areas and microhemorrhages did not collocate by visual inspection. Pituitary abnormalities were identified in a large proportion (29.0% [242 of 834]; OR, 16.8) of TBI participants. CONCLUSION: Blast-related injury and loss of consciousness is common in military TBI. Structural MR imaging demonstrates a high incidence of white matter T2-weighted hyperintense areas and pituitary abnormalities, with a low incidence of microhemorrhage in the chronic phase.

Three Scoring Approaches to the Neurobehavioral Symptom Inventory for Measuring Clinical Change in Service Members Receiving Intensive Treatment for Combat-Related mTBI.

OBJECTIVE: To examine the use of the Neurobehavioral Symptom Inventory to measure clinical changes over time in a population of US service members undergoing treatment of mild traumatic brain injury and comorbid psychological health conditions. SETTING: A 4-week, 8-hour per day, intensive, outpatient, interdisciplinary, comprehensive treatment program at the National Intrepid Center of Excellence in Bethesda, Maryland. PARTICIPANTS: Three hundred fourteen active-duty service members being treated for combat-related comorbid mild traumatic brain injury and psychological health conditions. DESIGN: Repeated-measures, retrospective analysis of a single-group using a pretest-posttest treatment design. MAIN MEASURES: Three Neurobehavioral Symptom Inventory scoring methods: (1) a total summated score, (2) the 3-factor method, and (3) the 4-factor method (with and without orphan items). RESULTS: All 3 scoring methods yielded statistically significant within-subject changes between admission and discharge. The evaluation of effect sizes indicated that the 3 different Neurobehavioral Symptom Inventory scoring methods were comparable. CONCLUSION: Findings indicate that the different scoring methods all have potential for assessing clinical changes in symptoms for groups of patients undergoing treatment, with no clear advantage with any one method.

NSI and PCL-5 Normative Tables for Active Duty Service Members Affected by Traumatic Brain Injury.

INTRODUCTION: Many service members (SMs) have been diagnosed with traumatic brain injury. Currently, military treatment facilities do not have access to established normative tables which can assist clinicians in gauging and comparing patient-reported symptoms. The aim of this study is to provide average scores for both the Neurobehavioral Symptom Inventory (NSI) and Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5) for active duty SMs based upon varying demographic groups. METHODS: Average scores were calculated for both the NSI and PCL-5 surveys from SMs who attended a military outpatient traumatic brain injury clinic. For this analysis, only the initial surveys for each SM were considered. The identifying demographics included age group, gender, grade, and race. RESULTS: Four normative tables were created to show the average scores of both the NSI and PCL-5 surveys grouped by demographics. The tables are grouped by Age Group/Gender/Race and Grade/Gender/Race. CONCLUSION: Clinicians and healthcare administrators can use the scores reported in this study to determine where SM NSI or PCL-5 scores fall within the average for their demographic group.

Enhancing image analytic tools by fusing quantitative physiological values with image features.

Computer-aided diagnosis systems (CADs) can quantify the severity of diseases by analyzing a set of images and employing prior statistical models. In general, CADs have proven to be effective at providing quantitative measurements of the extent of a particular disease, thus helping physicians to better monitor the progression of cancer, infectious diseases, and other health conditions. Electronic Health Records frequently include a large amount of clinical data and medical history that can provide critical information about the underlying condition of a patient. We hypothesize that the fusion of image and clinical-physiological features can be used to enhance the accuracy of automatic image classification models. In particular, this paper shows how image analytic tools can move beyond classical image interpretation models to broader systems where image and physiological measurements are fused and used to create more generic detection models. To test our hypothesis, a CAD system capable of quantifying the severity of patients with pulmonary fibrosis has been developed. Results show that CAD systems augmented with multimodal physiological values are more robust and accurate at determining the severity of the disease.

Baseline eHealth Behaviors of Service Members: A Retrospective, Cross-Sectional Analysis of Patient Portal Use Before the Pandemic.

INTRODUCTION: The use of electronic health (eHealth) tools has the potential to support the overall health, wellness, fitness status, and ability to deploy worldwide of active duty service members (SMs). Additionally, the Coronavirus Disease 2019 pandemic forced healthcare organizations to quickly convert to virtual care settings to decrease face-to-face interactions and increase access to healthcare using technology. The shift to virtual care and the push to increase use of eHealth tools heightened the need to understand how military members interact with eHealth tools. Little is known about the factors that influence SMs use of eHealth tools and if having a health condition increases or decreases use. To evaluate these factors, we completed a cross-sectional, retrospective analysis on a sample of 198,388 active duty SMs aged 18 to 68 years. MATERIALS AND METHODS: We used two Military Health System (MHS) data sources-Tricare Online (TOL) Patient Portal 2018 audit logs and outpatient electronic health record data. Using eHealth behaviors identified in the audit logs, we evaluated and compared individual characteristics (i.e., "gender", "age", "race", and "marital status"), environmental factors (i.e., "rank", "military branch", and "geographic location"), and six available health conditions (i.e., congenital health defects, amputation, anxiety, sleep, traumatic brain injury, and depression). Since moderate usage of eHealth tools is linked to improved health outcomes, adherence, communication, and increased consumer satisfaction, a logistic regression model was developed to find the factors most associated with moderate (3-11 logins per year) use of the portal. RESULTS: Electronic health use increased by SMs with underlying health conditions or if they were managing family member health. Most SMs who used the TOL Patient Portal were of ages 25-34 years, White, and married. The mean age is 32.53 for males and 29.98 for females. Over half of the TOL Patient Portal SM users utilized the portal one to two times. Most SMs used the TOL Patient Portal in Virginia, Texas, California, Florida, North Carolina, Georgia, and Maryland. The highest use was during the months of March to May. Frequent patient portal actions include searching for appointments, viewing health information, viewing medical encounters, and refilling medications. Although SMs with congenital health defects, anxiety, sleep issues, and depression have higher patient portal use rates, SMs with depression have a negative association with using the patient portal at a "moderate" rate. Viewing family member health information and searching for appointments were strongly associated with patient portal moderate use. CONCLUSIONS: Our findings support top military initiatives to improve the overall health, wellness, and readiness of SMs while decreasing the MHS's overall cost of care while providing a foundation to compare "pre" and "post" pandemic eHealth behaviors. It is essential to note that SMs are more likely to use a patient portal to seek information or manage family member health. This key factor identifies the significance of family health promotion and readiness in the active duty SM's life. The long-term goal of our study is to build the foundation for delivering tailored health information and eHealth tools to promote health and readiness-centric patient engagement.

Correlations Between the Neurobehavioral Symptom Inventory and Other Commonly Used Questionnaires for Traumatic Brain Injury.

OBJECTIVE: To evaluate the correlations between the Neurobehavioral Symptom Inventory (NSI) and other questionnaires commonly administered within military traumatic brain injury clinics. SETTING: Military outpatient traumatic brain injury clinics. PARTICIPANTS: In total, 15,428 active duty service members who completed 24,162 NSI questionnaires between March 2009 and May 2020. DESIGN: Observational retrospective analysis of questionnaires collected as part of standard clinical care. MAIN MEASURES: NSI, Post-Traumatic Stress Disorder Checklist for DSM-5 and Military Version, Patient Health Questionnaire (PHQ), Generalized Anxiety Disorder, Headache Impact Test (HIT-6), Insomnia Severity Index (ISI), Epworth Sleepiness Scale (ESS), Activities-Specific Balance Confidence Scale (ABC), Dizziness Handicap Inventory (DHI), Alcohol Use Disorders Identification Test (AUDIT), and the World Health Organization Quality of Life Instrument-Abbreviated Version. Only questionnaires completed on the same date as the NSI were examined. RESULTS: The total NSI score was moderately to strongly correlated with all questionnaires except for the AUDIT. The strongest correlation was between the NSI Affective Score and the PHQ9 (r = 0.86). The NSI Vestibular Score was moderately correlated with the ABC (r = -0.55) and strongly correlated with the DHI (r = 0.77). At the item level, the HIT-6 showed strong correlation with NSI headache (r = 0.80), the ISI was strongly correlated with NSI difficulty sleeping (r = 0.63), and the ESS was moderately correlated with NSI fatigue (r = 0.39). CONCLUSION: Clinicians and healthcare administrators can use the correlations reported in this study to determine if questionnaires add incremental value for their clinic as well as to make more informed decisions regarding which questionnaires to administer.

Burden of Behavioral Health Comorbidities on Outpatient Health Care Utilization by Active Duty Service Members With a First Documented mTBI.

OBJECTIVE: More than 280,000 Active Duty Service Members (ADSMs) sustained a mild traumatic brain injury (mTBI) between 2000 and 2019 (Q3). Previous studies of veterans have shown higher utilization of outpatient health clinics by veterans diagnosed with mTBI. Additionally, veterans with mTBI and comorbid behavioral health (BH) conditions such as post-traumatic stress disorder, depression, and substance use disorders have significantly higher health care utilization than veterans diagnosed with mTBI alone. However, few studies of the relationship between mTBI, health care utilization, and BH conditions in the active duty military population currently exist. We examined the proportion of ADSMs with a BH diagnosis before and after a first documented mTBI and quantified outpatient utilization of the Military Health System in the year before and following injury. MATERIALS AND METHODS: Retrospective analysis of 4,901,840 outpatient encounters for 39,559 ADSMs with a first documented diagnosis of mTBI recorded in the Department of Defense electronic health record, subsets of who had a BH diagnosis. We examined median outpatient utilization 1 year before and 1 year after mTBI using Wilcoxon signed rank test, and the results are reported with an effect size r. Outpatient utilization is compared by BH subgroups. RESULTS: Approximately 60% of ADSMs experience a first mTBI with no associated BH condition, but 17% of men and women are newly diagnosed with a BH condition in the year following mTBI. ADSMs with a history of a BH condition before mTBI increased their median outpatient utilization from 23 to 35 visits for men and from 32 to 42 visits for women. In previously healthy ADSMs with a new BH condition following mTBI, men more than tripled median utilization from 7 to 24 outpatient visits, and women doubled utilization from 15 to 32 outpatient visits. CONCLUSIONS: Behavioral health comorbidities affect approximately one-third of ADSMs following a first mTBI, and approximately 17% of previously healthy active duty men and women will be diagnosed with a new BH condition in the year following a first mTBI. Post-mTBI outpatient health care utilization is highly dependent on the presence or absence of BH condition and is markedly higher is ADSMs with a BH diagnosis in the year after a first documented mTBI.

Evaluation of Machine Learning Techniques to Predict the Likelihood of Mental Health Conditions Following a First mTBI.

OBJECTIVE: Limited research has evaluated the utility of machine learning models and longitudinal data from electronic health records (EHR) to forecast mental health outcomes following a traumatic brain injury (TBI). The objective of this study is to assess various data science and machine learning techniques and determine their efficacy in forecasting mental health (MH) conditions among active duty Service Members (SMs) following a first diagnosis of mild traumatic brain injury (mTBI). MATERIALS AND METHODS: Patient demographics and encounter metadata of 35,451 active duty SMs who have sustained an initial mTBI, as documented within the EHR, were obtained. All encounter records from a year prior and post index mTBI date were collected. Patient demographics, ICD-9-CM and ICD-10 codes, enhanced diagnostic related groups, and other risk factors estimated from the year prior to index mTBI were utilized to develop a feature vector representative of each patient. To embed temporal information into the feature vector, various window configurations were devised. Finally, the presence or absence of mental health conditions post mTBI index date were used as the outcomes variable for the models. RESULTS: When evaluating the machine learning models, neural network techniques showed the best overall performance in identifying patients with new or persistent mental health conditions post mTBI. Various window configurations were tested and results show that dividing the observation window into three distinct date windows [-365:-30, -30:0, 0:14] provided the best performance. Overall, the models described in this paper identified the likelihood of developing MH conditions at [14:90] days post-mTBI with an accuracy of 88.2%, an AUC of 0.82, and AUC-PR of 0.66. DISCUSSION: Through the development and evaluation of different machine learning models we have validated the feasibility of designing algorithms to forecast the likelihood of developing mental health conditions after the first mTBI. Patient attributes including demographics, symptomatology, and other known risk factors proved to be effective features to employ when training ML models for mTBI patients. When patient attributes and features are estimated at different time window, the overall performance increase illustrating the importance of embedding temporal information into the models. The addition of temporal information not only improved model performance, but also increased interpretability and clinical utility. CONCLUSION: Predictive analytics can be a valuable tool for understanding the effects of mTBI, particularly when identifying those individuals at risk of negative outcomes. The translation of these models from retrospective study into real-world validation models is imperative in the mitigation of negative outcomes with appropriate and timely interventions.

Case study on visualizing hurricanes using illustration-inspired techniques.

The devastating power of hurricanes was evident during the 2005 hurricane season, the most active season on record. This has prompted increased efforts by researchers to understand the physical processes that underlie the genesis, intensification, and tracks of hurricanes. This research aims at facilitating an improved understanding into the structure of hurricanes with the aid of visualization techniques. Our approach was developed by a mixed team of visualization and domain experts. To better understand these systems, and to explore their representation in NWP models, we use a variety of illustration-inspired techniques to visualize their structure and time evolution. Illustration-inspired techniques aid in the identification of the amount of vertical wind shear in a hurricane, which can help meteorologists predict dissipation. Illustration-style visualization, in combination with standard visualization techniques, helped explore the vortex rollup phenomena and the mesovortices contained within. We evaluated the effectiveness of our visualization with the help of six hurricane experts. The expert evaluation showed that the illustration-inspired techniques were preferred over existing tools. Visualization of the evolution of structural features is a prelude to a deeper visual analysis of the underlying dynamics.

Evaluating visual analytics for health informatics applications: a systematic review from the American Medical Informatics Association Visual Analytics Working Group Task Force on Evaluation.

OBJECTIVE: This article reports results from a systematic literature review related to the evaluation of data visualizations and visual analytics technologies within the health informatics domain. The review aims to (1) characterize the variety of evaluation methods used within the health informatics community and (2) identify best practices. METHODS: A systematic literature review was conducted following PRISMA guidelines. PubMed searches were conducted in February 2017 using search terms representing key concepts of interest: health care settings, visualization, and evaluation. References were also screened for eligibility. Data were extracted from included studies and analyzed using a PICOS framework: Participants, Interventions, Comparators, Outcomes, and Study Design. RESULTS: After screening, 76 publications met the review criteria. Publications varied across all PICOS dimensions. The most common audience was healthcare providers (n = 43), and the most common data gathering methods were direct observation (n = 30) and surveys (n = 27). About half of the publications focused on static, concentrated views of data with visuals (n = 36). Evaluations were heterogeneous regarding setting and measurements used. DISCUSSION: When evaluating data visualizations and visual analytics technologies, a variety of approaches have been used. Usability measures were used most often in early (prototype) implementations, whereas clinical outcomes were most common in evaluations of operationally-deployed systems. These findings suggest opportunities for both (1) expanding evaluation practices, and (2) innovation with respect to evaluation methods for data visualizations and visual analytics technologies across health settings. CONCLUSION: Evaluation approaches are varied. New studies should adopt commonly reported metrics, context-appropriate study designs, and phased evaluation strategies.

Texture-based transfer functions for direct volume rendering.

Visualization of volumetric data faces the difficult task of finding effective parameters for the transfer functions. Those parameters can determine the effectiveness and accuracy of the visualization. Frequently, volumetric data includes multiple structures and features that need to be differentiated. However, if those features have the same intensity and gradient values, existing transfer functions are limited at effectively illustrating those similar features with different rendering properties. We introduce texture-based transfer functions for direct volume rendering. In our approach, the voxel's resulting opacity and color are based on local textural properties rather than individual intensity values. For example, if the intensity values of the vessels are similar to those on the boundary of the lungs, our texture-based transfer function will analyze the textural properties in those regions and color them differently even though they have the same intensity values in the volume. The use of texture-based transfer functions has several benefits. First, structures and features with the same intensity and gradient values can be automatically visualized with different rendering properties. Second, segmentation or prior knowledge of the specific features within the volume is not required for classifying these features differently. Third, textural metrics can be combined and/or maximized to capture and better differentiate similar structures. We demonstrate our texture-based transfer function for direct volume rendering with synthetic and real-world medical data to show the strength of our technique.

Texture-based feature tracking for effective time-varying data visualization.

Analyzing, visualizing, and illustrating changes within time-varying volumetric data is challenging due to the dynamic changes occurring between timesteps. The changes and variations in computational fluid dynamic volumes and atmospheric 3D datasets do not follow any particular transformation. Features within the data move at different speeds and directions making the tracking and visualization of these features a difficult task. We introduce a texture-based feature tracking technique to overcome some of the current limitations found in the illustration and visualization of dynamic changes within time-varying volumetric data. Our texture-based technique tracks various features individually and then uses the tracked objects to better visualize structural changes. We show the effectiveness of our texture-based tracking technique with both synthetic and real world time-varying data. Furthermore, we highlight the specific visualization, annotation, registration, and feature isolation benefits of our technique. For instance, we show how our texture-based tracking can lead to insightful visualizations of time-varying data. Such visualizations, more than traditional visualization techniques, can assist domain scientists to explore and understand dynamic changes.

A Grammar-based Approach for Modeling User Interactions and Generating Suggestions During the Data Exploration Process.

Despite the recent popularity of visual analytics focusing on big data, little is known about how to support users that use visualization techniques to explore multi-dimensional datasets and accomplish specific tasks. Our lack of models that can assist end-users during the data exploration process has made it challenging to learn from the user's interactive and analytical process. The ability to model how a user interacts with a specific visualization technique and what difficulties they face are paramount in supporting individuals with discovering new patterns within their complex datasets. This paper introduces the notion of visualization systems understanding and modeling user interactions with the intent of guiding a user through a task thereby enhancing visual data exploration. The challenges faced and the necessary future steps to take are discussed; and to provide a working example, a grammar-based model is presented that can learn from user interactions, determine the common patterns among a number of subjects using a K-Reversible algorithm, build a set of rules, and apply those rules in the form of suggestions to new users with the goal of guiding them along their visual analytic process. A formal evaluation study with 300 subjects was performed showing that our grammar-based model is effective at capturing the interactive process followed by users and that further research in this area has the potential to positively impact how users interact with a visualization system.

A K-Reversible Approach to Model Clinical Trajectories.

A clinical trajectory can be defined as the path followed by patients between an initial heath state Si such as being healthy to another state Sj such as being diagnosed with a specific clinical condition. Being able to identify the common trajectories that a group of patients take can benefit clinicians at identifying the current state of patient and potentially provide early treatment to avoid going towards specific paths. In this paper we present our approach that enables a clinical dataset of patient encounters to be clustered into groups of similarity and run through our algorithm which produces an automaton displaying the most common trajectories taken by patients. Furthermore, we explore a dataset of patients that have experienced mild traumatic brain injuries (mTBI) to show that our approach is effective at clustering and identifying common trajectories for patients that develop headaches, sleep, and post traumatic stress disorder (PTSD) post concussion.