Making sense of personal health information: challenges for information visualization

Developing questions to assess and measure patients' perceived survival benefit from adjuvant endocrine therapy in breast cancer: a mixed methods pilot study

This mixed method study developed multiple question types to understand and measure women's perceived benefit from adjuvant endocrine therapy. We hypothesis that patients do not understand this benefit and sought to develop the questions needed to test this hypothesis and obtain initial patient estimates. From 8/2022 to 3/2023, qualitative interviews focused on assessing and modifying 9 initial varied question types asking about the overall survival (OS) benefit from adjuvant endocrine therapy. Subsequent focus groups modified and selected the optimal questions. Patients' self-assessment of their OS benefit was compared to their individualized PREDICT model results. Fifty-three patients completed the survey; 42% Hispanic, 30% rural, and 47% with income < $39,999 per year. Patients reported adequate health care literacy (61.5%) and average confidence about treatment and medication decisions 49.4 (95% CI 24.4-59.5). From the original 9 questions, 3 modified questions were ultimately found to capture patients' perception of this OS benefit, focusing on graphical and prose styles. Patients estimated an OS benefit of 42% compared to 4.4% calculated from the PREDICT model (p < 0.001). In this group with considerable representation from ethnic minority, rural and low-income patients, qualitative data showed that more than one modality of question type was needed to clearly capture patients' understanding of treatment benefit. Women with breast cancer significantly overestimated their 10-year OS benefit from adjuvant endocrine therapy compared to the PREDICT model.

Developing Questions to Assess and Measure Patients' Perceived Survival Benefit from Adjuvant Endocrine Therapy in Breast Cancer: A Mixed Methods Pilot Study

Purpose

This mixed methods study developed multiple question types to understand and measure women's perceived benefit from adjuvant endocrine therapy. We hypothesis that patients do not understand this benefit and sought to develop the questions needed to test this hypothesis and obtain initial patient estimates.

Methods

From 8/2022 to 3/2023, qualitative interviews focused on assessing and modifying 9 initial varied question types asking about the overall survival (OS) benefit from adjuvant endocrine therapy. Subsequent focus groups modified and selected the optimal questions. Patients' self-assessment of their OS benefit was compared to their individualized PREDICT model results.

Results

Fifty-three patients completed the survey; 42% Hispanic, 30% rural, and 47% with income <$39,999 per year. Patients reported adequate health care literacy (61.5%) and average confidence about treatment and medication decisions 49.4 (95% CI 24.4-59.5). From the original 9 questions, 3 modified questions were ultimately found to capture patients' perception of this OS benefit, focusing on graphical and prose styles. Patients estimated an OS benefit of 42% compared to 4.4% calculated from the PREDICT model (p < 0.001).

Conclusion

In this group with considerable representation from ethnic minority, rural and low-income patients, qualitative data showed that more than one modality of question type was needed to clearly capture patients' understanding of treatment benefit. Women with breast cancer significantly overestimated their 10-year OS benefit from adjuvant endocrine therapy compared to the PREDICT model.

MediCoSpace : Visual Decision-Support for Doctor-Patient Consultations using Medical Concept Spaces from EHRs

Healthcare systems are under pressure from an aging population, rising costs, and increasingly complex conditions and treatments. Although data are determined to play a bigger role in how doctors diagnose and prescribe treatments, they struggle due to a lack of time and an abundance of structured and unstructured information. To address this challenge, we introduce MediCoSpace , a visual decision-support tool for more efficient doctor-patient consultations. The tool links patient reports to past and present diagnoses, diseases, drugs, and treatments, both for the current patient and other patients in comparable situations. MediCoSpace uses textual medical data, deep-learning supported text analysis and concept spaces to facilitate a visual discovery process. The tool is evaluated with five medical doctors. The results show that MediCoSpace facilitates a promising, yet complex way to discover unlikely relations and thus suggests a path toward the development of interactive visual tools to provide physicians with more holistic diagnoses and personalized, dynamic treatments for patients.

Assessment of freely available online videos of cardiac electrophysiological procedures from a shared decision-making perspective

Background

Physicians recommend electrophysiological (EP) procedures to patients with arrhythmic risk. This involves shared decision-making (SDM). Patients increasingly search for additional information online. Freely available online videos are an attractive source.

Objective

We assessed freely available online videos for EP procedures from the perspective of SDM to determine if such videos can be shared with patients for SDM.

Methods

We searched for freely available online videos related to 6 common EP procedures limited to English language and duration between 1 and 10 minutes using Google and Bing. Data collected included date and source of upload, number of hits, and duration. Videos were assessed systematically for understandability, actionability (PEMAT tool), relatability, teamwork, and mention of risk.

Results

A total of 78 videos met our inclusion criteria, out of 960 video links. Overall inter-rater agreement was moderate to good. Video upload dates spanned 12 years and number of hits ranged from 87 to 594,000. The majority of videos (63%) were produced by health care systems or academic institutions. For all 78 videos the mean total PEMAT tool score was 48.6%. Thirty-five percent of videos showed a patient engaged in a conversation with the physician or a team member; 41% of videos showed other team members. The potential for complications was mentioned in 10%.

Conclusion

The majority of online, freely available videos for common EP procedures lack features useful for SDM and may not be helpful for sharing with patients from that perspective. It is possible to create high-quality videos that can facilitate SDM.

Interactive Mode of Visual Communication Based on Information Visualization Theory

In the modern environment, visual communication design has become a comprehensive subject that combines technology and art. The development of various technologies has promoted the emergence of new design art forms, and at the same time, it has also promoted the change of design concepts and thinking modes. Visual representation, as a representation practice to express the meaning of information in the form of visual symbols, is a method and means to realize information visualization. Based on visual interface design, this article takes interactivity and user experience as innovation points and digs into the design process of interactive information visualization to achieve the purpose of information transmission. By analyzing the cognitive tasks of users in each stage of visual thinking activities in visual terminals, this article proposes to optimize the interactive design of visual communication from the key points of attention, consciousness, and memory. In order to verify the feasibility of the interactive mode of visual communication based on information visualization theory, this article has carried out several comparative experiments with different models. Experimental results show that this algorithm has a faster convergence speed and a greater generalization ability, the accuracy of the algorithm reaches 96.03%, and the highest evaluation of user satisfaction is 95.93%. The interactive mode of visual communication in this article can provide means and reference value for the development direction of visual communication design.

Data Visualization for Chronic Neurological and Mental Health Condition Self-management: Systematic Review of User Perspectives

BACKGROUND

Remote measurement technologies (RMT) such as mobile health devices and apps are increasingly used by those living with chronic neurological and mental health conditions. RMT enables real-world data collection and regular feedback, providing users with insights about their own conditions. Data visualizations are an integral part of RMT, although little is known about visualization design preferences from the perspectives of those living with chronic conditions.

OBJECTIVE

The aim of this review was to explore the experiences and preferences of individuals with chronic neurological and mental health conditions on data visualizations derived from RMT to manage health.

METHODS

In this systematic review, we searched peer-reviewed literature and conference proceedings (PubMed, IEEE Xplore, EMBASE, Web of Science, Association for Computing Machinery Computer-Human Interface proceedings, and the Cochrane Library) for original papers published between January 2007 and September 2021 that reported perspectives on data visualization of people living with chronic neurological and mental health conditions. Two reviewers independently screened each abstract and full-text article, with disagreements resolved through discussion. Studies were critically appraised, and extracted data underwent thematic synthesis.

RESULTS

We identified 35 eligible publications from 31 studies representing 12 conditions. Coded data coalesced into 3 themes: desire for data visualization, impact of visualizations on condition management, and visualization design considerations. Data visualizations were viewed as an integral part of users' experiences with RMT, impacting satisfaction and engagement. However, user preferences were diverse and often conflicting both between and within conditions.

CONCLUSIONS

When used effectively, data visualizations are valuable, engaging components of RMT. They can provide structure and insight, allowing individuals to manage their own health more effectively. However, visualizations are not "one-size-fits-all," and it is important to engage with potential users during visualization design to understand when, how, and with whom the visualizations will be used to manage health.

The use of Web-based interactive technology to promote HPV vaccine uptake among young females: a randomized controlled trial

BACKGROUND

Currently no study has investigated whether Web-based interactive technology can influence females to adopt healthy behaviors. We investigated how and under what conditions do Web-based interactivity influence vaccination intentions among young females.

METHODS

In this randomized controlled trail, we conduct a 2 (mode of information presentation: narrative vs. data visualization) × 2 (interactivity: interactive information vs. noninteractive information) between-groups design. A total of 180 Chinese female undergraduate students who had never received HPV vaccination were randomly allocated to 4 experimental groups. Each participant was assessed for their information avoidance behavior and vaccination intention. The hypotheses were tested using a moderated mediation model. All analyses were performed using SPSS version 22.0 with probability set at 0.05 alpha level.

RESULTS

The indirect relationship between interactivity and behavioral intention though information avoidance was moderated by the mode of presentation. Under the narrative condition, interactivity (vs. non-interactivity) decreased information avoidance and increased the intention to receive HPV vaccination (B = -.23, SE = 0.10, P < 0.05). However, under data visualization condition, no significant difference was observed between the effects of interactivity and non-interactivity on intention.

CONCLUSION

The findings suggest that when young females experience difficulties in manipulating or understanding HPV-related information, their information-avoidance behavior is likely to increase. Rather than use interactive statistical or graphical information, young females are more likely to be persuaded by interactive narratives.

Utility, Value, and Benefits of Contemporary Personal Health Records: Integrative Review and Conceptual Synthesis

Background

Contemporary personal health record (PHR) technologies offer a useful platform for individuals to maintain a lifelong record of personally reported and clinically sourced data from various points of medical care.

Objective

This paper presents an integrative review and synthesis of the extant literature on PHRs. This review draws upon multiple lenses of analysis and deliberates value perspectives of PHRs at the product, consumer, and industry levels.

Methods

Academic databases were searched using multiple keywords related to PHRs for the years 2001-2020. Three research questions were formulated and used as selection criteria in our review of the extant literature relevant to our study.

Results

We offer a high-level functional utility model of PHR features and functions. We also conceptualize a consumer value framework of PHRs, highlighting the applications of these technologies across various health care delivery activities. Finally, we provide a summary of the benefits of PHRs for various health care constituents, including consumers, providers, payors, and public health agencies.

Conclusions

PHR products offer a myriad of content-, connectivity-, and collaboration-based features and functions for their users. Although consumers benefit from the tools provided by PHR technologies, their overall value extends across the constituents of the health care delivery chain. Despite advances in technology, our literature review identifies a shortfall in the research addressing consumer value enabled by PHR tools. In addition to scholars and researchers, our literature review and proposed framework may be especially helpful for value analysis committees in the health care sector that are commissioned for the appraisal of innovative health information technologies such as PHRs.

Patients' Experiences of Using Smartphone Apps to Support Self-Management and Improve Medication Adherence in Hypertension: Qualitative Study

BACKGROUND

Worldwide, hypertension control rates remain suboptimal despite clinically effective antihypertensive drug therapy. Patient failure to take medication as prescribed (ie, nonadherence) is the most important factor contributing to poor control. Smartphone apps can facilitate the delivery of evidence-based behavior change techniques to improve adherence and may provide a scalable, usable, and feasible method to deliver self-management support.

OBJECTIVE

The aim of this study is to explore patients' experiences of the usability and feasibility of smartphone apps to support self-management and improve medication adherence in hypertension.

METHODS

A qualitative descriptive study was conducted. A total of 11 people living with hypertension from the West of Ireland were sampled purposively and interviewed about their experience of using a self-management app for a 4-week period, which included two key functionalities: self-monitoring of blood pressure (BP) and medication reminders. Thematic analysis was carried out on the semistructured interview data.

RESULTS

Participants' age ranged from 43 to 74 years (mean 62 years, SD 9.13). Three themes were identified: digital empowerment of self-management, human versus digital systems, and digital sustainability. Although patients' experience of using the technology to self-monitor BP was one of empowerment, characterized by an enhanced insight and understanding into their condition, control, and personal responsibility, the reminder function was only feasible for patients who reported unintentional nonadherence to treatment. Patients experienced the app as a sustainable tool to support self-management and found it easy to use, including those with limited technological competence.

CONCLUSIONS

The study's findings provide new insights into the experience of using apps to support medication adherence in hypertension. Overall, the data support apps as a usable and feasible method to aid self-management of hypertension and highlight the need for personalized functionality, particularly with regard to medication adherence reminder strategies. The study's findings challenge the perspective that the use of these technologies to support self-management can inevitably add to the burden of treatment experienced by patients.

Visual Analytics for Dimension Reduction and Cluster Analysis of High Dimensional Electronic Health Records

Recent advancement in EHR-based (Electronic Health Record) systems has resulted in producing data at an unprecedented rate. The complex, growing, and high-dimensional data available in EHRs creates great opportunities for machine learning techniques such as clustering. Cluster analysis often requires dimension reduction to achieve efficient processing time and mitigate the curse of dimensionality. Given a wide range of techniques for dimension reduction and cluster analysis, it is not straightforward to identify which combination of techniques from both families leads to the desired result. The ability to derive useful and precise insights from EHRs requires a deeper understanding of the data, intermediary results, configuration parameters, and analysis processes. Although these tasks are often tackled separately in existing studies, we present a visual analytics (VA) system, called Visual Analytics for Cluster Analysis and Dimension Reduction of High Dimensional Electronic Health Records (VALENCIA), to address the challenges of high-dimensional EHRs in a single system. VALENCIA brings a wide range of cluster analysis and dimension reduction techniques, integrate them seamlessly, and make them accessible to users through interactive visualizations. It offers a balanced distribution of processing load between users and the system to facilitate the performance of high-level cognitive tasks in such a way that would be difficult without the aid of a VA system. Through a real case study, we have demonstrated how VALENCIA can be used to analyze the healthcare administrative dataset stored at ICES. This research also highlights what needs to be considered in the future when developing VA systems that are designed to derive deep and novel insights into EHRs.

Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3

Medication-induced acute kidney injury (AKI) is a well-known problem in clinical medicine. This paper reports the first development of a visual analytics (VA) system that examines how different medications associate with AKI. In this paper, we introduce and describe VISA_M3R3, a VA system designed to assist healthcare researchers in identifying medications and medication combinations that associate with a higher risk of AKI using electronic medical records (EMRs). By integrating multiple regression models, frequent itemset mining, data visualization, and human-data interaction mechanisms, VISA_M3R3 allows users to explore complex relationships between medications and AKI in such a way that would be difficult or sometimes even impossible without the help of a VA system. Through an analysis of 595 medications using VISA_M3R3, we have identified 55 AKI-inducing medications, 24,212 frequent medication groups, and 78 medication groups that are associated with AKI. The purpose of this paper is to demonstrate the usefulness of VISA_M3R3 in the investigation of medication-induced AKI in particular and other clinical problems in general. Furthermore, this research highlights what needs to be considered in the future when designing VA systems that are intended to support gaining novel and deep insights into massive existing EMRs.

Presenting patients with information on their oral health risk: the PREFER three-arm RCT and ethnography

Background

A new NHS dental practice contract is being tested using a traffic light (TL) system that categorises patients as being at red (high), amber (medium) or green (low) risk of poor oral health. This is intended to increase the emphasis on preventative dentistry, including giving advice on ways patients can improve their oral health. Quantitative Light-Induced Fluorescence (QLF™) cameras (Inspektor Research Systems BV, Amsterdam, the Netherlands) also potentially offer a vivid portrayal of information on patients’ oral health.

Methods

Systematic review – objective: to investigate how patients value and respond to different forms of information on health risks. Methods: electronic searches of nine databases, hand-searching of eight specialist journals and backwards and forwards citation-chasing followed by duplicate title, abstract- and paper-screening and data-extraction. Inclusion criteria limited studies to personalised information on risk given to patients as part of their health care. Randomised controlled trial (RCT) – setting: NHS dental practice. Objective: to investigate patients’ preferences for and response to different forms of information about risk given at check-ups. Design: a pragmatic, multicentred, three-arm, parallel-group, patient RCT. Participants: adults with a high/medium risk of poor oral health attending NHS dental practices. Interventions: (1) information given verbally supported by a card showing the patient’s TL risk category; (2) information given verbally supported by a QLF photograph of the patient’s mouth. The control was verbal information only (usual care). Main outcome measures: primary outcome – median valuation for the three forms of information measured by willingness to pay (WTP). Secondary outcomes included toothbrushing frequency and duration, dietary sugar intake, smoking status, self-rated oral health, a basic periodontal examination, Plaque Percentage Index and the number of tooth surfaces affected by caries (as measured by QLF). Qualitative study – an ethnography involving observations of 368 dental appointments and interviews with patients and dental teams.

Results

Systematic review – the review identified 12 papers (nine of which were RCTs). Eight studies involved the use of computerised risk assessments in primary care. Intervention effects were generally modest, even with respect to modifying risk perceptions rather than altering behaviour or clinical outcomes. RCT – the trial found that 51% of patients identified verbal information as their most preferred form, 35% identified QLF as most preferred and 14% identified TL information as most preferred. The median WTP for TL was about half that for verbal information alone. Although at 6 and 12 months patients reported taking less sugar in drinks, and at 12 months patients reported longer toothbrushing, there was no difference by information group. Qualitative study – there was very little explicit risk talk. Lifestyle discussions were often cursory to avoid causing shame or embarrassment to patients.

Limitations

Only 45% of patients were retained in the trial at 6 months and 31% were retained at 12 months. The trial was conducted in four dental practices, and five dental practices were involved in the qualitative work.

Conclusions

Patients prefer personal, detailed verbal advice on oral health at their check-up. A new NHS dental practice contract using TL categorisation might make this less likely.

Future work

Research on how to deliver, within time constraints, effective advice to patients on preventing poor oral health. More research on ‘risk work’ in wider clinical settings is also needed.

Trial registration

Current Controlled Trials ISRCTN71242343.

Funding

This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research; Vol. 8, No. 3. See the NIHR Journals Library website for further project information.

Informational Practices of Postacute Brain Injury Patients During Personal Recovery: Qualitative Study

BACKGROUND

The effects of brain injury, structural damage, or the physiological disruption of brain function last far beyond initial clinical treatment. Self-tracking and management technologies have the potential to help individuals experiencing brain injury in their personal recovery-helping them to function at their best despite ongoing symptoms of illness. However, current self-tracking technologies may be unsuited for measuring the interconnected, nonlinear ways in which brain injury manifests.

OBJECTIVE

This study aimed to investigate (1) the current informational practices and sensemaking processes used by postacute brain injury patients during personal recovery and (2) the potential role of quality-of-life instruments in improving patient awareness of brain injury recovery, advocacy, and involvement in care used outside the clinical context. Our objective was to explore the means of improving awareness through reflection that leads to compensatory strategies by anticipating or recognizing the occurrence of a problem caused by impairment.

METHODS

We conducted a qualitative study and used essentialist or realist thematic analysis to analyze the data collected through semistructured interviews and questionnaires, 2 weeks of structured data collection using brain injury-specific health-related quality of life instrument, quality of life after brain injury (QoLIBRI), and final interviews.

RESULTS

Informational practices of people with brain injury involve data collection, data synthesis, and obtaining and applying the insights to their lifestyles. Participants collected data through structured tools such as spreadsheets and wearable devices but switched to unstructured tools such as journals and blogs as changes in overall progress became more qualitative in nature. Although data collection helped participants summarize their progress better, the lack of conceptual understanding made it challenging to know what to monitor or communicate with clinicians. QoLIBRI served as an education tool in this scenario but was inadequate in facilitating reflection and sensemaking.

CONCLUSIONS

Individuals with postacute brain injury found the lack of conceptual understanding of recovery and tools for making sense of their health data as major impediments for tracking and being aware of their personal recovery. There is an urgent need for a better framework for recovery and a process model for choosing patient-generated health data tools that focus on the holistic nature of recovery and improve the understanding of brain injury for all stakeholders involved throughout recovery.

A Systematic Review of Patient-Facing Visualizations of Personal Health Data

OBJECTIVES

As personal health data are being returned to patients with increasing frequency and volume, visualizations are garnering excitement for their potential to facilitate patient interpretation. Evaluating these visualizations is important to ensure that patients are able to understand and, when appropriate, act upon health data in a safe and effective manner. The objective of this systematic review was to review and evaluate the state of the science of patient-facing visualizations of personal health data.

METHODS

We searched five scholarly databases (PubMed, Embase, Scopus, ACM Digital Library [Association for Computing Machinery Digital Library], and IEEE Computational Index [Institute of Electrical and Electronics Engineers Computational Index]) through December 1, 2018 for relevant articles. We included English-language articles that developed or tested one or more patient-facing visualizations for personal health data. Three reviewers independently assessed quality of included articles using the Mixed methods Appraisal Tool. Characteristics of included articles and visualizations were extracted and synthesized.

RESULTS

In 39 articles included in the review, there was heterogeneity in the sample sizes and methods for evaluation but not sample demographics. Few articles measured health literacy, numeracy, or graph literacy. Line graphs were the most common visualization, especially for longitudinal data, but number lines were used more frequently in included articles over past 5 years. Article findings suggested more patients understand the number lines and bar graphs compared with line graphs, and that color is effective at communicating risk, improving comprehension, and increasing confidence in interpretation.

CONCLUSION

In this review, we summarize types and components of patient-facing visualizations and methodologies for development and evaluation in the reviewed articles. We also identify recommendations for future work relating to collecting and reporting data, examining clinically actionable boundaries for diverse data types, and leveraging data science. This work will be critically important as patient access of their personal health data through portals and mobile devices continues to rise.

Developing Infographics to Facilitate HIV-Related Patient-Provider Communication in a Limited-Resource Setting

BACKGROUND

Productive patient-provider communication is a recognized component of high-quality health care that leads to better health outcomes. Well-designed infographics can facilitate effective communication, especially when culture, language, or literacy differences are present.

OBJECTIVES

This study aimed to rigorously develop infographics to improve human immunodeficiency virus (HIV)-related patient-provider communication in a limited-resource setting. A secondary purpose was to establish through participant feedback that infographics convey intended meaning in this clinical and cultural context.

METHODS

We adapted a participatory design methodology, developed in a high-resource setting, for use in the Dominican Republic. Initially, content to include was established using a data-triangulation method. Then, infographics were iteratively generated and refined during five phases of design sessions with three stakeholder groups: (1) 25 persons living with HIV, (2) 8 health care providers, and (3) 5 domain experts. Suggestions for improvement were incorporated between design sessions and questions to confirm interpretability of infographics were included at the end of each session.

RESULTS

Each participant group focused on different aspects of infographic designs. Providers drew on past experiences with patients and offered clinically and contextually relevant recommendations of symbols and images to include. Domain experts focused on technical design considerations and interpretations of infographics. While it was difficult for patient participants to provide concrete suggestions, they provided feedback on the meaning of infographics and responded clearly to direct questions regarding possible changes. Fifteen final infographics were developed and all participant groups qualitatively confirmed that they displayed the intended content in a culturally appropriate and clinically meaningful way.

CONCLUSION

Incorporating perspectives from various stakeholders led to the evolution of designs over time and generated design recommendations that will be useful to others creating infographics for use in similar populations. Next steps are to assess the feasibility of using infographics to improve clinical communication and patient outcomes.

"Seeing Pain Differently": A Qualitative Investigation Into the Differences and Similarities of Pain and Rheumatology Specialists' Interpretation of Multidimensional Mobile Health Pain Data From Children and Young People With Juvenile Idiopathic Arthritis

Background

In contrast to the use of traditional unidimensional paper-based scales, a mobile health (mHealth) assessment of pain in children and young people (CYP) with juvenile idiopathic arthritis (JIA) enables comprehensive and complex multidimensional pain data to be captured remotely by individuals. However, how professionals use multidimensional pain data to interpret and synthesize pain reports gathered using mHealth tools is not yet known.

Objective

The aim of this study was to explore the salience and prioritization of different mHealth pain features as interpreted by key stakeholders involved in research and management of pain in CYP with JIA.

Methods

Pain and rheumatology specialists were purposively recruited via professional organizations. Face-to-face focus groups were conducted for each specialist group. Participants were asked to rank order 9 static vignette scenarios created from real patient mHealth multidimensional pain data. These data had been collected by a researcher in a separate study using My Pain Tracker, a valid and acceptable mHealth iPad pain communication tool that collects information about intensity, severity, location, emotion, and pictorial pain qualities. In the focus groups, specialists discussed their decision-making processes behind each rank order in the focus groups. The total group rank ordering of vignette scenarios was calculated. Qualitative data from discussions were analyzed using latent thematic analysis.

Results

A total of 9 pain specialists took part in 1 focus group and 10 rheumatology specialists in another. In pain specialists, the consensus for the highest pain experience (44%) was poorer than their ranking of the lowest pain experiences (55%). Conversely, in rheumatology specialists, the consensus for the highest pain experience (70%) was stronger than their ranking of the lowest pain experience (50%). Pain intensity was a high priority for pain specialists, but rheumatology specialists gave high priority to intensity and severity taken together. Pain spread was highly prioritized, with the number of pain locations (particular areas or joints) being a high priority for both groups; radiating pain was a high priority for pain specialists only. Pain emotion was challenging for both groups and was only perceived to be a high priority when specialists had additional confirmatory evidence (such as information about pain interference or clinical observations) to validate the pain emotion report. Pain qualities such as particular word descriptors, use of the color red, and fire symbols were seen to be high priority by both groups in interpretation of CYP pain reports.

Conclusions

Pain interpretation is complex. Findings from this study of specialists’ decision-making processes indicate which aspects of pain are prioritized and weighted more heavily than others by those interpreting mHealth data. Findings are useful for developing electronic graphical summaries which assist specialists in interpreting patient-reported mHealth pain data more efficiently in clinical and research settings.

Task-Data Taxonomy for Health Data Visualizations: Web-Based Survey With Experts and Older Adults

Background

Increasingly, eHealth involves health data visualizations to enable users to better understand their health situation. Selecting efficient and ergonomic visualizations requires knowledge about the task that the user wants to carry out and the type of data to be displayed. Taxonomies of abstract tasks and data types bundle this knowledge in a general manner. Task-data taxonomies exist for visualization tasks and data. They also exist for eHealth tasks. However, there is currently no joint task taxonomy available for health data visualizations incorporating the perspective of the prospective users. One of the most prominent prospective user groups of eHealth are older adults, but their perspective is rarely considered when constructing tasks lists.

Objective

The aim of this study was to construct a task-data taxonomy for health data visualizations based on the opinion of older adults as prospective users of eHealth systems. eHealth experts served as a control group against the bias of lacking background knowledge. The resulting taxonomy would then be used as an orientation in system requirement analysis and empirical evaluation and to facilitate a common understanding and language in eHealth data visualization.

Methods

Answers from 98 participants (51 older adults and 47 eHealth experts) given in an online survey were quantitatively analyzed, compared between groups, and synthesized into a task-data taxonomy for health data visualizations.

Results

Consultation, diagnosis, mentoring, and monitoring were confirmed as relevant abstract tasks in eHealth. Experts and older adults disagreed on the importance of mentoring (χ²₄=14.1, P=.002) and monitoring (χ²₄=22.1, P<.001). The answers to the open questions validated the findings from the closed questions and added therapy, communication, cooperation, and quality management to the aforementioned tasks. Here, group differences in normalized code counts were identified for “monitoring” between the expert group (mean 0.18, SD 0.23) and the group of older adults (mean 0.08, SD 0.15; t₉₆=2431, P=.02). Time-dependent data was most relevant across all eHealth tasks. Finally, visualization tasks and data types were assigned to eHealth tasks by both experimental groups.

Conclusions

We empirically developed a task-data taxonomy for health data visualizations with prospective users. This provides a general framework for theoretical concession and for the prioritization of user-centered system design and evaluation. At the same time, the functionality dimension of the taxonomy for telemedicine—chosen as the basis for the construction of present taxonomy—was confirmed.

A Systematic Method for Exploring Data Attributes in Preparation for Designing Tailored Infographics of Patient Reported Outcomes

CONTEXT

Tailored visualizations of patient reported outcomes (PROs) are valuable health communication tools to support shared decision making, health self-management, and engagement with research participants, such as cohorts in the NIH Precision Medicine Initiative. The automation of visualizations presents some unique design challenges. Efficient design processes depend upon gaining a thorough understanding of the data prior to prototyping.

CASE DESCRIPTION

We present a systematic method to exploring data attributes, with a specific focus on application to self-reported health data. The method entails a) determining the meaning of the variable to be visualized, b) identifying the possible and likely values, and c) understanding how values are interpreted.

FINDINGS

We present two case studies to illustrate how this method affected our design decisions particularly with respect to outlier and non-missing zero values.

MAJOR THEMES

The use of a systematic method made our process of exploring data attributes easily manageable. The limitations of the data can narrow design options but can also prompt creative solutions and innovative design opportunities.

CONCLUSION

A systematic method of exploration of data contributes to an efficient design process, uncovers design opportunities, and alerts the designer to design challenges.

Visualizing risks in cancer communication: A systematic review of computer-supported visual aids

OBJECTIVE

Health websites are becoming important sources for cancer information. Lay users, patients and carers seek support for critical decisions, but they are prone to common biases when quantitative information is presented. Graphical representations of risk data can facilitate comprehension, and interactive visualizations are popular. This review summarizes the evidence on computer-supported graphs that present risk data and their effects on various measures.

METHODS

The systematic literature search was conducted in several databases, including MEDLINE, EMBASE and CINAHL. Only studies with a controlled design were included. Relevant publications were carefully selected and critically appraised by two reviewers.

RESULTS

Thirteen studies were included. Ten studies evaluated static graphs and three dynamic formats. Most decision scenarios were hypothetical. Static graphs could improve accuracy, comprehension, and behavioural intention. But the results were heterogeneous and inconsistent among the studies. Dynamic formats were not superior or even impaired performance compared to static formats.

CONCLUSIONS

Static graphs show promising but inconsistent results, while research on dynamic visualizations is scarce and must be interpreted cautiously due to methodical limitations.

PRACTICE IMPLICATIONS

Well-designed and context-specific static graphs can support web-based cancer risk communication in particular populations. The application of dynamic formats cannot be recommended and needs further research.

Does information form matter when giving tailored risk information to patients in clinical settings? A review of patients' preferences and responses

Neoliberal emphasis on "responsibility" has colonized many aspects of public life, including how health care is provided. Clinical risk assessment of patients based on a range of data concerned with lifestyle, behavior, and health status has assumed a growing importance in many health systems. It is a mechanism whereby responsibility for self (preventive) care can be shifted to patients, provided that risk assessment data is communicated to patients in a way which is engaging and motivates change. This study aimed to look at whether the form in which tailored risk information was presented in a clinical setting (for example, using photographs, online data, diagrams etc.), was associated with differences in patients' responses and preferences to the material presented. We undertook a systematic review using electronic searching of nine databases, along with handsearching specialist journals and backward and forward citation searching. We identified eleven studies (eight with a randomized controlled trial design). Seven studies involved the use of computerized health risk assessments in primary care. Beneficial effects were relatively modest, even in studies merely aiming to enhance patient-clinician communication or to modify patients' risk perceptions. In our paper, we discuss the apparent importance of the accompanying discourse between patient and clinician, which appears to be necessary in order to impart meaning to information on "risk," irrespective of whether the material is personalized, or even presented in a vivid way. Thus, while expanding computer technologies might be able to generate a highly personalized account of patients' risk in a time efficient way, the need for face-to-face interactions to impart meaning to the data means that these new technologies cannot fully address the resource issues attendant with this type of approach.

Commencing and Persisting With a Web-Based Cognitive Behavioral Intervention for Insomnia: A Qualitative Study of Treatment Completers

Background

Computerized cognitive behavioral therapy for insomnia (CCBT-I) has a growing evidence base as a stand-alone intervention, but it is less clear what factors may limit its acceptability and feasibility when combined with clinical care.

Objective

The purpose of this study was to explore barriers and facilitators to use of an adjunctive CCBT-I program among depressed patients in a psychiatric clinic by using both quantitative and qualitative approaches.

Methods

We conducted the qualitative component of the study using face-to-face or telephone interviews with participants who had enrolled in a clinical trial of a CCBT-I program as an adjunctive treatment in a psychiatric clinical setting. In line with the grounded theory approach, we used a semistructured interview guide with new thematic questions being formulated during the transcription and data analysis, as well as being added to the interview schedule. A range of open and closed questions addressing user experience were asked of all study participants who completed the 12-week trial in an online survey.

Results

Three themes emerged from the interviews and open questions, consistent with nonadjunctive CCBT-I implementation. Identification with the adjunctive intervention’s target symptom of insomnia and the clinical setting were seen as key reasons to engage initially. Persistence was related to factors to do with the program, its structure, and its content, rather than any nonclinical factors. The survey results showed that only the key active behavioral intervention, sleep restriction, was rated as a major problem by more than 15% of the sample. In this clinical setting, the support of the clinician in completing the unsupported program was highlighted, as was the need for the program and clinical treatment to be coordinated.

Conclusions

The use of a normally unsupported CCBT-I program as an adjunctive treatment can be aided by the clinician’s approach. A key behavioral component of the intervention, specific to insomnia treatment, was identified as a major problem for persistence. As such, clinicians need to be aware of when such components are delivered in the program and coordinate their care accordingly, if the use of the program is to be optimized.

ClinicalTrial

Australian and New Zealand Clinical Trials Registry ACTRN12612000985886; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=362875&isReview=true (Archived by WebCite at http://www.webcitation.org/6njjhl42X)

Beyond simple charts: Design of visualizations for big health data

Health data is often big data due to its high volume, low veracity, great variety, and high velocity. Big health data has the potential to improve productivity, eliminate waste, and support a broad range of tasks related to disease surveillance, patient care, research, and population health management. Interactive visualizations have the potential to amplify big data's utilization. Visualizations can be used to support a variety of tasks, such as tracking the geographic distribution of diseases, analyzing the prevalence of disease, triaging medical records, predicting outbreaks, and discovering at-risk populations. Currently, many health visualization tools use simple charts, such as bar charts and scatter plots, that only represent few facets of data. These tools, while beneficial for simple perceptual and cognitive tasks, are ineffective when dealing with more complex sensemaking tasks that involve exploration of various facets and elements of big data simultaneously. There is need for sophisticated and elaborate visualizations that encode many facets of data and support human-data interaction with big data and more complex tasks. When not approached systematically, design of such visualizations is labor-intensive, and the resulting designs may not facilitate big-data-driven tasks. Conceptual frameworks that guide the design of visualizations for big data can make the design process more manageable and result in more effective visualizations. In this paper, we demonstrate how a framework-based approach can help designers create novel, elaborate, non-trivial visualizations for big health data. We present four visualizations that are components of a larger tool for making sense of large-scale public health data.

Feast for the Eyes: An Introduction to Data Visualization

Data visualization is defined as the use of data presented in a graphical or pictorial manner. While data visualization is not a new concept, the ease with which anyone can create a data-drive chart, image, or visual has encouraged its growth. The increase of free sources of data and need for user-created content on social media has also led to a rise in data visualization's popularity. This column will explore what data visualization is and how it is currently being used. It will also discuss the benefits, potential problems, and uses in libraries. A brief list of visualization guides is included.

Sometimes more is more: iterative participatory design of infographics for engagement of community members with varying levels of health literacy

OBJECTIVE

To collaborate with community members to develop tailored infographics that support comprehension of health information, engage the viewer, and may have the potential to motivate health-promoting behaviors.

METHODS

The authors conducted participatory design sessions with community members, who were purposively sampled and grouped by preferred language (English, Spanish), age group (18-30, 31-60, >60 years), and level of health literacy (adequate, marginal, inadequate). Research staff elicited perceived meaning of each infographic, preferences between infographics, suggestions for improvement, and whether or not the infographics would motivate health-promoting behavior. Analysis and infographic refinement were iterative and concurrent with data collection.

RESULTS

Successful designs were information-rich, supported comparison, provided context, and/or employed familiar color and symbolic analogies. Infographics that employed repeated icons to represent multiple instances of a more general class of things (e.g., apple icons to represent fruit servings) were interpreted in a rigidly literal fashion and thus were unsuitable for this community. Preliminary findings suggest that infographics may motivate health-promoting behaviors.

DISCUSSION

Infographics should be information-rich, contextualize the information for the viewer, and yield an accurate meaning even if interpreted literally.

CONCLUSION

Carefully designed infographics can be useful tools to support comprehension and thus help patients engage with their own health data. Infographics may contribute to patients' ability to participate in the Learning Health System through participation in the development of a robust data utility, use of clinical communication tools for health self-management, and involvement in building knowledge through patient-reported outcomes.

Development, implementation and evaluation of an information model for archetype based user responsive medical data visualization

BACKGROUND

When medical data have been successfully recorded or exchanged between systems there appear a need to present the data consistently to ensure that it is clearly understood and interpreted. A standard based user interface can provide interoperability on the visual level.

OBJECTIVES

The goal of this research was to develop, implement and evaluate an information model for building user interfaces for archetype based medical data.

METHODS

The following types of knowledge were identified as important elements and were included in the information model: medical content related attributes, data type related attributes, user-related attributes, device-related attributes. In order to support flexible and efficient user interfaces an approach that represents different types of knowledge with different models separating the medical concept from a visual concept and interface realization was chosen. We evaluated the developed approach using Guideline for Good Evaluation Practice in Health Informatics (GEP-HI).

RESULTS

We developed a higher level information model to complement the ISO 13606 archetype model. This enabled the specification of the presentation properties at the moment of the archetypes' definition. The model allows realizing different users' perspectives on the data. The approach was implemented and evaluated within a functioning EHR system. The evaluation involved 30 patients of different age and IT experience and 5 doctors. One month of testing showed that the time required reading electronic health records decreased for both doctors (from average 310 to 220s) and patients (from average 95 to 39s). Users reported a high level of satisfaction and motivation to use the presented data visualization approach especially in comparison with their previous experience.

CONCLUSION

The introduced information model allows separating medical knowledge and presentation knowledge. The additional presentation layer will enrich the graphical user interface's flexibility and will allow an optimal presentation of medical data considering the different users' perspectives and different media used for data presentation.