Supporting Families in Reviewing and Communicating about Radiology Imaging Studies

Children's Designs for the Future of Telehealth

Telehealth has increased dramatically with COVID-19. However, current telehealth systems are designed for able-bodied adults, rather than for pediatric populations or for people with disabilities. Using a design scenario of a child with a communication disability who needs to access telehealth services, we explore children's ideas of the future of telehealth technology. We analyzed designs generated by six children and found three provocative over-arching design themes. The designs highlight how improving accessibility, accommodating communication preferences, and incorporating home based sensor technologies have the potential to improve telehealth for both pediatric patients and their physicians. We discuss how these themes can be incorporated into practical telehealth designs to serve a variety of patient populations-including adults, children, and people with disabilities.

User-Centered System Design for Communicating Clinical Laboratory Test Results: Design and Evaluation Study

Background

Personal clinical data, such as laboratory test results, are increasingly being made available to patients via patient portals. However, laboratory test results are presented in a way that is difficult for patients to interpret and use. Furthermore, the indications of laboratory test results may vary among patients with different characteristics and from different medical contexts. To date, little is known about how to design patient-centered technology to facilitate the interpretation of laboratory test results.

Objective

The aim of this study is to explore design considerations for supporting patient-centered communication and comprehension of laboratory test results, as well as discussions between patients and health care providers.

Methods

We conducted a user-centered, multicomponent design research consisting of user studies, an iterative prototype design, and pilot user evaluations, to explore design concepts and considerations that are useful for supporting patients in not only viewing but also interpreting and acting upon laboratory test results.

Results

The user study results informed the iterative design of a system prototype, which had several interactive features: using graphical representations and clear takeaway messages to convey the concerning nature of the results; enabling users to annotate laboratory test reports; clarifying medical jargon using nontechnical verbiage and allowing users to interact with the medical terms (eg, saving, favoriting, or sorting); and providing pertinent and reliable information to help patients comprehend test results within their medical context. The results of a pilot user evaluation with 8 patients showed that the new patient-facing system was perceived as useful in not only presenting laboratory test results to patients in a meaningful way but also facilitating in situ patient-provider interactions.

Conclusions

We draw on our findings to discuss design implications for supporting patient-centered communication of laboratory test results and how to make technology support informative, trustworthy, and empathetic.

Effect of AI Explanations on Human Perceptions of Patient-Facing AI-Powered Healthcare Systems

Ongoing research efforts have been examining how to utilize artificial intelligence technology to help healthcare consumers make sense of their clinical data, such as diagnostic radiology reports. How to promote the acceptance of such novel technology is a heated research topic. Recent studies highlight the importance of providing local explanations about AI prediction and model performance to help users determine whether to trust AI's predictions. Despite some efforts, limited empirical research has been conducted to quantitatively measure how AI explanations impact healthcare consumers' perceptions of using patient-facing, AI-powered healthcare systems. The aim of this study is to evaluate the effects of different AI explanations on people's perceptions of AI-powered healthcare system. In this work, we designed and deployed a large-scale experiment (N = 3,423) on Amazon Mechanical Turk (MTurk) to evaluate the effects of AI explanations on people's perceptions in the context of comprehending radiology reports. We created four groups based on two factors-the extent of explanations for the prediction (High vs. Low Transparency) and the model performance (Good vs. Weak AI Model)-and randomly assigned participants to one of the four conditions. Participants were instructed to classify a radiology report as describing a normal or abnormal finding, followed by completing a post-study survey to indicate their perceptions of the AI tool. We found that revealing model performance information can promote people's trust and perceived usefulness of system outputs, while providing local explanations for the rationale of a prediction can promote understandability but not necessarily trust. We also found that when model performance is low, the more information the AI system discloses, the less people would trust the system. Lastly, whether human agrees with AI predictions or not and whether the AI prediction is correct or not could also influence the effect of AI explanations. We conclude this paper by discussing implications for designing AI systems for healthcare consumers to interpret diagnostic report.

Retrieving Lab Test Related Questions from Social Q&A Sites by Combining Shallow Features and Deep Representations

Patients face challenges in accurately interpreting their lab test results. To fulfill their knowledge gap, patients often turn to online resources, such as Community Question-Answering (CQA) sites, to seek meaningful information and support from their peers. Retrieving the most relevant information to patients' queries is important to help patients understand lab test results. However, few studies investigated the retrieval of lab test-related questions on CQA platforms. To address this research gap, we build and evaluate a system that automatically ranks questions about lab tests based on their similarity to a given question. The system is tested using diabetes-related questions collected from Yahoo! Answers' health section. Experimental results show that the regression-weighted combination of deep representations and shallow features was most effective in the Yahoo! Answers dataset. The proposed system can be extended to medical question retrieval, where questions contain a variety of lab tests.

Utilization of Self-Diagnosis Health Chatbots in Real-World Settings: Case Study

Background

Artificial intelligence (AI)-driven chatbots are increasingly being used in health care, but most chatbots are designed for a specific population and evaluated in controlled settings. There is little research documenting how health consumers (eg, patients and caregivers) use chatbots for self-diagnosis purposes in real-world scenarios.

Objective

The aim of this research was to understand how health chatbots are used in a real-world context, what issues and barriers exist in their usage, and how the user experience of this novel technology can be improved.

Methods

We employed a data-driven approach to analyze the system log of a widely deployed self-diagnosis chatbot in China. Our data set consisted of 47,684 consultation sessions initiated by 16,519 users over 6 months. The log data included a variety of information, including users’ nonidentifiable demographic information, consultation details, diagnostic reports, and user feedback. We conducted both statistical analysis and content analysis on this heterogeneous data set.

Results

The chatbot users spanned all age groups, including middle-aged and older adults. Users consulted the chatbot on a wide range of medical conditions, including those that often entail considerable privacy and social stigma issues. Furthermore, we distilled 2 prominent issues in the use of the chatbot: (1) a considerable number of users dropped out in the middle of their consultation sessions, and (2) some users pretended to have health concerns and used the chatbot for nontherapeutic purposes. Finally, we identified a set of user concerns regarding the use of the chatbot, including insufficient actionable information and perceived inaccurate diagnostic suggestions.

Conclusions

Although health chatbots are considered to be convenient tools for enhancing patient-centered care, there are issues and barriers impeding the optimal use of this novel technology. Designers and developers should employ user-centered approaches to address the issues and user concerns to achieve the best uptake and utilization. We conclude the paper by discussing several design implications, including making the chatbots more informative, easy-to-use, and trustworthy, as well as improving the onboarding experience to enhance user engagement.

Patient Challenges and Needs in Comprehending Laboratory Test Results: Mixed Methods Study

Background

Patients are increasingly able to access their laboratory test results via patient portals. However, merely providing access does not guarantee comprehension. Patients could experience confusion when reviewing their test results.

Objective

The aim of this study is to examine the challenges and needs of patients when comprehending laboratory test results.

Methods

We conducted a web-based survey with 203 participants and a set of semistructured interviews with 13 participants. We assessed patients’ perceived challenges and needs (both informational and technological needs) when they attempted to comprehend test results, factors associated with patients’ perceptions, and strategies for improving the design of patient portals to communicate laboratory test results more effectively. Descriptive and correlation analysis and thematic analysis were used to analyze the survey and interview data, respectively.

Results

Patients face a variety of challenges and confusion when reviewing laboratory test results. To better comprehend laboratory results, patients need different types of information, which are grouped into 2 categories—generic information (eg, reference range) and personalized or contextual information (eg, treatment options, prognosis, what to do or ask next). We also found that several intrinsic factors (eg, laboratory result normality, health literacy, and technology proficiency) significantly impact people’s perceptions of using portals to view and interpret laboratory results. The desired enhancements of patient portals include providing timely explanations and educational resources (eg, a health encyclopedia), increasing usability and accessibility, and incorporating artificial intelligence–based technology to provide personalized recommendations.

Conclusions

Patients face significant challenges in interpreting the meaning of laboratory test results. Designers and developers of patient portals should employ user-centered approaches to improve the design of patient portals to present information in a more meaningful way.

Harnessing mobile health technology to support long-term chronic illness management: exploring family caregiver support needs in the outpatient setting

OBJECTIVE

Family caregiving is an important public health issue, particularly with the aging population. In recent years, mobile health (mHealth) technology has emerged as a potential low-cost, scalable platform to address caregiver support needs, and thereby alleviate the burden on caregivers. This study sought to examine the support needs of family caregivers in their lived experiences of outpatient care to inform the development of a future mHealth intervention.

MATERAILS AND METHODS

We conducted 20 semi-structured interviews in 2 outpatient hematopoietic cell transplant (HCT) clinics at a large academic medical center in the Midwestern United States. A thematic analysis was performed to define emerging themes.

RESULTS

Qualitative data analysis identified 5 primary themes that HCT caregivers faced: (I) lifestyle restrictions due to the patient's immunocompromised state; (II) Unmet needs due to limitations in the current resources, including unfamiliar medical tasks without necessary trainings; and (III) caregivers' adaptive strategies, including reformation of social relationships with family and friends. Based on these findings, we suggest 3 design considerations to guide the development of a future mHealth intervention.

CONCLUSIONS

The findings herein captured the family caregiver's lived experiences during outpatient care. There was broad agreement that caregiving was challenging and stressful. Thus, effective and scalable interventions to support caregivers are needed. This study provided data to guide the content and design of a future mHealth intervention in the outpatient setting.

Individuals with back and neck pain on medical forums: What do they mention? What do they fear?

BACKGROUND

The use of online medical forums is on the rise globally. Data scraping is a method of extracting website content using an automated computer program. We scraped users' questions regarding back and neck pain (BNP) from popular Israeli online medical forums. We aimed to identify the sort of questions being asked about BNP, and to analyse explicit themes that characterize their questions.

METHODS

Six leading Israeli BNP forums were identified. In phase 1, Python scripts scraped 12,418 questions into a data set. In phase 2 - five themes were identified: Surgery (n = 2,957); health care professions (n = 2,361); Sports (n = 2,304); drugs (n = 1,419) and interpretation of imaging (n = 845). Phase 3 - included the categorization of explicit fear-related words by the authors. Phase 4 - analysis of explicit fear-related themes yielded 402 questions.

RESULTS

Gender was identified for 394 users, and age was identified for 181 users. A total of 248 users (61.6%) were women and 146 men (36.3%). Mean age 36.3 ± 16.15 for women and 35.5 ± 16.1 for men. The most commonly expressed fears were related to: invasive procedures, 30.9% (131 questions); fear of serious condition or misdiagnosis, 17.0% (72 questions); General concerns, 13.7% (58 questions); fear of worsening or relapse, 12.3% (52 questions); adverse effects of oral drugs or radiation, 10.8% (46 questions) and concerns related to lifestyle, 9.7% (41 questions).

CONCLUSIONS

Web scraping is a feasible strategy with which to explore medical forums and the above-mentioned themes, all of which are of potential clinical significance.

SIGNIFICANCE

Using automated algorithms, a total of 12,369 questions from online back and neck medical forums were scraped and analysed. Secondary analysis categorized fear-related themes that were mentioned by users. Identifying and addressing patients' fear has potential to improve communication and therapeutic outcome. For example, questions regarding surgery were typically asked after the option was mentioned by a physician. This insight should encourage physicians to devote extra time explaining the possible implications of surgery, should they consider it as an option.

Enhancing clinician and patient understanding of radiology reports: a scoping review of international guidelines

Imaging reports are the primary method of communicating diagnostic imaging findings between the radiologist and the referring clinician. Guidelines produced by professional bodies provide guidance on content and format of imaging reports, but the extent to which they consider comprehensibility for referring clinicians and their patients is unclear. The objective of this review was to determine the extent to which radiology reporting guidelines consider comprehensibility of imaging reports for referring clinicians and patients.We performed a scoping review of English-language diagnostic imaging reporting guidelines. We searched electronic databases (OVID MEDLINE, Embase) and websites of radiological professional organisations to identify guidelines. The extent to which the guidelines recommended essential report features such as technical information, content, format and language, as well as features to enhance comprehensibility, such as lay language summaries, was recorded.Six guidelines from professional bodies representing radiologists from the USA, Canada, Australia and New Zealand, Hong Kong, the UK and Europe were identified from the search. Inconsistencies exist between guidelines in their recommendations, and they rarely consider that patients may read the report. No guideline made recommendations about the reporting of results considering the clinical context, and none recommended features preferred by patients such as lay language summaries. This review identifies an opportunity for future radiology reporting guidelines to give greater consideration to referring clinician and patient preferences.

Understanding Patient Information Needs About Their Clinical Laboratory Results: A Study of Social Q&A Site

Clinical data, such as laboratory test results, is increasingly being made available to patients through patient portals. However, patients often have difficulties understanding and acting upon the clinical data presented in portals. As such, many turn to online resources to fill their knowledge gaps and obtain actionable advice. In this work, we present a content analysis of the questions posted in a major social Q&A site to characterize lay people's general information needs concerning laboratory test results and to inform the design of patient portals for supporting patients' understanding of clinical data. We identified 15 information needs related to laboratory test results, and clustered them under four themes: understanding the results of lab test, interpreting doctor's diagnosis, learning about lab tests, and consulting the next steps. We draw on our findings to discuss design opportunities for supporting the understanding of laboratory results.

Supporting Collaborative Health Tracking in the Hospital: Patients' Perspectives

The hospital setting creates a high-stakes environment where patients' lives depend on accurate tracking of health data. Despite recent work emphasizing the importance of patients' engagement in their own health care, less is known about how patients track their health and care in the hospital. Through interviews and design probes, we investigated hospitalized patients' tracking activity and analyzed our results using the stage-based personal informatics model. We used this model to understand how to support the tracking needs of hospitalized patients at each stage. In this paper, we discuss hospitalized patients' needs for collaboratively tracking their health with their care team. We suggest future extensions of the stage-based model to accommodate collaborative tracking situations, such as hospitals, where data is collected, analyzed, and acted on by multiple people. Our findings uncover new directions for HCI research and highlight ways to support patients in tracking their care and improving patient safety.