Can Patient Contributed Data (PCD) Leverage Connected Health Technology for Cardiac Rehabilitation in Austria?

New technologies such as devices, apps, smartphones, and sensors not only enable people to self-monitor their health but also share their health data with healthcare professionals. Data collection and dissemination occur across a wide variety of environments and settings, tracking everything from biometric data to mood and behavior, which has been termed Patient Contributed Data (PCD). In this work, we created a patient journey, enabled by PCD, to shape a connected health model for Cardiac Rehabilitation (CR) in Austria. Consequently, we highlighted the potential PCD benefit, which is a postulated increasing uptake of CR and improved patient outcomes through apps in a home-based setting. Finally, we addressed the related challenges and policy barriers that hinder the implementation of CR-connected health in Austria and identified actions to be taken.

A guiding framework for creating a comprehensive strategy for mHealth data sharing, privacy, and governance in low- and middle-income countries (LMICs).

With the numerous advances and broad applications of mobile health (mHealth), establishing concrete data sharing, privacy, and governance strategies at national (or regional) levels is essential to protect individual privacy and data usage. This article applies the recent Health Data Governance Principles to provide a guiding framework for low- and middle-income countries (LMICs) to create a comprehensive mHealth data governance strategy. We provide three objectives: (1) establish data rights and ownership to promote equitable benefits from health data, (2) protect people through building trust and addressing patients' concerns, and (3) promote health value by enhancing health systems and services. We also recommend actions for realizing each objective to guide LMICs based on their unique mHealth data ecosystems. These objectives require adopting a regulatory framework for data rights and protection, building trust for data sharing, and enhancing interoperability to use new datasets in advancing healthcare services and innovation.

Auditing Learned Associations in Deep Learning Approaches to Extract Race and Ethnicity from Clinical Text.

Complete and accurate race and ethnicity (RE) patient information is important for many areas of biomedical informatics research, such as defining and characterizing cohorts, performing quality assessments, and identifying health inequities. Patient-level RE data is often inaccurate or missing in structured sources, but can be supplemented through clinical notes and natural language processing (NLP). While NLP has made many improvements in recent years with large language models, bias remains an often-unaddressed concern, with research showing that harmful and negative language is more often used for certain racial/ethnic groups than others. We present an approach to audit the learned associations of models trained to identify RE information in clinical text by measuring the concordance between model-derived salient features and manually identified RE-related spans of text. We show that while models perform well on the surface, there exist concerning learned associations and potential for future harms from RE-identification models if left unaddressed.

The messiness of the menstruator: assessing personas and functionalities of menstrual tracking apps.

OBJECTIVE: The aim of this study was to examine trends in the intended users and functionalities advertised by menstrual tracking apps to identify gaps in personas and intended needs fulfilled by these technologies. MATERIALS AND METHODS: Two types of materials were collected: a corpus of scientific articles related to the identities and needs of menstruators and a corpus of images and descriptions of menstrual tracking apps collected from the Google and Apple app stores. We conducted a scoping review of the literature to develop themes and then applied these as a framework to analyze the app corpus, looking for alignments and misalignments between the 2 corpora. RESULTS: A review of the literature showed a wide range of disciplines publishing work relevant to menstruators. We identified 2 broad themes: "who are menstruators?" and "what are the needs of menstruators?" Descriptions of menstrual trackers exhibited misalignments with these themes, with narrow characterizations of menstruators and design for limited needs. DISCUSSION: We synthesize gaps in the design of menstrual tracking apps and discuss implications for designing around: (1) an irregular menstrual cycle as the norm; (2) the embodied, leaky experience of menstruation; and (3) the varied biologies, identities, and goals of menstruators. An overarching gap suggests a need for a human-centered artificial intelligence approach for model and data provenance, transparency and explanations of uncertainties, and the prioritization of privacy in menstrual trackers. CONCLUSION: Comparing and contrasting literature about menstruators and descriptions of menstrual tracking apps provide a valuable guide to assess menstrual technology and their responsiveness to users and their needs.

Divided We Stand: The Collaborative Work of Patients and Providers in an Enigmatic Chronic Disease.

In chronic conditions, patients and providers need support in understanding and managing illness over time. Focusing on endometriosis, an enigmatic chronic condition, we conducted interviews with specialists and focus groups with patients to elicit their work in care specifically pertaining to dealing with an enigmatic disease, both independently and in partnership, and how technology could support these efforts. We found that the work to care for the illness, including reflecting on the illness experience and planning for care, is significantly compounded by the complex nature of the disease: enigmatic condition means uncertainty and frustration in care and management; the multi-factorial and systemic features of endometriosis without any guidance to interpret them overwhelm patients and providers; the different temporal resolutions of this chronic condition confuse both patients and provides; and patients and providers negotiate medical knowledge and expertise in an attempt to align their perspectives. We note how this added complexity demands that patients and providers work together to find common ground and align perspectives, and propose three design opportunities (considerations to construct a holistic picture of the patient, design features to reflect and make sense of the illness, and opportunities and mechanisms to correct misalignments and plan for care) and implications to support patients and providers in their care work. Specifically, the enigmatic nature of endometriosis necessitates complementary approaches from human-centered computing and artificial intelligence, and thus opens a number of future research avenues.

Data Quality of Chemotherapy-Induced Nausea and Vomiting Documentation.

OBJECTIVE: The objective of the study was to characterize the completeness and concordance of the electronic health record (EHR) documentation of cancer symptoms among multidisciplinary health care professionals. METHODS: We examined the EHRs of children, adolescents, and young adults who received highly emetogenic chemotherapy and characterized the completeness and concordance of chemotherapy-induced nausea and vomiting (CINV) documentation by clinician type and by the International Classification of Diseases 10th Revision (ICD-10) coding choice. RESULTS: The EHRs of 127 patients, comprising 870 patient notes, were abstracted and reviewed. A CINV assessment was documented by prescribers in 75% of patients, and by nurses in 58% of patients. Of the 60 encounters where both prescribers and nurses documented, 72% agreed on the presence/absence of CINV. CONCLUSION: Most patients receiving highly emetogenic chemotherapy had a documented assessment of CINV; however, many had incomplete or discordant documentation of CINV from different providers by role, implying the importance of incorporating pragmatic knowledge of EHR documentation patterns among multidisciplinary health professionals for EHR phenotyping and clinical decision support systems directed toward cancer-related symptom management.

Cognitive Function Characterization Using Electronic Health Records Notes.

Cognitive impairment is a defining feature of neurological disorders such as Alzheimer's disease (AD), one of the leading causes of disability and mortality in the elderly population. Assessing cognitive impairment is important for diagnostic, clinical management, and research purposes. The Folstein Mini-Mental State Examination (MMSE) is the most common screening measure of cognitive function, yet this score is not consistently available in the electronic health records. We conducted a pilot study to extract frequently used concepts characterizing cognitive function from the clinical notes of AD patients in an Aging and Dementia clinical practice. Then we developed a model to infer the severity of cognitive impairment and created a subspecialized taxonomy for concepts associated with MMSE scores. We evaluated the taxonomy and the severity prediction model and presented example use cases of this model.

Augmenting the Clinical Data Sources for Enigmatic Diseases: A Cross-Sectional Study of Self-Tracking Data and Clinical Documentation in Endometriosis.

BACKGROUND: Self-tracking through mobile health technology can augment the electronic health record (EHR) as an additional data source by providing direct patient input. This can be particularly useful in the context of enigmatic diseases and further promote patient engagement. OBJECTIVES: This study aimed to investigate the additional information that can be gained through direct patient input on poorly understood diseases, beyond what is already documented in the EHR. METHODS: This was an observational study including two samples with a clinically confirmed endometriosis diagnosis. We analyzed data from 6,925 women with endometriosis using a research app for tracking endometriosis to assess prevalence of self-reported pain problems, between- and within-person variability in pain over time, endometriosis-affected tasks of daily function, and self-management strategies. We analyzed data from 4,389 patients identified through a large metropolitan hospital EHR to compare pain problems with the self-tracking app and to identify unique data elements that can be contributed via patient self-tracking. RESULTS: Pelvic pain was the most prevalent problem in the self-tracking sample (57.3%), followed by gastrointestinal-related (55.9%) and lower back (49.2%) pain. Unique problems that were captured by self-tracking included pain in ovaries (43.7%) and uterus (37.2%). Pain experience was highly variable both across and within participants over time. Within-person variation accounted for 58% of the total variance in pain scores, and was large in magnitude, based on the ratio of within- to between-person variability (0.92) and the intraclass correlation (0.42). Work was the most affected daily function task (49%), and there was significant within- and between-person variability in self-management effectiveness. Prevalence rates in the EHR were significantly lower, with abdominal pain being the most prevalent (36.5%). CONCLUSION: For enigmatic diseases, patient self-tracking as an additional data source complementary to EHR can enable learning from the patient to more accurately and comprehensively evaluate patient health history and status.

Eye-tracking retrospective think-aloud as a novel approach for a usability evaluation.

OBJECTIVE: To report on the use of an eye-tracking retrospective think-aloud for usability evaluation and to describe its application in assessing the usability of a mobile health app. MATERIALS AND METHODS: We used an eye-tracking retrospective think-aloud to evaluate the usability of an HIV prevention mobile app among 20 young men (15-18 years) in New York City, NY; Birmingham, AL; and Chicago, IL. Task performance metrics, critical errors, a task completion rate per participant, and a task completion rate per task, were measured. Eye-tracking metrics including fixation, saccades, time to first fixation, time spent, and revisits were measured and compared among participants with/without a critical error. RESULTS: Using task performance analysis, we identified 19 critical errors on four activities, and of those, two activities had a task completion rate of less than 78%. To better understand these usability issues, we thoroughly analyzed participants' corresponding eye movements and verbal comments using an in-depth problem analysis. In areas of interest created for the activity with critical usability problems, there were significant differences in time spent (p = 0.008), revisits (p = 0.004), and total numbers of fixations (p = 0.007) by participants with/without a critical error. The overall mean score of perceived usability rated by the Health IT Usability Evaluation Scale was 4.64 (SD = 0.33), reflecting strong usability of the app. DISCUSSION AND CONCLUSION: An eye-tracking retrospective think-aloud enabled us to identify critical usability problems as well as gain an in-depth understanding of the usability issues related to interactions between end-users and the app. Findings from this study highlight the utility of an eye-tracking retrospective think-aloud in consumer health usability evaluation research.

A Mobile Health Intervention for HIV Prevention Among Racially and Ethnically Diverse Young Men: Usability Evaluation.

BACKGROUND: Mobile health (mHealth) apps have the potential to be a useful mode of delivering HIV prevention information, particularly for young men (13-24 years) who account for 21% of new HIV diagnoses in the United States. We translated an existing evidence-based, face-to-face HIV prevention curriculum into a portable platform and developed a mobile Web app: MyPEEPS Mobile. OBJECTIVE: The purpose of this study was to assess the usability of MyPEEPS Mobile from both expert and end user perspectives. METHODS: We conducted a heuristic evaluation with five experts in informatics to identify violations of usability principles and end user usability testing with 20 young men aged 15 to 18 years in New York, NY, Birmingham, AL, and Chicago, IL to identify potential obstacles to their use of the app. RESULTS: Mean scores of the overall severity of the identified heuristic violations rated by experts ranged from 0.4 and 2.6 (0=no usability problem to 4=usability catastrophe). Overall, our end users successfully completed the tasks associated with use case scenarios and provided comments/recommendations on improving usability of MyPEEPS Mobile. The mean of the overall Post-Study System Usability Questionnaire scores rated by the end users was 1.63 (SD 0.65), reflecting strong user acceptance of the app. CONCLUSIONS: The comments made by experts and end users will be used to refine MyPEEPS Mobile prior to a pilot study assessing the acceptability of the app across diverse sexual minority young men in their everyday lives.

Usability Testing of a mHealth App to Support Self-Management of HIV-Associated Non-AIDS Related Symptoms.

Mobile health (mHealth) applications (apps) are uniquely poised to offer the information that persons living with HIV (PLWH) need to manage the symptoms associated with their chronic condition. The purpose of this study was to assess the usability of a mHealth app designed to help PLWH self-man-age the symptoms associated with their HIV and HIV-associated non-AIDS (HANA) conditions. We conducted a heuristic evaluation with five experts in informatics and end-user testing with 20 PLWH. End-users completed the PSSUQ and Health-ITUES validated measures of system usability. Mean severity scores for the 10-item heuristic checklist com-pleted by experts ranged from 0.4-2.4. End-users gave the system high scores on the PSSUQ and Health-ITUES usability measures (mean 2.23 ± 0.83 and 4.24 ± 0.62 respectively). Results indicated the system is usable and will be ready for future efficacy testing after incorporation of recommended feedback.

Mobile Health Technology for Improving Symptom Management in Low Income Persons Living with HIV.

Persons living with HIV (PLWH) are living longer but experiencing more adverse symptoms associated with the disease and its treatment. This study aimed to examine the impact of a mHealth application (app) comprised of evidence-based self-care strategies on the symptom experience of PLWH. We conducted a 12-week feasibility study with 80 PLWH who were randomized (1:1) to a mHealth app, mobile Video Information Provider (mVIP), with self-care strategies for improving 13 commonly experienced symptoms in PLWH or to a control app. Intervention group participants showed a significantly greater improvement than the control group in 5 symptoms: anxiety (p = 0.001), depression (p = 0.001), neuropathy (p = 0.002), fever/chills/sweat (p = 0.037), and weight loss/wasting (p = 0.020). Participants in the intervention group showed greater improvement in adherence to their antiretroviral medications (p = 0.017) as compared to those in the control group. In this 12-week trial, mVIP was associated with improved symptom burden and increased medication adherence in PLWH.