Utilizing Digital Health to Collect Electronic Patient-Reported Outcomes in Prostate Cancer: Single-Arm Pilot Trial

Background: Measuring patient-reported outcomes (PROs) requires an individual’s perspective on their symptoms, functional status, and quality of life. Digital health enables remote electronic PRO (ePRO) assessments as a clinical decision support tool to facilitate meaningful provider interactions and personalized treatment. Objective: This study explored the feasibility and acceptability of collecting ePROs using validated health-related quality of life (HRQoL) questionnaires for prostate cancer. Methods: Using Apple ResearchKit software, the Strength Through Insight app was created with content from validated HRQoL tools 26-item Expanded Prostate Cancer Index Composite (EPIC) or EPIC for Clinical Practice and 8-item Functional Assessment of Cancer Therapy Advanced Prostate Symptom Index. In a single-arm pilot study with patients receiving prostate cancer treatment at Thomas Jefferson University Hospital and affiliates, participants were recruited, and instructed to download Strength Through Insight and complete ePROs once a week over 12 weeks. A mixed methods approach, including qualitative pre- and poststudy interviews, was used to evaluate the feasibility and acceptability of Strength Through Insight for the collection and care management of cancer treatment. Results: Thirty patients consented to the study; 1 patient failed to complete any of the questionnaires and was left out of the analysis of the intervention. Moreover, 86% (25/29) reached satisfactory questionnaire completion (defined as completion of 60% of weekly questions over 12 weeks). The lower bound of the exact one-sided 95% CI was 71%, exceeding the 70% feasibility threshold. Most participants self-identified with having a high digital literacy level (defined as the ability to use, understand, evaluate, and analyze information from multiple formats from a variety of digital sources), and only a few participants identified with having a low digital literacy level (defined as only having the ability to gather information on the Web). Interviews were thematically analyzed to reveal the following: (1) value of emotional support and wellness in cancer treatment, (2) rise of social patient advocacy in online patient communities and networks, (3) patient concerns over privacy, and (4) desire for personalized engagement tools. Conclusions: Strength Through Insight was demonstrated as a feasible and acceptable method of data collection for ePROs. A high compliance rate confirmed the app as a reliable tool for patients with localized and advanced prostate cancer. Nearly all participants reported that using the smartphone app is easier than or equivalent to the traditional paper-and-pen approach, providing evidence of acceptability and support for the use of remote PRO monitoring. This study expands on current research involving the value of digital health, as a social and behavioral science, augmented with technology, can begin to contribute to population health management, as it shapes psychographic segmentation by demographic, socioeconomic, health condition, or behavioral factors to group patients by their distinct personalities and motivations, which influence their choices

Building the case for actionable ethics in digital health research supported by artificial intelligence

The digital revolution is disrupting the ways in which health research is conducted, and subsequently, changing healthcare. Direct-to-consumer wellness products and mobile apps, pervasive sensor technologies and access to social network data offer exciting opportunities for researchers to passively observe and/or track patients ‘in the wild’ and 24/7. The volume of granular personal health data gathered using these technologies is unprecedented, and is increasingly leveraged to inform personalized health promotion and disease treatment interventions. The use of artificial intelligence in the health sector is also increasing. Although rich with potential, the digital health ecosystem presents new ethical challenges for those making decisions about the selection, testing, implementation and evaluation of technologies for use in healthcare. As the ‘Wild West’ of digital health research unfolds, it is important to recognize who is involved, and identify how each party can and should take responsibility to advance the ethical practices of this work. While not a comprehensive review, we describe the landscape, identify gaps to be addressed, and offer recommendations as to how stakeholders can and should take responsibility to advance socially responsible digital health research

Quality Assurance in Telehealth: Adherence to Evidence-Based Indicators.

Background: Value enhancing telehealth (TH) lacks a robust body of formal clinically focused quality assessment studies. Innovations such as telehealth must always demonstrate that it preserves or hopefully advances quality. Introduction: We sought to determine whether adherence to the evidence-based Choosing Wisely (CW) recommendations (antibiotic stewardship) for acute sinusitis differs for encounters through direct-to-consumer (DTC) telemedicine verses in-person care in an emergency department (ED) or an urgent care (UC) center. Materials and Methods: Study design was a retrospective review. Patients with a symptom complex consistent with acute sinusitis treated through DTC were matched with ED and UC patients, based upon time of visit. Charts were reviewed to determine patient characteristics, chief complaint, final diagnosis, presence or absence of criteria within the CW guidelines, and whether or not antibiotics were prescribed. The main outcome was adherence to the CW campaign recommendations. Results: A total of 570 visits were studied: 190 DTC, 190 ED, and 190 UC visits. The predominant chief complaints were upper respiratory infection (36%), sore throat (25%), and sinusitis (18%). Overall, there was a 67% (95% CI 62.3-71.7) adherence rate with the CW guidelines for sinusitis: DTC visits (71%), ED visits (68%), and UC visits (61%). There was a nonsignificant difference (p = 0.29) in adherence to CW guidelines based upon type of visit (DTC, UC, and ED). Discussion: The challenge is to demonstrate whether or not DTC TH compromises quality. Conclusion: In this study, DTC visits were associated with at least as good an adherence to the CW campaign recommendations as emergency medicine (EM) and UC in-person visits. © Daniel Halpren-Ruder et al

Between empowerment and self-discipline: governing patients' conduct through technological self-care

Recent health policy renders patients increasingly responsible for managing their health via digital technology such as health apps and online patient platforms. This paper discusses underlying tensions between empowerment and self-discipline embodied in discourses of technological self-care. It presents findings from documentary analysis and interviews with key players in the English digital health context including policy makers, health designers and patient organisations. We show how discourses ascribe to patients an enterprising identity, which is inculcated with economic interests and engenders self-discipline. However, this reading does not capture all implications of technological self-care. A governmentality lens also shows that technological self-care opens up the potential for a de-centring of medical knowledge and its subsequent communalization. The paper contributes to Foucauldian healthcare scholarship by showing how technology could engender agential actions that operate at the margins of an enterprising discourse

Implementing digital resources for clinicians' and patients' varying needs.

This paper presents an overview of several evidence-based medicine and patient information studies conducted across the UK health service over a 4 year period, investigating clinicians', managers', and patients' perceptions of digital resources (primarily digital libraries) in hospitals, Primary Care Trusts, NHS Direct (patient call centre) and patient groups. In-depth interviews and focus groups are analysed using grounded theory methodologies and through content analysis used to produce quantitative finding. The perceived impacts of three different methods employed for delivering health informatics are presented. The findings highlight some generic issues relevant for health informatics in the UK health sector as well as some specific issues for medical digital libraries. This paper reviews in more detail the issues of medical technology implementation (traditional implementation, on the wards, and intermediaries within in communities). A breakdown of the clinicians' and patients' information journey (information initiation, facilitation and interpretation) is also presented with regard to medical digital libraries and online resources. Broad guidelines derived from these findings are provided for health-informatics deployment

Targeting medication non-adherence behavior in selected autoimmune diseases: a systematic approach to digital health program development

Background 29 autoimmune diseases, including Rheumatoid Arthritis, gout, Crohn’s Disease, and Systematic Lupus Erythematosus affect 7.6-9.4% of the population. While effective therapy is available, many patients do not follow treatment or use medications as directed. Digital health and Web 2.0 interventions have demonstrated much promise in increasing medication and treatment adherence, but to date many Internet tools have proven disappointing. In fact, most digital interventions continue to suffer from high attrition in patient populations, are burdensome for healthcare professionals, and have relatively short life spans. Objective Digital health tools have traditionally centered on the transformation of existing interventions (such as diaries, trackers, stage-based or cognitive behavioral therapy programs, coupons, or symptom checklists) to electronic format. Advanced digital interventions have also incorporated attributes of Web 2.0 such as social networking, text messaging, and the use of video. Despite these efforts, there has not been little measurable impact in non-adherence for illnesses that require medical interventions, and research must look to other strategies or development methodologies. As a first step in investigating the feasibility of developing such a tool, the objective of the current study is to systematically rate factors of non-adherence that have been reported in past research studies. Methods Grounded Theory, recognized as a rigorous method that facilitates the emergence of new themes through systematic analysis, data collection and coding, was used to analyze quantitative, qualitative and mixed method studies addressing the following autoimmune diseases: Rheumatoid Arthritis, gout, Crohn’s Disease, Systematic Lupus Erythematosus, and inflammatory bowel disease. Studies were only included if they contained primary data addressing the relationship with non-adherence. Results Out of the 27 studies, four non-modifiable and 11 modifiable risk factors were discovered. Over one third of articles identified the following risk factors as common contributors to medication non-adherence (percent of studies reporting): patients not understanding treatment (44%), side effects (41%), age (37%), dose regimen (33%), and perceived medication ineffectiveness (33%). An unanticipated finding that emerged was the need for risk stratification tools (81%) with patient-centric approaches (67%). Conclusions This study systematically identifies and categorizes medication non-adherence risk factors in select autoimmune diseases. Findings indicate that patients understanding of their disease and the role of medication are paramount. An unexpected finding was that the majority of research articles called for the creation of tailored, patient-centric interventions that dispel personal misconceptions about disease, pharmacotherapy, and how the body responds to treatment. To our knowledge, these interventions do not yet exist in digital format. Rather than adopting a systems level approach, digital health programs should focus on cohorts with heterogeneous needs, and develop tailored interventions based on individual non-adherence patterns

Sentiment analysis of health care tweets: review of the methods used.

BACKGROUND: Twitter is a microblogging service where users can send and read short 140-character messages called "tweets." There are several unstructured, free-text tweets relating to health care being shared on Twitter, which is becoming a popular area for health care research. Sentiment is a metric commonly used to investigate the positive or negative opinion within these messages. Exploring the methods used for sentiment analysis in Twitter health care research may allow us to better understand the options available for future research in this growing field. OBJECTIVE: The first objective of this study was to understand which tools would be available for sentiment analysis of Twitter health care research, by reviewing existing studies in this area and the methods they used. The second objective was to determine which method would work best in the health care settings, by analyzing how the methods were used to answer specific health care questions, their production, and how their accuracy was analyzed. METHODS: A review of the literature was conducted pertaining to Twitter and health care research, which used a quantitative method of sentiment analysis for the free-text messages (tweets). The study compared the types of tools used in each case and examined methods for tool production, tool training, and analysis of accuracy. RESULTS: A total of 12 papers studying the quantitative measurement of sentiment in the health care setting were found. More than half of these studies produced tools specifically for their research, 4 used open source tools available freely, and 2 used commercially available software. Moreover, 4 out of the 12 tools were trained using a smaller sample of the study's final data. The sentiment method was trained against, on an average, 0.45% (2816/627,024) of the total sample data. One of the 12 papers commented on the analysis of accuracy of the tool used. CONCLUSIONS: Multiple methods are used for sentiment analysis of tweets in the health care setting. These range from self-produced basic categorizations to more complex and expensive commercial software. The open source and commercial methods are developed on product reviews and generic social media messages. None of these methods have been extensively tested against a corpus of health care messages to check their accuracy. This study suggests that there is a need for an accurate and tested tool for sentiment analysis of tweets trained using a health care setting-specific corpus of manually annotated tweets first

Beyond the trial: A systematic review of real-world uptake and engagement with digital self-help interventions for depression, low mood, or anxiety

Background: Digital self-help interventions (including online or computerized programs and apps) for common mental health issues have been shown to be appealing, engaging, and efficacious in randomized controlled trials. They show potential for improving access to therapy and improving population mental health. However, their use in the real world, that is, as implemented (disseminated) outside of research settings, may differ from that reported in trials, and implementation data are seldom reported. Objective: We aimed to review peer-reviewed articles reporting user uptake and/or ongoing use, retention, or completion data (hereafter ‘usage data’ or, for brevity, ‘engagement’) from implemented pure self-help (unguided) digital interventions for depression, anxiety, or the enhancement of mood. Methods: We conducted a systematic search of the Scopus, Embase, MEDLINE, and PsychINFO databases for studies reporting user uptake and/or usage data from implemented digital self-help interventions for the treatment or prevention of depression or anxiety, or the enhancement of mood, from 2002 to 2017. Additionally, we screened the reference lists of included articles, citations of these articles, and the titles of articles published in Internet Interventions, Journal of Medical Internet Research (JMIR), and JMIR Mental Health since their inception. We extracted data indicating the number of registrations or downloads and usage of interventions. Results: After the removal of duplicates, 970 papers were identified, of which ten met the inclusion criteria. Hand-searching identified one additional article. The included articles reported on seven publically available interventions. There was little consistency in the measures reported. The number of registrants or downloads ranged widely, from eight to over 40,000 per month. From 21% to 88% of users engaged in at least minimal use (e.g. used the intervention at least once or completed one module or assessment), while 7–42% engaged in moderate use (completing between 40% and 60% of modular fixed-length programs or continuing to use apps after four weeks). Indications of completion or sustained use (completion of all modules or the last assessment or continuing to use apps after six weeks or more) varied from 0.5% to 28.6%. Conclusions: Available data suggest that uptake and engagement vary widely among the handful of implemented digital self-help apps and programs which have reported this, and that usage may vary from that reported in trials. Implementation data should be routinely gathered and reported to facilitate improved uptake and engagement, arguably among the major challenges in digital health