Voice Assistants for Mental Health Services: Designing Dialogues with Homebound Older Adults.

The number of older adults who are homebound with depressive symptoms is increasing. Due to their homebound status, they have limited access to trained mental healthcare support, which leaves this support often to untrained family caregivers. To increase access, a growing interest is placed on using technology-mediated solutions, such as voice-assisted intelligent personal assistants (VIPAs), to deliver mental health services to older adults. To better understand how older adults and family caregivers intend to interact with a VIPA for mental health interventions, we conducted a participatory design study during which 6 older adults and 7 caregivers designed VIPA-human dialogues for various scenarios. Using conversation style preferences as a starting point, we present aspects of human-likeness older adults and family caregivers perceived as helpful or uncanny, specifically in the context of the delivery of mental health interventions, which helps inform potential roles VIPAs can play in mental healthcare for older adults.

Scalable Telehealth Cancer Care: integrated healthy lifestyle program to live well after cancer treatment.

Northwestern University's Center for Scalable Telehealth Cancer Care (STELLAR) is 1 of 4 Cancer Moonshot Telehealth Research Centers of Excellence programs funded by the National Cancer Institute to establish an evidence base for telehealth in cancer care. STELLAR is grounded in the Institute of Medicine's vision that quality cancer care includes not only disease treatment but also promotion of long-term health and quality of life (QOL). Cigarette smoking, insufficient physical activity, and overweight and obesity often co-occur and are associated with poorer treatment response, heightened recurrence risk, decreased longevity, diminished QOL, and increased treatment cost for many cancers. These risk behaviors are prevalent in cancer survivors, but their treatment is not routinely integrated into oncology care. STELLAR aims to foster patients' long-term health and QOL by designing, implementing, and sustaining a novel telehealth treatment program for multiple risk behaviors to be integrated into standard cancer care. Telehealth delivery is evidence-based for health behavior change treatment and is well suited to overcome access and workflow barriers that can otherwise impede treatment receipt. This paper describes STELLAR's 2-arm randomized parallel group pragmatic clinical trial comparing telehealth-delivered, coach-facilitated multiple risk behavior treatment vs self-guided usual care for the outcomes of reach, effectiveness, and cost among 3000 cancer survivors who have completed curative intent treatment. This paper also discusses several challenges encountered by the STELLAR investigative team and the adaptations developed to move the research forward.

Perceptions of Wearable Health Tools Post the COVID-19 Emergency in Low-Income Latin Communities: Qualitative Study.

BACKGROUND: Mobile health (mHealth) wearable devices are increasingly being adopted by individuals to help manage and monitor physiological signals. However, the current state of wearables does not consider the needs of racially minoritized low-socioeconomic status (SES) communities regarding usability, accessibility, and price. This is a critical issue that necessitates immediate attention and resolution. OBJECTIVE: This study's aims were 3-fold, to (1) understand how members of minoritized low-SES communities perceive current mHealth wearable devices, (2) identify the barriers and facilitators toward adoption, and (3) articulate design requirements for future wearable devices to enable equitable access for these communities. METHODS: We performed semistructured interviews with low-SES Hispanic or Latine adults (N=19) from 2 metropolitan cities in the Midwest and West Coast of the United States. Participants were asked questions about how they perceive wearables, what are the current benefits and barriers toward use, and what features they would like to see in future wearable devices. Common themes were identified and analyzed through an exploratory qualitative approach. RESULTS: Through qualitative analysis, we identified 4 main themes. Participants' perceptions of wearable devices were strongly influenced by their COVID-19 experiences. Hence, the first theme was related to the impact of COVID-19 on the community, and how this resulted in a significant increase in interest in wearables. The second theme highlights the challenges faced in obtaining adequate health resources and how this further motivated participants' interest in health wearables. The third theme focuses on a general distrust in health care infrastructure and systems and how these challenges are motivating a need for wearables. Lastly, participants emphasized the pressing need for community-driven design of wearable technologies. CONCLUSIONS: The findings from this study reveal that participants from underserved communities are showing emerging interest in using health wearables due to the COVID-19 pandemic and health care access issues. Yet, the needs of these individuals have been excluded from the design and development of current devices.

From formative design to service-ready therapeutic: A pragmatic approach to designing digital mental health interventions across domains.

As digital mental health interventions (DMHIs) proliferate, there is a growing need to understand the complexities of moving these tools from concept and design to service-ready products. We highlight five case studies from a center that specializes in the design and evaluation of digital mental health interventions to illustrate pragmatic approaches to the development of digital mental health interventions, and to make transparent some of the key decision points researchers encounter along the design-to-product pipeline. Case studies cover different key points in the design process and focus on partnership building, understanding the problem or opportunity, prototyping the product or service, and testing the product or service. We illustrate lessons learned and offer a series of questions researchers can use to navigate key decision points in the digital mental health intervention (DMHI) development process.

Augmented Intelligence to Identify Patients With Advanced Heart Failure in an Integrated Health System.

BACKGROUND: Timely referral for specialist evaluation in patients with advanced heart failure (HF) is a Class 1 recommendation. However, the transition from stage C HF to advanced or stage D HF often goes undetected in routine care, resulting in delayed referral and higher mortality rates. OBJECTIVES: The authors sought to develop an augmented intelligence-enabled workflow using machine learning to identify patients with stage D HF and streamline referral. METHODS: We extracted data on HF patients with encounters from January 1, 2007, to November 30, 2020, from a HF registry within a regional, integrated health system. We created an ensemble machine learning model to predict stage C or stage D HF and integrated the results within the electronic health record. RESULTS: In a retrospective data set of 14,846 patients, the model had a good positive predictive value (60%) and low sensitivity (25%) for identifying stage D HF in a 100-person, physician-reviewed, holdout test set. During prospective implementation of the workflow from April 1, 2021, to February 15, 2022, 416 patients were reviewed by a clinical coordinator, with agreement between the model and the coordinator in 50.3% of stage D predictions. Twenty-four patients have been scheduled for evaluation in a HF clinic, 4 patients started an evaluation for advanced therapies, and 1 patient received a left ventricular assist device. CONCLUSIONS: An augmented intelligence-enabled workflow was integrated into clinical operations to identify patients with advanced HF. Endeavors such as this require a multidisciplinary team with experience in design thinking, informatics, quality improvement, operations, and health information technology, as well as dedicated resources to monitor and improve performance over time.

How machine-learning recommendations influence clinician treatment selections: the example of the antidepressant selection.

Decision support systems embodying machine learning models offer the promise of an improved standard of care for major depressive disorder, but little is known about how clinicians' treatment decisions will be influenced by machine learning recommendations and explanations. We used a within-subject factorial experiment to present 220 clinicians with patient vignettes, each with or without a machine-learning (ML) recommendation and one of the multiple forms of explanation. We found that interacting with ML recommendations did not significantly improve clinicians' treatment selection accuracy, assessed as concordance with expert psychopharmacologist consensus, compared to baseline scenarios in which clinicians made treatment decisions independently. Interacting with incorrect recommendations paired with explanations that included limited but easily interpretable information did lead to a significant reduction in treatment selection accuracy compared to baseline questions. These results suggest that incorrect ML recommendations may adversely impact clinician treatment selections and that explanations are insufficient for addressing overreliance on imperfect ML algorithms. More generally, our findings challenge the common assumption that clinicians interacting with ML tools will perform better than either clinicians or ML algorithms individually.

Usability Evaluation of an Adaptive Information Recommendation System for Breast Cancer Patients.

We report on the usability of a mobile application, MyPath, that connects patients with personalized information based on their diagnosis and care plan and adapts over time as they progress through the cancer trajectory. We conducted usability tests with cancer survivors and health professionals, measuring three usability factors which could be affected by adaptive content: learnability, errors, and effectiveness. Our results indicate that the adaptive information did not obstruct usability of the system. Participants identified several strengths of the application, including the integration of clinical and non-clinical information, the segmentation of a large information set to reduce mental burden, and the inclusion of multiple media types to accommodate different learning styles. Participants also identified potential barriers to use and offered ideas for future developments. We share how we integrated this feedback into the MyPath system design and reflect on lessons for future personal health information systems.