Impact of Digital Literacy Training on Outcomes for People With Serious Mental Illness in Community and Inpatient Settings

OBJECTIVE

The authors explored whether a digital literacy program, Digital Outreach for Obtaining Resources and Skills (DOORS), could improve self-reported functional skills and clinical outcomes among people with serious mental illness.

METHODS

The 8-week program was offered to participants receiving treatment in community mental health centers (N=113) and an inpatient psychiatric unit (N=74). Pre- and postintervention self-report surveys were collected. Descriptive statistics and two-tailed t tests were used for analysis.

RESULTS

For patients treated in a community center, improvements were observed in 27 of the 29 self-reported functional skills that measured digital literacy. Changes in seven of these skills were statistically significant. Although these participants reported larger improvements in clinical outcomes than did inpatient participants, no statistically significant changes in symptoms were seen in either setting.

CONCLUSIONS

Digital skills training is necessary to increase access to care through technology. DOORS can improve self-reported digital literacy, but further research is necessary to determine its immediate impact on symptoms.

Focusing on Digital Research Priorities for Advancing the Access and Quality of Mental Health

Digital mental health solutions are now well recognized as critical to solving the global mental health crisis. As research accelerates, it is now clear that solutions ranging from computer-based therapy programs to virtual reality headsets and smartphone apps to large language model chatbots are of interest, feasible, and hold exciting potential to improve mental health. This research should now consider the next generation of scientific and clinical questions regarding if these new approaches are equitable, valid, effective, implementable, efficacious, and even cost-effective. This paper outlines several of the new frontiers for the next generation of research and introduces JMIR Publications' partnership with the Society of Digital Psychiatry to further advance these aims.

Telemental health policies for college students during COVID-19

Federal and institutional policy changes have accelerated the use of telemental health to care for college students distant from their mental health providers during the early part of the COVID-19 pandemic. Temporary measures have made telemental health more readily available, including relaxing of regulations related to interstate licensure, controlled substance prescribing, patient privacy, and reimbursement. Though early efforts are underway to sustain these changes during and in the wake of the pandemic, there are important areas in which federal and institutional policy are still lacking. Additional steps are needed to successfully implement and sustain telemental health for college students include ensuring student access to technology and Internet; proactive outreach to optimize the student's home environment, addressing concerns about safety and confidentiality; developing the means to track rapidly shifting telemental health policy changes; and developing centralized resources that enable remote providers to become familiar with involuntary commitment laws and emergency protocols.

Clinical Considerations for Digital Resources in Care for Patients With Suicidal Ideation

Smartphone apps offer accessible new tools that may help prevent suicide and that offer support for individuals with active suicidal ideation. Numerous smartphone apps for mental health conditions exist; however, their functionality is limited, and evidence is nascent. A new generation of apps using smartphone sensors and integrating real-time data on evolving risk offers the potential of more personalized support, but these apps present ethical risks and currently remain more in the research domain than in the clinical domain. Nevertheless, clinicians can use apps to benefit patients. This article outlines practical strategies to select safe and effective apps for the creation of a digital toolkit that can augment suicide prevention and safety plans. By creating a unique digital toolkit for each patient, clinicians can help ensure that the apps selected will be most relevant, engaging, and effective.

Digital phenotyping of student mental health during COVID-19: an observational study of 100 college students

Objective: This study assessed the feasibility of capturing smartphone based digital phenotyping data in college students during the COVID-19 pandemic with the goal of understanding how digital biomarkers of behavior correlate with mental health. Participants: Participants were 100 students enrolled in 4-year universities. Methods: Each participant attended a virtual visit to complete a series of gold-standard mental health assessments, and then used a mobile app for 28 days to complete mood assessments and allow for passive collection of GPS, accelerometer, phone call, and screen time data. Students completed another virtual visit at the end of the study to collect a second round of mental health assessments. Results: In-app daily mood assessments were strongly correlated with their corresponding gold standard clinical assessment. Sleep variance among students was correlated to depression scores (ρ = .28) and stress scores (ρ = .27). Conclusions: Digital Phenotyping among college students is feasible on both an individual and a sample level. Studies with larger sample sizes are necessary to understand population trends, but there are practical applications of the data today.

Information and communication technology-based interventions for suicide prevention implemented in clinical settings: a scoping review

BACKGROUND

A large number of information and communication technology (ICT) based interventions exist for suicide prevention. However, not much is known about which of these ICTs are implemented in clinical settings and their implementation characteristics. In response, this scoping review aimed to systematically explore the breadth of evidence on ICT-based interventions for suicide prevention implemented in clinical settings and then to identify and characterize implementation barriers and facilitators, as well as evaluation outcomes, and measures.

METHODS

We conducted this review following the Joanna Briggs Institute methodology for scoping reviews. A search strategy was applied to the following six databases between August 17-20, 2021: MEDLINE, Embase, CINAHL, PsycINFO, Web of Science, and Library, Information Science and Technology Abstracts. We also supplemented our search with Google searches and hand-searching reference lists of relevant reviews. To be included in this review, studies must include ICT-based interventions for any spectrum of suicide-related thoughts and behaviours including non-suicidal self-injury. Additionally, these ICTs must be implemented in clinical settings, such as emergency department and in-patient units. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist to prepare this full report.

RESULTS

This review included a total of 75 citations, describing 70 studies and 66 ICT-based interventions for suicide prevention implemented in clinical settings. The majority of ICTs were computerized interventions and/or applications (n = 55). These ICTs were commonly used as indicated strategies (n = 49) targeting patients who were actively presenting with suicide risk. The three most common suicide prevention intervention categories identified were post-discharge follow-up (n = 27), screening and/or assessment (n = 22), and safety planning (n = 20). A paucity of reported information was identified related to implementation strategies, barriers and facilitators. The most reported implementation strategies included training, education, and collaborative initiatives. Barriers and facilitators of implementation included the need for resource supports, knowledge, skills, motivation as well as engagement with clinicians with research teams. Studies included outcomes at patient, clinician, and health system levels, and implementation outcomes included acceptability, feasibility, fidelity, and penetration.

CONCLUSION

This review presents several trends of the ICT-based interventions for suicide prevention implemented in clinical settings and identifies a need for future research to strengthen the evidence base for improving implementation. More effort is required to better understand and support the implementation and sustainability of ICTs in clinical settings. The findings can also serve as a future resource for researchers seeking to evaluate the impact and implementation of ICTs.

Monitoring sleep using smartphone data in a population of college students

Sleep is fundamental to all health, especially mental health. Monitoring sleep is thus critical to delivering effective healthcare. However, measuring sleep in a scalable way remains a clinical challenge because wearable sleep-monitoring devices are not affordable or accessible to the majority of the population. However, as consumer devices like smartphones become increasingly powerful and accessible in the United States, monitoring sleep using smartphone patterns offers a feasible and scalable alternative to wearable devices. In this study, we analyze the sleep behavior of 67 college students with elevated levels of stress over 28 days. While using the open-source mindLAMP smartphone app to complete daily and weekly sleep and mental health surveys, these participants also passively collected phone sensor data. We used these passive sensor data streams to estimate sleep duration. These sensor-based sleep duration estimates, when averaged for each participant, were correlated with self-reported sleep duration (r = 0.83). We later constructed a simple predictive model using both sensor-based sleep duration estimates and surveys as predictor variables. This model demonstrated the ability to predict survey-reported Pittsburgh Sleep Quality Index (PSQI) scores within 1 point. Overall, our results suggest that smartphone-derived sleep duration estimates offer practical results for estimating sleep duration and can also serve useful functions in the process of digital phenotyping.

Digital Phenotyping Models of Symptom Improvement in College Mental Health: Generalizability Across Two Cohorts

Smartphones can be used to gain insight into mental health conditions through the collection of survey and sensor data. However, the external validity of this digital phenotyping data is still being explored, and there is a need to assess if predictive models derived from this data are generalizable. The first dataset (V1) of 632 college students was collected between December 2020 and May 2021. The second dataset (V2) was collected using the same app between November and December 2021 and included 66 students. Students in V1 could enroll in V2. The main difference between the V1 and V2 studies was that we focused on protocol methods in V2 to ensure digital phenotyping data had a lower degree of missing data than in the V1 dataset. We compared survey response counts and sensor data coverage across the two datasets. Additionally, we explored whether models trained to predict symptom survey improvement could generalize across datasets. Design changes in V2, such as a run-in period and data quality checks, resulted in significantly higher engagement and sensor data coverage. The best-performing model was able to predict a 50% change in mood with 28 days of data, and models were able to generalize across datasets. The similarities between the features in V1 and V2 suggest that our features are valid across time. In addition, models must be able to generalize to new populations to be used in practice, so our experiments provide an encouraging result toward the potential of personalized digital mental health care.

Exploring how informed mental health app selection may impact user engagement and satisfaction

The prevalence of mental health app use by people suffering from mental health disorders is rapidly growing. The integration of mental health apps shows promise in increasing the accessibility and quality of treatment. However, a lack of continued engagement is one of the significant challenges of such implementation. In response, the M-health Index and Navigation Database (MIND)- derived from the American Psychiatric Association's app evaluation framework- was created to support patient autonomy and enhance engagement. This study aimed to identify factors influencing engagement with mental health apps and explore how MIND may affect user engagement around selected apps. We conducted a longitudinal online survey over six weeks after participants were instructed to find mental health apps using MIND. The survey included demographic information, technology usage, access to healthcare, app selection information, System Usability Scale, the Digital Working Alliance Inventory, and the General Self-Efficacy Scale questions. Quantitative analysis was performed to analyze the data. A total of 321 surveys were completed (178 at the initial, 90 at the 2-week mark, and 53 at the 6-week mark). The most influential factors when choosing mental health apps included cost (76%), condition supported by the app (59%), and app features offered (51%), while privacy and clinical foundation to support app claims were among the least selected filters. The top ten apps selected by participants were analyzed for engagement. Rates of engagement among the top-ten apps decreased by 43% from the initial to week two and 22% from week two to week six on average. In the context of overall low engagement with mental health apps, implementation of mental health app databases like MIND can play an essential role in maintaining higher engagement and satisfaction. Together, this study offers early data on how educational approaches like MIND may help bolster mental health apps engagement.

Patient Online Record Access in English Primary Care: Qualitative Survey Study of General Practitioners' Views

BACKGROUND

In 2022, NHS England announced plans to ensure that all adult primary care patients in England would have full online access to new data added to their general practitioner (GP) record. However, this plan has not yet been fully implemented. Since April 2020, the GP contract in England has already committed to offering patients full online record access on a prospective basis and on request. However, there has been limited research into UK GPs' experiences and opinions about this practice innovation.

OBJECTIVE

This study aimed to explore the experiences and opinions of GPs in England about patients' access to their full web-based health record, including clinicians' free-text summaries of the consultation (so-called "open notes").

METHODS

In March 2022, using a convenience sample, we administered a web-based mixed methods survey of 400 GPs in the United Kingdom to explore their experiences and opinions about the impact on patients and GPs' practices to offer patients full online access to their health records. Participants were recruited using the clinician marketing service Doctors.net.uk from registered GPs currently working in England. We conducted a qualitative descriptive analysis of written responses ("comments") to 4 open-ended questions embedded in a web-based questionnaire.

RESULTS

Of 400 GPs, 224 (56%) left comments that were classified into 4 major themes: increased strain on GP practices, the potential to harm patients, changes to documentation, and legal concerns. GPs believed that patient access would lead to extra work for them, reduced efficiency, and increased burnout. The participants also believed that access would increase patient anxiety and incur risks to patient safety. Experienced and perceived documentation changes included reduced candor and changes to record functionality. Anticipated legal concerns encompassed fears about increased litigation risks and lack of legal guidance to GPs about how to manage documentation that would be read by patients and potential third parties.

CONCLUSIONS

This study provides timely information on the views of GPs in England regarding patient access to their web-based health records. Overwhelmingly, GPs were skeptical about the benefits of access both for patients and to their practices. These views are similar to those expressed by clinicians in other countries, including Nordic countries and the United States before patient access. The survey was limited by the convenience sample, and it is not possible to infer that our sample was representative of the opinions of GPs in England. More extensive, qualitative research is required to understand the perspectives of patients in England after experiencing access to their web-based records. Finally, further research is needed to explore objective measures of the impact of patient access to their records on health outcomes, clinician workload, and changes to documentation.

Digital Phenotyping Data to Predict Symptom Improvement and Mental Health App Personalization in College Students: Prospective Validation of a Predictive Model

BACKGROUND

Mental health apps offer a transformative means to increase access to scalable evidence-based care for college students. Yet low rates of engagement currently preclude the effectiveness of these apps. One promising solution is to make these apps more responsive and personalized through digital phenotyping methods able to predict symptoms and offer tailored interventions.

OBJECTIVE

Following our protocol and using the exact model shared in that paper, our primary aim in this study is to assess the prospective validity of mental health symptom prediction using the mindLAMP app through a replication study. We also explored secondary aims around app intervention personalization and correlations of engagement with the Technology Acceptance Model (TAM) and Digital Working Alliance Inventory scale in the context of automating the study.

METHODS

The study was 28 days in duration and followed the published protocol, with participants collecting digital phenotyping data and being offered optional scheduled and algorithm-recommended app interventions. Study compensation was tied to the completion of weekly surveys and was not otherwise tied to engagement or use of the app.

RESULTS

The data from 67 participants were used in this analysis. The area under the curve values for the symptom prediction model ranged from 0.58 for the UCLA Loneliness Scale to 0.71 for the Patient Health Questionnaire-9. Engagement with the scheduled app interventions was high, with a study mean of 73%, but few participants engaged with the optional recommended interventions. The perceived utility of the app in the TAM was higher (P=.01) among those completing at least one recommended intervention.

CONCLUSIONS

Our results suggest how digital phenotyping methods can be used to create generalizable models that may help create more personalized and engaging mental health apps. Automating studies is feasible, and our results suggest targets to increase engagement in future studies.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/37954.

Digital mental health: challenges and next steps

Digital innovations in mental health offer great potential, but present unique challenges. Using a consensus development panel approach, an expert, international, cross-disciplinary panel met to provide a framework to conceptualise digital mental health innovations, research into mechanisms and effectiveness and approaches for clinical implementation. Key questions and outputs from the group were agreed by consensus, and are presented and discussed in the text and supported by case examples in an accompanying appendix. A number of key themes emerged. (1) Digital approaches may work best across traditional diagnostic systems: we do not have effective ontologies of mental illness and transdiagnostic/symptom-based approaches may be more fruitful. (2) Approaches in clinical implementation of digital tools/interventions need to be creative and require organisational change: not only do clinicians and patients need training and education to be more confident and skilled in using digital technologies to support shared care decision-making, but traditional roles need to be extended, with clinicians working alongside digital navigators and non-clinicians who are delivering protocolised treatments. (3) Designing appropriate studies to measure the effectiveness of implementation is also key: including digital data raises unique ethical issues, and measurement of potential harms is only just beginning. (4) Accessibility and codesign are needed to ensure innovations are long lasting. (5) Standardised guidelines for reporting would ensure effective synthesis of the evidence to inform clinical implementation. COVID-19 and the transition to virtual consultations have shown us the potential for digital innovations to improve access and quality of care in mental health: now is the ideal time to act.

Increasing the value of digital phenotyping through reducing missingness: a retrospective review and analysis of prior studies

BACKGROUND

Digital phenotyping methods present a scalable tool to realise the potential of personalised medicine. But underlying this potential is the need for digital phenotyping data to represent accurate and precise health measurements.

OBJECTIVE

To assess the impact of population, clinical, research and technological factors on the digital phenotyping data quality as measured by rates of missing digital phenotyping data.

METHODS

This study analyses retrospective cohorts of mindLAMP smartphone application digital phenotyping studies run at Beth Israel Deaconess Medical Center between May 2019 and March 2022 involving 1178 participants (studies of college students, people with schizophrenia and people with depression/anxiety). With this large combined data set, we report on the impact of sampling frequency, active engagement with the application, phone type (Android vs Apple), gender and study protocol features on missingness/data quality.

FINDINGS

Missingness from sensors in digital phenotyping is related to active user engagement with the application. After 3 days of no engagement, there was a 19% decrease in average data coverage for both Global Positioning System and accelerometer. Data sets with high degrees of missingness can generate incorrect behavioural features that may lead to faulty clinical interpretations.

CONCLUSIONS

Digital phenotyping data quality requires ongoing technical and protocol efforts to minimise missingness. Adding run-in periods, education with hands-on support and tools to easily monitor data coverage are all productive strategies studies can use today.

CLINICAL IMPLICATIONS

While it is feasible to capture digital phenotyping data from diverse populations, clinicians should consider the degree of missingness in the data before using them for clinical decision-making.

Relapse prediction in schizophrenia with smartphone digital phenotyping during COVID-19: a prospective, three-site, two-country, longitudinal study

Smartphone technology provides us with a more convenient and less intrusive method of detecting changes in behavior and symptoms that typically precede schizophrenia relapse. To take advantage of the aforementioned, this study examines the feasibility of predicting schizophrenia relapse by identifying statistically significant anomalies in patient data gathered through mindLAMP, an open-source smartphone app. Participants, recruited in Boston, MA in the United States, and Bangalore and Bhopal in India, were invited to use mindLAMP for up to a year. The passive data (geolocation, accelerometer, and screen state), active data (surveys), and data quality metrics collected by the app were then retroactively fed into a relapse prediction model that utilizes anomaly detection. Overall, anomalies were 2.12 times more frequent in the month preceding a relapse and 2.78 times more frequent in the month preceding and following a relapse compared to intervals without relapses. The anomaly detection model incorporating passive data proved a better predictor of relapse than a naive model utilizing only survey data. These results demonstrate that relapse prediction models utilizing patient data gathered by a smartphone app can warn the clinician and patient of a potential schizophrenia relapse.

Assessing the Dynamics of the Mental Health Apple and Android App Marketplaces

Barriers to mental health care, including stigma, costs, and mental health professional shortages, have been exacerbated by the COVID-19 pandemic. Smartphone apps have the potential to increase scalability and improve access to mental health information, support, and interventions. However, evaluating these apps and selecting ones for use in care remain challenging, especially as apps are often updating and changing. Recommending apps requires knowledge of how stable apps are as the experience of one user several months ago may or may not be the same. A sample of 347 apps of the 650 apps on the M-health Index and Navigation Database (MIND) https://mindapps.org were reviewed between September 1, 2021, and January 5, 2022. Apps were selected by time since their last review, with updates occurring on average approximately 4 months from the last review. Eleven trained app evaluators reviewed apps across 105 evaluation criteria in 9 categories. Results were compared to initial ratings, identifying the changes that occurred. The average app updates every 433 days, though 19% were updated in the last 3 months and some nearly weekly. Changes in privacy and features made up the highest percentage of changes, both at 38%. The most frequently observed privacy-related change was increased privacy policy reading level. Functionality parameters changed in 28% of apps. The most common functionality change was the removal of an accessibility feature. Clinical foundations changed in 18% of apps and 9% added supporting studies. Cost structure changed in 17% of apps, with 10% adding a fee for use of the app. Engagement features changed in 17% of the apps, with additions and removals of validated assessments or screeners most common. The dynamic nature of the app stores is reflected in app privacy, features, and functionality. These changes, reflected by the increased reading levels required to understand privacy policies, the decrease in accessibility features, and the additions of fees to access mobile apps, reflect the need to constantly review apps and understand how they are evolving. Patient and clinicians should use the most recent and updated possible when evaluating apps.

An exploratory analysis of the effect size of the mobile mental health Application, mindLAMP

Objectives

Despite the proliferation of mobile mental health apps, evidence of their efficacy around anxiety or depression is inadequate as most studies lack appropriate control groups. Given that apps are designed to be scalable and reusable tools, insights concerning their efficacy can also be assessed uniquely through comparing different implementations of the same app. This exploratory analysis investigates the potential to report a preliminary effect size of an open-source smartphone mental health app, mindLAMP, on the reduction of anxiety and depression symptoms by comparing a control implementation of the app focused on self-assessment to an intervention implementation of the same app focused on CBT skills.

Methods

A total of 328 participants were eligible and completed the study under the control implementation and 156 completed the study under the intervention implementation of the mindLAMP app. Both use cases offered access to the same in-app self-assessments and therapeutic interventions. Multiple imputations were utilized to impute the missing Generalized Anxiety Disorder-7 and Patient Health Questionnaire-9 survey scores of the control implementation.

Results

Post hoc analysis revealed small effect sizes of Hedge's g = 0.34 for Generalized Anxiety Disorder-7 and Hedge's g = 0.21 for Patient Health Questionnaire-9 between the two groups.

Conclusions

mindLAMP shows promising results in improving anxiety and depression outcomes in participants. Though our results mirror the current literature in assessing mental health apps' efficacy, they remain preliminary and will be used to inform a larger, well-powered study to further elucidate the efficacy of mindLAMP.

Reverse Engineering of Digital Measures: Inviting Patients to the Conversation

Background

Digital measures offer an unparalleled opportunity to create a more holistic picture of how people who are patients behave in their real-world environments, thereby establishing a better connection between patients, caregivers, and the clinical evidence used to drive drug development and disease management. Reaching this vision will require achieving a new level of co-creation between the stakeholders who design, develop, use, and make decisions using evidence from digital measures.

Summary

In September 2022, the second in a series of meetings hosted by the Swiss Federal Institute of Technology in Zürich, the Foundation for the National Institutes of Health Biomarkers Consortium, and sponsored by Wellcome Trust, entitled "Reverse Engineering of Digital Measures," was held in Zurich, Switzerland, with a broad range of stakeholders sharing their experience across four case studies to examine how patient centricity is essential in shaping development and validation of digital evidence generation tools.

Key Messages

In this paper, we discuss progress and the remaining barriers to widespread use of digital measures for evidence generation in clinical development and care delivery. We also present key discussion points and takeaways in order to continue discourse and provide a basis for dissemination and outreach to the wider community and other stakeholders. The work presented here shows us a blueprint for how and why the patient voice can be thoughtfully integrated into digital measure development and that continued multistakeholder engagement is critical for further progress.

Smartphone app engagement and clinical outcomes in a hybrid clinic

Despite the growing prevalence of mental health-related smartphone apps, low real-world engagement has prevented these apps from transforming the mental health landscape. Integrating mental health apps into more traditional therapeutic models appears to support better clinical outcomes, but also raises questions about the relationship between app engagement, the app itself, and the coach or clinician. This study explores patient app engagement patterns and the associated clinical outcomes gathered from piloting a digital clinic. Patients with anxiety or depression completed eight clinical visits and coach visits over a median of 83 days with a standard deviation of 17.25 days. Between clinical visits, patients completed therapeutic activities on the mindLAMP app. Mean PHQ-9 and GAD-7 scores decreased from the intake visit to both visit 4 and visit 8. Patients had high app engagement, but engagement did not correlate with outcomes. From intake visit to visit 4, the interaction effects indicate significant differences in the change of both PHQ-9 and GAD-7 depending on participants' average app satisfaction and clinician/coach satisfaction (as measured by WAI-SR) with engagement. Overall, results support the feasibility of incorporating an app into a hybrid clinic.

Exploring the Potential of Apple SensorKit and Digital Phenotyping Data as New Digital Biomarkers for Mental Health Research

The use of digital phenotyping continues to expand across all fields of health. By collecting quantitative data in real-time using devices such as smartphones or smartwatches, researchers and clinicians can develop a profile of a wide range of conditions. Smartphones contain sensors that collect data, such as GPS or accelerometer data, which can inform secondary metrics such as time spent at home, location entropy, or even sleep duration. These metrics, when used as digital biomarkers, are not only used to investigate the relationship between behavior and health symptoms but can also be used to support personalized and preventative care. Successful phenotyping requires consistent long-term collection of relevant and high-quality data. In this paper, we present the potential of newly available, for approved research, opt-in SensorKit sensors on iOS devices in improving the accuracy of digital phenotyping. We collected opt-in sensor data over 1 week from a single person with depression using the open-source mindLAMP app developed by the Division of Digital Psychiatry at Beth Israel Deaconess Medical Center. Five sensors from SensorKit were included. The names of the sensors, as listed in official documentation, include the following: phone usage, messages usage, visits, device usage, and ambient light. We compared data from these five new sensors from SensorKit to our current digital phenotyping data collection sensors to assess similarity and differences in both raw and processed data. We present sample data from all five of these new sensors. We also present sample data from current digital phenotyping sources and compare these data to SensorKit sensors when applicable. SensorKit offers great potential for health research. Many SensorKit sensors improve upon previously accessible features and produce data that appears clinically relevant. SensorKit sensors will likely play a substantial role in digital phenotyping. However, using these data requires advanced health app infrastructure and the ability to securely store high-frequency data.

IT and the Quality and Efficiency of Mental Health Care in a Time of COVID-19: Case Study of Mental Health Providers in England

BACKGROUND

In England, COVID-19 has significantly affected mental health care and tested the resilience of health care providers. In many areas, the increased use of IT has enabled traditional modes of service delivery to be supported or even replaced by remote forms of provision.

OBJECTIVE

This study aimed to assess the use and impact of IT, in remote service provision, on the quality and efficiency of mental health care during the pandemic. We drew on sociotechnical systems theory as a conceptual framework to help structure the gathering, analysis, and interpretation of data.

METHODS

We conducted a national scoping survey that involved documentary analysis and semistructured interviews with 6 national stakeholders and case studies of 4 purposefully selected mental health providers in England involving interviews with 53 staff members.

RESULTS

Following the outbreak of COVID-19, mental health providers rapidly adjusted their traditional forms of service delivery, switching to digital and telephone consultations for most services. The informants provided nuanced perspectives on the impact on the quality and efficiency of remote service delivery during the pandemic. Notably, it has allowed providers to attend to as many patients as possible in the face of COVID-19 restrictions, to the convenience of both patients and staff. Among its negative effects are concerns about the unsuitability of remote consultation for some people with mental health conditions and the potential to widen the digital divide and exacerbate existing inequalities. Sociotechnical systems theory was found to be a suitable framework for understanding the range of systemic and sociotechnical factors that influence the use of technology in mental health care delivery in times of crisis and normalcy.

CONCLUSIONS

Although the use of IT has boosted mental health care delivery during the pandemic, it has had mixed effects on quality and efficiency. In general, patients have benefited from the convenience of remote consultation when face-to-face contact was impossible. In contrast, patient choice was often compromised, and patient experience and outcomes might have been affected for some people with mental health conditions for which remote consultation is less suitable. However, the full impact of IT on the quality and efficiency of mental health care provision along with the systemic and sociotechnical determinants requires more sustained and longitudinal research.

Current challenges for evaluating mobile health applications

As mobile health applications continue to proliferate without clear regulation, the need for app evaluation frameworks to offer guidance to patients and clinicians also expands. However, this expanding number of app evaluation frameworks itself can be a source of confusion and often contradictory recommendations. In pursuit of better frameworks that offer innovation for app evaluation, we present 4 challenges that app evaluation frameworks must overcome as well as examples from our own experience toward overcoming them. The recommendations are applicable to all health apps from any field of medicine, although we use examples from mental health as they are illustrative.

Introducing an implementation framework for augmenting care with digital technology for early psychosis patients: theory and motivation

BACKGROUND

Treatment programs for early-course psychosis are evidence-based interventions that provide specialty care to improve outcomes in patients. Digital technologies offer the potential to augment services and meet the growing demand for care.

AIMS

We introduce a framework to guide the assessment of site readiness for technology and their ability to successfully introduce, implement, and sustain digital technology use. While broader in use that early course psychosis, we focus on this use case to introduce the theory and clinical application.

METHODS

Adapting the replicating effective programs framework, we present an early psychosis focused model. Considering the unique opportunities and challenges of these programs, we present a five-stage evaluation framework. Informed by our clinical experience and recent literature, we present tools and examples to help programs plan and execute successful technology implementation.

RESULTS

The AACCS framework is comprised of five stages: (1) Access (e.g. identifying access to and comfort with technology), (2) Align (e.g. understanding aspects technology can augment), (3) Connect (e.g. customizing technology to stakeholder needs), (4) Care (e.g. implementing technology into treatment), and (5) Sustain (e.g. creating sustainable services). Site visits revealed patients have access to digital tools and are open to implementation into care, while staff prefers digital skills training.

CONCLUSIONS

This framework assists programs in identifying clinical targets to be augmented with technology, stages of implementation, and recommendations for sustaining meaningful technology use.