A Clinically Integrated mHealth App and Practice Model for Collecting Patient-Reported Outcomes between Visits for Asthma Patients: Implementation and Feasibility

Tailoring Rheumatoid Arthritis Visit Timing Based on mHealth App Data: Mixed Methods Assessment of Implementation and Usability

Background

Visits to medical subspecialists are common, with follow-up timing often based on heuristics rather than evidence. Unnecessary visits contribute to long wait times for new patients. Specialists could enhance visit timing and reduce frequency by systematically monitoring patients' symptoms between visits, especially for symptom-driven conditions like rheumatoid arthritis (RA). We previously designed an intervention using a mobile health (mHealth) app to collect patient-reported outcomes (PRO). One of several aims of the app was to assist rheumatologists in determining visit timing for patients with RA. The intervention did not reduce visit frequency.

Objective

To explore possible reasons for the lack of association between the intervention and visit frequency, we describe app usage, assess usability, and identify barriers and facilitators for using between-visit PRO data to reduce visits when patients' symptoms are stable.

Methods

We analyzed patients' use of the app by reporting adherence (percent of PRO questionnaires completed during the 12-month study) and retention (use in the last month of the study). To examine rheumatologists' experiences, we summarized views of the electronic health record (EHR)-embedded PRO dashboard and EHR inbox messages suggesting early or deferred visits. We assessed app usability using the interactive mHealth App Usability Questionnaire for Ease of Use and Usefulness for patients and the System Usability Scale for rheumatologists. We assessed rheumatologist-level effects of intervention usage using Kruskal-Wallis rank sum and equality of proportion tests. We identified barriers and facilitators through interviews and surveys.

Results

The analysis included 150 patients with RA and their 11 rheumatologists. Patients answered a median of 53.3% (IQR 34.1%-69.2%) of PRO questionnaires; this proportion varied by rheumatologist (range 40.7%-67%). Over half of the patients used the app during the final month of the study (56%, range 51%-65%, by rheumatologists); the median number of months of use was 12 (IQR 9-12). Rheumatologists viewed the dashboard 78 times (17.6% of 443 visits) with significant differences in viewing rates by rheumatologist (range 10%-66%; P<.01). There were 108 generated messages sent to rheumatologists suggesting a deferred visit (24.4% of 443 visits) with significant differences in message counts received per visit by rheumatologist (range 10.8%-22.6%; P=.03). Rheumatologists' reported barriers to offering visit deferrals included already scheduling as far out as they were comfortable and rescheduling complexities for staff. Based on 39 patient interviews and 44 surveys, patients reported 2 main barriers to app usage: questionnaire frequency not being tailored to them and reduced motivation after not discussing PRO data with their rheumatologist. A total of 5 interviewed patients received the option to defer their visits, of which 3 elected to defer the appointment and 2 chose to keep it.

Conclusions

While an mHealth app for reporting RA PROs was used frequently by patients, using these data to reduce the frequency of unneeded visits was not straightforward. Better engagement of clinicians may improve the use of PRO data.

Between-Visit Asthma Symptom Monitoring With a Scalable Digital Intervention: A Randomized Clinical Trial

Importance

Asthma affects an estimated 7.7% of the US population and 262 million people worldwide. Symptom monitoring has demonstrated benefits but has not achieved widespread use.

Objective

To assess the effect of a scalable asthma symptom monitoring intervention on asthma outcomes.

Design, Setting, and Participants

This randomized clinical trial was conducted between July 2020 and March 2023 at 7 primary care clinics affiliated with an academic medical center (Brigham and Women's Hospital in Boston, Massachusetts). Candidate patients with a diagnosis of asthma over a 20-month recruitment period (July 2020 to March 2022) were identified and categorized into tiers of varying disease activity based on electronic health record data. Eligible patients were adults (aged ≥18 years) and had a primary care practitioner in 1 of the 7 participating clinics.

Intervention

Intervention group patients were asked to use a mobile health app to complete weekly symptom questionnaires; track notes, peak flows, and triggers; and view educational information. Patients who reported worsening or severe symptoms were offered clinical callback requests. App data were available in the electronic health record. Usual care group patients received general asthma guidance.

Main Outcomes and Measures

The primary outcome was the mean change in Mini Asthma Quality of Life Questionnaire (MiniAQLQ) score for the intended 12-month study period. A change of 0.5 on a scale of 1 to 7 was considered a minimally important change. The secondary outcome was the mean number of asthma-related health care utilization events (urgent care visits, emergency department visits, or hospitalizations). Mean differences for all outcomes between groups were compared using robust linear regression models (generalized estimating equations) with treatment group as the only covariate.

Results

Baseline questionnaires were completed by 413 patients (mean [SD] age, 52.2 [15.4] years; 321 women [77.7%]). Of these, 366 patients completed final questionnaires and were included in the primary analysis. MiniAQLQ scores increased 0.34 (95% CI, 0.19-0.49) in the intervention group and 0.11 (95% CI, -0.11 to 0.33) in the usual care group from baseline to final questionnaire completion (adjusted difference-in-difference, 0.23 [95% CI, 0.06-0.40]; P = .01); although the difference was statistically significant, it did not reach the threshold for a minimally important change. Intervention subgroups showed positive differences in MiniAQLQ scores relative to the usual care group, with noteworthy increases among individuals aged 18 to 44 years (adjusted difference-in-difference, 0.40 [95% CI, 0.13-0.66]), those with low baseline patient activation (adjusted difference-in-difference, 0.77 [95% CI, 0.30-1.24]), those with a low baseline MiniAQLQ score (adjusted difference-in-difference, 0.33 [95% CI, 0.07-0.59]), and those with uncontrolled asthma at baseline (adjusted difference-in-difference, 0.30 [95% CI, 0.05-0.54]). The intervention group had a mean of 0.59 (95% CI, 0.42-0.77) nonroutine asthma-related utilization events compared with 0.76 (95% CI, 0.55-0.96) in the usual care group (adjusted effect size, -0.16 [95% CI, -0.42 to 0.17]; P = .23).

Conclusions and Relevance

In this randomized clinical trial of a scalable symptom monitoring intervention, the increase in asthma-related quality of life did not reach the threshold for a minimally important change. Exploratory analyses suggest possible benefits for patients with low levels of activation.

Trial Registration

ClinicalTrials.gov Identifier: NCT04401332.

Collection of Patient-Reported Outcome Measures in Rural and Underserved Populations

BACKGROUND

 The NIH Pragmatic Trials Collaboratory supports the design and conduct of 31 embedded pragmatic clinical trials, and many of these trials use patient-reported outcome measures (PROMs) to provide valuable information about the patients' health and wellness. Often these trials enroll medically underserved patients, including people with incomes below the federal poverty threshold, racial or ethnic minority groups, or rural or frontier communities.

OBJECTIVES

 In this series of trial case reports, we provide lessons learned about collecting PROMs in these populations. Unbiased collection of PROM data is critical to increase the generalizability of trial outcomes and to address health inequities. Use of electronic health records (EHRs) and other digital modes of PROM administration has gained traction. However, engagement with these modes is often low among populations prone to disparity due to lower digital proficiency, device access, and uptake of EHR portals and web interfaces.

METHODS

 To maximize the completeness and representativeness of their trial outcome data, study teams tested a range of strategies to improve PROM response rates with emphasis on disparities prone and underserved patient groups. This manuscript describes the approaches, their implementation, and the targeted populations.

CONCLUSION

 Optimized PROM collection required hybrid approaches with multiple outreach modes, high-touch methods, creativity in promoting digital uptake, multimodal participant engagement, and text messaging.

Current Implementation of Digital Health in Chronic Disease Management: Scoping Review

BACKGROUND

Approximately 1 in 3 adults live with multiple chronic diseases. Digital health is being harnessed to improve continuity of care and management of chronic diseases. However, meaningful uptake of digital health for chronic disease management remains low. It is unclear how these innovations have been implemented and evaluated.

OBJECTIVE

This scoping review aims to identify how digital health innovations for chronic disease management have been implemented and evaluated: what implementation frameworks, methods, and strategies were used; how successful these strategies were; key barriers and enablers to implementation; and lessons learned and recommendations shared by study authors.

METHODS

We used the Joanna Briggs Institute methodology for scoping reviews. Five databases were searched for studies published between January 2015 and March 2023: PubMed, Scopus, CINAHL, PsycINFO, and IEEE Xplore. We included primary studies of any study design with any type of digital health innovations for chronic diseases that benefit patients, caregivers, or health care professionals. We extracted study characteristics; type of digital health innovation; implementation frameworks, strategies, and outcome measures used; barriers and enablers to implementation; lessons learned; and recommendations reported by study authors. We used established taxonomies to synthesize extracted data. Extracted barriers and enablers were grouped into categories for reporting. Descriptive statistics were used to consolidate extracted data.

RESULTS

A total of 252 studies were included, comprising mainly mobile health (107/252, 42.5%), eHealth (61/252, 24.2%), and telehealth (97/252, 38.5%), with some studies involving more than 1 innovation. Only 23 studies (23/252, 9.1%) reported using an implementation science theory, model, or framework; the most common were implementation theories, classic theories, and determinant frameworks, with 7 studies each. Of 252 studies, 144 (57.1%) used 2 to 5 implementation strategies. Frequently used strategies were "obtain and use patient or consumer feedback" (196/252, 77.8%); "audit and provide feedback" (106/252, 42.1%); and piloting before implementation or "stage implementation scale-up" (85/252, 33.7%). Commonly measured implementation outcomes were acceptability, feasibility, and adoption of the digital innovation. Of 252 studies, 247 studies (98%) did not measure service outcomes, while patient health outcomes were measured in 89 studies (35.3%). The main method used to assess outcomes was surveys (173/252, 68.7%), followed by interviews (95/252, 37.7%). Key barriers impacting implementation were data privacy concerns and patient preference for in-person consultations. Key enablers were training for health care workers and personalization of digital health features to patient needs.

CONCLUSIONS

This review generated a summary of how digital health in chronic disease management is currently implemented and evaluated and serves as a useful resource for clinicians, researchers, health system managers, and policy makers planning real-world implementation. Future studies should investigate whether using implementation science frameworks, including how well they are used, would yield better outcomes compared to not using them.

Enhancing Adult Asthma Management: A Review on the Utility of Remote Home Spirometry and Mobile Applications

Asthma is a prevalent chronic disease, contributing significantly to the global burden of disease and economic costs. Despite advances in treatment, inadequate disease management and reliance on reliever medications lead to preventable deaths. Telemedicine, defined as the use of information and communication technology to improve healthcare access, has gained global attention, especially during the COVID-19 pandemic. This systematic review examines the effectiveness of home monitoring systems in managing severe asthma. A systematic literature search was conducted in PubMed, Web of Science, Scopus, and Cochrane Library, focusing on studies from 2014 to 2024. Fourteen studies involving 9093 patients were analyzed. The results indicate that telemedicine, through tools such as mobile applications and portable spirometers, positively impacts asthma control, self-management, and quality of life. Home spirometry, in particular, shows strong agreement with clinic spirometry, offering a feasible alternative for continuous monitoring. Digital coaching and machine learning-based telemedicine applications also demonstrate significant potential in improving asthma outcomes. However, challenges such as technology accessibility, data privacy, and the need for standardized protocols remain. This review highlights the promise of telemedicine in asthma management and calls for further research to optimize its implementation and address existing barriers.

Evaluation of a Primary Care-Integrated Mobile Health Intervention to Monitor between-Visit Asthma Symptoms

OBJECTIVES

 This study aimed to evaluate implementation of a digital remote symptom monitoring intervention that delivered weekly symptom questionnaires and included the option to receive nurse callbacks via a mobile app for asthma patients in primary care.

METHODS

 Research questions were structured by the NASSS (Nonadoption, Abandonment, Scale-up Spread, and Sustainability) framework. Quantitative and qualitative methods assessed scalability of the electronic health record (EHR)-integrated app intervention implemented in a 12-month randomized controlled trial. Data sources included patient asthma control questionnaires; app usage logs; EHRs; and interviews and discussions with patients, primary care providers (PCPs), and nurses.

RESULTS

 We included app usage data from 190 patients and interview data from 21 patients and several clinician participants. Among 190 patients, average questionnaire completion rate was 72.3% and retention was 78.9% (i.e., 150 patients continued to use the app at the end of the trial period). App use was lower among Hispanic and younger patients and those with fewer years of education. Of 1,185 nurse callback requests offered to patients. Thirty-three (2.8%) were requested. Of 84 PCP participants, 14 (16.7%) accessed the patient-reported data in the EHR. Analyses showed that the intervention was appropriate for all levels of asthma control; had no major technical barriers; was desirable and useful for patient treatment; involved achievable tasks for patients; required modest role changes for clinicians; and was a minimal burden on the organization.

CONCLUSION

 A clinically integrated symptom monitoring intervention has strong potential for sustained adoption. Inequitable adoption remains a concern. PCP use of patient-reported data during visits could improve intervention adoption but may not be required for patient benefits.

Artificial intelligence in diabetes management: Advancements, opportunities, and challenges

The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures. Here, we review the recent progress in the application of AI in the management of diabetes and then discuss the opportunities and challenges of AI application in clinical practice. Furthermore, we explore the possibility of combining and expanding upon existing digital health technologies to develop an AI-assisted digital health-care ecosystem that includes the prevention and management of diabetes.

Examining primary care provider experiences with using a clinical decision support tool for pain management

Objective

To evaluate primary care provider (PCP) experiences using a clinical decision support (CDS) tool over 16 months following a user-centered design process and implementation.

Materials and Methods

We conducted a qualitative evaluation of the Chronic Pain OneSheet (OneSheet), a chronic pain CDS tool. OneSheet provides pain- and opioid-related risks, benefits, and treatment information for patients with chronic pain to PCPs. Using the 5 Rights of CDS framework, we conducted and analyzed semi-structured interviews with 19 PCPs across 2 academic health systems.

Results

PCPs stated that OneSheet mostly contained the right information required to treat patients with chronic pain and was correctly located in the electronic health record. PCPs used OneSheet for distinct subgroups of patients with chronic pain, including patients prescribed opioids, with poorly controlled pain, or new to a provider or clinic. PCPs reported variable workflow integration and selective use of certain OneSheet features driven by their preferences and patient population. PCPs recommended broadening OneSheet access to clinical staff and patients for data entry to address clinician time constraints.

Discussion

Differences in patient subpopulations and workflow preferences had an outsized effect on CDS tool use even when the CDS contained the right information identified in a user-centered design process.

Conclusions

To increase adoption and use, CDS design and implementation processes may benefit from increased tailoring that accommodates variation and dynamics among patients, visits, and providers.

Are older adults considered in asthma and chronic obstructive pulmonary disease mobile health research? A scoping review

BACKGROUND

The use of mobile health (mHealth) for asthma and chronic obstructive pulmonary disease (COPD) is rapidly growing and may help address the complex respiratory care needs of our ageing population. However, little is currently known about how airways mHealth is developed and used among older adults (≥65 years).

OBJECTIVE

To identify if and how older adults with asthma and COPD have been incorporated across the mHealth research cycle.

METHODS

We searched Ovid MEDLINE, EMBASE, CINAHL and the Cochrane Central Registry of Controlled Trials for studies pertaining to the development or evaluation of asthma and COPD mHealth for adults published after 2010. Study, participant and mHealth details, including any considerations of older age, were extracted, synthesised and charted.

RESULTS

A total of 334 studies of 191 mHealth tools were identified. Adults ≥65 years old were included in 33.3% of asthma mHealth studies and 85.3% of COPD studies. Discussions of older age focused on barriers to technology use. Methodologic and/or analytic considerations of older age were mostly absent throughout the research cycle. Among the 28 instances quantitative age-related analyses were detailed, 12 described positive mHealth use and satisfaction outcomes in older adults versus negative or equivocal outcomes.

CONCLUSION

We identified an overall lack of consideration for older age throughout the airways mHealth research cycle, even among COPD mHealth studies that predominantly included older adults. We also found a contrast between the perceptions of how older age might negatively influence mHealth use and available quantitative evaluations. Future airways mHealth research must better integrate the needs and concerns of older adults.

Rudin R, Sulca Flores J, Goolkasian G, Sousa J, Rodriguez J, Lipsitz S, Foer D, K. Dalal A. Assessing Equitable Recruitment in a Digital Health Trial for Asthma

OBJECTIVE

This study aimed to assess a multipronged strategy using primarily digital methods to equitably recruit asthma patients into a clinical trial of a digital health intervention.

METHODS

We approached eligible patients using at least one of eight recruitment strategies. We recorded approach dates and the strategy that led to completion of a web-based eligibility questionnaire that was reported during the verbal consent phone call. Study team members conducted monthly sessions using a structured guide to identify recruitment barriers and facilitators. The proportion of participants who reported being recruited by a portal or nonportal strategy was measured as our outcomes. We used Fisher's exact test to compare outcomes by equity variable, and multivariable logistic regression to control for each covariate and adjust effect size estimates. Using grounded theory, we coded and extracted themes regarding recruitment barriers and facilitators.

RESULTS

The majority (84.4%) of patients who met study inclusion criteria were patient portal enrollees. Of 6,366 eligible patients who were approached, 627 completed the eligibility questionnaire and were less frequently Hispanic, less frequently Spanish-speaking, and more frequently patient portal enrollees. Of 445 patients who consented to participate, 241 (54.2%) reported completing the eligibility questionnaire after being contacted by a patient portal message. In adjusted analysis, only race (odds ratio [OR]: 0.46, 95% confidence interval [CI]: 0.28-0.77, p = 0.003) and college education (OR: 0.60, 95% CI: 0.39-0.91, p = 0.016) remained significant. Key recruitment barriers included technology issues (e.g., lack of email access) and facilitators included bilingual study staff, Spanish-language recruitment materials, targeted phone calls, and clinician-initiated "1-click" referrals.

CONCLUSION

A primarily digital strategy to recruit patients into a digital health trial is unlikely to achieve equitable participation, even in a population overrepresented by patient portal enrollees. Nondigital recruitment methods that address racial and educational disparities and less active portal enrollees are necessary to ensure equity in clinical trial enrollment.

Smart dry powder inhalers and intelligent adherence management

Adherence to inhaled treatments is a complex challenge for patients with chronic obstructive pulmonary disease (COPD) and asthma, it not only involves following the prescribed treatment plans but also administering the medications correctly. When using a dry powder inhaler (DPI), the inhalation flow is particularly critical. Patients frequently fail to use a rapid enough onset and fast enough inhalation when using DPIs. At the same time, there is increasing pressure on physicians to switch patients to DPIs, to minimise the environmental impact of pMDI propellants. This makes it critical to understand whether a patient will maintain or improve disease control by using their new inhaler correctly. However, it is challenging for health care professionals to understand how a patient behaves away from the clinic. Therefore, it would be beneficial to obtain real-world data through the use of monitoring tools, i.e., "smart inhalers". This paper reviews the technologies used to monitor DPIs, how effective they have been in a clinical setting, and how well these have been adopted by patients and health care providers.

Development and Testing of an Electronic Health Record-Integrated Patient-Reported Outcome Application and Intervention to Improve Efficiency of Rheumatoid Arthritis Care

OBJECTIVE

Many patients with rheumatoid arthritis (RA) have difficulty finding clinicians to treat them because of workforce shortages. We developed an app to address this problem by improving care efficiency. The app collects patient-reported outcomes (PROs) and can be used to inform visit timing, potentially reducing the volume of low-value visits. We describe the development process, intervention design, and planned study for testing the app.

METHODS

We employed user-centered design, interviewing patients and clinicians, to develop the app. To improve visit efficiency, symptom tracking logic alerts clinicians to PRO trends: worsening PROs generate alerts suggesting an earlier visit, and stable or improving PROs generate notifications that scheduled visits could be delayed. An interrupted time-series analysis with a nonrandomized control population will allow assessment of the impact of the app on visit frequency.

RESULTS

Patient interviews identified several of the following needs for effective app and intervention design: the importance of a simple user interface facilitating rapid answering of PROs, the availability of condensed summary information with links to more in-depth answers to common questions regarding RA, and the need for clinicians to discuss the PRO data during visits with patients. Clinician interviews identified the following user needs: PRO data must be easy to view and use during the clinical workflow, and there should be reduced interval visits when PROs are trending worse. Some clinicians believed visits could be delayed for patients with stable PROs, whereas others raised concerns.

CONCLUSION

PRO apps may improve care efficiency in rheumatology. Formal evaluation of an integrated PRO RA app is forthcoming.

Toward an asthma patient-reported outcome measure for use in digital remote monitoring

OBJECTIVE

To develop and test a patient-reported outcome measure (PROM) for suitability in digital remote asthma symptom monitoring to identify uncontrolled asthma.

METHODS

We modified a 5-item PROM that does not require a license, the asthma control measure (ACM), from a one-month to one-week lookback period, and evaluated it using the 5-item asthma control questionnaire (ACQ-5). We recruited subjects with asthma through MTurk, an online platform.

RESULTS

In a sample of 498 subjects, the ACM identified uncontrolled asthma with sensitivity 0.99 and specificity 0.65. The two measures correlated with r = 0.81.

CONCLUSION

The ACM modified to a weekly lookback period can differentiate patients with well-controlled asthma from those with uncontrolled asthma. This PROM does not require a license and can be used in digital remote monitoring interventions.

Nurses' experiences of using AsthmaTuner - an eHealth self-management system for healthcare of patients with asthma

Objective

This study describes nurses' experiences of using eHealth for assessment in the healthcare of patients with asthma.

Methods

Five nurses with experience of using AsthmaTuner in the healthcare of patients with asthma participated in the study. Individual semi-structured interviews were conducted with the nurses to understand their experiences of using the eHealth system. The transcribed interviews were analyzed using qualitative content analysis.

Results

The results show that nurses as well as patients find the tool useful and easy-to-handle. AsthmaTuner gives the nurses access to more and better information about the patients, which facilitates assessments and makes their work more efficient. The patients become more involved in their care, gain increased control and take more responsibility for their illness and treatment.

Conclusions

The nurses appreciate eHealth in asthma care. Using AsthmaTuner makes the nurses' work more efficient and the patients become more involved in their care.

The development of an instrument to predict patients’ adoption of mHealth in the developing world

Introduction:

Method:

Results:

Conclusion:

Challenges in Participant Engagement and Retention using Mobile Health Apps: A Literature Review (Preprint)

Background

Mobile health (mHealth) apps are revolutionizing the way clinicians and researchers monitor and manage the health of their participants. However, many studies using mHealth apps are hampered by substantial participant dropout or attrition, which may impact the representativeness of the sample and the effectiveness of the study. Therefore, it is imperative for researchers to understand what makes participants stay with mHealth apps or studies using mHealth apps.

Objective

This study aimed to review the current peer-reviewed research literature to identify the notable factors and strategies used in adult participant engagement and retention.

Methods

We conducted a systematic search of PubMed, MEDLINE, and PsycINFO databases for mHealth studies that evaluated and assessed issues or strategies to improve the engagement and retention of adults from 2015 to 2020. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Notable themes were identified and narratively compared among different studies. A binomial regression model was generated to examine the factors affecting retention.

Results

Of the 389 identified studies, 62 (15.9%) were included in this review. Overall, most studies were partially successful in maintaining participant engagement. Factors related to particular elements of the app (eg, feedback, appropriate reminders, and in-app support from peers or coaches) and research strategies (eg, compensation and niche samples) that promote retention were identified. Factors that obstructed retention were also identified (eg, lack of support features, technical difficulties, and usefulness of the app). The regression model results showed that a participant is more likely to drop out than to be retained.

Conclusions

Retaining participants is an omnipresent challenge in mHealth studies. The insights from this review can help inform future studies about the factors and strategies to improve participant retention.

Implementing an Application Programming Interface for PROMIS Measures at Three Medical Centers

BACKGROUND

There is an increasing body of literature advocating for the collection of patient-reported outcomes (PROs) in clinical care. Unfortunately, there are many barriers to integrating PRO measures, particularly computer adaptive tests (CATs), within electronic health records (EHRs), thereby limiting access to advances in PRO measures in clinical care settings.

OBJECTIVE

To address this obstacle, we created and evaluated a software integration of an Application Programming Interface (API) service for administering and scoring Patient-Reported Outcomes Measurement Information System (PROMIS) measures with the EHR system.

METHODS

We created a RESTful API and evaluated the technical feasibility and impact on clinical workflow at three academic medical centers.

RESULTS

Collaborative teams (i.e., clinical, information technology [IT] and administrative staff) performed these integration efforts addressing issues such as software integration as well as impact on clinical workflow. All centers considered their implementation successful based on the high rate of completed PROMIS assessments (between January 2016 and January 2021) and minimal workflow disruptions.

CONCLUSION

These case studies demonstrate not only the feasibility but also the pathway for the integration of PROMIS CATs into the EHR and routine clinical care. All sites utilized diverse teams with support and commitment from institutional leadership, initial implementation in a single clinic, a process for monitoring and optimization, and use of custom software to minimize staff burden and error.

User-centered design of a scalable, electronic health record-integrated remote symptom monitoring intervention for patients with asthma and providers in primary care

OBJECTIVE

To determine user and electronic health records (EHR) integration requirements for a scalable remote symptom monitoring intervention for asthma patients and their providers.

METHODS

Guided by the Non-Adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework, we conducted a user-centered design process involving English- and Spanish-speaking patients and providers affiliated with an academic medical center. We conducted a secondary analysis of interview transcripts from our prior study, new design sessions with patients and primary care providers (PCPs), and a survey of PCPs. We determined EHR integration requirements as part of the asthma app design and development process.

RESULTS

Analysis of 26 transcripts (21 patients, 5 providers) from the prior study, 21 new design sessions (15 patients, 6 providers), and survey responses from 55 PCPs (71% of 78) identified requirements. Patient-facing requirements included: 1- or 5-item symptom questionnaires each week, depending on asthma control; option to request a callback; ability to enter notes, triggers, and peak flows; and tips pushed via the app prior to a clinic visit. PCP-facing requirements included a clinician-facing dashboard accessible from the EHR and an EHR inbox message preceding the visit. PCP preferences diverged regarding graphical presentations of patient-reported outcomes (PROs). Nurse-facing requirements included callback requests sent as an EHR inbox message. Requirements were consistent for English- and Spanish-speaking patients. EHR integration required use of custom application programming interfaces (APIs).

CONCLUSION

Using the NASSS framework to guide our user-centered design process, we identified patient and provider requirements for scaling an EHR-integrated remote symptom monitoring intervention in primary care. These requirements met the needs of patients and providers. Additional standards for PRO displays and EHR inbox APIs are needed to facilitate spread.

Evaluation of electronic health record-integrated digital health tools to engage hospitalized patients in discharge preparation

OBJECTIVE

To evaluate the effect of electronic health record (EHR)-integrated digital health tools comprised of a checklist and video on transitions-of-care outcomes for patients preparing for discharge.

MATERIALS AND METHODS

English-speaking, general medicine patients (>18 years) hospitalized at least 24 hours at an academic medical center in Boston, MA were enrolled before and after implementation. A structured checklist and video were administered on a mobile device via a patient portal or web-based survey at least 24 hours prior to anticipated discharge. Checklist responses were available for clinicians to review in real time via an EHR-integrated safety dashboard. The primary outcome was patient activation at discharge assessed by patient activation (PAM)-13. Secondary outcomes included postdischarge patient activation, hospital operational metrics, healthcare resource utilization assessed by 30-day follow-up calls and administrative data and change in patient activation from discharge to 30 days postdischarge.

RESULTS

Of 673 patients approached, 484 (71.9%) enrolled. The proportion of activated patients (PAM level 3 or 4) at discharge was nonsignificantly higher for the 234 postimplementation compared with the 245 preimplementation participants (59.8% vs 56.7%, adjusted OR 1.23 [0.38, 3.96], P = .73). Postimplementation participants reported 3.75 (3.02) concerns via the checklist. Mean length of stay was significantly higher for postimplementation compared with preimplementation participants (10.13 vs 6.21, P < .01). While there was no effect on postdischarge outcomes, there was a nonsignificant decrease in change in patient activation within participants from pre- to postimplementation (adjusted difference-in-difference of -16.1% (9.6), P = .09).

CONCLUSIONS

EHR-integrated digital health tools to prepare patients for discharge did not significantly increase patient activation and was associated with a longer length of stay. While issues uncovered by the checklist may have encouraged patients to inquire about their discharge preparedness, other factors associated with patient activation and length of stay may explain our observations. We offer insights for using PAM-13 in context of real-world health-IT implementations.

TRIAL REGISTRATION

NIH US National Library of Medicine, NCT03116074, clinicaltrials.gov.

Smartphone App for monitoring Asthma in children and adolescents

PURPOSE

The asthma stepwise treatment approach recommended is based on monitoring patients' symptoms. The Asthma Research in Children and Adolescents (ARCA) cohort was created to provide evidence about the evolution of persistent asthma. This manuscript describes the development of an electronic health tool, comprising a mobile health application for patients with asthma and its associated online platform for pediatricians to monitor them.

METHODS

The development process followed 7 phases: the first 5 (Conceptualization, Preparation, Assessment scheduling, Image and user interface, and Technical development) defined and designed the tool, followed by a testing phase (functionality assessment and pilot test with ARCA patients), and a last phase which evaluated usability. Since the target population was aged 6-16 years, three versions were designed within the same smartphone application: parents/proxy, children, and adolescents. The online platform for pediatricians provides real-time information from the application: patients' responses over time with color-coded charts (red/amber/green, as in traffic lights).

RESULTS

The pilot test through semi-structured phone interviews of the first 50 participants included in the ARCA study (n = 53) detected their misunderstandings. Pediatricians were trained to emphasize that the application is free of charge and requires monthly answers. Median of the System Usability Scale scores (n = 85), ranging 0 (negative)-100 (positive), was > 93 in the three age versions of the application.

CONCLUSIONS

Technology has the capability of transforming the use of patient-reported outcomes. Describing all the development phases of a mobile health application for monitoring children and adolescents with asthma may increase the knowledge on how to design applications for young patients.

Empowering Caseworkers to Better Serve the Most Vulnerable with a Cloud-Based Care Management Solution

BACKGROUND

Care-management tools are typically utilized for chronic disease management. Sonoma County government agencies employed advanced health information technologies, artificial intelligence (AI), and interagency process improvements to help transform health and health care for socially disadvantaged groups and other displaced individuals.

OBJECTIVES

The objective of this case report is to describe how an integrated data hub and care-management solution streamlined care coordination of government services during a time of community-wide crisis.

METHODS

This innovative application of care-management tools created a bridge between social and clinical determinants of health and used a three-step approach-access, collaboration, and innovation. The program Accessing Coordinated Care to Empower Self Sufficiency Sonoma was established to identify and match the most vulnerable residents with services to improve their well-being. Sonoma County created an Interdepartmental Multidisciplinary Team to deploy coordinated cross-departmental services (e.g., health and human services, housing services, probation) to support individuals experiencing housing insecurity. Implementation of a data integration hub (DIH) and care management and coordination system (CMCS) enabled integration of siloed data and services into a unified view of citizen status, identification of clinical and social determinants of health from structured and unstructured sources, and algorithms to match clients across systems.

RESULTS

The integrated toolset helped 77 at-risk individuals in crisis through coordinated care plans and access to services in a time of need. Two case examples illustrate the specific care and services provided individuals with complex needs after the 2017 Sonoma County wildfires.

CONCLUSION

Unique application of a care-management solution transformed health and health care for individuals fleeing from their homes and socially disadvantaged groups displaced by the Sonoma County wildfires. Future directions include expanding the DIH and CMCS to neighboring counties to coordinate care regionally. Such solutions might enable innovative care-management solutions across a variety of public, private, and nonprofit services.

Digital health technologies: opportunities and challenges in rheumatology

The past decade in rheumatology has seen tremendous innovation in digital health technologies, including the electronic health record, virtual visits, mobile health, wearable technology, digital therapeutics, artificial intelligence and machine learning. The increased availability of these technologies offers opportunities for improving important aspects of rheumatology, including access, outcomes, adherence and research. However, despite its growth in some areas, particularly with non-health-care consumers, digital health technology has not substantially changed the delivery of rheumatology care. This Review discusses key barriers and opportunities to improve application of digital health technologies in rheumatology. Key topics include smart design, voice enablement and the integration of electronic patient-reported outcomes. Smart design involves active engagement with the end users of the technologies, including patients and clinicians through focus groups, user testing sessions and prototype review. Voice enablement using voice assistants could be critical for enabling patients with hand arthritis to effectively use smartphone apps and might facilitate patient engagement with many technologies. Tracking many rheumatic diseases requires frequent monitoring of patient-reported outcomes. Current practice only collects this information sporadically, and rarely between visits. Digital health technology could enable patient-reported outcomes to inform appropriate timing of face-to-face visits and enable improved application of treat-to-target strategies. However, best practice standards for digital health technologies do not yet exist. To achieve the potential of digital health technology in rheumatology, rheumatology professionals will need to be more engaged upstream in the technology design process and provide leadership to effectively incorporate the new tools into clinical care.