Original Paper
Abstract
Background: Major depressive disorder (MDD) is a leading cause of disability worldwide. Management of chronic conditions such as MDD can be improved by enhanced patient engagement, measurement-based care (MBC), and shared decision-making (SDM). A user-centered design approach can improve the understanding of the patient journey and care team workflows and thus aid the development of digital health care innovations optimized for the needs of patients living with MDD and their primary care teams.
Objective: This study aims to use qualitative research methods for the user-centered design of a digitally enabled MDD care platform, PathwayPlatform, intended to enhance patient engagement, MBC, and SDM.
Methods: Insights were gathered through 2 stages of qualitative interviews by a study team with expertise in qualitative research and user-centered design methods. Thematic analysis was used to generate an overarching understanding of a set of shared experiences, thoughts, or behaviors across a broad qualitative data set, including transcripts of interviews, to allow both inductive and deductive insights to emerge. Thematic analysis of interviews was supported by Dedoose (SocioCultural Research Consultants, LLC), a qualitative data analysis software tool that enables systematized coding. Findings and insights were presented based on code frequency, salience, and relevance to the research project.
Results: In stage 1, interviews were conducted with 20 patients living with MDD and 15 health care providers from September 2018 to January 2019 to understand the experiences with and perceptions about the initial functionality of the Pathway app while also exploring the perceptions about potential additional features and functionality. Feedback about care team workflows and treatment approaches was collected in stage-2 interviews with 36 health care providers at 8 primary care sites. Inductive and deductive thematic analyses revealed several themes related to app functionality, patient-provider engagement, workflow integration, and patient education. Both patients and their care teams perceived the remote tracking of patient-reported outcomes via digital tools to be clinically useful and reliable and to promote MBC and SDM. However, there was emphasis on the need to enhance the flow of real-time data shared with the care team, improve trend visualizations, and integrate the data within the existing clinical workflow and educational programs for patients and their care teams. User feedback was incorporated into the iterative development of the Pathway app.
Conclusions: Ongoing communication with patients living with MDD and their care teams provided an opportunity for user-centric developmental iterations of the Pathway Platform. Key insights led to further development of the patient-facing and care team–facing visit preparation features, collaborative goal-setting and goal-tracking features, patient-reported outcome summaries, and trend visualizations. The result is an enhanced digital platform with the potential to improve treatment outcomes and provide patients living with MDD additional support throughout their treatment journey.
doi:10.2196/42768
Keywords
Introduction
Background
Depression is a leading cause of disability worldwide, affecting nearly 300 million people [
, ]. Major depressive disorder (MDD) is a growing problem in the United States, with the total number of US adults with MDD increasing by 12.9%, from 15.5 million to 17.5 million between 2010 and 2018, and it is associated with a significant economic burden [ ]. Primary care centers are the largest mental health service providers for people living with MDD, with up to two-thirds of visits to health care providers (HCPs) for depression occurring in a primary care setting [ - ]. Time constraints and the need to frequently manage multiple conditions during a single visit to a primary care setting can make it difficult for HCPs to fully engage with patients when it comes to their treatment for MDD [ ]. Frequent communication and engagement between people living with MDD and their care teams may improve therapeutic outcomes, especially for chronic conditions such as MDD [ ].Measurement-Based Care
The American Psychiatric Association clinical guidelines recommend measurement-based care (MBC) for treating depression. MBC includes the routine use of standardized outcome measures to assess changes in depression symptoms, level of functioning, and quality of life across the treatment course [
]. MBC for MDD is effective because it allows the primary care provider to quantify clinical outcomes. This provides guidance for timely treatment modifications that may better meet the needs of the person being treated [ ]. Furthermore, treatment decisions based on MBC give people living with MDD a better understanding of how their condition is changing over time and therefore potentially empowers them in terms of their own care [ ]. In addition, compared with usual care, MBC in the management of depression has been shown to improve treatment adherence, thereby leading to improvements in clinical outcomes [ , ]. Despite the demonstrated benefits of MBC in improving treatment outcomes and patient engagement, adoption of MBC in routine clinical practice has been slow, with only 20% of HCPs using it in their practice [ ]. Increased consultation times are often cited as a barrier to MBC implementation.Shared Decision-Making
Along with MBC, another critical factor shown to improve treatment outcomes is the involvement of people living with MDD in the decision-making processes of their treatment journey [
]. Several studies have found that people experiencing a mental illness want to play a large role in the treatment decision-making process [ ]. A shared decision-making (SDM) model of interaction can foster patient-provider engagement by empowering patients to play a great role in the decision-making process, thus creating an opportunity for them to have their voices, beliefs, values, goals, experiences, and preferences reflected in the treatment planning and monitoring process. This, in turn, can help increase treatment satisfaction and overall treatment adherence [ ].Among people receiving treatment for mental health disorders (including depression), increased adherence to treatments (including both psychopharmaceutical and psychotherapeutic) has been reported when they recognize that their treatments reflect their unique needs and preferences. A strong alignment between the treatment goals of the person living with depression and their treatment provider is another critical factor shown to be important in promoting adherence [
, ]. Several studies have reported misalignment between what patients and their treatment providers consider to be the most important treatment goal [ ]. Thus, involvement in SDM is associated with a high probability of receiving quality care and improvement in symptoms [ ] through increased adherence to drug treatment [ ].Digital Tool Development in MDD
Digital tool development that enhances patient engagement, MBC, and SDM has the potential to improve treatment outcomes in MDD [
]. Digital communications and information technologies have previously been shown to improve health care delivery by improving communication between providers, and by decreasing the need for face-to-face appointments, thus helping to alleviate the workload of HCPs [ , ]. With rapid advances and the adoption of smartphone technology, mobile health apps have generated interest from both the public and medical communities [ ]. For the HCP, digital technology platforms, such as mobile apps, can offer low-cost interventions to monitor and improve services for patient populations that are difficult to retain during treatment [ ]. A mobile app could help save time because people living with MDD could engage with symptom assessments (such as the 9-item Patient Health Questionnaire [PHQ-9]) and other clinical instruments outside their visit, whereas HCPs would only need to review these results instead of administering the instrument during the visit itself [ ]. A mobile app could therefore promote MBC by allowing patients to remotely engage with validated quantitative measures of assessments, which can then be uploaded into their electronic health records (EHRs) for in-office visits and physician monitoring [ ]. Collection of PHQ-9 results through a mobile app has been shown to be as sensitive as, or even more sensitive than, the traditional (in-person and paper-based) PHQ-9 data collection method [ ]. This may be because people living with MDD might feel more comfortable revealing their symptoms in remote settings through a mobile app rather than in traditional in-person settings.From the patient’s perspective, several studies using smartphone app–based interventions for depressive disorders have shown that depressive symptoms were reduced significantly more with smartphone apps [
, , ] through motivating some users to consult medical professionals for diagnosis and management [ , ] and through self-observation [ , ]. Furthermore, apps dedicated to the caregivers of people living with MDD have also been shown to help caregivers in better supporting their loved ones and to destigmatize mental health care [ ].User-Centered Design Approach
Achieving this potential of digital tools to improve treatment outcomes requires a nuanced understanding of the desirability and usability of patient-facing and care team−facing digital interfaces alongside the practical requirements for patient and care team adoption. Perceived utility and overall value of the product to care teams and privacy and confidentiality concerns are often cited by care teams as examples of barriers that can limit the adoption of these types of apps [
]. Various mobile apps are available for depression management; however, many are patient facing only and do not include a care team interface [ ]. The lack of guidance or feedback from the care team has often been cited by people living with depression as a barrier to the adoption of many apps that are dedicated to mental health care [ , , ]. Patients might, for example, perceive the lack of feedback or engagement from the care team as indicating that the information they are providing through the app is not being monitored or integrated into their care processes, thus disincentivizing their engagement with these products [ ].A user-centered design approach can improve the understanding of the patient journey (
) [ ]. Patient insights help develop an understanding of user needs, with iterative designs and prototypes playing a key role in how these insights and needs can be unearthed [ ]. However, studies describing the user-centered design approach in health-related technology remain limited [ ]. Takeda, Lundbeck, and Advocate Aurora Health (AAH) partnered to cocreate a digitally enabled care experience with users (patients living with MDD and care teams), software developers, and health-technology product development specialists (Ctrl Group and Fora Health) [ ]. Specifically, a digital mobile patient interface, the Pathway App, was designed with a conversational interface and tested via a pilot feasibility study with 40 patients living with MDD [ ]. The study showed a trend toward high patient activation and patient-provider engagement for people who used the app in addition to usual care compared with those who were assigned to usual care only [ ]. Building on these results, a new iteration of the app was created and incorporated into a new digital platform, Pathway Platform, which includes the Pathway App; EHR-integrated, real-time, patient-level data sharing; and educational programming that is both care team and patient facing. This was guided by a more comprehensive understanding of care team workflows and patient and care team insights [ ]. In this paper, we describe how user-centered design was applied to develop a digitally enabled MDD care platform that is optimized for the needs of patients living with MDD and their primary care teams.Methods
Overview
This report describes the qualitative research undertaken to understand, iterate, and integrate Pathway Platform into primary care in the AAH system within and around Chicago, Illinois. In stage 1, interviews were conducted with 20 patients with MDD who participated in the pilot feasibility study and 15 HCPs (from September 19, 2018, to January 30, 2019). The stage-1 interview sought to understand the experiences with and perceptions about the initial functionality of the Pathway app (
) while also exploring perceptions about potential additional features such as HCP visit preparation, patient education, and goal setting and tracking. Interviews with patients lasted up to 60 minutes, and interviews with HCPs lasted up to 30 minutes. Another round of follow-up feedback was collected from 36 HCPs at 8 primary care sites through stage-2 interviews to understand care team workflows in the treatment of patients with MDD, the extent to which Pathway Platform can help optimize care, and what support the care team will need to make Pathway Platform work at their respective sites. HCPs were included if they were involved in primary care and specifically managed the care of patients with MDD. The semistructured, in-person interviews were conducted by a study team with expertise in qualitative research, user-centered design methods, workflow assessment, and educational support (Ctrl Group and PRIME Education LLC). Care teams were interviewed about topics related to workflow, perceptions about and experiences with MBC and SDM, and educational needs of patients with MDD and their care team members. The interviews were conducted until observational and analytical saturation was achieved. Saturation was defined as when no new inductive themes emerged during analysis and a priori or deductive themes were exemplified in the data [ ].Data Analysis
The qualitative data were first coded and analyzed independently by 2 Ctrl Group researchers using a set of foundational structural codes. A structural coding framework, based on the agreed-upon discussion guide from the qualitative interviews, was created for the “top-down” codes; “bottom-up” codes that organically arose from the data were also used. The top-down and bottom-up coding was then refined collaboratively as the researchers progressed through the data set by, for example, splitting preexisting codes into more specific subcodes and combining existing subcodes where appropriate. Once refined, the codes enabled thematic analysis and identification of recurring themes across participants’ accounts and perceptions. Guided by prior research findings [
- ], we adopted thematic analysis to generate an overarching understanding of shared experiences, thoughts, and behaviors across a broad, qualitative data set, to allow both inductive and deductive insights to emerge. This was particularly important because our study aimed to validate preexisting product features (through deductive reasoning) while also allowing respondents to provide suggestions that can inform the development of completely new features (inductive reasoning). This type of mixed reasoning would have been very difficult to achieve if the participants had only been surveyed about their thoughts around specific product features. The flexibility allowed by the thematic analyses was also important because it enabled engagement with both personal accounts of patients’ experiences and understandings and broad social constructs (eg, “workflow efficiency” or “medication adherence”) in slightly different social contexts (ie, different primary care environments) in the same research [ ].Thematic analysis of interviews was supported by Dedoose (SocioCultural Research Consultants, LLC), a qualitative data analysis software tool that enables systematized coding. Audio recordings from the interviews were uploaded to the web-based transcription service, Rev. Transcripts were uploaded to Dedoose [
], the top-down codes created during the coding framework phase were applied to appropriate excerpts for each transcript, and new bottom-up codes were created and applied as they arose over the course of the analysis. High-level analyses were conducted by reviewing the codes that had been applied to the interview transcripts. This was done to identify key and recurring themes from the insight-gathering stage. Interpretive insights were formulated through subsequent in-depth analyses. Findings and insights were presented based on code frequency, salience, and relevance to the research project.The Standards for Reporting Qualitative Research recommendations were followed in the reporting of the study [
].Ethics Approval
Ethics approval from the AAH institutional review board was obtained by making amendments to the existing Advocate Pathway study protocol (approval number: AHC-6680-75000249).
Results
Participant Characteristics
A total of 37 patients living with MDD completed the 18-week primary follow-up period in the pilot feasibility study—19% (n=7) were Black and 38% (n=14) were Hispanic [
]. Overall, 54% (20/37) of patients from the pilot feasibility study (9/18, 50% from the Pathway App arm and 11/19, 58% from the usual care arm) participated in the qualitative interviews in stage 1. In addition, 15 HCPs also participated in the stage-1 interviews. Overall, 53% (8/15) of HCPs in the group were previously involved in the pilot study, 25% (2/8) of whom were assigned to the PathwayApp arm and had used the app to derive patient reports. A total of 36 HCPs from primary care medicine with experience in managing people with depression participated in the stage-2 interviews ( ). These HCPs were sampled for diversity of roles both within and across sites, and it was found that 33% (12/36) were physicians, followed by certified medical assistants (8/36, 22%), registered nurses (6/36, 17%), and licensed practical or advanced practice nurses (6/36, 17%).Users’ Experiences With and Perceptions of the Pathway App
Overview
Thematic analysis identified major themes to describe the users’ experiences with and perceptions about the Pathway App, which included functionality, support, and patient-provider engagement, along with subthemes such as ease of use, utility, reliability, motivation, reducing burden, communication, understanding, and shared vision (
and ).Themes, subthemes, and the Pathway App features | Participant quotes | |||
Functionality | ||||
Ease of use | ||||
Side effects |
| |||
Mood |
| |||
Utility | ||||
Medication tracking |
| |||
Side effects |
| |||
Mood |
| |||
Reliability | ||||
Mood |
| |||
Medication tracking |
| |||
Support | ||||
Motivation | ||||
Medication |
| |||
Side effects |
| |||
In-app report |
| |||
Goal setting and tracking |
| |||
Patient education |
| |||
Reducing burden | ||||
HCP visit preparation |
| |||
Side effects |
| |||
Patient education |
|
aRN: registered nurse.
bHCP: health care provider.
cLPN: licensed practical nurse.
Themes, subthemes, and the Pathway App features | Participant quotes | ||
Communication | |||
Goal setting and tracking |
| ||
Side effects |
| ||
Patient education |
| ||
Understanding | |||
For all tracking features |
| ||
Shared decision-making | |||
For all tracking features |
|
aHCP: health care provider.
bRN: registered nurse.
cMD: doctor of medicine.
Functionality
Functionality was a major theme that emerged from the interviews, with 3 associated subthemes—ease of use, utility, and reliability. Patients living with MDD and HCPs emphasized ease of use and overall value as 2 of the most beneficial app features. Participants appreciated the simplicity of the design, which made it easy to understand and use the app effectively, and many reported that the app provided clinically meaningful information, such as tracking of symptoms, mood, and medication adherence, which helped them manage their symptoms more effectively. HCPs viewed mood, medication, and side effect tracking to be clinically valuable, owing in part to the continuous and direct input from the patient.
Support
Support was an important theme that emerged, which could be split into 2 subthemes—motivation and burden reduction. Patients living with MDD reported that the goal-setting and goal-tracking features provided emotional support and motivation through regular reminders and encouragement, which helped them stay engaged in their treatment.
Similarly, HCP visit preparation was noted as very useful by patients living with MDD. Many reported that the ability to prepare for HCP visits using the Pathway App reduced their anxiety and improved the ease and accuracy of communication with their HCP.
HCPs felt that the visit preparation feature reduced their administrative burden and improved the efficiency and effectiveness of appointments. Similarly, HCPs stated that patient education features reduced the education burden on them, providing a constructive focus for patients living with MDD and improving patient-provider interactions.
Patient-Provider Engagement
Patients living with MDD reported that the app helped to improve their interactions with their HCP by providing easy-to-use tools for tracking symptoms and progress. They also appreciated the ability to share their data with their HCP, which they said helped to facilitate better communication and collaboration. Patients and HCPs found that the goal-setting and goal-tracking features of the app supported clinical conversations while also helping patients focus on their treatment goals. HCPs reported that patients may remain engaged in their care by knowing that their input and concerns are being taken seriously, thereby promoting SDM.
Both patients living with MDD and HCPs expressed positive feedback about the patient education feature of the Pathway App. They found the feature to be visually appealing and engaging and deemed the content to be suitable in length and depth. Furthermore, HCPs reported that the education feature would facilitate productive conversations with patients.
Concerns
Concerns about the Pathway App were that it was difficult to use and had a lack of interactivity, with subthemes such as being confusing, repetitive, and time consuming and lacking workflow integration (
).The initial well-being tracker, a visual analog scale from 0 to 100, received the most critical feedback from patients living with MDD, as many of them found the construct and scale to be unnecessarily complicated. HCPs also expressed skepticism about the clinical utility of the well-being tracker feature and had concerns about the ease with which they could interpret the responses. Both HCPs and patients suggested changes to include a simple response format and visual indicators to support interpretation. The cognition 2-back feature also generated negative reviews, with both patients and HCPs expressing their frustration, as many found it to be confusing and anxiety inducing, and they expressed skepticism about the usefulness and interpretation of data. Participants also expressed frustration about the daily repetition of certain questions and expressed concerns about disengagement.
Categories, themes, subthemes, and the Pathway App features | Participant quotes | ||||
Concerns | |||||
Cumbersome to use | |||||
Time consuming | |||||
Pathway report |
| ||||
Confusing | |||||
Well-being tracker |
| ||||
Cognition 2-back |
| ||||
Repetitive and redundant | |||||
Side effects |
| ||||
Mood |
| ||||
Lack of interactivity | |||||
In-app report |
| ||||
Recommendations | |||||
Workflow integration | |||||
Pathway report |
| ||||
HCP visit preparation |
| ||||
Goal setting and tracking |
| ||||
Increase interactivity | |||||
Patient education |
| ||||
Explanation and visualization | |||||
Cognition 2-back |
| ||||
Well-being tracker |
|
aHCP: health care provider.
bEMR: electronic medical record.
cCBT: cognitive behavioral therapy.
Recommendations
For the well-being tracker, participants suggested a simple response format and the inclusion of visual indicators to support interpretation (
). A better explanation and provision of an alternative cognitive exercise were suggested by participants for the cognition 2-back feature. Providing examples of goals and assisting patients living with MDD with setting their own goals independently of their care teams were among the HCP-suggested changes to the goal-setting feature. Both HCPs and patients suggested changes to goal tracking, such as options to set goal reminders, record goal progress, and provide rewards to motivate goal attainment.Many of the HCP-suggested changes included an emphasis on the value of EHR integration for easy access and to save time. They suggested that features such as HCP visit preparation, goal setting and tracking, and the Pathway report would benefit from integration with the EHR to be more useful and effective for HCPs. In addition to workflow integration, HCP-suggested changes included increasing the interactivity and variety of the given content for patient education, while also making the value and purpose of the educational materials clear.
Optimization of the Integration of the Pathway App Into a Primary Care Clinical Workflow
Overview
Care team members made several recommendations regarding how to facilitate the integration of the Pathway App into clinical workflows. These included product-specific recommendations and suggested changes to existing workflow scopes. Key themes that emerged were the importance of MBC, SDM, educational needs for the care team, and patient education (
).Categories, themes, and subthemes | Participant quotes | |||
Perceptions | ||||
Importance of MBCa | ||||
PROsb |
| |||
Shared decision-making | ||||
Goal setting |
| |||
Side effects |
| |||
Concerns | ||||
Overwhelming | ||||
Pathway data |
| |||
Educational support | ||||
Cognition tests |
| |||
PROs |
| |||
Recommendations | ||||
Patient education | ||||
Educational resources for patients |
|
aMBC: measurement-based care.
bPRO: patient-reported outcome.
cRN: registered nurse.
dPA: physician assistant.
eMD: doctor of medicine.
fLPN: licensed practical nurse.
gWHO: World Health Organization.
hPDQ-D-5: Perceived Deficits Questionnaire–Depression.
iAPN: advanced practice nurse.
jEMR: electronic medical record.
Importance of MBC
Although many primary care professionals within Advocate Aurora primary care perceived MBC, collaborative care, and SDM as important, various barriers limited their inclusion in day-to-day clinical workflows. Care teams agreed that Pathway administration of the PHQ-9 builds on and improves current clinical practices. HCPs viewed the PHQ-9 as a key component of current clinical practice; therefore, its continuous use to track patients’ treatment progress outside visits, as enabled by Pathway Platform, was perceived as clinically useful.
Shared Decision-Making
Tracking collaboratively devised goals was perceived as useful for the care team, with many also stating that this would help engage people living with MDD in their own care. HCPs reported that the presentation of patient-reported outcome (PRO) trajectories for medication adherence, PHQ-9 scores, and side effects provided a clinically useful view of how each patient’s condition has changed over time, which, in turn, supports better clinical decision-making. HCPs suggested including more information about patient care in the Pathway-EHR interface, such as medication refill data for comparison with reported adherence.
Workflow Integration and Data Visibility
Interviews also highlighted the importance of understanding care team needs, such as interpreting PRO measures and trend visualizations, to ensure that Pathway Platform can support care team workflows. Data about medication adherence, PHQ-9 scores, and side effects were perceived to be the most clinically important PROs. However, there were also concerns that Pathway data would be overwhelming and may lead to key data being missed.
Educational Needs
Many expressed low familiarity and desire for education regarding PROs other than PHQ-9 and clinical use of cognitive tests. In addition to their own education, the care team members also expressed a desire for patient-directed educational materials. Many care team members spoke about patients living with MDD being provided with educational handouts at their respective practices (including handouts printed from web-based searches, those that have been externally printed, and those taken from web-based medical reference platforms and the EHR). Many care team members stated that, from the list of educational topics (which included understanding one’s diagnosis, treatment options, side effect management, goal setting, and patient engagement), they were most interested in accessing materials about helping patients understand their diagnosis and their available treatment options. Care team members also thought that patients would benefit from educational resources about the types of local behavioral health resources that are available to them and information about what type of role they can play in their depression care and how often they should follow-up with their physician.
Discussion
Principal Findings
Qualitative research and thematic analyses conducted in this study allowed us to capture the user experiences and perceptions of patients living with MDD and their care teams. These results can guide researchers and app developers in designing effective digital health interventions that will be readily accepted by their intended end users [
].Most of the currently available apps developed for depression management have only been assessed for effectiveness in a research setting and have not been integrated within clinical workflows; this has resulted in a lack of adoption by care teams and broad health care systems [
]. Incorporating the voices of people living with MDD and their care teams into the product development process aligns with the broad paradigm shift toward patient-focused decision-making and SDM between patients and their providers, ultimately leading to high-quality, fully informed, and preference-based treatment plans [ ]. Our analyses revealed that both patients and their care teams perceived the remote tracking of PROs via digital tools to be clinically useful and reliable. Other highlights of our study included the need to enhance the flow of real-time data shared with the care team and the need to integrate within the care team workflow, including real-time sharing of the patient’s app data within the EHR. Results from our analyses also highlighted the need for care team education about MBC and SDM and about how to use the Pathway App to improve these processes by using features such as visit preparation and collaborative goal setting and tracking. Using the broad insights gathered from our thematic analyses, we were able to understand, iterate, and integrate a digitally enabled platform, Pathway Platform, into a primary care setting in the United States. The first iteration of the Pathway App included PRO measures related to depression, well-being, cognitive symptom tracking, medication adherence, and side effects [ ]. Pilot results confirmed the feasibility of using the Pathway App among patients living with MDD and showed a trend in enhanced patient activation in the app arm, albeit in a small sample size [ ]. Building on the results from the pilot study, Pathway Platform was developed to consist of 3 components: the latest iteration of the Pathway App; EHR-integrated, real-time, patient-level data sharing; and educational programming that is both care team and patient facing (including a web-based educational resource center that describes the utility of Pathway Platform to the care team through reading materials, presentations, and videos; ). The current version of the app prompts patients living with MDD to complete the following scales every 2 weeks: PHQ-9 [ ] and Perceived Deficits Questionnaire—Depression [ ] to assess depression status and subjective cognitive impairment, World Health Organization Well-being Index [ ] to assess quality of life and emotional well-being, and Digit Symbol Substitution Test [ ] as an objective measure of cognition to assess working memory and processing speed.The Pathway App also includes a daily “evening check-in” to collect information about medication adherence and side effects. Data collected by the Pathway App are electronically transmitted and stored in an EHR-integrated web interface. These data are accessible to the care team and provide a longitudinal summary that may assist them in clinical decision-making and overarching depression management. Care team members can view these data either before or during the clinical visit and then use the data to collaboratively discuss future treatment decisions with the people they are treating. A web-based educational training program for primary care team members was also developed by using evidence-based medicine, building on the concepts of MBC and SDM, as they relate to depression management.
In addition, audit and feedback sessions will be conducted to benchmark performance measures, reflect on current clinical practice and improvement strategies, and set team-based action plans. Specific training sessions were conducted for the care team members about how to instruct patients to use Pathway Platform and how to use EHRs to view data collected via Pathway Platform. A training manual was also developed for patients living with MDD that describes the functionality of Pathway Platform and how to use and interpret their data.
Pathway Platform was cocreated with input from all users (people living with MDD, care teams, health system information technology personnel, and study collaborators), along with input from software developers and health-technology product development researchers, to optimize usability, utility, iteration speed, and integrated system performance and to ultimately enable nuanced care focused on SDM and MBC (
). Continued reassessment based on user feedback has allowed for fast iterations, optimized system performance, and sustainability [ ]. This user-centric approach has, in turn, led to an enhanced digital platform to improve treatment outcomes by supporting an expanded understanding of MDD treatment, bolstering care team workflows, and providing patients with additional support throughout their treatment journey.Limitations and Future Directions
A limitation of our study is its small sample size of patients with MDD and their care teams. In addition, among the care teams, only 33% (12/36) were clinicians. Future studies would benefit from an even split between clinicians and nurses in the sample. Moreover, this was a single-provider network study, and the results of this study may have limited generalizability. In addition, although the design and development of Pathway Platform were guided by a deep understanding of care team workflows, the extent to which clinical workflows can be modified to accommodate the adoption of Pathway Platform will ultimately depend on the clinical team.
Furthermore, although thematic analyses offered the necessary tools for organizing, interpreting, and transforming data without the need for separate theories, the depth of our conclusions may have been increased by additional methods such as modeling and theory building [
]. However, creating an overarching or generalizable theory to explain the way people reacted to the product’s feature set as a whole [ ] would have gone beyond the primary interests of the study, which were focused on understanding and analyzing specific feedback such that it could be mindfully applied to iterations of the product features. We also used a highly systematized and enumerative approach to coding and generating themes, as recommended by the guidelines [ ]. In addition, we minimized interpretive inconsistencies by using a single code tree and 2 analysts to evaluate each other’s work for analytical consistency. The improved iteration of Pathway Platform is being evaluated in an ongoing large-scale implementation study (Use of a Digitally Enabled App With Clinical Team Interface in the Management of Depression; NCT04891224). The study will include up to 200 patients at 20 primary care clinics. The implementation study aims to test the scaling and integration of Pathway Platform, along with educational interventions, at multiple primary care sites within the AAH system, with the primary objective of determining improvement in adherence to MBC practices [ ]. Results are expected to provide insights into the improvements in clinical workflows that are necessary to enhance collaborative care, depression management, clinician and patient experience, adherence to medication, patient-provider engagement, and depression outcomes in the primary care setting [ ]. EHR integration and how it enables decision-making, and efficiencies with current AAH information technology platforms, such as ease of access of data in real time by the care team, will also be assessed. Together, insights from this study will allow further amendment of workflows to ensure the optimal use of Pathway Platform [ ].Providing effective care for MDD has become more important than ever, with the prevalence of symptoms of anxiety disorder and depressive disorder having increased more than 3-fold in the United States during the COVID-19 pandemic [
, ]. Furthermore, people with few social and economic resources had high likelihood of exhibiting depression symptoms during this time [ ]. Digital tools are therefore increasingly relevant in the era of COVID-19, owing to increased use of telehealth services to facilitate access to care [ ]. In addition, low-cost interventions, such as digital tools, could provide increased monitoring and improved services to at-risk populations [ , ].Moreover, future research methodologies, analysis protocols, and publications should provide a more explicit account of the impact of people’s social intersectionality on their perceptions about remote MDD monitoring. The imperative for this is heightened by the increasing attention that reimbursement entities are paying to the way any given intervention can help minimize the negative effects that social determinants have on treatment outcomes [
].Conclusions
Ongoing communication with patients with MDD and their care teams (cocreation) provided an opportunity for continued reassessment and developmental iteration of Pathway Platform. These insights included the need for rapid communication of updated and current patient data with the care team, integration of the app into the MDD care pathway via the EHR, and education of the care team about the interpretation and use of these data. This cocreation model using qualitative research findings has resulted in fast iterations and optimized system performance and will allow for eventual sustainability outside the research environment. Future development of Pathway Platform will continue, consistent with the evolving needs of people living with MDD and their care teams.
Acknowledgments
This study was funded by Takeda Pharmaceuticals U.S.A., Inc., and Lundbeck LLC. Under the direction of the authors, medical writing assistance was provided by Sangeetha De, PhD, on behalf of Syneos Health Medical Communications LLC. Takeda Pharmaceuticals U.S.A., Inc., and H. Lundbeck A/S provided funding to Syneos Health for support in writing this manuscript.
Data Availability
The data sets generated and analyzed during this study are available from the corresponding author upon reasonable request.
Authors' Contributions
MM, RK, CK, CB, BF, JK, LC, and DEK contributed to the conceptualization of the study. MM, CB, BF, JK, LC, and DEK supported the development of the study methodology. BF and JK supported the software components of the study and performed data validation. RK, BF, JK, AS, and LZ performed the formal data analysis. BF, JK, and DEK contributed to data collection and provision of study resources. RK, BF, JK, AS, and LZ contributed to data interpretation. MM, RK, CB, BF, JK, LZ, and AR contributed to data visualization. MM, RK, CK, CB, BF, JK, and DEK provided oversight for research planning and execution. MM, RK, CK, CB, BF, JK, and AR managed, coordinated, and executed research activities. All authors contributed to draft review and critical revision of the manuscript and approved the final version to be published.
Conflicts of Interest
MM is an employee of Takeda Pharmaceuticals U.S.A., Inc., and receives annual stock options. LC and AR were employees of Takeda Pharmaceuticals U.S.A., Inc., at the time of the study. CB was an employee of Advocate Aurora Health at the time of the study and is currently an employee of Takeda Pharmaceuticals U.S.A., Inc. RK, CK, and DEK are employees of Advocate Aurora Health. DEK has received remuneration from Takeda Pharmaceuticals U.S.A., Inc., for activities unrelated to the conduct of the study. JK and BF are employees of Ctrl Group. JK is also a founder and owner of Fora Health, the software used in this study. BF is also a director of Cognition Kit. LZ and AS were employees of Ctrl Group at the time of the study.
The treatment journey for patients with major depressive disorder (MDD).
DOCX File , 534 KBScreenshot of the Pathway app and a sample report from the pilot study.
DOCX File , 1043 KBDetails of health care provider sample in stage-2 interviews.
DOCX File , 50 KBDesign of Pathway Platform.
DOCX File , 156 KBUser-centered design approach.
DOCX File , 68 KBReferences
- Depression. World Health Organization. Sep 13, 2021. URL: https://www.who.int/news-room/fact-sheets/detail/depression [accessed 2022-06-01]
- Global health data exchange (GHDx). Institute of Health Metrics and Evaluation. URL: http://ghdx.healthdata.org/gbd-results-tool?params=gbd-api-2019-permalink/d780dffbe8a381b25e1416884959e88b[accessed [accessed 2022-02-01]
- Greenberg PE, Fournier AA, Sisitsky T, Pike CT, Kessler RC. The economic burden of adults with major depressive disorder in the United States (2005 and 2010). J Clin Psychiatry. Feb 2015;76(2):155-162. [FREE Full text] [CrossRef] [Medline]
- Unützer J, Park M. Strategies to improve the management of depression in primary care. Prim Care. Jun 2012;39(2):415-431. [FREE Full text] [CrossRef] [Medline]
- Harman JS, Veazie PJ, Lyness JM. Primary care physician office visits for depression by older Americans. J Gen Intern Med. Sep 2006;21(9):926-930. [FREE Full text] [CrossRef] [Medline]
- Park LT, Zarate Jr CA. Depression in the primary care setting. Reply. N Engl J Med. Jun 06, 2019;380(23):2279-2280. [FREE Full text] [CrossRef] [Medline]
- Silverman JJ, Galanter M, Jackson-Triche M, Jacobs DG, Lomax 2nd JW, Riba MB, et al. American Psychiatric Association. The American Psychiatric Association practice guidelines for the psychiatric evaluation of adults. Am J Psychiatry. Aug 01, 2015;172(8):798-802. [CrossRef] [Medline]
- Hong RH, Murphy JK, Michalak EE, Chakrabarty T, Wang Z, Parikh SV, et al. Implementing measurement-based care for depression: practical solutions for psychiatrists and primary care physicians. Neuropsychiatr Dis Treat. Jan 14, 2021;17:79-90. [FREE Full text] [CrossRef] [Medline]
- Chang TE, Jing Y, Yeung AS, Brenneman SK, Kalsekar ID, Hebden T, et al. Depression monitoring and patient behavior in the clinical outcomes in MEasurement-based treatment (COMET) trial. Psychiatr Serv. Aug 01, 2014;65(8):1058-1061. [CrossRef] [Medline]
- Hopwood M. The shared decision-making process in the pharmacological management of depression. Patient. Feb 2020;13(1):23-30. [FREE Full text] [CrossRef] [Medline]
- Thompson L, McCabe R. The effect of clinician-patient alliance and communication on treatment adherence in mental health care: a systematic review. BMC Psychiatry. Jul 24, 2012;12:87. [FREE Full text] [CrossRef] [Medline]
- Clever SL, Ford DE, Rubenstein LV, Rost KM, Meredith LS, Sherbourne CD, et al. Primary care patients' involvement in decision-making is associated with improvement in depression. Med Care. May 2006;44(5):398-405. [CrossRef] [Medline]
- van Os J, Delespaul P, Barge D, Bakker RP. Testing an mHealth momentary assessment routine outcome monitoring application: a focus on restoration of daily life positive mood states. PLoS One. Dec 16, 2014;9(12):e115254. [FREE Full text] [CrossRef] [Medline]
- Gonçalves-Bradley DC, J Maria AR, Ricci-Cabello I, Villanueva G, Fønhus MS, Glenton C, et al. Mobile technologies to support healthcare provider to healthcare provider communication and management of care. Cochrane Database Syst Rev. Aug 18, 2020;8(8):CD012927. [FREE Full text] [CrossRef] [Medline]
- Rathbone AL, Prescott J. The use of mobile apps and SMS messaging as physical and mental health interventions: systematic review. J Med Internet Res. Aug 24, 2017;19(8):e295. [FREE Full text] [CrossRef] [Medline]
- Wu A, Scult MA, Barnes ED, Betancourt JA, Falk A, Gunning FM. Smartphone apps for depression and anxiety: a systematic review and meta-analysis of techniques to increase engagement. NPJ Digit Med. Feb 11, 2021;4(1):20. [FREE Full text] [CrossRef] [Medline]
- Torous J, Staples P, Shanahan M, Lin C, Peck P, Keshavan M, et al. Utilizing a personal smartphone custom app to assess the Patient Health Questionnaire-9 (PHQ-9) depressive symptoms in patients with major depressive disorder. JMIR Ment Health. Mar 24, 2015;2(1):e8. [FREE Full text] [CrossRef] [Medline]
- Firth J, Torous J, Nicholas J, Carney R, Pratap A, Rosenbaum S, et al. The efficacy of smartphone-based mental health interventions for depressive symptoms: a meta-analysis of randomized controlled trials. World Psychiatry. Oct 2017;16(3):287-298. [FREE Full text] [CrossRef] [Medline]
- Linardon J, Cuijpers P, Carlbring P, Messer M, Fuller-Tyszkiewicz M. The efficacy of app-supported smartphone interventions for mental health problems: a meta-analysis of randomized controlled trials. World Psychiatry. Oct 2019;18(3):325-336. [FREE Full text] [CrossRef] [Medline]
- BinDhim NF, Alanazi EM, Aljadhey H, Basyouni MH, Kowalski SR, Pont LG, et al. Does a mobile phone depression-screening app motivate mobile phone users with high depressive symptoms to seek a health care professional's help? J Med Internet Res. Jun 27, 2016;18(6):e156. [FREE Full text] [CrossRef] [Medline]
- Chung K, Jeon MJ, Park J, Lee S, Kim CO, Park JY. Development and evaluation of a mobile-optimized daily self-rating depression screening app: a preliminary study. PLoS One. Jun 26, 2018;13(6):e0199118. [FREE Full text] [CrossRef] [Medline]
- Beitman BD, Soth AM. Activation of self-observation: a core process among the psychotherapies. J Psychother Integr. Dec 2006;16(4):383-397. [FREE Full text] [CrossRef]
- Morris ME, Kathawala Q, Leen TK, Gorenstein EE, Guilak F, Labhard M, et al. Mobile therapy: case study evaluations of a cell phone application for emotional self-awareness. J Med Internet Res. Apr 30, 2010;12(2):e10. [FREE Full text] [CrossRef] [Medline]
- Lorca-Cabrera J, Grau C, Martí-Arques R, Raigal-Aran L, Falcó-Pegueroles A, Albacar-Riobóo N. Effectiveness of health web-based and mobile app-based interventions designed to improve informal caregiver's well-being and quality of life: a systematic review. Int J Med Inform. Feb 2020;134:104003. [CrossRef] [Medline]
- Patoz MC, Hidalgo-Mazzei D, Blanc O, Verdolini N, Pacchiarotti I, Murru A, et al. Patient and physician perspectives of a smartphone application for depression: a qualitative study. BMC Psychiatry. Jan 29, 2021;21(1):65. [FREE Full text] [CrossRef] [Medline]
- Qu C, Sas C, Daudén Roquet C, Doherty G. Functionality of top-rated mobile apps for depression: systematic search and evaluation. JMIR Ment Health. Jan 24, 2020;7(1):e15321. [FREE Full text] [CrossRef] [Medline]
- Stiles-Shields C, Montague E, Lattie EG, Kwasny MJ, Mohr DC. What might get in the way: barriers to the use of apps for depression. Digit Health. Jun 08, 2017;3:2055207617713827. [FREE Full text] [CrossRef] [Medline]
- Fuller-Tyszkiewicz M, Richardson B, Klein B, Skouteris H, Christensen H, Austin D, et al. A mobile app-based intervention for depression: end-user and expert usability testing study. JMIR Ment Health. Aug 23, 2018;5(3):e54. [FREE Full text] [CrossRef] [Medline]
- Mohr DC, Cuijpers P, Lehman K. Supportive accountability: a model for providing human support to enhance adherence to eHealth interventions. J Med Internet Res. Mar 10, 2011;13(1):e30. [FREE Full text] [CrossRef] [Medline]
- Peng W, Kanthawala S, Yuan S, Hussain SA. A qualitative study of user perceptions of mobile health apps. BMC Public Health. Nov 14, 2016;16(1):1158. [FREE Full text] [CrossRef] [Medline]
- McCue M, Blair C, Fehnert B, King J, Cormack F, Sarkey S, et al. Mobile app to enhance patient activation and patient-provider communication in major depressive disorder management: collaborative, randomized controlled pilot study. JMIR Form Res. Oct 27, 2022;6(10):e34923. [FREE Full text] [CrossRef] [Medline]
- Khatib R, McCue M, Blair C, Roy A, Franco J, Fehnert B, et al. Design and implementation of a digitally enabled care pathway to improve management of depression in a large health care system: protocol for the implementation of a patient care platform. JMIR Res Protoc. Jun 23, 2023;12:e43788. [FREE Full text] [CrossRef] [Medline]
- Saunders B, Sim J, Kingstone T, Baker S, Waterfield J, Bartlam B, et al. Saturation in qualitative research: exploring its conceptualization and operationalization. Qual Quant. 2018;52(4):1893-1907. [FREE Full text] [CrossRef] [Medline]
- Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;56(8):77-101. [CrossRef]
- Clarke V, Braun V. Teaching thematic analysis: overcoming challenges and developing strategies for effective learning. The Psychologist. 2013;26(2):120-123.
- Kiger ME, Varpio L. Thematic analysis of qualitative data: AMEE guide no. 131. Med Teach. Aug 2020;42(8):846-854. [CrossRef] [Medline]
- Features. Dedoose. URL: https://www.dedoose.com/home/features [accessed 2021-11-08]
- O'Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA. Standards for reporting qualitative research: a synthesis of recommendations. Acad Med. Sep 2014;89(9):1245-1251. [FREE Full text] [CrossRef] [Medline]
- Dowie J, Kaltoft MK. The future of health is self-production and co-creation based on apomediative decision support. Med Sci (Basel). Aug 22, 2018;6(3):66. [FREE Full text] [CrossRef] [Medline]
- Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. Sep 2001;16(9):606-613. [FREE Full text] [CrossRef] [Medline]
- Lam RW, Lamy FX, Danchenko N, Yarlas A, White MK, Rive B, et al. Psychometric validation of the Perceived Deficits Questionnaire-Depression (PDQ-D) instrument in US and UK respondents with major depressive disorder. Neuropsychiatr Dis Treat. Oct 29, 2018;14:2861-2877. [FREE Full text] [CrossRef] [Medline]
- Topp CW, Østergaard SD, Søndergaard S, Bech P. The WHO-5 well-being index: a systematic review of the literature. Psychother Psychosom. 2015;84(3):167-176. [FREE Full text] [CrossRef] [Medline]
- Jaeger J. Digit symbol substitution test: the case for sensitivity over specificity in neuropsychological testing. J Clin Psychopharmacol. Oct 2018;38(5):513-519. [FREE Full text] [CrossRef] [Medline]
- Morgan DL. Themes, theories, and models. Qual Health Res. Feb 2018;28(3):339-345. [CrossRef] [Medline]
- Czeisler MÉ, Lane RI, Petrosky E, Wiley JF, Christensen A, Njai R, et al. Mental health, substance use, and suicidal ideation during the COVID-19 pandemic - United States, June 24-30, 2020. MMWR Morb Mortal Wkly Rep. Aug 14, 2020;69(32):1049-1057. [FREE Full text] [CrossRef] [Medline]
- Ettman CK, Abdalla SM, Cohen GH, Sampson L, Vivier PM, Galea S. Prevalence of depression symptoms in US adults before and during the COVID-19 pandemic. JAMA Netw Open. Sep 01, 2020;3(9):e2019686. [FREE Full text] [CrossRef] [Medline]
- Demeke HB, Merali S, Marks S, Pao LZ, Romero L, Sandhu P, et al. Trends in use of telehealth among health centers during the COVID-19 pandemic - United States, June 26-November 6, 2020. MMWR Morb Mortal Wkly Rep. Feb 19, 2021;70(7):240-244. [FREE Full text] [CrossRef] [Medline]
- Batra S, Baker RA, Wang T, Forma F, DiBiasi F, Peters-Strickland T. Digital health technology for use in patients with serious mental illness: a systematic review of the literature. Med Devices (Auckl). Oct 04, 2017;10:237-251. [FREE Full text] [CrossRef] [Medline]
- Solomon LS, Kanter MH. Health care steps up to social determinants of health: current context. Perm J. Dec 22, 2018;22(4S) [FREE Full text] [CrossRef]
Abbreviations
AAH: Advocate Aurora Health |
EHR: electronic health record |
HCP: health care provider |
MBC: measurement-based care |
MDD: major depressive disorder |
PHQ-9: 9-item Patient Health Questionnaire |
PRO: patient-reported outcome |
SDM: shared decision-making |
Edited by A Kushniruk; submitted 20.09.22; peer-reviewed by M Opler, M Mirro, J Murphy; comments to author 20.11.22; revised version received 10.05.23; accepted 25.05.23; published 26.07.23.
Copyright©Maggie McCue, Rasha Khatib, Christopher Kabir, Chris Blair, Ben Fehnert, James King, Alexander Spalding, Lara Zaki, Lambros Chrones, Anit Roy, David E Kemp. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 26.07.2023.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Human Factors, is properly cited. The complete bibliographic information, a link to the original publication on https://humanfactors.jmir.org, as well as this copyright and license information must be included.