Use Of Patient-generated Health Data Research Articles

Patient and Provider Perspectives About the Use of Patient-Generated Health Data During Pregnancy: Qualitative Exploratory Study.

There is increasing interest in using patient-generated health data (PGHD) to improve patient-centered care during pregnancy. However, little research has examined the perspectives of patients and providers as they report, collect, and use PGHD to inform obstetric care. This study aims to explore the perspectives of patients and providers about the use of PGHD during pregnancy, including the benefits and challenges of reporting, collecting, and using these data, as well as considerations for expanding the use of PGHD to improve obstetric care. We conducted one-on-one interviews with 30 pregnant or postpartum patients and 14 health care providers from 2 obstetrics clinics associated with an academic medical center. Semistructured interview guides included questions for patients about their experience and preferences for sharing PGHD and questions for providers about current processes for collecting PGHD, opportunities to improve or expand the collection of PGHD, and challenges faced when collecting and using this information. Interviews were conducted by phone or videoconference and were audio recorded, transcribed verbatim, and deidentified. Interview transcripts were analyzed deductively and inductively to characterize and explore themes in the data. Patients and providers described how PGHD, including physiologic measurements and experience of symptoms, were currently collected during and between in-person clinic visits for obstetric care. Both patients and providers reported positive perceptions about the collection and use of PGHD during pregnancy. Reported benefits of collecting PGHD included the potential to use data to directly inform patient care (eg, identify issues and adjust medication) and to encourage ongoing patient involvement in their care (eg, increase patient attention to their health). Patients and providers had suggestions for expanding the collection and use of PGHD during pregnancy, and providers also shared considerations about strategies that could be used to expand PGHD collection and use. These strategies included considering the roles of both patients and providers in reporting and interpreting PGHD. Providers also noted the need to consider the unintended consequences of using PGHD that should be anticipated and addressed. Acknowledging the challenges, suggestions, and considerations voiced by patients and providers can inform the development and implementation of strategies to effectively collect and use PGHD to support patient-centered care during pregnancy.

The Use of Patient-Generated Health Data From Consumer-Grade Mobile Devices in Clinical Workflows: Protocol for a Systematic Review.

With the rapid advancement of mobile technology, the scope of mobile health (mHealth) has expanded to include consumer-grade devices such as smartphones and wearable sensors. These solutions have typically been used for fitness purposes; however, due to their ubiquitous capabilities for data collection, they have the potential to bridge information gaps and supplement data from clinical visits. Patient-generated health data (PGHD) can be derived from mHealth solutions and be used by health care professionals (HCPs) as complementary tools in the care process, yet their integration into clinical workflows presents a myriad of challenges. PGHD might be a new and unfamiliar source of information for most HCPs, and the majority of mHealth solutions have not been designed to be used by HCPs as active reviewers. As mHealth solutions become more available and attractive to patients, HCPs may see an increase in the influx of data and related inquiries from their patients. This mismatch in expectations can result in disruptions to clinical workflows and negatively impact patient-clinician relationships. For PGHD to be integrated into clinical workflows, its use should be proven beneficial for both patients and HCPs. However, so far, only limited research has been done on the concrete experiences of HCPs as active reviewers of PGHD from consumer-grade mobile devices. We aimed to systematically guide the review of existing literature to identify what types of PGHD from consumer-grade mobile devices are currently being used by HCPs as complementary tools in the care process. The PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) 2015 was followed for the design of the search, selection, and data synthesis processes. Electronic searches will be done on PubMed, ACM Digital Library, IEEE Xplore, and Scopus. Preliminary searches have been conducted, and previous related systematic and scoping reviews have been found and evaluated. The review is expected to be completed in February 2023. This protocol will guide the review of existing literature on the use of PGHD produced by consumer-grade mobile devices. Although there have been previous reviews related to this topic, our proposed approach seeks to understand the specific opinions and experiences of different types of HCPs who are already using PGHD in their clinical practice and the motives for deeming these data useful and worth reviewing. Depending on the studies that will be included, there may be an opportunity to provide a wider understanding of what types of HCPs trust PGHD, despite the possible challenges that its use might convey, potentially contributing with the knowledge to support the design strategies of mHealth tools that could be integrated into clinical workflows. PRR1-10.2196/39389.

Bridging New Technology Into Clinical Practice With Mobile Apps, Electronic Patient-Reported Outcomes, and Wearables.

With sophisticated mobile and wearable technologies available, there has been interest in leveraging these devices to help gather and analyze patient-generated health data (PGHD). This information could be used to better address health concerns, aid in treatment decision-making, and guide interventional strategies to improve outcomes. Among PGHD, electronic patient-reported outcomes, direct reports of patient experience usually collected via validated scales and questionnaires, are increasingly integrated into routine clinical practice to monitor patient status. Electronic patient-reported outcomes have been shown to improve outcomes, including symptom control, quality of life, and overall survival, in several clinical trials. Electronic patient-reported outcome collection is now being implemented across broader clinical practice settings but with limited evaluation of impact thus far. Wearable devices and mobile apps provide opportunities to collect additional PGHD, including continuous physiologic measures, and to generate algorithms with which to monitor patients with cancer and guide interventions. In this article, we discuss several topics related to PGHD and technology, including electronic patient-reported outcomes, mobile apps, and wearable devices and how their introduction into oncology care has the potential to improve the collection and use of PGHD in the future. We also highlight the challenges and future directions needed for mobile and wearable technologies to provide meaningful information that can be acted upon and thus can improve oncologic care.

Adoption of Patient-Generated Health Data in Oncology: A Report From the NCCN EHR Oncology Advisory Group.

Collecting, monitoring, and responding to patient-generated health data (PGHD) are associated with improved quality of life and patient satisfaction, and possibly with improved patient survival in oncology. However, the current state of adoption, types of PGHD collected, and degree of integration into electronic health records (EHRs) is unknown. The NCCN EHR Oncology Advisory Group formed a Patient-Reported Outcomes (PRO) Workgroup to perform an assessment and provide recommendations for cancer centers, researchers, and EHR vendors to advance the collection and use of PGHD in oncology. The issues were evaluated via a survey of NCCN Member Institutions. Questions were designed to assess the current state of PGHD collection, including how, what, and where PGHD are collected. Additionally, detailed questions about governance and data integration into EHRs were asked. Of 28 Member Institutions surveyed, 23 responded. The collection and use of PGHD is widespread among NCCN Members Institutions (96%). Most centers (90%) embed at least some PGHD into the EHR, although challenges remain, as evidenced by 88% of respondents reporting the use of instruments not integrated. Forty-seven percent of respondents are leveraging PGHD for process automation and adherence to best evidence. Content type and integration touchpoints vary among the members, as well as governance maturity. The reported variability regarding PGHD suggests that it may not yet have reached its full potential for oncology care delivery. As the adoption of PGHD in oncology continues to expand, opportunities exist to enhance their utility. Among the recommendations for cancer centers is establishment of a governance process that includes patients. Researchers should consider determining which PGHD instruments confer the highest value. It is recommended that EHR vendors collaborate with cancer centers to develop solutions for the collection, interpretation, visualization, and use of PGHD.

Enabling Research and Clinical Use of Patient-Generated Health Data (the mindLAMP Platform): Digital Phenotyping Study.

BackgroundThere is a growing need for the integration of patient-generated health data (PGHD) into research and clinical care to enable personalized, preventive, and interactive care, but technical and organizational challenges, such as the lack of standards and easy-to-use tools, preclude the effective use of PGHD generated from consumer devices, such as smartphones and wearables.ObjectiveThis study outlines how we used mobile apps and semantic web standards such as HTTP 2.0, Representational State Transfer, JSON (JavaScript Object Notation), JSON Schema, Transport Layer Security (version 1.3), Advanced Encryption Standard-256, OpenAPI, HTML5, and Vega, in conjunction with patient and provider feedback to completely update a previous version of mindLAMP.MethodsThe Learn, Assess, Manage, and Prevent (LAMP) platform addresses the abovementioned challenges in enhancing clinical insight by supporting research, data analysis, and implementation efforts around PGHD as an open-source solution with freely accessible and shared code.ResultsWith a simplified programming interface and novel data representation that captures additional metadata, the LAMP platform enables interoperability with existing Fast Healthcare Interoperability Resources–based health care systems as well as consumer wearables and services such as Apple HealthKit and Google Fit. The companion Cortex data analysis and machine learning toolkit offer robust support for artificial intelligence, behavioral feature extraction, interactive visualizations, and high-performance data processing through parallelization and vectorization techniques.ConclusionsThe LAMP platform incorporates feedback from patients and clinicians alongside a standards-based approach to address these needs and functions across a wide range of use cases through its customizable and flexible components. These range from simple survey-based research to international consortiums capturing multimodal data to simple delivery of mindfulness exercises through personalized, just-in-time adaptive interventions.

Clinicians and Older Adults' Perceptions of the Utility of Patient-Generated Health Data in Caring for Older Adults: Exploratory Mixed Methods Study.

BackgroundMany people are motivated to self-track their health and optimize their well-being through mobile health apps and wearable devices. The diversity and complexity of these systems have evolved over time, resulting in a large amount of data referred to as patient-generated health data (PGHD), which has recently emerged as a useful set of data elements in health care systems around the world. Despite the increased interest in PGHD, clinicians and older adults’ perceptions of PGHD are poorly understood. In particular, although some clinician barriers to using PGHD have been identified, such as concerns about data quality, ease of use, reliability, privacy, and regulatory issues, little is known from the perspectives of older adults.ObjectiveThis study aims to explore the similarities and differences in the perceptions of older adults and clinicians with regard to how various types of PGHD can be used to care for older adults.MethodsA mixed methods study was conducted to explore clinicians and older adults’ perceptions of PGHD. Focus groups were conducted with older adults and health care providers from the Greater Toronto area and the Kitchener-Waterloo region. The participants were asked to discuss their perceptions of PGHD, including facilitators and barriers. A questionnaire aimed at exploring the perceived usefulness of a range of different PGHD was also embedded in the study design. Focus group interviews were transcribed for thematic analysis, whereas the questionnaire results were analyzed using descriptive statistics.ResultsOf the 9 participants, 4 (44%) were clinicians (average age 38.3 years, SD 7 years), and 5 (56%) were older adults (average age 81.0 years, SD 9.1 years). Four main themes were identified from the focus group interviews: influence of PGHD on patient-provider trust, reliability of PGHD, meaningful use of PGHD and PGHD-based decision support systems, and perceived clinical benefits and intrusiveness of PGHD. The questionnaire results were significantly correlated with the frequency of PGHD mentioned in the focus group interviews (r=0.42; P=.03) and demonstrated that older adults and clinicians perceived blood glucose, step count, physical activity, sleep, blood pressure, and stress level as the most useful data for managing health and delivering high-quality care.ConclusionsThis embedded mixed methods study generated several important findings about older adults and clinicians’ perceptions and perceived usefulness of a range of PGHD. Owing to the exploratory nature of this study, further research is needed to understand the concerns about data privacy, potential negative impact on the trust between older adults and clinicians, data quality and quantity, and usability of PGHD-related technologies for older adults.

Patient generated health data: Benefits and challenges

Patient Generated Health Data (PGHD) is defined as data generated by and from patients.1 The use of PGHD has rapidly increased with the widespread availability of smart phone mobile health applications (mHealth apps) and wearable devices. Currently, the vast majority of PGHD is generated via the use of mHealth apps and wearables like "Fitbit" or medical devices such as a continuous glucose monitoring device. There are many benefits of PGHD including increased monitoring of children's chronic health conditions outside clinical care to supplement ambulatory clinic visits, improved health outcomes, increased patient awareness and engagement and improved patient-provider communication. When leveraged properly, PGHD can be a powerful tool in delivering safe, effective, patient centered, efficient and equitable care as outlined by the Institute of Medicine (IOM).2 The challenges that limit collection, use and acceptance of PGHD include limited access to the internet, inability to incorporate PGHD into clinical workflows, data privacy and security concerns and apprehension about accuracy and safety of mHealth apps. These issues can lead to a lack of use or compliance with devices or apps associated with PGHD. To ensure optimal health benefits, agreement to leverage PGHD should be a joint decision between the clinician and the patient/caregiver. Future steps to ensure safety and clinical relevance of PGHD include involving regulatory authorities, device manufacturers and professional bodies to develop standards for mHealth apps and wearables to promote uncomplicated PGHD integration into workflows, easy and secure sharing of PGHD. Wearable technology, medical devices and smart phone apps become more advanced and widespread among the population, there will be an increasing potential for PGHD to facilitate personalized, efficient, and collaborative care resulting in improved health outcomes for children and adolescent and young adult. More research and innovation is needed to facilitate this transition.

Correction to: Changing Categorical Work in Healthcare: the Use of Patient-Generated Health Data in Cancer Rehabilitation

Correction to: Changing Categorical Work in Healthcare: the Use of Patient-Generated Health Data in Cancer Rehabilitation

Perceptions of Mobile Health Technology in Elective Surgery: A Qualitative Study of North American Surgeons.

To explore the surgeon-perceived added value of mobile health technologies (mHealth), and determine facilitators of and barriers to implementing mHealth. Despite the growing popularity of mHealth and evidence of meaningful use of patient-generated health data in surgery, implementation remains limited. This was an exploratory qualitative study following the Consolidated Criteria for Reporting Qualitative Research. Purposive sampling was used to identify surgeons across the United States and Canada. The Consolidated Framework for Implementation Research informed development of a semistructured interview guide. Video-based interviews were conducted (September-November 2020) and interview transcripts were thematically analyzed. Thirty surgeons from 8 specialties and 6 North American regions were interviewed. Surgeons identified opportunities to integrate mHealth data pre- operatively (eg, expectation-setting, decision-making) and during recovery (eg, remote monitoring, earlier detection of adverse events) among higher risk patients. Perceived advantages of mHealth data compared with surgical and patient-reported outcomes included easier data collection, higher interpretability and objectivity of mHealth data, and the potential to develop more patientcentered and functional measures of health. Surgeons identified a variety of implementation facilitators and barriers around surgeon- and patient buy-in, integration with electronic medical records, regulatory/reimbursement concerns, and personnel responsible for mHealth data. Surgeons described similar considerations regarding perceptions of mHealth among patients, including the potential to address or worsen existing disparities in surgical care. These findings have the potential to inform the effective and equitable implementation of mHealth for the purposes of supporting patients and surgical care teams throughout the delivery of surgical care.

Investigating the possibilities of using patient-generated health data in emergency care: an explorative study

AbstractIt is well known that emergency departments (EDs) are exposed to human errors and unintended events due to large patient flow, high work pressure and overload of information. Strategies for providing efficient and effective health care are therefore imperative, and health information technologies are suggested to be one of the solutions. This study sought to investigate if the use of patient-generated health data gathered through a digital patient questionnaire and visualised as a patient-generated journal (PGJ v.1.0) has the potential to improve the care delivered in EDs.MethodsUsing a mixed-method approach, the PGJ (v.1.0) was investigatedfrom key stakeholders’ perspectives. First, we examined the PGJ from a patient perspective via participant observation (n = 18) and interviews (n = 18), supported by statistical data from the PGJ (n = 56). Second, we used questionnaires to explore the physicians’ perspectives (n = 9). Lastly, two interviews were conducted with healthcare leaders from the ED. The data were compared and analysed using descriptive statistics and hermeneutic analysis.Results From the findings, it appears that patients in need of urgent care accept the use of patient-generated data, and patients highly favoured being active in their patient pathway. However, the system needed some adjustments to fit the patients’abilities in urgent situations.The physicians expressed mixed attitudes towards the PGJ: the majority agreed that the system needed some adjustments in order for the full benefits to be gained, but thought that it had potential to improve their work processes when fully developed.Conclusions This study concludes that the use of patient-generated data is well accepted by patients in an urgent setting, and that the PGJ has the potential to improve quality of care in patient pathways by adding value to patient flows as well as clinical workflows. The concept of utilising patient-generated health data in emergency care should therefore be further developed and investigated.

Use of patient-generated health data for shared decision-making in the clinical environment: ready for prime time

Use of patient-generated health data for shared decision-making in the clinical environment: ready for prime time

The impact of the COVID-19 pandemic on people with rheumatic and musculoskeletal diseases: insights from patient-generated data on social media.

ObjectivesDuring the COVID-19 pandemic, much communication occurred online, through social media. This study aimed to provide patient perspective data on how the COVID-19 pandemic impacted people with rheumatic and musculoskeletal diseases (RMDs), using Twitter-based patient-generated health data (PGHD).MethodsA convenience sample of Twitter messages in English posted by people with RMDs was extracted between March 1, and July 12, 2020 and examined using thematic analysis. Included were Twitter messages that mentioned keywords and hashtags related to both COVID-19 (or SARS-CoV-2) and select RMDs. The RMDs monitored included inflammatory-driven (joint) conditions (Ankylosing Spondylitis, Rheumatoid Arthritis, Psoriatic Arthritis, Lupus/Systemic Lupus Erythematosus, and Gout).ResultsThe analysis included 569 tweets by 375 Twitter users with RMDs across several countries. Eight themes emerged regarding the impact of the COVID-19 pandemic on people with RMDs: (1) lack of understanding of SARS-CoV-2/COVID-19; (2) critical changes in health behaviour; (3) challenges in healthcare practice and communication with healthcare professionals; (4) difficulties with access to medical care; (5) negative impact on physical and mental health, coping strategies; (6) issues around work participation, (7) negative effects of the media; (8) awareness-raising.ConclusionThe findings show that Twitter serves as a real-time data source to understand the impact of the COVID-19 pandemic on people with RMDs. The platform provided “early signals” of potentially critical health behaviour changes. Future epidemics might benefit from the real-time use of Twitter-based PGHD to identify emerging health needs, facilitate communication, and inform clinical practice decisions.

Using Data to Approach the Unknown

Medical practices have always been data oriented: healthcare providers decisions are based on data, both clinically generated and patient reported. With the increased use of patient-generated health data (PGHD), other types of data are entering providers? practices and influencing patient-provider collaboration. We studied the use of PGHD and its related data practices in the context of fertility, a health concern that is uncertain, complex, and data intensive. We interviewed 14 patients who are facing or have faced challenges to conceive and 5 healthcare providers specialized in infertility. Our findings show that patients and providers use PGHD in different ways but with the common goal of exploring 'the unknown' generated by the uncertainties of fertility. Providers use patients? data in a rational protocol, aiming to identify possible causes of infertility and define a treatment course. Patients use data in a much more emotional way, learning about their bodies while struggling with data interpretation challenges. By analyzing these data practices, we discuss the principles behind their differences and describe how they have individual benefits for each specific group. We then suggest that fertility technologies need to consider such principles, highlight the existing boundary between patients' and providers' data practices, and focus on bridging instead of merging them in order to facilitate collaboration and maintain their independent benefits.

Changing Categorical Work in Healthcare: the Use of Patient-Generated Health Data in Cancer Rehabilitation

Categorical work in chronic care is increasingly dependent on digital technologies for remote patient care. However, remote care takes many forms and while various types of digital technologies are currently being used, we lack a nuanced understanding of how to design such technologies for specific novel usages. In this paper, we focus on digital technologies for patient-generated health data and how their use changes categorical work in chronic care. Our aim is to understand how categorical work changes, which novel forms of categorical work emerge and what the implications are for the care relation. This paper is based on an ethnographic study of healthcare professionals’ work at a pelvic cancer rehabilitation clinic and their interactions with patients. In this setting, supportive talks between patients and nurses are central. To understand the complexities of categorical work in chronic care when patient-generated health data are introduced, we contrast the traditional supportive talks with supportive talks where the nurses had access to the patients’ patient-generated health data. We identify and analyze challenges connected to novel forms of categorical work. Specifically, we focus on categorical work and how it can undergo changes. Our empirical findings show how changes occur in the way patients’ lived experience of the chronic disease aligns with the categories from chronic care, as well as in the way the nurse works with clinical categories during the talk. These insights help us further understand the implications of patient generated-data use in supportive talks. We contribute to an improved understanding of the use of patient-generated health data in clinical practice and based on this, we identify design implications for how to make categorical work more collaborative.

Learning endometriosis phenotypes from patient-generated data

Endometriosis is a systemic and chronic condition in women of childbearing age, yet a highly enigmatic disease with unresolved questions: there are no known biomarkers, nor established clinical stages. We here investigate the use of patient-generated health data and data-driven phenotyping to characterize endometriosis patient subtypes, based on their reported signs and symptoms. We aim at unsupervised learning of endometriosis phenotypes using self-tracking data from personal smartphones. We leverage data from an observational research study of over 4000 women with endometriosis that track their condition over more than 2 years. We extend a classical mixed-membership model to accommodate the idiosyncrasies of the data at hand, i.e., the multimodality and uncertainty of the self-tracked variables. The proposed method, by jointly modeling a wide range of observations (i.e., participant symptoms, quality of life, treatments), identifies clinically relevant endometriosis subtypes. Experiments show that our method is robust to different hyperparameter choices and the biases of self-tracking data (e.g., the wide variations in tracking frequency among participants). With this work, we show the promise of unsupervised learning of endometriosis subtypes from self-tracked data, as learned phenotypes align well with what is already known about the disease, but also suggest new clinically actionable findings. More generally, we argue that a continued research effort on unsupervised phenotyping methods with patient-generated health data via new mobile and digital technologies will have significant impact on the study of enigmatic diseases in particular, and health in general.

MHealth and patient generated health data: stakeholder perspectives on opportunities and barriers for transforming healthcare.

Wearable devices, mobile health apps, and geolocation technologies place the ability to track, monitor and report data in the individuals' hands - or on their bodies. These innovations create an opportunity for "connected health," where individuals collect data outside of the healthcare encounter and report it to care providers. Collection of such patient-generated health data (PGHD) has the potential to impact the delivery of healthcare through remote monitoring, and by allowing patients and healthcare teams to provide targeted and efficient care that aligns with the health status of individual patients. To understand the value and barriers associated with clinical integration of PGHD we engaged a range of stakeholders, examining their perspectives and experiences of PGHD use. We conducted open-ended interviews with healthcare consumers (patients and care partners), healthcare providers, and healthcare administrators. Open recruitment and purposive sampling were utilized to identify participants that represented the breadth of PGHD use in research and clinical care. Interview guides focused on the value and barriers of PGHD use. Interviews were recorded, transcribed, and analyzed for emergent themes. Themes emerged around the value of PGHD to support care decisions and improve patient-provider communication and engagement, and the promise of applying PGHD to formal care pathways and measurement-based care. Significant barriers included data validity and actionability, and the burden of integrating PGHD into existing care processes. Interviews highlighted areas for future research to better understand how PGHD can advance care transformation. These findings provide rich context for understanding the experiences and needs of the individuals who interface with PGHD. Translating advances in technology and data tracking into successful clinical implementation requires understanding how stakeholders conceptualize and make use of PGHD, the potential value that PGHD can add to care, and the challenges that may limit PGHD's promise. Our results illustrate the value and challenges associated with health-system implementation of PGHD. Efforts to increase the scale and spread of PGHD will benefit from an approach that addresses the value and challenges PGHD brings to clinical care.

Use of patient-generated health data across healthcare settings: implications for health systems.

ObjectiveThe growing prevalence of chronic conditions requiring changes in lifestyle and at-home self-management has increased interest in and need for supplementing clinic visits with data generated by patients outside the clinic. Patient-generated health data (PGHD) support the ability to diagnose and manage chronic conditions, to improve health outcomes, and have the potential to facilitate more “connected health” between patients and their care teams; however, health systems have been slow to adopt PGHD use in clinical care.Materials and MethodsWe surveyed current and potential users of PGHD to catalog how PGHD is integrated into clinical care at an academic health center. The survey included questions about data type, method of collection, and clinical uses of PGHD. Current users were asked to provide detailed case studies of PGHD use in research and care delivery.ResultsThirty-one respondents completed the survey. Seventeen individuals contributed detailed case studies of PGHD use across diverse areas of care, including behavioral health, metabolic and gastrointestinal conditions, musculoskeletal/progressive functional conditions, cognitive symptoms, and pain management. Sensor devices and mobile technologies were the most commonly reported platforms for collection. Clinicians and researchers involved in PGHD use cited the potential for PGHD to enhance care delivery and outcomes, but also indicated substantial barriers to more widespread PGHD adoption across healthcare systems.ConclusionThe results of our survey illustrate how PGHD is used in targeted areas of one healthcare system and provide meaningful insights that can guide health systems in supporting the widespread use of PGHD in care delivery.

Engaging Patients in Co-Design of Mobile Health Tools for Surgical Site Infection Surveillance: Implications for Research and Implementation

Background: As the use of patient-owned devices, including smartphones and tablets, to manage day-to-day activities grows, so does healthcare industry's interest to better leverage technology to engage patients. For surgical care, a unique opportunity exists to capture patient-generated health data (PGHD) including photographs. As part of a broader initiative to evaluate PGHD for surgical site infection (SSI) surveillance, we sought evidence regarding patient involvement and experience with PGHD for SSI monitoring and surveillance.Methods: Through a scoping review of the literature and semi-structured stakeholder interviews we gathered evidence on what is currently known about patient perspectives of and experiences with mobile health (mHealth) interventions for post-operative recovery. We presented findings to and discussed with the ASSIST PGHD Stakeholder Advisory Group (PSAG) to generate priorities for further examination.Results: Our scoping review yielded 34 studies that addressed post-discharge use of PGHD for monitoring and surveillance of SSI. Of these, 16 studies addressed at least one outcome regarding patient experience; the most commonly measured outcome was patient satisfaction. Only three studies reported on patient involvement in the development of PGHD tools and interventions. We conducted interviews (n = 24) representing a range of stakeholder perspectives. Interviewees stressed the importance of patient involvement in tool and program design, noting patient involvement ensures the “work” that patients do in their daily lives to manage their health and healthcare is recognized. Discussion of evidence with the ASSIST PSAG resulted in formal recommendations for direct involvement of patients and caregivers for future work.Conclusions: While mHealth initiatives to advance post-operative management offer the ability to improve patient engagement, work is needed to ensure the patient voice is reflected. Active engagement with patients and caregivers in the development of new technology, the design of new workflows, and the conduct of research and evaluation ensures that the patient experiences and values are incorporated.

A Stakeholder-Driven Framework for Evaluating Surgical Site Infection Surveillance Technologies.

Background: Patients increasingly use mobile devices to send text messages and photographic data to surgeons. There is potential to harness this patient-generated health data (PGHD) for clinical and public health surveillance of surgical site infection (SSI). Leveraging PGHD collected via remote monitoring in the post-operative period has the potential to produce important benefits for patients, surgeons, care teams, and infection surveillance and prevention. Methods: We conducted a health technology assessment (HTA), drawing heavily on stakeholder engagement to better understand current and potential uses of PGHD in post-operative care. Stakeholder engagement activities included assembling an advisory board composed of stakeholder experts, interviewing key informants, and seeking out stakeholder guidance to synthesize evidence from interviews, literature review, and technical app review in order to develop recommendations on the use of PGHD in SSI surveillance. Results: We conducted a review of the published literature, a technical/market scan of available apps for capturing post-operative PGHD, and two rounds of key informant interviews with stakeholders. In addition, we held a day-long workshop to solicit stakeholder feedback on initial findings of the project and to guide additional work. These activities culminated in an HTA report that provides guidance and recommendations on the use of PGHD in SSI surveillance, including practice, research, and public health surveillance, and identifies open issues on post-operative use of PGHD for which additional evidence and experience are needed to optimize application of those data for clinical and public health purposes. Conclusion: Stakeholders, individuals with direct experience, or interest in a given topic are critical to the HTA process. They provide insight to guide the work conducted, ensure that the topics addressed are relevant and important, and that products of the work are accessible and meaningful to the individuals who will be most impacted.

Harnessing the power of social media: how can it help in axial spondyloarthritis research?

Axial spondyloarthritis (axSpA) is a chronic inflammatory rheumatic disease that is relatively unknown among the general public. Most patients with axSpA are young or middle-aged adults and more likely to use some social media. This review highlights trends in the application of social media and different ways in which these tools do already or may benefit clinical research, delivery of care, and education in rheumatology, particularly in the field of axSpA. This article discusses four areas in the biomedical field that social media has infused with novel ideas: (i) the use of patient-generated health data from social media to learn about their disease experience, (ii) delivering health education and interventions, (iii) recruiting study participants, and (iv) reform, transfer, and disseminate medical education. We conclude with promising studies in rheumatology that have incorporated social media and suggestions for future directions. Rheumatologists now have the opportunity to use social media and innovate on many aspects of their practice. We propose further exploration of multiple ways in which social media might help with the identification, diagnosis, education, and research study enrollment of axSpA patients. However, standardization in study design, reporting, and managing ethical and regulatory aspects will be required to take full advantage of this opportunity.