Integration Of Patient-generated Health Data Research Articles

An Exploratory Study on the Utility of Patient-Generated Health Data as a Tool for Health Care Professionals in Multiple Sclerosis Care.

Patient-generated health data (PGHD) are data collected through technologies such as mobile devices and health apps. The integration of PGHD into health care workflows can support the care of chronic conditions such as multiple sclerosis (MS). Patients are often willing to share data with health care professionals (HCPs) in their care team; however, the benefits of PGHD can be limited if HCPs do not find it useful, leading patients to discontinue data tracking and sharing eventually. Therefore, understanding the usefulness of mobile health (mHealth) solutions, which provide PGHD and serve as enablers of the HCPs' involvement in participatory care, could motivate them to continue using these technologies. The objective of this study is to explore the perceived utility of different types of PGHD from mHealth solutions which could serve as tools for HCPs to support participatory care in MS. A mixed-methods approach was used, combining qualitative research and participatory design. This study includes three sequential phases: data collection, assessment of PGHD utility, and design of data visualizations. In the first phase, 16 HCPs were interviewed. The second and third phases were carried out through participatory workshops, where PGHD types were conceptualized in terms of utility. The study found that HCPs are optimistic about PGHD in MS care. The most useful types of PGHD for HCPs in MS care are patients' habits, lifestyles, and fatigue-inducing activities. Although these subjective data seem more useful for HCPs, it is more challenging to visualize them in a useful and actionable way. HCPs are optimistic about mHealth and PGHD as tools to further understand their patients' needs and support care in MS. HCPs from different disciplines have different perceptions of what types of PGHD are useful; however, subjective types of PGHD seem potentially more useful for MS care.

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.

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.

Provider Preferences for Patient-Generated Health Data Displays in Pediatric Asthma: A Participatory Design Approach.

Objective There is a lack of evidence on how to best integrate patient-generated health data (PGHD) into electronic health record (EHR) systems in a way that supports provider needs, preferences, and workflows. The purpose of this study was to investigate provider preferences for the graphical display of pediatric asthma PGHD to support decisions and information needs in the outpatient setting. Methods In December 2019, we conducted a formative evaluation of information display prototypes using an iterative, participatory design process. Using multiple types of PGHD, we created two case-based vignettes for pediatric asthma and designed accompanying displays to support treatment decisions. Semi-structured interviews and questionnaires with six participants were used to evaluate the display usability and determine provider preferences. Results We identified provider preferences for display features, such as the use of color to indicate different levels of abnormality, the use of patterns to trend PGHD over time, and the display of environmental data. Preferences for display content included the amount of information and the relationship between data elements. Conclusion Overall, provider preferences for PGHD include a desire for greater detail, additional sources, and visual integration with relevant EHR data. In the design of PGHD displays, it appears that the visual synthesis of multiple PGHD elements facilitates the interpretation of the PGHD. Clinicians likely need more information to make treatment decisions when PGHD displays are introduced into practice. Future work should include the development of interactive interface displays with full integration of PGHD into EHR systems.

Patient-Generated Health Data in Pediatric Asthma: Exploratory Study of Providers' Information Needs

BackgroundAdolescents are using mobile health apps as a form of self-management to collect data on symptoms, medication adherence, and activity. Adding functionality to an electronic health record (EHR) to accommodate disease-specific patient-generated health data (PGHD) may support clinical care. However, little is known on how to incorporate PGHD in a way that informs care for patients. Pediatric asthma, a prevalent health issue in the United States with 6 million children diagnosed, serves as an exemplar condition to examine information needs related to PGHD.ObjectiveIn this study we aimed to identify and prioritize asthma care tasks and decisions based on pediatric asthma guidelines and identify types of PGHD that might support the activities associated with the decisions. The purpose of this work is to provide guidance to mobile health app developers and EHR integration.MethodsWe searched the literature for exemplar asthma mobile apps and examined the types of PGHD collected. We identified the information needs associated with each decision in accordance with consensus-based guidelines, assessed the suitability of PGHD to meet those needs, and validated our findings with expert asthma providers.ResultsWe mapped guideline-derived information needs to potential PGHD types and found PGHD that may be useful in meeting information needs. Information needs included types of symptoms, symptom triggers, medication adherence, and inhaler technique. Examples of suitable types of PGHD were Asthma Control Test calculations, exposures, and inhaler use. Providers suggested uncontrolled asthma as a place to focus PGHD efforts, indicating that they preferred to review PGHD at the time of the visit.ConclusionsWe identified a manageable list of information requirements derived from clinical guidelines that can be used to guide the design and integration of PGHD into EHRs to support pediatric asthma management and advance mobile health app development. Mobile health app developers should examine PGHD information needs to inform EHR integration efforts.

Integrating Patient-Generated Health Data in an Electronic Medical Record: Stakeholders' Perspectives.

Patient-generated health data (PGHD), when shared with the provider, provides potential as an approach to improve quality of care. Based on interviews and a focus group with stakeholders involved in PGHD integration in the electronic medical record (EMR), we explore the benefits, barriers and possible risks. We propose solutions to address liability concerns, such as clarifying patient and provider expectations for the analyses of PGHD and emphasize considerations for future steps, which include the need to screen PGHD for patient safety.

Patient generated health data and electronic health record integration in oncologic surgery: A call for artificial intelligence and machine learning.

In this review, we aim to assess the current state of science in relation to the integration of patient-generated health data (PGHD) and patient-reported outcomes (PROs) into routine clinical care with a focus on surgical oncology populations. We will also describe the critical role of artificial intelligence and machine-learning methodology in the efficient translation of PGHD, PROs, and traditional outcome measures into meaningful patient care models.

Innovations in research and clinical care using patient-generated health data.

Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.

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.

Patient-Generated Health Data Integration and Advanced Analytics for Diabetes Management: The AID-GM Platform.

Diabetes is a high-prevalence disease that leads to an alteration in the patient’s blood glucose (BG) values. Several factors influence the subject’s BG profile over the day, including meals, physical activity, and sleep. Wearable devices are available for monitoring the patient’s BG value around the clock, while activity trackers can be used to record his/her sleep and physical activity. However, few tools are available to jointly analyze the collected data, and only a minority of them provide functionalities for performing advanced and personalized analyses. In this paper, we present AID-GM, a web application that enables the patient to share with his/her diabetologist both the raw BG data collected by a flash glucose monitoring device, and the information collected by activity trackers, including physical activity, heart rate, and sleep. AID-GM provides several data views for summarizing the subject’s metabolic control over time, and for complementing the BG profile with the information given by the activity tracker. AID-GM also allows the identification of complex temporal patterns in the collected heterogeneous data. In this paper, we also present the results of a real-world pilot study aimed to assess the usability of the proposed system. The study involved 30 pediatric patients receiving care at the Fondazione IRCCS Policlinico San Matteo Hospital in Pavia, Italy.

Patient-generated health data and electronic health record integration: protocol for a scoping review

IntroductionThe objective of this study is to determine the extent and describe the nature of patient-generated health data (PGHD) integration into electronic health records (EHRs) using systematic scoping methods to...

Executive Summary of the Assessing Surgical Site Infection Surveillance Technologies (ASSIST) Project.

Background: The expert panel that conducted the Assessing Surgical Site Infection Surveillance Technologies (ASSIST) project elaborates on the key findings of the health technologies assessment (HTA) report in a series of articles addressing topics from workflow challenges to implementation strategies to new big data analytics tailored to incorporate serial patient-generated health data (PGHD).Conclusion: By reporting on the methodology, with an emphasis on stakeholder engagement, the ASSIST investigators provide the basis for a future deep dive into the next phase of PGHD integration into surgical site infection (SSI) surveillance.

Patient generated health data use in clinical practice: A systematic review

BackgroundPrecision health calls for collecting and analyzing large amounts of data to capture an individual's unique behavior, lifestyle, genetics, and environmental context. The diffusion of digital tools has led to a significant growth of patient generated health data (PGHD), defined as health-related data created, gathered or inferred by or from patients and for which the patient controls data collection and data sharing. PurposeWe assessed the current evidence of the impact of PGHD use in clinical practice and provide recommendations for the formal integration of PGHD in clinical care. MethodsWe searched PubMed, Ovid, Embase, CINAHL, Web of Science, and Scopus up to May 2018. Inclusion criteria were applied and four reviewers screened titles and abstracts and consequently full articles. FindingsOur systematic literature review identified 21 studies that examined the use of PGHD in clinical settings. Integration of PGHD into electronic records was extremely limited, and decision support capabilities were for the most part basic. DiscussionPGHD and other types of patient-reported data will be part of the health care system narrative and we must continue efforts to understand its impact on health outcomes, costs, and patient satisfaction. Nursing scientists need to lead the process of defining the role of PGHD in the era of precision health.

Bridging the integration gap between patient-generated blood glucose data and electronic health records.

Telemedicine can facilitate population health management by extending the reach of providers to efficiently care for high-risk, high-utilization populations. However, for telemedicine to be maximally useful, data collected using telemedicine technologies must be reliable and readily available to healthcare providers. To address current gaps in integration of patient-generated health data into the electronic health record (EHR), we examined 2 patient-facing platforms, Epic MyChart and Apple HealthKit, both of which facilitated the uploading of blood glucose data into the EHR as part of a diabetes telemedicine intervention. All patients were offered use of the MyChart platform; we subsequently invited a purposive sample of patients who used the MyChart platform effectively (n = 5) to also use the Apple HealthKit platform. Patients reported both platforms helped with diabetes self-management, and providers appreciated the convenience of the processes for obtaining patient data. Providers stated that the EHR data presentation format for Apple HealthKit was challenging to interpret; however, they also valued the greater perceived accuracy the Apple HealthKit data. Our findings indicate that patient-facing platforms can feasibly facilitate transmission of patient-generated health data into the EHR and support telemedicine-based care.

Potential and challenges of patient-generated health data for high-quality cancer care.

Electronic health records and information technology that allow for customizable alerts, intelligent filtering of data, and meaningful aggregation of multiple streams of patient-generated health data with clinical data will be integral to the successful integration of patient-generated health data into routine cancer care.