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Abstract

Objectives: Consumer Health Informatics (CHI) and the use of Patient-Generated Health Data (PGHD) are rapidly growing focus areas in healthcare. The objective of this paper is to briefly review the literature that has been published over the past few years and to provide a sense of where the field is going. Methods: We searched PubMed and the ACM Digital Library for articles published between 2014 and 2016 on the topics of CHI and PGHD. The results of the search were screened for relevance and categorized into a set of common themes. We discuss the major topics covered in these articles. Results: We retrieved 65 articles from our PubMed query and 32 articles from our ACM Digital Library query. After a review of titles, we were left with 47 articles to conduct our full article survey of the activities in CHI and PGHD. We have summarized these articles and placed them into major categories of activity. Within the domain of consumer health informatics, articles focused on mobile health and patient-generated health data comprise the majority of the articles published in recent years. Conclusions: Current evidence indicates that technological advancements and the widespread availability of affordable consumer-grade devices are fueling research into using PGHD for better care. As we observe a growing number of (pilot) developments using various mobile health technologies to collect PGHD, major gaps still exist in how to use the data by both patients and providers. Further research is needed to understand the impact of PGHD on clinical outcomes.

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... Patient-Generated Health Data (PGHD) is defined as "health-related data created and recorded by or from patients outside of the clinical setting to help address a health concern" [1]. Innovations in Digital Health (DH) tools and technologies-such as mobile apps, smartphones, wearables, and connected medical devices-have facilitated PGHD capture, use, and sharing [2]. PGHD usually include biometric data, medication effects, symptoms, and activity levels [3]. ...
... The use of PGHD in clinical practice and research is currently in a promising and expanding stage [2,3,6]. There are initiatives in several countries for utilizing PGHD on a national scale, for example: -In the United States of America, the office of the National Coordinator project for Health IT provided a white paper on PGHD in care delivery informed by two pilot demonstrations (2018) [7]. ...
... There are still no best practices on how to incorporate PGHD into clinical workflows. Table 1 summarizes stakeholder perspectives (patients, healthcare providers, and researchers) on PGHD opportunities and barriers for transforming healthcare [2][3][4]. ...
Chapter
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With advances in Digital Health (DH) tools, it has become much easier to collect, use, and share patient-generated health data (PGHD). This wealth of data could be efficiently used in monitoring and controlling chronic illnesses as well as predicting health outcome. Although integrating PGHD into clinical practice is currently in a promising stage, there are several technical challenges and usage barriers that hinder the full utilization of the PGHD potential in clinical care and research. This paper aims to address PGHD opportunities and challenges while developing the DH-Convener project to integrate PGHD into the Electronic Health Record in Austria (ELGA). Accordingly, it provides an integrative technical-clinical-user approach for developing a fully functional health ecosystem for exchanging integrated data among patients, healthcare providers, and researchers.
... anywhere and anytime (Chow, Ariyarathna, Islam, Thiagalingam, & Redfern, 2016;Milani, Lavie, Bober, Milani, & Ventura, 2017). The use of mHealth apps generate diverse and complex data streams, also called patient-generated health data (PGHD; Hsueh et al., 2017;Lai, Hsueh, Choi, & Austin, 2017). PGHD may reveal a whole-patient perspective by including environmental, psychosocial, physiological, or health-related behaviors that influence the capability to manage one's health (Hull, 2015;Rosenbloom, 2016;Woods, Evans, & Frisbee, 2016). ...
... Informatics solutions, such as mHealth apps, enable patients to track and monitor their health (Ernsting et al., 2017;Lai et al., 2017). The PGHD from mHealth apps have the ability to include individuals' perspectives. ...
Article
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Purpose: The purpose of this data visualization study was to identify patterns in patient-generated health data (PGHD) of women with and without Circulation signs or symptoms. Specific aims were to (a) visualize and interpret relationships among strengths, challenges, and needs of women with and without Circulation signs or symptoms; (b) generate hypotheses based on these patterns; and (c) test hypotheses generated in Aim 2. Design: The design of this visualization study was retrospective, observational, case controlled, and exploratory. Methods: We used existing de-identified PGHD from a mobile health application, MyStrengths+MyHealth (N = 383). From the data, women identified with Circulation signs or symptoms (n = 80) were matched to an equal number of women without Circulation signs or symptoms. Data were analyzed using data visualization techniques and descriptive and inferential statistics. Findings: Based on the patterns, we generated nine hypotheses, of which four were supported. Visualization and interpretation of relationships revealed that women without Circulation signs or symptoms compared to women with Circulation signs or symptoms had more strengths, challenges, and needs-specifically, strengths in connecting; challenges in emotions, vision, and health care; and needs related to info and guidance. Conclusions: This study suggests that visualization of whole-person health including strengths, challenges, and needs enabled detection and testing of new health patterns. Some findings were unexpected, and perspectives of the patient would not have been detected without PGHD, which should be valued and sought. Such data may support improved clinical interactions as well as policies for standardization of PGHD as sharable and comparable data across clinical and community settings. Clinical relevance: Standardization of patient-generated whole-person health data enabled clinically relevant research that included the patients' perspective.
... Connection refers to the idea whether data offers a space or a starting point for a conversation between different actors in health and wellness network [11]. Patient portals [37] and patient-generated health data [25,38] are some examples of data spaces that at best may promote communication between patient and service provider. In general, data-driven consultation may facilitate in-depth conversations between service provider and patient [39]. ...
... The technology service provider in this study highlighted the increasing importance of person-generated data in the future due to the emerging amount of virtual health check-ups. Patient portals [37] and the increasing interest of using patient generated health data [38] can be seen as a foundation for a more dynamic communication between customers/patients and service providers. This study suggests, however, that it is necessary to be sensitive and recognize the context of the data when having conversations initiated by person-generated data as otherwise, it can be challenging to make appropriate interpretations based on data. ...
Article
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The healthcare and wellness sector currently attempts to provide more proactive service models with data-driven solutions. This study examines the expectations and values related to personal data i.e. data valences from the perspective of service providers and individual users. The study is based on the analysis of extensive empirical material collected through interviews and a collaborative workshop. The data was collected in one cultural context, Finland. The results suggest that the potential service providers and users have similar expectations regarding self-evidence of data while the main differences concern the expectations of transparency. The results of the study propose some basic requirements for the development of personalised data-driven services in future. The study suggests that basic requirements for the development of future data driven services concern expectations to usable data visualisations, data as a motivator, data accuracy and data transparency. Even though there are varying expectations to personal health data and even some concerns, it can be seen that here different ecosystem actors primarily perceived the wider use of personal health and wellness data as a positive trend. It can be concluded that collaborative personal data-driven service ecosystems are an integral part of development towards proactive service models in healthcare.
... 4,5 Patients can immediately visualize their PROs and other patient-generated health data (PGHD) easily and quickly through the same electronic systems, including smartphone applications and wearables. 6,7 The potential benefits of returning health data to the patient include increased comprehension of health status, engagement in care, and adoption of positive health behaviors. [8][9][10] As such, it represents an opportunity for the patient to become a more active participant in their health and wellbeing. ...
... Nonetheless, there is reticence to allow patients direct access to their health data without clinician interpretation due to concerns about poor comprehension, risk perception, and possibly dangerous or unhealthy behaviors in response to the data. 7,11 Personal health information can be complex, especially when including numerous data points and medical jargon. It may also require contextualization based on age, gender, baseline status, and other personal characteristics. ...
Article
Objectives As personal health data are being returned to patients with increasing frequency and volume, visualizations are garnering excitement for their potential to facilitate patient interpretation. Evaluating these visualizations is important to ensure that patients are able to understand and, when appropriate, act upon health data in a safe and effective manner. The objective of this systematic review was to review and evaluate the state of the science of patient-facing visualizations of personal health data. Methods We searched five scholarly databases (PubMed, Embase, Scopus, ACM Digital Library [Association for Computing Machinery Digital Library], and IEEE Computational Index [Institute of Electrical and Electronics Engineers Computational Index]) through December 1, 2018 for relevant articles. We included English-language articles that developed or tested one or more patient-facing visualizations for personal health data. Three reviewers independently assessed quality of included articles using the Mixed methods Appraisal Tool. Characteristics of included articles and visualizations were extracted and synthesized. Results In 39 articles included in the review, there was heterogeneity in the sample sizes and methods for evaluation but not sample demographics. Few articles measured health literacy, numeracy, or graph literacy. Line graphs were the most common visualization, especially for longitudinal data, but number lines were used more frequently in included articles over past 5 years. Article findings suggested more patients understand the number lines and bar graphs compared with line graphs, and that color is effective at communicating risk, improving comprehension, and increasing confidence in interpretation. Conclusion In this review, we summarize types and components of patient-facing visualizations and methodologies for development and evaluation in the reviewed articles. We also identify recommendations for future work relating to collecting and reporting data, examining clinically actionable boundaries for diverse data types, and leveraging data science. This work will be critically important as patient access of their personal health data through portals and mobile devices continues to rise.
... As part of recent efforts to create a more patient-centered health care system, patient generated health data (PGHD) has received significant attention because of its potential to foster better patient-provider communication, improve care coordination, and strengthen patient engagement. [1][2][3][4][5] Although smartphones and other mobile devices have rapidly expanded opportunities for patients to capture and monitor their own health data, PGHD efforts to date are predominantly smallscale pilots focused on specific conditions or technologies. 3,[6][7][8][9][10][11] Health care delivery organizations have been slow to build PGHD capabilities at scale. 3 This is likely because working with PGHD at scale requires addressing not only technical challenges, 12 but also determining how to incorporate the data into multiple workflows and clinical decisions in a safe, efficient, and effective way. ...
... PGHD has received significant attention because of its potential to foster better patient engagement, offer providers a clearer picture of patient health status, and improve patient care. [1][2][3][4][5] Whereas the technical capabilities to support PGHD exist, health care delivery organizations have been slow to build PGHD capabilities at scale. To our knowledge, our work is the first to describe PGHD efforts that go beyond pilot programs to scalable approaches and we identify 3 emerging models being pursued at scale, each based around a different type of data: health history, validated questionnaires/surveys, and biometric/activity. ...
Article
Objective: Although patient generated health data (PGHD) has stimulated excitement about its potential to increase patient engagement and to offer clinicians new insights into patient health status, we know little about these efforts at scale and whether they align with patient preferences. This study sought to characterize provider-led PGHD approaches, assess whether they aligned with patient preferences, and identify challenges to scale and impact. Materials and methods: We interviewed leaders from a geographically diverse set of health systems (n = 6), leaders from large electronic health record vendors (n = 3), and leaders from vendors providing PGHD solutions to health systems (n = 3). Next, we interviewed patients with 1 or more chronic conditions (n = 10), half of whom had PGHD experience. We conducted content analysis to characterize health system PGHD approaches, assess alignment with patient preferences, and identify challenges. Results: In this study, 3 primary approaches were identified, and each was designed to support collection of a different type of PGHD: 1) health history, 2) validated questionnaires and surveys, and 3) biometric and health activity. Whereas patient preferences aligned with health system approaches, patients raised concerns about data security and the value of reporting. Health systems cited challenges related to lack of reimbursement, data quality, and clinical usefulness of PGHD. Discussion: Despite a federal policy focus on PGHD, it is not yet being pursued at scale. Whereas many barriers contribute to this narrow pursuit, uncertainty around the value of PGHD, from both patients and providers, is a primary inhibitor. Conclusion: Our results reveal a fairly narrow set of approaches to PGHD currently pursued by health systems at scale.
... The growing availability and widespread adoption of these technologies have led to a number of key informatics issues, and research has not caught up with the rapidly growing market of these services [7]. Several authors call for a need to further study the role of patient generated data [8]- [10]. It is therefore important to provide an update on the recent stage of the research on patient generated data (PGD) and to pinpoint areas that need further investigation. ...
... A report by IMS [62] shows that two thirds of health related apps are were wellness oriented. Whereas, with sickness-related technologies patients might need to use more complex devices, for example replacing the old glucose meter with a Bluetooth enabled glucose meter -even though as noted by [10] these devices are becoming more affordable. The high numbers of papers that discuss system-aided interventions seem to be connected to the frequent studies on mobile apps. ...
Conference Paper
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In recent years, patient-centered care has gained significant momentum in healthcare and the patient is more involved as an active participant in data generation. In this state of the art review we identify trends in patient generated data (PGD) and areas in need of further research by reviewing papers published in the health tracks of five high-ranked IS conferences. Our results suggest that research is mostly empirically grounded and primarily focuses on sickness rather than wellness issues. There is an emphasis on chronic diseases and self-management, dealing with user motivation, and a focus mostly on mobile apps. Though technology plays an important part, there is scarce problematization of and theorization on PGD. Further studies are needed that investigate the effects of PGD on patients and healthcare providers, include a wider range of issues and incorporate wearable devices more comprehensively.
... [5] Patients may promptly and easily access their patient-reported outcomes (PROs) and other health data provided by patients (PGHD) using the same electronic systems, such as smartphone apps and wearable devices. [6,7]. Returning health data to the patient has the potential to bring about many advantages, such as enhancing the patient's understanding of their health condition, increasing their involvement in their own healthcare, and encouraging the adoption of beneficial health practices [8], [9], [10]. ...
Article
As patients are receiving their own health data more often and in larger amounts, visualizations are generating enthusiasm for their ability to help patients understand the information. It is crucial to assess these representations to guarantee that patients can comprehend and, when suitable, take action based on health data in a secure and efficient way. The aim of this systematic study was to assess and analyze the current status of patient-facing representations of personal health data. We conducted a thorough search on five reputable academic databases, namely PubMed, Embase, Scopus, ACM Digital Library (Association for Computing Machinery Digital Library), and IEEE Computational Index (Institute of Electrical and Electronics Engineers Computational Index). We included English-language papers that created or examined patient-oriented visual representations for personal health information. The results of the article indicated that a greater proportion of patients were able to comprehend number lines and bar graphs as opposed to line graphs. Additionally, it was shown that the use of color was useful in conveying danger, enhancing understanding, and boosting confidence in interpretation. This review provides a concise overview of the many kinds and components of visuals that are directly accessible to patients. Additionally, it outlines the different approaches used in the production and assessment of these visualizations, as described in the examined publications. In addition, we provide suggestions for future research on the collection and presentation of data, exploration of clinically significant thresholds for various kinds of data, and use of data science techniques. This effort will be crucially significant as the use of patient portals and mobile devices for accessing personal health data continues to increase.
... However, despite the bene ts for improved health and access, rural residents appear less likely to electronically communicate with doctors and manage personal health information online compared to urban residents [9]. In addition, several barriers to PHR use have been identi ed, including lack of provider recommendation [10], lack of meaningful pre-implementation involvement and decision making, and lack of engagement strategies during and after deployment [1,11]. ...
Preprint
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Background Demand is emerging for personal health records (PHRs), a patient-centric digital tool for engaging in shared decision-making and healthcare data management. This study uses a RE-AIM framework to explore rural patients and providers’ perceptions prior to and following implementation of a PHR. Methods Health care providers and their patients were recruited from early-adopter patient medical home clinics and a local patient advisory group. Focus groups were used to explore patient and provider pre-implementation perceptions of PHRs and post-implementation provider perspectives. Patients were invited through participating clinics to use the PHR. Data sources included pre-/post-intervention patient surveys and PHR/EHR administrative data. ResultsBoth patient and provider focus groups described PHRs as providing a comprehensive health story and enhanced communication. Patients prioritized collection of health promotion data while providers endorsed health-related, clinical data. Both groups expressed the need for managing expectations and setting boundaries on PHR use. The evaluation indicated Reach: 16% of targeted patients participated and an additional 127 patients used the PHR as a tool during the COVID-19 pandemic. Effectiveness: Patient satisfaction with use was neutral, with no significant changes to quality of life, self-efficacy, or patients’ activation. Adoption: 44% of eligible clinics participated, primarily those operated publicly versus privately, in smaller communities, and farther from a regional hospital. Implementation: Despite system interoperability expectations, at time of roll out, information exchange standards had not been reached. Additional implementation complications arose from the onset of the pandemic. One clinic on-boarded additional patients resulting in a rapid spike in PHR use. Maintenance: All clinics discontinued PHR within the study period, citing several key barriers to use. Conclusions RE-AIM offers a valuable process evaluation framework for a comprehensive depiction of impact, and how to drive future success. Interoperability, patient agency and control, and provider training and support are critical obstacles to overcome in PHR implementation.
... Although consumer health information preferences are highly personal, they are not always predicted by sociodemographic char- acteristics. 8 Although our survey received over 6000 responses, our survey participants reflect only a small percentage of TWC COVID-19 hub users, and may not represent the entire United States, especially when it comes to race, with over 90% of our respondents identifying as White and over half (51.5%) having a Bachelor's or Graduate degree. Our survey respondents were similar to the TWC user base in terms of demographics, which may be indicative of the growing digital divide and should be further explored. ...
Article
Full-text available
We describe implementation and usage of a coronavirus disease 2019 (COVID-19) digital information hub delivered through the widely adopted The Weather Company (TWC) application and explore COVID-19 knowledge, behaviors, and information needs of users. TWC deployed the tool, which displayed local case counts and trends, in March 2020. Unique users, visits, and interactions with tool features were measured. In August 2020, a cross-sectional survey assessed respondent characteristics, COVID-19 knowledge, behaviors, and preferences. TWC COVID-19 hub averaged 1.97 million unique users with over 2.6 million visits daily and an average interaction time of 1.63 min. Respondents reported being knowledgeable about COVID-19 (92.3%) and knowing relevant safety precautions (90.9%). However, an average of 35.3% of respondents reported not increasing preventive practices across behaviors surveyed due to information about COVID-19. In conclusion, we find a free weather application delivered COVID-19 data to millions of Americans. Despite confidence in knowledge and best practices for prevention, over one-third of survey respondents did not increase practice of preventive behaviors due to information about COVID-19.
... Data, including information on social determinants (race, ethnicity, education, housing, and employment) and patient-generated health data (PGHD), have proven to be an effective tool to improve people's health [8]. In particular, PGHD and patient-reported outcomes have been emphasized [9][10][11]. According to the Office of the National Coordinator for Health Information Technology (ONC), "PGHD are health related data created, recorded, or gathered by or from patients (or family members or other caregivers) to help address a health concern" [12]. ...
Article
Full-text available
Objectives: An increasing emphasis has been placed on the integration of clinical data and patient-generated health data (PGHD), which are generated outside of hospitals. This study explored the possibility of using standard terminologies to represent PGHD for data integration. Methods: We chose the 2020 general health checkup questionnaire of the Korean Health Screening Program as a resource. We divided every component of the questionnaire into entities and values, which were mapped to standard terminologies-Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) version 2020-07-31 and Logical Observation Identifiers Names and Codes (LOINC) version 2.68. Results: Eighty-nine items were derived from the 17 questions of the 2020 health examination questionnaire, of which 76 (85.4%) were mapped to standard terms. Fifty-two items were mapped to SNOMED CT and 24 items were mapped to LOINC. Among the items mapped to SNOMED CT, 35 were mapped to pre-coordinated expressions and 17 to post-coordinated expressions. Forty items had one-to-one relationships, and 17 items had one-to-many relationships. Conclusions: We achieved a high mapping rate (85.4%) by using both SNOMED CT and LOINC. However, we noticed some issues while mapping the Korean general health checkup questionnaire (i.e., lack of explanations, vague questions, and overly narrow concepts). In particular, items combining two or more concepts into a single item were not appropriate for mapping using standard terminologies. Although it is not the case that all items need to be expressed in standard terminology, essential items should be presented in a way suitable for mapping to standard terminology by revising the questionnaire in the future.
... However, recent large observational studies (e.g., UK Biobank, U.S. National Health and Nutrition Examination Survey (NHANES)) largely shifted to wrist-worn devices to improve the comfort of participants and adherence (Troiano et al. 2014). Controlled scientific data collection is thus aligning with the consumer market, where affordability of wrist-worn consumer-grade devices has generated a wealth of data that is increasingly being used for health research (Lai et al. 2017). While wrist-worn accelerometry data has become increasingly available, there is a substantial gap in methods dedicated to quantifying gait characteristics in the free-living environment. ...
Article
Objective: We evaluate the stride segmentation performance of the Adaptive Empirical Pattern Transformation (ADEPT) for subsecond-level accelerometry data collected in the free-living environment using a wrist-worn sensor. Approach: We substantially expand the scope of the existing ADEPT pattern-matching algorithm. Methods are applied to subsecond-level accelerometry data collected continuously for 4 weeks in 45 participants, including 30 arthritis and 15 control patients. We estimate the daily walking cadence for each participant and quantify its association with SF-36 quality of life (QoL) measures. Main results: We provide free, open-source software to segment individual walking strides in subsecond-level accelerometry data. Walking cadence is significantly associated with the Role physical score reported via SF-36 after adjusting for age, gender, weight and height. Significance: Methods provide automatic, precise walking stride segmentation, which allows estimation of walking cadence from free-living wrist-worn accelerometry data. Results provide new evidence of associations between free-living walking parameters and health outcomes.
... Moreover, data fusion and integration from various medical devices are improving, and mobile technologies are accelerating. Patient-generated health data using mobile health are attracting [23][24][25]. ...
Article
Full-text available
The 10-s grip and release is a method to evaluate hand dexterity. Current evaluations only visually determine the presence or absence of a disability, but experienced physicians may also make other diagnoses. In this study, we investigated a method for evaluating hand movement function by acquiring and analyzing fingertip data during a 10-s grip and release using a wearable sensor that can measure triaxial acceleration and strain. The subjects were two healthy females. The analysis was performed on the x-, y-, and z-axis data, and absolute acceleration and contact force of all fingertips. We calculated the variability of the data, the number of grip and release, the frequency response, and each finger’s correlation. Experiments with some grip-and-release patterns have resulted in different characteristics for each. It was suggested that this could be expressed in radar charts to intuitively know the state of grip and release. Contact-force data of each finger were found to be useful for understanding the characteristics of grip and release and improving the accuracy of calculating the number of times to grip and release. Frequency analysis suggests that knowing the periodicity of grip and release can detect unnatural grip and release and tremor states. The correlations between the fingers allow us to consider the finger’s grip-and-release characteristics, considering the hand’s anatomy. By taking these factors into account, it is thought that the 10-s grip-and-release test could give us a new value by objectively assessing the motor functions of the hands other than the number of times of grip and release.
... IoB devices collect PGHD on virtually all aspects of lifestyles and behaviors, creating a treasure trove of information that can potentially advance understanding of long-term population health and precision public health interventions. PGHD collected by IoB devices allow for continuous monitoring of health status in real time, as well as collection of longitudinal data outside of-or in addition to-the intermittent monitoring that takes place in clinical settings (Lai et al., 2017). PGHD can inform correlations between individual behaviors, sociodemographics, and population-level factors, uncovering the complex relationships between acute and chronic stressors, diet, lifestyles, and overall health. ...
... 4 In addition, the ubiquitous use of consumer and patient health technologies capture data that could be coupled with electronic health data for clinical care and research. 5,6 The increased use of application programming interfaces (APIs) to share health IT data, driven through legislation, 7 and rulemaking, 8,9 furthers the potential to open new and existing streams of data for research. ...
Article
In the last decade, expanding use of health information technology (IT) across the United States has created opportunities for use of electronic health data for health services and biomedical research, but efforts may be hampered by limited data access, data quality, and system functionality. We identify five opportunities to advance the use of health IT for health services and biomedical research, which informed a federal government-led, collaborative effort to develop a relevant policy and development agenda. In particular, the health IT infrastructure should more effectively support the use of electronic health data for research; provide adaptable technologies; incorporate relevant research-related functionality; support patient and caregiver engagement in research; and support effective integration of knowledge into practice. While not exhaustive, these represent important opportunities that the biomedical and health informatics communities can pursue to better leverage health IT and electronic health data for research.
... This is especially true in United States where healthcare providers relied heavily on paper records about a decade ago. 1 Additionally, Health Information Management was considered a specialization of library science where individual health record managers were trained in assembling and organizing health care records. There has been a paradigm shift in this process with the inception of HITECH Act of 2009. ...
Article
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The purpose of this article is to perform a scientific analysis of the definitions associated with healthcare informatics and healthcare data analytics. Additionally, the authors attempt to redefine the scientific pursuit of healthcare informatics and healthcare data analytics. This commentary can assist the thinking of informaticians and data analysts working in healthcare management and practice. The authors also provide a brief insight on the possible future direction of informatics and analytics associated with healthcare.
... Other methods of capturing patient data, like PROMIS measures, typically excludes patients with low literacy 29 , while PRO data often requires significant administrative burdens to both patients and providers 30 . Data from smartwatches allow the patient, instead of the provider, to take ownership of generating and capturing data 31 . We found that few providers were concerned about PGHD_SW adding to workloads. ...
Article
Full-text available
Wearable devices, like smartwatches, are increasingly used for tracking physical activity, community mobility, and monitoring symptoms. Data generated from smartwatches (PGHD_SW) is a form of patient-generated health data, which can benefit providers by supplying frequent temporal information about patients. The goal of this study was to understand providers’ perceptions towards PGHD_SW adoption and its integration with electronic medical records. In-depth, semi-structured qualitative interviews were conducted with 12 providers from internal medicine, family medicine, geriatric medicine, nursing, surgery, rehabilitation, and anesthesiology. Diffusion of Innovations was used as a framework to develop questions and guide data analysis. The constant comparative method was utilized to formulate salient themes from the interviews. Four main themes emerged: (1) PGHD_SW is perceived as a relative advantage; (2) data are viewed as compatible with current practices; (3) barriers to overcome to effectively use PGHD_SW; (4) assessments from viewing sample data. Overall, PGHD_SW was valued because it enabled access to information about patients that were traditionally unattainable. It also can initiate discussions between patients and providers. Providers consider PGHD_SW important, but data preferences varied by specialty. The successful adoption of PGHD_SW will depend on tailoring data, frequencies of reports, and visualization preferences to correspond with the demands of providers.
... Moreover, wearables and mobile technology is going to impact clinical trials and drug development to a great extent [2,3] The use of wearables for patients monitoring and integration in healthcare delivery relies on emerging technologies. At this early stage, several kinds of challenges can be identified in order to unlock the potential of PGHD for low cost data collection and for comparative effectiveness of research in oncology at a population level [1,[4][5][6][7]. Several aspects should be considered when setting up an information technology system for their management. ...
Article
This paper presents the extension of a service-oriented architecture framework for precision oncology to the management of patient generated health data from wearables. The solution follows the indication provided by the Health Level 7 (HL7) and Object Management Group (OMG) initiative Healthcare Service Specification Project (HSSP) and is compliant to Retrieve Locate and Update Service (RLUS) Release 1 standard adopting Clinical Document Architecture Release 2 (CDA R2) as semantic signifier. The system which has been developed supports the management of visits, the setting up of a clinical diary and a comprehensive view of the patients from the wearables data for improve clinical care and for research. The system structure is highly modular and the parameters relating to wearables data are only present in one module. Extension of the systems to other aspects, such as genomics and immune therapy, are planned following the same modular design criteria.
... [1][2][3] These data, termed patient-generated health data (PGHD), may include physiologic measures, symptoms, and lifestyle data. 4,5 PGHD has garnered excitement for its ability to uncover fluctuations in health-related factors that may play an important role in an individual's health and wellness. [6][7][8][9] PGHD also is valuable for centering care on the patient and their unique environmental, lifestyle, and biological circumstances. ...
Article
Background Patient-generated health data (PGHD) collected digitally with mobile health (mHealth) technology has garnered recent excitement for its potential to improve precision management of chronic conditions such as atrial fibrillation (AF), a common cardiac arrhythmia. However, sustained engagement is a major barrier to collection of PGHD. Little is known about barriers to sustained engagement or strategies to intervene upon engagement through application design. Objective This article investigates individual patient differences in sustained engagement among individuals with a history of AF who are self-monitoring using mHealth technology. Methods This qualitative study involved patients, health care providers, and research coordinators previously involved in a randomized, controlled trial involving electrocardiogram (ECG) self-monitoring of AF. Patients were adults with a history of AF randomized to the intervention arm of this trial who self-monitored using ECG mHealth technology for 6 months. Semistructured interviews and focus groups were conducted separately with health care providers and research coordinators, engaged patients, and unengaged patients. A validated model of sustained engagement, an adapted unified theory of acceptance and use of technology (UTAUT), guided data collection, and analysis through directed content analysis. Results We interviewed 13 patients (7 engaged, 6 unengaged), 6 providers, and 2 research coordinators. In addition to finding differences between engaged and unengaged patients within each predictor in the adapted UTAUT model (perceived ease of use, perceived usefulness, facilitating conditions), four additional factors were identified as being related to sustained engagement in this population. These are: (1) internal motivation to manage health, (2) relationship with health care provider, (3) supportive environments, and (4) feedback and guidance. Conclusion Although it required some modification, the adapted UTAUT model was useful in understanding of the parameters of sustained engagement. The findings of this study provide initial requirement specifications for the design of applications that engage patients in this unique population of adults with AF.
... We chose an exploratory, selective approach, instead of a comprehensive systematic review because researchers in the different fields classify their publications quite differently. For example in health informatics, researchers who work with people who manage their personal health utilize keywords such as "patient" or "consumer" [10]. However, researchers in human computer interaction rarely utilize these terms -instead they utilize keywords that note the action (e.g., self tracking, photo sharing, blogging), design method (e.g., participatory design, mixed methods), target population (e.g., family, HIV, eating disorder, children, chronic illness), or technology (e.g., social media, online health communities, Twitter). ...
Article
Full-text available
Objectives: To review innovative human computer interaction methods researchers utilize to identify stakeholders' needs that inform the design of personal health systems outside of clinical environments. Methods: A selective review of recent literature. Results: Summaries of exemplar needs analysis papers showing how researchers utilize novel methods to surface the lived experiences of users. Conclusions: The medical informatics community is encouraged to ensure that we are designing health technology for all individuals - including underrepresented and underserved populations — by investigating the complex needs of target users. This paper summarizes the novel ways researchers have explored target populations via social media and engaged populations as part of the design team. Medical informaticians should continue investigating the soundness of these methods by comparing the design outcomes with currently utilized user-centered methods and to report on unintended consequences
... Current evidence on the clinical benefit of PGHD is sparse but emerging as technology and policy provide the means to incorporate it into clinical practice. [8][9][10] On a policy level, digital PGHD may contribute to healthcare quality by augmenting the type, amount, and detail of health information exchanged between patients and providers. 11,12 Healthcare costs associated with unnecessary office visits and hospitalizations may decrease when patients share PGHD by allowing the provider to proactively manage illnesses and prevent complications. 4 Patients with previous barriers to healthcare for cost-or location-related reasons may now exchange health information more easily and affordably with providers because mobile device ownership is prevalent across diverse populations. ...
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Objective: This integrative review identifies convergent and divergent areas of need for collecting and using patient-generated health data (PGHD) identified by patients and providers (i.e., physicians, nurses, advanced practice nurses, physician assistants, and dietitians). Methods: A systematic search of 9 scholarly databases targeted peer-reviewed studies published after 2010 that reported patients' and/or providers' needs for incorporating PGHD in clinical care. The studies were assessed for quality and bias with the Mixed-Methods Appraisal Tool. The results section of each article was coded to themes inductively developed to categorize patient and provider needs. Distinct claims were extracted and areas of convergence and divergence identified. Results: Eleven studies met inclusion criteria. All had moderate to low risk of bias. Three themes (clinical, logistic, and technological needs), and 13 subthemes emerged. Forty-eight claims were extracted. Four were divergent and twenty were convergent. The remainder was discussed by only patients or only providers. Conclusion: As momentum gains for integrating PGHD into clinical care, this analysis of primary source data is critical to understanding the requirements of the 2 groups directly involved in collection and use of PGHD.
... There has been some work on identifying the potential opportunities and difficulties of using PGHD for clinical and research purposes [4,5] and on improving the processing (collection, integration, and management, etc.) of mobile sensor data [6]. Additionally, the popularity and availability of consumer-grade devices may be responsible for an increased amount of research on using PGHD for improving health outcomes [7]. However, most of the existing work on data quality for health applications is focused on other types of medical data and not PGHD [8][9][10]. ...
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Person-generated health data (PGHD) generated by wearable devices and smartphone applications are growing rapidly. There is increasing effort to employ advanced analytical methods to generate insights from these data in order to help people change their lifestyle and improve their health. PGHD—such as step counts, exercise logs, nutritional diaries, and sleep records—are often incomplete, inaccurate, and collected over too short a duration. Insufficient user engagement with wearable and mobile technologies, as well as lack of sensor validation, standardization of data collection, transparency of data processing assumptions, and accessibility to relevant data from consumer-grade sensors, also negatively affects data quality. The literature on data quality for PGHD is sparse and fragmented, providing little guidance to data analysts on how to assess and prioritize data quality concerns. In this paper, we summarize our experiences as data analysts working with PGHD, outline some of the challenges in using PGHD for insight generation, and discuss some established methods for addressing these challenges. We review the literature on PGHD data quality, identify the major stakeholders in the PGHD ecosystem, and apply an established data quality framework to present the most relevant data quality challenges for each stakeholder.
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Patient-generated health data (PGHD) is crucial for healthcare providers' decision making, as it complements clinical data by providing a more holistic view of patients' daily conditions. We interviewed 20 healthcare providers in asthma care to envision future technologies to support their PGHD use. We found that healthcare providers want future artificial intelligence (AI) systems to enhance their ability to treat patients by analyzing PGHD for profiling risk and predicting deterioration. Despite the potential benefits of AI, providers perceived various challenges of AI use with PGHD, including AI-driven data inequity, added burden, lack of trust toward AI, and fear of being replaced by AI. Clinicians wished for a future of co-dependent human-AI collaboration, where AI will help them to improve their clinical practice. In turn, healthcare providers can improve AI systems by making AI outputs more trustworthy and humane. Through the lens of data feminism, we discuss the importance of considering context and aligning the complex human infrastructure before designing or deploying PGHD-based AI systems in clinical settings. We highlight the opportunity to design for human-AI enrichment, where humans and AI not only partner with each other for improved performance, but also enrich each other to enhance each other's work overtime.
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Background Individuals increasingly want to access, contribute to, and share their personal health information to improve outcomes, such as through shared decision-making (SDM) with their care teams. Health systems' growing capacity to use person-generated health data (PGHD) expands the opportunities for SDM. However, SDM not only lacks organizational and information infrastructure support but also is actively undermined, despite public interest in it. Objectives This work sought to identify challenges to individual–clinician SDM and policy changes needed to mitigate barriers to SDM. Methods Two multi-stakeholder group of consumers, patients, caregivers; health services researchers; and experts in health policy, informatics, social media, and user experience used a consensus process based on Bardach's policy analysis framework to identify barriers to SDM and develop recommendations to reduce these barriers. Results Technical, legal, organizational, cultural, and logistical obstacles make data sharing difficult, thereby undermining use of PGHD and realization of SDM. Stronger privacy, security, and ethical protections, including informed consent; promoting better consumer access to their data; and easier donation of personal data for research are the most crucial policy changes needed to facilitate an environment that supports SDM. Conclusion Data protection policy lags far behind the technical capacity for third parties to share and reuse electronic information without appropriate permissions, while individuals' right to access their own health information is often restricted unnecessarily, poorly understood, and poorly communicated. Sharing of personal information in a private, secure environment in which data are shared only with individuals' knowledge and consent can be achieved through policy changes.
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Background One prominent barrier faced by healthcare consumers when accessing health services is a common requirement to complete repetitive, inefficient paper-based documentation at multiple registration sites. Digital innovation has a potential role to reduce the burden in this area, through the collection and sharing of data between healthcare providers. While there is growing evidence for digital innovations to potentially improve the effectiveness and efficiency of health systems, there is less information on the willingness of healthcare consumers to embrace and utilise technology to provide data. Aim The study aims to improve understanding of consumers’ preference for utilising a digital health administration mobile app. Methods The online study used a stated preference experiment design to explore aspects of consumers’ preference for a mobile health administration app and its impact on the likelihood of using the app. The survey was answered by a representative sample (by age and gender) of Australian adults, and sociodemographic factors were also recorded for analysis. Each participant answered eight choice sets in which a hypothetical app (defined by a set of dimensions and levels) was presented and the respondent was asked if they would be willing to provide data using that app. Analysis was conducted using bivariate logistic regression. Results For the average respondent, the two most important dimensions were the time it took to register on the app and the electronic governance arrangements around their personal information. Willingness to use any app was found to differ based on respondent characteristics: people with higher education, and women, were relatively more willing to utilise the mobile health app. Conclusion This study investigated consumers’ willingness to utilise a digital health administration mobile app. The identification of key characteristics of more acceptable apps provide valuable insight and recommendations for developers of similar digital health administration technologies. This would increase the likelihood of achieving successful acceptance and utilisation by consumers. The results from this study provide evidence-based recommendations for future research and policy development, planning and implementation of digital health administration mobile applications in Australia.
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Background Patient-generated health data (PGHD) are health-related data created or recorded by patients to inform their self-care and understanding about their own health. PGHD is different from other patient-reported outcome data because the collection of data is patient-driven, not practice- or research-driven. Technical applications for assisting patients to collect PGHD supports self-management activities such as healthy eating and exercise and can be important for preventing and managing disease. Technological innovations (eg, activity trackers) are making it more common for people to collect PGHD, but little is known about how PGHD might be used in outpatient clinics. Objective The objective of our study was to examine the experiences of health care professionals who use PGHD in outpatient clinics. Methods We conducted an evaluation of Project HealthDesign Round 2 to synthesize findings from 5 studies funded to test tools designed to help patients collect PGHD and share these data with members of their health care team. We conducted semistructured interviews with 13 Project HealthDesign study team members and 12 health care professionals that participated in these studies. We used an immersion-crystallization approach to analyze data. Our findings provide important information related to health care professionals’ attitudes toward and experiences with using PGHD in a clinical setting. Results Health care professionals identified 3 main benefits of PGHD accessibility in clinical settings: (1) deeper insight into a patient’s condition; (2) more accurate patient information, particularly when of clinical relevance; and (3) insight into a patient’s health between clinic visits, enabling revision of care plans for improved health goal achievement, while avoiding unnecessary clinic visits. Study participants also identified 3 areas of consideration when implementing collection and use of PGHD data in clinics: (1) developing practice workflows and protocols related to PGHD collection and use; (2) data storage, accessibility at the point of care, and privacy concerns; and (3) ease of using PGHD data. Conclusions PGHD provides value to both patients and health care professionals. However, more research is needed to understand the benefit of using PGHD in clinical care and to identify the strategies and clinic workflow needs for optimizing these tools.
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Importance: Effective long-term treatments are needed to address the obesity epidemic. Numerous wearable technologies specific to physical activity and diet are available, but it is unclear if these are effective at improving weight loss. Objective: To test the hypothesis that, compared with a standard behavioral weight loss intervention (standard intervention), a technology-enhanced weight loss intervention (enhanced intervention) would result in greater weight loss. Design, setting, participants: Randomized clinical trial conducted at the University of Pittsburgh and enrolling 471 adult participants between October 2010 and October 2012, with data collection completed by December 2014. Interventions: Participants were placed on a low-calorie diet, prescribed increases in physical activity, and had group counseling sessions. At 6 months, the interventions added telephone counseling sessions, text message prompts, and access to study materials on a website. At 6 months, participants randomized to the standard intervention group initiated self-monitoring of diet and physical activity using a website, and those randomized to the enhanced intervention group were provided with a wearable device and accompanying web interface to monitor diet and physical activity. Main outcomes and measures: The primary outcome of weight was measured over 24 months at 6-month intervals, and the primary hypothesis tested the change in weight between 2 groups at 24 months. Secondary outcomes included body composition, fitness, physical activity, and dietary intake. Results: Among the 471 participants randomized (body mass index [BMI], 25 to <40; age range, 18-35 years; 28.9% nonwhite, 77.2% women), 470 (233 in the standard intervention group, 237 in the enhanced intervention group) initiated the interventions as randomized, and 74.5% completed the study. For the enhanced intervention group, mean baseline weight was 96.3 kg (95% CI, 94.2-98.5) and 24-month weight 89.3 kg (95% CI, 87.1-91.5). For the standard intervention group, mean baseline weight was 95.2 kg (95% CI, 93.0-97.3) and 24-month weight was 92.8 kg (95% CI, 90.6-95.0). Weight change at 24 months differed significantly by intervention group (estimated mean weight loss, 3.5 kg [95% CI, 2.6-4.5} in the enhanced intervention group and 5.9 kg [95% CI, 5.0-6.8] in the standard intervention group; difference, 2.4 kg [95% CI, 1.0-3.7]; P?=?.002). Both groups had significant improvements in body composition, fitness, physical activity, and diet, with no significant difference between groups. Conclusions and relevance: Among young adults with a BMI between 25 and less than 40, the addition of a wearable technology device to a standard behavioral intervention resulted in less weight loss over 24 months. Devices that monitor and provide feedback on physical activity may not offer an advantage over standard behavioral weight loss approaches. Trial registration: clinicaltrials.gov Identifier: NCT01131871.
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It has been widely recognized that discovering potential contributing factors to personal sleep is as important as understanding sleep pattern per se. However, in large quantified-self datasets, contributing factors may only show correlations to sleep when their values are within certain ranges. Existing correlation analysis using Pearson Correlation Coefficient cannot identify such hidden dependencies. We propose a new method based on association rules mining. Our method not only can discover hidden correlations that existing methods cannot, but also provides users with actionable knowledge to guide sleep improvement through lifestyle change.
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Smart technology, like wearable sensors or biochips, presents a vast capacity for monitoring vital signs, assess patients' behaviour and context, and simultaneously provide feedback with a significant effect in diagnosis, treatment, and control of diseases. Many chronic disease, in particular Inflammatory Bowel Disease (IBD), patients need to monitor their behaviour and register their disease history (e.g. symptoms, medication intake), as well as collect their physiological data, in order to control the disease, find correlations between their behaviour and the disease progress and help doctors to adjust treatment and promote patients behaviour changes. We have been working in the use of m-health applications by chronic disease patients to facilitate self-management of their diseases and increase their autonomy. We are now studying the use of wearable devices and biochips to automatically collect patients' data and empower them in managing their own health conditions.
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Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung disease that is characterized by airway obstruction, coughing, shortness of breath and increased sputum production. An acute exacerbation of COPD is a sudden worsening of the disease. Acute exacerbations result in more frequent and severe coughing and increased difficulty breathing. If not treated quickly, hospitalization may be required which is expensive and decreases patient's quality of life. If untreated, an acute exacerbation can lead to death. We present WearCOPD, an application that uses a smartwatch and smartphone to continuously monitor physiological signs from patients with the goal of predicting exacerbations before they happen.
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As technologies such as personal health records and symptom trackers become more common, we are seeing an increase in patients actively engaging in health tracking behaviors. Patient collected data can provide valuable insight for healthcare providers, particularly in the area of breast cancer. Thus far, little work has examined whether the health information that patients are willing to track and share aligns with the information needs of healthcare providers. Our work provides a comparison between the health information sharing preferences of breast cancer patients, doctors and navigators. We identify discrepancies between stakeholders' preferences, such as patients' hesitation to share feelings of loneliness, signifying where technology can play an important role in helping patients prioritize the health information shared with providers. We present design implications from this work to guide the development of future health information sharing tools that consider the differing needs of healthcare stakeholders.
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Self-monitoring is an integral component of many chronic diseases; however few theoretical frameworks address how individuals understand self-monitoring data and use it to guide self-management. To articulate a theoretical framework of sensemaking in diabetes self-management that integrates existing scholarship with empirical data. The proposed framework is grounded in theories of sensemaking adopted from organizational behavior, education, and human-computer interaction. To empirically validate the framework the researchers reviewed and analyzed reports on qualitative studies of diabetes self-management practices published in peer-reviewed journals from 2000 to 2015. The proposed framework distinguishes between sensemaking and habitual modes of self-management and identifies three essential sensemaking activities: perception of new information related to health and wellness, development of inferences that inform selection of actions, and carrying out daily activities in response to new information. The analysis of qualitative findings from 50 published reports provided ample empirical evidence for the proposed framework; however, it also identified a number of barriers to engaging in sensemaking in diabetes self-management. The proposed framework suggests new directions for research in diabetes self-management and for design of new informatics interventions for data-driven self-management. Copyright © 2015. Published by Elsevier Inc.
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With cures and long-term survival rates increasing in hematologic malignancies, increased focus has been placed on gaining a better understanding of the patient experience from disease and treatment effects. This has been the basis for the utilization of patient reported outcomes (PRO) and other patient-generated health data (PGHD) in efforts to improve long-term health-related quality of life (HRQOL). This review will summarize the impact PROs have had on the evolving standard of care for patients with hematologic malignant conditions and will conclude with a template for the integration of PRO and PGHD to enhance the patient experience, using stem cell transplantation as an example.
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Health reform initiatives around the world require active participation of individuals in their own health and in the health care process. Technologies designed to support engagement in health and health care provide the foundation for active participation of patients and engender a new vision of health services. This vision is grounded in a two-way exchange of information between patients and clinicians. Information, preferences, knowledge, and responsibility are shared to achieve the outcomes of the cooperative health efforts. Health and illness management both require more than the traditional, clinically derived signs and symptoms. They also require patient-generated data, including self-monitoring, tracking, and observations made in everyday living—the unique observations and insights that bring the everyday life of the person into the clinical encounter. Personal and ubiquitous technologies present opportunities to go beyond the traditional observation of signs and symptoms to i ...
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Inclusion of the patient perspective in research need not, and must not, reduce the rigor of the research. Patient input informs the design of research components that are already a standard part of studies, such as inclusion/exclusion criteria, comparators, and outcomes. The special training and skill set of researchers guide the approaches to study design, data collection, and analysis, just as the experience and perspectives of patients, clinicians, and other stakeholders help to make the research more patient-centered and relevant to health care decisions. Physician input is needed because patient-centered outcomes research is intended to influence clinical practice. There are practical reasons to incorporate patient-centeredness in research, including improved choice of research questions and improved selection and refinement of outcomes through elicitation of stakeholder perspectives, enhanced accrual and participant retention strategies, and more appropriate dissemination and implementation strategies for findings. The promise of speeding implementation is particularly salient for PCORI, which has a legislative mandate to improve practice and reduce practice variation and disparities. PCORI proposals must include an implementation plan. Clinicians have a role in putting results into practice and should be aware of the intention of patient-centered research to enhance relevance and, ideally, trust in results by those who use them.
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The wave of digital health is continuously growing and promises to transform the experience of patients, redefining their role as empowered actors of the healthcare processes rather than passive receivers of medical help. Mobile technologies are a fundamental component of this transformation since they have provided a platform for the development of novel solutions, allowing a gradual shift of healthcare closer to the patients' daily living and away from the traditional clinical environment. Chronic diseases are in the center of these developments as they require the continuous and active involvement of not only healthcare professionals but also patients both of who can be empowered through the use of specialized mobile applications and the analysis of data from modern miniaturized and wearable sensing devices. Furthermore, the communication channels introduced by mobile technologies can significantly increase the efficiency of the healthcare system and facilitate the communication between patients and healthcare professionals. The current workshop invites researchers from the fields of Information Technologies and Medical Sciences as well as healthcare professionals and technology developers to demonstrate and discuss innovative approaches related to the utilization of mobile Human Computer Interaction approaches in the modern healthcare environment.
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Persons that suffered from a cardiac disease are often recommended to integrate a sufficient level of physical exercise in their daily life. Initially, cardiac rehabilitation takes place in a closely monitored setting in a hospital or a rehabilitation center. Sustaining the effort once the patient has left the ambulatory, supervised environment is a challenge, and drop-out rates are high. Emerging approaches such as telemonitoring and telerehabilitation have been proven to show the potential to support the cardiac patient in adhering to the advised physical exercise. However, most telerehabilitation solutions only support a limited range of physical exercise, such as step-counting during walking. We propose BoB (Back on Bike), a mobile application that guides cardiac patients while cycling. Design choices are explained according to three pillars: ease of use, reduce fear, and direct and indirect motivation. In this paper, we report the results from a field study with cardiac patients.
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Physical injury, stroke, trauma, traumatic brain injury and spinal cord injury rank among the top causes of disability. There are a total of 54 million people in the US requiring rehabilitative assistance of which 15.3 million people are in the age groups of 18-44. However, the compliance rate for patients performing rehabilitation exercises in the home environment is poor. In this paper, we design and prototype a personalized home rehabilitation system, MotionTalk, for the real time quantitative assessment of mobility. Performance of rehabilitation is designed to be assessed using the changes in mobility, reflected in the exercises performed by patients at home with respect to the same exercises performed in the clinic. Our system is capable of capturing motion using Microsoft Kinect and analyzing the position and rotation information to give scores for assessing rehabilitation progress. In comparison to conventional rehabilitation systems, MotionTalk is an inexpensive (<150comparedtoconventionalsystemscosting>150 compared to conventional systems costing >1000), less intrusive and personalized home rehabilitation system, which was developed and tested using data from ablebodied volunteers at Georgia Institute of Technology.
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Patients and carers frequently get their health information and advice from websites containing patient-led, shared health experiences. This means that they often engage in a very idiosyncratic selection process in order to determine which websites have personally resonant material. In this paper we used a Repertory Grid (repgrid) technique to elicit the very personal constructs that individuals use to discriminate between websites. We recruited patients with chronic asthma and carers of people with multiple sclerosis (MS), presenting each patient/carer with a set of health websites relevant to their condition and asking them to sort them using a standard repgrid procedure. We were then able to generate hyperpersonal representations of those constructs associated with liked and trusted vs. disliked and mistrusted sites, giving us new insights into the ways individual patients can navigate the health web. H.5.2 Group and Organization Interfaces: Web-based interaction; J.3 LIFE AND MEDICAL SCIENCES: Health;
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Patient-generated health data are coming into broader use across the health care spectrum and hold great promise as a means to improve care and health outcomes. At the same time, rapid evolution in the social media and mobile health (mHealth) market has promoted an environment in which creation and transmission of personal health information is easy, quick, and appealing to patients. However, adoption of social media and mHealth by providers is hampered by legal and regulatory concerns with regard to data ownership and data use. This article defines common forms of patient-generated health data (PGHD) and describes how PGHD is used in clinical settings. It explores issues related to protection of personal health information, including that of children and adolescents, data security, and other potential barriers such as physician licensure. It also discusses regulatory and legal considerations providers and patients should consider before using social media and mobile health apps. Citation: Petersen C, DeMuro P. Legal and regulatory considerations associated with use of patient-generated health data from social media and mobile health (mHealth) devices. Appl Clin Inf 2015; 6: 16–26 http://dx.doi.org/10.4338/ACI-2014-09-R-0082
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Recent advancements in consumer directed personal computing technology have led to the generation of biomedically-relevant data streams with potential health applications. This has catalyzed international interest in Patient Generated Health Data (PGHD), defined as “health-related data – including health history, symptoms, biometric data, treatment history, lifestyle choices, and other information-created, recorded, gathered, or inferred by or from patients or their designees (i.e. care partners or those who assist them) to help address a health concern.”(Shapiro et al., 2012) PGHD offers several opportunities to improve the efficiency and output of clinical trials, particularly within oncology. These range from using PGHD to understand mechanisms of action of therapeutic strategies, to understanding and predicting treatment-related toxicity, to designing interventions to improve adherence and clinical outcomes. To facilitate the optimal use of PGHD, methodological research around considerations related to feasibility, validation, measure selection, and modeling of PGHD streams is needed. With successful integration, PGHD can catalyze the application of “big data” to cancer clinical research, creating both “n of 1” and population-level observations, and generating new insights into the nature of health and disease.
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Background: Personal health records (PHRs) connected to a physician's electronic health record system hold substantial promise for supporting and engaging patients with chronic disease. Objectives: To explore how U.S. health care organizations are currently utilizing PHRs for chronic disease populations. Methods: A mixed methods study including semi-structured interviews and a questionnaire was conducted. A purposive sample was developed of health care organizations which were recognized as exemplars for PHRs and were high performers in national patient satisfaction surveys (H-CAHPS or CAHPS). Within each organization, participants were health IT leaders or those managing high-risk or chronic disease populations. Results: Interviews were conducted with 30 informants and completed questionnaires were received from 16 organizations (84% response rate). Most PHRs allowed patients to access health records and educational material, message their provider, renew prescriptions and request appointments. Patient generated data was increasingly being sought and combined with messaging, resulted in greater understanding of patient health and functioning outside of the clinic visit. However for chronic disease populations, there was little targeted involvement in PHR design and few tools to help interpret and manage their conditions beyond those offered for all. The PHR was largely uncoupled from high risk population management interventions and no clear framework for future PHR development emerged. Conclusion: This technology is currently underutilized and represents a major opportunity given the potential benefits of patient engagement and shared decision making. A coherent patient-centric PHR design and evaluation strategy is required to realize its potential and maximize this natural hub for multidisciplinary care co-ordination.
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Patient engagement is important to help patients become more informed and active in managing their health. Effective patient engagement demands short, yet valid instruments for measuring self-efficacy in various care dimensions. However, the static instruments are often too lengthy to be effective for assessment purposes. Furthermore, these tests could neither account for the dynamicity of measurements over time, nor differentiate care dimensions that are more critical to certain sub-populations. To remedy these disadvantages, we devise a dynamic instrument composition approach that can model the measurement of patient self-efficacy over time and iteratively select critical care dimensions and appropriate assessment questions based on dynamic user categorization. The dynamically composed instruments are expected to guide patients through self-management reinforcement cycles within or across care dimensions, while tightly integrated into clinical workflow and standard care processes.
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Background: Consumer health informatics (CHI) is an emerging field that utilizes technology to provide health information to enhance health-care decision making by the public. There is, however, no widely accepted or uniform definition of CHI. A consensus definition would be important for pedagogical reasons, to build capacity and to reduce confusion about what the discipline consists of. Aim: We undertook a systematic review of published definitions of CHI and evaluated them using five quality assessment criteria and measures of similarity. Methods: Five databases were searched (Embase, Web of Science, MEDLINE, CINAHL and Business Source Complete) resulting in 1101 citations. Twenty-three studies met the inclusion criteria. Definitions were appraised using five criteria (with each scoring out of one): use of published citation, multi-disciplinarity, journal impact, definition comprehensibility, text readability. Results: Most definitions scored low on citation (Mean ± SD: 0.22 ± 0.42), multi-disciplinarity (0.15 ± 0.28) and readability (0.04 ± 0.21) and somewhat higher on IF (0.35 ± 0.45) and definition comprehensibility (idea density) (0.87 ± 0.34) criteria. Overall, the quality of the published definitions was low 1.63 ± 0.80 (out of five). Conclusions: The definitions of CHI were variable in terms of the quality assessment criteria. This suggests the need for continued discussion amongst consumer health informaticians to develop a clear consensus definition about CHI.
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Introduction: There is a critical need for public health interventions to support the independence of older adults as the world's population ages. Health smart homes (HSH) and home-based consumer health (HCH) technologies may play a role in these interventions. Methods: We conducted a systematic review of HSH and HCH literature from indexed repositories for health care and technology disciplines (e.g., MEDLINE, CINAHL, and IEEE Xplore) and classified included studies according to an evidence-based public health (EBPH) typology. Results: One thousand, six hundred and thirty-nine candidate articles were identified. Thirty-one studies from the years 1998-2011 were included. Twenty-one included studies were classified as emerging, 10 as promising and 3 as effective (first tier). Conclusion: The majority of included studies were published in the period beginning in the year 2005. All 3 effective (first tier) studies and 9 of 10 of promising studies were published during this period. Almost all studies included an activity sensing component and most of them used passive infrared motion sensors. The three effective (first tier) studies all used a multicomponent technology approach that included activity sensing, reminders and other technologies tailored to individual preferences. Future research should explore the use of technology for self-management of health by older adults; social support; and self-reported health measures incorporated into personal health records, electronic medical records, and community health registries.
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The purpose of this study was to evaluate participants' perceptions of the weight-loss intervention used in a hypertension prevention clinical trial. A total of 308 overweight and moderately obese subjects participated in the weight-management intervention. After the 18-month program, 281 participants completed a questionnaire designed to evaluate their perceptions of the program's effectiveness. Adult participants (224 men and 84 women) in the weight-loss modality of the Trials of Hypertension Prevention Phase I, surveyed in 1991. chi 2 Analyses were used to test for statistical significance of group differences. Intervention components that were most useful are presented. Older participants (older than 50 years) were most likely to attend sessions and women were most likely to identify stress and frustration because of disappointing results. Successful participants were more likely to incorporate exercise into their daily activities, exercise regularly, and use self-monitoring strategies. Few participants found group exercise to be useful. These findings suggest that interventionists in weight-loss programs need to find flexible and creative ways to maintain contact with participants, continue to develop better methods of self-monitoring, obtain the skills needed to recognize frustration and provide timely support, continue to couple the message of diet and exercise, and emphasize helping participants develop their problem-solving skills. This may require training outside the traditional field of dietetics.
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Self-monitoring promotes behavior changes by promoting awareness of eating habits and creates self-efficacy. It is an important component of the Women's Health Initiative dietary intervention. During the first year of intervention, 74% of the total sample of 19,542 dietary intervention participants self-monitored. As the study progressed the self-monitoring rate declined to 59% by spring 2000. Participants were challenged by inability to accurately estimate fat content of restaurant foods and the inconvenience of carrying bulky self-monitoring tools. In 1996, a Self-Monitoring Working Group was organized to develop additional self-monitoring options that were responsive to participant needs. This article describes the original and additional self-monitoring tools and trends in tool use over time. Original tools were the Food Diary and Fat Scan. Additional tools include the Keeping Track of Goals, Quick Scan, Picture Tracker, and Eating Pattern Changes instruments. The additional tools were used by the majority of participants (5,353 of 10,260 or 52% of participants who were self-monitoring) by spring 2000. Developing self-monitoring tools that are responsive to participant needs increases the likelihood that self-monitoring can enhance dietary reporting adherence, especially in long-term clinical trials.
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This study has two objectives: first, to identify and characterize consumer health terms not found in the Unified Medical Language System (UMLS) Metathesaurus (2007 AB); second, to describe the procedure for creating new concepts in the process of building a consumer health vocabulary. How do the unmapped consumer health concepts relate to the existing UMLS concepts? What is the place of these new concepts in professional medical discourse? The consumer health terms were extracted from two large corpora derived in the process of Open Access Collaboratory Consumer Health Vocabulary (OAC CHV) building. Terms that could not be mapped to existing UMLS concepts via machine and manual methods prompted creation of new concepts, which were then ascribed semantic types, related to existing UMLS concepts, and coded according to specified criteria. This approach identified 64 unmapped concepts, 17 of which were labeled as uniquely "lay" and not feasible for inclusion in professional health terminologies. The remaining terms constituted potential candidates for inclusion in professional vocabularies, or could be constructed by post-coordinating existing UMLS terms. The relationship between new and existing concepts differed depending on the corpora from which they were extracted. Non-mapping concepts constitute a small proportion of consumer health terms, but a proportion that is likely to affect the process of consumer health vocabulary building. We have identified a novel approach for identifying such concepts.
Smartphone Ownership and Internet Usage Continues to Climb in Emerging Economies
  • J Poushter
Poushter J. Smartphone Ownership and Internet Usage Continues to Climb in Emerging Economies [Internet]. Pew Research Center; 2016. Available from: http://www.pewglobal.org/2016/02/22/ smartphone-ownership-and-internet-usage-continues-to-climb-in-emerging-economies/
Patients Want a Heavy Dose of Digital
  • K Safavi
  • R Ratli
  • K Webb
  • L Maccracken
Safavi K, Ratli R, Webb K, MacCracken L. Patients Want a Heavy Dose of Digital. 2016; Available from: https://www.accenture.com/_acnmedia/PDF-8/ Accenture-Patients-Want-A-Heavy-Dose-of-Digital-Infographic-v2.pdf
Evaluating health interest profiles extracted from patient-generated data
  • A L Hartzler
  • D W Mcdonald
  • A Park
  • J Huh
  • C Weaver
  • W Pratt
Hartzler AL, McDonald DW, Park A, Huh J, Weaver C, Pratt W. Evaluating health interest profiles extracted from patient-generated data. AMIA Annu Symp Proc 2014 Nov 14;2014:626-35.
Delivering digital health and well-being at scale: lessons learned during the implementation of the dallas program in the United Kingdom
  • A M Devlin
  • M Mcgee-Lennon
  • O' Donnell
  • C A Bouamrane
  • M-M Agbakoba
  • O' Connor
Devlin AM, McGee-Lennon M, O'Donnell CA, Bouamrane M-M, Agbakoba R, O'Connor S, et al. Delivering digital health and well-being at scale: lessons learned during the implementation of the dallas program in the United Kingdom. J Am Med Inform Assoc 2016 Jan;23(1):48-59.