Pricing through health apps generated data-Digital dividend as a game changer: Discrete choice experiment.

OBJECTIVES: The objective of this paper is to study under which circumstances wearable and health app users would accept a compensation payment, namely a digital dividend, to share their self-tracked health data. METHODS: We conducted a discrete choice experiment alternative, a separated adaptive dual response. We chose this approach to reduce extreme response behavior, considering the emotionally-charged topic of health data sales, and to measure willingness to accept. Previous experiments in lab settings led to demands for high monetary compensation. After a first online survey and two pre-studies, we validated four attributes for the final online study: monthly bonus payment, stakeholder handling the data (e.g., health insurer, pharmaceutical or medical device companies, universities), type of data, and data sales to third parties. We used a random utility framework to evaluate individual choice preferences. To test the expected prices of the main study for robustness, we assigned respondents randomly to one of two identical questionnaires with varying price ranges. RESULTS: Over a period of three weeks, 842 respondents participated in the main survey, and 272 respondents participated in the second survey. The participants considered transparency about data processing and no further data sales to third parties as very important to the decision to share data with different stakeholders, as well as adequate monetary compensation. Price expectations resulting from the experiment were high; pharmaceutical and medical device companies would have to pay an average digital dividend of 237.30€/month for patient generated health data of all types. We also observed an anchor effect, which means that people formed price expectations during the process and not ex ante. We found a bimodal distribution between relatively low price expectations and relatively high price expectations, which shows that personal data selling is a divisive societal issue. However, the results indicate that a digital dividend could be an accepted economic incentive system to gather large-scale, self-tracked data for research and development purposes. After the COVID-19 crisis, price expectations might change due to public sensitization to the need for big data research on patient generated health data. CONCLUSION: A continuing success of existing data donation models is highly unlikely. The health care sector needs to develop transparency and trust in data processing. An adequate digital dividend could be an effective long-term measure to convince a diverse and large group of people to share high-quality, continuous data for research purposes.

How the Use of a Patient-Accessible Health Record Contributes to Patient-Centered Care: Scoping Review.

BACKGROUND: Worldwide, patient-centered care is becoming a widely used concept in medical practice, getting more and more attention because of its proven ability to improve quality of care and reduce costs. Although several studies show that patient-accessible electronic health records (PAEHRs) influence certain aspects of patient-centered care, the possible contribution of PAEHR implementation to patient-centered care as a comprehensive concept has not, to our knowledge, been structurally evaluated to date. OBJECTIVE: The objective of this study is to review whether and how the use of PAEHRs contributes to patient-centered care both in general and among specific population groups. METHODS: We followed PRISMA Extension for Scoping Reviews reporting guidelines. We identified literature in 5 databases, using the terms "patient-accessible medical records," "patient experiences," and "professional experiences" as key concepts. A total of 49 articles were included and analyzed with a charting code list containing 10 elements of patient-centered care. RESULTS: Studies were diverse in design, country of origin, functionalities of the investigated PAEHR, and target population. Participants in all studies were adults. Most studies reported positive influence of PAEHR use on patient-centered care; patient accessible health records were appreciated for their opportunity to empower patients, inform patients about their health, and involve patients in their own care. There were mixed results for the extent to which PAEHRs affected the relation between patients and clinicians. Professionals and patients in mental health care held opposing views concerning the impact of transparency, where professionals appeared more worried about potential negative impact of PAEHRs on the patient-clinician relationship. Their worries seemed to be influenced by a reluctant attitude toward patient-centered care. Disadvantaged groups appeared to have less access to and make less use of patient-accessible records than the average population but experienced more benefits than the average population when they actually used PAEHRs. CONCLUSIONS: The review indicates that PAEHRs bear the potential to positively contribute to patient-centered care. However, concerns from professionals about the impact of transparency on the patient-clinician relationship as well as the importance of a patient-centered attitude need to be addressed. Potentially significant benefits for disadvantaged groups will be achieved only through easily accessible and user-friendly PAEHRs.

Personal Health Information Inference Using Machine Learning on RNA Expression Data from Patients With Cancer: Algorithm Validation Study.

BACKGROUND: As the need for sharing genomic data grows, privacy issues and concerns, such as the ethics surrounding data sharing and disclosure of personal information, are raised. OBJECTIVE: The main purpose of this study was to verify whether genomic data is sufficient to predict a patient's personal information. METHODS: RNA expression data and matched patient personal information were collected from 9538 patients in The Cancer Genome Atlas program. Five personal information variables (age, gender, race, cancer type, and cancer stage) were recorded for each patient. Four different machine learning algorithms (support vector machine, decision tree, random forest, and artificial neural network) were used to determine whether a patient's personal information could be accurately predicted from RNA expression data. Performance measurement of the prediction models was based on the accuracy and area under the receiver operating characteristic curve. We selected five cancer types (breast carcinoma, kidney renal clear cell carcinoma, head and neck squamous cell carcinoma, low-grade glioma, and lung adenocarcinoma) with large samples sizes to verify whether predictive accuracy would differ between them. We also validated the efficacy of our four machine learning models in analyzing normal samples from 593 cancer patients. RESULTS: In most samples, personal information with high genetic relevance, such as gender and cancer type, could be predicted from RNA expression data alone. The prediction accuracies for gender and cancer type, which were the best models, were 0.93-0.99 and 0.78-0.94, respectively. Other aspects of personal information, such as age, race, and cancer stage, were difficult to predict from RNA expression data, with accuracies ranging from 0.0026-0.29, 0.76-0.96, and 0.45-0.79, respectively. Among the tested machine learning methods, the highest predictive accuracy was obtained using the support vector machine algorithm (mean accuracy 0.77), while the lowest accuracy was obtained using the random forest method (mean accuracy 0.65). Gender and race were predicted more accurately than other variables in the samples. On average, the accuracy of cancer stage prediction ranged between 0.71-0.67, while the age prediction accuracy ranged between 0.18-0.23 for the five cancer types. CONCLUSIONS: We attempted to predict patient information using RNA expression data. We found that some identifiers could be predicted, but most others could not. This study showed that personal information available from RNA expression data is limited and this information cannot be used to identify specific patients.

Placing volunteered geographic health information: Socio-spatial bias in 311 bed bug report data for New York City.

Health researchers and policy-makers increasingly use volunteered geographic information (VGI) to analyze spatial variation in health and wellbeing and to develop interventions. As socially constructed data, health VGI reflect the people who perceive issues and choose to report them, and the digital systems that structure the reporting process. We propose a conceptual framework that describes the interlocking effects of socioeconomic, behavioral, geographic, and technological processes on VGI accuracy and credibility. GIS and statistical methods are used to analyze social and geographical biases in health-related VGI through a case study of bed bug complaint data from New York City's 311 system. Reports of bed bug infestation from 311 are mapped and modeled to uncover associations with socioeconomic and built environment characteristics. Factors associated with bed bug report credibility are examined by comparing characteristics of confirmed reports with those for reports in which inspectors found no evidence of infestation (negative reports). A multilevel model of credibility incorporating report-, building-, and tract-level variables reveals strong geographical and socioeconomic biases, with negative reports generated more frequently from high-value residential buildings located in high-income neighborhoods with predominately white, non-Hispanic populations. Using 311 data for all bed bug reports, rather than confirmed reports, obscures the burden of these pests in high poverty neighborhoods and diminishes socioeconomic disparities. Mistaken reporting also has economic costs, as each report triggers an inspection by city inspectors that entails time, monetary, and opportunity costs.

Willingness of Patients Prescribed Medications for Lifestyle-Related Diseases to Use Personal Health Records: Questionnaire Study.

BACKGROUND: Personal health record (PHR) systems let individuals utilize their own health information to maintain and improve quality of life. Using PHRs is expected to support self-management in patients with lifestyle-related diseases. OBJECTIVE: The aim of this study was to identify predictors of the willingness to use PHRs among patients who are prescribed medications for lifestyle-related diseases. METHODS: We recruited pharmacy patrons, aged 20 years or older, who had received at least one medication indicated for hypertension, dyslipidemia, or diabetes. Participants completed self-administered questionnaires regarding their previous diseases, awareness of health care, experience in using PHRs, willingness to use PHRs, and barriers to using PHRs. Data were analyzed using multivariate logistic regression models. RESULTS: Of the 3708 subjects meeting eligibility criteria, 2307 replies (62.22%) were collected. While only 174 (7.54%) participants had previous PHR experience, 853 (36.97%) expressed willingness to use PHRs. In the multivariate analysis, considering exercise to be important for health management (odds ratio [OR] 1.57, 95% CI 1.12-2.21; P=.009), obtaining medical information from books or magazines (OR 1.23, 95% CI 0.96-1.59; P=.10), and obtaining medical information from the internet (OR 1.45, 95% CI 1.13-1.87; P=.004) were newly identified predictors. These were in addition to known predictors, such as being employed, owning information terminals, and previous PHR experience. CONCLUSIONS: Patients who have an active and positive attitude toward health seem to be more willing to use PHRs. Investigating willingness should contribute to the development of more useful PHRs for self-management among patients prescribed medications for lifestyle-related diseases.

Factors that affect health information technology adoption by seniors: A systematic review.

The number of seniors and prevalence of chronic conditions are increasing worldwide, resulting in more pressure on health systems. Health Information Technologies (HIT) present opportunities to support the healthcare needs of seniors. Although prior studies have investigated HIT and seniors, it remains unclear what factors significantly affect the adoption of different HIT by elderly people in the community. A Systematic Review (SR) was conducted between December 2017 and February 2018 (with a search update in 2018-2019) to critically appraise and synthesise existing evidence on HIT adoption factors among seniors. Following the PRISMA guidelines, five major databases were consulted (PubMed, Medline, CINAHL, Scopus and Web of Science). The inclusion criteria consisted of empirical studies, published in English, and reporting impacts of specific factors on HIT adoption among seniors in the community. A total of 41 studies were included in this review, mostly published between 2014 and 2017 in Europe and the US; the level of evidence in these studies was low to moderate. The factors that affect HIT adoption did not differ across types of technologies or age groups. The findings reveal that seniors adopt HIT that are perceived as useful and requiring low effort commitment; price/cost value were reported as adoption barriers. Social influence, facilitating conditions, senior-friendly product design, self-efficacy, Intrinsic Technology Quality, experience/training and technology anxiety may affect HIT adoption by seniors, although the evidence on these impacts remains weak and limited. Mixed and inconclusive evidence was observed on the impacts of socio-demographic variables, health condition, habit and privacy/security. Given the reported low level of HIT adoption among seniors, we call for more rigorous research in this area using a 'senior-centred' approach, which takes into account the discourse/interaction between seniors and their collective environment to better understand the factors that affect their technology adoption and address their needs.

Access to Personal Health Records and Screening for Breast and Cervical Cancer Among Women with a Family History of Cancer.

It is essential for at-risk women to be screened for breast and cervical cancer in a timely manner. Despite a growing interest in the role of health information technology including personal health records (PHRs) to improve quality and outcomes in health care, less is known about the effectiveness of PHRs to promote breast and cervical cancer screening among women with a family history of cancer (FHC). We examined the association between access to PHRs and the use of a recommended mammography and a Pap smear testing among women with a FHC using data from the 2015 Health Information National Trends Survey (HINTS 4-cycle 4) and the 2016 Area Health Resource Files. The study sample was comprised of 1250 women aged 20-75 years with a FHC, a subsample of 3677 survey respondents. Of the 1250 women, 64.96% received a mammogram, and 75.44% underwent a Pap testing. Among women with a FHC, there was a significant and positive association between access to PHRs and the receipt of a mammogram (adjusted odds ratio (aOR) 4.20; 95% CI, 2.23-7.94; p < .001) and a Pap testing (aOR 3.13; 95% CI, 1.56-6.28; p < .01). Our findings suggest that at-risk women can benefit from greater access to PHRs. Policymakers should consider incentivizing providers and healthcare organizations who provide access to PHRs to their patients as well as developing programs that can help improve access to PHRs among at-risk women.

The desktop, or the top of the desk? The relative usefulness of household features for personal health information management.

Sixty percent of the US population manages at least one chronic illness. For these patients, personal health information management (PHIM) is an integral part of daily life, and largely occurs within the home. However, the way in which the home supports PHIM has not been systematically investigated. The present study examined how members of the diabetic population use features of the home environment to support PHIM. Participants (N = 60) explored a simulated home environment, the VR CAVE, and identified the most useful features for performing three examples of PHIM tasks. The computer was perceived as the most useful feature for PHIM. However, perceived usefulness of features varied based on the PHIM task performed and the rooms in which features appeared. We conclude that a detailed study of the affordances of features is necessary to ease the burden of managing chronic illness, particularly diabetes mellitus, in the sociotechnical system of the home.

Psychology of personal data donation.

Advances in digital technology have led to large amounts of personal data being recorded and retained by industry, constituting an invaluable asset to private organizations. The implementation of the General Data Protection Regulation in the EU, including the UK, fundamentally reshaped how data is handled across every sector. It enables the general public to access data collected about them by organisations, opening up the possibility of this data being used for research that benefits the public themselves; for example, to uncover lifestyle causes of poor health outcomes. A significant barrier for using this commercial data for academic research, however, is the lack of publicly acceptable research frameworks. Data donation-the act of an individual actively consenting to donate their personal data for research-could enable the use of commercial data for the benefit of society. However, it is not clear which motives, if any, would drive people to donate their personal data for this purpose. In this paper we present the results of a large-scale survey (N = 1,300) that studied intentions and reasons to donate personal data. We found that over half of individuals are willing to donate their personal data for research that could benefit the wider general public. We identified three distinct reasons to donate personal data: an opportunity to achieve self-benefit, social duty, and the need to understand the purpose of data donation. We developed a questionnaire to measure those three reasons and provided further evidence on the validity of the scales. Our results demonstrate that these reasons predict people's intentions to donate personal data over and above generic altruistic motives. We show that a social duty is the strongest predictor of the intention to donate personal data, while understanding the purpose of data donation also positively predicts the intentions to donate personal data. In contrast, self-serving motives show a negative association with intentions to donate personal data. The findings presented here examine people's reasons for data donation to help inform the ethical use of commercially collected personal data for academic research for public good.

Personal health information in research: Perceived risk, trustworthiness and opinions from patients attending a tertiary healthcare facility.

BACKGROUND: Personal health information is a valuable resource to the advancement of research. In order to achieve a comprehensive reform of data infrastructure in Australia, both public engagement and building social trust is vital. In light of this, we conducted a study to explore the opinions, perceived risks and trustworthiness regarding the use of personal health information for research, in a sample of the public attending a tertiary healthcare facility. METHODS: The Consumer Opinions of Research Data Sharing (CORDS) study was a questionnaire-based design with 249 participants who were attending a public tertiary healthcare facility located on the Gold Coast, Australia. The questionnaire was designed to explore opinions and evaluate trust and perceived risk in research that uses personal health information. Concept analysis was used to identify key dimensions of perceived risk. RESULTS: Overall participants were supportive of research, highly likely to participate and mostly willing to share their personal health information. However, where the perceived risk of data misuse was high and trust in others was low, participants expressed hesitation to share particular types of information. Performance, physical and privacy risks were identified as key dimensions of perceived risk. CONCLUSION: This study highlights that while participant views on the use of personal health information in research is mostly positive, where there is perceived risk in an environment of low trust, support for research decreases. The three key findings of this research are that willingness to share data is contingent upon: (i) data type; (ii) risk perception; and (iii) trust in who is accessing the data. Understanding which factors play a key role in a person's decision to share their personal health information for research is vital to securing a social license.

The Challenges in Personal Health Record Adoption.

EXECUTIVE SUMMARY: Communication between patient and healthcare provider is an essential part of care planning. A patient's personal health record (PHR) contains medical information that can be easily accessed, updated, and used via the Internet to facilitate and improve treatment plans. However, PHRs are not widely adopted. The goal of this study was to explore the challenges and design a roadmap to adoption. Data were collected via semistructured face-to-face interviews and analyzed with a qualitative approach. This study included 35 subjects selected from a sample of CEOs, chief information officers (CIOs), and health informatics project managers (HIPMs); the sample included key executives and project managers in the health information technology business. This study revealed that executives see the PHR as a tool to improve communication and patient care and reduce cost. However, a lack of electronic health record (EHR) systems, poor PHR integration with EHR systems, lack of user experience, and computer and health illiteracy were identified as barriers to PHR adoption. Therefore, adoption was not found to be a top priority for executives. Nevertheless, the PHR ought to be part of the information technology strategy in every hospital. Effective staff training, better patient orientation, and dedicated health informaticians can ensure a smooth and safe journey toward a useful PHR.

The usefulness and acceptability of a personal health record to children and young people living with a complex health condition: A realist review of the literature.

BACKGROUND: There are a growing number of children and young people (CYP) with chronic health needs or complex disabilities. Increasingly, CYP with life-limiting or life-threatening conditions are surviving into adulthood. Communication between CYP, their family, and health professionals can be challenging. The use of a personal health record (PHR) is one potential strategy for improving communication by promoting CYP's health advocacy skills. However, PHR implementation has proved difficult due to technical, organisational, and professional barriers. The aim of this realist review is to identify the factors, which help or hinder the use of PHRs with CYP living with a complex health condition. METHODS: Systematic realist review. Literature was sourced from six databases: Medline, Embase, CINAHL, PsychInfo, The Cochrane Library, and Science Direct (from 1946 to August Week 3 2018). The web was searched to identify grey literature. Articles were sourced from reference lists of included studies. Data were extracted using a standardised data extraction form. Two reviewers completed data extraction and synthesis. Methodological rigor was assessed using the relevant Critical Appraisal Skills Programme tool. RESULTS: Nine articles were included. Contextual factors, which helped implementation, included the CYP having a high perception of need for a PHR and a high level of desire for self-management. Service providers and CYP need knowledge about the purpose and benefits of the PHR, and organisations need a dedicated person to facilitate PHR use. Mechanisms triggered by the PHR included improved understanding and knowledge of health care condition(s) for CYP, an increased feeling of control over condition(s), and more active engagement in their health care. Outcomes for CYP included improved self-advocacy and communication. CONCLUSION: Clearer definitions of which young people would benefit from using a PHR must be established to inform which organisations and service providers would be best suited to PHR implementation.

Impact of Personal Health Records and Wearables on Health Outcomes and Patient Response: Three-Arm Randomized Controlled Trial.

BACKGROUND: Although using the technologies for a variety of chronic health conditions such as personal health record (PHR) is reported to be acceptable and useful, there is a lack of evidence on the associations between the use of the technologies and the change of health outcome and patients' response to a digital health app. OBJECTIVE: This study aimed to examine the impact of the use of PHR and wearables on health outcome improvement and sustained use of the health app that can be associated with patient engagement. METHODS: We developed an Android-based mobile phone app and used a wristband-type activity tracker (Samsung Charm) to collect data on health-related daily activities from individual patients. Dietary record, daily step counts, sleep log, subjective stress amount, blood pressure, and weight values were recorded. We conducted a prospective randomized clinical trial across 4 weeks on those diagnosed with obstructive sleep apnea (OSA) who had visited the outpatient clinic of Seoul National University Bundang Hospital. The trial randomly assigned 60 patients to 3 subgroups including 2 intervention groups: (1) mobile app and wearable device users (n=20), (2) mobile app-only users (n=20), and (3) controls (n=20). The primary outcome measure was weight change. Body weights before and after the trial were recorded and analyzed during clinic visits. Changes in OSA-related respiratory parameters such as respiratory disturbance, apnea-hypopnea, and oxygenation desaturation indexes and snoring comprised the secondary outcome and were analyzed for each participant. RESULTS: We collected the individual data for each group during the trial, specifically anthropometric measurement and laboratory test results for health outcomes, and the app usage logs for patient response were collected and analyzed. The body weight showed a significant reduction in the 2 intervention groups after intervention, and the mobile app-only group showed more weight loss compared with the controls (P=.01). There were no significant changes in sleep-related health outcomes. From a patient response point of view, the average daily step counts (8165 steps) from the app plus wearable group were significantly higher than those (6034 steps) from the app-only group because they collected step count data from different devices (P=.02). The average rate of data collection was not different in physical activity (P=.99), food intake (P=.98), sleep (P=.95), stress (P=.70), and weight (P=.90) in the app plus wearable and app-only groups, respectively. CONCLUSIONS: We tried to integrate PHR data that allow clinicians and patients to share lifelog data with the clinical workflow to support lifestyle interventions. Our results suggest that a PHR-based intervention may be successful in losing body weight and improvement in lifestyle behavior. TRIAL REGISTRATION: ClinicalTrials.gov NCT03200223; https://clinicaltrials.gov/ct2/show/NCT03200223 (Archived by WebCite at http://www.webcitation.org/74baZmnCX).

The role of powerful audiences in health information avoidance.

BACKGROUND: Although people may want to learn information, concerns about how audiences (persons or entities privy to one's behavior or information) might respond may motivate people to avoid information that audiences could use to threaten resources or harm them. PURPOSE: We examined whether powerful audiences prompt health risk information avoidance. METHODS: We tested in two studies (N = 843 adults 25 and older, 75% White) the influence of different audiences on medical information avoidance. We manipulated the audience and examined the consequences for avoidance of health risk information. RESULTS: Participants avoided personal health risk information significantly more when they believed that a powerful audience (an employer or insurance company) might learn their results from a health risk test than when they believed a non-powerful audience (health researchers) might learn their results. Exploratory mediation analyses revealed that these effects were partially mediated by participant expectations of likelihood of powerful audience harm. CONCLUSIONS: Results suggest that people may choose to remain ignorant of potentially important health risks if they believe that powerful audiences can use that information to harm them.

'Two clicks and I'm in!' Patients as co-actors in managing health data through a personal health record infrastructure.

One of the most significant changes in the healthcare field in the past 10 years has been the large-scale digitalization of patients' healthcare data, and an increasing emphasis on the importance of patients' roles in cooperating with healthcare professionals through digital infrastructures. A project carried out in the North of Italy with the aim of creating a personal health record has been evaluated over the course of 5 years by means of mixed method fieldwork. Two years after the infrastructure was put into regular service, the way in which patients are represented in the system and patient practices have been studied using surveys and qualitative interviews. The data show that, first, patients have become co-actors in describing their clinical histories; second, that they have become co-actors in the diagnosis process; and finally, they have become co-actors in the management of time and space as regards their specific state of health.

Development of a techno-humanist model for e-health adoption of innovative technology.

BACKGROUND AND PURPOSE: After investing billions of dollars in an integrated Electronic Medical Records (physicians) and Personal Health Records (patients) system to allow both parties to manage and communicate through e-health innovative technologies, Canada is still making slow adoption progress. In an attempt to bridge the human and technological perspectives by developing and testing a holistic model, this study purports to predict patients' behavioral intentions to use e-health applications. METHODS: An interdisciplinary approach labelled as a techno-humanism model (THM) is testing twelve constructs identified from the technological, sociological, psychological, and organizational research literature and deemed to have a significant effect upon and positive relationship with patients' e-health applications adoption. Subjects were Canadians recruited in a mall-intercept mode from a region representing a demographically diverse population, including rural and urban residents. The SmartPLS measurement tool was used to evaluate the reliability and validity of study constructs. The twelve constructs were separately tested with quantitative data such as factor analysis, single, multiple, and hierarchical multiple regression. RESULTS: The hierarchical multiple regression analysis process led us to formulate four models, each hinged on a combination of interdisciplinary variables. Model 1 consisted of the technological predictors and explained 62.3% (p < .001) of variance in the behavioral intention to use e-health. Model 2 added the sociological predictors to the equation and explained 72.3% (p < .001) of variance. Model 3 added the psychological predictors to Model 2 and explained 72.8% (p < .001). Finally, Model 4 included all twelve predictors and explained 73% (p < .001) of variance in the behavioral intention to use e-health applications. CONCLUSIONS: One of the greatest barriers to applying e-health records in Canada resides in the lack of coordination among stakeholders. The present study implies that healthcare policy makers must consider the twelve variables with their findings and implications as a whole. The techno-humanist model (THM) we are proposing is a more holistic and continuous approach. It pushes back to a breakdown of the various technological, sociological, psychological, and managerial factors and stakeholders that are at the root cause of behavioral intentions to use e-health, as opposed to merely observing behavioral outcomes at the end of the "assembly line". Active participation and coordination of all stakeholders is a key feature.