Patient Adoption of Smart Cards

This paper adapts the Unified Theory of Acceptance and Use Technology Model (UTAUT) to assess the factors impacting the adoption of smart cards in Medicaid Health Home context. We contribute to the theory by including three constructs specific to smart card and health devices: (i) concern for error, (ii) sickness orientation (iii) concern for data security. Utilizing a survey design we collected responses from 116 participants who are ethnic minorities, enrolled in a Medicaid Health Home program or from a high-risk population. We developed a conceptual model and an instrument to measure the patient’s likelihood to use the smart card. The concern for error, social influence and sickness orientation significantly impact the likelihood to use the smart card. Our results show that patients are more concerned about prevention of errors as compared to security breaches

Federated learning in gaze recognition (FLIGR)

The efficiency and generalizability of a deep learning model is based on the amount and diversity of training data. Although huge amounts of data are being collected, these data are not stored in centralized servers for further data processing. It is often infeasible to collect and share data in centralized servers due to various medical data regulations. This need for diversely distributed data and infeasible storage solutions calls for Federated Learning (FL). FL is a clever way of utilizing privately stored data in model building without the need for data sharing. The idea is to train several different models locally with same architecture, share the model weights between the collaborators, aggregate the model weights and use the resulting global weights in furthering model building. FL is an iterative algorithm which repeats the above steps over defined number of rounds. By doing so, we negate the need for centralized data sharing and avoid several regulations tied to it. In this work, federated learning is applied to gaze recognition, a task to identify where the doctor’s gaze at. A global model is built by repeatedly aggregating local models built from 8 local institutional data using the FL algorithm for 4 federated rounds. The results show increase in the performance of the global model over federated rounds. The study also shows that the global model can be trained one more time locally at the end of FL on each institutional level to fine-tune the model to local data

Association between patient-provider communication and withholding information due to privacy concerns among women in the United States: an analysis of the 2011 to 2018 Health Information National Trends Survey

Background Electronic medical record software is common in healthcare settings. However, data privacy and security challenges persist and may impede patients’ willingness to disclose health information to their clinicians. Positive patient-provider communication may foster patient trust and subsequently reduce information nondisclosure. This study sought to characterize information-withholding behaviors among women and evaluate the association between positive patient-provider communication and women’s health information-withholding behavior in the United States. Methods Data were pooled from the 2011 to 2018 Health Information National Trends Survey. We used descriptive statistics, bivariate, and logistic regression analyses to investigate whether positive patient-provider communication significantly impacted health information-withholding behaviors. Data from 7,738 women were analyzed. Results About 10.8% or 1 in 10 women endorsed withholding health information from their providers because of privacy or security concerns about their medical records. After adjusting for the covariates, higher positive patient-provider communication scores were associated with lower odds of withholding information from the provider because of privacy and security concerns (aOR 0.93; 95% CI = 0.90–0.95). Additionally, we found that age, race/ethnicity, educational status, psychological distress, and smoking status significantly predicted women’s willingness to disclose health information. Conclusions Findings suggest that improving positive patient-provider communication quality may reduce women’s privacy and security concerns and encourage them to disclose sensitive medical information

The Ethics in Synthetics: Statistics in the Service of Ethics and Law in Health-Related Research in Big Data from Multiple Sources

An ethical advancement of scientific knowledge demands a delicate equilibrium between benefits and harms, in particular in health-related research. When applying and advancing scientific knowledge or technologies, Article 4 of UNESCO’s Universal Declaration on Bioethics and Human Rights, ethically justifiable research requires maximizing direct and indirect benefits and minimizing possible harms. The National Institution of Health [NIH] Data Sharing Policy and Implementation Guidance similarly states that data necessary for drawing valid conclusions and advancing medical research should be made as widely and freely available as possible (in order to share the benefits) while safeguarding the privacy of participants from potentially harmful disclosure of sensitive information. This paper discusses the challenges in the maximization of research benefit and the minimization of potential harms in the unique context of health-related research in Big Data from multiple sources, which are differently protected by the law. Part I frames the ethical dilemma by discussing potential benefits and harms, showing the constant misalignment in health-related research in Big Data from multiple sources, between the benefits in the use of confidential information for scientific purposes and the value in keeping confidentiality. Part II addresses existing regulations, including their nature and legal coverage. It highlights the prevailing challenges when combining data from multiple sources that are differently protected by the law. Part III compares different requirements for consent or authorization to use persons’ health information for research. It focuses on the difficulty of existing regulation to ensure those requirements when using multiple sources of data. Part IV investigates whether exemptions from the authorization requirement could prevail in the context of information that exceeds the protection of HIPAA and the Protection of Human Subjects Regulations. In Part V the paper proposes a solution of a statistical nature, using the method of synthetic data to balance conflicting considerations. Part VI shows how the use of synthetic data can overcome some of the ethical challenges

Nativity Status and Dietary and Physical Activity Behavior among United States Adults: Findings from the Health Information National Trends Survey (HINTS 4 Cycle 3)

Background: Non-communicable diseases and chronic conditions continue to emerge as public health crises in the United States (U.S.) and globally. Obesity, one of the most notable of such conditions, is associated with significant morbidity and mortality. Compared to non-immigrants, U.S. immigrants are known to have lower risks of obesity. However, there is paucity of literature on how U.S. immigrants compare to native-born adults regarding obesity-related behavior. Objective: We aim to describe demographic characteristics, weight distribution, and distributions of specific obesity-related behaviors among immigrant and native-born U.S. adults. Secondarily, we aim to estimate associations between nativity status and obesity-related behavior among U.S. adults. Methodology: We analyzed data from the Health Information National Trends Survey conducted between September and December 2013 (HINTS 4 Cycle 3). The independent variable was nativity status (immigrant vs. native-born). Outcomes of interest were indicators of dietary behavior (fruit, vegetable, and soda intake) and indicators of physical activity level (sitting time spent on television/computer games/web surfing, participation in physical exercise, and participation in muscle training exercise). Bivariate analyses and multivariable logistic regression models were utilized in describing demographics, weight distribution, and associations between variables of interest. Statistical significance was determined using p-values \u3c 0.05 and 95% CI around adjusted odds ratios. Results: A total of 3185 individuals participated in the survey. The overall male to female ratio was 1:1.6. Approximately 17% of participants were immigrants and roughly 83% were native-born U.S. adults. The mean age was 51 years (SD +/- 15) for immigrants and 55 years (SD+/-16) for native-born respondents. Among immigrants, the racial distribution was 55.3% Hispanic, 18.9% Asian, 14.7% White, 9.9% Black and 1.3% other races. About 25% of immigrants were obese, compared to 34% of non-immigrants. Immigrants were more likely than native-born respondents to take some quantity of fruit daily (adjusted OR = 1.88; 95% CI: 1.07 - 3.32; p = 0.0290); and less likely than native-born respondents to consume soda every week (adjusted OR = 0.74; 95% CI: 0.55 - 0.98; p = 0.0383). Immigrants were less likely than non-immigrants to spend 6 hours or more a day on sedentary leisure activities (adjusted OR = 0.64; 95% CI: 0.42 - 0.97; p = 0.0350). Immigrants were also more likely than non-immigrants to engage in physical activity of at least moderate intensity, at least once a week (adjusted OR = 1.48; 95% CI: 1.07 - 2.05; p = 0.0192). Conclusion: Compared to non-immigrants, U.S. immigrants appear to have a tendency towards healthier lifestyles regarding diet and physical activity behavior. Strategies to sustain such tendencies among immigrants will promote health and reduce risks of obesity, cancer and other chronic diseases in the U.S. More robust studies are needed to shed more light on various socio-economic, cultural and demographic factors that influence proximal determinants of obesity

Patient generated health data and electronic health record integration, governance and socio-technical issues: A narrative review

Patients’ health records have the potential to include patient generated health data (PGHD), which can aid in the provision of personalized care. Access to these data can allow healthcare professionals to receive additional information that will assist in decision-making and the provision of additional support. Given the diverse sources of PGHD, this review aims to provide evidence on PGHD integration with electronic health records (EHR), models and standards for PGHD exchange with EHR, and PGHD-EHR policy design and development. The review also addresses governance and socio-technical considerations in PGHD management. Databases used for the review include PubMed, Scopus, ScienceDirect, IEEE Xplore, SpringerLink and ACM Digital Library. The review reveals the significance, but current deficiency, of provenance, trust and contextual information as part of PGHD integration with EHR. Also, we find that there is limited work on data quality, and on new data sources and associated data elements, within the design of existing standards developed for PGHD integration. New data sources from emerging technologies like mixed reality, virtual reality, interactive voice response system, and social media are rarely considered. The review recommends the need for well-developed designs and policies for PGHD-EHR integration that promote data quality, patient autonomy, privacy, and enhanced trust