The German initiative \"National Research Data Infrastructure for Personal Health Data\" (NFDI4Health) focuses on research data management in health research. It aims to foster and develop harmonized informatics standards for public health, epidemiological studies, and clinical trials, facilitating access to relevant data and metadata standards. This publication lists syntactic and semantic data standards of potential use for NFDI4Health and beyond, based on interdisciplinary meetings and workshops, mappings of study questionnaires and the NFDI4Health metadata schema, and literature search. Included are 7 syntactic, 32 semantic and 9 combined syntactic and semantic standards. In addition, 101 ISO Standards from ISO/TC 215 Health Informatics and ISO/TC 276 Biotechnology could be identified as being potentially relevant. The work emphasizes the utilization of standards for epidemiological and health research data ensuring interoperability as well as the compatibility to NFDI4Health, its use cases, and to (inter-)national efforts within these sectors. The goal is to foster collaborative and inter-sectoral work in health research and initiate a debate around the potential of using common standards.

OBJECTIVE: Online access to medical records is expected to foster patient empowerment and patient-centred healthcare. However, data on actual experienced effects remain limited. We aimed to examine the development of effects patients perceive from online access.
METHODS: A nationwide online survey (N = 1769) evaluated Dutch patients\' use of online access and beliefs about its effects on 16 outcomes at baseline and one-year follow-up. Analyses of Variance (ANOVA) were used to examine within-person belief changes across three user groups: patients who 1) used online access before the study, 2) started use during the study, and 3) did not use it at all.
RESULTS: There was a small decline in five beliefs around online access facilitating patient empowerment and participation in participants who started using online access during the study compared to at least one other user group. Most changes in beliefs did not differ between groups.
CONCLUSIONS: No evidence of benefits from online access was found. The findings might indicate inadequacies in the current system of online access. Possibly, the benefits of online access are contingent upon portal improvements and changes in documentation practices.
CONCLUSIONS: Records need to be easily accessible and comprehensible for patients. Consultation practices should enable patient participation.

BACKGROUND: Understanding patient preference regarding taking tablet or capsule formulations plays a pivotal role in treatment efficacy and adherence. Therefore, these preferences should be taken into account when designing formulations and prescriptions.
OBJECTIVE: This study investigates the factors affecting patient preference in patients who have difficulties swallowing large tablets or capsules and aims to identify appropriate sizes for tablets and capsules.
METHODS: A robust data set was developed based on a questionnaire survey conducted from December 1, 2022, to December 7, 2022, using the harmo smartphone app operated by harmo Co, Ltd. The data set included patient input regarding their tablet and capsule preferences, personal health records (including dispensing history), and drug formulation information (available from package inserts). Based on the medication formulation information, 6 indices were set for each of the tablets or capsules that were considered difficult to swallow owing to their large size and concomitant tablets or capsules (used as controls). Receiver operating characteristic (ROC) analysis was used to evaluate the performance of each index. The index demonstrating the highest area under the curve of the ROC was selected as the best index to determine the tablet or capsule size that leads to swallowing difficulties. From the generated ROCs, the point with the highest discriminative performance that maximized the Youden index was identified, and the optimal threshold for each index was calculated. Multivariate logistic regression analysis was performed to identify the risk factors contributing to difficulty in swallowing oversized tablets or capsules. Additionally, decision tree analysis was performed to estimate the combined risk from several factors, using risk factors that were significant in the multivariate logistic regression analysis.
RESULTS: This study analyzed 147 large tablets or capsules and 624 control tablets or capsules. The \"long diameter + short diameter + thickness\" index (with a 21.5 mm threshold) was identified as the best indicator for causing swallowing difficulties in patients. The multivariate logistic regression analysis (including 132 patients with swallowing difficulties and 1283 patients without) results identified the following contributory risk factors: aged <50 years (odds ratio [OR] 1.59, 95% CI 1.03-2.44), female (OR 2.54, 95% CI 1.70-3.78), dysphagia (OR 3.54, 95% CI 2.22-5.65), and taking large tablets or capsules (OR 9.74, 95% CI 5.19-18.29). The decision tree analysis results suggested an elevated risk of swallowing difficulties for patients with taking large tablets or capsules.
CONCLUSIONS: This study identified the most appropriate index and threshold for indicating that a given tablet or capsule size will cause swallowing difficulties, as well as the contributory risk factors. Although some sampling biases (eg, only including smartphone users) may exist, our results can guide the design of patient-friendly formulations and prescriptions, promoting better medication adherence.

The Electronic Personal Health Record (EPHR) provides an innovative service for citizens and professionals to manage health data, promoting patient-centred care. It enhances communication between patients and physicians and improves accessibility to documents for remote medical information management. The study aims to assess the prevalence of awareness and acceptance of the EPHR in northern Italy and define determinants and barriers to its implementation. In 2022, a region-wide cross-sectional study was carried out through a paper-based and online survey shared among adult citizens. Univariable and multivariable regression models analysed the association between the outcome variables (knowledge and attitudes toward the EPHR) and selected independent variables. Overall, 1634 people were surveyed, and two-thirds were aware of the EPHR. Among those unaware of the EPHR, a high prevalence of specific socio-demographic groups, such as foreign-born individuals and those with lower educational levels, was highlighted. Multivariable regression models showed a positive association between being aware of the EPHR and educational level, health literacy, and perceived poor health status, whereas age was negatively associated. A higher knowledge of the EPHR was associated with a higher attitude towards the EPHR. The current analysis confirms a lack of awareness regarding the existence of the EPHR, especially among certain disadvantaged demographic groups. This should serve as a driving force for a powerful campaign tailored to specific categories of citizens for enhancing knowledge and usage of the EPHR. Involving professionals in promoting this tool is crucial for helping patients and managing health data.

Experiencing the death of a family member and providing end-of-life caregiving can be stressful on families - this is well-documented in both the caregiving and bereavement literatures. Adopting a linked-lived theoretical perspective, exposure to the death and dying of one family member could be conceptualized as a significant life stressor that produces short and long-term health consequences for surviving family members. This study uses familial-linked administrative records from the Utah Population Database to assess how variations in family hospice experiences affect mortality risk for surviving spouses and children. A cohort of hospice decedents living in Utah between 1998 and 2016 linked to their spouses and adult children (n = 37,271 pairs) provides an ideal study population because 1) hospice typically involves family members in the planning and delivery of end-of-life care, and 2) hospice admission represents a conscious awareness and acknowledgment that the decedent is entering an end-of-life experience. Thus, hospice duration (measured as the time between admission and death) is a precise measure of the family\'s exposure to an end-of-life stressor. Linking medical records, vital statistics, and other administrative microdata to describe decedent-kin pairs, event-history models assessed how hospice duration and characteristics of the family, including familial network size and coresidence with the decedent, were associated with long-term mortality risk of surviving daughters, sons, wives (widows), and husbands (widowers). Longer hospice duration increased mortality risk for daughters and husbands, but not sons or wives. Having other family members in the state was protective, and living in the same household as the decedent prior to death was a risk factor for sons. We conclude that relationship type and sex likely modify the how of end-of-life stressors (i.e., potential caregiving demands and bereavement experiences) affect health because of normative gender roles. Furthermore, exposure to dementia deaths may be particularly stressful, especially for women.

The exact definition of a personal health record (PHR) is not clear, but it is generally defined as an electronic record of an individual\'s medical examination results and medication history that can be accurately tracked by the individual and family members. There are many opportunities to obtain personal PHR data in the field of occupational health, and the amount of information in a worker\'s PHR is enormous. It is necessary in this field to comprehensively integrate and accumulate not only information from medical examinations, but also records of the work environment and work records as personal health information.

Cloud-based personal health records increase globally. The GPOC series introduces the concept of a Global Patient co-Owned Cloud (GPOC) of personal health records. Here, we present the GPOC series\' Prospective Register of Systematic Reviews (PROSPERO) registered and Preferred Reporting Items Systematic and Meta-Analyses (PRISMA)-guided systematic review and meta-analysis. It examines cloud-based personal health records and factors such as data security, efficiency, privacy and cost-based measures. It is a meta-analysis of twelve relevant axes encompassing performance, cryptography and parameters based on efficiency (runtimes, key generation times), security (access policies, encryption, decryption) and cost (gas). This aims to generate a basis for further research, a GPOC sandbox model, and a possible construction of a global platform. This area lacks standard and shows marked heterogeneity. A consensus within this field would be beneficial to the development of a GPOC. A GPOC could spark the development and global dissemination of artificial intelligence in healthcare.

What clinicians document about patients can have important consequences for those patients. Paternalistic language in patients\' health records is of specific ethical concern because it emphasizes clinicians\' power and patients\' vulnerabilities and can be demeaning and traumatizing. This article considers the importance of person-centered, trauma-informed language in clinical documentation and suggests strategies for teaching students and trainees documentation practices that express clinical neutrality and respect.

Modern healthcare has a sharp focus on data aggregation and processing technologies. Consequently, from a data perspective, a patient may be regarded as a timestamped list of medical conditions and their corresponding corrective interventions. Technologies to securely aggregate and access data for individual patients in the quest for precision medicine have led to the adoption of Digital Twins in healthcare. Digital Twins are used in manufacturing and engineering to produce digital models of physical objects that capture the essence of device operation to enable and drive optimization. Thus, a patient\'s Digital Twin can significantly improve health data sharing. However, creating the Digital Twin from multiple data sources, such as the patient\'s electronic medical records (EMR) and personal health records (PHR) from wearable devices, presents some risks to the security of the model and the patient. The constituent data for the Digital Twin should be accessible only with permission from relevant entities and thus requires authentication, privacy, and provable provenance. This paper proposes a blockchain-secure patient Digital Twin that relies on smart contracts to automate the updating and communication processes that maintain the Digital Twin. The smart contracts govern the response the Digital Twin provides when queried, based on policies created for each patient. We highlight four research points: access control, interaction, privacy, and security of the Digital Twin and we evaluate the Digital Twin in terms of latency in the network, smart contract execution times, and data storage costs.

UNASSIGNED: Using data in research often requires that the data first be de-identified, particularly in the case of health data, which often include Personal Identifiable Information (PII) and/or Personal Health Identifying Information (PHII). There are established procedures for de-identifying structured data, but de-identifying clinical notes, electronic health records, and other records that include free text data is more complex. Several different ways to achieve this are documented in the literature. This scoping review identifies categories of de-identification methods that can be used for free text data.
UNASSIGNED: We adopted an established scoping review methodology to examine review articles published up to May 9, 2022, in Ovid MEDLINE; Ovid Embase; Scopus; the ACM Digital Library; IEEE Explore; and Compendex. Our research question was: What methods are used to de-identify free text data? Two independent reviewers conducted title and abstract screening and full-text article screening using the online review management tool Covidence.
UNASSIGNED: The initial literature search retrieved 3,312 articles, most of which focused primarily on structured data. Eighteen publications describing methods of de-identification of free text data met the inclusion criteria for our review. The majority of the included articles focused on removing categories of personal health information identified by the Health Insurance Portability and Accountability Act (HIPAA). The de-identification methods they described combined rule-based methods or machine learning with other strategies such as deep learning.
UNASSIGNED: Our review identifies and categorises de-identification methods for free text data as rule-based methods, machine learning, deep learning and a combination of these and other approaches. Most of the articles we found in our search refer to de-identification methods that target some or all categories of PHII. Our review also highlights how de-identification systems for free text data have evolved over time and points to hybrid approaches as the most promising approach for the future.