Protecting Personal Health Data through Privacy Awareness

Digital health data is important to keep secure, and patients' perception around the privacy of it is essential to the development of digital health records. In this paper we present people's perceptions of the communication of data protection, in relation to their personal health data and the access to it; we focused particularly on people with chronic or long-term illness. Based on their use of personally accessible health records, we inquired into their explicit perception of security and sense of data privacy in relation to their health data. Our goal was to provide insights and guidelines to designers and developers on the communication of data protection in health records in an accessible way for the users. We analyzed their approach to and experience with their own health care records and describe the details of their challenges. A conceptual framework called "Privacy Awareness' was developed from the findings and reflects the perspectives of the users. The conceptual framework forms the basis of a proposal for design guidelines for Digital Health Record systems, which aim to address, facilitate and improve the users' awareness of the protection of their online health data.

Covid-19 Space-time Cluster Detection Using Retrospective Analysis

Background: As of the 31st of January 2021, there had been 102,399,513 confirmed cases of COVID-19 worldwide, with 2,217,005 deaths reported to WHOThe goal of this study is to uncover the spatiotemporal patterns of COVID 19 in Ethiopia, which will aid in the planning and implementation of essential preventative measures. Methods We obtained data on COVID 19 cases reported in Ethiopia from November 23 to December 29, 2021, from an Ethiopian health data website that is open to the public.Kulldorff's retrospective space-time scan statistics were utilized to detect the temporal, geographical, and spatiotemporal clusters of COVID 19 at the county level in Ethiopia, using the discrete Poisson probability model. Results: In Ethiopia, between November 23 and December 29, 2021, a total of 22,199 COVID 19 cases were reported.The COVID 19 cases in Ethiopia were strongly clustered in spatial, temporal, and spatiotemporal distribution, according to the results of Kulldorff's scan. statisticsThe most likely Spatio-temporal cluster (LLR = 70369.783209, RR = 412.48, P 0.001) was mostly concentrated in Addis Ababa and clustered between 2021/11/1 and 2021/11/30.Conclusion: From November 23 to December 29, 2021, this study found three large COVID 19 space-time clusters in Ethiopia, which could aid in future resource allocation in high-risk locations for COVID 19 management and prevention.

Remote Monitoring of Patient- and Family-Generated Health Data in Pediatrics

Remote patient monitoring (RPM) is a form of telemedicine that involves the collection and transmission of health data from a patient to their health care team by using digital health technologies. RPM can be leveraged to aggregate and visualize longitudinal patient-generated health data for proactive clinical management and engagement of the patient and family in a child’s health care. Collection of remote data has been considered standard of care for years in some chronic pediatric conditions. However, software limitations, gaps in access to the Internet and technology devices, digital literacy, insufficient reimbursement, and other challenges have prevented expansion of RPM in pediatric medicine on a wide scale. Recent technological advances in remote devices and software, coupled with a shift toward virtual models of care, have created a need to better understand how RPM can be leveraged in pediatrics to improve the health of more children, especially for children with special health care needs who are reliant on high-quality chronic disease management. In this article, we define RPM for the general pediatric health care provider audience, provide case examples of existing RPM models, discuss advantages of and limitations to RPM (including how data are collected, evaluated, and managed), and provide a list of current RPM resources for clinical practitioners. Finally, we propose considerations for expansion of this health care delivery approach for children, including clinical infrastructure, equitable access to digital health care, and necessary reimbursement. The overarching goal is to advance health for children by adapting RPM technologies as appropriate and beneficial for patients, families, and providers alike.

Mapping European research networks providing health data: results from the InfAct Joint Action on health information

Background Research networks offer multidisciplinary expertise and promote information exchange between researchers across Europe. They are essential for the European Union’s (EU) health information system as providers of health information and data. The aim of this mapping exercise was to identify and analyze EU research networks in terms of health data collection methods, quality assessment, availability and accessibility procedures. Methods A web-based search was performed to identify EU research networks that are not part of international organizations (e.g., WHO-Europe, OECD) and are involved in collection of data for health monitoring or health system performance assessment. General characteristics of the research networks (e.g., data sources, representativeness), quality assessment procedures, availability and accessibility of health data were collected through an ad hoc extraction form. Results Fifty-seven research networks, representative at national, international or regional level, were identified. In these networks, data are mainly collected through administrative sources, health surveys and cohort studies. Over 70% of networks provide information on quality assessment of their data collection procedures. Most networks share macrodata through articles and reports, while microdata are available from ten networks. A request for data access is required by 14 networks, of which three apply a financial charge. Few networks share data with other research networks (8/49) or specify the metadata-reporting standards used for data description (9/49). Conclusions Improving health information and availability of high quality data is a priority in Europe. Research networks could play a major role in tackling health data and information inequalities by enhancing quality, availability, and accessibility of health data and data sharing across European networks.

Sharing of military Veterans’ mental health data across Canada: A scoping review

LAY SUMMARY Information on Canadian military Veterans’ mental health is needed to develop and improve mental health services. It is not clear to what extent such information is available and connected across its sources. A comprehensive review of scientific and other authorized publications was conducted to identify information sources related to Canadian Veteran mental health, connections between them, and related policies or guidelines. Ten data sources related to military Veterans’ mental health in Canada were found, but no policies or guidelines specifically addressing information sharing across these data sets were discovered. Secure, Accessible, eFfective, and Efficient (SAFE) information sharing across these sources was implied but not confirmed. The authors recommend consideration be given to establishing a repository of relevant data sets and policies and guidelines for information sharing and standardization across all relevant data sets.

Evaluating and extending the Informed Consent Ontology for representing permissions from the clinical domain

The purpose of this study was to evaluate, revise, and extend the Informed Consent Ontology (ICO) for expressing clinical permissions, including reuse of residual clinical biospecimens and health data. This study followed a formative evaluation design and used a bottom-up modeling approach. Data were collected from the literature on US federal regulations and a study of clinical consent forms. Eleven federal regulations and fifteen permission-sentences from clinical consent forms were iteratively modeled to identify entities and their relationships, followed by community reflection and negotiation based on a series of predetermined evaluation questions. ICO included fifty-two classes and twelve object properties necessary when modeling, demonstrating appropriateness of extending ICO for the clinical domain. Twenty-six additional classes were imported into ICO from other ontologies, and twelve new classes were recommended for development. This work addresses a critical gap in formally representing permissions clinical permissions, including reuse of residual clinical biospecimens and health data. It makes missing content available to the OBO Foundry, enabling use alongside other widely-adopted biomedical ontologies. ICO serves as a machine-interpretable and interoperable tool for responsible reuse of residual clinical biospecimens and health data at scale.

Enabling Research and Clinical Use of Patient-Generated Health Data (the mindLAMP Platform): Digital Phenotyping Study

Background There is a growing need for the integration of patient-generated health data (PGHD) into research and clinical care to enable personalized, preventive, and interactive care, but technical and organizational challenges, such as the lack of standards and easy-to-use tools, preclude the effective use of PGHD generated from consumer devices, such as smartphones and wearables. Objective This study outlines how we used mobile apps and semantic web standards such as HTTP 2.0, Representational State Transfer, JSON (JavaScript Object Notation), JSON Schema, Transport Layer Security (version 1.3), Advanced Encryption Standard-256, OpenAPI, HTML5, and Vega, in conjunction with patient and provider feedback to completely update a previous version of mindLAMP. Methods The Learn, Assess, Manage, and Prevent (LAMP) platform addresses the abovementioned challenges in enhancing clinical insight by supporting research, data analysis, and implementation efforts around PGHD as an open-source solution with freely accessible and shared code. Results With a simplified programming interface and novel data representation that captures additional metadata, the LAMP platform enables interoperability with existing Fast Healthcare Interoperability Resources–based health care systems as well as consumer wearables and services such as Apple HealthKit and Google Fit. The companion Cortex data analysis and machine learning toolkit offer robust support for artificial intelligence, behavioral feature extraction, interactive visualizations, and high-performance data processing through parallelization and vectorization techniques. Conclusions The LAMP platform incorporates feedback from patients and clinicians alongside a standards-based approach to address these needs and functions across a wide range of use cases through its customizable and flexible components. These range from simple survey-based research to international consortiums capturing multimodal data to simple delivery of mindfulness exercises through personalized, just-in-time adaptive interventions.

Health data collection methods and procedures across EU member states: findings from the InfAct Joint Action on health information

Background Health-related data are collected from a variety of sources for different purposes, including secondary use for population health monitoring (HM) and health system performance assessment (HSPA). Most of these data sources are not included in databases of international organizations (e.g., WHO, OECD, Eurostat), limiting their use for research activities and policy making. This study aims at identifying and describing collection methods, quality assessment procedures, availability and accessibility of health data across EU Member States (MS) for HM and HSPA. Methods A structured questionnaire was developed and administered through an online platform to partners of the InfAct consortium form EU MS to investigate data collections applied in HM and HSPA projects, as well as their methods and procedures. A descriptive analysis of the questionnaire results was performed. Results Information on 91 projects from 18 EU MS was collected. In these projects, data were mainly collected through administrative sources, population health interview or health examination surveys and from electronic medical records. Tools and methods used for data collection were mostly mandatory reports, self-administered questionnaires, or record linkage of various data sources. One-third of the projects shared data with EU research networks and less than one-third performed quality assessment of their data collection procedures using international standardized criteria. Macrodata were accessible via open access and reusable in 22 projects. Microdata were accessible upon specific request and reusable in 15 projects based on data usage licenses. Metadata was available for the majority of the projects, but followed reporting standards only in 29 projects. Overall, compliance to FAIR Data principles (Findable, Accessible, Interoperable, and Reusable) was not optimal across the EU projects. Conclusions Data collection and exchange procedures differ across EU MS and research data are not always available, accessible, comparable or reusable for further research and evidence-based policy making. There is a need for an EU-level health information infrastructure and governance to promote and facilitate sharing and dissemination of standardized and comparable health data, following FAIR Data principles, across the EU.

CrowdMed-II: a blockchain-based framework for efficient consent management in health data sharing

The healthcare industry faces serious problems with health data. Firstly, health data is fragmented and its quality needs to be improved. Data fragmentation means that it is difficult to integrate the patient data stored by multiple health service providers. The quality of these heterogeneous data also needs to be improved for better utilization. Secondly, data sharing among patients, healthcare service providers and medical researchers is inadequate. Thirdly, while sharing health data, patients’ right to privacy must be protected, and patients should have authority over who can access their data. In traditional health data sharing system, because of centralized management, data can easily be stolen, manipulated. These systems also ignore patient’s authority and privacy. Researchers have proposed some blockchain-based health data sharing solutions where blockchain is used for consensus management. Blockchain enables multiple parties who do not fully trust each other to exchange their data. However, the practice of smart contracts supporting these solutions has not been studied in detail. We propose CrowdMed-II, a health data management framework based on blockchain, which could address the above-mentioned problems of health data. We study the design of major smart contracts in our framework and propose two smart contract structures. We also introduce a novel search contract for searching patients in the framework. We evaluate their efficiency based on the execution costs on Ethereum. Our design improves on those previously proposed, lowering the computational costs of the framework. This allows the framework to operate at scale and is more feasible for widespread adoption.