Secure Health Knowledge: Balancing Security, Privacy and Access

While decision analysis and treatment protocols have begun to move from health insurance companies into medical settings, secure knowledge management initiatives are being driven by HIPAA (Health Insurance Portability and Accountability Act) legislation. The needs of practitioners, researchers and students require that access be granted to pertinent patient data; balancing access and compliance in an environment that embraces new technological advances is difficult. HIPAA privacy and security guidelines curtailed the enthusiasm of open access with risk analysts placing an emphasis on risk-neutral behavior. This research in progress paper uses a case-based approach to address the role of security within a teaching institution. A research plan to test knowledge security and access is formulated

Enhancing Confidentiality and Privacy Preservation in e-Health to Enhanced Security

Electronic health (e-health) system use is growing, which has improved healthcare services significantly but has created questions about the privacy and security of sensitive medical data. This research suggests a novel strategy to overcome these difficulties and strengthen the security of e-health systems while maintaining the privacy and confidentiality of patient data by utilising machine learning techniques. The security layers of e-health systems are strengthened by the comprehensive framework we propose in this paper, which incorporates cutting-edge machine learning algorithms. The suggested framework includes data encryption, access control, and anomaly detection as its three main elements. First, to prevent unauthorised access during transmission and storage, patient data is secured using cutting-edge encryption technologies. Second, to make sure that only authorised staff can access sensitive medical records, access control mechanisms are strengthened using machine learning models that examine user behaviour patterns. This research's inclusion of machine learning-based anomaly detection is its most inventive feature. The technology may identify variations from typical data access and usage patterns, thereby quickly spotting potential security breaches or unauthorised activity, by training models on past e-health data. This proactive strategy improves the system's capacity to successfully address new threats. Extensive experiments were carried out employing a broad dataset made up of real-world e-health scenarios to verify the efficacy of the suggested approach. The findings showed a marked improvement in the protection of confidentiality and privacy, along with a considerable decline in security breaches and unauthorised access events

Supporting security-oriented, inter-disciplinary research: crossing the social, clinical and geospatial domains

How many people have had a chronic disease for longer than 5-years in Scotland? How has this impacted upon their choices of employment? Are there any geographical clusters in Scotland where a high-incidence of patients with such long-term illness can be found? How does the life expectancy of such individuals compare with the national averages? Such questions are important to understand the health of nations and the best ways in which health care should be delivered and measured for their impact and success. In tackling such research questions, e-Infrastructures need to provide tailored, secure access to an extensible range of distributed resources including primary and secondary e-Health clinical data; social science data, and geospatial data sets amongst numerous others. In this paper we describe the security models underlying these e-Infrastructures and demonstrate their implementation in supporting secure, federated access to a variety of distributed and heterogeneous data sets exploiting the results of a variety of projects at the National e-Science Centre (NeSC) at the University of Glasgow

Semantic Security for E-Health: A Case Study in Enhanced Access Control

Data collection, access and usage are essential for many forms of collaborative research. E-Health represents one area with much to gain by sharing of data across organisational boundaries. In such contexts, security and access control are essential to protect the often complex, privacy and information governance concerns of associated stakeholders. In this paper we argue that semantic technologies have unique benefits for specification and enforcement of security policies that cross organisation boundaries. We illustrate this through a case study based around the International Niemann-Pick Disease (NPD) Registry (www.inpdr.org) - which typifies many current e-Health security processes and policies. We show how approaches based upon ontology-based policy specification overcome many of the current security challenges facing the development of such systems and enhance access control by leveraging existing security information associated with clinical collaborators

Towards a Secure Web Based Health Care Application

Even though security requirements in health care are traditionally high, most computerized health care applications lack sophisticated security measures or focus only on single security objectives. This paper describes special security problems that arise when processing health care data using public networks such as the Internet. It proposes a structured approach using a context-dependent access control mechanism over the Internet as well as other security mechanisms to counter the threats against the major security objectives: confidentiality, integrity, availability, and accountability. The feasibility of the proposed security measures is shown through a prototype, which has been developed in a research project focussed on security in health care

Healthcare Equity: Questions of Access and Security

Abstract The rapid growth of mobile technology to improve healthcare conditions, support patient engagement, and enhance patient education is expected to continue¬ its upward trend. Physicians feel that simplified access to health information is one of the greatest benefits of technology. This research connects the growth of patients’ healthcare data access via mobile applications and the growth of access to wireless communication. This article proposes the following questions to investigate potential healthcare equity barriers: “What is the available Wi-Fi coverage?” and “What types of security protocols are used in the wireless access points?” The results indicate that there is a difference in community access to available Wi-Fi coverage. This difference could influence healthcare equity barriers. In addition, communities had identical security protocol usage. This indicates an opportunity to improve knowledge of security protocols and maintenance of access points, as well as influences on health care equity barriers

EFFECTS OF FOOD ASSISTANCE AND NUTRITION PROGRAMS ON NUTRITION AND HEALTH: VOLUME 4, EXECUTIVE SUMMARY OF THE LITERATURE REVIEW

This report provides a summary of a comprehensive review and synthesis of published research on the impact of USDA's domestic food and nutrition assistance programs on participants' nutrition and health outcomes. The outcome measures reviewed include food expenditures, household nutrient availability, dietary intake, other measures of nutrition status, food security, birth outcomes, breastfeeding behaviors, immunization rates, use and cost of health care services, and selected nonhealth outcomes, such as academic achievement and school performance (children) and social isolation (elderly). The report is one of four volumes produced by a larger study that includes Volume 1, Research Design; Volume 2, Data Sources; Volume 3, Literature Review; and Volume 4, Executive Summary of the Literature Review. The review examines the research on 15 USDA food assistance and nutrition programs but tends to focus on the largest ones for which more research is available: food stamps, school feeding programs, and the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). Over half of USDA's budget - $41.6 billion in fiscal year 2003 - was devoted to food assistance and nutrition programs that provide low-income families and children with access to a healthy diet.Dietary intake, food expenditures, nutrient availability, nutrient intake, nutritional status, nutrition and health outcomes, USDAs food assistance and nutrition programs, Food Security and Poverty,

EFFECTS OF FOOD ASSISTANCE AND NUTRITION PROGRAMS ON NUTRITION AND HEALTH: VOLUME 3, LITERATURE REVIEW

This report provides a comprehensive review and synthesis of published research on the impact of USDA's domestic food and nutrition assistance programs on participants' nutrition and health outcomes. The outcome measures reviewed include food expenditures, household nutrient availability, dietary intake, other measures of nutrition status, food security, birth outcomes, breastfeeding behaviors, immunization rates, use and cost of health care services, and selected nonhealth outcomes, such as academic achievement and school performance (children) and social isolation (elderly). The report is one of four volumes produced by a larger study that includes Volume 1, Research Design; Volume 2, Data Sources; Volume 3, Literature Review; and Volume 4, Executive Summary of the Literature Review. The review examines the research on 15 USDA food assistance programs but tends to focus on the largest ones for which more research is available: food stamps, school feeding programs, and the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). Over half of USDA's budget - $41.6 billion in fiscal year 2003 - was devoted to food assistance and nutrition programs that provide low-income families and children with access to a healthy diet.Dietary intake, food expenditures, nutrient availability, nutrient intake, nutritional status, nutrition and health outcomes, USDA, Food Security and Poverty,

A sensitive data access model in support of learning health systems

Given the ever-growing body of knowledge, healthcare improvement hinges more than ever on efficient knowledge transfer to clinicians and patients. Promoted initially by the Institute of Medicine, the Learning Health System (LHS) framework emerged in the early 2000s. It places focus on learning cycles where care delivery is tightly coupled with research activities, which in turn is closely tied to knowledge transfer, ultimately injecting solid improvements into medical practice. Sensitive health data access across multiple organisations is therefore paramount to support LHSs. While the LHS vision is well established, security requirements to support them are not. Health data exchange approaches have been implemented (e.g., HL7 FHIR) or proposed (e.g., blockchain-based methods), but none cover the entire LHS requirement spectrum. To address this, the Sensitive Data Access Model (SDAM) is proposed. Using a representation of agents and processes of data access systems, specific security requirements are presented and the SDAM layer architecture is described, with an emphasis on its mix-network dynamic topology approach. A clinical application benefiting from the model is subsequently presented and an analysis evaluates the security properties and vulnerability mitigation strategies offered by a protocol suite following SDAM and in parallel, by FHIR

A case study in open source innovation: developing the Tidepool Platform for interoperability in type 1 diabetes management.

OBJECTIVE:Develop a device-agnostic cloud platform to host diabetes device data and catalyze an ecosystem of software innovation for type 1 diabetes (T1D) management. MATERIALS AND METHODS:An interdisciplinary team decided to establish a nonprofit company, Tidepool, and build open-source software. RESULTS:Through a user-centered design process, the authors created a software platform, the Tidepool Platform, to upload and host T1D device data in an integrated, device-agnostic fashion, as well as an application ("app"), Blip, to visualize the data. Tidepool's software utilizes the principles of modular components, modern web design including REST APIs and JavaScript, cloud computing, agile development methodology, and robust privacy and security. DISCUSSION:By consolidating the currently scattered and siloed T1D device data ecosystem into one open platform, Tidepool can improve access to the data and enable new possibilities and efficiencies in T1D clinical care and research. The Tidepool Platform decouples diabetes apps from diabetes devices, allowing software developers to build innovative apps without requiring them to design a unique back-end (e.g., database and security) or unique ways of ingesting device data. It allows people with T1D to choose to use any preferred app regardless of which device(s) they use. CONCLUSION:The authors believe that the Tidepool Platform can solve two current problems in the T1D device landscape: 1) limited access to T1D device data and 2) poor interoperability of data from different devices. If proven effective, Tidepool's open source, cloud model for health data interoperability is applicable to other healthcare use cases