A secure query assurance approach for distributed health records

Health information system architectures inherently include distributed systems and data repositories across multiple organizations, health providers and with potentially some data stored with the health consumer. This is part of the shift to more fully integrated electronic health systems. Due to the varied stakeholders of these systems, it will become more important to provide a high level of query quality assurance for the parties utilizing these distributed and shared data repositories. A core consideration of health records is providing data confidentiality, including to protect against insider security threats. As such, it will often be desirable that electronic health information be stored in an encrypted format. In this paper, we present and describe the implementation and evaluation of a query assurance model that implements the three requirements of query assurance across sources of searchable encrypted health data. Furthermore, we consider the issue of freshness and data persistence in a multiple data owner environment,including a discussion of the characteristics of consumer interfacing health information systems

Secure and reliable distributed health records : achieving query assurance across repositories of encrypted health data

Future health information system architectures will intrinsically include distributed systems and data repositories across multiple organizations. As such it will become more important to provide a high level of query quality assurance for the organizations utilizing these distributed and shared data repositories. Query assurance is defined as the data source accurately responding to queries by meeting the requirements of correctness, completeness and freshness. Secure and private health information is a necessity and as one of the significant threats to this security is from insider activities, it will often be desirable that electronic health information be stored in an encrypted format to provide data confidentiality. Providing data confidentially and query assurance within the same approach will be a necessity, while simultaneously ensuring the usability of the health information is not substantially diminished. In this paper, we present a query assurance model that implements the three requirements of query assurance across sources of searchable encrypted data. Further, we consider the issue of freshness and data persistence in a multiple data-owner environment. This is a novel contribution to query assurance and one driven by and increasingly important in the specific context of emerging distributed health information systems. The approach is tested against a large dataset of Continuity of Care Records (CCR) in a key-value store and evaluation results are presented

Local processing to achieve anonymity in a participatory health e-research system

The use of participatory sensing in health e-Research applications is rapidly becoming a possibility due to the adoption of mobile computing technologies and sensing platforms. Such a change will have important benefits in the access to near real-time, large-scale up to population-wide data collection and analysis. However, there are numerous issues implied. Primarily of concern is how to ensure anonymity and privacy within these methodologies, and further the related issue of how to incentivize participants and remove barriers/concerns over participation. To address these concerns, in this paper we introduce a novel system to capture aggregate population health research data via utilizing smartphone capabilities while fully maintaining the anonymity and privacy of each individual contributing such data. A key and novel capability of this system is the support for customizable data collection; without the need to know specific details about an individual. The customized collection rules can be deployed on the local device based on detailed local data, and the resultant collection can be measured by the anonymous data collection network. In this paper we provide a conceptual architecture and describe a method for local processing of aggregate e-Research health data utilizing adaptive privacy thresholds to createa multi-party flexible approach to participatory data submission to support this novel health e-Research capability

A real-time, composite healthy building measurement architecture drawing upon occupant smartphone-collected data

Fundamentally, it is the health of those individuals occupying a building and how this is affected by a building that is a central concern in modeling and measuring healthy buildings. Emerging and increasingly sophisticated smartphones enable a novel approach to analyzing Healthy Buildings through real-time, location-specific, anonymous data capture associated with each building occupant over time. This involves manual, semi-automated and automated data capture via the occupants’ smartphones. This in effect can create a composite, time-varying healthy building measure that is constituted from these occupant-centric data sources. In this paper we present a framework and architecture for such a system to measure the state of a healthy building continuously over time. The benefits and challenges of this approach are considered via case study examples and a comparative analysis approach. Finally, some aspects of the technical deploy-ability of a system based on such a framework and architecture are discussed

A smartphone-based system for population-scale anonymized public health data collection and intervention

The wide availability and sophisticated functionalities of current mobile devices or smartphones can provide a new form of data collection capability relevant to public health. However, current data that is collected is typically siloed on individual devices and/or specific proprietary systems, only intended for individual use, limiting possible utilization for public health purposes. Additionally, the current aggregate data collection approaches do not incorporate key public health components such as support for interventions and demographic data. To address these limitations, in this paper we introduce and evaluate a system to provide aggregate population health data via utilizing smartphone capabilities, whilst fully maintaining the anonymity and privacy of each individual. In this paper we provide a detailed architecture, a method for local processing of aggregate population health data utilizing adaptive privacy thresholds to create a multi-party flexible approach to participatory data submission and evaluate its privacy properties at large scale

Summarized data to achieve population-wide anonymized wellness measures

The growth in smartphone market share has seen the increasing emergence of individuals collecting quantitative wellness data. Beyond the potential health benefits for the individual in regards to managing their own health, the data is highly related to preventative and risk factors for a number of lifestyle related diseases. This data has often been a component of public health data collection and epidemiological studies due to its large impact on the health system with chronic and lifestyle diseases increasingly being a major burden for the health service. However, collection of this kind of information from large segments of the community in a usable fashion has not been specifically explored in previous work. In this paper we discuss some of the technologies that increase the ease and capability of gathering quantitative wellness data via smartphones, how specific and detailed this data needs to be for public health use and the challenges of such anonymized data collection for public health. Additionally, we propose a conceptual architecture that includes the necessary components to support this approach to data collection

A Smartphone-Based System for Population-Scale Anonymized Public Health Data Collection and Intervention

The wide availability and sophisticated functionalities of current mobile devices or smartphones can provide a new form of data collection capability relevant to public health. However, current data that is collected is typically siloed on individual devices and/or specific proprietary systems, only intended for individual use, limiting possible utilization for public health purposes. Additionally, the current aggregate data collection approaches do not incorporate key public health components such as support for interventions and demographic data. To address these limitations, in this paper we introduce and evaluate a system to provide aggregate population health data via utilizing smartphone capabilities, whilst fully maintaining the anonymity and privacy of each individual. In this paper we provide a detailed architecture, a method for local processing of aggregate population health data utilizing adaptive privacy thresholds to create a multi-party flexible approach to participatory data submission and evaluate its privacy properties at large scale

Health participatory sensing networks

The use of participatory sensing in relation to the capture of health-related data is rapidly becoming a possibility due to the widespread consumer adoption of emerging mobile computing technologies and sensing platforms. This has the potential to revolutionize data collection for population health, aspects of epidemiology, and health-related e-Science applications and as we will describe, provide new public health intervention capabilities, with the classifications and capabilities of such participatory sensing platforms only just beginning to be explored. Such a development will have important benefits for access to near real-time, large-scale, up to population-scale data collection. However, there are also numerous issues to be addressed first: provision of stringent anonymity and privacy within these methodologies, user interface issues, and the related issue of how to incentivize participants and address barriers/concerns over participation. To provide a step towards describing these aspects, in this paper we present a first classification of health participatory sensing models, a novel contribution to the literature, and provide a conceptual reference architecture for health participatory sensing networks (HPSNs) and user interaction example case study

Summarized data to achieve population-wide anonymized wellness measures

The growth in smartphone market share has seen the increasing emergence of individuals collecting quantitative wellness data. Beyond the potential health benefits for the individual in regards to managing their own health, the data is highly related to preventative and risk factors for a number of lifestyle related diseases. This data has often been a component of public health data collection and epidemiological studies due to its large impact on the health system with chronic and lifestyle diseases increasingly being a major burden for the health service. However, collection of this kind of information from large segments of the community in a usable fashion has not been specifically explored in previous work. In this paper we discuss some of the technologies that increase the ease and capability of gathering quantitative wellness data via smartphones, how specific and detailed this data needs to be for public health use and the challenges of such anonymized data collection for public health. Additionally, we propose a conceptual architecture that includes the necessary components to support this approach to data collection

How personal fitness data can be re-used by smart cities

The growing trend to use mobile devices and applications to collect data relating to fitness activities has resulted in large amounts of sensor data being generated. Further, in some cases the fitness data is shared over social networks. This collected data has potential uses in a number of fields including: public health and population health data, urban planning, fitness trends analysis, social network analysis and personalization of health information. As the motivation for creating this sensor data already exists for the individual in relation to fitness benefits and health monitoring, this type of participatory sensing approach has a lower barrier to entry. However, there is currently no structured approach to collect and re-use this sensor data. This paper describes the distinct types of fitness sensor applications; conceptual architecture for data collection and aggregation and the steps and developments that would improve the quality and usability of data collected. Further, the types of secondary uses for smart cities of this collected data are explored