A rights management approach to protection of privacy in a cloud of electronic health records

A patient-centric DRM approach is proposed for protecting privacy of health records stored in a cloud storage based on the patient's preferences and without the need to trust the service provider. Contrary to the current server-side access control solutions, this approach protects the privacy of records from the service provider, and also controls the usage of data after it is released to an authorized user.

Towards semantic search and inference in electronic medical records : an approach using concept-based information retrieval

For more than a decade research in the field of context aware computing has aimed to find ways to exploit situational information that can be detected by mobile computing and sensor technologies. The goal is to provide people with new and improved applications, enhanced functionality and better use experience (Dey, 2001). Early applications focused on representing or computing on physical parameters, such as showing your location and the location of people or things around you. Such applications might show where the next bus is, which of your friends is in the vicinity and so on. With the advent of social networking software and microblogging sites such as Facebook and Twitter, recommender systems and so on context-aware computing is moving towards mining the social web in order to provide better representations and understanding of context, including social context. In this paper we begin by recapping different theoretical framings of context. We then discuss the problem of context- aware computing from a design perspective.

Privacy oriented access control for electronic health records

Security and privacy in electronic health record systems have been hindering the growth of e-health systems since their emergence. The development of policies that satisfy the security and privacy requirements of different stakeholders in healthcare has proven to be difficult. But, these requirements have to be met if the systems developed are to succeed in achieving their intended goals. Access control is a fundamental security barrier for securing data in healthcare information systems. In this paper we present an access control model for electronic health records. We address patient privacy requirements, confidentiality of private information and the need for flexible access for health professionals for electronic health records. We carefully combine three existing access control models and present a novel access control model for EHRs which satisfies requirements of electronic health records.

Towards semantic search and inference in electronic medical records : an approach using concept-based information retrieval

Background This paper presents a novel approach to searching electronic medical records that is based on concept matching rather than keyword matching. Aim The concept-based approach is intended to overcome specific challenges we identified in searching medical records. Method Queries and documents were transformed from their term-based originals into medical concepts as defined by the SNOMED-CT ontology. Results Evaluation on a real-world collection of medical records showed our concept-based approach outperformed a keyword baseline by 25% in Mean Average Precision. Conclusion The concept-based approach provides a framework for further development of inference based search systems for dealing with medical data.

Is there a place for pixels in our practice? Dietitians' attitudes towards the use of photographic dietary records within the nutrition care process

Photographic records of dietary intake (PhDRs) are an innovative method for the dietary assessment and may alleviate the burden of recording intake compared to traditional methods of recording intake. While the performance of PhDRs has been evaluated, no investigation into the application of this method had occurre within dietetic practice. This study examined the attitudes of dietitians towards the use of PhDRs in the provision of nutrition care. A web-based survey on the practices and beliefs with regards to technology use among Dietitians Association of Australia members was conducted in August 2011. Of the 87 dietitians who responded, 86% assessed the intakes of clients as part of individualised medical nutrition therapy, with the diet history the most common method used. The majority (91%) of dietitians surveyed believed that a PhDR would be of use in their current practice to estimate intake. Information contained in the PhDR would primarily be used to obtain a qualitative evaluation of diet (84%) or to supplement an existing assessment method (69%), as opposed to deriving an absolute measure of nutrient intake (31%). Most (87%) indicated that a PhDR would also be beneficial in both the delivery of the intervention and to evaluate and monitor goals and outcomes, while only 46% felt that a PhDR would assist in determining the nutrition diagnosis. This survey highlights the potential for the use of PhDRs within practice. Future endeavours lie in establishing resources which support the inclusion of PhDRs within the nutrition care process.

Perceptions and attitudes of nursing students’ towards electronic health records

Introduction The onset of Personally Controlled Electronic Health Records in Australia demand healthcare decision making processes to comprise, understand and accept electronic health records (EHR). Nurses play a key, central role in the healthcare decision making process and their perceptions and attitudes of EHRs are significant [1], which develop during their academic life. However, studies aimed at nursing students’ attitudes of EHRs are very limited [2-4]. A proper understanding of these attitudes and how they evolve with academic progress is important. This paper presents results from a survey conducted at a leading University in Queensland, Australia as a first step to filling this gap.

Informed use of patients' records on trusted health care services

Health care is an information-intensive business. Sharing information in health care processes is a smart use of data enabling informed decision-making whilst ensuring. the privacy and security of patient information. To achieve this, we propose data encryption techniques embedded Information Accountability Framework (IAF) that establishes transitions of the technological concept, thus enabling understanding of shared responsibility, accessibility, and efficient cost effective informed decisions between health care professionals and patients. The IAF results reveal possibilities of efficient informed medical decision making and minimisation of medical errors. Of achieving this will require significant cultural changes and research synergies to ensure the sustainability, acceptability and durability of the IAF

Data warehousing model for integrating fragmented electronic health records from disparate and heterogeneous clinical data stores

This research was a step forward in developing a data integration framework for Electronic Health Records. The outcome of the research is a conceptual and logical Data Warehousing model for integrating Cardiac Surgery electronic data records. This thesis investigated the main obstacles for the healthcare data integration and proposes a data warehousing model suitable for integrating fragmented data in a Cardiac Surgery Unit.

Climatic records over the past 30 ka from temperate Australia – a synthesis from the Oz-INTIMATE workgroup

Temperate Australia sits between the heat engine of the tropics and the cold Southern Ocean, encompassing a range of rainfall regimes and falling under the influence of different climatic drivers. Despite this heterogeneity, broad-scale trends in climatic and environmental change are evident over the past 30 ka. During the early glacial period (∼30–22 ka) and the Last Glacial Maximum (∼22–18 ka), climate was relatively cool across the entire temperate zone and there was an expansion of grasslands and increased fluvial activity in regionally important Murray–Darling Basin. The temperate region at this time appears to be dominated by expanded sea ice in the Southern Ocean forcing a northerly shift in the position of the oceanic fronts and a concomitant influx of cold water along the southeast (including Tasmania) and southwest Australian coasts. The deglacial period (∼18–12 ka) was characterised by glacial recession and eventual disappearance resulting from an increase in temperature deduced from terrestrial records, while there is some evidence for climatic reversals (e.g. the Antarctic Cold Reversal) in high resolution marine sediment cores through this period. The high spatial density of Holocene terrestrial records reveals an overall expansion of sclerophyll woodland and rainforest taxa across the temperate region after ∼12 ka, presumably in response to increasing temperature, while hydrological records reveal spatially heterogeneous hydro-climatic trends. Patterns after ∼6 ka suggest higher frequency climatic variability that possibly reflects the onset of large scale climate variability caused by the El Niño/Southern Oscillation.

Identify disorders in health records using Conditional Random Fields and Metamap: AEHRC at ShARe/CLEF 2013 eHealth Evaluation Lab Task 1

The Australian e-Health Research Centre (AEHRC) recently participated in the ShARe/CLEF eHealth Evaluation Lab Task 1. The goal of this task is to individuate mentions of disorders in free-text electronic health records and map disorders to SNOMED CT concepts in the UMLS metathesaurus. This paper details our participation to this ShARe/CLEF task. Our approaches are based on using the clinical natural language processing tool Metamap and Conditional Random Fields (CRF) to individuate mentions of disorders and then to map those to SNOMED CT concepts. Empirical results obtained on the 2013 ShARe/CLEF task highlight that our instance of Metamap (after ltering irrelevant semantic types), although achieving a high level of precision, is only able to identify a small amount of disorders (about 21% to 28%) from free-text health records. On the other hand, the addition of the CRF models allows for a much higher recall (57% to 79%) of disorders from free-text, without sensible detriment in precision. When evaluating the accuracy of the mapping of disorders to SNOMED CT concepts in the UMLS, we observe that the mapping obtained by our ltered instance of Metamap delivers state-of-the-art e ectiveness if only spans individuated by our system are considered (`relaxed' accuracy).

Privacy oriented access control for electronic health records

Information privacy is a critical success/failure factor in information technology supported healthcare (eHealth). eHealth systems utilise electronic health records (EHR) as the main source of information, thus, implementing appropriate privacy preserving methods for EHRs is vital for the proliferation of eHealth. Whilst information privacy may be a fundamental requirement for eHealth consumers, healthcare professionals demand non-restricted access to patient information for improved healthcare delivery, thus, creating an environment where stakeholder requirements are contradictory. Therefore, there is a need to achieve an appropriate balance of requirements in order to build successful eHealth systems. Towards achieving this balance, a new genre of eHealth systems called Accountable-eHealth (AeH) systems has been proposed. In this paper, an access control model for EHRs is presented that can be utilised by AeH systems to create information usage policies that fulfil both stakeholders’ requirements. These policies are used to accomplish the aforementioned balance of requirements creating a satisfactory eHealth environment for all stakeholders. The access control model is validated using a Web based prototype as a proof of concept.

Automatic de-identification of electronic health records : an Australian perspective

We present an approach to automatically de-identify health records. In our approach, personal health information is identified using a Conditional Random Fields machine learning classifier, a large set of linguistic and lexical features, and pattern matching techniques. Identified personal information is then removed from the reports. The de-identification of personal health information is fundamental for the sharing and secondary use of electronic health records, for example for data mining and disease monitoring. The effectiveness of our approach is first evaluated on the 2007 i2b2 Shared Task dataset, a widely adopted dataset for evaluating de-identification techniques. Subsequently, we investigate the robustness of the approach to limited training data; we study its effectiveness on different type and quality of data by evaluating the approach on scanned pathology reports from an Australian institution. This data contains optical character recognition errors, as well as linguistic conventions that differ from those contained in the i2b2 dataset, for example different date formats. The findings suggest that our approach compares to the best approach from the 2007 i2b2 Shared Task; in addition, the approach is found to be robust to variations of training size, data type and quality in presence of sufficient training data.