Retrieval of health advice on the web: AEHRC at ShARe/CLEF eHealth Evaluation Lab Task 3

This paper details the participation of the Australian e- Health Research Centre (AEHRC) in the ShARe/CLEF 2013 eHealth Evaluation Lab { Task 3. This task aims to evaluate the use of information retrieval (IR) systems to aid consumers (e.g. patients and their relatives) in seeking health advice on the Web. Our submissions to the ShARe/CLEF challenge are based on language models generated from the web corpus provided by the organisers. Our baseline system is a standard Dirichlet smoothed language model. We enhance the baseline by identifying and correcting spelling mistakes in queries, as well as expanding acronyms using AEHRC's Medtex medical text analysis platform. We then consider the readability and the authoritativeness of web pages to further enhance the quality of the document ranking. Measures of readability are integrated in the language models used for retrieval via prior probabilities. Prior probabilities are also used to encode authoritativeness information derived from a list of top-100 consumer health websites. Empirical results show that correcting spelling mistakes and expanding acronyms found in queries signi cantly improves the e ectiveness of the language model baseline. Readability priors seem to increase retrieval e ectiveness for graded relevance at early ranks (nDCG@5, but not precision), but no improvements are found at later ranks and when considering binary relevance. The authoritativeness prior does not appear to provide retrieval gains over the baseline: this is likely to be because of the small overlap between websites in the corpus and those in the top-100 consumer-health websites we acquired.

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.

Overview of the ShARe/CLEF eHealth Evaluation Lab 2013

Discharge summaries and other free-text reports in healthcare transfer information between working shifts and geographic locations. Patients are likely to have difficulties in understanding their content, because of their medical jargon, non-standard abbreviations,and ward-specific idioms. This paper reports on an evaluation lab with an aim to support the continuum of care by developing methods and resources that make clinical reports in English easier to understand for patients, and which helps them in finding information related to their condition.

ShARe/CLEF eHealth Evaluation Lab 2013, Task 3: Information retrieval to address patients' questions when reading clinical reports

This paper presents the results of task 3 of the ShARe/CLEF eHealth Evaluation Lab 2013. This evaluation lab focuses on improving access to medical information on the web. The task objective was to investigate the effect of using additional information such as the discharge summaries and external resources such as medical ontologies on the IR effectiveness. The participants were allowed to submit up to seven runs, one mandatory run using no additional information or external resources, and three each using or not using discharge summaries.

De-Identification of health records using Anonym: Effectiveness and robustness across datasets

Objective Evaluate the effectiveness and robustness of Anonym, a tool for de-identifying free-text health records based on conditional random fields classifiers informed by linguistic and lexical features, as well as features extracted by pattern matching techniques. De-identification of personal health information in electronic health records is essential for the sharing and secondary usage of clinical data. De-identification tools that adapt to different sources of clinical data are attractive as they would require minimal intervention to guarantee high effectiveness. Methods and Materials The effectiveness and robustness of Anonym are evaluated across multiple datasets, including the widely adopted Integrating Biology and the Bedside (i2b2) dataset, used for evaluation in a de-identification challenge. The datasets used here vary in type of health records, source of data, and their quality, with one of the datasets containing optical character recognition errors. Results Anonym identifies and removes up to 96.6% of personal health identifiers (recall) with a precision of up to 98.2% on the i2b2 dataset, outperforming the best system proposed in the i2b2 challenge. The effectiveness of Anonym across datasets is found to depend on the amount of information available for training. Conclusion Findings show that Anonym compares to the best approach from the 2006 i2b2 shared task. It is easy to retrain Anonym with new datasets; if retrained, the system is robust to variations of training size, data type and quality in presence of sufficient training data.

eHealth-as-a-Service (eHaaS) : towards universal stakeholder engagement

With the introduction of the Personally Controlled Health Record (PCEHR), the Australian public is being asked to accept greater responsibility for their healthcare by taking an active role in the management of personal health information. Although well designed, constructed and intentioned, policy and privacy concerns have resulted in an eHealth model that may impact future health sharing requirements. Hence, as a case study for a consumer eHealth initative in the Australian context, eHealth-as-a-Service (eHaaS) serves as a disruptive step in in the aggregation and transformation of health information for use as real-world knowledge. The strategic value of extending the community Health Record Bank (HRB) model lies in the ability to automatically draw on a multitude of relevant data repositories and sources to create a single source of the truth and to engage market forces to create financial sustainability. The opportunity to transform the beleaguered Australian PCEHR into a realisable and sustainable technology consumption model for patient safety is explored. Moreover, the current clerical focus of healthcare practitioners acting in the role of de facto record keepers is renegotiated to establish a shared knowledge creation landscape of action for safer patient interventions. To achieve this potential however requires a platform that will facilitate efficient and trusted unification of all health information available in real-time across the continuum of care. eHaaS provides a sustainable environment and encouragement to realise this potential.

Consumer acceptance of Accountable-eHealth systems

In this paper, we present the results of a survey conducted to measure the attitudes of the consumers of eHealth towards Accountable-eHealth systems which are designed for information privacy management. A research model is developed that can identify the factors contributing to system acceptance and is validated using quantitative data from 187 completed survey responses from university students studying non-health related courses at a university in Queensland, Australia. The research model is validated using structural equation modelling and can be used to identify how specific characteristics of Accountable-eHealth systems would affect their overall acceptance by future eHealth consumers.

Barriers to implementing eHealth : a multi-dimensional perspective

This paper strives to identify barriers that hamper eHealth implementation from different perspectives. The benefits offered by eHealth and the need for eHealth preparedness is first discussed. This is followed by a discussion on the integral components of a robust eHealth infrastructure. Then, the barriers to eHealth such as technical interoperability issues, lack of holistic approach and technology disconnect are explained in detail. Finally, solutions to promote better adoption of eHealth through government policies, standardisation and training are also discussed.

Progress and challenges in the implementation of Electronic Medical Records in Saudi Arabia : a systematic review

Electronic Medical Record (EMR) systems are being implemented increasingly worldwide. Saudi Arabia is one of the developing countries that commenced implementing such systems in 1988. Whilst EMR uptake has been low in Saudi Arabia until now, a number of hospitals have implemented EMR systems successfully. This paper analyses available studies (n = 28) in the literature regarding EMR implementation in Saudi Arabia to identify the progress of EMR implementation to date and to identify the facilitators and barriers to implementation.

Implementing a new genre of eHealth system in a reticent environment

This tutorial primarily focuses on the social aspects of implementing a novel eHealth systems called Accountable-eHealth (AeH) systems. The main focus of AeH systems is mitigating information privacy concerns whilst facilitating appropriate access to information for users, and is based on the principles of information accountability (IA).

1st International Workshop on Secure and Privacy-Aware Information Management in eHealth

Information security and privacy in the healthcare domain is a complex and challenging problem for computer scientists, social scientists, law experts and policy makers. Appropriate healthcare provision requires specialized knowledge, is information intensive and much patient information is of a particularly sensitive nature. Electronic health record systems provide opportunities for information sharing which may enhance healthcare services, for both individuals and populations. However, appropriate information management measures are essential for privacy preservation...

Overview of the ShARe/CLEF eHealth Evaluation Lab 2014

This paper reports on the 2nd ShARe/CLEFeHealth evaluation lab which continues our evaluation resource building activities for the medical domain. In this lab we focus on patients' information needs as opposed to the more common campaign focus of the specialised information needs of physicians and other healthcare workers. The usage scenario of the lab is to ease patients and next-of-kins' ease in understanding eHealth information, in particular clinical reports. The 1st ShARe/CLEFeHealth evaluation lab was held in 2013. This lab consisted of three tasks. Task 1 focused on named entity recognition and normalization of disorders; Task 2 on normalization of acronyms/abbreviations; and Task 3 on information retrieval to address questions patients may have when reading clinical reports. This year's lab introduces a new challenge in Task 1 on visual-interactive search and exploration of eHealth data. Its aim is to help patients (or their next-of-kin) in readability issues related to their hospital discharge documents and related information search on the Internet. Task 2 then continues the information extraction work of the 2013 lab, specifically focusing on disorder attribute identification and normalization from clinical text. Finally, this year's Task 3 further extends the 2013 information retrieval task, by cleaning the 2013 document collection and introducing a new query generation method and multilingual queries. De-identified clinical reports used by the three tasks were from US intensive care and originated from the MIMIC II database. Other text documents for Tasks 1 and 3 were from the Internet and originated from the Khresmoi project. Task 2 annotations originated from the ShARe annotations. For Tasks 1 and 3, new annotations, queries, and relevance assessments were created. 50, 79, and 91 people registered their interest in Tasks 1, 2, and 3, respectively. 24 unique teams participated with 1, 10, and 14 teams in Tasks 1, 2 and 3, respectively. The teams were from Africa, Asia, Canada, Europe, and North America. The Task 1 submission, reviewed by 5 expert peers, related to the task evaluation category of Effective use of interaction and targeted the needs of both expert and novice users. The best system had an Accuracy of 0.868 in Task 2a, an F1-score of 0.576 in Task 2b, and Precision at 10 (P@10) of 0.756 in Task 3. The results demonstrate the substantial community interest and capabilities of these systems in making clinical reports easier to understand for patients. The organisers have made data and tools available for future research and development.

Adoption of accountable-eHealth systems by future healthcare professionals: An empirical research model based on the Australian context

This paper provides a first look at the acceptance of Accountable-eHealth systems, a new genre of eHealth systems, designed to manage information privacy concerns that hinder the proliferation of eHealth. The underlying concept of AeH systems is appropriate use of information through after-the-fact accountability for intentional misuse of information by healthcare professionals. An online questionnaire survey was utilised for data collection from three educational institutions in Queensland, Australia. A total of 23 hypothesis relating to 9 constructs were tested using a structural equation modelling technique. A total of 334 valid responses were received. The cohort consisted of medical, nursing and other health related students studying at various levels in both undergraduate and postgraduate courses. The hypothesis testing disproved 7 hypotheses. The empirical research model developed was capable of predicting 47.3% of healthcare professionals’ perceived intention to use AeH systems. A validation of the model with a wider survey cohort would be useful to confirm the current findings.