Siaw-Teng Liaw

Stroke in rural areas and small communities

The management of stroke in rural and regional areas is variable in both the developed and developing world. Informed by best-practice guidelines and recommendations for systems of stroke care, adaptable models of care that are appropriate for local needs should be devised for rural and regional settings. This review addresses the issue of the provision of appropriate services in rural and regional settings, with particular attention to the barriers involved, according to the classification of Low Human Development Country (LHDC), Medium Human Development Country (MHDC) and High Human Development Country (HHDC). We discuss the need and feasibility of developing implementing stroke care in rural settings according to best-practice recommendations, within models of care adapted to local conditions.

Perceived need for mental health care: influences of diagnosis, demography and disability.

BACKGROUND Recent major epidemiological studies have adopted increasingly multidimensional approaches to assessment. Several of these have included some assessment of perceived need for mental health care. The Australian National Survey of Mental Health and Wellbeing, conducted in 1997, included a particularly detailed examination of this construct, with an instrument with demonstrated reliability and validity. METHODS A clustered probability sample of 10641 Australians responded to the field questionnaire for this survey, including questions on perceived need either where there had been service utilization, or where a disorder was detected by administration of sections of the Composite International Diagnostic Interview. The confidentialized unit record file generated from the survey was analysed for determinants of perceived need. RESULTS Perceived need is increased in females, in people in the middle years of adulthood, and in those who have affective disorders or co-morbidity. Effects of diagnosis and disability can account for most of the differences in gender specific rates. With correction for these effects through regression, there is less perceived need for social interventions and possibly more for counselling in females; disability is confirmed as strongly positively associated with perceived need, as are the presence of affective disorders or co-morbidity. CONCLUSIONS The findings of this study underscore the imperative for mental health services to be attentive and responsive to consumer perceived need. The substantial majority of people who are significantly disabled by mental health problems are among those who see themselves as having such needs.

Towards an ontology for data quality in integrated chronic disease management: A realist review of the literature

PURPOSE Effective use of routine data to support integrated chronic disease management (CDM) and population health is dependent on underlying data quality (DQ) and, for cross system use of data, semantic interoperability. An ontological approach to DQ is a potential solution but research in this area is limited and fragmented. OBJECTIVE Identify mechanisms, including ontologies, to manage DQ in integrated CDM and whether improved DQ will better measure health outcomes. METHODS A realist review of English language studies (January 2001-March 2011) which addressed data quality, used ontology-based approaches and is relevant to CDM. RESULTS We screened 245 papers, excluded 26 duplicates, 135 on abstract review and 31 on full-text review; leaving 61 papers for critical appraisal. Of the 33 papers that examined ontologies in chronic disease management, 13 defined data quality and 15 used ontologies for DQ. Most saw DQ as a multidimensional construct, the most used dimensions being completeness, accuracy, correctness, consistency and timeliness. The majority of studies reported tool design and development (80%), implementation (23%), and descriptive evaluations (15%). Ontological approaches were used to address semantic interoperability, decision support, flexibility of information management and integration/linkage, and complexity of information models. CONCLUSION DQ lacks a consensus conceptual framework and definition. DQ and ontological research is relatively immature with little rigorous evaluation studies published. Ontology-based applications could support automated processes to address DQ and semantic interoperability in repositories of routinely collected data to deliver integrated CDM. We advocate moving to ontology-based design of information systems to enable more reliable use of routine data to measure health mechanisms and impacts.

Successful chronic disease care for Aboriginal Australians requires cultural competence

Objective: To review the literature to determine the attributes of culturally appropriate healthcare to inform the design of chronic disease management (CDM) models for Aboriginal patients in urban general practice.

Australian rural football club leaders as mental health advocates: an investigation of the impact of the Coach the Coach project

BackgroundMental ill health, especially depression, is recognised as an important health concern, potentially with greater impact in rural communities. This paper reports on a project, Coach the Coach, in which Australian rural football clubs were the setting and football coaches the leaders in providing greater mental health awareness and capacity to support early help seeking behaviour among young males experiencing mental health difficulties, especially depression. Coaches and other football club leaders were provided with Mental Health First Aid (MHFA) training.MethodPre-post measures of the ability of those club leaders undertaking mental health training to recognise depression and schizophrenia and of their knowledge of evidence supported treatment options, and confidence in responding to mental health difficulties were obtained using a questionnaire. This was supplemented by focus group interviews. Pre-post questionnaire data from players in participating football clubs was used to investigate attitudes to depression, treatment options and ability to recognise depression from a clinical scenario. Key project stakeholders were also interviewed.ResultsClub leaders (n = 36) who were trained in MHFA and club players (n = 275) who were not trained, participated in this evaluation. More than 50% of club leaders who undertook the training showed increased capacity to recognise mental illness and 66% reported increased confidence to respond to mental health difficulties in others. They reported that this training built upon their existing skills, fulfilled their perceived social responsibilities and empowered them. Indirect benefit to club players from this approach seemed limited as minimal changes in attitudes were reported by players. Key stakeholders regarded the project as valuable.ConclusionsRural football clubs appear to be appropriate social structures to promote rural mental health awareness. Club leaders, including many coaches, benefit from MHFA training, reporting increased skills and confidence. Benefit to club players from this approach was less obvious. However, the generally positive findings of this study suggest further research in this area is desirable.

Validating an ontology-based algorithm to identify patients with Type 2 Diabetes Mellitus in Electronic Health Records

BACKGROUND Improving healthcare for people with chronic conditions requires clinical information systems that support integrated care and information exchange, emphasizing a semantic approach to support multiple and disparate Electronic Health Records (EHRs). Using a literature review, the Australian National Guidelines for Type 2 Diabetes Mellitus (T2DM), SNOMED-CT-AU and input from health professionals, we developed a Diabetes Mellitus Ontology (DMO) to diagnose and manage patients with diabetes. This paper describes the manual validation of the DMO-based approach using real world EHR data from a general practice (n=908 active patients) participating in the electronic Practice Based Research Network (ePBRN). METHOD The DMO-based algorithm to query, using Semantic Protocol and RDF Query Language (SPARQL), the structured fields in the ePBRN data repository were iteratively tested and refined. The accuracy of the final DMO-based algorithm was validated with a manual audit of the general practice EHR. Contingency tables were prepared and Sensitivity and Specificity (accuracy) of the algorithm to diagnose T2DM measured, using the T2DM cases found by manual EHR audit as the gold standard. Accuracy was determined with three attributes - reason for visit (RFV), medication (Rx) and pathology (path) - singly and in combination. RESULTS The Sensitivity and Specificity of the algorithm were 100% and 99.88% with RFV; 96.55% and 98.97% with Rx; and 15.6% and 98.92% with Path. This suggests that Rx and Path data were not as complete or correct as the RFV for this general practice, which kept its RFV information complete and current for diabetes. However, the completeness is good enough for this purpose as confirmed by the very small relative deterioration of the accuracy (Sensitivity and Specificity of 97.67% and 99.18%) when calculated for the combination of RFV, Rx and Path. The manual EHR audit suggested that the accuracy of the algorithm was influenced by data quality such as incorrect data due to mistaken units of measurement and unavailable data due to non-documentation or documented in the wrong place or progress notes, problems with data extraction, encryption and data management errors. CONCLUSION This DMO-based algorithm is sufficiently accurate to support a semantic approach, using the RFV, Rx and Path to define patients with T2DM from EHR data. However, the accuracy can be compromised by incomplete or incorrect data. The extent of compromise requires further study, using ontology-based and other approaches.

Integrating electronic health record information to support integrated care

BACKGROUND Information in Electronic Health Records (EHRs) are being promoted for use in clinical decision support, patient registers, measurement and improvement of integration and quality of care, and translational research. To do this EHR-derived data product creators need to logically integrate patient data with information and knowledge from diverse sources and contexts. OBJECTIVE To examine the accuracy of an ontological multi-attribute approach to create a Type 2 Diabetes Mellitus (T2DM) register to support integrated care. METHODS Guided by Australian best practice guidelines, the T2DM diagnosis and management ontology was conceptualized, contextualized and validated by clinicians; it was then specified, formalized and implemented. The algorithm was standardized against the domain ontology in SNOMED CT-AU. Accuracy of the implementation was measured in 4 datasets of varying sizes (927-12,057 patients) and an integrated dataset (23,793 patients). Results were cross-checked with sensitivity and specificity calculated with 95% confidence intervals. RESULTS Incrementally integrating Reason for Visit (RFV), medication (Rx), and pathology in the algorithm identified nearly100% of T2DM cases. Incrementally integrating the four datasets improved accuracy; controlling for sample size, data incompleteness and duplicates. Manual validation confirmed the accuracy of the algorithm. CONCLUSION Integrating multiple data elements within an EHR using ontology-based case-finding algorithms can improve the accuracy of the diagnosis and compensate for suboptimal data quality, and hence creating a dataset that is more fit-for-purpose. This clinical and pragmatic application of ontologies to EHR data improves the integration of data and the potential for better use of data to improve the quality of care.

Which Bundles of Features in a Web-Based Personally Controlled Health Management System Are Associated With Consumer Help-Seeking Behaviors for Physical and Emotional Well-Being?

Background Personally controlled health management systems (PCHMS), which include a personal health record (PHR), health management tools, and consumer resources, represent the next stage in consumer eHealth systems. It is still unclear, however, what features contribute to an engaging and efficacious PCHMS. Objective To identify features in a Web-based PCHMS that are associated with consumer utilization of primary care and counselling services, and help-seeking rates for physical and emotional well-being concerns. Methods A one-group pre/posttest online prospective study was conducted on a university campus to measure use of a PCHMS for physical and emotional well-being needs during a university academic semester (July to November 2011). The PCHMS integrated an untethered personal health record (PHR) with well-being journeys, social forums, polls, diaries, and online messaging links with a health service provider, where journeys provide information for consumer participants to engage with clinicians and health services in an actionable way. 1985 students and staff aged 18 and above with access to the Internet were recruited online. Logistic regression, the Pearson product-moment correlation coefficient, and chi-square analyses were used to associate participants’ help-seeking behaviors and health service utilization with PCHMS usage among the 709 participants eligible for analysis. Results A dose-response association was detected between the number of times a user logged into the PCHMS and the number of visits to a health care professional (P=.01), to the university counselling service (P=.03), and help-seeking rates (formal or informal) for emotional well-being matters (P=.03). No significant association was detected between participant pre-study characteristics or well-being ratings at different PCHMS login frequencies. Health service utilization was strongly correlated with use of a bundle of features including: online appointment booking (primary care: OR 1.74, 95% CI 1.01-3.00; counselling: OR 6.04, 95% CI 2.30-15.85), personal health record (health care professional: OR 2.82, 95% CI 1.63-4.89), the poll (health care professional: OR 1.47, 95% CI 1.02-2.12), and diary (counselling: OR 4.92, 95% CI 1.40-17.35). Help-seeking for physical well-being matters was only correlated with use of the personal health record (OR 1.73, 95% CI 1.18-2.53). Help-seeking for emotional well-being concerns (including visits to the university counselling service) was correlated with a bundle comprising the poll (formal or informal help-seeking: OR 1.03, 95% CI 1.00-1.05), diary (counselling: OR 4.92, 95% CI 1.40-17.35), and online appointment booking (counselling: OR 6.04, 95% CI 2.30-15.85). Conclusions Frequent usage of a PCHMS was significantly associated with increased consumer health service utilization and help-seeking rates for emotional health matters in a university sample. Different bundles of PCHMS features were associated with physical and emotional well-being matters. PCHMS appears to be a promising mechanism to engage consumers in help-seeking or health service utilization for physical and emotional well-being matters.

A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data.

Objective: Harmonized data quality (DQ) assessment terms, methods, and reporting practices can establish a common understanding of the strengths and limitations of electronic health record (EHR) data for operational analytics, quality improvement, and research. Existing published DQ terms were harmonized to a comprehensive unified terminology with definitions and examples and organized into a conceptual framework to support a common approach to defining whether EHR data is ‘fit’ for specific uses. Materials and Methods: DQ publications, informatics and analytics experts, managers of established DQ programs, and operational manuals from several mature EHR-based research networks were reviewed to identify potential DQ terms and categories. Two face-to-face stakeholder meetings were used to vet an initial set of DQ terms and definitions that were grouped into an overall conceptual framework. Feedback received from data producers and users was used to construct a draft set of harmonized DQ terms and categories. Multiple rounds of iterative refinement resulted in a set of terms and organizing framework consisting of DQ categories, subcategories, terms, definitions, and examples. The harmonized terminology and logical framework’s inclusiveness was evaluated against ten published DQ terminologies. Results: Existing DQ terms were harmonized and organized into a framework by defining three DQ categories: (1) Conformance (2) Completeness and (3) Plausibility and two DQ assessment contexts: (1) Verification and (2) Validation. Conformance and Plausibility categories were further divided into subcategories. Each category and subcategory was defined with respect to whether the data may be verified with organizational data, or validated against an accepted gold standard, depending on proposed context and uses. The coverage of the harmonized DQ terminology was validated by successfully aligning to multiple published DQ terminologies. Discussion: Existing DQ concepts, community input, and expert review informed the development of a distinct set of terms, organized into categories and subcategories. The resulting DQ terms successfully encompassed a wide range of disparate DQ terminologies. Operational definitions were developed to provide guidance for implementing DQ assessment procedures. The resulting structure is an inclusive DQ framework for standardizing DQ assessment and reporting. While our analysis focused on the DQ issues often found in EHR data, the new terminology may be applicable to a wide range of electronic health data such as administrative, research, and patient-reported data. Conclusion: A consistent, common DQ terminology, organized into a logical framework, is an initial step in enabling data owners and users, patients, and policy makers to evaluate and communicate data quality findings in a well-defined manner with a shared vocabulary. Future work will leverage the framework and terminology to develop reusable data quality assessment and reporting methods.

Coronary artery disease risk assessment from unstructured electronic health records using text mining

Coronary artery disease (CAD) often leads to myocardial infarction, which may be fatal. Risk factors can be used to predict CAD, which may subsequently lead to prevention or early intervention. Patient data such as co-morbidities, medication history, social history and family history are required to determine the risk factors for a disease. However, risk factor data are usually embedded in unstructured clinical narratives if the data is not collected specifically for risk assessment purposes. Clinical text mining can be used to extract data related to risk factors from unstructured clinical notes. This study presents methods to extract Framingham risk factors from unstructured electronic health records using clinical text mining and to calculate 10-year coronary artery disease risk scores in a cohort of diabetic patients. We developed a rule-based system to extract risk factors: age, gender, total cholesterol, HDL-C, blood pressure, diabetes history and smoking history. The results showed that the output from the text mining system was reliable, but there was a significant amount of missing data to calculate the Framingham risk score. A systematic approach for understanding missing data was followed by implementation of imputation strategies. An analysis of the 10-year Framingham risk scores for coronary artery disease in this cohort has shown that the majority of the diabetic patients are at moderate risk of CAD.