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Dive into the research topics where Jane Taggart is active.

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Featured researches published by Jane Taggart.


BMC Family Practice | 2012

A systematic review of interventions in primary care to improve health literacy for chronic disease behavioral risk factors

Jane Taggart; Anna Williams; Sarah Dennis; Anthony T. Newall; Tim Shortus; Nicholas Zwar; Elizabeth Denney-Wilson; Mark Harris

BackgroundTo evaluate the effectiveness of interventions used in primary care to improve health literacy for change in smoking, nutrition, alcohol, physical activity and weight (SNAPW).MethodsA systematic review of intervention studies that included outcomes for health literacy and SNAPW behavioral risk behaviors implemented in primary care settings.We searched the Cochrane Library, Johanna Briggs Institute, Medline, Embase, CINAHL, Psychinfo, Web of Science, Scopus, APAIS, Australasian Medical Index, Google Scholar, Community of Science and four targeted journals (Patient Education and Counseling, Health Education and Behaviour, American Journal of Preventive Medicine and Preventive Medicine).Study inclusion criteria: Adults over 18 years; undertaken in a primary care setting within an Organisation for Economic Co-operation and Development (OECD) country; interventions with at least one measure of health literacy and promoting positive change in smoking, nutrition, alcohol, physical activity and/or weight; measure at least one outcome associated with health literacy and report a SNAPW outcome; and experimental and quasi-experimental studies, cohort, observational and controlled and non-controlled before and after studies.Papers were assessed and screened by two researchers (JT, AW) and uncertain or excluded studies were reviewed by a third researcher (MH). Data were extracted from the included studies by two researchers (JT, AW). Effectiveness studies were quality assessed. A typology of interventions was thematically derived from the studies by grouping the SNAPW interventions into six broad categories: individual motivational interviewing and counseling; group education; multiple interventions (combination of interventions); written materials; telephone coaching or counseling; and computer or web based interventions. Interventions were classified by intensity of contact with the subjects (High ≥ 8 points of contact/hours; Moderate >3 and <8; Low ≤ 3 points of contact hours) and setting (primary health, community or other).Studies were analyzed by intervention category and whether significant positive changes in SNAPW and health literacy outcomes were reported.Results52 studies were included. Many different intervention types and settings were associated with change in health literacy (73% of all studies) and change in SNAPW (75% of studies). More low intensity interventions reported significant positive outcomes for SNAPW (43% of studies) compared with high intensity interventions (33% of studies). More interventions in primary health care than the community were effective in supporting smoking cessation whereas the reverse was true for diet and physical activity interventions.ConclusionGroup and individual interventions of varying intensity in primary health care and community settings are useful in supporting sustained change in health literacy for change in behavioral risk factors. Certain aspects of risk behavior may be better handled in clinical settings while others more effectively in the community. Our findings have implications for the design of programs.


International Journal of Medical Informatics | 2013

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

Siaw-Teng Liaw; Alireza Rahimi; Pradeep Ray; Jane Taggart; Sarah Dennis; S de Lusignan; Bin Jalaludin; A.E.T. Yeo; Amir Talaei-Khoei

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.


BMC Family Practice | 2012

Which providers can bridge the health literacy gap in lifestyle risk factor modification education: a systematic review and narrative synthesis

Sarah Dennis; Anna Williams; Jane Taggart; Anthony T. Newall; Elizabeth Denney-Wilson; Nicholas Zwar; Tim Shortus; Mark Harris

BackgroundPeople with low health literacy may not have the capacity to self-manage their health and prevent the development of chronic disease through lifestyle risk factor modification. The aim of this narrative synthesis is to determine the effectiveness of primary healthcare providers in developing health literacy of patients to make SNAPW (smoking, nutrition, alcohol, physical activity and weight) lifestyle changes.MethodsStudies were identified by searching Medline, Embase, Cochrane Library, CINAHL, Joanna Briggs Institute, Psychinfo, Web of Science, Scopus, APAIS, Australian Medical Index, Community of Science and Google Scholar from 1 January 1985 to 30 April 2009. Health literacy and related concepts are poorly indexed in the databases so a list of text words were developed and tested for use. Hand searches were also conducted of four key journals. Studies published in English and included males and females aged 18 years and over with at least one SNAPW risk factor for the development of a chronic disease. The interventions had to be implemented within primary health care, with an aim to influence the health literacy of patients to make SNAPW lifestyle changes. The studies had to report an outcome measure associated with health literacy (knowledge, skills, attitudes, self efficacy, stages of change, motivation and patient activation) and SNAPW risk factor.The definition of health literacy in terms of functional, communicative and critical health literacy provided the guiding framework for the review.Results52 papers were included that described interventions to address health literacy and lifestyle risk factor modification provided by different health professionals. Most of the studies (71%, 37/52) demonstrated an improvement in health literacy, in particular interventions of a moderate to high intensity.Non medical health care providers were effective in improving health literacy. However this was confounded by intensity of intervention. Provider barriers impacted on their relationship with patients.ConclusionCapacity to provide interventions of sufficient intensity is an important condition for effective health literacy support for lifestyle change. This has implications for workforce development and the organisation of primary health care.


International Journal of Medical Informatics | 2014

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

Alireza Rahimi; Siaw-Teng Liaw; Jane Taggart; Pradeep Ray; Hairong Yu

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.


Journal of Evaluation in Clinical Practice | 2011

Patients Assessment of Chronic Illness Care (PACIC) in two Australian studies: structure and utility

Jane Taggart; Bibiana Chan; Upali W. Jayasinghe; Bettina Christl; Judy Proudfoot; Patrick A Crookes; Justin Beilby; Deborah Black; Mark Harris

AIMS To validate the Patients Assessment of Chronic Illness Care (PACIC) among patients with chronic disease in the Australian context and to examine the relationship between patient-assessed quality of care and patient and practice characteristics. METHODS Cross-sectional analysis of baseline data in two independent health service intervention studies that involved patients with type 2 diabetes, ischaemic heart disease and/or hypertension in general practice. The first study involved 2552 patients from 60 urban and rural general practices. The second involved 989 patients from 26 practices in Sydney. Patients were mailed a questionnaire, which included the PACIC and Short Form Health Survey. Factor analysis was performed and the factor scores and total PACIC were analysed using multi-level regression models against practice and patient characteristics. RESULTS Factor analysis revealed a two-factor solution with similar loading of PACIC items in both studies: one for shared decision making and self-management and the other for planned care. Practice characteristics were not related to PACIC scores. Scores were related to patient characteristics - education, retirement, type and number and duration of conditions. CONCLUSIONS The two-factor structure of the PACIC found in these Australian studies is different from the five-factor structure found in the US and the European studies. This may be related to differences in the way patients interact with the health system especially the use of Team Care plans. The association of total scores with patient characteristics was consistent with those found in other studies including a lack of association with gender, age and ethnicity. These findings should be taken into consideration when comparing patient-assessed quality of care between countries using this tool.


Journal of Biomedical Informatics | 2014

Integrating electronic health record information to support integrated care

Siaw-Teng Liaw; Jane Taggart; Hairong Yu; Simon de Lusignan; Craig E. Kuziemsky; Andrew Hayen

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.


Health and Quality of Life Outcomes | 2013

Gender differences in health-related quality of life of Australian chronically-ill adults: patient and physician characteristics do matter.

Upali W. Jayasinghe; Mark Harris; Jane Taggart; Bettina Christl; Deborah Black

BackgroundThe aims of this study were to explore the health-related quality of life (HRQoL) in a large sample of Australian chronically-ill patients (type 2 diabetes and/or hypertension/ischaemic heart disease), to investigate the impact of characteristics of patients and their general practitioners on their HRQoL and to examine clinically significant differences in HRQoL among males and females.MethodsThis was a cross-sectional study with 193 general practitioners and 2181 of their chronically-ill patients aged 18 years or more using the standard Short Form Health Survey (SF-12) version 2. SF-12 physical component score (PCS-12) and mental component score (MCS-12) were derived using the standard US algorithm. Multilevel regression analysis (patients at level 1 and general practitioners at level 2) was applied to relate PCS-12 and MCS-12 to patient and general practitioner (GP) characteristics.ResultsEmployment was likely to have a clinically significant larger positive effect on HRQoL of males (regression coefficient (B) (PCS-12) = 7.29, P < 0.001, effect size = 1.23 and B (MCS-12) = 3.40, P < 0.01, effect size = 0.55) than that of females (B(PCS-12) = 4.05, P < 0.001, effect size = 0.78 and B (MCS-12) = 1.16, P > 0.05, effect size = 0.16). There was a clinically significant difference in HRQoL among age groups. Younger men (< 39 years) were likely to have better physical health than older men (> 59 years, B = −5.82, P < 0.05, effect size = 0.66); older women tended to have better mental health (B = 5.62, P < 0.001, effect size = 0.77) than younger women. Chronically-ill women smokers reported clinically significant (B = −3.99, P < 0.001, effect size = 0.66) poorer mental health than women who were non-smokers. Female GPs were more likely to examine female patients than male patients (33% vs. 15%, P < 0.001) and female patients attending female GPs reported better physical health (B = 1.59, P < 0.05, effect size = 0.30).ConclusionsSome of the associations between patient characteristics and SF-12 physical and/or mental component scores were different for men and women. This finding underlines the importance of considering these factors in the management of chronically-ill patients in general practice. The results suggest that chronically ill women attempting to quit smoking may need more psychological support. More quantitative studies are needed to determine the association between GP gender and patient gender in relation to HRQoL.


BMC Medical Research Methodology | 2008

Engaging participants in a complex intervention trial in Australian General Practice

David Perkins; Mark Harris; Jocelyn Tan; Bettina Christl; Jane Taggart; Mahnaz Fanaian

BackgroundThe paper examines the key issues experienced in recruiting and retaining practice involvement in a large complex intervention trial in Australian General Practice.MethodsReflective notes made by research staff and telephone interviews with staff from general practices which expressed interest, took part or withdrew from a trial of a complex general practice intervention.ResultsRecruitment and retention difficulties were due to factors inherent in the demands and context of general practice, the degree of engagement of primary care organisations (Divisions of General Practice), perceived benefits by practices, the design of the trial and the timing and complexity of data collection.ConclusionThere needs to be clearer articulation to practices of the benefits of the research to participants and streamlining of the design and processes of data collection and intervention to fit in with their work practices. Ultimately deeper engagement may require additional funding and ongoing participation through practice research networks.Trial RegistrationCurrent Controlled Trials ACTRN12605000788673


Emergency Medicine Australasia | 2012

Health reform: Is routinely collected electronic information fit for purpose?

Siaw-Teng Liaw; Huei-Yang Chen; Della Maneze; Jane Taggart; Sarah Dennis; Sanjyot Vagholkar; Jeremy Bunker

Objective: Little has been reported about the completeness and accuracy of data in existing Australian clinical information systems. We examined the accuracy of the diagnoses of some chronic diseases in an ED information system (EDIS), a module of the NSW Health electronic medical record (EMR), and the consistency of the reports generated by the EMR.


Australian Journal of Primary Health | 2013

The Teamwork Study: enhancing the role of non-GP staff in chronic disease management in general practice

Deborah Black; Jane Taggart; Upali W. Jayasinghe; Judith Proudfoot; Patrick A Crookes; Justin Beilby; G. Powell-Davis; Leigh Wilson; Mark Harris

There is evidence for a team-based approach in the management of chronic disease in primary health care. However, the standard of care is variable, probably reflecting the limited organisational capacity of health services to provide the necessary structured and organised care for this group of patients. This study aimed to evaluate the impact of a structured intervention involving non-GP staff in GP practices on the quality of care for patients with diabetes or cardiovascular disease. A cluster randomised trial was undertaken across 60 GP practices. The intervention was implemented in 30 practices with staff and patients interviewed at baseline and at 12-15 months follow up. The change in team roles was evaluated using a questionnaire completed by practice staff. The quality of care was evaluated using the Patient Assessment of Chronic Illness Care questionnaire. We found that although the team roles of staff improved in the intervention practices and there were significant differences between practices, there was no significant difference between those in the intervention and control groups in patient-assessed quality of care after adjusting for baseline-level score and covariates at the 12-month follow up. Practice team roles were not significantly associated with change in Patient Assessment of Chronic Illness Care scores. Patients with multiple conditions were more likely to assess their quality of care to be better. Thus, although previous research has shown a cross-sectional association between team work and quality of care, we were unable to replicate these findings in the present study. These results may be indicative of insufficient time for organisational change to result in improved patient-assessed quality of care, or because non-GP staff roles were not sufficiently focussed on the aspects of care assessed. The findings provide important information for researchers when designing similar studies.

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Mark Harris

University of New South Wales

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Siaw-Teng Liaw

University of New South Wales

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Hairong Yu

University of New South Wales

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Pradeep Ray

University of New South Wales

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Upali W. Jayasinghe

University of New South Wales

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Bettina Christl

University of New South Wales

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Anna Williams

University of New South Wales

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Anthony T. Newall

University of New South Wales

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