“…The Diabetes Care Project (DCP) was a large Australian multicentre cluster randomised controlled trial conducted in general practice to assess the impact of new coordinated care interventions for patients with diabetes (Fountaine & Bennett, ; Leach et al, ). To assess the accuracy of the three recall periods, the following question was added to the final follow‐up questionnaire: “How many visits have you made to a doctor (general practitioner GP or specialist) in the last X/weeks or months?”Study participants were pseudo‐randomised to a recall period by asking them to answer the question over a 2‐week period ( w = 14) if their date of birth (DOB) fell between the first and the 10th of the month, to answer over a 3‐month period ( w = 90) if their DOB fell between the 11th and 20th of the month and to answer over a 12‐month period ( w = 365) if their DOB fell between the 21st and 31st of the month (where w is defined as the recall period in days).…”
Section: Methodsmentioning
confidence: 99%
“…The Diabetes Care Project (DCP) was a large Australian multicentre cluster randomised controlled trial conducted in general practice to assess the impact of new coordinated care interventions for patients with diabetes (Fountaine & Bennett, 2016;Leach et al, 2013). To assess the accuracy of the three recall periods, the following question was added to the final follow-up questionnaire:…”
In health economics, the use of patient recall of health care utilisation information is common, including in national health surveys. However, the types and magnitude of measurement error that relate to different recall periods are not well understood. This study assessed the accuracy of recalled doctor visits over 2-week, 3-month, and 12-month periods by comparing self-report with routine administrative Australian Medicare data. Approximately 5,000 patients enrolled in an Australian study were pseudo-randomised using birth dates to report visits to a doctor over three separate recall periods. When comparing patient recall with visits recorded in administrative information from Medicare Australia, both bias and variance were minimised for the 12-month recall period. This may reflect telescoping that occurs with shorter recall periods (participants pulling in important events that fall outside the period). Using shorter recall periods scaled to represent longer periods is likely to bias results. There were associations between recall error and patient characteristics. The impact of recall error is demonstrated with a cost-effectiveness analysis using costs of doctor visits and a regression example predicting number of doctor visits. The findings have important implications for surveying health service utilisation for use in economic evaluation, econometric analyses, and routine national health surveys.
“…The Diabetes Care Project (DCP) was a large Australian multicentre cluster randomised controlled trial conducted in general practice to assess the impact of new coordinated care interventions for patients with diabetes (Fountaine & Bennett, ; Leach et al, ). To assess the accuracy of the three recall periods, the following question was added to the final follow‐up questionnaire: “How many visits have you made to a doctor (general practitioner GP or specialist) in the last X/weeks or months?”Study participants were pseudo‐randomised to a recall period by asking them to answer the question over a 2‐week period ( w = 14) if their date of birth (DOB) fell between the first and the 10th of the month, to answer over a 3‐month period ( w = 90) if their DOB fell between the 11th and 20th of the month and to answer over a 12‐month period ( w = 365) if their DOB fell between the 21st and 31st of the month (where w is defined as the recall period in days).…”
Section: Methodsmentioning
confidence: 99%
“…The Diabetes Care Project (DCP) was a large Australian multicentre cluster randomised controlled trial conducted in general practice to assess the impact of new coordinated care interventions for patients with diabetes (Fountaine & Bennett, 2016;Leach et al, 2013). To assess the accuracy of the three recall periods, the following question was added to the final follow-up questionnaire:…”
In health economics, the use of patient recall of health care utilisation information is common, including in national health surveys. However, the types and magnitude of measurement error that relate to different recall periods are not well understood. This study assessed the accuracy of recalled doctor visits over 2-week, 3-month, and 12-month periods by comparing self-report with routine administrative Australian Medicare data. Approximately 5,000 patients enrolled in an Australian study were pseudo-randomised using birth dates to report visits to a doctor over three separate recall periods. When comparing patient recall with visits recorded in administrative information from Medicare Australia, both bias and variance were minimised for the 12-month recall period. This may reflect telescoping that occurs with shorter recall periods (participants pulling in important events that fall outside the period). Using shorter recall periods scaled to represent longer periods is likely to bias results. There were associations between recall error and patient characteristics. The impact of recall error is demonstrated with a cost-effectiveness analysis using costs of doctor visits and a regression example predicting number of doctor visits. The findings have important implications for surveying health service utilisation for use in economic evaluation, econometric analyses, and routine national health surveys.
“…A further reason that documented diabetes follow‐up was not more frequent in intervention hospitals may have been that care for patients with type 2 diabetes was transferred from specialists to general practitioners. The shift over the past 15 years to a GP management model of diabetes care, together with limited hospital resources, has led many hospital diabetes services to focus on patients with complex needs, discharging patients with milder disease to general practice. Poor communication between teams caring for patients with diabetes and the completion of discharge summaries to GPs by medical staff not involved in their care may have also contributed to documentation of follow‐up plans not being more frequent in intervention hospitals.…”
Objective
To determine whether routine blood glucose assessment of patients admitted to hospital from emergency departments (EDs) results in higher rates of new diagnoses of diabetes and documentation of follow‐up plans.
Design, setting
Cluster randomised trial in 18 New South Wales public district and tertiary hospitals, 31 May 2011 – 31 December 2012; outcomes follow‐up to 31 March 2016.
Participants
Patients aged 18 years or more admitted to hospital from EDs.
Intervention
Routine blood glucose assessment at control and intervention hospitals; automatic requests for glycated haemoglobin (HbA1c) assessment and notification of diabetes services about patients at intervention hospitals with blood glucose levels of 14 mmol/L or more.
Main outcome measure
New diagnoses of diabetes and documented follow‐up plans for patients with admission blood glucose levels of 14 mmol/L or more.
Results
Blood glucose was measured in 133 837 patients admitted to hospital from an ED. The numbers of new diabetes diagnoses with documented follow‐up plans for patients with blood glucose levels of 14 mmol/L or more were similar in intervention (83/506 patients, 16%) and control hospitals (73/278, 26%; adjusted odds ratio [aOR], 0.83; 95% CI 0.42–1.7; P = 0.61), as were new diabetes diagnoses with or without plans (intervention, 157/506, 31%; control, 86/278, 31%; aOR, 1.51; 95% CI, 0.83–2.80; P = 0.18). 30‐day re‐admission (31% v 22%; aOR, 1.34; 95% CI, 0.86–2.09; P = 0.21) and post‐hospital mortality rates (24% v 22%; aOR, 1.07; 95% CI, 0.74–1.55; P = 0.72) were also similar for patients in intervention and control hospitals.
Conclusion
Glucose and HbA1c screening of patients admitted to hospital from EDs does not alone increase detection of previously unidentified diabetes. Adequate resourcing and effective management pathways for patients with newly detected hyperglycaemia and diabetes are needed.
Trial registration
Australian New Zealand Clinical Trials Registry, ACTRN12611001007921.
“…The Australian Commonwealth Government and States continue developing policies to fund exponential growth in diabetes-related healthcare needs [ 10 , 11 , 12 ]. The Australian National Diabetes Strategy 2021–2030 identified the workforce as an enabler of access to equitable person-focused integrated quality care spanning the health continuum [ 13 ].…”
Section: Introductionmentioning
confidence: 99%
“…Internationally, it is recognised that this access will not be sustainable without increasing health professionals’ diabetes capabilities [ 14 ]. Australian policies to better support diabetes care include Medicare-incentivised models (publicly funded universal healthcare) for general medical practitioners to manage diabetes with generalists, primary healthcare nurses, and allied health professionals in a coordinated manner [ 10 , 11 , 12 ]. Driven by policy and employer expectations, primary health professionals must now undertake activities once performed by specialist CDEs [ 15 , 16 ].…”
Consumers access health professionals with varying levels of diabetes-specific knowledge and training, often resulting in conflicting advice. Conflicting health messages lead to consumer disengagement. The study aimed to identify capabilities required by health professionals to deliver diabetes education and care to develop a national consensus capability-based framework to guide their training. A 3-staged modified Delphi technique was used to gain agreement from a purposefully recruited panel of Australian diabetes experts from various disciplines and work settings. The Delphi technique consisted of (Stage I) a semi-structured consultation group and pre-Delphi pilot, (Stage II) a 2-phased online Delphi survey, and (Stage III) a semi-structured focus group and appraisal by health professional regulatory and training organisations. Descriptive statistics and central tendency measures calculated determined quantitative data characteristics and consensus. Content analysis using emergent coding was used for qualitative content. Eighty-four diabetes experts were recruited from nursing and midwifery (n = 60[71%]), allied health (n = 17[20%]), and pharmacy (n = 7[9%]) disciplines. Participant responses identified 7 health professional practice levels requiring differences in diabetes training, 9 capability areas to support care, and 2 to 16 statements attained consensus for each capability—259 in total. Additionally, workforce solutions were identified to expand capacity for diabetes care. The rigorous consultation process led to the design and validation of a Capability Framework for Diabetes Care that addresses workforce enablers identified by the Australian National Diabetes Strategy. It recognises diversity, creating shared understandings of diabetes across health professional disciplines. The findings will inform diabetes policy, practice, education, and research.
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