Clinical quality registries for clinician‐level reporting: strengths and limitations

Ahern, Susannah; Hopper, Ingrid; Evans, Sue

doi:10.5694/mja16.00659

Cited by 20 publications

(6 citation statements)

References 10 publications

(24 reference statements)

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“…The specific health outcomes monitored by registries vary depending on the patient population and registry purpose, but commonly include mortality, complication and patient-reported outcome measures. Many registries also have clinician agreed clinical quality indicators that can be derived from the collected data,9 10 which may include process indicators such as time to referral and diagnostic test administration, or clinical and functional measures.…”

Section: Introductionmentioning

confidence: 99%

“…This will allow for consistency and reduce the rate of false positive and negative results. Further, increasing interest in the public reporting of benchmarking results makes it vital that methods are appropriate and robust to ensure accurate information is being communicated to the public, as well as stakeholders 9 33 34. The potential reputational and employment consequences for medical providers and practitioners from a publicly reported underperformance status is great, adding to the importance of accurate methods.…”

Section: Introductionmentioning

confidence: 99%

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Evaluations of statistical methods for outlier detection when benchmarking in clinical registries: a systematic review

2023

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ObjectivesBenchmarking is common in clinical registries to support the improvement of health outcomes by identifying underperforming clinician or health service providers. Despite the rise in clinical registries and interest in publicly reporting benchmarking results, appropriate methods for benchmarking and outlier detection within clinical registries are not well established, and the current application of methods is inconsistent. The aim of this review was to determine the current statistical methods of outlier detection that have been evaluated in the context of clinical registry benchmarking.DesignA systematic search for studies evaluating the performance of methods to detect outliers when benchmarking in clinical registries was conducted in five databases: EMBASE, ProQuest, Scopus, Web of Science and Google Scholar. A modified healthcare modelling evaluation tool was used to assess quality; data extracted from each study were summarised and presented in a narrative synthesis.ResultsNineteen studies evaluating a variety of statistical methods in 20 clinical registries were included. The majority of studies conducted application studies comparing outliers without statistical performance assessment (79%), while only few studies used simulations to conduct more rigorous evaluations (21%). A common comparison was between random effects and fixed effects regression, which provided mixed results. Registry population coverage, provider case volume minimum and missing data handling were all poorly reported.ConclusionsThe optimal methods for detecting outliers when benchmarking clinical registry data remains unclear, and the use of different models may provide vastly different results. Further research is needed to address the unresolved methodological considerations and evaluate methods across a range of registry conditions.PROSPERO registration numberCRD42022296520.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluations of statistical methods for outlier detection when benchmarking in clinical registries: a systematic review

2023

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“…These registries are governed by specific operational, governance and technical requirements 21 . The use of clinical quality registries for data collection and research has been broadly utilised worldwide and originated as epidemiological data to measure population trends and overall health 22 . Key advantages include their ability to provide benchmarks and performance data about practice standards and quality of care.…”

Section: Introductionmentioning

confidence: 99%

“… 21 The use of clinical quality registries for data collection and research has been broadly utilised worldwide and originated as epidemiological data to measure population trends and overall health. 22 Key advantages include their ability to provide benchmarks and performance data about practice standards and quality of care. Facilitating feedback on the variables they collect in a combined database of trends and patterns in treatments or interventions.…”

Section: Introductionmentioning

confidence: 99%

The prototype of a frailty learning health system: The HARMONY Model

Parker,

Hickman,

McDonagh

et al. 2023

Learning Health Systems

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IntroductionRapid translation of research findings into clinical practice through innovation is critical to improve health systems and patient outcomes. Access to efficient systems of learning underpinned with real‐time data are the future of healthcare. This type of health system will decrease unwarranted clinical variation, accelerate rapid evidence translation, and improve overall healthcare quality.MethodsThis paper aims to describe The HARMONY model (acHieving dAta‐dRiven quality iMprovement to enhance frailty Outcomes using a learNing health sYstem), a new frailty learning health system model of implementation science and practice improvement. The HARMONY model provides a prototype for clinical quality registry infrastructure and partnership within health care.ResultsThe HARMONY model was applied to the Western Sydney Clinical Frailty Registry as the prototype exemplar. The model networks longitudinal frailty data into an accessible and useable format for learning. Creating local capability that networks current data infrastructures to translate and improve quality of care in real‐time.ConclusionThis prototype provides a model of registry data feedback and quality improvement processes in an inpatient aged care and rehabilitation hospital setting to help reduce clinical variation, enhance research translation capacity, and improve care quality.

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“…They facilitate public health reporting and transparency (e.g. cardiac surgery or assisted reproductive technology treatments) ( Bridgewater et al 2013 , Ahern et al 2017 ) ( accessed 14 October 2022). They may provide evidence to improve the healthcare system, for example, the UK National Joint Registry identified specific devices (some metal-on-metal articulations) had higher-than-expected revisions rates, leading to their removal from the UK market ( Porter et al 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Establishing the Australian National Endometriosis Clinical and Scientific Trials (NECST) Registry: a protocol paper

Michelmore

Mishra

et al. 2023

Reproduction and Fertility

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Endometriosis is a common yet under-recognised chronic inflammatory disease, affecting 176 million women, trans and gender diverse people globally. The National Endometriosis Clinical and Scientific Trials (NECST) Registry is a new clinical registry, collecting and tracking diagnostic and treatment data, and patient-reported outcomes on people with endometriosis. The registry is a research priority action item from the 2018 National Action Plan for Endometriosis and aims to provide, large-scale, national and longitudinal population-based data on endometriosis. Working groups (consisting of patients with endometriosis, clinicians and researchers) developing the NECST Registry data dictionary and data collection platform started in 2019. Our data dictionary was developed based on existing and validated questionnaires, tools, meta-data and data cubes – World Endometriosis Research Foundation (WERF) Endometriosis Phenome and Biobanking Harmonisation Project (EPHect), endometriosis CORE outcomes set, patient-reported outcome measures, the International Statistical Classification of Diseases-10th Revision Australian Modification diagnosis codes, and Australian Government datasets: Australian Institute for Health and Welfare (for sociodemographic data), Medicare Benefits Schedule (MBS; for medical procedures) and the Pharmaceutical Benefits Scheme (PBS; for medical therapies). The resulting NECST Registry is an online, secure cloud-based database; prospectively collecting minimum core clinical and health data across eight patient and clinician modules and longitudinal data tracking disease life course. The NECST Registry has ethics approval (HREC/62508/MonH-2020) and is registered on the Australian New Zealand Clinical Trials Registry (ACTRN12622000987763).

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Clinical quality registries for clinician‐level reporting: strengths and limitations

Cited by 20 publications

References 10 publications

Evaluations of statistical methods for outlier detection when benchmarking in clinical registries: a systematic review

Evaluations of statistical methods for outlier detection when benchmarking in clinical registries: a systematic review

The prototype of a frailty learning health system: The HARMONY Model

Establishing the Australian National Endometriosis Clinical and Scientific Trials (NECST) Registry: a protocol paper

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