The NSW Pathology Atlas of Variation: Part I—Identifying Emergency Departments With Outlying Laboratory Test–Ordering Practices

Wabe, Nasir; Thomas, Judith; Scowen, Craig; Eigenstetter, Alex; Lindeman, Robert; Georgiou, Andrew

doi:10.1016/j.annemergmed.2021.01.013

Cited by 4 publications

(3 citation statements)

References 51 publications

(78 reference statements)

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“…Wennberg and colleagues' Atlas of Variation, introduced in 1999 [65], and its UK equivalent, introduced in 2010 [66], described wide regional differences in the rates of procedures from arthroscopy to hysterectomy, and were used to prompt services to identify and address examples of under-treatment, mis-treatment and over-treatment. Numerous similar initiatives, mostly based on hospital activity statistics, have been introduced around the world [66][67][68][69]. Sutherland and Levesque's proposed framework for analysing variation, for example, has three domains: capacity (broadly, whether sufficient resources are allocated at organizational level and whether individuals have the time and headspace to get involved), evidence (the extent to which evidence-based guidelines exist and are followed), and agency (e.g.…”

Section: Warranted and Unwarranted Variation In Clinical Practicementioning

confidence: 99%

What is quality in long covid care? Lessons from a national quality improvement collaborative and multi-site ethnography

Greenhalgh,

Darbyshire,

Lee

et al. 2024

BMC Med

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Background Long covid (post covid-19 condition) is a complex condition with diverse manifestations, uncertain prognosis and wide variation in current approaches to management. There have been calls for formal quality standards to reduce a so-called “postcode lottery” of care. The original aim of this study—to examine the nature of quality in long covid care and reduce unwarranted variation in services—evolved to focus on examining the reasons why standardizing care was so challenging in this condition. Methods In 2021–2023, we ran a quality improvement collaborative across 10 UK sites. The dataset reported here was mostly but not entirely qualitative. It included data on the origins and current context of each clinic, interviews with staff and patients, and ethnographic observations at 13 clinics (50 consultations) and 45 multidisciplinary team (MDT) meetings (244 patient cases). Data collection and analysis were informed by relevant lenses from clinical care (e.g. evidence-based guidelines), improvement science (e.g. quality improvement cycles) and philosophy of knowledge. Results Participating clinics made progress towards standardizing assessment and management in some topics; some variation remained but this could usually be explained. Clinics had different histories and path dependencies, occupied a different place in their healthcare ecosystem and served a varied caseload including a high proportion of patients with comorbidities. A key mechanism for achieving high-quality long covid care was when local MDTs deliberated on unusual, complex or challenging cases for which evidence-based guidelines provided no easy answers. In such cases, collective learning occurred through idiographic (case-based) reasoning, in which practitioners build lessons from the particular to the general. This contrasts with the nomothetic reasoning implicit in evidence-based guidelines, in which reasoning is assumed to go from the general (e.g. findings of clinical trials) to the particular (management of individual patients). Conclusion Not all variation in long covid services is unwarranted. Largely because long covid’s manifestations are so varied and comorbidities common, generic “evidence-based” standards require much individual adaptation. In this complex condition, quality improvement resources may be productively spent supporting MDTs to optimise their case-based learning through interdisciplinary discussion. Quality assessment of a long covid service should include review of a sample of individual cases to assess how guidelines have been interpreted and personalized to meet patients’ unique needs. Study registration NCT05057260, ISRCTN15022307.

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Section: Warranted and Unwarranted Variation In Clinical Practicementioning

confidence: 99%

What is quality in long covid care? Lessons from a national quality improvement collaborative and multi-site ethnography

Greenhalgh,

Darbyshire,

Lee

et al. 2024

BMC Med

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“…Wennberg and colleagues' Atlas of Variation, introduced in 1999 [39], and its UK equivalent, introduced in 2010 [40], described wide regional differences in the rates of procedures from arthroscopy to hysterectomy, and were used to prompt re ection on and (sometimes) performance management of assumed under-treatment, mis-treatment and over-treatment. Numerous similar initiatives, mostly based on hospital activity statistics, have been introduced around the world [40][41][42][43]. Sutherland and Levesque's proposed framework for analyzing variation has three domains: capacity (broadly, whether su cient resources are allocated at organizational level and whether individuals have the time and headspace to get involved), evidence (the extent to which evidence-based guidelines exist and are followed) and agency (e.g.…”

Section: Warranted and Unwarranted Variation In Clinical Practicementioning

confidence: 99%

Reducing variation in the quality of long covid care: Lessons from a national quality improvement collaborative and multi-site ethnography

Greenhalgh,

Darbyshire,

Lee

et al. 2023

Preprint

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Context Long covid (post covid-19 condition) is a complex condition with diverse manifestations and uncertain prognosis. There is wide variation in how patients are investigated and managed. There have been calls for formal quality standards so as to reduce a so-called “postcode lottery” of care. We aimed to examine the nature of quality in long covid care and reduce unwarranted variation in care provided by long covid services. Methods In a mixed-method study (2021-2023), we ran a quality improvement collaborative across 10 UK sites. We also gathered data on the origins and current context of each clinic, interviewed staff and patients, and observed 12 clinics (52 consultations) and 34 multidisciplinary team (MDT) meetings (230 patient cases). Data collection and analysis were informed by relevant lenses from clinical care (e.g. evidence-based guidelines), improvement science (e.g. quality improvement cycles, reducing unwarranted variation) and philosophy of knowledge. Results The collaborative made progress towards standardizing assessment and management in some topics, but much variation remained. Clinics had different histories and path-dependencies, occupied a different place in their healthcare ecosystem and served a varied caseload including (in most clinics) a high proportion of patients with comorbidities. Dimensions of quality prioritized by patients related to the service (e.g. accessibility, ease of navigation), and human qualities of staff (e.g. attentiveness, compassion). A key route to quality long covid care was when local MDTs deliberated on unusual, complex or challenging cases for which evidence-based guidelines provided no easy answers. In such cases, collective learning occurred through idiographic reasoning, in which practitioners build lessons from the particular to the general. This contrasts with the nomothetic reasoning implicit in evidence-based guidelines, in which reasoning is assumed to go from the general (e.g. findings of clinical trials) to the particular (management of individual patients). Conclusion Not all variation in long covid services is unwarranted. Largely because long covid’s manifestations are so varied, universal ‘evidence-based’ standards are hard to define and implement. In this complex condition, quality improvement resources may be better spent supporting team-based learning locally than attempting to standardize care across widely differing services. Trial registration NCT05057260, ISRCTN15022307.

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“…The provider-related subcategory includes six features. The type of provider (e.g., private, public, academic, community), 10,16,20,21,24,26,36,38,47,52 location (e.g., rural, urban), 10,16,21,29,36,38,52 provider size, 36 case handling volume (such as low, medium, high), 10,20,21,24,38,45,47 capacity or resource availability (such as onsite diagnostic facilities or radiotherapy equipment), 10,20,21,24,38,45,47 and practice pattern (e.g., opioid prescription pattern 45 or IVF pattern 51 ).…”

Section: Local Context Featuresmentioning

confidence: 99%

Identification and classification of principal features for analyzing unwarranted clinical variation

McOwiti,

Tao,

Tao

2023

Evaluation Clinical Practice

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Rationale, Aims, and ObjectiveUnwarranted clinical variation (UCV) is an undesirable aspect of a healthcare system, but analyzing for UCV can be difficult and time‐consuming. No analytic feature guidelines currently exist to aid researchers. We performed a systematic review of UCV literature to identify and classify the features researchers have identified as necessary for the analysis of UCV.MethodsThe literature search followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses. We looked for articles with the terms ‘medical practice variation’ and ‘unwarranted clinical variation’ from four databases: Medline, Web of Science, EMBASE and CINAHL. The search was performed on 24 March 2023. The articles selected were original research articles in the English language reporting on UCV analysis in adult populations. Most of the studies were retrospective cohort analyses. We excluded studies reporting geographic variation based on the Atlas of Variation or small‐area analysis methods. We used ASReview Lab software to assist in identifying articles for abstract review. We also conducted subsequent reference searches of the primary articles to retrieve additional articles.ResultsThe search yielded 499 articles, and we reviewed 46. We identified 28 principal analytic features utilized to analyze for unwarranted variation, categorised under patient‐related or local healthcare context factors. Within the patient‐related factors, we identified three subcategories: patient sociodemographics, clinical characteristics, and preferences, and classified 17 features into seven subcategories. In the local context category, 11 features are classified under two subcategories. Examples are provided on the usage of each feature for analysis.ConclusionTwenty‐eight analytic features have been identified, and a categorisation has been established showing the relationships between features. Identifying and classifying features provides guidelines for known confounders during analysis and reduces the steps required when performing UCV analysis; there is no longer a need for a UCV researcher to engage in time‐consuming feature engineering activities.

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The NSW Pathology Atlas of Variation: Part I—Identifying Emergency Departments With Outlying Laboratory Test–Ordering Practices

Cited by 4 publications

References 51 publications

What is quality in long covid care? Lessons from a national quality improvement collaborative and multi-site ethnography

What is quality in long covid care? Lessons from a national quality improvement collaborative and multi-site ethnography

Reducing variation in the quality of long covid care: Lessons from a national quality improvement collaborative and multi-site ethnography

Identification and classification of principal features for analyzing unwarranted clinical variation

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