Developing indicators for measuring low-value care: mapping Choosing Wisely recommendations to hospital data

Chalmers, Kelsey; Badgery‐Parker, Tim; Pearson, Sallie‐Anne; Brett, Jonathan; Scott, Ian A; Elshaug, Adam G

doi:10.1186/s13104-018-3270-4

Cited by 42 publications

(67 citation statements)

References 21 publications

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“…De-implementation refers to ending ineffective or low-value programs, and is studied more often in medicine than in public health [1,[4][5][6][7][8][9][10][11][12]. The international Choosing Wisely initiative has recommended numerous medical procedures for de-implementation [13,14].…”

Section: Introductionmentioning

confidence: 99%

Perspectives on program mis-implementation among U.S. local public health departments

Allen

Jacob

Parks

et al. 2020

BMC Health Serv Res

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Background: Public health resources are limited and best used for effective programs. This study explores associations of mis-implementation in public health (ending effective programs or continuing ineffective programs) with organizational supports for evidence-based decision making among U.S. local health departments. Methods: The national U.S. sample for this cross-sectional study was stratified by local health department jurisdiction population size. One person was invited from each randomly selected local health department: the leader in chronic disease, or the director. Of 600 selected, 579 had valid email addresses; 376 completed the survey (64.9% response). Survey items assessed frequency of and reasons for mis-implementation. Participants indicated agreement with statements on organizational supports for evidence-based decision making (7-point Likert). Results: Thirty percent (30.0%) reported programs often or always ended that should have continued (inappropriate termination); organizational supports for evidence-based decision making were not associated with the frequency of programs ending. The main reason given for inappropriate termination was grant funding ended (86.0%). Fewer (16.4%) reported programs often or always continued that should have ended (inappropriate continuation). Higher perceived organizational supports for evidence-based decision making were associated with less frequent inappropriate continuation (odds ratio = 0.86, 95% confidence interval 0.79, 0.94). All organizational support factors were negatively associated with inappropriate continuation. Top reasons were sustained funding (55.6%) and support from policymakers (34.0%). Conclusions: Organizational supports for evidence-based decision making may help local health departments avoid continuing programs that should end. Creative mechanisms of support are needed to avoid inappropriate termination. Understanding what influences mis-implementation can help identify supports for de-implementation of ineffective programs so resources can go towards evidence-based programs.

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Section: Introductionmentioning

confidence: 99%

Perspectives on program mis-implementation among U.S. local public health departments

Allen

Jacob

Parks

et al. 2020

BMC Health Serv Res

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“…We have previously developed indicators for 27 low-value procedures 10 23. Briefly, we scanned recommendations from Choosing Wisely (USA, Canada, Australia, and the UK), the UK National Institute of Health and Care Excellence ‘do not do’ database, and other guidelines and identified recommendations that could be measured using our hospital admissions data.…”

Section: Methodsmentioning

confidence: 99%

Estimating misclassification error in a binary performance indicator: case study of low value care in Australian hospitals

2020

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ObjectiveIndicators based on hospital administrative data have potential for misclassification error, especially if they rely on clinical detail that may not be well recorded in the data. We applied an approach using modified logistic regression models to assess the misclassification (false-positive and false-negative) rates of low-value care indicators.Design and settingWe applied indicators involving 19 procedures to an extract from the New South Wales Admitted Patient Data Collection (1 January 2012 to 30 June 2015) to label episodes as low value. We fit four models (no misclassification, false-positive only, false-negative only, both false-positive and false-negative) for each indicator to estimate misclassification rates and used the posterior probabilities of the models to assess which model fit best.ResultsFalse-positive rates were low for most indicators—if the indicator labels care as low value, the care is most likely truly low value according to the relevant recommendation. False-negative rates were much higher but were poorly estimated (wide credible intervals). For most indicators, the models allowing no misclassification or allowing false-negatives but no false-positives had the highest posterior probability. The overall low-value care rate from the indicators was 12%. After adjusting for the estimated misclassification rates from the highest probability models, this increased to 35%.ConclusionBinary performance indicators have a potential for misclassification error, especially if they depend on clinical information extracted from administrative data. Indicators should be validated by chart review, but this is resource-intensive and costly. The modelling approach presented here can be used as an initial validation step to identify and revise indicators that may have issues before continuing to a full chart review validation.

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“…A strong healthcare system will consistently choose high-value services that generate large health benefits as compared to low-value services that do not. An example of low-value healthcare is endovascular repair of abdominal aortic aneurysms in an asymptomatic high-risk patient with limited life expectancy [2]. By contrast, a total hip replacement for a patient with diseases of the hip that causes chronic discomfort and functional impairment can give excellent longterm health benefits [3].…”

Section: What Is Economics and Why Is It Useful?mentioning

confidence: 99%

Make economics your friend

Graves

2018

Journal of Hospital Infection

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In this essay I define the discipline of economics and say why it's useful. I review methods for measuring economic outcomes relevant to those working to prevent infections, and review how decision-makers should use the information to help choose the best among competing programmes. I finish by arguing that good economics can improve the amount of health gained from an infection prevention service working under conditions of scarce resources.

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Developing indicators for measuring low-value care: mapping Choosing Wisely recommendations to hospital data

Cited by 42 publications

References 21 publications

Perspectives on program mis-implementation among U.S. local public health departments

Perspectives on program mis-implementation among U.S. local public health departments

Estimating misclassification error in a binary performance indicator: case study of low value care in Australian hospitals

Make economics your friend

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