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Prospective monitoring of imaging guideline adherence by physicians in a surgical collaborative: comparison of statistical process control methods for detecting outlying performance.

Identifieur interne : 000178 ( Main/Exploration ); précédent : 000177; suivant : 000179

Prospective monitoring of imaging guideline adherence by physicians in a surgical collaborative: comparison of statistical process control methods for detecting outlying performance.

Auteurs : Michael Inadomi [États-Unis] ; Karandeep Singh [États-Unis] ; Ji Qi [États-Unis] ; Rodney Dunn [États-Unis] ; Susan Linsell [États-Unis] ; Brian Denton [États-Unis] ; Patrick Hurley [États-Unis] ; Eduardo Kleer [États-Unis] ; James Montie [États-Unis] ; Khurshid R. Ghani [États-Unis]

Source :

RBID : pubmed:32404086

Descripteurs français

English descriptors

Abstract

BACKGROUND

Systematic, automated methods for monitoring physician performance are necessary if outlying behavior is to be detected promptly and acted on. In the Michigan Urological Surgery Improvement Collaborative (MUSIC), we evaluated several statistical process control (SPC) methods to determine the sensitivity and ease of interpretation for assessing adherence to imaging guidelines for patients with newly diagnosed prostate cancer.

METHODS

Following dissemination of imaging guidelines within the Michigan Urological Surgery Improvement Collaborative (MUSIC) for men with newly diagnosed prostate cancer, MUSIC set a target of imaging < 10% of patients for which bone scan is not indicated. We compared four SPC methods using Monte Carlo simulation: p-chart, weighted binomial CUSUM, Bernoulli cumulative sum (CUSUM), and exponentially weighted moving average (EWMA). We simulated non-indicated bone scan rates ranging from 5.9% (within target) to 11.4% (above target) for a representative MUSIC practice. Sensitivity was determined using the average run length (ARL), the time taken to signal a change. We then plotted actual non-indicated bone scan rates for a representative MUSIC practice using each SPC method to qualitatively assess graphical interpretation.

RESULTS

EWMA had the lowest ARL and was able to detect changes significantly earlier than the other SPC methodologies (p < 0.001). The p-chart had the highest ARL and thus detected changes slowest (p < 0.001). EWMA and p-charts were easier to interpret graphically than CUSUM methods due to their ability to display historical imaging rates.

CONCLUSIONS

SPC methods can be used to provide informative and timely feedback regarding adherence to healthcare performance target rates in quality improvement collaboratives. We found the EWMA method most suited for detecting changes in imaging utilization.


DOI: 10.1186/s12911-020-1126-z
PubMed: 32404086
PubMed Central: PMC7218839


Affiliations:


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<term>Diagnostic Imaging (MeSH)</term>
<term>Guideline Adherence (MeSH)</term>
<term>Humans (MeSH)</term>
<term>Male (MeSH)</term>
<term>Monte Carlo Method (MeSH)</term>
<term>Physicians (MeSH)</term>
<term>Prospective Studies (MeSH)</term>
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<term>Adhésion aux directives (MeSH)</term>
<term>Humains (MeSH)</term>
<term>Imagerie diagnostique (MeSH)</term>
<term>Mâle (MeSH)</term>
<term>Médecins (MeSH)</term>
<term>Méthode de Monte Carlo (MeSH)</term>
<term>Études prospectives (MeSH)</term>
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<term>Diagnostic Imaging</term>
<term>Guideline Adherence</term>
<term>Humans</term>
<term>Male</term>
<term>Monte Carlo Method</term>
<term>Physicians</term>
<term>Prospective Studies</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr">
<term>Adhésion aux directives</term>
<term>Humains</term>
<term>Imagerie diagnostique</term>
<term>Mâle</term>
<term>Médecins</term>
<term>Méthode de Monte Carlo</term>
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<front>
<div type="abstract" xml:lang="en">
<p>
<b>BACKGROUND</b>
</p>
<p>Systematic, automated methods for monitoring physician performance are necessary if outlying behavior is to be detected promptly and acted on. In the Michigan Urological Surgery Improvement Collaborative (MUSIC), we evaluated several statistical process control (SPC) methods to determine the sensitivity and ease of interpretation for assessing adherence to imaging guidelines for patients with newly diagnosed prostate cancer.</p>
</div>
<div type="abstract" xml:lang="en">
<p>
<b>METHODS</b>
</p>
<p>Following dissemination of imaging guidelines within the Michigan Urological Surgery Improvement Collaborative (MUSIC) for men with newly diagnosed prostate cancer, MUSIC set a target of imaging < 10% of patients for which bone scan is not indicated. We compared four SPC methods using Monte Carlo simulation: p-chart, weighted binomial CUSUM, Bernoulli cumulative sum (CUSUM), and exponentially weighted moving average (EWMA). We simulated non-indicated bone scan rates ranging from 5.9% (within target) to 11.4% (above target) for a representative MUSIC practice. Sensitivity was determined using the average run length (ARL), the time taken to signal a change. We then plotted actual non-indicated bone scan rates for a representative MUSIC practice using each SPC method to qualitatively assess graphical interpretation.</p>
</div>
<div type="abstract" xml:lang="en">
<p>
<b>RESULTS</b>
</p>
<p>EWMA had the lowest ARL and was able to detect changes significantly earlier than the other SPC methodologies (p < 0.001). The p-chart had the highest ARL and thus detected changes slowest (p < 0.001). EWMA and p-charts were easier to interpret graphically than CUSUM methods due to their ability to display historical imaging rates.</p>
</div>
<div type="abstract" xml:lang="en">
<p>
<b>CONCLUSIONS</b>
</p>
<p>SPC methods can be used to provide informative and timely feedback regarding adherence to healthcare performance target rates in quality improvement collaboratives. We found the EWMA method most suited for detecting changes in imaging utilization.</p>
</div>
</front>
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<Day>16</Day>
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<DateRevised>
<Year>2020</Year>
<Month>12</Month>
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<Title>BMC medical informatics and decision making</Title>
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<ArticleTitle>Prospective monitoring of imaging guideline adherence by physicians in a surgical collaborative: comparison of statistical process control methods for detecting outlying performance.</ArticleTitle>
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<MedlinePgn>89</MedlinePgn>
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<Abstract>
<AbstractText Label="BACKGROUND">Systematic, automated methods for monitoring physician performance are necessary if outlying behavior is to be detected promptly and acted on. In the Michigan Urological Surgery Improvement Collaborative (MUSIC), we evaluated several statistical process control (SPC) methods to determine the sensitivity and ease of interpretation for assessing adherence to imaging guidelines for patients with newly diagnosed prostate cancer.</AbstractText>
<AbstractText Label="METHODS">Following dissemination of imaging guidelines within the Michigan Urological Surgery Improvement Collaborative (MUSIC) for men with newly diagnosed prostate cancer, MUSIC set a target of imaging < 10% of patients for which bone scan is not indicated. We compared four SPC methods using Monte Carlo simulation: p-chart, weighted binomial CUSUM, Bernoulli cumulative sum (CUSUM), and exponentially weighted moving average (EWMA). We simulated non-indicated bone scan rates ranging from 5.9% (within target) to 11.4% (above target) for a representative MUSIC practice. Sensitivity was determined using the average run length (ARL), the time taken to signal a change. We then plotted actual non-indicated bone scan rates for a representative MUSIC practice using each SPC method to qualitatively assess graphical interpretation.</AbstractText>
<AbstractText Label="RESULTS">EWMA had the lowest ARL and was able to detect changes significantly earlier than the other SPC methodologies (p < 0.001). The p-chart had the highest ARL and thus detected changes slowest (p < 0.001). EWMA and p-charts were easier to interpret graphically than CUSUM methods due to their ability to display historical imaging rates.</AbstractText>
<AbstractText Label="CONCLUSIONS">SPC methods can be used to provide informative and timely feedback regarding adherence to healthcare performance target rates in quality improvement collaboratives. We found the EWMA method most suited for detecting changes in imaging utilization.</AbstractText>
</Abstract>
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