Validation of a knowledge based reminder system for diagnostic test ordering in general practice

Bindels, Rianne; Winkens, Ron; Pop, P.; Wersch, J. W. J. Van; Talmon, Jan L.; Hasman, Arie

doi:10.1016/s1386-5056(01)00207-6

Cited by 9 publications

(8 citation statements)

References 29 publications

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“…The estimation in variation in request behaviour of GPs in this power calculation is based on results of a previous pilot study. 13 Since most recommendations suggest the removal of one or more tests, the direction of the effect is one-sided. Therefore we also chose to present the mean value and the 95% confidence interval (CI) of the effects.…”

Section: Discussionmentioning

confidence: 99%

“…12,13 GRIF is meant to stimulate adherence to accepted practice guidelines on diagnostic tests. This system was developed to support or even replace the written feedback given by the Transmural Care Unit of the Maastricht University Hospital since 1985.…”

Section: Refereed Papersmentioning

confidence: 99%

“…13 The diagnoses for test cluster A and B are listed in Box 1. The GPs were randomly assigned to receive either recommendations on test cluster A or on test cluster B.…”

Section: The Interventionmentioning

confidence: 99%

See 2 more Smart Citations

The efficacy of an automated feedback system for general practitioners

Bindels¹,

Hasman

Kester³

et al. 2003

Journal of Innovation in Health Informatics

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Section: Discussionmentioning

confidence: 99%

Section: Refereed Papersmentioning

confidence: 99%

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The efficacy of an automated feedback system for general practitioners

Bindels¹,

Hasman

Kester³

et al. 2003

Journal of Innovation in Health Informatics

Self Cite

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“…Diagnostic CDSS are seen as an attractive method of assisting clinicians who are not experts in a particular field to integrate the ever‐expanding clinical knowledge requirements and assist in providing accurate and reliable diagnosis [10–14,18,19]. Potentially, a CDSS could reduce health care cost through avoiding delays in treatment, redundant tests or referrals due to misdiagnosis [17].…”

Section: Introductionmentioning

confidence: 99%

An update and further testing of a knowledge‐based diagnostic clinical decision support system for musculoskeletal disorders of the shoulder for use in a primary care setting

Farmer

2014

Evaluation Clinical Practice

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“…A diagnostic CDSS provides clinical support and diagnostic results, or a recommendation, to a clinician by using artificial intelligence reasoning to synthesize clinical information [1], [2], [6][7][8][9]–[10]. CDSS are not developed to replace a clinicians’ assessment but, instead, to facilitate correct assessment and reasoning [6], [7], [10].…”

Section: Introductionmentioning

confidence: 99%

A Knowledge-based Diagnostic Clinical Decision Support System for Musculoskeletal Disorders of the Shoulder for Use in a Primary Care Setting

Farmer¹,

Schilstra

2012

Shoulder & Elbow

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Background Twenty percent of cases seen by primary care clinicians (general practitioners; GPs) are musculoskeletal in nature, and approximately one‐quarter of these are shoulder complaints. GPs are increasingly overloaded with clinical information and unfamiliarity with current research can easily lead to misdiagnosis and, in turn, to unnecessary test requests or onward specialist referrals. Well‐designed diagnostic clinical decision support systems (CDSS) have been shown to facilitate clinical decision‐making and reduce diagnostic errors. However, no CDSS have been developed or tested for musculoskeletal disorders. Methods We have developed a prototype knowledge‐based diagnostic CDSS for musculoskeletal shoulder conditions. The CDSS uses Bayesian reasoning to diagnose six common musculoskeletal shoulder pathologies, based on current evidence and expert opinion. The CDSS was tested by comparing its diagnostic outcome against 50 case studies with known diagnosis by radiological imaging. Results The CDSS diagnostic validity and reliability was shown to be 88% with a Kappa value of 0.85 to a confidence level of 99% compared to known diagnosis by radiological imaging. Conclusions The results suggest that a Bayesian network‐based CDSS is a promising instrument in the diagnosis of musculoskeletal shoulder conditions, having been shown to be valid and reliable for 50 case studies.

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Validation of a knowledge based reminder system for diagnostic test ordering in general practice

Cited by 9 publications

References 29 publications

The efficacy of an automated feedback system for general practitioners

The efficacy of an automated feedback system for general practitioners

An update and further testing of a knowledge‐based diagnostic clinical decision support system for musculoskeletal disorders of the shoulder for use in a primary care setting

A Knowledge-based Diagnostic Clinical Decision Support System for Musculoskeletal Disorders of the Shoulder for Use in a Primary Care Setting

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