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

Farmer, Nicholas; Schilstra, Maria J.

doi:10.1111/j.1758-5740.2011.00165.x

Cited by 9 publications

(6 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite their popularity, there is no established framework to evaluate the performance of SCs [3,4]. The use of case vignettes, based on real or fictitious patients, has been a common approach for rating SCs [5][6][7][8][9]. The 2 most recent non-industry-funded audit studies using this methodology rated SC triage capability as unreliable, with an average of only 49% and 58% of appraisals deemed correct [10,11].…”

Section: Evidence On Scsmentioning

confidence: 99%

Benchmarking Triage Capability of Symptom Checkers Against That of Medical Laypersons: Survey Study

Schmieding¹,

Mörgeli²,

Schmieding³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

Background Symptom checkers (SCs) are tools developed to provide clinical decision support to laypersons. Apart from suggesting probable diagnoses, they commonly advise when users should seek care (triage advice). SCs have become increasingly popular despite prior studies rating their performance as mediocre. To date, it is unclear whether SCs can triage better than those who might choose to use them. Objective This study aims to compare triage accuracy between SCs and their potential users (ie, laypersons). Methods On Amazon Mechanical Turk, we recruited 91 adults from the United States who had no professional medical background. In a web-based survey, the participants evaluated 45 fictitious clinical case vignettes. Data for 15 SCs that had processed the same vignettes were obtained from a previous study. As main outcome measures, we assessed the accuracy of the triage assessments made by participants and SCs for each of the three triage levels (ie, emergency care, nonemergency care, self-care) and overall, the proportion of participants outperforming each SC in terms of accuracy, and the risk aversion of participants and SCs by comparing the proportion of cases that were overtriaged. Results The mean overall triage accuracy was similar for participants (60.9%, SD 6.8%; 95% CI 59.5%-62.3%) and SCs (58%, SD 12.8%). Most participants outperformed all but 5 SCs. On average, SCs more reliably detected emergencies (80.6%, SD 17.9%) than laypersons did (67.5%, SD 16.4%; 95% CI 64.1%-70.8%). Although both SCs and participants struggled with cases requiring self-care (the least urgent triage category), SCs more often wrongly classified these cases as emergencies (43/174, 24.7%) compared with laypersons (56/1365, 4.10%). Conclusions Most SCs had no greater triage capability than an average layperson, although the triage accuracy of the five best SCs was superior to the accuracy of most participants. SCs might improve early detection of emergencies but might also needlessly increase resource utilization in health care. Laypersons sometimes require support in deciding when to rely on self-care but it is in that very situation where SCs perform the worst. Further research is needed to determine how to best combine the strengths of humans and SCs.

show abstract

Section: Evidence On Scsmentioning

confidence: 99%

Benchmarking Triage Capability of Symptom Checkers Against That of Medical Laypersons: Survey Study

Schmieding¹,

Mörgeli²,

Schmieding³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Farmer et al designed a tool that diagnosed the six most common musculoskeletal pathologies in the shoulder. In a first trial [13], it obtained a validity of 88%; and in a second trial, with an updated version [14], it obtained a validity of 91%. However, this tool was managed by a medical professional and not self-administered by the patient as in our study, which may explain the much higher values obtained.…”

Section: Discussionmentioning

confidence: 99%

Design and Validation of a Computer Application for Diagnosis of Shoulder Locomotor System Pathology

et al. 2019

View full text Add to dashboard Cite

Objectives To design and validate a computer application for the diagnosis of shoulder locomotor system pathology. Methods The first phase involved the construction of the application using the Delphi method. In the second phase, the application was validated with a sample of 250 patients with shoulder pathology. Validity was measured for each diagnostic group using sensitivity, specificity, and positive and negative likelihood ratio (LR(+) and LR(−)). The correct classification ratio (CCR) for each patient and the factors related to worse classification were calculated using multivariate binary logistic regression (odds ratio, 95% confidence interval). Results The mean time to complete the application was 15 ± 7 minutes. The validity values were the following: LR(+) 7.8 and LR(−) 0.1 for cervical radiculopathy, LR(+) 4.1 and LR(−) 0.4 for glenohumeral arthrosis, LR(+) 15.5 and LR(−) 0.2 for glenohumeral instability, LR(+) 17.2 and LR(−) 0.2 for massive rotator cuff tear, LR(+) 6.2 and LR(−) 0.2 for capsular syndrome, LR(+) 4.0 and LR(−) 0.3 for subacromial impingement/rotator cuff tendinopathy, and LR(+) 2.5 and LR(−) 0.6 for acromioclavicular arthropathy. A total of 70% of the patients had a CCR greater than 85%. Factors that negatively affected accuracy were massive rotator cuff tear, acromioclavicular arthropathy, age over 55 years, and high pain intensity ( p < 0.05). Conclusions The developed application achieved an acceptable validity for most pathologies. Because the tool had a limited capacity to identify the full clinical picture in the same patient, improvements and new studies applied to other groups of patients are required.

show abstract

“…The CDSS facilitated a thorough assessment by prompting for key subjective questions and objective tests, before providing a diagnosis with recommended management for six common shoulder musculoskeletal conditions. Testing showed the CDSS diagnostic validity (accuracy) to be 88% compared with known diagnosis by radiological imaging for 50 case studies, with a kappa value of 0.85 to a confidence level of 99% [20].…”

Section: Introductionmentioning

confidence: 96%

“…Although numerous diagnostic CDSS exist [10,12,14], none had been designed for the diagnosis of musculoskeletal disorders. Therefore in previous work by this author, a prototype CDSS was developed to assist primary care clinicians in diagnosing musculoskeletal shoulder complaints [20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Self Cite

View full text Add to dashboard Cite

show abstract

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

Cited by 9 publications

References 45 publications

Benchmarking Triage Capability of Symptom Checkers Against That of Medical Laypersons: Survey Study

Benchmarking Triage Capability of Symptom Checkers Against That of Medical Laypersons: Survey Study

Design and Validation of a Computer Application for Diagnosis of Shoulder Locomotor System Pathology

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

Contact Info

Product

Resources

About