BACKGROUND: Looking up information regarding a medical condition is the third most popular activity online, and there are a variety of web-based symptom-checking programs available to the patient. However, the authors are not aware of any that have been scientifically evaluated as an accurate measure for the cause of one's knee pain. PURPOSE/HYPOTHESIS: The purpose of this study was to design and evaluate an Internet-based program that generates a differential diagnosis based on a history of knee pain entered by the patient. The hypothesis was that the program would accurately generate a differential diagnosis for patients presenting with knee pain. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 2. METHODS: A web-based program was created to collect knee pain history and generate a differential diagnosis for ambulatory patients with knee pain. The program selected from 26 common knee diagnoses. A total of 527 consecutive patients aged ≥18 years, who presented with a knee complaint to 7 different board-certified orthopaedic surgeons during a 3-month period, were asked to complete the questionnaire in the program. Upon completion, patients were examined by a board-certified orthopaedic surgeon. Both the patient and physician were blinded to the differential diagnosis generated by the program. A third party was responsible for comparing the diagnosis(es) generated by the program with that determined by the physician. The level of matching between diagnoses determined the accuracy of the program. RESULTS: A total of 272 male and 255 female patients, with an average age of 47 years (range, 18-84 years), participated in the study. The median number of diagnoses generated by the program was 4.8 (range, 1-10), with this list containing the physician's diagnosis(es) 89% of the time. The specificity was 27%. CONCLUSION: Despite a low specificity, the results of this study show the program to be an accurate method for generating a differential diagnosis for knee pain.
BACKGROUND: Looking up information regarding a medical condition is the third most popular activity online, and there are a variety of web-based symptom-checking programs available to the patient. However, the authors are not aware of any that have been scientifically evaluated as an accurate measure for the cause of one's knee pain. PURPOSE/HYPOTHESIS: The purpose of this study was to design and evaluate an Internet-based program that generates a differential diagnosis based on a history of knee pain entered by the patient. The hypothesis was that the program would accurately generate a differential diagnosis for patients presenting with knee pain. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 2. METHODS: A web-based program was created to collect knee pain history and generate a differential diagnosis for ambulatory patients with knee pain. The program selected from 26 common knee diagnoses. A total of 527 consecutive patients aged ≥18 years, who presented with a knee complaint to 7 different board-certified orthopaedic surgeons during a 3-month period, were asked to complete the questionnaire in the program. Upon completion, patients were examined by a board-certified orthopaedic surgeon. Both the patient and physician were blinded to the differential diagnosis generated by the program. A third party was responsible for comparing the diagnosis(es) generated by the program with that determined by the physician. The level of matching between diagnoses determined the accuracy of the program. RESULTS: A total of 272 male and 255 female patients, with an average age of 47 years (range, 18-84 years), participated in the study. The median number of diagnoses generated by the program was 4.8 (range, 1-10), with this list containing the physician's diagnosis(es) 89% of the time. The specificity was 27%. CONCLUSION: Despite a low specificity, the results of this study show the program to be an accurate method for generating a differential diagnosis for knee pain.
Authors: Leslie J Bisson; Jorden T Komm; Geoffrey A Bernas; Marc S Fineberg; John M Marzo; Michael A Rauh; Robert J Smolinski; William M Wind Journal: Orthop J Sports Med Date: 2016-02-19
Authors: Peter Smittenaar; Jennifer C Erhart-Hledik; Rose Kinsella; Simon Hunter; Gabriel Mecklenburg; Daniel Perez Journal: JMIR Rehabil Assist Technol Date: 2017-04-05
Authors: Nicolas Munsch; Alistair Martin; Stefanie Gruarin; Jama Nateqi; Isselmou Abdarahmane; Rafael Weingartner-Ortner; Bernhard Knapp Journal: J Med Internet Res Date: 2020-10-06 Impact factor: 5.428