Accuracy of a Computer-Based Diagnostic Program for Ambulatory Patients With Knee Pain

Bisson, Leslie J.; Komm, Jorden T.; Bernas, Geoffrey A.; Fineberg, Marc S.; Marzo, John M.; Rauh, Michael A.; Smolinski, Robert J.; Wind, William M.

doi:10.1177/0363546514541654

Cited by 30 publications

(31 citation statements)

References 6 publications

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“…In line with these recommendations, we have developed a 12-week DCP for CKP. The program builds on previous work in digital musculoskeletal care, which studied individual components of digital care in isolation, such as diagnosis [30], CBT [25], exercise with telephone-based coaching [31], exercise with pain coping training [32], and behavioral change approaches [33]. …”

Section: Introductionmentioning

confidence: 99%

Translating Comprehensive Conservative Care for Chronic Knee Pain Into a Digital Care Pathway: 12-Week and 6-Month Outcomes for the Hinge Health Program

Smittenaar¹,

Erhart‐Hledik²,

Kinsella³

et al. 2017

JMIR Rehabil Assist Technol

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BackgroundChronic knee pain (CKP) affects a large number of adults, many of whom do not receive best-practice care and are at high risk for unnecessary surgery.ObjectiveThe aim of this study was to investigate the effect of the Hinge Health 12-week digital care program (DCP) for CKP on knee pain and function, with secondary outcomes of surgery interest and satisfaction, at 12 weeks and 6 months after starting the program.MethodsIndividuals with CKP were recruited onto the 12-week program, comprising sensor-guided physical exercises, weekly education, activity tracking, and psychosocial support such as personal coaching and cognitive behavioral therapy (CBT). We used a single-arm design with assessment of outcomes at baseline, 12 weeks, and 6 months after starting the program. We used a linear mixed effects model with Tukey contrasts to compare timepoints and report intention-to-treat statistics with last observation carried forward.ResultsThe cohort consisted of 41 individuals (32 female, mean age 52 years, SD 9 years). Between baseline and week 12, participants reported clinically significant improvements in the Knee Injury and Osteoarthritis Outcome Score (KOOS) pain and Knee Injury and Osteoarthritis Outcome Score-Physical Function Short Form (KOOS-PS) function scales of 16 points (95% CI 12-21, P<.001) and 10 points (95% CI 6-14, P<.001), respectively. Significant reductions of 57% (mean difference 30, 95% CI 21-38, P<.001) and 51% (mean difference 25, 95% CI 16-33, P<.001) in visual analog scale (VAS) knee pain and stiffness, respectively, were observed at 12 weeks, as well as a 67% reduction in surgery interest (mean reduction 2.3 out of 10, 95% CI 1.5-3.1, P<.001). Average satisfaction at week 12 was 9.2 out of 10. Critically, all improvements were maintained at 6 months at similar or greater magnitude.ConclusionsParticipants on the Hinge Health DCP for CKP showed substantial clinical improvements that were maintained 6 months after enrolling in the program. This shows that DCPs carry strong potential to deliver evidence-based, cost-effective care to those suffering from CKP.

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Section: Introductionmentioning

confidence: 99%

Translating Comprehensive Conservative Care for Chronic Knee Pain Into a Digital Care Pathway: 12-Week and 6-Month Outcomes for the Hinge Health Program

Smittenaar¹,

Erhart‐Hledik²,

Kinsella³

et al. 2017

JMIR Rehabil Assist Technol

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“…We utilized a web-based symptom checker for knee pain that has been described previously, which generates a differential diagnosis following patient-entered symptoms and has a reported accuracy of 89%. 1 Briefly, after entry of symptoms, the program supplies a list of potential diagnoses that are selected from 26 possible diagnoses ( Table 1 ). As in the original study, 1 for the purposes of analysis, the 26 diagnoses were consolidated to 21.…”

Section: Methodsmentioning

confidence: 99%

“… 1 Briefly, after entry of symptoms, the program supplies a list of potential diagnoses that are selected from 26 possible diagnoses ( Table 1 ). As in the original study, 1 for the purposes of analysis, the 26 diagnoses were consolidated to 21. First, osteoarthritis and osteoarthritis exacerbation were considered a single diagnosis.…”

Section: Methodsmentioning

confidence: 99%

“…4 A recent study has demonstrated that a web-based program can generate an accurate differential diagnosis in 89% of ambulatory patients with knee pain based solely on the history entered by the patient. 1 Despite the regularity of online symptom checking, we are not aware of any study that has evaluated a patient’s ability to self-diagnose the cause of their knee pain with the assistance of a symptom checker. The purpose of this study was to determine patients’ ability to select their diagnosis from a list of possible diagnoses supplied by an accurate symptom checker.…”

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confidence: 99%

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How Accurate Are Patients at Diagnosing the Cause of Their Knee Pain With the Help of a Web-based Symptom Checker?

Bisson

Komm

Bernas

et al. 2016

Orthopaedic Journal of Sports Medicine

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Background:Researching medical information is the third most popular activity online, and there are a variety of web-based symptom checker programs available.Purpose:This study evaluated a patient’s ability to self-diagnose their knee pain from a list of possible diagnoses supplied by an accurate symptom checker.Study Design:Cohort study (diagnosis); Level of evidence, 2.Methods:All patients older than 18 years who presented to the office of 7 different fellowship-trained sports medicine surgeons over an 8-month period with a complaint of knee pain were asked to participate. A web-based symptom checker for knee pain was used; the program has a reported accuracy of 89%. The symptom checker generates a list of potential diagnoses after patients enter symptoms and links each diagnosis to informative content. After exploring the informative content, patients selected all diagnoses they felt could explain their symptoms. Each patient was later examined by a physician who was blinded to the differential generated by the program as well as the patient-selected diagnoses. A blinded third party compared the diagnoses generated by the program with those selected by the patient as well as the diagnoses determined by the physician. The level of matching between the patient-selected diagnoses and the physician’s diagnoses determined the patient’s ability to correctly diagnose their knee pain.Results:There were 163 male and 165 female patients, with a mean age of 48 years (range, 18-76 years). The program generated a mean 6.6 diagnoses (range, 2-15) per patient. Each patient had a mean 1.7 physician diagnoses (range, 1-4). Patients selected a mean 2 diagnoses (range, 1-9). The patient-selected diagnosis matched the physician’s diagnosis 58% of the time.Conclusion:With the aid of an accurate symptom checker, patients were able to correctly identify the cause of their knee pain 58% of the time.

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“…Knowledge graphs enable the information to be read and understood by machines with the support of machine learning algorithms upon presenting the proof of automation by extracting the facts as human brains derive to certain level. Several applications working on ontologies have been noted in recent years particularly in the domain of medicine [1] for automated disease classification and conclusion of the diagnosis [2] based on the clinical observations. Automation through inference learning [3] in medicine has huge demand as it enables knowledge sharing across the world coupling the experience of experts over the practical fields.…”

Section: Related Workmentioning

confidence: 99%

Integration of Healthcare domain Ontologies using Bayesian Networks

2019

IJITEE

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Semantic Web (SW) was created with the vision of knowledge sharing. Knowledge from the past and present help predict the future with the use of Machine Learning (ML) algorithms. SW powered with ontologies help in realizing machine interactions supporting automated knowledge extraction. Healthcare as a field of medical domain gives lot of importance for timely accurate decisions with the available features. Representing existing information in terms of ontologies, retrieving the decisions upon establishing interaction between the relevant ontologies within the same domain, knowledge sharing & reusing the existing facts are of great benefit to the medical practitioners and researchers which has lot of open challenges to be resolved in order to realize the same. To address the stated issues, an algorithmic approach – Ontologies Integration algorithm using Bayesian Networks (OIBN) based on Bayesian Belief Networks (BBN) working on Naïve beliefs has been proposed which works on symptoms through the attributes of related ontologies within the same domain exploring the symptom dependencies and their probability of occurrences in combination. Selection of features for integration will follow the steps proposed in Sequential Forward Feature Selection algorithm (SFFS). The observation on the correctness of the presented method over diabetic datasets represented in ontological form with integration of relevant features reveals that the knowledge graphs have been efficiently explored discovering the facts based on the probability theory. The experimental results conclude that the proposed technique is showing enhanced prediction accuracy of 80.95% which is better compared to accuracies of the individual ontologies prior to integration and existing state-of-art technique.

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Accuracy of a Computer-Based Diagnostic Program for Ambulatory Patients With Knee Pain

Abstract: 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.

Cited by 30 publications

References 6 publications

Translating Comprehensive Conservative Care for Chronic Knee Pain Into a Digital Care Pathway: 12-Week and 6-Month Outcomes for the Hinge Health Program

Translating Comprehensive Conservative Care for Chronic Knee Pain Into a Digital Care Pathway: 12-Week and 6-Month Outcomes for the Hinge Health Program

How Accurate Are Patients at Diagnosing the Cause of Their Knee Pain With the Help of a Web-based Symptom Checker?

Integration of Healthcare domain Ontologies using Bayesian Networks

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