The Role of a Decision Support System in Back Pain Diagnoses: A Pilot Study

Benditz, Achim; Faber, Florian; Wenk, Gabriela; Fuchs, Tina; Salak, Natalie; Grifka, Joachim; Vogl, Matthias; Menke, M.; Jansen, Petra

doi:10.1155/2019/1314028

Cited by 7 publications

(21 citation statements)

References 21 publications

(17 reference statements)

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“…Every age group is affected by back pain. The incidence of low back pain is 60-90%, and low back pain is the main cause of working disability in most countries [6][7][8][9][10]. Also neck pain is in increasing problem with incidences of between 10.4% and 71.5%, and an annual prevalence varying between 30 and 50% [7,[11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…Because of these enormous costs for the health care system, more and more attempts are made to support health care providers with a computerized decision support systems (DSS). The DSS can be considered as one specific form of a symptom checker [ 10 ]. The study group of the authors has already developed and published a first use of a computerized algorithm which is based on the next best questions to find out the most possible diagnosis [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…The DSS can be considered as one specific form of a symptom checker [ 10 ]. The study group of the authors has already developed and published a first use of a computerized algorithm which is based on the next best questions to find out the most possible diagnosis [ 10 ]. Also, other institutions use these systems.…”

Section: Introductionmentioning

confidence: 99%

“…A study in 2018 showed significant medium correlation between the DSS and the medical recommendation. In 49.6% the diagnoses were concordant [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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A clinical decision support system in back pain helps to find the diagnosis: a prospective correlation study

Benditz

Pulido

Grifka

et al. 2021

Arch Orthop Trauma Surg

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The aim of this study is to show the concordance of an app-based decision support system and the diagnosis given by spinal surgeons in cases of back pain. 86 patients took part within 2 months. They were seen by spine surgeons in the daily routine and then completed an app-based questionnaire that also led to a diagnosis independently. The results showed a Cramer’s V = .711 (p < .001), which can be taken as a strong relation between the tool and the diagnosis of the medical doctor. Besides, in 67.4% of the cases, the diagnosis was concordant. An overestimation of the severity of the diagnosis occurred more often than underestimation (15.1% vs. 7%). The app-based tool is a safe tool to support healthcare professionals in back pain diagnosis.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…A study in 2018 showed significant medium correlation between the DSS and the medical recommendation. In 49.6% the diagnoses were concordant [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A clinical decision support system in back pain helps to find the diagnosis: a prospective correlation study

Benditz

Pulido

Grifka

et al. 2021

Arch Orthop Trauma Surg

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“…Here, the use of AI-based algorithms in a diagnostic decision support system (DDSS) can be seen as another potential of AI in medicine. Data has already shown that such a DDSS can help patients to estimate the severity of their complaints and can also be useful to support medical personnel [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Use of artificial intelligence in sports medicine: a report of 5 fictional cases

Rigamonti

Estel

Gehlen

et al. 2021

BMC Sports Sci Med Rehabil

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Background Artificial intelligence (AI) is one of the most promising areas in medicine with many possibilities for improving health and wellness. Already today, diagnostic decision support systems may help patients to estimate the severity of their complaints. This fictional case study aimed to test the diagnostic potential of an AI algorithm for common sports injuries and pathologies. Methods Based on a literature review and clinical expert experience, five fictional “common” cases of acute, and subacute injuries or chronic sport-related pathologies were created: Concussion, ankle sprain, muscle pain, chronic knee instability (after ACL rupture) and tennis elbow. The symptoms of these cases were entered into a freely available chatbot-guided AI app and its diagnoses were compared to the pre-defined injuries and pathologies. Results A mean of 25–36 questions were asked by the app per patient, with optional explanations of certain questions or illustrative photos on demand. It was stressed, that the symptom analysis would not replace a doctor’s consultation. A 23-yr-old male patient case with a mild concussion was correctly diagnosed. An ankle sprain of a 27-yr-old female without ligament or bony lesions was also detected and an ER visit was suggested. Muscle pain in the thigh of a 19-yr-old male was correctly diagnosed. In the case of a 26-yr-old male with chronic ACL instability, the algorithm did not sufficiently cover the chronic aspect of the pathology, but the given recommendation of seeing a doctor would have helped the patient. Finally, the condition of the chronic epicondylitis in a 41-yr-old male was correctly detected. Conclusions All chosen injuries and pathologies were either correctly diagnosed or at least tagged with the right advice of when it is urgent for seeking a medical specialist. However, the quality of AI-based results could presumably depend on the data-driven experience of these programs as well as on the understanding of their users. Further studies should compare existing AI programs and their diagnostic accuracy for medical injuries and pathologies.

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Diagnostic clinical prediction rules for categorising low back pain: A systematic review

Hill,

Banerjee,

Hill

et al. 2023

Musculoskeletal Care

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BackgroundLow back pain (LBP) is a common complex condition, where specific diagnoses are hard to identify. Diagnostic clinical prediction rules (CPRs) are known to improve clinical decision‐making. A review of LBP diagnostic‐CPRs by Haskins et al. (2015) identified six diagnostic‐CPRs in derivation phases of development, with one tool ready for implementation. Recent progress on these tools is unknown. Therefore, this review aimed to investigate developments in LBP diagnostic‐CPRs and evaluate their readiness for implementation.MethodsA systematic review was performed on five databases (Medline, Amed, Cochrane Library, PsycInfo, and CINAHL) combined with hand‐searching and citation‐tracking to identify eligible studies. Study and tool quality were appraised for risk of bias (Quality Assessment of Diagnostic Accuracy Studies‐2), methodological quality (checklist using accepted CPR methodological standards), and CPR tool appraisal (GRade and ASsess Predictive).ResultsOf 5021 studies screened, 11 diagnostic‐CPRs were identified. Of the six previously known, three have been externally validated but not yet undergone impact analysis. Five new tools have been identified since Haskin et al. (2015); all are still in derivation stages. The most validated diagnostic‐CPRs include the Lumbar‐Spinal‐Stenosis‐Self‐Administered‐Self‐Reported‐History‐Questionnaire and Diagnosis‐Support‐Tool‐to‐Identify‐Lumbar‐Spinal‐Stenosis, and the StEP‐tool which differentiates radicular from axial‐LBP.ConclusionsThis updated review of LBP diagnostic CPRs found five new tools, all in the early stages of development. Three previously known tools have now been externally validated but should be used with caution until impact evaluation studies are undertaken. Future funding should focus on externally validating and assessing the impact of existing CPRs on clinical decision‐making.

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The Role of a Decision Support System in Back Pain Diagnoses: A Pilot Study

Cited by 7 publications

References 21 publications

A clinical decision support system in back pain helps to find the diagnosis: a prospective correlation study

A clinical decision support system in back pain helps to find the diagnosis: a prospective correlation study

Use of artificial intelligence in sports medicine: a report of 5 fictional cases

Diagnostic clinical prediction rules for categorising low back pain: A systematic review

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