Towards Improving the Identification of Anterior Cruciate Ligament Tears in Primary Point-of-Care Settings

Abstract Background: Only a small proportion of anterior cruciate ligament (ACL) tears are diagnosed on initial healthcare consultation. Current clinical guidelines do not acknowledge that primary point-of-care practitioners rely more heavily on a clinical history than special clinical tests for diagnosis of an ACL tear. This research will assess the accuracy of combinations of patient-reported variables alone, and in combination with clinician-generated variables to identify an ACL tear as a preliminary step to designing a primary point-of-care clinical decision support tool. Methods: Electronic medical records (EMRs) of individuals aged 15-45 years, with ICD-9 codes corresponding to a knee condition, and confirmed (ACL + ) or denied (ACL - ) first-time ACL tear seen at a University-based Clinic between 2014 and 2016 were eligible for inclusion. Demographics, relevant diagnostic indicators and ACL status based on orthopaedic surgeon assessment and/or MRI reports were manually extracted. Descriptive statistics calculated for all variables by ACL status. Univariate between group comparisons, clinician surveys (n=17), availability of data and univariable logistic regression (95%CI) were used to select variables for inclusion into multivariable logistic regression models that assessed the odds (95%CI) of an ACL-tear based on patient-reported variables alone (consistent with primary point-of-care practice), or in combination with clinician-generated variables. Model performance was assessed by accuracy, sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios (95%CI). Results: Of 1,512 potentially relevant EMRs, 725 were included. Participant median age was 26 years (range 15-45), 48% were female and 60% had an ACL tear. A combination of patient-reported (age, sport-related injury, immediate swelling, family history of ACL tear) and clinician-generated variables (Lachman test result) were superior for ACL tear diagnosis [accuracy; 0.95 (90,98), sensitivity; 0.97 (0.88,0.98), specificity; 0.95 (0.82,0.99)] compared to the patient-reported variables alone [accuracy; 84% (77,89), sensitivity; 0.60 (0.44,0.74), specificity; 0.95 (0.89,0.98)]. Conclusions: A high proportion of individuals without an ACL tear can be accurately identified by considering patient-reported age, injury setting, immediate swelling and family history of ACL tear. These findings directly inform the development of a clinical decision support tool to facilitate timely and accurate ACL tear diagnosis in primary care settings.

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Towards Improving the Identification of Anterior Cruciate Ligament Tears in Primary Point-of-Care Settings

10.21203/rs.2.19203/v2 ◽

2020 ◽

Author(s):

Jackie L Whittaker ◽

Michelle Chan ◽

Bo Pan ◽

Imran Hassan ◽

Terry Defreitas ◽

...

Keyword(s):

Point Of Care ◽

Cruciate Ligament ◽

Decision Support Tool ◽

Clinical Decision ◽

Support Tool ◽

Acl Tear ◽

Patient Reported ◽

History Of ◽

Clinical Decision Support Tool ◽

Anterior Cruciate

Abstract Background: Only a small proportion of anterior cruciate ligament (ACL) tears are diagnosed on initial healthcare consultation. Current clinical guidelines do not acknowledge that primary point-of-care practitioners rely more heavily on a clinical history than special clinical tests for diagnosis of an ACL tear. This research will assess the accuracy of combinations of patient-reported variables alone, and in combination with clinician-generated variables to identify an ACL tear as a preliminary step to designing a primary point-of-care clinical decision support tool.Methods: Electronic medical records (EMRs) of individuals aged 15-45 years, with ICD-9 codes corresponding to a knee condition, and confirmed (ACL+) or denied (ACL-) first-time ACL tear seen at a University-based Clinic between 2014 and 2016 were eligible for inclusion. Demographics, relevant diagnostic indicators and ACL status based on orthopaedic surgeon assessment and/or MRI reports were manually extracted. Descriptive statistics calculated for all variables by ACL status. Univariate between group comparisons, clinician surveys (n=17), availability of data and univariable logistic regression (95%CI) were used to select variables for inclusion into multivariable logistic regression models that assessed the odds (95%CI) of an ACL-tear based on patient-reported variables alone (consistent with primary point-of-care practice), or in combination with clinician-generated variables. Model performance was assessed by accuracy, sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios (95%CI).Results: Of 1,512 potentially relevant EMRs, 725 were included. Participant median age was 26 years (range 15-45), 48% were female and 60% had an ACL tear. A combination of patient-reported (age, sport-related injury, immediate swelling, family history of ACL tear) and clinician-generated variables (Lachman test result) were superior for ACL tear diagnosis [accuracy; 0.95 (90,98), sensitivity; 0.97 (0.88,0.98), specificity; 0.95 (0.82,0.99)] compared to the patient-reported variables alone [accuracy; 84% (77,89), sensitivity; 0.60 (0.44,0.74), specificity; 0.95 (0.89,0.98)].Conclusions: A high proportion of individuals without an ACL tear can be accurately identified by considering patient-reported age, injury setting, immediate swelling and family history of ACL tear. These findings directly inform the development of a clinical decision support tool to facilitate timely and accurate ACL tear diagnosis in primary care settings.

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Towards Improving the Identification of Anterior Cruciate Ligament Tears in Primary Point-of-Care Settings

10.21203/rs.2.19203/v1 ◽

2019 ◽

Author(s):

Jackie L Whittaker ◽

Michelle Chan ◽

Bo Pan ◽

Imran Hassan ◽

Terry Defreitas ◽

...

Keyword(s):

Point Of Care ◽

Cruciate Ligament ◽

Decision Support Tool ◽

Clinical Decision ◽

Support Tool ◽

Acl Tear ◽

Patient Reported ◽

History Of ◽

Clinical Decision Support Tool ◽

Anterior Cruciate

Abstract BackgroundOnly a small proportion of anterior cruciate ligament (ACL) tears are diagnosed on initial healthcare consultation. Current clinical guidelines do not acknowledge that primary point-of-care practitioners rely more heavily on a clinical history than special clinical tests for diagnosis of an ACL tear. This research will assess the accuracy of combinations of patient-reported variables alone, and in combination with clinician-generated variables to identify an ACL tear as a preliminary step to designing a primary point-of-care care clinical decision support tool.MethodsElectronic medical records (EMRs) of individuals aged 15-45 years, with ICD-9 codes corresponding to a knee condition, and confirmed (ACL+) or denied (ACL-) first-time ACL tear seen at a University-based Clinic between 2014 and 2016 were eligible for inclusion. Demographics, relevant diagnostic indicators and ACL status based on orthopaedic surgeon assessment and/or MRI reports were manually extracted. Descriptive statistics calculated for all variables by ACL status. Univariate between group comparisons, clinician surveys (n=17), availability of data and univariable logistic regression (95%CI) were used to select variables for inclusion into multivariable logistic regression models that assessed the odds (95%CI) of an ACL-tear based on patient-reported variables alone (consistent with primary point-of-care practice), or in combination with clinician-generated variables. Model performance was assessed by accuracy, sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios (95%CI).ResultsOf 1,512 potentially relevant EMRs, 725 were included. Participant median age was 26 years (range 15-45), 48% were female and 60% had an ACL tear. A combination of patient-reported (age, sport-related injury, immediate swelling, family history of ACL tear) and clinician-generated variables (Lachman test result) were superior for ACL tear diagnosis [accuracy; 95%CI (90,98), sensitivity; 0.97 (0.88,0.98), specificity; 0.95 (0.82,0.99)] compared to the patient-reported variables alone [accuracy; 84% (77,89), sensitivity; 0.60 (0.44,0.74), specificity; 0.95 (0.89,0.98)].ConclusionsA high proportion of individuals without an ACL tear can be accurately identified by considering patient-reported age, injury setting, immediate swelling and family history of ACL tear. These findings directly inform the development of a clinical decision support tool to facilitate timely and accurate ACL tear diagnosis in primary care settings.

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The effectiveness of an electronic clinical decision support tool embedded into the patient medical record at the point of care to guide Mohs surgery utilization: A cohort study

Journal of the American Academy of Dermatology ◽

10.1016/j.jaad.2021.06.886 ◽

2021 ◽

Author(s):

Sepideh Ashrafzadeh ◽

Sameer Gupta ◽

Anne-Maria Fiorino ◽

Elizabeth T. Cafiero-Fonseca ◽

Timothy G. Ferris ◽

...

Keyword(s):

Cohort Study ◽

Decision Support ◽

Clinical Decision Support ◽

Point Of Care ◽

Decision Support Tool ◽

Clinical Decision ◽

Patient Medical Record ◽

Support Tool ◽

Clinical Decision Support Tool ◽

Electronic Clinical Decision Support

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Utilization and uptake of the UpToDate clinical decision support tool at the Makerere University College of Health Sciences (MakCHS), Uganda

African Health Sciences ◽

10.4314/ahs.v21i2.52 ◽

2021 ◽

Vol 21 (2) ◽

pp. 904-911

Author(s):

Alison Annet Kinengyere ◽

Julie Rosenberg ◽

Olivia Pickard ◽

Moses Kamya

Keyword(s):

Clinical Practice ◽

Decision Support ◽

Clinical Decision Support ◽

Point Of Care ◽

Decision Support Tool ◽

Clinical Decision ◽

Health Sciences ◽

Support Tool ◽

Makerere University ◽

Clinical Decision Support Tool

Background: The use of point-of-care, evidence-based tools is becoming increasingly popular. They can provide easy-to- use, high-quality information which is regularly updated and has been shown to improve clinical outcomes. Integrating such tools into clinical practice is an important component of improving the quality of health care. However, because such tools are rarely used in resource-limited settings, there is limited research on uptake especially among medical students. Objective: This paper explores the uptake of one such tool, Up-To-Date, when provided free of cost at a medical school in Africa. Methods: In partnership with the Better Evidence at Ariadne Labs free access to UpToDate was granted through the MakCHS IP address. On-site librarians facilitated training sessions and spread awareness of the tool. Usage data was aggre- gated, based on log ins and content views, presented and analyzed using Excel tables and graphs. Results: The data shows evidence of meaningful usage, with 43,043 log ins and 15,591 registrations between August 2019 and August 2020. The most common topics viewed were in obstetrics and gynecology, pediatrics, drug information, and infectious diseases. Access occurred mainly through the mobile phone app. Conclusion: Findings show usage by various user categories, but with inconsistent uptake and low usage. Librarians can draw upon these results to encourage institutions to support uptake of point-of-care tools in clinical practice. Keywords: UpToDate clinical decision support tool; Makerere University College of Health Sciences; Uganda.

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Impact of electronic clinical decision support on adherence to guideline-recommended treatment for hyperlipidaemia, atrial fibrillation and heart failure: protocol for a cluster randomised trial

BMJ Open ◽

10.1136/bmjopen-2017-019087 ◽

2017 ◽

Vol 7 (12) ◽

pp. e019087 ◽

Cited By ~ 3

Author(s):

Maya Elizabeth Kessler ◽

Rickey E Carter ◽

David A Cook ◽

Daryl Jon Kor ◽

Paul M McKie ◽

...

Keyword(s):

Heart Failure ◽

Atrial Fibrillation ◽

Decision Support ◽

Clinical Decision Support ◽

Primary Outcome ◽

Point Of Care ◽

Decision Support Tool ◽

Clinical Decision ◽

Support Tool ◽

Clinical Decision Support Tool

IntroductionClinical practice guidelines facilitate optimal clinical practice. Point of care access, interpretation and application of such guidelines, however, is inconsistent. Informatics-based tools may help clinicians apply guidelines more consistently. We have developed a novel clinical decision support tool that presents guideline-relevant information and actionable items to clinicians at the point of care. We aim to test whether this tool improves the management of hyperlipidaemia, atrial fibrillation and heart failure by primary care clinicians.Methods/analysisClinician care teams were cluster randomised to receive access to the clinical decision support tool or passive access to institutional guidelines on 16 May 2016. The trial began on 1 June 2016 when access to the tool was granted to the intervention clinicians. The trial will be run for 6 months to ensure a sufficient number of patient encounters to achieve 80% power to detect a twofold increase in the primary outcome at the 0.05 level of significance. The primary outcome measure will be the percentage of guideline-based recommendations acted on by clinicians for hyperlipidaemia, atrial fibrillation and heart failure. We hypothesise care teams with access to the clinical decision support tool will act on recommendations at a higher rate than care teams in the standard of care arm.Ethics and disseminationThe Mayo Clinic Institutional Review Board approved all study procedures. Informed consent was obtained from clinicians. A waiver of informed consent and of Health Insurance Portability and Accountability Act (HIPAA) authorisation for patients managed by clinicians in the study was granted. In addition to publication, results will be disseminated via meetings and newsletters.Trial registration numberNCT02742545.

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