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The Best Seizure Diagnostic Tool Is Not a Medical Device: Why Stand-Alone Video Review Needs a Current Procedural Terminology Code.

ABSTRACT: The diagnosis of seizures and epilepsy is primarily based on the history, but history-taking is fraught with difficulties and has serious limitations, which is one reason for the common misdiagnosis of seizures. EEG is a very useful tool, but routine EEG has poor sensitivity, and prolonged EEG-video monitoring, the gold-standard for diagnosis, is only useful for patients with frequent events. Smartphones are ubiquitous, and their videos are increasingly used as an extension of the history and a diagnostic tool. Stand-alone videos should be considered a diagnostic tool and treated as such, including with a Current Procedural Terminology (CPT) code, the American uniform nomenclature for medical procedures, which is used for billing and reimbursement.

SUBMITTER: Benbadis SR

PROVIDER: S-EPMC9987202 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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The Best Seizure Diagnostic Tool Is Not a Medical Device: Why Stand-Alone Video Review Needs a Current Procedural Terminology Code.

Benbadis Selim R SR

Neurology. Clinical practice 20230113 1

The diagnosis of seizures and epilepsy is primarily based on the history, but history-taking is fraught with difficulties and has serious limitations, which is one reason for the common misdiagnosis of seizures. EEG is a very useful tool, but routine EEG has poor sensitivity, and prolonged EEG-video monitoring, the gold-standard for diagnosis, is only useful for patients with frequent events. Smartphones are ubiquitous, and their videos are increasingly used as an extension of the history and a ...[more]

PMID: 36891282

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Project description:IntroductionUnlike antibiotic and perfusion support, guidelines for sepsis source control lack high-quality evidence and are ungraded. Internally valid administrative data methods are needed to identify cases representing source control procedures to evaluate outcomes.MethodsOver five modified Delphi rounds, two independent reviewers identified Current Procedural Terminology (CPT) codes pertinent to source control. In each round, codes with perfect agreement were retained or excluded, whereas disagreements were reviewed by the panelists. Manual review of 400 patient records meeting Sepsis-3 criteria (2010-2017) clinically adjudicated which encounters included source control procedures (gold standard). The performance of consensus codes was compared with the gold standard to assess sensitivity, specificity, predictive values, and likelihood ratios.ResultsOf 5752 CPT codes, 609 consensus codes represented source control procedures. Of 400 hospitalizations for sepsis, 39 (9.8%; 95% confidence interval [CI] 7.0%-13.1%) underwent gold standard source control procedures and 29 (7.3%; 95% CI 4.9-10.3%) consensus code-defined source control procedures. Thirty consensus codes were identified (20.0% gastrointestinal/intraabdominal, 10.0% genitourinary, 13.3% hepatopancreatobiliary, 23.3% orthopedic/cranial, 23.3% soft tissue, and 10.0% intrathoracic), which had 61.5% (95% CI 44.6%-76.6%) sensitivity, 98.6% (95% CI 96.8%-99.6%) specificity, 83.2% (95% CI 66.6%-92.4%) positive, and 95.9% (95% CI 93.9%-97.2%) negative predictive values. With pretest probability at sample prevalence, an identified consensus code had a posttest probability of 83.0% (95% CI 66.0%-92.0%), whereas consensus code absence had a probability of 4.0% (95% CI 3.0-6.0) for undergoing a source control procedure.ConclusionsUsing modified Delphi methodology, we created and validated CPT codes identifying source control procedures, providing a framework for evaluation of the surgical care of patients with sepsis.

Project description:BackgroundFully digital preoperative information could save valuable time and resources. However, compared with face to face consultations, equivalent levels of safety, patient satisfaction and participation need to be maintained when using other methods to inform patients. This trial compared knowledge retention between preoperative stand-alone video education and face-to-face education by an anaesthesiologist.ObjectivesTo assess if video education, alone or combined with face-to-face education, leads to better knowledge retention more than conventional face-to-face education.DesignA randomised controlled trial with four arms: Video, Anaesthesiologist, Video & Anaesthesiologist, and Reference for baseline measurements and exploration of a test-enhanced learning effect.SettingA Dutch tertiary care centre from February 2022 to February 2023.PatientsA total of 767 adult patients undergoing preoperative consultation for elective non-cardiothoracic surgery, with 677 included in the complete case analysis.InterventionsStand-alone preoperative video education and video education in combination with face-to-face education in the preoperative outpatient clinic.Main outcome measuresPrimary outcome, measured by the Rotterdam Anaesthesia Knowledge Questionnaire, was knowledge retention on day 0. Secondary outcomes included knowledge retention at 14 and 42 days, preoperative anxiety, and the need for additional information using the Amsterdam Preoperative Anxiety and Information Scale. Other outcomes were satisfaction, self-assessed knowledge, and test-enhanced learning effect.ResultsStand-alone video education led to higher Rotterdam Anaesthesia Knowledge Questionnaire scores than face-to-face education on day 0: median [IQR], 87.5 [81.3 to 93.8] vs. 81.3 [68.8 to 87.5], P < 0.001. Combined education in the "Video & Anaesthesiologist" group led to better knowledge retention compared with both the "Anaesthesiologist" group and the Video group: 93.8 [87.5 to 93.8] vs. 81.3 [68.8 to 87.5], P < 0.001; 93.8 [87.5 to 93.8] vs. 87.5 [81.3 to 93.8], P = 0.01, respectively. No differences in the patients' preoperative anxiety and satisfaction levels were found.ConclusionCompared with face-to-face education by an anaesthesiologist, stand-alone video and combined video education improve short-term knowledge retention, without increasing patient anxiety.Trial registrationClinicalTrials.gov Identifier: NCT05188547.

Project description:BACKGROUND:Accurate anesthesiology procedure code data are essential to quality improvement, research, and reimbursement tasks within anesthesiology practices. Advanced data science techniques, including machine learning and natural language processing, offer opportunities to develop classification tools for Current Procedural Terminology codes across anesthesia procedures. METHODS:Models were created using a Train/Test dataset including 1,164,343 procedures from 16 academic and private hospitals. Five supervised machine learning models were created to classify anesthesiology Current Procedural Terminology codes, with accuracy defined as first choice classification matching the institutional-assigned code existing in the perioperative database. The two best performing models were further refined and tested on a Holdout dataset from a single institution distinct from Train/Test. A tunable confidence parameter was created to identify cases for which models were highly accurate, with the goal of at least 95% accuracy, above the reported 2018 Centers for Medicare and Medicaid Services (Baltimore, Maryland) fee-for-service accuracy. Actual submitted claim data from billing specialists were used as a reference standard. RESULTS:Support vector machine and neural network label-embedding attentive models were the best performing models, respectively, demonstrating overall accuracies of 87.9% and 84.2% (single best code), and 96.8% and 94.0% (within top three). Classification accuracy was 96.4% in 47.0% of cases using support vector machine and 94.4% in 62.2% of cases using label-embedding attentive model within the Train/Test dataset. In the Holdout dataset, respective classification accuracies were 93.1% in 58.0% of cases and 95.0% among 62.0%. The most important feature in model training was procedure text. CONCLUSIONS:Through application of machine learning and natural language processing techniques, highly accurate real-time models were created for anesthesiology Current Procedural Terminology code classification. The increased processing speed and a priori targeted accuracy of this classification approach may provide performance optimization and cost reduction for quality improvement, research, and reimbursement tasks reliant on anesthesiology procedure codes.

Dataset Information

The Best Seizure Diagnostic Tool Is Not a Medical Device: Why Stand-Alone Video Review Needs a Current Procedural Terminology Code.

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The Best Seizure Diagnostic Tool Is Not a Medical Device: Why Stand-Alone Video Review Needs a Current Procedural Terminology Code.

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