Israel Medical Association Journal

Background: Group A Streptococcus (GAS) is the predominant bacterial pathogen of pharyngitis in children. However, distinguishing GAS from viral pharyngitis is sometimes difficult. Unnecessary antibiotic use contributes to unwanted side effects, such as allergic reactions and diarrhea. It also may increase antibiotic resistance. 

Objectives: To evaluate the effect of a machine learning algorithm on the clinical evaluation of bacterial pharyngitis in children.

Methods: We assessed 54 children aged 2–17 years who presented to a primary healthcare clinic with a sore throat and fever over 38°C from 1 November 2021 to 30 April 2022. All children were tested with a streptococcal rapid antigen detection test (RADT). If negative, a throat culture was performed. Children with a positive RADT or throat culture were considered GAS-positive and treated antibiotically for 10 days, as per guidelines. Children with negative RADT tests throat cultures were considered positive for viral pharyngitis. The children were allocated into two groups: Group A streptococcal pharyngitis (GAS-P) (n=36) and viral pharyngitis (n=18). All patients underwent a McIsaac score evaluation. A linear support vector machine algorithm was used for classification.

Results: The machine learning algorithm resulted in a positive predictive value of 80.6 % (27 of 36) for GAS-P infection. The false discovery rates for GAS-P infection were 19.4 % (7 of 36).

Conclusions: Applying the machine-learning strategy resulted in a high positive predictive value for the detection of streptococcal pharyngitis and can contribute as a medical decision aid in the diagnosis and treatment of GAS-P.

Background: In the past decade, numerous new imaging and laboratory tests have been implemented that significantly contribute to improved medical diagnostic capabilities. However, inappropriate utilization, which occurs on a large scale, has significant ramifications for both patient care and health systems.

Objectives: To assess the impact of a novel clinical decision support system (CDSS) applied to our electronic medical records on abdominal ultrasonography utilization pattern.

Methods: We conducted a retrospective cohort study comparing patterns of abdominal ultrasound utilization in cases of liver enzyme elevation, with and without CDSS, between February and May in 2017 (before CDSS implementation) and during the same months in 2018 (after CDSS implementation). The following parameters were collected: number of tests ordered according to the guidelines, tests with a diagnostic value, and order forms completed with any data or a diagnostic question. The comparison was conducted using chi-square test.

Results: Of 152 abdominal ultrasound tests, 72 were ordered in the pre-implementation period and 80 in the post-implementation period. The system failed to reach statistical significance regarding the rates of ordered tests according to the guidelines and/or tests with a diagnostic value. However, the use of the CDSS had a statistically significant impact regarding completing the order form with data, including a specific diagnostic question.

Conclusions: The effect of the system on the efficiency of test utilization was partial. However, our findings strongly suggested that CDSS has the potential to promote proper usage of complementary technologies.