Medical visit time and predictors in health facilities: a mega systematic review and meta-analysis

Analysis of factors affecting ultrasound examination time: A quantitative study

BACKGROUND

Numerous studies have focused on reducing patient absences and effectively scheduling exams. However, very few studies have analyzed the factors influencing examination time and predicted examination time.

OBJECTIVES

To investigate the factors affecting ultrasound examination visit length and provide a reference for interventions to optimize ultrasound appointments.

METHODS

This cross-sectional study was conducted at a fertility clinic in China. Ultrasound examination time and clinical characteristics were obtained from the electronic records. Univariate and multivariate analyses used 33,432 patients who attended our clinic center between August 1 and October 30, 2018. A quantile regression model was constructed to examine associations between ultrasound examination time and statistically significant variables in the univariate analysis.

RESULTS

Of the 33,432 patients included in this study, 29,085 (87%) were female and 4,347 (13%) were male. Their mean examination time was 6 ± 3 minutes. The doctor's title and gender, equipment, and patient's age, examination site, gender, and origin were all statistically significant. Physical examination and outpatient clinic patients had shorter examination times than inpatients. Female physicians had longer examination times than male physicians. Examination time was positively correlated with thyroid, breast, liver, gallbladder, spleen, pancreas, kidney, heart, vascular, adrenal, gynecological, early pregnancy, nuchal translucency, prostate, scrotum, and mid-to-late pregnancy fetal sites. Moreover, NT and mid-to-late pregnancy fetal sites showed a clear and continuous positive trend with increasing examination time.

CONCLUSION

The length of the ultrasound examination was correlated with the examination site, physician title, physician gender, patient age, patient gender, patient origin, and instrumentation. The reliability of inspection time predicted by variables such as the physicians' title, sex, sites examined, and the number of sites examined was higher when they were longer.

Consultation length and no-show prediction for improving appointment scheduling efficiency at a cardiology clinic: A data analytics approach

BACKGROUND

The observed consultation length at specialty clinics, such as cardiology care, is represented by two underlying groups - one with zero service time due to patient no-shows, and the other characterized by positive values with high variance. This inconstancy affects the scheduler's ability to accurately estimate consultation length, which, in turn, hinders effective utilization of the clinic's resources and timely access to care. The objectives of this study were to: (i) predict the consultation length by accounting for its semicontinuous nature (i.e., zero in case of no-shows and positive otherwise), using machine learning (ML) algorithms, (ii) identify important features for predicting no-shows and non-zero consultation length, and (iii) assess the impact of integrating the ML-based prediction with the appointment scheduling system.

METHODS

We used two-years of data extracted from the electronic medical records of a cardiology clinic. By leveraging 16 predictors pertaining to the patient, appointment, and doctor, a two-part ML-based approach was developed to handle the semicontinuous consultation length. Supervised classification models were employed to predict no-shows (i.e., categorize the consultation length as zero or positive), and regression algorithms were developed for estimating non-zero consultation lengths. Three algorithms, namely, random forests, stochastic gradient boosting, and deep neural networks, were individually employed for both no-show classification and positive consultation length prediction. Finally, the best performing classification and regression models were combined to establish the complete two-part model, and its prediction error on new data is benchmarked against the clinic's current performance. The evaluation metrics for classification models were area under the receiver operating characteristic curve (AUC-ROC) and area under the precision-recall curve (AUC-PR). The prediction performance of regression algorithms was evaluated by mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). A simulation modeling approach was adopted to ascertain the effectiveness of using ML-based prediction for scheduling decisions as opposed to the clinic's current strategy.

RESULTS

Among the classification models tested, stochastic gradient boosted classification tree (SGBCT) demonstrated best performance (AUC-ROC = 0.85, AUC-PR = 0.64). For positive consultation length prediction, deep neural network regressor (DNNR) resulted in lowest prediction error (MAE = 8.55, RMSE = 6.88, MAPE = 12.24). The complete two-part model (SGBCT + DNNR) outperformed the clinic's approach to consultation length estimation by achieving 50 % and 52 % reduction in RMSE and MAE, respectively. Further adopting it for appointment scheduling could reduce the patient waiting time and doctor idle time by 56 % and 52 %, respectively. Besides, several clinical insights, along with critical features for no-show and consultation length prediction, were also identified from our proof-of-concept study.

CONCLUSION

This study demonstrates that routine clinical tasks such as estimation of consultation length and no-shows can be accurately predicted using ML algorithms, and subsequently integrated into the clinical scheduling system to improve resource utilization and reduce patient waiting time.