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

Srinivas, Sharan; Salah, Haya

doi:10.1016/j.ijmedinf.2020.104290

Cited by 39 publications

(18 citation statements)

References 21 publications

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“…These characteristics make them particularly appealing for developing risk prediction algorithms that can be deployed at the level of populations for health system planning. While there is an increasing number of risk prediction models intended for clinical use in individuals in an ambulatory setting,25–28 there are few examples of a single, unified model that can be deployed on routinely collected data to regularly support population health and health system management 29. Databases with analogous AHD are available in most single-payer healthcare systems such as the UK, Australia and New Zealand.…”

Section: Introductionmentioning

confidence: 99%

Predicting hospitalisations related to ambulatory care sensitive conditions with machine learning for population health planning: derivation and validation cohort study

Harish

Gutierrez³

et al. 2022

BMJ Open

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ObjectiveTo predict older adults’ risk of avoidable hospitalisation related to ambulatory care sensitive conditions (ACSC) using machine learning applied to administrative health data of Ontario, Canada.Design, setting and participantsA retrospective cohort study was conducted on a large cohort of all residents covered under a single-payer system in Ontario, Canada over the period of 10 years (2008–2017). The study included 1.85 million Ontario residents between 65 and 74 years old at any time throughout the study period.Data sourcesAdministrative health data from Ontario, Canada obtained from the (ICES formely known as the Institute for Clinical Evaluative Sciences Data Repository.Main outcome measuresRisk of hospitalisations due to ACSCs 1 year after the observation period.ResultsThe study used a total of 1 854 116 patients, split into train, validation and test sets. The ACSC incidence rates among the data points were 1.1% for all sets. The final XGBoost model achieved an area under the receiver operating curve of 80.5% and an area under precision–recall curve of 0.093 on the test set, and the predictions were well calibrated, including in key subgroups. When ranking the model predictions, those at the top 5% of risk as predicted by the model captured 37.4% of those presented with an ACSC-related hospitalisation. A variety of features such as the previous number of ambulatory care visits, presence of ACSC-related hospitalisations during the observation window, age, rural residence and prescription of certain medications were contributors to the prediction. Our model was also able to capture the geospatial heterogeneity of ACSC risk in Ontario, and especially the elevated risk in rural and marginalised regions.ConclusionsThis study aimed to predict the 1-year risk of hospitalisation from ambulatory-care sensitive conditions in seniors aged 65–74 years old with a single, large-scale machine learning model. The model shows the potential to inform population health planning and interventions to reduce the burden of ACSC-related hospitalisations.

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Section: Introductionmentioning

confidence: 99%

Predicting hospitalisations related to ambulatory care sensitive conditions with machine learning for population health planning: derivation and validation cohort study

Harish

Gutierrez³

et al. 2022

BMJ Open

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“…Further, these studies did not explain the DOS model’s predictions. Studies whose feature set is similar to ours [ 25 , 26 ] developed ML models to predict length of physician appointment and length of stay in the emergency department, however, they have not analyzed DOS, nor have they generated explanations for their predictions.…”

Section: Introductionmentioning

confidence: 99%

Surgery duration: Optimized prediction and causality analysis

et al. 2022

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Accurate estimation of duration of surgery (DOS) can lead to cost-effective utilization of surgical staff and operating rooms and decrease patients’ waiting time. In this study, we present a supervised DOS nonlinear regression prediction model whose accuracy outperforms earlier results. In addition, unlike previous studies, we identify the features that influence DOS prediction. Further, in difference from others, we study the causal relationship between the feature set and DOS. The feature sets used in prior studies included a subset of the features presented in this study. This study aimed to derive influential effectors of duration of surgery via optimized prediction and causality analysis. We implemented an array of machine learning algorithms and trained them on datasets comprising surgery-related data, to derive DOS prediction models. The datasets we acquired contain patient, surgical staff, and surgery features. The datasets comprised 23,293 surgery records of eight surgery types performed over a 10-year period in a public hospital. We have introduced new, unstudied features and combined them with features adopted from previous studies to generate a comprehensive feature set. We utilized feature importance methods to identify the influential features, and causal inference methods to identify the causal features. Our model demonstrates superior performance in comparison to DOS prediction models in the art. The performance of our DOS model in terms of the mean absolute error (MAE) was 14.9 minutes. The algorithm that derived the model with the best performance was the gradient boosted trees (GBT). We identified the 10 most influential features and the 10 most causal features. In addition, we showed that 40% of the influential features have a significant (p-value = 0.05) causal relationship with DOS. We developed a DOS prediction model whose accuracy is higher than that of prior models. This improvement is achieved via the introduction of a novel feature set on which the model was trained. Utilizing our prediction model, hospitals can improve the efficiency of surgery schedules, and by exploiting the identified causal relationship, can influence the DOS. Further, the feature importance methods we used can help explain the model’s predictions.

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“…e application of machine learning techniques in topics related to healthcare has been varied. For example, Srinivas and Salah [20] applied classification techniques, Random Forest, and deep neuronal networks to estimate consultation length and to predict noshows at a cardiology clinic; in [16], artificial neural networks models and multiple regression models were used to forecast blood supply at blood centers; in [21], supervised machine learning classifiers were induced to develop predictive models that identify the risk of a patient no-show to a clinical site; in [22], the authors compared four ML algorithms, namely, logistic regression, Random Forest, gradient boosting machine, and artificial neural networks to identify which one has the best performance to predict the patientspecific risk of late arrival to some ambulatory care clinics. In general, the research works report an effectiveness of around 80% to predict the event of interest, which provide evidence of the viability to apply ML techniques to help in healthcare problems.…”

Section: Introductionmentioning

confidence: 99%

Comparing Machine Learning Methods to Improve Fall Risk Detection in Elderly with Osteoporosis from Balance Data

Cuaya-Simbro

Sanpablo

Morales

et al. 2021

Journal of Healthcare Engineering

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Falls are a multifactorial cause of injuries for older people. Subjects with osteoporosis are particularly vulnerable to falls. We study the performance of different computational methods to identify people with osteoporosis who experience a fall by analysing balance parameters. Balance parameters, from eyes open and closed posturographic studies, and prospective registration of falls were obtained from a sample of 126 community-dwelling older women with osteoporosis (age 74.3 ± 6.3) using World Health Organization Questionnaire for the study of falls during a follow-up of 2.5 years. We analyzed model performance to determine falls of every developed model and to validate the relevance of the selected parameter sets. The principal findings of this research were (1) models built using oversampling methods with either IBk (KNN) or Random Forest classifier can be considered good options for a predictive clinical test and (2) feature selection for minority class (FSMC) method selected previously unnoticed balance parameters, which implies that intelligent computing methods can extract useful information with attributes which otherwise are disregarded by experts. Finally, the results obtained suggest that Random Forest classifier using the oversampling method to balance the data independent of the set of variables used got the best overall performance in measures of sensitivity (>0.71), specificity (>0.18), positive predictive value (PPV >0.74), and negative predictive value (NPV >0.66) independent of the set of variables used. Although the IBk classifier was built with oversampling data considering information from both eyes opened and closed, using all variables got the best performance (sensitivity >0.81, specificity >0.19, PPV = 0.97, and NPV = 0.66).

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

Cited by 39 publications

References 21 publications

Predicting hospitalisations related to ambulatory care sensitive conditions with machine learning for population health planning: derivation and validation cohort study

Predicting hospitalisations related to ambulatory care sensitive conditions with machine learning for population health planning: derivation and validation cohort study

Surgery duration: Optimized prediction and causality analysis

Comparing Machine Learning Methods to Improve Fall Risk Detection in Elderly with Osteoporosis from Balance Data

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