Modeling and forecasting monthly patient volume at a primary health care clinic using univariate time-series analysis

Abdel-Aal, R.E.; Mangoud, Abdallah M

doi:10.1016/s0169-2607(98)00032-7

Cited by 44 publications

(49 citation statements)

References 7 publications

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“…R 2 values were significant, even if in the best cases the models failed to account for approximately 25% of the variability. MAPE was 4.23%—a good value—in a study of a healthcare clinic in which patient visits were similar from week to week,22 with little random variation, compared with approximately 10% for ED visits 17. When available, MAPE and RMSE ranged from 4.2% to 14.4%, indicating a good statistical predictability.…”

Section: Resultsmentioning

confidence: 94%

“…Daily data were used in five articles14 17 18 20 21 and monthly data in two articles 15 22. The data collection period varied widely, from a few months14 19 to nearly 10 years 16 17 22. Studies that used linear regression models14 18 20 21 consistently found that the day of the week was the best predictor of patient visits.…”

Section: Resultsmentioning

confidence: 99%

“…From the reference lists of the selected articles, we identified two additional, older studies 20 21. Finally, another article22 was found by using the Science Citation Index (Web of Science, Thomson Reuters, New York, USA) to look for publications citing any of the eight selected articles. Our study is thus based on nine articles.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

A systematic review of models for forecasting the number of emergency department visits

Wargon

Guidet²,

Hoang³

et al. 2009

Emergency Medicine Journal

123

131

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The ability to predict patient visits to emergency departments (ED) is crucial for designing strategies aimed at avoiding overcrowding. A good working knowledge of the mathematical models used to predict patient volume and of their results is therefore essential. Articles retrieved by a Medline search were reviewed for studies designed to predict patient attendance at ED or walk-in clinics. Nine studies were identified. Most of the models used to predict patient volume were either linear regression models including calendar variables or time series models. These models explained 31-75% of patient-volume variability. Although the day of the week had the strongest effect, this variable explained only part of the variability. Other causes of this variability are to be defined. However, the performance of the models was good, with errors ranging from 4.2% to 14.4%. Adding meteorological data failed to improve model performance. The mathematical methods developed to predict ED visits have a low rate of error, but the prediction of daily patient visits should be used carefully and therefore does not allow day-to-day adjustments of staff. ED directors or managers should be aware of the model limitations. These models should certainly be used on a larger scale to assess future needs.

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Section: Resultsmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A systematic review of models for forecasting the number of emergency department visits

Wargon

Guidet²,

Hoang³

et al. 2009

Emergency Medicine Journal

123

131

View full text Add to dashboard Cite

show abstract

“…After applying in studied case, authors found improvements in quality performance of hospital. [1] were researching patient volume forecasting methods in walk-in outpatient clinics. On a large set of data they compared implementation of two forecasting models -autoregressive and growth curve of means.…”

Section: Literature Reviewmentioning

confidence: 99%

Health Care Provider Processes Analysis

Kawczynski

Taisch

2010

IFIP Advances in Information and Communication Technology

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Abstract. In every society there is a need for an efficient health care system. This is a case study paper that summarizes process analysis performed at a US provider clinic. This paper provides an analysis of factors (arrival accuracy and no shows) influencing main processes within the clinic. The numerical relations between influencing factors and key processes are exhibited. Moreover, the abilities of a health care provider to deal with variations of arrival time are exhibited. The predicted probabilities for arrival accuracy and no shows are discussed. The paper provides an interesting statistical approach to analyze operations of a health care provider, which can be beneficial for stakeholders of the clinic.

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“…For instance, Milner (1997) modeled attendances at accident and emergency departments by one-off original ARIMA. Abdel-Aal and Mangoud (1998) applied an ARIMA model in forecasting the monthly patient volume at a health care clinic. Diaz et al (2001) used ARIMA to forecast emergency hospital admissions.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Patient Visits to Hospitals using a WD&ANN-based Decomposition and Ensemble Model

Hang

Tang

et al. 2017

EURASIA J MATH SCI T

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Forecasting the number of patient visits to hospitals has aroused an increasingly large interest from both theoretic and application perspectives. To enhance the accuracy of forecasting hospital visits, this paper proposes a hybrid approach by coupling wavelet decomposition (WD) and artificial neural network (ANN) under the framework of "decomposition and ensemble". In this model, the WD is first employed to decompose the original monthly data of the number of patient visits to hospitals into several components and one residual term. Then, the ANN as a powerful prediction tool is implemented to fit each decomposed component and generate individual prediction results. Finally, all individual prediction values are fused into the final prediction output by simple addition method. For illustration and verification, four sets of monthly series data of the number of patient visits to hospitals are used as the sample data, and the results show that the proposed model can obtain significantly more accurate forecasting results than all considered popular forecasting techniques.

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Modeling and forecasting monthly patient volume at a primary health care clinic using univariate time-series analysis

Cited by 44 publications

References 7 publications

A systematic review of models for forecasting the number of emergency department visits

A systematic review of models for forecasting the number of emergency department visits

Health Care Provider Processes Analysis

Forecasting Patient Visits to Hospitals using a WD&ANN-based Decomposition and Ensemble Model

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Modeling and forecasting monthly patient volume at a primary health care clinic using univariate time-series analysis

Cited by 44 publications

References 7 publications

A systematic review of models for forecasting the number of emergency department visits

A systematic review of models for forecasting the number of emergency department visits

Health Care Provider Processes Analysis

Forecasting Patient Visits to Hospitals using a WD&amp;ANN-based Decomposition and Ensemble Model

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Product

Resources

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Forecasting Patient Visits to Hospitals using a WD&ANN-based Decomposition and Ensemble Model