Development of a revisit prediction model for the outpatient in a hospital

Hwang, Sung Wan; Lee, Hae Jong

doi:10.4258/jksmi.2008.14.2.137

Cited by 4 publications

(10 citation statements)

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“…Considering that patients in Korea consist of the following three types -1) those in an emergency situation, who require immediate treatment, 2) non-emergency patients who seek treatment on holidays or at night, and 3) non-emergency patients who seek outpatient treatment without a doctor's reference- it can be inferred that the non-emergency patients are impacted most profoundly in this situation. In fact, Hwang et al [ 14 ] has reports that 2,276 out of 4,273 new patients (53.3%) obtain treatment at an ED. As a consequence, considering the number of non-emergency patients who visit the ED, it is necessary to take into account weather factors when constructing demand forecasting models for emergency medical centers.…”

Section: Discussionmentioning

confidence: 99%

“…A variety of measures have been taken thus far to address this ED crowding problem [ 9 - 11 ], including staff supplementation, expansions of beds and spaces, diversification of test equipment, the establishment of walk-in clinics for the treatment of light illnesses, hallways, operation of observation units, and allocations of staff and resources according to demand [ 12 , 13 ]. Several studies concerning demand forecasting for allocation of staff and resources have been already been conducted [ 14 - 16 ]. In the emergency medical services field, Spencer et al previously attempted to forecast demand in the ED using a variety of time series analysis methods [ 17 ], and Sun et al [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Daily Patient Numbers for a Regional Emergency Medical Center using Time Series Analysis

2010

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ObjectivesTo develop and evaluate time series models to predict the daily number of patients visiting the Emergency Department (ED) of a Korean hospital.MethodsData were collected from the hospital information system database. In order to develop a forecasting model, we used, 2 years of data from January 2007 to December 2008 data for the following 3 consecutive months were processed for validation. To establish a Forecasting Model, calendar and weather variables were utilized. Three forecasting models were established: 1) average; 2) univariate seasonal auto-regressive integrated moving average (SARIMA); and 3) multivariate SARIMA. To evaluate goodness-of-fit, residual analysis, Akaike information criterion and Bayesian information criterion were compared. The forecast accuracy for each model was evaluated via mean absolute percentage error (MAPE).ResultsThe multivariate SARIMA model was the most appropriate for forecasting the daily number of patients visiting the ED. Because it's MAPE was 7.4%, this was the smallest among the models, and for this reason was selected as the final model.ConclusionsThis study applied explanatory variables to a multivariate SARIMA model. The multivariate SARIMA model exhibits relativelyhigh reliability and forecasting accuracy. The weather variables play a part in predicting daily ED patient volume.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Daily Patient Numbers for a Regional Emergency Medical Center using Time Series Analysis

2010

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“…In previous study, the loyalty of customers was shown as being affected by selectable treatments [1]. In selectable treatments, patients choose doctors who they want to receive treatments from and are based on the relationship of the doctor and patient.…”

Section: Discussionmentioning

confidence: 99%

“…According to recent changes of medical service from treatment focus to prevention focus, the dominant market of providing treatment services is being replaced by the market of consumers visiting hospitals to receive prevention services [ 1 ]. Also clients now select hospitals more freely by collecting information from various sources and therefore, hospitals have a higher threat to their business environment due to leaving patients [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Data Mining Application in Customer Relationship Management for Hospital Inpatients

Lee

2012

Healthc Inform Res

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ObjectivesThis study aims to discover patients loyal to a hospital and model their medical service usage patterns. Consequently, this study proposes a data mining application in customer relationship management (CRM) for hospital inpatients.MethodsA recency, frequency, monetary (RFM) model has been applied toward 14,072 patients discharged from a university hospital. Cluster analysis was conducted to segment customers, and it modeled the patterns of the loyal customers' medical services usage via a decision tree.ResultsPatients were divided into two groups according to the variables of the RFM model and the group which had significantly high frequency of medical use and expenses was defined as loyal customers, a target market. As a result of the decision tree, the predictable factors of the loyal clients were; length of stay, certainty of selectable treatment, surgery, number of accompanying treatments, kind of patient room, and department from which they were discharged. Particularly, this research showed that when a patient within the internal medicine department who did not have surgery stayed for more than 13.5 days, their probability of being a classified as a loyal customer was 70.0%.ConclusionsTo discover a hospital's loyal patients and model their medical usage patterns, the application of data-mining has been suggested. This paper suggests practical use of combining segmentation, targeting, positioning (STP) strategy and the RFM model with data-mining in CRM.

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“…In addition, full-time attending physicians in the PED decreased length of stay and the mortality rate in the emergency department [10]. The study also raised the need for indepth analysis on emergency department patients [11].…”

Section: Introductionmentioning

confidence: 92%

The Impact of Full-Time Attending Physician in the Pediatric Emergency Department

Kim

2017

Soonchunhyang Med Sci

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The purpose of this study was to indirectly evaluate the impact of full-time pediatric emergency physician on the quality of medical services. Methods: This study was a retrospective study that was performed by using the medical records of new patients aged less than 16 years old, residing in Cheonan and Asan, who visited the pediatric emergency department (PED) of Soonchyunhayng University Cheonan Hospital from December 1, 2011 to December 31, 2016. The correlations between the outpatient revisit following the emergency department visit and socio-demographic characteristics, number of physicians, reasons for the visit, severity based on the Korean Triage and Acuity System, length of stay, and whether or not tests were conducted were analyzed. Results: Total of 30,728 patients were included. The significant factors affecting the outpatient revisit were the patient's age (P= 0.00; odds ratio [OR], 1.004), number of physicians (P= 0.03; OR, 1.066), and length of stay (P= 0.00; OR, 1.004). When the number of physicians was appropriate, the length of stay was reduced (P= 0.00; OR, 0.999), and the rate of test (P= 0.00; OR, 0.835) were reduced. Conclusion: It is believed that having an appropriate number of physicians will indirectly increase the patient's satisfaction, and be helpful in improving the quality of the medical services in PED.

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Development of a revisit prediction model for the outpatient in a hospital

Cited by 4 publications

References 0 publications

Prediction of Daily Patient Numbers for a Regional Emergency Medical Center using Time Series Analysis

Prediction of Daily Patient Numbers for a Regional Emergency Medical Center using Time Series Analysis

Data Mining Application in Customer Relationship Management for Hospital Inpatients

The Impact of Full-Time Attending Physician in the Pediatric Emergency Department

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