An Application of the Short-Term Forecasting with Limited Data in the Healthcare Traveling Industry

Dang, Hoang-Sa; Huang, Yun‐Feng; Wang, Chia–Nan; Nguyen, Thuy-Mai-Trinh

doi:10.3390/su8101037

Cited by 23 publications

(12 citation statements)

References 28 publications

(35 reference statements)

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“…Since then, it finds wide-ranging applications in various research fields [ 38 , 39 ]. GM (1,1) is a well-studied prediction model based on the Grey System theory, which is known as a first-order model with one variable [ 40 ]. Compared with the traditional statistical forecast models, the historical data requirement for model construction of grey predictions is very flexible, which can be minimized to four sample points.…”

Section: Methods and Datamentioning

confidence: 99%

Prediction and Decomposition of Efficiency Differences in Chinese Provincial Community Health Services

Liu

Mohiuddin

2018

IJERPH

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The objective of this paper is to analyze the provincial efficiency of the Chinese community health care service and its differences. This study allows us to predict the provincial differences in the efficiency of the Chinese community health care service from 2017 to 2026. This study analyzes the contributions of inter-regional and intra-regional differences in the total efficiency difference. We use the Super-SBM (Slacks-based Model) data envelopment analysis (DEA) model, Grey Model GM (1,1) for grey prediction, and the group-based Theil index decomposition method to study Chinese provincial panel data from 2008 to 2016. Results show that a fluctuating trend existed in the average provincial efficiency of community health services from 2008 to 2016. The community health services in a considerable number of provincial areas were inefficient. This study also reveals that there existed apparent inter-provincial differences in efficiency in Chinese community health services. The inter-provincial differences of the efficiency of Chinese community health services revealed by the Theil index declined at a relatively slow pace. With regard to the provincial efficiency difference of the Chinese community health service, the intra-regional efficiency difference is the most important structural reason for the overall efficiency difference, which explains the overall difference to a large extent. The inter-regional efficiency difference among the eastern, central, and western regions becomes the secondary structural reason, which should not be ignored. In conclusion, focus should be put on restructuring the investments into medical resources for community health service in each Chinese province. More attentions should be put into narrowing the inter-regional efficiency differences of the Chinese provincial community health service. The strategies targeted at reducing the inter-regional efficiency differences should not be ignored, so as to facilitate the improvement of overall efficiency of the Chinese community health service.

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Section: Methods and Datamentioning

confidence: 99%

Prediction and Decomposition of Efficiency Differences in Chinese Provincial Community Health Services

Liu

Mohiuddin

2018

IJERPH

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“…Trends from the medical tourism industry in the Asia-Pacific region. 1 Data collected from the following sources: [19,21,22].…”

Section: Int J Environ Res Public Health 2020 17 X 3 Of 23mentioning

confidence: 99%

“…In addition, for the market size estimation of the medical tourist industry in general or in Taiwan in particular, there is a small number of studies addressed to propose an effective forecasting model for the medical tourism industry. A recent study of [21] applied three forecasting models to predict the healthcare travelling industry, including the Lotka-Vottera model, the exponential smoothing model, and the Grey model-GM (1,1). While, literature in [35] applied time series models to predict the foreign patients' arrivals in India up to 2015 based on the data of patient origin.…”

Section: Int J Environ Res Public Health 2020 17 X 3 Of 23mentioning

confidence: 99%

“…Grey forecasting is a high accuracy, short-term forecasting technique, which is successful in its application to various fields [38,49]. It can be employed in situations with limited data-down to as little as four observations [21]. For this reason, Grey forecasting can be used as an effective tool for understanding the uncertain environment under the limitation of information [50].…”

Section: Literatures Review On Grey System Theorymentioning

confidence: 99%

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Grey System Theory in the Study of Medical Tourism Industry and Its Economic Impact

Dang

Nguyen

Wang

et al. 2020

IJERPH

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The Asia-Pacific region is known as a favorite destination for global medical travelers due to its medical expertise, innovative technology, safety, attractive tourism destination and cost advantage in the recent decade. This study contributes to propose an approach which effectively assesses performance of medical tourism industry based on considering the economic impact factors as well as provides a conceptual framework for the industry analysis. Grey system theory is utilized as a major analyzing approach. According to that, factors impact on the sustainable development of medical tourism in Asia-Pacific region could be identified. The performance of each destination in this region was simultaneously revealed. The results presented an overall perspective of the medical tourism industry in the scope of the Asia-Pacific region, and in Taiwan particularly. Data was collected on six major destinations including Singapore, Thailand, India, South Korea, Malaysia and Taiwan. The results proved that tourism sources and healthcare medical infrastructures play a crucial role in promoting the healthcare travel industry, while cost advantage and marketing effectiveness were less considered. In addition, performance analyse indicated that Thailand has a good performance and stands in the top ranking, followed by Malaysia, India, Singapore, South Korea and Taiwan, respectively. The revenue of Taiwan has increased slowly in the last six years, with a market worth approximately NT$20.5 billion, and the number of medical travelers is expected to increase to 777,523 by 2025. The findings of this study are expected to provide useful information for the medical tourism industry and related key players in strategic planning.

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“…The results obtained from these various models show that the forecasting accuracy of the GM(1,1) is better than the other models. In addition, [6] used three models including the exponential smoothing model, the Grey model GM (1,1), and the modified Lotka-Volterra model (L.V.) to conduct forecasting analyses based on the data of foreign patients from 2001 to 2013 in six different places.…”

Section: Introductionmentioning

confidence: 99%

Forecasts of female breast cancer referrals using grey prediction model GM(1,1)

Iqelan¹

2017

ams

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Breast Cancer forecasting is an important matter for governments, health sector investors, and other related companies. Although there are different forecasting models, choosing the suitable model is of great significance. This paper focuses on utilizing the performance of grey prediction model GM(1,1) to the monthly total number of women referrals for Breast Cancer in Gaza Strip\Palestine from January 2002 to December 2016. The results show that the GM(1,1) model exhibits good forecasting ability according to the MAPE criteria. Moreover, the forecasting results are compared with the results of exponential smoothing state space (ETS) and ARIMA models. The three techniques do similarly well in forecasting process. However, GM(1,1) outperforms the ETS and ARIMA techniques according to forecasting error accuracy measure MAPE.

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An Application of the Short-Term Forecasting with Limited Data in the Healthcare Traveling Industry

Cited by 23 publications

References 28 publications

Prediction and Decomposition of Efficiency Differences in Chinese Provincial Community Health Services

Prediction and Decomposition of Efficiency Differences in Chinese Provincial Community Health Services

Grey System Theory in the Study of Medical Tourism Industry and Its Economic Impact

Forecasts of female breast cancer referrals using grey prediction model GM(1,1)

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