Crowd Wisdom Models in Demand Forecasting in China’s Health Sector: Cases from Guanxi and Jiangsu Provinces

Authors

  • Henry Asante Antwi  School of Management, Jiangsu University, 301 Xuefu Road, 212012, Zhenjiang, Jiangsu, P.R. China and Institute of Medical Insurance and Healthcare Management, Jiangsu University, Zhenjiang, Jiangsu, P.R. China
  • Zhou Lulin  School of Management, Jiangsu University, 301 Xuefu Road, 212012, Zhenjiang, Jiangsu, P.R. China and Institute of Medical Insurance and Healthcare Management, Jiangsu University, Zhenjiang, Jiangsu, P.R. China
  • Mary Opokua Ansong  School of Management, Jiangsu University, 301 Xuefu Road, 212012, Zhenjiang, Jiangsu, P.R. China and Department of Computer Science, School of Applied Science, Kumasi Polytechnic, P. O. Box 854, Kumasi, Ghana
  • Samuel Owusu Mensah  School of Management, Jiangsu University, 301 Xuefu Road, 212012, Zhenjiang, Jiangsu, P.R. China
  • Tehzeeb Mustafa  School of Management, Jiangsu University, 301 Xuefu Road, 212012, Zhenjiang, Jiangsu, P.R. China

Keywords:

Prediction Market, Time Series, Regression, Health, Demand, China

Abstract

Available literature shows that a large group's aggregated answers to questions involving quantity estimation, general world knowledge, and spatial reasoning has generally been found to be as good as, and often better than, the answer given by any of the individuals within the group. This has inspired the development of new models of crowd wisdom techniques such as prediction markets. Using cases in selected hospitals in the Guanxi and Jiangsu Provinces in China, we evaluate the efficacy of prediction markets in predicting patient arrivals in the emergency departments using an enhanced prediction market model. Our results are compared with the prediction estimate using a benchmark time series regression model on historical records. The time series regression model outperformed the prediction market in the short term prediction but the latter was superior in estimating patient flow in a long term forecasting across the healthcare facilities.

References

  1. Caporale, Guglielmo Maria, & Gil-Alana, Luis A. (2013). Long memory and fractional integration in high frequency data on the US dollar/British pound spot exchange rate. International Review of Financial Analysis, 29, 1-9.
  2. Chang, Rene WS, Lee, Berni, Jacobs, Sydney, & Lee, Brian. (1989). Accuracy of decisions to withdraw therapy in critically ill patients: Clinical judgment versus a computer model. Critical care medicine, 17(11), 1091-1097.
  3. Chatfield C (2013). The analysis of time series: an introduction. In: The Analysis of Time Series: An Introduction, Sixth Edition. London: Chapman & Hall/CRC; 2003.
  4. Chen, Long-jian, Lian, Guo-ping, & Han, Lu-jia. (2007). Prediction of human skin permeability using artificial neural network (ANN) modeling. Acta Pharmacologica Sinica, 28(4), 591.
  5. Duru, Okan, Bulut, Emrah, & Yoshida, Shigeru. (2012). A fuzzy extended DELPHI method for adjustment of statistical time series prediction: An empirical study on dry bulk freight market case. Expert Systems with Applications, 39(1), 840-848.
  6. Garcia KA, Chan PK (2012) Estimating Hospital Admissions with a Randomized Regression Approach. 2012 Presented at: 11th International Conference on Machine Learning and Applications ICMLA 2012; December 12-15, 2012; Boca Raton, FL p. 179-184.
  7. Kittur, Aniket, Chi, Ed, Pendleton, Bryan A, Suh, Bongwon, & Mytkowicz, Todd. (2007). Power of the few vs. wisdom of the crowd: Wikipedia and the rise of the bourgeoisie. World wide web, 1(2), 19.
  8. Kitzinger, Celia, & Kitzinger, Jenny. (2015). Withdrawing artificial nutrition and hydration from minimally conscious and vegetative patients: family perspectives. Journal of Medical Ethics, 41(2), 157-160.
  9. Rajakovich, David, & Vladimirov, Vladimir. (2009). Prediction markets as a medical forecasting tool: Demand for hospital services. Journal of Prediction Markets, 3(2), 78-106.
  10. Smart, Keith M, Blake, Catherine, Staines, Anthony, & Doody, Catherine. (2010). Clinical indicators of ‘nociceptive’,‘peripheral neuropathic’and ‘central’mechanisms of musculoskeletal pain. A Delphi survey of expert clinicians. Manual Therapy, 15(1), 80-87.
  11. Sniezek, Janet A, & Buckley, Timothy. (1995). Cueing and cognitive conflict in judge-advisor decision making. Organizational Behavior and Human Decision Processes, 62(2), 159-174.
  12. Wilson, Andrew M, Thabane, Lehana, & Holbrook, Anne. (2004). Application of data mining techniques in pharmacovigilance. British journal of clinical pharmacology, 57(2), 127-134.
  13. Wolfers, Justin (2008). Polls Glance Backward, Markets Peer Ahead. The Wall Street Journal, July 8, 2008.
  14. Wolfers, Justin and Eric Zitzewitz (2014). Prediction Markets. Journal of Economic Perspectives, Vol. 18, No. 2.
  15. Wong H, Wu RC, Caesar M, Abrams H, Morra D (2010). Real-time operational feedback: daily discharge rate as a novel hospital efficiency metric. Qual Saf Health Care 2010 Dec;19(6):e32.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2017-02-28

Issue

Volume 2, Issue 1, January-February-2017

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Henry Asante Antwi, Zhou Lulin, Mary Opokua Ansong, Samuel Owusu Mensah, Tehzeeb Mustafa, " Crowd Wisdom Models in Demand Forecasting in China’s Health Sector: Cases from Guanxi and Jiangsu Provinces, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 1, pp.148-155 , January-February-2017.