Abstract
The increase in digital/data resources available in the healthcare sector has heightened the emphasis of applying analytics to extract information to provide solutions to problems. However, the process of providing analytic-based healthcare solutions may introduce factors that require multiple analytic techniques or a hybrid approach. Data resources can involve complexities including formatting and volume issues or multiplicity of sub-tasks in achieving a full problem solution. This work extends the previous research on AI in forecasting patient demand and adds clustering methods to identify the types of ailments that need to be treated according to diagnostic codes. The hybrid approach is applied to data from a US-based psychiatry/behavioral health center and the results indicate clustering can add value to demand forecasts established by AI by identifying the type of ailments that patients require treatment for. With this information, care providers can better optimize staffing resources to meet demand in a cost-effective and efficient way by better understanding not only the amount of patient demand, but also the type of treatment that is required for select ailments.
Similar content being viewed by others
References
Acharya UR, Sree SV, Ribeiro R, Krishnamurthi G, Marinho RT, Sanches J, Suri JS (2012) Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm. Med Phys 39(7):4255–4264
Austin PC, Lee DS, Steyerberg EW, Tu JV (2012) Regression trees for predicting mortality in patients with cardiovascular disease: what improvement is achieved by using ensemble-based methods? Biomed J 54:657–673
Ayadi MG, Bouslimi R, Akaichi J (2016) A framework for medical and health care databases and data warehouses conceptual modeling support. Netw Model Anal Health Inf Bioinf 5(1):13
Barr J (2017) Ensembles and regularization—analytics super heros, elder research data science and predictive analytics. https://www.elderresearch.com/company/blog/regularization-and-ensembles. Accessed 15 Dec 2017
Batal H, Tench J, McMillan S, Adams J, Mehler PS (2001) Predicting patient visits to an urgent care clinic using calendar variables. Acad Emerg Med 8:48–53
Catalano R (1991) The health effects of economic insecurity. Am J Public Health 18(9):1448–1552
Cios KJ, More GW (2002) Uniqueness of medical data mining. Artif Intell Med 26(1–2):1–24
Davis D, Chawla N, Blumm N, Christakis N, Barabasi A (2008) Predicting individual disease risk based on medical history. In: Proceedings of the 17th ACM conference on information and knowledge management, California, pp 769–778
Dooley D, Prause J, Ham-Rowbottom K (2000) Underemployment and depression: longitudinal relationships. J Health Soc Behav 41:421–436
Fayyad U, Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. Artif Intell 13(3):37
Fullerton KJ, Crawford V (1999) The winter bed crisis—quantifying seasonal affects on hospital bed usage. QJM 92:199–206
Ha S, Joo S (2010) A hybrid data mining method for the medical classification of chest pain. World Acad Sci Eng Technol 37:608–613
Hinton G (1992) How neural networks learn from experience. Sci Am 267(3):144–151
Holleman DR, Bowling RL, Gathy C (1996) Predicting daily visits to a walk-in clinic and emergency department using calendar and weather data. J Gen Intern Med 11(4):237–239
Hu J, Wang F, Sun J, Sorrentino R, Shahram EA (2012) Healthcare utilization analysis framework for hot spotting and contextual anomaly detection. AMIA annual symposium proceedings, pp 360–369
Jones SS, Thomas A, Evans RS, Welch SJ, Haug PJ, Snow GL (2008) Forecasting daily patient volumes in the emergency department. Acad Emerg Med 15(2):159
Jones SS, Evans RS, Allen TL, Thomas A, Haug PJ, Welch SJ, Snow GL (2009) A multivariate time series approach to modeling and forecasting demand in the emergency department. J Biomed Inf 42(1):123–139
Kudyba S (2012) Utilizing multivariate analytics for decision support to enhance patient demand forecasting. Research and Insights. International Institute of Analytics, Portland, pp 1–7
Kudyba S (2016) Healthcare informatics: increasing efficiency through technology, analytics and management. Taylor Francis, New York
Kudyba S, Perry T (2015) A data mining approach for estimating patient demand for mental health services. Health Syst 4(1):5–11
Kuo MH, Hung CM, Barnett J, Pinheiro F (2012) Assessing the feasibility of data mining techniques for early liver cancer detection. Stud Health Technol Inf 180:584–588
LaMantia MA, Platts-Mills TF, Biese K, Khandelwal C, Forbach C, Cairns CB, Busby-Whitehead J, Kizer JS (2010) Predicting hospital admission and returns to the emergency department for elderly patients. Acad Emerg Med 17:252–259
Lurie SJ, Gawinski B, Pierce D, Rousseau SJ (2006) Seasonal affective disorder. Am Fam Physician 74(9):1521–1524
Meystre S, Thibault J, Shen S, Hurdle J, South B (2010) Textractor: a hybrid system for medications and reason for their prescription extraction from clinical text documents. JAMIA 17(5):559–562
Peck JS, Benneyan JC, Nightingale DJ, Gaehde SA (2012) Predicting emergency department inpatient admissions to improve same-day patient flow. Acad Emerg Med 19:E1045–E1054
Polikar R (2006) Ensemble based systems in decision making. IEEE Circuits Syst 6(3):21–45
Rumelhart D, McClelland J (1996) Learning internal representations by error propagation. MIT Press, London
Sridhar S (2012) Improving diagnostic accuracy using agent-based distributed data mining systems. Inform Health Soc Care, September 7
Sun Y, Heng BH, Tay SY, Seow E (2011) Predicting hospital admissions at emergency department triage using routine administrative data. Acad Emerg Med 18:844–850
Tomar D, Agarwal S (2013) A survey on data mining approaches for healthcare. Int J Bio Sci Bio Technol 5(5):241–266
Vulik S, Mayer E, Darzi A (2016) Enhancing risk stratification for use in integrated care: a cluster analysis of high-risk patients in a retrospective cohort study. BMJ 6(12):e012903
Wargon M, Guidet B, Hoang TD, Hejblum G (2009) A systematic review of models for forecasting the number of emergency department visits. Emerg Med J 26(6):395
Wu X, Kotagiri R, Korb K (1998) Research and development in knowledge discovery and data mining. Second Pacific-Asia Conference, Melbourne Australia
Acknowledgements
I would like to thank Thad Perry PhD for his assistance and guidance in this research.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
There are no conflicting interests and no fundings connected to this paper.
Rights and permissions
About this article
Cite this article
Kudyba, S. A hybrid analytic approach for understanding patient demand for mental health services. Netw Model Anal Health Inform Bioinforma 7, 3 (2018). https://doi.org/10.1007/s13721-018-0164-2
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13721-018-0164-2