Predicting Cases of Ambulatory Care Sensitive Conditions

W. Haque, D. C. Finke


Proper management of ambulatory care sensitive conditions does not only enhance patient care, but also reduces healthcare costs by minimizing hospitalizations. In order to strategically allocate resources, it is essential to rely on informed forecasting decisions. Among other factors, the healthcare data is deeply affected by seasonality, granularity, missing information and the sheer volume. We have used the ten-year history from a Discharge Abstract Database to build predictive models and perform multi-dimensional analysis on key metrics such as age, gender, and demographics. The valuable insights suggest that investments in some areas appear to be working and should continue whereas other areas suggest a need for reallocation of resources. The results have been confirmed using two distinct time series models. The forecasted data is integrated with existing data and presented to users through data visualization tools with capabilities to drill down to reports of finer granularity. It is observed that though some diagnoses appear to be on an upward trend in prevalence over the next few years, other ACSC-related diagnoses will continue to occur with either the same or slightly less frequency.


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Paper Citation

in Harvard Style

Haque W. and Finke D. (2013). Predicting Cases of Ambulatory Care Sensitive Conditions . In Proceedings of the 2nd International Conference on Data Technologies and Applications - Volume 1: DATA, ISBN 978-989-8565-67-9, pages 72-79. DOI: 10.5220/0004479800720079

in Bibtex Style

author={W. Haque and D. C. Finke},
title={Predicting Cases of Ambulatory Care Sensitive Conditions},
booktitle={Proceedings of the 2nd International Conference on Data Technologies and Applications - Volume 1: DATA,},

in EndNote Style

JO - Proceedings of the 2nd International Conference on Data Technologies and Applications - Volume 1: DATA,
TI - Predicting Cases of Ambulatory Care Sensitive Conditions
SN - 978-989-8565-67-9
AU - Haque W.
AU - Finke D.
PY - 2013
SP - 72
EP - 79
DO - 10.5220/0004479800720079