TY - GEN
T1 - A Whole-System Approach to Identify the Sources of Variation in Patient Flow
AU - Arbabzadeh, Nasim
AU - Jafari, Mohsen A.
AU - Seyed, Kian
PY - 2014
Y1 - 2014
N2 - The main objective of this paper is to develop a quantitative framework to identify the main sources of variation in patient flow. Since 1983, under Health Care Financing Administration (HCFA)'s system, generally referred to as the Prospective Payment System (PPS), each hospital inpatient is classified into one of around 500 Diagnosis-Related Groups (DRGs), and the hospital is paid the amount that HCFA has assigned to each DRG. In other words, irrespective of what the hospital charges for, it will be paid only a fixed price for each DRG through major reimbursement plans. Therefore, it is logical to expect that by reducing the within DRG discrepancies, hospitals can cut cost and improve patient safety and satisfaction. In order to reach this goal the first step is to identify the main sources of variations. In this paper, we apply classical quality/process control tools and well known data mining methods to determine significant factors affecting the patient sequence among tens or hundreds of potential factors.
AB - The main objective of this paper is to develop a quantitative framework to identify the main sources of variation in patient flow. Since 1983, under Health Care Financing Administration (HCFA)'s system, generally referred to as the Prospective Payment System (PPS), each hospital inpatient is classified into one of around 500 Diagnosis-Related Groups (DRGs), and the hospital is paid the amount that HCFA has assigned to each DRG. In other words, irrespective of what the hospital charges for, it will be paid only a fixed price for each DRG through major reimbursement plans. Therefore, it is logical to expect that by reducing the within DRG discrepancies, hospitals can cut cost and improve patient safety and satisfaction. In order to reach this goal the first step is to identify the main sources of variations. In this paper, we apply classical quality/process control tools and well known data mining methods to determine significant factors affecting the patient sequence among tens or hundreds of potential factors.
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U2 - 10.1007/978-3-319-01848-5_16
DO - 10.1007/978-3-319-01848-5_16
M3 - Conference contribution
AN - SCOPUS:84893477535
SN - 9783319018478
T3 - Springer Proceedings in Mathematics and Statistics
SP - 203
EP - 214
BT - Proceedings of the International Conference on Health Care Systems Engineering
PB - Springer New York LLC
T2 - International Conference on Health Care Systems Engineering, HCSE 2013
Y2 - 22 May 2013 through 24 May 2013
ER -