Functional regression-based monitoring of quality of service in hospital emergency departments

Research output: Contribution to journalArticle

Abstract

This article focuses on building a statistical monitoring scheme for service systems that experience time-varying arrivals of customers and have time-varying service rates. There is lack of research in the systematic statistical monitoring of large-scale service systems, which is critical for maintaining a high quality of service. Motivated by the emergency department at a major academic medical center, this article intends to fill this research gap and provide a practical statistical monitoring scheme capable of detecting changes in service using readily available time stamp data. The proposed method is focused on building a functional regression model based on customer arrival and departure time instances from an in-control system. The model finds the expected departure intensity function for an observed arrival intensity on any given day of operation. The mean squared difference between the expected departure intensity function and the observed departure intensity functions is used to generate an alarm indicating a significant change in service. This methodology is validated using simulation and real data case studies. The proposed method can identify patterns of inefficiency or delay in service that are hard to detect using traditional statistical monitoring algorithms. The method offers a practical approach for monitoring service systems and determining when staffing levels need to be re-optimized.

Original languageEnglish (US)
JournalIISE Transactions
DOIs
StatePublished - Jan 1 2019

Fingerprint

Quality of service
Monitoring
Control systems

Keywords

  • functional regression
  • non-stationary queues
  • Statical process control of service systems

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Cite this

@article{d6ef01f99a7b46d3afbc2a12b7a223d8,
title = "Functional regression-based monitoring of quality of service in hospital emergency departments",
abstract = "This article focuses on building a statistical monitoring scheme for service systems that experience time-varying arrivals of customers and have time-varying service rates. There is lack of research in the systematic statistical monitoring of large-scale service systems, which is critical for maintaining a high quality of service. Motivated by the emergency department at a major academic medical center, this article intends to fill this research gap and provide a practical statistical monitoring scheme capable of detecting changes in service using readily available time stamp data. The proposed method is focused on building a functional regression model based on customer arrival and departure time instances from an in-control system. The model finds the expected departure intensity function for an observed arrival intensity on any given day of operation. The mean squared difference between the expected departure intensity function and the observed departure intensity functions is used to generate an alarm indicating a significant change in service. This methodology is validated using simulation and real data case studies. The proposed method can identify patterns of inefficiency or delay in service that are hard to detect using traditional statistical monitoring algorithms. The method offers a practical approach for monitoring service systems and determining when staffing levels need to be re-optimized.",
keywords = "functional regression, non-stationary queues, Statical process control of service systems",
author = "Devashish Das and Pasupathy, {Kalyan S} and Curtis Storlie and Mustafa Sir",
year = "2019",
month = "1",
day = "1",
doi = "10.1080/24725854.2018.1536303",
language = "English (US)",
journal = "IISE Transactions",
issn = "2472-5854",
publisher = "Taylor and Francis Ltd.",

}

TY - JOUR

T1 - Functional regression-based monitoring of quality of service in hospital emergency departments

AU - Das, Devashish

AU - Pasupathy, Kalyan S

AU - Storlie, Curtis

AU - Sir, Mustafa

PY - 2019/1/1

Y1 - 2019/1/1

N2 - This article focuses on building a statistical monitoring scheme for service systems that experience time-varying arrivals of customers and have time-varying service rates. There is lack of research in the systematic statistical monitoring of large-scale service systems, which is critical for maintaining a high quality of service. Motivated by the emergency department at a major academic medical center, this article intends to fill this research gap and provide a practical statistical monitoring scheme capable of detecting changes in service using readily available time stamp data. The proposed method is focused on building a functional regression model based on customer arrival and departure time instances from an in-control system. The model finds the expected departure intensity function for an observed arrival intensity on any given day of operation. The mean squared difference between the expected departure intensity function and the observed departure intensity functions is used to generate an alarm indicating a significant change in service. This methodology is validated using simulation and real data case studies. The proposed method can identify patterns of inefficiency or delay in service that are hard to detect using traditional statistical monitoring algorithms. The method offers a practical approach for monitoring service systems and determining when staffing levels need to be re-optimized.

AB - This article focuses on building a statistical monitoring scheme for service systems that experience time-varying arrivals of customers and have time-varying service rates. There is lack of research in the systematic statistical monitoring of large-scale service systems, which is critical for maintaining a high quality of service. Motivated by the emergency department at a major academic medical center, this article intends to fill this research gap and provide a practical statistical monitoring scheme capable of detecting changes in service using readily available time stamp data. The proposed method is focused on building a functional regression model based on customer arrival and departure time instances from an in-control system. The model finds the expected departure intensity function for an observed arrival intensity on any given day of operation. The mean squared difference between the expected departure intensity function and the observed departure intensity functions is used to generate an alarm indicating a significant change in service. This methodology is validated using simulation and real data case studies. The proposed method can identify patterns of inefficiency or delay in service that are hard to detect using traditional statistical monitoring algorithms. The method offers a practical approach for monitoring service systems and determining when staffing levels need to be re-optimized.

KW - functional regression

KW - non-stationary queues

KW - Statical process control of service systems

UR - http://www.scopus.com/inward/record.url?scp=85061224781&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85061224781&partnerID=8YFLogxK

U2 - 10.1080/24725854.2018.1536303

DO - 10.1080/24725854.2018.1536303

M3 - Article

AN - SCOPUS:85061224781

JO - IISE Transactions

JF - IISE Transactions

SN - 2472-5854

ER -