Support for a Waiting Room Time Tracker: A Survey of Patients Waiting in an Urban ED

Shaikh, Sanober; Witting, Michael D.; Winters, Michael E.; Brodeur, Michael N.; Jerrard, David A.

doi:10.1016/j.jemermed.2012.02.053

Cited by 19 publications

(18 citation statements)

References 5 publications

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“…Clinically suitable results were achieved regarding the accuracy metric, by inducing the DM technique Decision Trees on data from scenario 0, achieving approximately 96% of accuracy, when using a five level target approach. The best DM models to predict the triage waiting times can also be used to implement a time tracker in the emergency room, since studies indicate that the awareness of waiting times increases the individuals' satisfaction and reduces anxiety and unattended leaves [26]. Accordingly, the best models and the results achieved will be included in the IDSS and the BI platforms, allowing the improvement of the GO patient flow and satisfaction, supporting the physicians' decision-making, leading to quality improvements in the maternity care.…”

Section: Discussionmentioning

confidence: 99%

Predicting Triage Waiting Time in Maternity Emergency Care by Means of Data Mining

Pereira

Torres

Portela

et al. 2016

New Advances in Information Systems and Technologies

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Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times.

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Section: Discussionmentioning

confidence: 99%

Predicting Triage Waiting Time in Maternity Emergency Care by Means of Data Mining

Pereira

Torres

Portela

et al. 2016

New Advances in Information Systems and Technologies

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“…Providing real-time realistic information about wait times and providing reasons for delays could also improve patient satisfaction with wait time 5 6 18 20 21. Shaikh et al 22 have shown that most respondents would prefer a display with a time tracker to provide information about their wait time when visiting the emergency department. A display, automatically presenting the phase of the patient to the escorts and the estimated wait times, could also reduce the number of questions concerning the patient's progress, stimulate active involvement and actions of the patients/escorts (eg, go to the intake room themselves without a nurse assisting them) and reduce anxiety 13 20 22.…”

Section: Discussionmentioning

confidence: 99%

“…Shaikh et al 22 have shown that most respondents would prefer a display with a time tracker to provide information about their wait time when visiting the emergency department. A display, automatically presenting the phase of the patient to the escorts and the estimated wait times, could also reduce the number of questions concerning the patient's progress, stimulate active involvement and actions of the patients/escorts (eg, go to the intake room themselves without a nurse assisting them) and reduce anxiety 13 20 22. Kim et al 23 have shown that mean wait times were shortened when patients were automatically allocated to examination rooms; this also increased workflow efficiency by reducing staff effort and consequently, reducing costs.…”

Section: Discussionmentioning

confidence: 99%

Tracking surgical day care patients using RFID technology

2015

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ObjectiveMeasure wait times, characterise current information flow and define requirements for a technological information system that supports the patient's journey.DesignFirst, patients were observed during eight random weekdays and the durations of actions performed at each phase of the surgical trajectory were measured. Patients were grouped into patients receiving general anaesthesia or local (or topical) anaesthesia. Second (active) Radio Frequency IDentification (RFID) technology was installed and patients were tracked during 52 weekdays. Length of hospital stay, length of stay and wait times per phase, and differences in wait times between the two types of administered anaesthesia were analysed. Third, interviews were conducted to characterise the current information flow between staff, and between staff and escorts (patients’ family/friends escorting them throughout their journey).ResultsObservations (198 patients) showed that the average duration of actions for general anaesthesia patients took longer than for local anaesthesia patients, especially at the recovery phase (general anaesthesia: 0h16, local anaesthesia: 0h01).RFID tracking (622 patients): Significant differences were seen for wait times between general and local anaesthesia patients at: preoperative ward (p=0.014), recovery (p<0.001) and postoperative ward (p<0.001). The average percentage of wait time during the entire hospital stay ranged from 64% to 68% (with variation in groups being substantial).Interviews (30 escorts, 9 ward nurses and 8 holding/recovery nurses): Escorts did not use the current information system and ward nurses indicated problems with exchanging information concerning bringing/picking up patients to/from the holding/recovery that resulted in unnecessary wait times for some patients (mainly local anaesthesia patients).ConclusionsMost time spent in hospital is wait time. A Patient Tracking System was designed to automatically display the phase in which a patient is in. It provides transparency for patients and staff in the surgical trajectory and is expected to reduce intermittent communication, improve patient flow, reduce wait times and improve patient and staff satisfaction.

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“…General services Antonides, Verhoef, and van Aalst (2002); Luo, J. Liberatore, L. Nydick, B. Chung, and Sloane (2004) Internet services Hong, Hess, and Hardin (2013) ED services Boudreaux and O'Hea (2004); Hedges (2002); Nanda et al (2012); Shaikh, Witting, Winters, Brodeur, and Jerrard (2013); Soremekun, Takayesu, and Bohan (2011); Thompson, Yarnold, Adams, and Spacone (1996); Thompson, Yarnold, Williams, and Adams (1996); Welch (2009) PWT is related to and impacts satisfaction strongly Boudreaux and O'Hea (2004); Soremekun et al (2011); Thompson, Yarnold, Williams, and Adams (1996); Welch (2009) Satisfaction depends more on PWT than AWT Hedges (2002); Thompson, Yarnold, Williams, and Adams (1996) Patients inaccurately estimate AWT and PWT Thompson, Yarnold, Adams, and Spacone (1996) Distraction shortens PWT Antonides et al (2002); Nanda et al (2012); Shaikh et al (2013); Thompson, Yarnold, Williams, and Adams (1996); Welch (2009) Distraction increases PWT Hong et al 2013Process reengineering impacts PWT Luo et al (2004) (b) Overview of Perception-related Outcomes Table 1. Classification of Publications in the Field of Waiting Time Perceptions Table 1(a) reveals that there are two relevant studies related to general services.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine learning for healthcare behavioural OR: Addressing waiting time perceptions in emergency care

Gärtner

Padman

2019

Journal of the Operational Research Society

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Recent research has highlighted the need to improve patient satisfaction by reducing perceived waiting times in hospitals. This study examines factors that are associated with waiting time estimation behaviour and how to control flow of patients who overestimate waiting times. Using data from more than 250 patients, we test the applicability of machine learning methods to understand under-, correct and overestimation behaviour of waiting times in two emergency department areas. Our attribute ranking and selection methods reveal that actual waiting time, clinical attributes, and the service environment are among the top ranked and selected attributes. The classification methods reveal that the precision to classify a patient to the true outcome of overestimating waiting times reaches almost 70% in the first waiting area. If a patient waits in a treatment room which is the second waiting area under study, this precision level reaches almost 78%. We developed a discrete-event simulation model which we linked with the machine learning models of each waiting area. Our scenario analysis revealed that changing staffing patterns can lead to a substantial drop-off in the number of patients overestimating waiting times. Our results can be employed to control waiting time perceptions and, potentially, increase patient satisfaction.

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Support for a Waiting Room Time Tracker: A Survey of Patients Waiting in an Urban ED

Cited by 19 publications

References 5 publications

Predicting Triage Waiting Time in Maternity Emergency Care by Means of Data Mining

Predicting Triage Waiting Time in Maternity Emergency Care by Means of Data Mining

Tracking surgical day care patients using RFID technology

Machine learning for healthcare behavioural OR: Addressing waiting time perceptions in emergency care

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