Radiology scheduling with consideration of patient characteristics to improve patient access to care and medical resource utilization

Huang, Yu-Li; Marcak, Justin

doi:10.1057/hs.2013.1

Cited by 13 publications

(9 citation statements)

References 31 publications

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“…For instance, Cayirli, Veral and Rosen (2008) make simplifying assumptions that the no-show rates were identical for the two patient groups they study. Huang and Marcak (2013) do use decision trees for classification of patients. By doing so, they determine the slot length.…”

Section: (Ii) Simulation Modelling and Analysismentioning

confidence: 99%

Simulation modelling and analysis of appointment system performance for multiple classes of patients in a hospital: A case study

Bhattacharjee

Ray

2016

Operations Research for Health Care

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Section: (Ii) Simulation Modelling and Analysismentioning

confidence: 99%

Simulation modelling and analysis of appointment system performance for multiple classes of patients in a hospital: A case study

Bhattacharjee

Ray

2016

Operations Research for Health Care

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“…Decision trees are popular in the healthcare setting for their relative efficiency, computational ease, ease of interpretation, and visual appeal in terms of display. [27][28][29] A decision tree method is used to identify when the proposed method or the current ED practice should be used. The decision is generated using R software.…”

Section: Simulation Runs and Analysismentioning

confidence: 99%

Treatment allocation for the potential urgent patients in hospital emergency department to reduce length of stay – a simulation approach

Huang

2015

IJH

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Extended waiting in the emergency room continues to be a recognized problem; to alleviate this many hospitals have adopted triage systems which classify patients based on their priority level and fast track processes for low acuity patients. This paper aims to reduce the length of stay in Emergency Department (ED) by allocating urgent patients in either the traditional treatment or the fast track route without compromising care for higher acuity patients. This study focuses on the five-priority level system and evaluates the concept using simulation and classification techniques. Literature has reported that priority three patients, whose conditions are initially not life threatening but may progress to a critical level, are the majority and normally encounter the longest wait. The simulation was built based on a difined ED patient flow considering factors such as patient arrival patterns, treatment times, and resource capacity. Analysis of Variance was conducted to determine the impact of each factor to the decision on level 3 patient allocation. A regression model was constructed to estimate the cycle time savings. Decision tree was used to further provide ED a general guideline on the allocation of level 3 patients. The study concludes that the fast track route as an option for level 3 patients is adequate if the fast track has higher or similar capacity as the traditional treatment route especially for the rural EDs where level 3 patients are identified 61% of time and required longer treatment. The patient cycle time saving estimated as much as three hours.

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“…It impacts patient and medical staff productivity, stress, service quality and efficiency of medical care, as well as health-care cost and availability. Many appointment scheduling methods have been developed to undertake the problems with consideration of patient classifications [3][4][5][6], appointment demand uncertainty [7][8][9], urgent care [10][11][12], patient arrival patterns [13], service interruptions and physician lateness [14], walk-ins [15], ancillary services [16], and no-shows for effective overbooking [17][18][19][20][21][22][23]. These considerations aim to provide a better appointment scheduling template by mainly determining the most appropriate appointment intervals in subject to an objective function that either maximizing profit [8,[24][25], minimizing patient and/or physician waiting [13-14, 16, 19, 26-28], or minimizing costs of patient waiting, physician idling and overtime [3, 10-11, 15, 17-18, 20-21, 29-30].…”

Section: Introductionmentioning

confidence: 99%

Appointment Template Redesign in a Women’s Health Clinic Using Clinical Constraints to Improve Service Quality and Efficiency

Huang

Verduzco

2015

Appl Clin Inform

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SummaryBackground: Patient wait time is a critical element of access to care that has long been recognized as a major problem in modern outpatient health care delivery systems. It impacts patient and medical staff productivity, stress, quality and efficiency of medical care, as well as health-care cost and availability. Objectives: This study was conducted in a Women's Health Clinic. The objective was to improve clinic service quality by redesigning patient appointment template using the clinical constraints. Methods: The proposed scheduling template consisted of two key elements: the redesign of appointment types and the determination of the length of time slots using defined constraints. The reclassification technique was used for the redesign of appointment visit types to capture service variation for scheduling purposes. Then, the appointment length was determined by incorporating clinic constraints or goals, such as patient wait time, physician idle time, overtime, finish time, lunch hours, when the last appointment was scheduled, and the desired number of appointment slots, to converge the optimal length of appointment slots for each visit type. Results: The redesigned template was implemented and the results indicated a 73% reduction in average patient waiting from the reported 40 to 11 minutes. The patient no-show rate was reduced by 4% from 24% to 20%. The morning section on average finished about 11:50 am. The clinic day was finished around 4:45 pm. Provider average idle time was estimated to be about 5 minutes, which can be used for charting/documenting patients. Conclusions: This study provided an alternative method of redesigning appointment scheduling templates using only the clinical constraints rather than the traditional way that required an objective function. This paper also documented the employed methods step by step in a real clinic setting. The implementation results concluded a significant improvement on patient wait time and noshow rate.

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Radiology scheduling with consideration of patient characteristics to improve patient access to care and medical resource utilization

Cited by 13 publications

References 31 publications

Simulation modelling and analysis of appointment system performance for multiple classes of patients in a hospital: A case study

Simulation modelling and analysis of appointment system performance for multiple classes of patients in a hospital: A case study

Treatment allocation for the potential urgent patients in hospital emergency department to reduce length of stay – a simulation approach

Appointment Template Redesign in a Women’s Health Clinic Using Clinical Constraints to Improve Service Quality and Efficiency

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