Patient Decision and Experience in the Multi-channel Appointment Context: An Empirical Study (Preprint)

2021 ◽  
Author(s):  
qing Ye ◽  
Hong Wu
Keyword(s):  
Risk Factors ◽  
Waiting Time ◽  
Ordinary Least Squares ◽  
Channel Choice ◽  
Related Risk ◽  
Disease Costs ◽  
Health Related ◽  
Patient Waiting Time ◽  
Patient Appointment ◽  
Patient Waiting

BACKGROUND Long waiting time for treatment in the outpatient department has long been a complaint and influence patient experience. It is critical to schedule patients for doctors to reduce patient waiting time. Nowadays, the multi-channel appointment has been provided for patients to get medical services, especially for those with severe illnesses and remote distance. OBJECTIVE This study aims to explore the factors influencing patient appointment channel choice in the context of multi-channel appointments, and how channel choice affects the waiting time for offline visiting. METHODS We collected outpatient appointment records from both online and offline appointment channels to conduct our empirical research. The empirical analysis is conducted into two steps. We first analyze the relationship between appointment channel choice and patient waiting time, and then the relationships between three determinants and appointment channel choice. The ordinary least squares and the logistic regression model are used to obtain empirical results. RESULTS Our results show that a patient with an online appointment decision has a shorter consultation waiting time compared with a patient with on-site appointment (β = -0.320, p<0.001). High-quality resource demand (β = 0.349, p<0.001), high-severity disease (β = 0.011, p<0.001), and high non-disease costs (β = 0.039, p<0.001) create an obvious incentive for patients to make appointments via the Internet. Further, only the effect of non-disease cost on channel choice is lower for patients with multiple visit histories (β = -0.021, p<0.001). CONCLUSIONS Our study confirms the effect of Internet use on reducing patient waiting time. Patients consider both health-related risk factors and cost-related risk factors to make decisions on appointment channels. Our study produces several insights, which have implications for channel choice and patient behavior literature. More importantly, these insights as a whole, contribute to the design of appointment systems of hospitals.

Patient Decision and Experience in the Multi-channel Appointment Context (Preprint)

2020 ◽  
Author(s):  
Hong Wu
Keyword(s):  
Waiting Time ◽  
Tertiary Hospital ◽  
The Internet ◽  
High Quality ◽  
Channel Choice ◽  
High Severity ◽  
Patient Decision ◽  
Multiple Visit ◽  
Disease Costs ◽  
Patient Waiting Time

BACKGROUND Long waiting time for treatment in the outpatient department has long been a complaint and influence patient experience. It is critical to schedule patients for doctors to reduce patient waiting time. Nowadays, the multi-channel appointment has been provided for patients to get medical services, especially for ones with severe illnesses and remote distance. OBJECTIVE Under the multi-channel appointment context, the determinants of patients’ appointment decision and experience become more complicated and are needed to be investigated. METHODS From the point of patient, this paper uses a real operation dataset (1,241 doctors from 119 departments, involving 308,085 patients) from a tertiary hospital in China to investigate the antecedents and consequence of patients’ appointment decision. RESULTS Our results show that a patient with online appointment decision has a shorter consultation waiting time compared with a patient with on-site appointment. High-quality resource demand, high-severity disease, and high non-disease costs create an obvious incentive for patients to make appointments via the Internet. Further, only the effect of non-disease cost on channel choice is lower for patients with multiple visit histories. CONCLUSIONS This study produces several insights, which have implications for channel choice and patient behavior literature. More importantly, these insights as a whole, contribute to the design of appointment systems of hospitals.

Appointment Tracking System for Improving Patient Waiting Time

2015 ◽  
Vol 781 ◽  
pp. 591-594
Author(s):  
Adrian Wattananupong ◽  
Pichitpong Soontornpipit
Keyword(s):  
Waiting Time ◽  
Cell Phone ◽  
Tracking System ◽  
Core System ◽  
Average Waiting Time ◽  
Appointment Time ◽  
Patient Waiting Time ◽  
Patient Appointment ◽  
Appointment System ◽  
Patient Waiting

This research aim to design and develop a system that improve the patient waiting time. Appointment system is designed to be the core system that uses to inform the patient’s appointment information. Patient appointment is informed by email and SMS alert to their cell phone. The system provides the appointment time and average waiting time before their queue. When the patients get their treatment, the system tracks down the timestamp for each process from the start till the end to calculate average waiting time. The time results are used for both other patients to determine their waiting time and for hospital management team to verify the quality system.

scholarly journals The Impact of Longer Consultation Time: A Simulation-Based Approach

2020 ◽  
Vol 11 (05) ◽  
pp. 857-864
Author(s):  
Abdulrahman M. Jabour
Keyword(s):  
Waiting Time ◽  
Quality Care ◽  
Working Hours ◽  
Discrete Events ◽  
Simulation Based ◽  
Patient Waiting Time ◽  
Free Environment ◽  
The Impact ◽  
Consultation Time ◽  
Patient Waiting

Abstract Background Maintaining a sufficient consultation length in primary health care (PHC) is a fundamental part of providing quality care that results in patient safety and satisfaction. Many facilities have limited capacity and increasing consultation time could result in a longer waiting time for patients and longer working hours for physicians. The use of simulation can be practical for quantifying the impact of workflow scenarios and guide the decision-making. Objective To examine the impact of increasing consultation time on patient waiting time and physician working hours. Methods Using discrete events simulation, we modeled the existing workflow and tested five different scenarios with a longer consultation time. In each scenario, we examined the impact of consultation time on patient waiting time, physician hours, and rate of staff utilization. Results At baseline scenarios (5-minute consultation time), the average waiting time was 9.87 minutes and gradually increased to 89.93 minutes in scenario five (10 minutes consultation time). However, the impact of increasing consultation time on patients waiting time did not impact all patients evenly where patients who arrive later tend to wait longer. Scenarios with a longer consultation time were more sensitive to the patients' order of arrival than those with a shorter consultation time. Conclusion By using simulation, we assessed the impact of increasing the consultation time in a risk-free environment. The increase in patients waiting time was somewhat gradual, and patients who arrive later in the day are more likely to wait longer than those who arrive earlier in the day. Increasing consultation time was more sensitive to the patients' order of arrival than those with a shorter consultation time.

Occupational health-related risk factors in mining industry

2021 ◽  
Author(s):  
Farnad Nasirzadeh ◽  
Nazi Soltanmohammadlou ◽  
Sanaz Sadeghi ◽  
Abbas Khosravi
Keyword(s):  
Risk Factors ◽  
Occupational Health ◽  
Mining Industry ◽  
Related Risk ◽  
Health Related

Announcing Waiting Time in Emergency Departments

2019 ◽  
pp. 1-6
Author(s):  
Martin Lariviere ◽  
Sarang Deo
Keyword(s):  
Waiting Time ◽  
Emergency Departments ◽  
Waiting Times ◽  
Large Network ◽  
Regional Networks ◽  
Marketing Campaign ◽  
National Healthcare ◽  
Time Estimates ◽  
Patient Waiting Time ◽  
Patient Waiting

First National Healthcare (FNH) runs a large network of hospitals and has worked to systematically reduce waiting times in its emergency departments. One of FNH's regional networks has run a successful marketing campaign promoting its low ED waiting times that other regions want to emulate. The corporate quality manager must now determine whether to allow these campaigns to be rolled out and, if so, which waiting time estimates to use. Are the numbers currently being reported accurate? Is there a more accurate way of estimating patient waiting time that can be easily understood by consumers?

scholarly journals Immigration and Acculturation in Relation to Health and Health-Related Risk Factors Among Specific Asian Subgroups in a Health Maintenance Organization

2004 ◽  
Vol 94 (11) ◽  
pp. 1977-1984 ◽  
Author(s):  
Scarlett L. Gomez ◽  
Jennifer L. Kelsey ◽  
Sally L. Glaser ◽  
Marion M. Lee ◽  
Stephen Sidney
Keyword(s):  
Risk Factors ◽  
Health Maintenance Organization ◽  
Health Maintenance ◽  
Related Risk ◽  
Health Related

scholarly journals Psychosocial and health-related risk factors for depressive symptom trajectories among midlife women over 15 years: Study of Women's Health Across the Nation (SWAN)

2018 ◽  
Vol 49 (2) ◽  
pp. 250-259 ◽  
Author(s):  
Joyce T. Bromberger ◽  
Laura L. Schott ◽  
Nancy E. Avis ◽  
Sybil L. Crawford ◽  
Sioban D. Harlow ◽  
...  
Keyword(s):  
Risk Factors ◽  
Social Support ◽  
Depressive Symptoms ◽  
Depressive Symptom ◽  
Midlife Women ◽  
Role Functioning ◽  
Symptom Trajectories ◽  
Related Risk ◽  
Health Related ◽  
Symptom Trajectory

AbstractBackgroundPsychosocial and health-related risk factors for depressive symptoms are known. It is unclear if these are associated with depressive symptom patterns over time. We identified trajectories of depressive symptoms and their risk factors among midlife women followed over 15 years.MethodsParticipants were 3300 multiracial/ethnic women enrolled in a multisite longitudinal menopause and aging study, Study of Women's Health Across the Nation. Biological, psychosocial, and depressive symptom data were collected approximately annually. Group-based trajectory modeling identified women with similar longitudinal patterns of depressive symptoms. Trajectory groups were compared on time-invariant and varying characteristics using multivariable multinomial analyses and pairwise comparisons.ResultsFive symptom trajectories were compared (50% very low; 29% low; 5% increasing; 11% decreasing; 5% high). Relative to whites, blacks were less likely to be in the increasing trajectory and more likely to be in the decreasing symptom trajectory and Hispanics were more likely to have a high symptom trajectory than an increasing trajectory. Psychosocial/health factors varied between groups. A rise in sleep problems was associated with higher odds of having an increasing trajectory and a rise in social support was associated with lower odds. Women with low role functioning for 50% or more visits had three times the odds of being in the increasing symptom group.ConclusionsChanges in psychosocial and health characteristics were related to changing depressive symptom trajectories. Health care providers need to evaluate women's sleep quality, social support, life events, and role functioning repeatedly during midlife to monitor changes in these and depressive symptoms.

scholarly journals Impact of value added services on patient waiting time at the ambulatory pharmacy Queen Elizabeth Hospital

2017 ◽  
Vol 15 (1) ◽  
pp. 846-846 ◽  
Author(s):  
Benjamin C. Loh ◽  
Kheng F. Wah ◽  
Carolyn A. Teo ◽  
Nadia M. Khairuddin ◽  
Fairenna B. Fairuz ◽  
...  
Keyword(s):  
Waiting Time ◽  
Value Added ◽  
Queen Elizabeth Hospital ◽  
Patient Waiting Time ◽  
Patient Waiting
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