A Prototype Mobile Application for Clinic Appointment Reminder and Scheduling System in Erbil City

In healthcare, the goal of clinics and hospitals is to serve the patient in a skillful way, due to the sensitivity of patients and the high competitiveness among healthcare service providers. Across many countries systems with cutting-edge solutions have been introduced in order to enhance healthcare services throughout last years. However, in Iraq and Kurdistan region still there is a lack for such systems and particularly for clinics. Patients need to go through a time-consuming process which might require a considerable effort especially for the time of making appointment to the consultation hour. Moreover, the current available appointment systems come up with problems such as missing appointments by the patients due to the long queues given by clinics. By implementing technological solutions it’s possible to obtain benefits such as eliminating waiting lists, enhancing patient’s timely access to services and reducing no show problem. Therefore, we suggest creating a mobile application system to enhance the process of booking appointments at clinics. Thus, providing a mobile application tailored to the clinic’s needs is the main goals of this paper. Our approach is to design a prototype mobile system to facilitate the whole appointment process from the beginning to the end. The system will cover both client and clinics side. In this paper, we will propose a mobile system to improve appointment scheduling system for clinics. The findings show that 91 % of patients whom participated in the study were ready to use any sort of online appointment systems to book their next consultation hour

Characteristics of Walk-In Patients and Related Factors in a Dental University Hospital

This study aimed to identify the characteristics of walk-in patients who visited a dental university hospital more than once (returning patient). The factors affecting walk-in visits were analyzed in relation to demographic, appointment, and treatment characteristics among 146,567 cases treated between 1 March 2019 and 29 February 2020. Multiple logistic regression was used to assess factors influencing walk-in visits. The walk-in rate was 14.1%. The likelihood of walk-in visits was higher in men, patients aged 20-39 years, patients residing in Seoul, and hospital employees or their family members. Walk-in visits were more likely to take place from 8:00 to 9:59 and on Saturdays and Mondays. The walk-in odds ratios differed depending on the treatment department and diagnosis. Return patients had higher odds of walk-in visits for treatments not covered by insurance. The probability of being a walk-in patient was lower among patients who also received treatment in other departments on the same day than among those who did not. These results indicate that each examined factor has a predictable pattern. The findings also suggest a relatively high percentage of walk-in cases in dental university hospitals and that walk-in patients differ in their characteristics from patients with appointments.ope

Assessing the queuing process using data envelopment analysis:an application in health centres

Queuing is one of the very important criteria for assessing the performance and efficiency of any service industry, including healthcare. Data Envelopment Analysis (DEA) is one of the most widely-used techniques for performance measurement in healthcare. However, no queue management application has been reported in the health-related DEA literature. Most of the studies regarding patient flow systems had the objective of improving an already existing Appointment System. The current study presents a novel application of DEA for assessing the queuing process at an Outpatients’ department of a large public hospital in a developing country where appointment systems do not exist. The main aim of the current study is to demonstrate the usefulness of DEA modelling in the evaluation of a queue system. The patient flow pathway considered for this study consists of two stages; consultation with a doctor and pharmacy. The DEA results indicated that waiting times and other related queuing variables included need considerable minimisation at both stages

Determining patient outcomes from patient letters: A comparison of text analysis approaches

This paper presents a case study comparing text analysis approaches used to classify the current status of a patient to inform scheduling. It aims to help one of the UKs largest healthcare providers systematically capture patient outcome information following a clinic attendance, ensuring records are closed when a patient is discharged and follow-up appointments can be scheduled to occur within the time-scale required for safe, effective care. Analysing patient letters allows systematic extraction of discharge or follow-up information to automatically update a patient record. This clarifies the demand placed on the system, and whether current capacity is a barrier to timely access. Three approaches for systematic information capture are compared: phrase identification (using lexicons), word frequency analysis and supervised text mining. Approaches are evaluated according to their precision and stakeholder acceptability. Methodological lessons are presented to encourage project objectives to be considered alongside text classification methods for decision support tools

A Simulation-Based Evaluation Of Efficiency Strategies For A Primary Care Clinic With Unscheduled Visits

In the health care industry, there are strategies to remove inefficiencies from the health delivery process called efficiency strategies. This dissertation proposed a simulation model to evaluate the impact of the efficiency strategies on a primary care clinic with unscheduled walk-in patient visits. The simulation model captures the complex characteristics of the Orlando Veteran\u27s Affairs Medical Center (VAMC) primary care clinic. This clinic system includes different types of patients, patient paths, and multiple resources that serve them. Added to the problem complexity is the presence of patient no-shows characteristics and unscheduled patient arrivals, a problem which has been until recently, largely neglected. The main objectives of this research were to develop a model that captures the complexities of the Orlando VAMC, evaluate alternative scenarios to work in unscheduled patient visits, and examine the impact of patient flow, appointment scheduling, and capacity management decisions on the performance of the primary care clinic system. The main results show that only a joint policy of appointment scheduling rules and patient flow decisions has a significant impact on the wait time of scheduled patients. It is recommended that in the future the clinic addresses the problem of serving additional walk-in patients from an integrated scheduling and patient flow viewpoint

Implications of Non-Operating Room Anesthesia Policy for Operating Room Efficiency

This thesis focuses on examining the use of Non-Operating Room Anesthesia (NORA) policy in Operating Room (OR) scheduling. A NORA policy involves a practice whereby the administration of anesthesia stage is performed outside the OR. The goal of the thesis is to determine whether NORA policy can improve OR efficiency measured by the performance of total costs, which consists of a weighted sum of patient waiting time, OR overtime and idle time. A simulation optimization method is adopted to find near-optimal schedules for elective surgeries in an outpatient setting. The results of a traditional OR scheduling model, where all stages of the surgery are performed in the OR, will be compared to the results of a NORA OR model where the initial anesthesia stage is performed outside of the OR. Two cases are considered for the NORA model given the decrease on mean durations: (1) a model with the same number of surgery appointments and shorter session length and (2) a models with the same session length and more surgery appointments. . The impact of a NORA policy on OR performance is further analyzed by considering scenarios that capture Surgery duration variability and mean surgery durations which are two traits for surgeries that have been shown to impact OR performance. This thesis aims to investigate how a NORA policy performs when standard deviations and mean surgery durations change. The results show that NORA policy can improve OR efficiency in all settings

Appointment planning and scheduling in primary care

The Affordable Care Act (ACA) puts greater emphasis on disease prevention and better quality of care; as a result, primary care is becoming a vital component in the health care system. However, long waits for the next available appointments and delays in doctors offices combined with no-shows and late cancellations have resulted in low efficiency and high costs. This dissertation develops an innovative stochastic model for patient planning and scheduling in order to reduce patients’ waiting time and optimize primary care providers’ utility. In order to facilitate access to patients who request a same-day appointment, a new appointment system is presented in which a proportion of capacity is reserved for urgent patients while the rest of the capacity is allocated to routine patients in advance. After the examination of the impact of no-shows on scheduling, a practical double-booking strategy is proposed to mitigate negative impacts of the no-show. Furthermore, proposed model demonstrates the specific circumstances under which each type of scheduling should be adopted by providers to reach higher utilization. Moreover, this dissertation extends the single physician’s model to a joint panel scheduling and investigates the efficiency of such systems on the urgent patients’ accessibility, the physicians’ utilization, and the patients’ waiting time. Incorporating the newsvendor approach and stochastic optimization, these models are robust and practical for planning and scheduling in primary care settings. All the analytical results are supported with numerical examples in order to provide better managerial insights for primary care providers

Managerial Intervention Strategies to Reduce Patient No-Show Rates

High patient no-show rates increase health care costs, decrease healthcare access, and reduce the clinical efficiency and productivity of health care facilities. The purpose of this exploratory qualitative single case study was to explore and analyze the managerial intervention strategies healthcare administrators use to reduce patient no-show rates. The targeted research population was active American College of Healthcare Executives (ACHE), Hawaii-Pacific Chapter healthcare administrative members with operational and supervisory experience addressing administrative patient no-show interventions. The conceptual framework was the theory of planned behavior. Semistructured interviews were conducted with 4 healthcare administrators, and appointment cancellation policy documents were reviewed. Interpretations of the data were subjected to member checking to ensure the trustworthiness of the findings. Based on the methodological triangulation of the data collected, 5 common themes emerged after the data analysis: reform appointment cancellation policies, use text message appointment reminders, improve patient accessibility, fill patient no-show slots immediately, and create organizational and administrative efficiencies. Sharing the findings of this study may help healthcare administrators to improve patient health care accessibility, organizational performance and the social well-being of their communities

Outpatient appointment scheduling in presence of seasonal walk-ins

Due to copyright restrictions, the access to the full text of this article is only available via subscription.This study investigates appointment systems (AS), as combinations of access rules and appointment-scheduling rules, explicitly designed for dealing with walk-in seasonality. In terms of 'access rules', strategies are tested for adjusting capacity through intra-week, or monthly seasonality of walk-ins, or their combined effects. In terms of 'appointment rules', strategies are tested to determine which particular slots to double-book or leave open in cases where seasonal walk-in rates exceed or fall short of the overall yearly rate. In that regard, this study integrates capacity and appointment decisions, which are usually addressed in an isolated manner in previous studies. Simulation optimization is used to derive heuristic solutions to the appointment-scheduling problem, and the findings are compared in terms of in-clinic measures of patient wait time, physician idle time and overtime. The goal is to provide practical guidelines for healthcare practitioners on how to best design their AS when seasonal walk-ins exist.TÜBİTA