Relationship Between Waiting Time on Patient Satisfaction at Baloi Permai Batam Puskesmas Year 2021

Background: Medical records are an important part in assisting the implementation of service delivery to patients in hospitals. This research aimsMethods: Quantitative with survey research, a quantitative approach is used to find out how effective the relationship between waiting time and patient satisfaction is at the Baloi Permai Health Center.Results: The results of the chi square statistical test showed that the p-value of 0.001 was less than 0.050, so it can be said that there is a significant relationship between waiting time and patient satisfaction. The odds ratio for the relationship between waiting time and patient satisfaction is 7.263 with 95% CI between 2.143- 24.614. Patients with long waiting times are 7,263 or 7 times more likely to have a low level of satisfaction compared to patients whose waiting times are not too long.Conclusions: Based on the results of the study, it can be concluded that there is an effect of patient waiting time on outpatient satisfaction. The staff of the Baloi Perma Batam outpatient unit should further improve services, especially for waiting time for outpatients. Based on the results of the study, it can be concluded that there is an effect of patient waiting time on outpatient satisfaction. The staff of the Baloi Perma Batam outpatient unit should further improve services, especially for waiting time for outpatients.

Indoor air quality in wards of dental school clinic: Variations of VOCs, CO2 and airborne particle levels

Introduction: The indoor environment of dental clinics may endanger dental patients and personnel and due to a great variety of air pollutants throughout the usual dental operation. The purpose of the present cross-sectional study was the evaluation of Indoor Air Quality (IAQ) and factors affecting it in a dentistry faculty of Arak University of Medical Sciences. Material and methods: The IAQ of five dental active wards and the patient waiting room was evaluated. The concentrations of Total Volatile Organic Compounds (TVOC), CO2, particulate matter, and bioaerosols were measured. Results: The TVOCs concentration in sampling locations ranged between 817 to 3670 μg/m3 during dental work and exceeded the Leadership in Energy and Environmental Design (LEED) guideline in all sampling locations. The highest values of Particulate Matter (PM) for PM10, PM2.5, and PM1 were observed in the periodontics ward, while the lowest values were observed in the endodontics ward. The PM2.5 concentrations exceeded the WHO limit in periodontics and pediatric wards. TVOC levels had a significant positive correlation with temperature (r=0.374, p<0.01) and RH (r=0.265, p<0.05). The predominant bacterial genus of the patient waiting area was Bacillus (36%), while the dominant bacterial genus of the other sampling site was Micrococcus spp. Penicillium (35.5%) and Cladosporium (28%) were the predominant fungi detected. Conclusion: Controlling of airborne particles is to be standardized by the infection control actions of dental clinics and improved ventilation capacity in the air conditioning system was suggested for reducing VOCs and PM concentrations.

Factors Associated With Patient Waiting Time at a Medical Outpatient Clinic: A Case Study of University of Nairobi Health Services

Background: Long waiting time in outpatient clinics is a constant challenge for patients and the health care providers. Prolonged waiting times are associated with poor adherence to treatment, missed appointment and failure or delay in initiation of treatment and is a major factor towards the perception of the patient towards the care received. Objective: To determine the waiting time and associated factors among out patients attending staff clinic at University of Nairobi health services. Method: A cross-sectional study design was used and data collected from 384 ambulatory patients over a period of four weeks using an interviewer administered pretested structured exit questionnaire with a time-tracking section. Simple random sampling was used to select respondents in a walk- in outpatient clinic set up. Data was cleaned and analysed using Statistical Package for Social Sciences (SPSS) 20. Analysis of variance (ANOVA), and cross tabulation was used to establish associations between the independent variable and dependent variables. Results: In total 384 patients were tracked and interviewed. The average patient waiting time was 55.3mins.Most respondents (52%) suggested that improving availability of staff at their stations would help to reduce patient waiting time. In this study, gender (P=0.005) and availability of doctors (p=0.000) were found to affect patient waiting time with women waiting longer than the male patients. Conclusion: Majority of the patients spent about an hour at the facility to be served. Inadequate number of health workers was the main cause of long waiting time.

Measurement of Neutron Dose Equivalent within and Outside of a LINAC Treatment Vault Using a Neutron Survey Meter

This work concerns neutron doses associated with the use of a Siemens Primus M5497 electron accelerator, which is operated in the photon mode at 15 MV. The conditions offer a situation within which a fraction of the bremsstrahlung emission energies exceed the photoneutron threshold. For different field sizes, an investigation has been made of neutron dose equivalent values at various measurement locations, including: (i) At the treatment table, at a source-surface distance of 100 cm; (ii) at the level of the floor directly adjacent to the treatment table; and (iii) in the control room and patient waiting area. The evaluated neutron dose equivalent was found to range from 0.0001 to 8.6 mSv/h, notably with the greatest value at the level of the floor directly adjacent to the treatment couch (8.6 mSv/h) exceeding the greatest value on the treatment table (5.5 mSv/h). Low values ranging from unobservable to between 0.0001 to 0.0002 mSv/h neutron dose were recorded around the control room and patient waiting area. For measurements on the floor, the study showed the dose equivalent to be greatest with the jaws closed. These data, most particularly concerning neutron distribution within the treatment room, are of great importance in making steps towards improving patient safety via the provision of protective measures.

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

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.

Predicting Patient Waiting Time in the Queue System Using Deep Learning Algorithms in the Emergency Room

Many hospitals consider the length of time waiting in queue to be a measure of emergency room (ER) overcrowding. Long waiting times plague many ER departments, hindering the ability to effectively provide medical attention to those in need and increasing overall costs. Advanced techniques such as machine learning and deep learning (DL) have played a central role in queuing system applications. This study aims to apply DL algorithms for historical queueing variables to predict patient waiting time in a system alongside, or in place of, queueing theory (QT). We applied four optimization algorithms, including SGD, Adam, RMSprop, and AdaGrad. The algorithms were compared to find the best model with the lowest mean absolute error (MAE). A traditional mathematical simulation was used for additional comparisons. The results showed that the DL model is applicable using the SGD algorithm by activating a lowest MAE of 10.80 minutes (24% error reduction) to predict patients' waiting times. This work presents a theoretical contribution of predicting patients’ waiting time with alternative techniques by achieving the highest performing model to better prioritize patients waiting in the queue. Also, this study offers a practical contribution by using real-life data from ERs. Furthermore, we proposed models to predict patients' waiting time with more accurate results than a traditional mathematical method. Our approach can be easily implemented for the queue system in the healthcare sector using electronic health records (EHR) data.

1618 Improving the Clinic Experience for Surgical Patients Visiting the Melanoma Multidisciplinary Team Clinic at Guy’s and St Thomas’ NHS Foundation Trust

Aim The incidence of malignant melanoma has been increasing in the UK over the last decade. Effective melanoma management requires a multidisciplinary team (MDT) approach, often involving dermatologists, oncologists, radiologists, histopathologists, skin cancer nurse specialists and plastic surgeons. Patient waiting times at our melanoma MDT clinic at the St John’s Institute of Dermatology at Guy’s Hospital, London had anecdotally been reported as excessive, specifically for same-day ‘in-clinic referrals’ from dermatology to plastic surgery. We aimed to ascertain the reasons for the delay and implement changes to improve patient satisfaction in the clinic. Method A patient satisfaction questionnaire was devised, measuring satisfaction on a numerical scale of 1 (unsatisfied) to 5 (very satisfied) in addition to a clinic staff perception questionnaire on patient satisfaction. Lack of instruction from staff after the dermatology appointment was identified as the predominant factor contributing towards waiting delays to the plastic surgery clinic. This led us to highlight and educate the issue to all team members involved and create a system whereby patients re-reported to the administrative staff after their dermatology appointment to be re-entered into the system. Results As a result of this, the mean surgical patient satisfaction score, pre-intervention of 3.83, improved to 4.50 post-intervention. Similarly, mean scores from staff assessing perception of how well the clinic ran and patient waiting times also increased. Conclusions The introduction of a new patient in-clinic referral protocol and increased staff education of the issue has improved both patient and staff satisfaction within the clinic’s health care provision.

Using lean techniques and discrete-event simulation for performance improvement in an outpatient clinic

Purpose Increased demand and the pressure to reduce health-care costs have led to longer waiting time for patients to make appointments and during the day of hospital visits. The purpose of this study is to identify opportunities to reduce waiting time using lean techniques and discrete-event simulation (DES). Design/methodology/approach A five-step procedure is proposed to facilitate the effective utilization of lean and DES to improve the performance of the Otolaryngology Head and Neck Surgery Outpatient Clinic at Cleveland Clinic Abu Dhabi. While lean techniques were applied to reduce the potential sources of waste by aligning processes, a DES model was developed to validate the proposed solutions and plan patient arrivals under dynamic conditions and different scenarios. Findings Aligning processes resulted in an efficient patient flow reducing both waiting times. DES played a complementary role in verifying lean solutions under dynamic conditions, helping to plan the patient arrivals and striking a balance between the waiting times. The proposed solutions offered flexibility to improve the clinic capacity from the current 176 patients up to 479 (without violating the 30 min waiting time policy) or to reduce the patient waiting time during the visit from the current 33 min to 4.5 min (without violating the capacity goal of 333 patients). Research limitations/implications Proposing and validating lean solutions require reliable data to be collected from the clinic and such a process could be laborious as data collection require patient and resource tracing without interfering with the regular functions of the clinic. Practical implications The work enables health-care managers to conveniently conduct a trade-off analysis and choose a suitable inter-arrival time – for every physician – that would satisfy their objectives between resource utilization (clinic capacity) and average patient waiting time. Social implications Successful implementation of lean requires a supportive and cooperative culture from all stakeholders involved. Originality/value This study presents an original and detailed application of lean techniques with DES to reduce patient waiting times. The adopted approach in this study could be generalized to other health-care settings with similar objectives.

COMPARING SCHEDULING APPOINTMENT RULES PERFORMANCE IN HEALTH CARE UNITS: A DISCRETE EVENT SIMULATION APPROACH

Health care systems are affected by sudden increases in demand that can be generated by factors such as natural disasters, terrorist attacks, epidemics, among others. Patient demand can be divided between scheduled and walk-in and, in pandemic scenarios, both of them must be managed in order to avoid higher patient waiting times or number in queue. A discrete event simulation model is proposed in order to evaluate critical indicators like: patient waiting times, number in queue, resource utilization (doctors), using four different patient schedule appointment rules. In this study it was also considered patients impunctuality, walk-in patients and no-show in different scenarios. The best schedule appointment rules for each demand scenario were evaluated. After comparing six performance indicators, four schedule appointment rules in nine different scenarios it was found that the most known scheduling rule had the lowest queue sizes at scenarios with low or no walk-in patients, whereas, as the unpredictability of the scenarios rose, other rules outperformed it. It was also presented to exist an inverse relation between queue size and the physician idle time. Keywords: discrete event simulation, idle-time, queue management, appointment scheduling, health care.