Association between age and outpatient clinic arrival time: myth or reality?

Technologically Punctual? A Preliminary Evaluation of Differences between Face-to-Face and Video Check-In Times for Initial Mental Health Services

Video-based telehealth provides mental health services to underserved populations. As decision makers reevaluate service offerings following COVID-19, it remains prudent to evaluate the utility of ongoing telehealth options among rural healthcare facilities, the primary healthcare source for many rural individuals. As research continues to compare video and face-to-face services, one understudied component is attendance. Although video-based telehealth has demonstrated improved show-rates for mental health services when compared to face-to-face methods, limited work has clarified whether video improves patient punctuality for these appointments, a documented challenge prevalent for patients with mental health-related concerns. A retrospective electronic record review of psychiatry, psychology, and social work initial patient visits between 2018-2022 was conducted (N = 14,088). Face-to-face visits demonstrated a mean check-in time of -10.78 min (SD = 26.77), while video visits demonstrated a mean check-in time of -6.44 (SD = 23.87). Binary logistic regressions suggested that increased video usage was associated with a decreased likelihood of late check-in (B = -0.10, S. E. = 0.05, Exp(B) = 0.91, 95% CI = 0.83 - 1.00). Exploratory binary logistic regressions evaluated age, sex, race, ethnicity, specialty, insurance type, and diagnostic classification influence on video initial visits. Increased video usage was associated with a statistically decreased likelihood of late check-in; however, clinically, both face-to-face and video visits exhibited mean check-in times prior to the initial visit's scheduled time. As such, mental health organizations are encouraged to continue offering both face-to-face and video as options to foster evidence-based practices to the broadest population.

Patient's punctuality in an outpatient clinic: the role of age, medical branch and geographical factors

BACKGROUND

The efficiency of the management of an outpatient clinic largely depends on the administration of patient flows and waiting times increase costs and affect clinical quality. In this study, we verify if the visit acceptance times are influenced by demographic or geographical factors in a large cohort of patients referred to a city and suburban private outpatient multidisciplinary clinic.

METHODS

We included all scheduled visits of patients aged from 18 to 75 years who arrived in 2021, 2022 and 2023 in our private outpatient clinics, consisting of 34 medical clinics scattered in Milan metropolitan city and hinterland. The variables collected were age, visit time, check-in time, address of the medical clinic and its distance from the closest underground station, patient typology (new business vs. follow-up patient), and the medical branch of the visit. Outcome is'punctuality', defined as check-in time minus visit time (in minutes).

RESULTS

We considered a sample of 410.808 visits from January 2021 to April 2023. The majority of patients check-in early (84.4%) and we found that the percentage of punctual patients increases linearly with age. Earlier hours in the morning show the worst punctuality pattern as well as Blood Draws in the analysis of different medical branches. We also observed that patients who already had some activity recorded in our systems show the worst pattern of punctuality. No particular differences emerged considering the geographical location of the clinics.

CONCLUSIONS

Younger patients have worse punctuality than older patients. Moreover, earlier hour slots are the most disadvantaged and the medical specialty has an influence on the arrival habits. This data should be considered for better clinical quality and efficiency.

Mapping out the arguments for and against patient non-attendance fees in healthcare: an analysis of public consultation documents

BACKGROUND

Patients not attending their appointments without giving notice burden healthcare services. To reduce non-attendance rates, patient non-attendance fees have been introduced in various settings. Although some argue in narrow economic terms that behavioural change as a result of financial incentives is a voluntary transaction, charging patients for non-attendance remains controversial. This paper aims to investigate the controversies of implementing patient non-attendance fees.

OBJECTIVE

The aim was to map out the arguments in the Norwegian public debate concerning the introduction and use of patient non-attendance fees at public outpatient clinics.

METHODS

Public consultation documents (2009-2021) were thematically analysed (n=84). We used a preconceived conceptual framework based on the works of Grant to guide the analysis.

RESULTS

A broad range of arguments for and against patient non-attendance fees were identified, here referring to the acceptability of the fees' purpose, the voluntariness of the responses, the effects on the individual character and institutional norms and the perceived fairness and comparative effectiveness of patient non-attendance fees. Whereas the aim of motivating patients to keep their appointments to avoid poor utilisation of resources and increased waiting times was widely supported, principled and practical arguments against patient non-attendance fees were raised.

CONCLUSION

A narrow economic understanding of incentives cannot capture the breadth of arguments for and against patient non-attendance fees. Policy makers may draw on this insight when implementing similar incentive schemes. The study may also contribute to the general debate on ethics and incentives.

Outpatient visit behavior in patients with epilepsy: Generalized Epilepsy is more frequently non-attendance than Focal Epilepsy

BACKGROUND

Patients with epilepsy (PWE), especially those with Idiopathic Epilepsy (GE), are at a high risk of disadvantage caused by non-adherence. It has been suggested that medical visit behavior may be a surrogate indicator of medication adherence. We hypothesized that patients with IGE would adhere poorly to visits.

METHODS

This was a retrospective study of PWE who visited the Department of Psychiatry and Neurology at Hokkaido University Hospital between January 2017 and December 2019. Demographic and clinical information on PWE were extracted from medical records and visit data from the medical information system. Non-attendance of outpatient appointments was defined as "not showing up for the day of an appointment without prior notice." Mixed-effects logistic regression analysis was conducted with non-attendance as the objective variable.

RESULTS

Of the 9151 total appointments, 413 were non-attendances, with an overall non-attendance rate of 4.5%. IGE was a more frequent non-attendance than Focal Epilepsy (FE) (odds ratio (OR) 1.94; 95% confidence interval (CI) 1.17-3.21; p = 0.010). History of public assistance receipt was associated with higher non-attendance (OR 2.04; 95% CI 1.22-3.43; p = 0.007), while higher education (OR 0.64; 95% CI 0.43-0.93; p = 0.021) and farther distance to a hospital (OR 0.33; 95% CI 0.13-0.88; p = 0.022), and higher frequency of visits (OR 0.18; 95% CI 0.04-0.86; p = 0.031) were associated with fewer non-attendances. In a subgroup analysis of patients with GE, women were associated with fewer non-attendance (OR 0.31; 95% CI 0.14-0.72; p = 0.006).

CONCLUSIONS

GE was more frequent in the non-attendance group than in the FE group. Among patients with GE, females were found to have non-attendance less frequently; however, there was no clear difference in the odds of non-attendance between Juvenile Myoclonic Epilepsy (JME) and IGE other than JME.

The Use of Machine Learning to Predict Late Arrivals at the Adult Outpatient Department

INTRODUCTION

Patient unpunctuality leads to delays in the delivery of care and increased waiting times, resulting in crowdedness. Late arrivals for adult outpatient appointments are a challenge for healthcare, contributing to negative effects on the efficiency of health services as well as wasted time, budget, and resources. This study aims to identify factors and characteristics associated with tardy arrivals at adult outpatient appointments using machine learning and artificial intelligence. The goal is to create a predictive model using machine learning models capable of predicting adult patients arriving late to their appointments. This would support effective and accurate decision-making in scheduling systems, leading to better utilization and optimization of healthcare resources.

METHODS

A retrospective cohort review of adult outpatient appointments between January 1, 2019, and December 31, 2019, was undertaken at a tertiary hospital in Riyadh. Four machine learning models were used to identify the best prediction model that could predict late-arriving patients based on multiple factors.

RESULTS

A total of 1,089,943 appointments for 342,974 patients were conducted. There were 128,121 visits (11.7%) categorized as late arrivals. The best prediction model was Random Forest, with a high accuracy of 94.88%, a recall of 99.72%, and a precision of 90.92%. The other models showed different results, such as XGBoost with an accuracy of 68.13%, Logistic Regression with an accuracy of 56.23%, and GBoosting with an accuracy of 68.24%.

CONCLUSION

This paper aims to identify the factors associated with late-arriving patients and improve resource utilization and care delivery. Despite the overall good performance of the machine learning models developed in this study, not all variables and factors included contribute significantly to the algorithms' performance. Considering additional variables could improve machine learning performance outcomes, further enhancing the practical application of the predictive model in healthcare settings.

Research on outpatient capacity planning combining lean thinking and integer linear programming

BACKGROUND

The size and cost of outpatient capacity directly affect the operational efficiency of a whole hospital. Many scholars have faced the study of outpatient capacity planning from an operations management perspective.

OBJECTIVE

The outpatient service is refined, and the quantity allocation problem of each type of outpatient service is modeled as an integer linear programming problem. Thus, doctors' work efficiency can be improved, patients' waiting time can be effectively reduced, and patients can be provided with more satisfactory medical services.

METHODS

Outpatient service is divided into examination and diagnosis service according to lean thinking. CPLEX is used to solve the integer linear programming problem of outpatient service allocation, and the maximum working time is minimized by constraint solution.

RESULTS

A variety of values are taken for the relevant parameters of the outpatient service, using CPLEX to obtain the minimum and maximum working time corresponding to each situation. Compared with no refinement stratification, the work efficiency of senior doctors has increased by an average of 25%. In comparison, the patient flow of associate senior doctors has increased by an average of 50%.

CONCLUSION

In this paper, the method of outpatient capacity planning improves the work efficiency of senior doctors and provides outpatient services for more patients in need; At the same time, it indirectly reduces the waiting time of patients receiving outpatient services from senior doctors. And the patient flow of the associate senior doctors is improved, which helps to improve doctors' technical level and solve the problem of shortage of medical resources.

Impact of Socio-demographic Characteristics on Time in Outpatient Cardiology Clinics: A Retrospective Analysis

Inequitable access to health services influences health outcomes. Some studies have found patients of lower socio-economic status (SES) wait longer for surgery, but little data exist on access to outpatient services. This study analyzed patient-level data from outpatient public cardiology clinics and assessed whether low SES patients spend longer accessing ambulatory services. Retrospective analysis of cardiology clinic encounters across 3 public hospitals between 2014 and 2019 was undertaken. Data were linked to age, gender, Indigenous status, country of birth, language spoken at home, number of comorbidities, and postcode. A cox proportional hazards model was applied adjusting for visit type (new/follow up), clinic, and referral source. Higher hazard ratio (HR) indicates shorter clinic time. Overall, 22 367 patients were included (mean [SD] age 61.4 [15.2], 14 925 (66.7%) male). Only 7823 (35.0%) were born in Australia and 8452 (37.8%) were in the lowest SES quintile. Median total clinic time was 84 min (IQR 58-130). Visit type, clinic, and referral source were associated with clinic time (R² = 0.23, 0.35, 0.20). After adjusting for these variables, older patients spent longer in clinic (HR 0.94 [0.90-0.97]), though there was no difference according to SES (HR 1.02 [0.99-1.06]) or other variables of interest. Time spent attending an outpatient clinic is substantial, amplifying an already significant time burden faced by patients with chronic health conditions. SES was not associated with longer clinic time in our analysis. Time spent in clinics could be used more productively to optimize care, improve health outcomes and patient experience.

Factors predicting first appointment attendance at a traumatic brain injury clinical neuropsychology outpatient clinic: a logistic regression analysis

BACKGROUND

The purpose of our study was to investigate factors which predicted first appointment attendance within a traumatic brain injury (TBI) neuropsychology outpatient department.

MATERIALS AND METHODS

A newly introduced telephone triaging system was implemented in a clinical neuropsychology service for individuals with a TBI. The effects of receiving a triage telephone call, amongst other variables, were analysed as predictors of attendance at the first face-to-face clinic appointment. The data from 161 individuals were analysed using routine patient information collected by the clinical neuropsychology service. Logistic regression analyses were performed to investigate predictors of first appointment clinic attendance.

RESULTS

Logistic regression analyses identified higher age, shorter waiting times, and answering the triage call as potential predictors of attendance, highlighting where the service might focus efforts to facilitate attendance.

CONCLUSIONS

Both patient and service factors were found to be significant predictors of patient attendance. Further service evaluation could explore patients' experiences of triage telephone calls, and investigate relationships between waiting times and neuropsychological outcomes.IMPLICATIONS FOR REHABILITATIONIdentifying predictors of appointment attendance can allow the service to focus on the needs of particular patient groups.Implementing a telephone triage initiative had positive effects, both on waiting times and efficient use of face-to-face clinic time.The analysis highlighted the need to think about better ways of reaching out to younger individuals and those who have waited longer to attend appointments, who are less likely to attend once invited.

Predictors of No-Show in Neurology Clinics

In this study, we aim to identify predictors of a no-show in neurology clinics at our institution. We conducted a retrospective review of neurology clinics from July 2013 through September 2018. We compared odds ratio of patients who missed appointments (no-show) to those who were present at appointments (show) in terms of age, lead-time, subspecialty, race, gender, quarter of the year, insurance type, and distance from hospital. There were 60,012 (84%) show and 11,166 (16%) no-show patients. With each day increase in lead time, odds of no-show increased by a factor of 1.0019 (p < 0.0001). Odds of no-show were higher in younger (p ≤ 0.0001, OR = 0.49) compared to older (age ≥ 60) patients and in women (p < 0.001, OR = 1.1352) compared to men. They were higher in Black/African American (p < 0.0001, OR = 1.4712) and lower in Asian (p = 0.03, OR = 0.6871) and American Indian/Alaskan Native (p = 0.055, OR = 0.6318) as compared to White/Caucasian. Patients with Medicare (p < 0.0001, OR = 1.5127) and Medicaid (p < 0.0001, OR = 1.3354) had higher odds of no-show compared to other insurance. Young age, female, Black/African American, long lead time to clinic appointments, Medicaid/Medicare insurance, and certain subspecialties (resident and stroke clinics) are associated with high odds of no show. Possible suggested interventions include better communication and flexible appointments for the high-risk groups as well as utilizing telemedicine.

Patient factors associated with dermatology outpatient non-attendance: An analysis of racial and ethnic diversity

Non-attendance to dermatology outpatient appointments is a risk factor for poorer patient outcomes. The culturally and linguistically diverse (CALD) communities in Australia have been identified as at risk of poorer health outcomes, but there is a paucity of data assessing patient factors that may increase outpatient non-attendance. To investigate this, we performed a retrospective cross-sectional study of dermatology appointments from patients attending a tertiary, referral public hospital located in one of Australia's most racially and ethnically diverse communities. Patients within the 18-45 years age bracket were 61% more likely to not attend compared to older age groups. Those born in Oceania, Middle East Asia, and surprisingly Australia were more likely to miss an appointment, whilst those born in East and Southeast Asia were more likely to attend. Those who spoke Arabic at home were more likely to not attend, whilst those who spoke Vietnamese at home were more likely to attend. This study sheds further light on health disparities in non-attendance and emphasizes the importance of not collectively amalgamating all groups of the CALD community.

Research on obstetric ward planning combining lean thinking and mixed-integer programming

BACKGROUND

In recent years, there are many studies on scheduling methods of patient flow, nurse scheduling, bed allocation, operating room scheduling and other problems, but there is no report on the research methods of how to plan ward allocation from a more macroscopic perspective.

OBJECTIVE

Refine and stratify the obstetric ward to provide more accurate medical service for pregnant women and improve the work efficiency of obstetricians and midwives. The problem of how to allocate the number of each type of ward is modeled as a mixed integer programming problem, which maximizes the patient flow of pregnant women in obstetric hospitals.

METHODS

The obstetric wards are divided into observation ward, cesarean section ward and natural delivery ward according to lean thinking. CPLEX is used to solve the mixed-integer programming problem of ward allocation. In R software, multivariate Generalized Linear Models (GLM) regression model is used to analyze the influence of each factor on patient flow.

RESULTS

The maximum patient flow of each case was obtained by CPLEX, which was 19-25% higher than that of patients without refinement, stratification and planning. GLM regression analysis was carried out on the abovementioned data, and the positive and negative correlation factors were obtained.

CONCLUSION

According to lean thinking, obstetric wards are divided into three types of wards. Obstetricians and midwives work more efficiently and get more rest time. Pregnant women also enjoy more detailed medical services. By modeling the delivery ward allocation problem as a mixed-integer programming problem, we can improve the capacity of the service in obstetric hospitals from a macro perspective. Through GLM regression model analysis, it is conducive to improve the obstetric hospital capacity from the perspective of positive and negative correlation factors.

Missing the Care in Health Care

ABSTRACT

This article describes the case of a chronically ill patient whose care was grossly mismanaged as a result of the policies and practices of a dysfunctional health system. This case illustrates the importance of truly listening to patients and communicating effectively with colleagues within the health care system. It also discusses appropriate steps for the practice of patient-centered care, including a reevaluation of late arrival policies at hospitals and clinics.

A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers

Late-arriving patients have become a prominent concern in several ambulatory care clinics across the globe. Accommodating them could lead to detrimental ramifications such as schedule disruption and increased waiting time for forthcoming patients, which, in turn, could lead to patient dissatisfaction, reduced care quality, and physician burnout. However, rescheduling late arrivals could delay access to care. This paper aims to predict the patient-specific risk of late arrival using machine learning (ML) models. Data from two different ambulatory care facilities are extracted, and a comprehensive list of predictor variables is identified or derived from the electronic medical records. A comparative analysis of four ML algorithms (logistic regression, random forests, gradient boosting machine, and artificial neural networks) that differ in their training mechanism is conducted. The results indicate that ML algorithms can accurately predict patient lateness, but a single model cannot perform best with respect to predictive performance, training time, and interpretability. Prior history of late arrivals, age, and afternoon appointments are identified as critical predictors by all the models. The ML-based approach presented in this research can serve as a decision support tool and could be integrated into the appointment system for effectively managing and mitigating tardy arrivals.

Capacity and patient flow planning in post-term pregnancy outpatient clinics: a computer simulation modelling study

BACKGROUND

The demand for a large Norwegian hospital's post-term pregnancy outpatient clinic has increased substantially over the last 10 years due to changes in the hospital's catchment area and to clinical guidelines. Planning the clinic is further complicated due to the high did not attend rates as a result of women giving birth. The aim of this study is to determine the maximum number of women specified clinic configurations, combination of specified clinic resources, can feasibly serve within clinic opening times.

METHODS

A hybrid agent based discrete event simulation model of the clinic was used to evaluate alternative configurations to gain insight into clinic planning and to support decision making. Clinic configurations consisted of six factors: X0: Arrivals. X1: Arrival pattern. X2: Order of midwife and doctor consultations. X3: Number of midwives. X4: Number of doctors. X5: Number of cardiotocography (CTGs) machines. A full factorial experimental design of the six factors generated 608 configurations.

RESULTS

Each configuration was evaluated using the following measures: Y1: Arrivals. Y2: Time last woman checks out. Y3: Women's length of stay (LoS). Y4: Clinic overrun time. Y5: Midwife waiting time (WT). Y6: Doctor WT. Y7: CTG connection WT. Optimisation was used to maximise X0 with respect to the 32 combinations of X1-X5. Configuration 0a, the base case Y1 = 7 women and Y3 = 102.97 [0.21] mins. Changing the arrival pattern (X1) and the order of the midwife and doctor consultations (X2) configuration 0d, where X3, X4, X5 = 0a, Y1 = 8 woman and Y3 86.06 [0.10] mins.

CONCLUSIONS

The simulation model identified the availability of CTG machines as a bottleneck in the clinic, indicated by the WT for CTG connection effect on LoS. One additional CTG machine improved clinic performance to the same degree as an extra midwife and an extra doctor. The simulation model demonstrated significant reductions to LoS can be achieved without additional resources, by changing the clinic pathway and scheduling of appointments. A more general finding is that a simulation model can be used to identify bottlenecks, and efficient ways of restructuring an outpatient clinic.