Patients' intention to make an up-front payment at private outpatient clinics in Malaysia as a no-show reduction method.

Regulating patients' no-show behavior is critical from the standpoints of sustainable medical services and operational efficiencies. The purpose of this study was to evaluate the patients' intention to make partial up-front payments at outpatient clinics. This was a cross-sectional study design introducing a self-administered questionnaire to 221 outpatients at a private health facility. The questionnaire measured the patient's demographic characteristics, perceived usefulness (PU), trust in the health facility, and intention to make upfront partial payments. Out of the total respondents, 57.4% were female. There were 34.8% Malays, 40.6% Chinese and 24.6% Indians. The majority (66.5%) of the respondents attained tertiary education. Nearly a third of the respondents (30.5%) reported an income between 3000 and 5000 Malaysian Ringgit (RM). Regarding payment mode, just more than half (51.1%) made self-payment, and 21.8% by guaranteed letter. A quarter (24.9%) waited more than 3 h for consultation and 59.6% visited the health facility more than 2 times in a year. Initial analysis showed that PU, trust, age, education, number of visits, and hours of waiting were significantly associated with the intention to make a partial payment. Multiple linear regression showed that perceived usefulness (B = 0.517, p < 0.001); trust in hospital management (B = 0.288, p < 0.001) and number of visits (B = 0.216, p < 0.001) were associated with the intention to make partial payment. Intention to make partial up-front payments is associated with higher perceived usefulness in making such payments and hospital trust. Visiting the health facility frequently was associated with a higher intention to make upfront partial payment. The result may guide further studies on potential remedies to no-show.

Predicting Patient No-Shows: Situated Machine Learning with Imperfect Data.

Patients who do not show up for scheduled appointments are a considerable cost and concern in healthcare. In this study, we predict patient no-shows for outpatient surgery at the endoscopy ward of a hospital in Denmark. The predictions are made by training machine leaning (ML) models on available data, which have been recorded for purposes other than ML, and by doing situated work in the hospital setting to understand local data practices and fine-tune the models. The best performing model (XGBoost with oversampling) predicts no-shows at sensitivity = 0.97, specificity = 0.66, and accuracy = 0.95. Importantly, the situated work engaged local hospital staff in the design process and led to substantial quantitative improvements in the performance of the models. We consider the results promising but acknowledge that they are from a single ward. To transfer the no-show models to other wards and hospitals, the situated work must be redone.

Prevalence of financial hardship and health-related social needs among patients with missed radiology appointments.

PURPOSE: We aimed to evaluate the prevalence of financial hardship and Health-Related Social Needs (HRSN) among patients who missed their radiology appointment. METHODS: English-speaking adult patients, with a missed outpatient imaging appointment at any of a tertiary care imaging centers between 11/2022 and 05/2023 were eligible. We measured self-reported general financial worry using Comprehensive Score for Financial Toxicity (COST), imaging hardship (worry that the current imaging is a financial hardship to patient and their family), material hardship (e.g., medical debt), cost-related care nonadherence, and HRSNs including housing instability, food insecurity, transportation problems, and utility help needs. RESULTS: 282 patients were included (mean age 54.7 ± 15.0 years; 70.7 % female). Majority were non-Hispanic White (52.4 %), followed by Asian (23.0 %) and Hispanic (16.0 %) racial/ethnic background. Most missed appointments were patient-initiated (74.8 %); 13.5 % due to cost or insurance coverage and 6.4 % due to transportation and parking. Mean COST score was 26.8 with 44.4 % and 28.8 % reporting their illness and imaging as a source of financial hardship. 18.3 % and 35.2 % endorsed cost-related care nonadherence and material hardship. 32.7 % had at least one HRSNs with food insecurity the most common (25.4 %). Only 12.5 % were previously screened for financial hardship or HRSNs. Having comorbidity and living in more disadvantaged neighborhoods was associated with higher report of financial hardship and HRSNs. CONCLUSION: Financial hardship and HRSNs are common among those who miss radiology appointments. There needs to be more rigorous screening for financial hardship and HRSNs at every health encounter and interventions should be implemented to address these.

Predictors of No-Shows and Cancellations in an Outpatient Neuropsychology Clinic in a Large Healthcare System.

BACKGROUND: The purpose of this study was to evaluate potential predictors of no-shows and late cancellations in an outpatient clinic within a large healthcare system serving vulnerable communities. METHODS: Demographic data and appointment status were recorded for 537 consecutive patients scheduled for neuropsychological evaluation in an outpatient psychiatry clinic. Patients include 220 males and 317 females with an average formal education of 11.01 years (SD = 3.87) and age of 55.64 years (SD = 16.20). RESULTS: The overall rate of no-shows or late cancellations was 20%. Of the 106 patients who no-showed/late cancelled, 41% rescheduled, and of those, 23% missed or late cancelled their second appointment. No-shows and late cancellations were associated with historical/prior no-show rate, while race/ethnicity and activation of MyChart had slight impacts. CONCLUSIONS: These data suggest that prior no-show rates and MyChart access may be targets for interventions to improve show rates. This is important for the patients' gaining access to care as well as minimizing financial strains for the system and increasing wait times/delays to care for other patients.

Plastic and Reconstructive Surgery Resident Indigent Care Clinic Optimization by Improving "No-Show" Rates.

ABSTRACT: Absenteeism among clinical patients is a significant source of inefficiency in the modern American health care system. Routine absenteeism limits access to care for indigent patients, thus providing additional strain on the health care system and timely administration of care.This quality improvement project set out to quantify, understand, and potentially reduce patient absenteeism in our weekly plastic and reconstructive surgery resident indigent care clinic. One year prior to our study was retrospectively reviewed to determine a baseline rate of absenteeism (no shows). The daily and monthly no-show percentages were calculated. Then, three consecutive 2-month Plan, Do, Study, Act (PDSA) cycles were performed and data were recorded.The initial year analysis demonstrated an average no-show rate of 25%. The first PDSA cycle attempted to ascertain factors contributing to absenteeism and to get patients rescheduled. The rate of clinical absenteeism was 27% over this period compared with a rate of 18% in the control period. During this period, we discovered a limitation of our institution's electronic medical record (EMR). Rescheduled patients were removed from the original schedule and were not counted as a missed appointment even though the opportunity for care was missed. The second PDSA cycle attempted to collect raw data while trying to understand the EMR error and rescheduling process. During this period, there was a 33% no-show rate compared with 27% in the control period. The third PDSA cycle attempted again to establish factors contributing to clinical absenteeism with a better understanding of the limitations of our EMR. A 33% no-show rate during this cycle was recorded compared with 22% in the control period. After three PDSA cycles were completed, our clinic had an average no-show rate of 31% compared with 25% during the same months in the previous year.This project brought to realization that our data were initially skewed by our ignorance of an EMR flaw that did not track patients who either canceled or rescheduled their appointments. We also learned that there is a certain subset of patients who are not able to be contacted and who do not follow up.

Association of Unmet Social Needs With Missed In-Person Urology Clinic Appointments.

INTRODUCTION: Social determinants of health (SDH) are nonbiologic influencers of disease and health care disparities. This study focused on understanding the association between SDH and urology clinic "no-show" visits within a diverse urban population. METHODS: We retrospectively identified patients scheduled for urology clinic visits from October 2015 to June 2022 who completed a 10-question social needs screener. For each patient, demographic variables, and number of missed clinic appointments were abstracted. Multivariable logistic regression was performed to determine the association of unmet social needs and no-shows. RESULTS: Of 5761 unique patients seen in clinic, 5293 completed a social needs screener. Respondents were most commonly male (62.8%), Hispanic (50.3%), English-speaking (75.5%), and insured by Medicare (46.0%). Overall, 8.2%, 4.6%, and 6.1% reported 1, 2, and 3+ unmet social needs, respectively. Most patients (61.7%) had 0 no-shows; 38.3% had 1+ no-shows. Between the 0 and 1+ no-show groups, we found significant differences with respect to gender (P =.05), race/ethnicity (P = .002), preferred language (P = .006), insurance payer (P < .001), SDH status (P = .003), and total number of unmet social needs (P = .006). On multivariable analysis, patients concerned about housing quality (odds ratio [OR] = 1.50, P = .002), legal help (OR = 1.53, P = .009), and with 3+ unmet social needs (OR = 1.39, P = .006) were more likely to have 1+ no-shows. CONCLUSIONS: Unmet social needs were associated with increased no-show urology clinic visits. Routine social needs screening could identify at-risk patients who would benefit from services. This may be particularly pertinent for patients with urgent diagnoses or those requiring frequent office visits where missing appointments could impact morbidity and mortality.

Sociodemographic Factors Associated with Outpatient Radiology No-shows Versus Cancellations.

RATIONALE AND OBJECTIVES: To assess prevalence of missed outpatient radiology appointments and sociodemographic factors associated with no-shows vs. cancellations. METHODS: Adults with outpatient radiology appointments in 2022 and January 2023 at a single tertiary academic health center were included. Generalized estimating equation regression was used to evaluate sociodemographic factors associated with missed vs. completed appointments, no-shows vs. cancellations and time interval between cancellations and appointments. RESULTS: 19,262 (24.3%) examinations were either a cancellation (22.3%) or no-show (2.0%) among 9713 patients (mean age 60.8 ± 15.5; 67.1% female, 63.9% White, 20.0% Asian, 22.0% Hispanics). Among cancellations, 70.19% were patient-initiated. Age ≥ 65 significantly decreased the probability of missed appointments by 5.4% point (pp) (95% CI: 3.7-7.2) or no-shows (4.2 pp; 95% CI, 1.4-6.9), while being single increased probability of missed appointments (2.2 pp; 95% CI, 1.2-3.1) or no-shows (2.6 pp; 95% CI, 1.2-4.1). Those uninsured or with public insurance were 1.3-4.9 pp more likely to miss appointments than commercial insurance, and 2.2-7.6 pp more likely to have no-shows than cancellations. Living in disadvantaged neighborhoods 4.9 pp (95% CI, 3.9-6.0) increased likelihood of missing appointment and was associated with shorter time interval between cancellation and appointment. English speakers were 2.2 pp (95% CI, 1.1-3.3) more likely to miss their exam, while 2.7 pp (95% CI, 1.1-0.4.3) less likely to be a no-show than cancellation. CONCLUSION: Cancellations represented a significant portion of missed appointments. Specific sociodemographic subgroups exhibited higher tendencies for having missed appointments and no-shows.

Factors associated with the absence of postpartum consultations in a high-risk population.

Objective: To assess the rate of missed postpartum appointments at a referral center for high-risk pregnancy and compare puerperal women who did and did not attend these appointments to identify related factors. Methods: This was a retrospective cross-sectional study with all women scheduled for postpartum consultations at a high-risk obstetrics service in 2018. The variables selected to compare women were personal, obstetric, and perinatal. The variables of interest were obtained from the hospital's electronic medical records. Statistical analyses were performed using the Chi-square, Fisher's exact, or Mann-Whitney tests. For the variable of the interbirth interval, a receiver operating characteristic curve (ROC) was used to best discriminate whether or not patients attended the postpartum consultation. The significance level for the statistical tests was 5%. Results: A total of 1,629 women scheduled for postpartum consultations in 2018 were included. The rate of missing the postpartum consultation was 34.8%. A shorter interbirth interval (p = 0.039), previous use of psychoactive substances (p = 0.027), current or former smoking (p = 0.003), and multiparity (p < 0.001) were associated with non-attendance. Conclusion: This study showed a high rate of postpartum appointment non-attendance. This is particularly relevant because it was demonstrated in a high-risk obstetric service linked to clinical severity or social vulnerability cases. This highlights the need for new approaches to puerperal women before hospital discharge and new tools to increase adherence to postpartum consultations, especially for multiparous women.

Public health nurse perspectives on predicting nonattendance for cervical cancer screening through classification, ensemble, and deep learning models.

OBJECTIVES: Women's attendance to cervical cancer screening (CCS) is a major concern for healthcare providers in community. This study aims to use the various algorithms that can accurately predict the most barriers of women for nonattendance to CS. DESIGN: The real-time data were collected from women presented at OPD of primary health centers (PHCs). About 1046 women's data regarding attendance and nonattendance to CCS were included. In this study, we have used three models, classification, ensemble, and deep learning models, to compare the specific accuracy and AU-ROC for predicting non-attenders for CC. RESULTS: The current model employs 22 predictors, with soft voting in ensemble models showing slightly higher specificity (96%) and sensitivity (93%) than weighted averaging. Bagging excels with the highest accuracy (98.49%), specificity (97.3%), and ideal sensitivity (100%) with an AUC of 0.99. Classification models reveal Naive Bayes with higher specificity (97%) but lower sensitivity (91%) than Logistic Regression. Random Forest and Neural Network achieve the highest accuracy (98.49%), with an AUC of 0.98. In deep learning, LSTM has an accuracy of 95.68%, higher specificity (97.60%), and lower sensitivity (93.42%) compared to other models. MLP and NN showed the highest AUC values of 0.99. CONCLUSION: Employing ensemble and deep learning models proved most effective in predicting barriers to nonattendance in cervical screening.

Telehealth Visits have Lower Non-attendance than Traditional Orthopaedic Clinic Visits.

The use of telemedicine has rapidly expanded in the wake of the COVID pandemic, but its effect on patient attendance remains unknown for different clinicians. This study compared traditional in-clinic visits with telehealth visits by retrospectively reviewing all scheduled orthopaedic clinic visits. Results demonstrated lower rates of cancellations in patients scheduled for telehealth visits as compared to in-clinic visits, during the initial COVID pandemic. In general, physicians can expect a lower cancellation rate than non-physician practitioners.

Patient Factors Associated with Missed Otolaryngology Appointments at an Urban Safety-Net Hospital.

OBJECTIVE: To determine if patient factors related to ethnicity, socioeconomic status (SES), medical comorbidities, or appointment characteristics increase the risk of missing an initial adult otolaryngology appointment. METHODS: This study is a retrospective case control study at Boston Medical Center (BMC) in Boston, Massachusetts, that took place in 2019. Patient demographic and medical comorbidity data as well as appointment characteristic data were collected and compared between those that attended their initial otolaryngology appointment versus those who missed their initial appointment. Chi-square and ANOVA tests were used to calculate differences between attendance outcomes. Multivariate analysis was used to compare the odds of missing an appointment based on various patient- and appointment-related factors. RESULTS: Patients who were more likely to miss their appointments were more often female, of lower education, disabled, not employed, Black or Hispanic, and Spanish-speaking. Spring and Fall appointments were more likely to be missed. When a multivariate regression was conducted to control for social determinants of health (SDOH) such as race, insurance status, employment, and education status, the odds of females, Spanish-speaking, students, and disabled patients missing their appointment were no longer statistically significant. CONCLUSION: A majority of patients at BMC come from lower SES backgrounds and have multiple medical comorbidities. Those who reside closer to BMC, often areas of lower average income, had higher rates of missed appointments. Interventions such as decreasing lag time, providing handicap-accessible free transportation, and increasing accessibility of telemedicine for patients could help improve attendance rates at BMC. LEVEL OF EVIDENCE: IV Laryngoscope, 134:4003-4010, 2024.

The Impact of Telemedicine on Human Immunodeficiency Virus (HIV)-Related Clinical Outcomes During the COVID-19 Pandemic.

The coronavirus disease of 2019 (COVID-19) pandemic exacerbated barriers to care for people living with human immunodeficiency virus (HIV) (PLWH). The quick uptake of telemedicine in the outpatient setting provided promise for care continuity. In this study, we compared appointment and laboratory no-show rates in an urban outpatient HIV clinic during three time periods: (1) Pre-COVID-19: 9/15/2019-3/14/2020 (predominately in-person), (2) "Early" COVID-19: 3/15/2020-9/14/2020 (predominately telemedicine), and (3) "Later" COVID-19: 9/15/2020-3/14/2021 (mixed in-person/telemedicine). Multivariable logistic regression models evaluated the two study hypotheses: (i) equivalence of Period 2 with Period 1 and of Period 3 with Period 1 and (ii) improved outcomes with telemedicine over in-person visits. No-show rates were 1% in Period 1, 4% in Period 2, and 18% in Period 3. Compared to the pre-pandemic period, individuals had a higher rate of appointment no-shows during Period 2 [OR (90% CI): 7.67 (2.68, 21.93)] and 3 [OR (90% CI): 30.91 (12.83 to 75.06). During the total study period, those with telemedicine appointments were less likely to no-show than those with in-person appointments [OR (95% CI): 0.36 (0.16-0.80), p = 0.012]. There was no statistical difference between telemedicine and in-person appointments for laboratory completion rates. Our study failed to prove that no-show rates before and during the pandemic were similar; in fact, no-show rates were higher during both the early and later pandemic. Overall, telemedicine was associated with lower no-show rates compared to in-person appointments. In future pandemics, telemedicine may be a valuable component to maintain care in PLWH.

Telemedicine Reduces Missed Appointments but Disparities Persist.

INTRODUCTION: Missed appointments also known as no-shows adversely affect clinical outcomes, clinic efficiency, and quality of care and have been attributed to barriers such as work schedule conflicts and lack of transportation. The widespread telemedicine implementation and adoption that has occurred as a consequence of the COVID-19 pandemic has the potential to address these barriers and improve missed appointment rates. This study aims to analyze the relationship between telemedicine and missed appointments. METHODS: This retrospective cohort study used electronic health records data from a safety-net academic health center with federally qualified clinics (March 2020-December 2022). Bivariate and multivariable generalized estimating equations were used to analyze the relationship between no-show and appointment type (in-person versus telemedicine appointment). Stratified adjusted regression analyses were used to calculate the average change in the marginal effect of telemedicine appointments on no-shows across covariates. The data were analyzed from September 2023 to October 2023. RESULTS: Hispanic patients accounted for 60% of the 474,212 appointments, followed by non-Hispanic White (22.5%), non-Hispanic Black (13.3%), Asian (2.7%), Native American (1%), and other race/ethnicity patients (0.6%). The no-show rate for telemedicine appointments was 12% compared with 25% for in-person appointments. Multivariable analysis showed that telemedicine appointment was associated with a decreased likelihood of no-show compared with in-person appointments (OR=0.40, 95% CI=0.40, 0.41). The average change in the marginal effect of telemedicine appointments on the reduction of no-shows across race/ethnicity was greatest for Native American and non-Hispanic Black patients. CONCLUSIONS: Telemedicine appointments were associated with a decreased likelihood of no-shows, and the protective effect of telemedicine appointments on missed appointments was greatest for underserved groups. Strategies to increase telemedicine uptake, especially for underserved groups, are critical.

Effect of a Patient Portal Reminder Message After No-Show on Appointment Reattendance in Ophthalmology: A Randomized Clinical Trial.

PURPOSE: To assess the efficacy of electronic health record (EHR) messaging for re-engaging patients with ophthalmology care after a missed appointment. DESIGN: Prospective, randomized clinical trial. METHODS: The study setting was an academic ophthalmology department. The patient population comprised of return patients age 18 years or older with an appointment "no show," or missed appointment. Over 2 phases of recruitment, 362 patients with an active patient portal in the EHR were selected consecutively each business day. Patients were randomized using a web-based tool to receive a reminder to reschedule via a standard mailed letter only (control) or the mailed letter plus an electronic message through the EHR within 1 business day of the missed appointment (intervention). Reengagement with eye care was defined as attendance of a rescheduled appointment within 30 days of the no-show visit. Patient charts were reviewed for demographic and clinical data. RESULTS: The average age of recruited patients was 59.9 years, just under half of the sample was male (42.5%, 154/362), and most patients were White (56.9%, 206/362) or Black (36.2%, 131/362). Patients were most commonly recruited from the retina service (39.2%, 142/362) followed by the glaucoma service (29.3%, 106/362). Many patients in this study had previous no-show appointments, with an average no-show rate of 18.8% out of all scheduled visits across our health system. In total, 22.2% (42/189) of patients in the intervention group attended a follow-up appointment within 30 days of their no-show visit compared to 11.6% (20/173) of the control group (OR, 2.186; 95% CI, 1.225-3.898; P = .008). When including only the 74 patients in the intervention group who read the intervention message in the patient portal, 28.4% (21/74) attended a follow-up compared to 11.6% (20/173) of the control group (P = .001). CONCLUSIONS: EHR-based reminder messages sent within a business day of a missed appointment may promote re-engagement in ophthalmology care after appointment no-show.

Appointment Non-attendance for Telehealth Versus In-Person Primary Care Visits at a Large Public Healthcare System.

BACKGROUND: Appointment non-attendance has clinical, operational, and financial implications for patients and health systems. How telehealth services are associated with non-attendance in primary care is not well-described, nor are patient characteristics associated with telehealth non-attendance. OBJECTIVE: We sought to compare primary care non-attendance for telehealth versus in-person visits and describe patient characteristics associated with telehealth non-attendance. DESIGN: An observational study of electronic health record data. PARTICIPANTS: Patients with primary care encounters at 23 adult primary care clinics at a large, urban public healthcare system from November 1, 2019, to August 31, 2021. MAIN MEASURES: We analyzed non-attendance by modality (telephone, video, in-person) during three time periods representing different availability of telehealth using hierarchal multiple logistic regression to control for patient demographics and variation within patients and clinics. We stratified by modality and used hierarchal multiple logistic regression to assess for associations between patient characteristics and non-attendance in each modality. KEY RESULTS: There were 1,219,781 scheduled adult primary care visits by 329,461 unique patients: 754,149 (61.8%) in-person, 439,295 (36.0%) telephonic, and 26,337 (2.2%) video visits. Non-attendance for telephone visits was initially higher than that for in-person visits (adjusted odds ratio 1.04 [95% CI 1.02, 1.07]) during the early telehealth availability period, but decreased later (0.82 [0.81, 0.83]). Non-attendance for video visits was higher than for in-person visits during the early (4.37 [2.74, 6.97]) and later (2.02 [1.95, 2.08]) periods. Telephone visits had fewer differences in non-attendance by demographics; video visits were associated with increased non-attendance for patients who were older, male, had a primary language other than English or Spanish, and had public or no insurance. CONCLUSIONS: Telephonic visits may improve access to care and be more easily adoptable among diverse populations. Further attention to implementation may be needed to avoid impeding access to care for certain populations using video visits.

Effects of a brief pre-admission telephone reminder on no-show and dropout rates in substance use disorder treatment: a quasi-experimental study.

BACKGROUND: Appointment no-show and early dropout from treatment represent major challenges in outpatient substance use disorder treatment, adversely affecting clinical outcomes and health care productivity. In this quasi-experimental study, we examined how a brief reminder intervention for new patients before their first appointment affected treatment participation and retention. No-shows (not attending any sessions) and dropouts (discontinuation of initiated treatment because of three consecutively missed appointments) were compared between a period with pre-admission telephone calls (intervention) and a period without such reminders (non-intervention). METHODS: Participants were all eligible patients (N = 262) admitted to a Norwegian specialist clinic for substance use disorder treatment. We used the Chi-square test for the no-show analysis. Of the eligible patients, 147 were included in a subsequent dropout analysis. We used the number of visits up to 10 appointments as a measure for time to event. Group differences were analyzed using a Kaplan-Meier plot and the log-rank test. To control for relevant sociodemographic variables, as well as substance use and mental distress severity, we used Cox regression. RESULTS: No-show rates did not differ between the two periods (12% for non-intervention vs. 14% for intervention; χ2 = 0.20, p = 0.653). Of those consenting to participate in the dropout analysis (n = 147), 28 (19%) discontinued treatment within the time frame of 10 appointments, with no differences between the two periods (log-rank test = 0.328, p = 0.567). Controlling for baseline characteristics did not alter this finding. In fact, of the registered covariates at baseline, only higher education level was associated with attrition, linked to a reduced risk for dropout (hazard ratio = 0.85, 95% CI = 0.74-0.98, p = 0.025). CONCLUSION: These findings do not provide support for the systematic use of a brief pre-admission telephone reminder in the current treatment setting. TRIAL REGISTRATION: The study was retrospectively registered 13 Jan 2021 at ClinicalTrials.gov, NCT04707599.

Factors Contributing to Non-Attendance of GI Endoscopic Procedures in a Tertiary Care Center in the Middle East.

INTRODUCTION: The cancer burden in the Middle East is high and growing. Colorectal cancer (CRC) is the second most common cancer for both men and women in the UAE. Although early diagnosis of malignancy reduces morbidity and increases the survival rates, non-attendance of gastroenterology (GI) endoscopic procedures is a significant global problem, which can lead to delay in cancer diagnosis and treatment. Several factors have been found to contribute to non-attendance behavior, including socioeconomic, cultural, and organizational related barriers. The purpose of this study was to identify factors contributing to non-attendance behavior among outpatients scheduled for GI endoscopic procedures in a tertiary hospital in the United Arab Emirates. We conclude with recommendations that can help in reducing the rate of patient no-shows for GI endoscopic procedures in the region. METHODS: In a tertiary medical center in the Middle East, we surveyed patients who did not attend their scheduled GI endoscopic procedures over a period of one year. The questionnaire sought to identify possible reasons for patient's non-attendance. Descriptive measures including means, standard deviation, frequencies, and percentages were used to analyze the demographic characteristics of the study participants. The chi-square test was performed to analyze gender differences. RESULTS: Of 314 outpatients who met study inclusion criteria, 168 agreed to participate (53.5% response rate). The majority of participants were women (n=96, 60.4 %), aged 18 to 73, with a mean of 42 years. The largest age group was between 35 and 44 (n=46, 28.9 %). Approximately equal numbers of non-attendance appointments were scheduled for combined colonoscopy and upper endoscopy (36.3 %), colonoscopy alone (31.3 %), or upper endoscopy alone (31.3 %). The most common causes for cancellation or non-attendance included concerns about the appointment (35.5%), inconvenient timing of the appointment (27.9%) and changes in medical status (26.4%). Gender differences were noted for non-attendance behaviors, with women significantly more likely than men to report feelings of embarrassment (Chi-square 6.261, df=1, p=.012). CONCLUSION: Our study has identified several barriers to patient attendance of endoscopic procedures, as well as opportunities to reduce the rate of patient no-shows, including patient education, scheduling options, and protocols to minimize discomfort and misconceptions around GI endoscopic procedures, particularly accommodating for same gender endoscopists, with the ultimate goal of increasing early cancer screening and prevention.

Association of Differences in Treatment Intensification, Missed Visits, and Scheduled Follow-up Interval With Racial or Ethnic Disparities in Blood Pressure Control.

Importance: Black patients with hypertension often have the lowest rates of blood pressure (BP) control in clinical settings. It is unknown to what extent variation in health care processes explains this disparity. Objective: To assess whether and to what extent treatment intensification, scheduled follow-up interval, and missed visits are associated with racial and ethnic disparities in BP control. Design, Setting, and Participants: In this cohort study, nested logistic regression models were used to estimate the likelihood of BP control (defined as a systolic BP [SBP] level <140 mm Hg) by race and ethnicity, and a structural equation model was used to assess the association of treatment intensification, scheduled follow-up interval, and missed visits with racial and ethnic disparities in BP control. The study included 16 114 adults aged 20 years or older with hypertension and elevated BP (defined as an SBP level ≥140 mm Hg) during at least 1 clinic visit between January 1, 2015, and November 15, 2017. A total of 11 safety-net clinics within the San Francisco Health Network participated in the study. Data were analyzed from November 2019 to October 2020. Main Outcomes and Measures: Blood pressure control was assessed using the patient's most recent BP measurement as of November 15, 2017. Treatment intensification was calculated using the standard-based method, scored on a scale from -1.0 to 1.0, with -1.0 being the least amount of intensification and 1.0 being the most. Scheduled follow-up interval was defined as the mean number of days to the next scheduled visit after an elevated BP measurement. Missed visits measured the number of patients who did not show up for visits during the 4 weeks after an elevated BP measurement. Results: Among 16 114 adults with hypertension, the mean (SD) age was 58.6 (12.1) years, and 8098 patients (50.3%) were female. A total of 4658 patients (28.9%) were Asian, 3743 (23.2%) were Black, 3694 (22.9%) were Latinx, 2906 (18.0%) were White, and 1113 (6.9%) were of other races or ethnicities (including American Indian or Alaska Native [77 patients (0.4%)], Native Hawaiian or Pacific Islander [217 patients (1.3%)], and unknown [819 patients (5.1%)]). Compared with patients from all racial and ethnic groups, Black patients had lower treatment intensification scores (mean [SD], -0.33 [0.26] vs -0.29 [0.25]; ß = -0.03, P < .001) and missed more visits (mean [SD], 0.8 [1.5] visits vs 0.4 [1.1] visits; ß = 0.35; P < .001). In contrast, Asian patients had higher treatment intensification scores (mean [SD], -0.26 [0.23]; ß = 0.02; P < .001) and fewer missed visits (mean [SD], 0.2 [0.7] visits; ß = -0.20; P < .001). Black patients were less likely (odds ratio [OR], 0.82; 95% CI, 0.75-0.89; P < .001) and Asian patients were more likely (OR, 1.13; 95% CI, 1.02-1.25; P < .001) to achieve BP control than patients from all racial or ethnic groups. Treatment intensification and missed visits accounted for 21% and 14%, respectively, of the total difference in BP control among Black patients and 26% and 13% of the difference among Asian patients. Conclusions and Relevance: This study's findings suggest that racial and ethnic inequities in treatment intensification may be associated with more than 20% of observed racial or ethnic disparities in BP control, and racial and ethnic differences in visit attendance may also play a role. Ensuring more equitable provision of treatment intensification could be a beneficial health care strategy to reduce racial and ethnic disparities in BP control.

Efficient Prediction of Missed Clinical Appointment Using Machine Learning.

Public health and its related facilities are crucial for thriving cities and societies. The optimum utilization of health resources saves money and time, but above all, it saves precious lives. It has become even more evident in the present as the pandemic has overstretched the existing medical resources. Specific to patient appointment scheduling, the casual attitude of missing medical appointments (no-show-ups) may cause severe damage to a patient's health. In this paper, with the help of machine learning, we analyze six million plus patient appointment records to predict a patient's behaviors/characteristics by using ten different machine learning algorithms. For this purpose, we first extracted meaningful features from raw data using data cleaning. We applied Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling Method (Adasyn), and random undersampling (RUS) to balance our data. After balancing, we applied ten different machine learning algorithms, namely, random forest classifier, decision tree, logistic regression, XG Boost, gradient boosting, Adaboost Classifier, Naive Bayes, stochastic gradient descent, multilayer perceptron, and Support Vector Machine. We analyzed these results with the help of six different metrics, i.e., recall, accuracy, precision, F1-score, area under the curve, and mean square error. Our study has achieved 94% recall, 86% accuracy, 83% precision, 87% F1-score, 92% area under the curve, and 0.106 minimum mean square error. Effectiveness of presented data cleaning and feature selection is confirmed by better results in all training algorithms. Notably, recall is greater than 75%, accuracy is greater than 73%, F1-score is more significant than 75%, MSE is lesser than 0.26, and AUC is greater than 74%. The research shows that instead of individual features, combining different features helps make better predictions of a patient's appointment status.