The Psychological, Physiological, and Behavioral Responses of Patients to Magnetic Resonance Imaging (MRI): A Systematic Review and Meta-Analysis.

BACKGROUND: MRI is generally well-tolerated although it may induce physiological stress responses and anxiety in patients. PURPOSE: Investigate the psychological, physiological, and behavioral responses of patients to MRI, their evolution over time, and influencing factors. STUDY TYPE: Systematic review with meta-analysis. POPULATION: 181,371 adult patients from 44 studies undergoing clinical MRI. ASSESSMENT: Pubmed, PsycInfo, Web of Science, and Scopus were systematically searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Quality appraisal was conducted with the Joanna Briggs Institute critical appraisal tools. Meta-analysis was conducted via Meta-Essentials workbooks when five studies were available for an outcome. Psychological and behavioral outcomes could be analyzed. Psychological outcomes were anxiety (State-Trait-Anxiety Inventory, STAI-S; 37) and willingness to undergo MRI again. Behavioral outcomes included unexpected behaviors: No shows, sedation, failed scans, and motion artifacts. Year of publication, sex, age, and positioning were examined as moderators. STATISTICAL TESTS: Meta-analysis, Hedge's g. A P value <0.05 was considered to indicate statistical significance. RESULTS: Of 12,755 initial studies, 104 studies were included in methodological review and 44 (181,371 patients) in meta-analysis. Anxiety did not significantly reduce from pre- to post-MRI (Hedge's g = -0.20, P = 0.051). Pooled values of STAI-S (37) were 44.93 (pre-MRI) and 40.36 (post-MRI). Of all patients, 3.9% reported unwillingness to undergo MRI again. Pooled prevalence of unexpected patient behavior was 11.4%; rates for singular behaviors were: Failed scans, 2.1%; no-shows, 11.5%; sedation, 3.3%; motion artifacts, 12.2%. Year of publication was not a significant moderator (all P > 0.169); that is, the patients' response was not improved in recent vs. older studies. Meta-analysis of physiological responses was not feasible since preconditions were not met for any outcome. DATA CONCLUSION: Advancements of MRI technology alone may not be sufficient to eliminate anxiety in patients undergoing MRI and related unexpected behaviors. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 5.

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.

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.

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.

Reducing No-Show Appointments in Preanesthesia Screening.

PURPOSE: Preanesthesia screening is critical to evaluate the patient's medical and surgical history before any procedure or surgery to assess for risks and to optimize outcomes during the perioperative period. The purpose of this quality improvement project was to decrease the number of missed appointments in the outpatient preanesthesia and surgical screening clinic and the impact on provider satisfaction. DESIGN: The design of this quality improvement project was pre and post design. Automated and live phone calls reminders were provided for patients scheduled in the outpatient preanesthesia. Data were collected to compare missed appointment rates from a 3-month period before the project implementation and a 3-month period afterward. METHODS: Predata collection included the number of no-shows in the electronic health record system from the previous 3 months. Participants included all adult patients who are scheduled for a preanesthesia surgical screening appointment. Provider satisfaction was assessed using a 5-question survey, pre and postinnovation. FINDINGS: Reminder systems had a statistically significant impact on reducing the number of no-shows in the preanesthesia and surgical screening clinic. No significant impact was shown in provider satisfaction. CONCLUSIONS: Implementation of a reminder system can help to reduce no-show rates in clinics. Patient no-shows overload the health system by reducing the productivity of providers and waste resources including use of clinic staff, longer wait times for other patients, and the timing providers put into chart preparation.

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.

Missed Physiotherapy Appointment and Its Influence on Cost, Efficiency and Patients' Outcomes.

Background: Missed appointments is a significant challenge to efficient running of physiotherapy departments and it has cost implications. In this study, wait time, and pattern, predictors and impact of Missed appointments (MAs) on cost, efficiency and recovery time was assessed among Nigerian patients receiving physiotherapy. Method: In this retrospective study a total of 3,243 physiotherapy appointments were booked between 2009 and 2019 at an Outpatient Physiotherapy Clinic in Nigeria. Data were collected on Missed appointments, on costs of of treatment and on socio-demographic characteristics. The total revenue loss due to missed appointments was calculated as a product of the total of Missed appointments and cost per treatment; recovery time was also estimated. Results: Missed appointments were 1,701 out of 3,243 booked (52.5%) and the average wait time for the first appointment was 9.6 ± 23.2 days. The proportion of Missed appointments was higher among females (50.2%), patients who were not resident of the same location as the clinic (45.3%), patients with orthopaedic conditions (56.2%) and patients referred from an orthopaedic surgeon (32.8%). Females, those who live within the city, and those with neurological/medical conditions were 1.68, 1.24, and 1.52 times more likely to have Missed appointments compared to males (OR = 1.68, Confidence intervals = 1.44 - 1.96, p = < 0.001), those who live outside the city (OR = 1.24, CI = 1.05 - 1.46, p = 0.01), and to those who have orthopaedic conditions (OR = 1.52, CI = 1.20 - 1.93, p = < 0.001), respectively. Using per treatment schedule cost of N1000 (an equivalent of $ 2.31), a 52.5% Missed appointments rate resulted in lower efficiency of 76.6% with an efficiency ratio of 0.23. Further, a 52.5% Missed appointments rate could potentially impact patient recovery time by 3402 days if Missed appointments slow a patient recovery process by 2 days. Conclusions: Missed appointments for physiotherapy treatment pose a significant challenge in terms of costs, efficiency, and patient recovery time. Thus, an innovative reminder system may help reduce patients' non-attendance to physiotherapy and its consequences.

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.

Telehealth as a Tool to Improve Access and Reduce No-Show Rates in a Large Safety-Net Population in the USA.

Low-income populations are at higher risk of missing appointments, resulting in fragmented care and worsening disparities. Compared to face-to-face encounters, telehealth visits are more convenient and could improve access for low-income populations. All outpatient encounters at the Parkland Health between March 2020 and June 2022 were included. No-show rates were compared across encounter types (face-to-face vs telehealth). Generalized estimating equations were used to evaluate the association of encounter type and no-show encounters, clustering by individual patient and adjusting for demographics, comorbidities, and social vulnerability. Interaction analyses were performed. There were 355,976 unique patients with 2,639,284 scheduled outpatient encounters included in this dataset. 59.9% of patients were of Hispanic ethnicity, while 27.0% were of Black race. In a fully adjusted model, telehealth visits were associated with a 29% reduction in odds of no-show (aOR 0.71, 95% CI: 0.70-0.72). Telehealth visits were associated with significantly greater reductions in probability of no-show among patients of Black race and among those who resided in the most socially vulnerable areas. Telehealth encounters were more effective in reducing no-shows in primary care and internal medicine subspecialties than surgical specialties or other non-surgical specialties. These data suggest that telehealth may serve as a tool to improve access to care in socially complex patient populations.

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.

Intraday dynamic rescheduling under patient no-shows.

Patient no-shows are a major source of uncertainty for outpatient clinics. A common approach to hedge against the effect of no-shows is to overbook. The trade-off between patient's waiting costs and provider idling/overtime costs determines the optimal level of overbooking. Existing work on appointment scheduling assumes that appointment times cannot be updated once they have been assigned. However, advances in communication technology and the adoption of online (as opposed to in-person) appointments make it possible for appointments to be flexible. In this paper, we describe an intraday dynamic rescheduling model that adjusts upcoming appointments based on observed no-shows. We formulate the problem as a Markov Decision Process in order to compute the optimal pre-day schedule and the optimal policy to update the schedule for every scenario of no-shows. We also propose an alternative formulation based on the idea of 'atomic' actions that allows us to apply a shortest path algorithm to solve for the optimal policy more efficiently. Based on a numerical study using parameter estimates from existing literature, we find that intraday dynamic rescheduling can reduce expected cost by 15% compared to static scheduling.

Lost in Follow-Up: Predictors of Patient No-Shows to Clinic Follow-Up After Abdominal Injury.

BACKGROUND: The aim of this study is to evaluate risk factors for non-attendance to post-discharge, hospital follow-up appointments for traumatically injured patients who underwent exploratory laparotomy. METHODS: This is a retrospective chart review of patients who underwent exploratory laparotomy for traumatic abdominal injury at an urban, Midwestern, level I trauma center with clinic follow-up scheduled after discharge. Clinically, relevant demographic characteristics, patients' distance from hospital, and the presence of staples, sutures, and drains requiring removal were collected. Descriptive statistics of categorical variables were calculated as totals and percentages and compared with a chi-squared test or Fisher's exact when appropriate. RESULTS: The sample included 183 patients who were largely assaultive trauma survivors (68%), male (80%), and black (53%) with a mean age of 35.4 ± 14.9 years. Overall, 18.5% no-showed for their follow-up appointment. On multivariate analysis for clinic no-show; length of stay (odds ratio = 0.92 [0.84-0.99], P = 0.04) and the need for suture, staple, or drain removal were protective for clinic attendance (odds ratio = 5.59 [1.07-7.01], P = 0.04). Overall, 12 patients (6.4%) were readmitted. Forty patients (18.3%) had their follow-up in the emergency department (ED). On multivariate regression of risk factors for ED visits, the only statistically significant factors (P < 0.05) were clinic appointment no-show (OR = 2.81) and self-pay insurance (OR = 4.78). CONCLUSIONS: Abdominal trauma patients are at high risk of no-show for follow-up appointments and no-show visits are associated with ED visits. Future work is needed evaluating interventions to improve follow-up.

The Role of Cognitive Impairment on Physical Therapy Attendance and Outcomes in Multiple Sclerosis.

BACKGROUND AND PURPOSE: Many persons with multiple sclerosis (PwMS) experience cognitive impairments, which may affect their ability to engage in physical therapy. There is limited information on how cognitive impairments are associated with PwMS' ability to participate and improve their functional outcomes. This study aimed to assess the relationship between cognitive functioning and PwMS' attendance, total goal attainment, and functional improvement following physical therapy intervention. METHODS: Participants (n = 45) were PwMS who participated in a larger self-management study and enrolled in physical therapy within the past 2 years. Objective cognitive functioning was examined using tests of prospective memory, retrospective memory, working memory, and processing speed, along with a self-report measure. Bivariate analyses were conducted to examine the relationship between cognitive functioning and each physical therapy outcome (session attendance, attaining goals, and changes in functional outcome measures), followed by logistic regressions with age, education, gender, and disability level as covariates. RESULTS: Difficulty learning new verbal information was associated with a greater likelihood of "no showing" one or more of their physical therapy sessions. Reductions in working memory and processing speed were associated with PwMS not meeting all their rehabilitation goals. Despite deficits in new learning, memory, and processing speed, 85.2% of those with pre-/postscores showed improvements in at least one functional outcome measure following physical therapy intervention. DISCUSSION AND CONCLUSIONS: These findings demonstrate the ability for PwMS to make functional motor gains despite the presence of cognitive impairments and highlight the potential contributions of cognitive functioning on attendance and goal attainment of physical therapy intervention.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A362, which includes background, methods, results, and discussion in the authors' own voices).

Prospective Evaluation of a Machine-Learning Prediction Model for Missed Radiology Appointments.

The term "no-show" refers to scheduled appointments that a patient misses, or for which she arrives too late to utilize medical resources. Accurately predicting no-shows creates opportunities to intervene, ensuring that patients receive needed medical resources. A machine-learning (ML) model can accurately identify individuals at high no-show risk, to facilitate strategic and targeted interventions. We used 4,546,104 non-same-day scheduled appointments in our medical system from 1/1/2017 through 1/1/2020 for training data, including 631,386 no-shows. We applied eight ML techniques, which yielded cross-validation AUCs of 0.77-0.93. We then prospectively tested the best performing model, Gradient Boosted Regression Trees, over a 6-week period at a single outpatient location. We observed 123 no-shows. The model accurately identified likely no-show patients retrospectively (AUC 0.93) and prospectively (AUC 0.73, p < 0.0005). Individuals in the highest-risk category were three times more likely to no-show than the average of all other patients. No-show prediction modeling based on machine learning has the potential to identify patients for targeted interventions to improve their access to medical resources, reduce waste in the medical system and improve overall operational efficiency. Caution is advised, due to the potential for bias to decrease the quality of service for patients based on race, zip code, and gender.

Conversion of No-Show Patients to Telehealth in a Primary Medicine Clinic.

No-shows in primary care clinics prevent patients from receiving essential care and decrease clinic productivity. The COVID-19 pandemic forced physicians to adjust to telemedicine as a necessary method to provide care. In this study no-show patients were converted to telehealth visits thereby allowing physicians to care for their patients and maintain hospital revenue. The most common reasons for "no-shows" were found to be forgetting appointments and transportation issues.

Mitigating Nonattendance Using Clinic-Resourced Incentives Can Be Mutually Beneficial: A Contingency Management-Inspired Partially Observable Markov Decision Process Model.

OBJECTIVES: Nonattendance of appointments in outpatient clinics results in many adverse effects including inefficient use of valuable resources, wasted capacity, increased delays, and gaps in patient care. This research presents a modeling framework for designing positive incentives aimed at decreasing patient nonattendance. METHODS: We develop a partially observable Markov decision process (POMDP) model to identify optimal adaptive reinforcement schedules with which financial incentives are disbursed. The POMDP model is conceptually motivated based on contingency management evidence and practices. We compare the expected net profit and trade-offs for a clinic using data from the literature for a base case and the optimal positive incentive design resulting from the POMDP model. To accommodate a less technical audience, we summarize guidelines for reinforcement schedules from a simplified Markov decision process model. RESULTS: The results of the POMDP model show that a clinic can increase its net profit per recurrent patient while simultaneously increasing patient attendance. An increase in net profit of 6.10% was observed compared with a policy with no positive incentive implemented. Underlying this net profit increase is a favorable trade-off for a clinic in investing in a targeted contingency management-based positive incentive structure and an increase in patient attendance rates. CONCLUSIONS: Through a strategic positive incentive design, the POMDP model results show that principles from contingency management can support decreasing nonattendance rates and improving outpatient clinic efficiency of its appointment capacity, and improved clinic efficiency can offset the costs of contingency management.

Patient Engagement with Interactive Text Message System Improves Successful Colonoscopy Rates in an Outpatient Endoscopy Center.

INTRODUCTION: Text message-based interventions reduce colonoscopy no-show rates and improve bowel preparation scores. In this non-randomized study, we assessed whether an interactive text messaging system could improve colonoscopy outcomes. METHODS: Colonoscopy pre-procedural instructions were programmed into a dedicated software platform created for this study. In the intervention arm, text messages were sent to veterans during a 4-week study period. Validated pre-procedural satisfaction questionnaires were completed by patients during standard protocol and intervention periods. Demographics and colonoscopy outcomes data were compared between the standard protocol and intervention arms, including procedure completion rate on scheduled date, Boston bowel preparation score (BPPS), adenoma detection rate, and satisfaction. RESULTS: Of 241 patients, 128 were in the standard protocol arm and 113 in the intervention arm. Higher proportions of patients receiving text messages underwent colonoscopy on their scheduled date (69.9%) compared to the ones in the standard protocol (50.8%, p = 0.015). Patients with ≥3 interactions with the system had 80.6% likelihood of completing colonoscopy on the scheduled date compared to 56.9% with <3 interactions and 50.8% with standard protocol (p < 0.001). Frequency of interaction with the system was similar between older (>65 years) and younger patients (p = 1.0). Among older patients, colonoscopy was completed successfully in 84.2% when alert-based human interactions occurred compared to 65.6% in those without and 47.9% with standard protocol (p = 0.018). More than 90% indicated they would recommend the system to patients undergoing future colonoscopy. CONCLUSION: An interactive text messaging system improves successful colonoscopy rates in a VA setting, with greatest impact in older patients.

Short Message Service reminders reduce outpatient colonoscopy nonattendance rate: A randomized controlled study.

BACKGROUND AND AIM: Nonattendance of outpatient colonoscopy leads to inefficient use of health-care resources. We aimed to study the effectiveness of using Short Message Service (SMS) reminder prior in patients scheduled for outpatient colonoscopy on their nonattendance rate. METHODS: Patients who scheduled for an outpatient colonoscopy and had access of SMS were recruited from three clinics in Hong Kong. Patients were randomized to SMS group and standard care (SC) group. All patients were given a written appointment slip on the booking date. In addition, patients in the SMS group received an SMS reminder 7-10 days before their colonoscopy appointment. Patients' demographics, attendance, colonoscopy completion, and bowel preparation quality were recorded. Logistic regression was performed to identify predictors of nonattendance. RESULTS: From November 2013 to October 2019, a total of 2225 eligible patients were recruited. A total of 1079 patients were allocated to the SMS group and 1146 to the SC group. The nonattendance rate of patients in the SMS group was significantly lower than that in the SC group (8.9% vs 11.9%, P = 0.022). There were no significant differences in their baseline characteristics and colonoscopy completion rate and bowel preparation quality. A trend towards a higher rate of adequate bowel preparation was observed in the SMS group when compared with the SC group (69.9% vs 65.8%, P = 0.053). Independent predictors for nonattendance included younger age, underprivilege, and existing diabetes. CONCLUSIONS: An SMS reminder for outpatient colonoscopy is effective in reducing the nonattendance rate and may potentially improve the bowel preparation quality.

Cigarette Smoking in an Acute Partial Hospital Program.

ABSTRACT: This study explored demographic and clinical features, plus clinical outcomes, in a smoke-free acute partial hospital (PH) among current smokers, former smokers, and those who had never smoked (nonsmokers). Compared with nonsmokers, current smokers were younger and more likely to be unmarried and unpartnered, unemployed, or receiving disability benefits. They had more prior inpatient (IP) and PH episodes. They also had more problems with interpersonal relationships, mood lability, psychosis, and substance use. Compared with nonsmokers, current smokers were more likely to miss PH treatment days and drop out. They also had longer time to readmission to PH or IP. Former smokers resembled nonsmokers, except that former smokers also had a high rate of dropout. Changes in symptoms and functioning for patients who completed PH were the same among all groups. In an acute PH setting, smoking is a marker for psychiatric and psychosocial impairment plus treatment interruption.