Patient No-show Rates Research Articles

Effect of Standardized Reminder Calls on Trauma Patient No-Show Rate.

Most patients who sustain a traumatic injury require outpatient follow-up. A common barrier to outpatient postadmission care is patient failure to follow-up. One of the most significant factors resulting in failure to follow-up is age more than 35 years. Recent work has shown that follow-up telephone calls reduce readmission rates. Our aim was to decrease no-show appointments by 10% in 12 months. The electronic medical records at our level I and II trauma centers were queried for all outpatient appointments for trauma between July 1, 2020, and June 9, 2021, and whether the patient attended their follow-up appointment. Patients with visits scheduled after August 1, 2021, received 24- and 48-hour previsit reminder calls. Patients with visits scheduled between July 1, 2020, and August 1, 2021, did not receive previsit calls. Both groups were compared using multivariable direct logistic regression models. A total of 1,822 follow-up opportunities were included in the study. During the pre-implementation phase, there was a no-show rate of 30.9% (329 of 1,064 visits). Postintervention, a 12.2% reduction in overall no-show rate occurred. A statistically significant 11.2% decrease (p < 0.001) was seen in elderly patients. Multivariate analysis showed standardized calls resulted in significantly decreased odds of failing to keep an appointment (adjusted odds ratio = 0.610, p < 0.001). Reminder calls led to a 12.2% reduction in no-show rate and were an independent predictor of a patient's likelihood of attending their appointment. Other predictors of attendance included insurance status and abdominal injury.

Effect of telehealth-based versus in-person nutritional and exercise intervention on type II diabetes mellitus improvement and efficiency of human resources utilization inpatients with obesity.

Telehealth became a patient necessity during the COVID pandemic and evolved into a patient preference in the post-COVID era. This study compared the % total body weight loss (%TBWL), HbA1c reduction, and resource utilization among patients with obesity and diabetes who participated in lifestyle interventions with or without telehealth. A total of 150 patients with obesity and diabetes who were followed every 4-6weeks either in-person (n=83) or via telehealth (n=67), were included. All patients were provided with an individualized nutritional plan that included a weight-based daily protein intake from protein supplements and food, an activity/sleep schedule-based meal times, and an aerobic exercise goal of a 2000-calorie burn/week, customized to patient's preferences, physical abilities, and comorbidities. The goal was to lose 10%TBWL. Telehealth-based follow-up required transmission via texting of weekly body composition measurements and any blood glucose levels below 100mg/dl for medication adjustments. Weight, BMI, %TBWL, HbA1c (%), and medication effect score (MES) were compared. Patient no-show rates, number of visits, program duration, and drop-out rate were used to assess resource utilization based on cumulative staff and provider time spent (CSPTS), provider lost time (PLT) and patient spent time (PST). Mean age was 47.2±10.6years and 74.6% were women. Mean Body Mass Index (BMI) decreased from 44.1±7.7-39.7±6.7kg/m2 (p<0.0001). Mean program duration was 189.4±169.3days. An HbA1c% unit decline of 1.3±1.5 was achieved with a 10.1±5.1%TBWL. Diabetes was cured in 16% (24/150) of patients. %TBWL was similar in regards to telehealth or in-person appointments (10.6%±5.1 vs. 9.6%±4.9, p=0.14). Age, initial BMI, MES, %TBWL, and baseline HbA1c had a significant independent effect on HbA1c reduction (p<0.0001). Program duration was longer for in-person follow-up (213.8±194 vs. 159.3±127, p=0.019). The mean annual telehealth and in-person no-show rates were 2.7% and 11.2%, respectively (p<0.0001). Mean number of visits (5.7±3.0 vs. 8.6±5.1) and drop-out rates (16.49% vs. 25.83%) were lower in telehealth group (p<0.0001). The CSPTS (440.4±267.5min vs. 200.6±110.8min), PLT (28.9±17.5min vs. 3.1±1.6min), and PST (1033±628min vs. 113.7±61.4min) were significantly longer (p<0.0001) for the in-person group. Telehealth offered comparable %TBWL and HbA1c decline as in-person follow-up, but with a shorter follow-up, fewer appointments, and no-shows. If improved resource utilization is validated by other studies, telehealth should become the standard of care for the management of obesity and diabetes.

Evaluation of pharmacist impact on diabetes outcomes for telehealth and hybrid care delivery versus in-office visits

BackgroundFew studies have examined the effect of pharmacist-led telemanagement on diabetes outcomes during the COVID-19 pandemic. ObjectivesAssess for noninferiority for the absolute change in mean A1C between telehealth and hybrid groups versus the in-office group during the COVID-19 pandemic. Secondary objectives were to compare the percentage of patients achieving population health A1C goals and patient no-show rates between study groups. MethodsA retrospective, noninferiority analysis was conducted for patients seen by a primary care pharmacist from November 1, 2020 to May 31, 2021 across 17 primary care clinics in the Northeast Ohio region of Cleveland Clinic. The noninferiority margin was prespecified at > 0.3% A1C reduction. Patients with a baseline A1C of 8% or greater were included. Patients were separated into 3 study groups (telehealth, in-office, and hybrid) based on the visit types that were conducted by the pharmacist during the study period. ResultsHybrid care delivery (N = 366) was noninferior to in-office care delivery (N = 180), with regards to absolute change in mean A1C reduction (0.24% [95% CI: −0.13, 0.61], P = 0.002). Similar results were shown when comparing the telehealth group (N = 691) to the in-office group (0.04 [95% CI: −0.28, 0.36], P = 0.02). The mean A1C reduction in the in-office (1.36 ± 1.9), hybrid (1.60 ± 2.2), and telehealth (1.40 ± 2.0) groups were not significantly different (P = 0.23). Subgroup analyses showed that newly consulted patients had a larger reduction in A1C compared to the overall population, in all groups. No-show rates and percentage of patients achieving population health A1C goals were not significantly different based on visit type. ConclusionTelehealth and hybrid visit types were noninferior to in-office visits with regards to mean change in A1C reduction. Results demonstrate the importance of primary care pharmacists continuing to offer diverse visit types based on patient preference.

Reduction of No-Show Rate for New Patients in a Pediatric Neurology Clinic.

The rate of patients not keeping their appointments at our children's hospital outpatient pediatric neurology clinic (no-shows) was high. We conducted a quality improvement project to reduce no-show rates and improve operational efficiency. Specifically, we aimed to decrease the new patient no-show mean rate from 7% to 4% at the main campus and from 17% to 12% at the south campus. After reviewing the previous literature on this topic and institutional data, we used the simplified failure mode and effects analysis (sFMEA) to identify the key drivers. Of the patients at the main campus who failed to keep their appointment, 84% had not confirmed their appointment. Errors in inpatient/family contact information, limited use of the electronic patient portal, and miscommunication were other key drivers identified. Three Plan-Do-Study-Act (PDSA) cycles were completed over seven months. The key interventions we implemented were bidirectional text triage, telephone reminders, and promoting the use of the electronic patient portal. A run chart was used to assess the results of these interventions. A statistically significant shift was noted in the run chart for the median rate of no-shows, which declined from 7% to 4% at the main campus and 17% to 10% at the south campus. We were able to successfully reduce no-shows among new patients in the neurology clinic. The limitations of our study include unknown external factors, the potential impact of COVID-19, and the brief length of the study.

Implementing Lean Six Sigma Concepts in Audiology Practices

Implementing Lean Six Sigma Concepts in Audiology Practices

Using Machine Learning to Identify No-Show Telemedicine Encounters in a New York City Hospital.

No-show visits are a serious problem for healthcare centers. It costs a major hospital over 15 million dollars annually. The goal of this paper was to build machine learning models to identify potential no-show telemedicine visits and to identify significant factors that affect no-show visits. 257,293 telemedicine sessions and 152,164 unique patients were identified in Mount Sinai Health System between March 2020 and December 2020. 5,124 (2%) of these sessions were no-show encounters. Extreme Gradient Boosting (XGB) with under-sampling was the best machine learning model to identify no-show visits using telemedicine service. The accuracy was 0.74, with an AUC score of 0.68. Patients with previous no-show encounters, non-White or non-Asian patients, and patients living in Bronx and Manhattan were all important factors for no-show encounters. Furthermore, providers' specialties in psychiatry and nutrition, and social workers were more susceptible to higher patient no-show rates.

966-P: Reducing Patient No-Show Rates in a Diabetes Clinic

Background: Patients missing scheduled appointments, or no-shows, is a pervasive issue across outpatient clinics. The estimated no-show rate among primary care clinics ranges between 14% and 50%. No-shows are estimated to cost the US healthcare system $150 billion a year. At many clinics no-shows have necessitated overbooking clinic slots to maintain efficiency. Ultimately, patient no-shows and the overbooking they necessitate causes problems for both the patient and provider. Methods: We used the PDSA (plan/do/study/act) cycle from quality improvement methodology. We determined the baseline no-show rate at the Parkland Diabetes Clinic, an endocrinology clinic with 16 providers (including specialists and advance practice providers) which sees ∼1200 visits a month. The baseline no-show rate from June 2018 to May 20was ∼31.9% ± 2.19. We then examined clinic processes to determine reasons for no-shows through process mapping and root cause analysis. Mapping of the scheduling process identified process inefficiencies. Phone call surveys were conducted with patients with high no-show rates. Using the process map and survey data, we brainstormed interventions to target root causes of no-shows. Our first intervention was ensuring that every patient received a phone call reminder for their appointment. Our second intervention was limiting the degree to which a third-party call center handled scheduling for the Diabetes clinic. Results: Interventions were implemented in August 2020, and subsequent data collected from November 2020 to October 2021 was analyzed to see changes in the no-show rate. The average no-show rate during this post-intervention period was ∼22.2% ± 2.50 (t-test, p &amp;lt;0.05) , a significant decrease from the baseline rate of ∼31.9% ± 2.19. We predict this reduction will result in more consistent follow-up care, less risk of disease progression, and increased clinical efficiency. Our next step will be to transition our interventions into long-term sustainable solutions to maintain the lower no-show rate. Disclosure F.Jafri: None. U.Gunasekaran: None.

Heart Failure Clinic No-show Rates And Effect On Heart Failure Hospitalizations: A Real-World Experience From The Middle East

<h3>Introduction</h3> Adherence to scheduled appointments in heart failure (HF) patients remains an important factor for optimum HF management. Recognizing patterns of adherence to appointments will provide insight and help to improve compliance in attending future HF clinics. <h3>Aim</h3> We aim to describe the frequency and characteristics of no-show appointments in relation to patient demographics and distance traveled, and evaluate the effect on HF hospitalizations in our newly established advanced HF program in the Middle East-Gulf Region. <h3>Methods</h3> A total of 230 patients who have been followed up in our HF clinic were included. Retrospective chart review was used to collect data on patients' demographics and adherence to appointments. Based on the average no-show rate, a head-to-head comparison was conducted using appropriate testing methods between those who were adherent (no-show rate ≤10%) vs. non-adherent (no-show rate >10%) patients. The comparison included the 12 months HF-admissions as well. <h3>Results</h3> A total of 1667 appointments were scheduled and confirmed. Patients did not show in 199 (11.9%) of the appointments, but attended an average of 6.5± 2 appointments during 12 months follow-up. Wednesday and Monday clinic appointments were more likely to be missed (32.6% and 24.1%, respectively). Afternoon appointments represented 57.8% of all missed appointments. Non-convenient appointment was the most documented reason for not showing (29.4%, Table.1). When comparing the adherent vs. the non-adherent group, (Table.2). Travel distance was similar in both groups. Males and smokers were more predominant in the non-adherent group (p=0.02 and p=0.02, respectively). The adherent group had a total of 35 HF hospitalizations compared to the non-adherent group who had a total of 42 HF hospitalizations (p=0.15). <h3>Conclusion</h3> This study explores important real-world data and adds to the available evidence on HF patient adherence. Highlighting key patient characteristics, including distance traveled to specialized HF programs increases our understanding and helps formulate future strategic planning to predict and reduce the impact of patient no-show rates and future HF hospitalizations.

A Medical Student-Run Telehealth Primary Care Clinic During the COVID-19 Pandemic: Maintaining Care for the Underserved.

Background:In this report, we outline our approach to implementing a hybrid in-person and virtual clinic model at a student-run free clinic (SRFC) during the COVID-19 pandemic. Individuals of low socioeconomic status (SES) are at an increased risk for COVID-19 infection and severe clinical outcomes. It is unclear if telehealth is a viable continuity of care enabler for the underserved.Methods:The Weill Cornell Community Clinic (WCCC) implemented a novel telehealth clinic model to serve uninsured patients in May 2020. A phone survey of was conducted to assess WCCC patients access to technology needed for telehealth visits (eg, personal computers, smartphones). Patient no-show rates were retrospectively assessed for both in-person (pre-pandemic) and hybrid continuity of care models.Results:The phone survey found that 90% of WCCC patients had access to technology needed for telehealth visits. In the 8 months following implementation of the hybrid model, telehealth and in-person no-show rates were 11% (14/128) and 15% (10/67) respectively; the combined hybrid no-show rate was 12% (24/195). For comparison, the in-person 2019 no-show rate was 23% (84/367). This study aligns with previous reports that telehealth improves patient attendance.Conclusion:Literature on the transition of SRFCs from in-person to telehealth care delivery models is limited. At the WCCC, the reduction in no-show rates supports the feasibility and benefits of adopting telehealth for the delivery of care to underserved patient populations. We believe the hybrid telehealth model described here is a viable model for other student run free clinics to increase access to care in low SES communities.

Impact of Physician Names on Missed Appointments in Psychiatry Resident Clinics: A Pilot Study

The authors undertook the current study to investigate patient and provider factors and their relationship to patient show rates. The authors included as eligible all new patient appointments in psychiatry resident clinics at a large academic hospital-based psychiatry clinic between July 2017 and June 2018. Prior to data extraction, a resident focus group ( n = 5) was conducted to examine ways in which patients might perceive provider names as a proxy for implicit bias. Multilevel logistic regression modeling was used with appointment show status (yes/no) as the binary dependent variable. Data were extracted for a total of 14 providers from July 2017 through June 2018, yielding 295 scheduled new-patient evaluation appointments; of these, 102 (35%) were missed (“no-shows”). Ambiguous-sounding provider names were associated with lower patient show rates compared with non–ambiguous-sounding provider names (odds ratio = 0.41, P &lt; .01). Our results demonstrate some relationship between provider name ambiguity and patient no-show rate. Our small sample size and high prevalence of ambiguous-sounding names does not permit further characterization of name types, although the question may warrant further study. [ Psychiatr Ann . 2021;51(10):486–491.]

Machine learning-based prediction models for patients no-show in online outpatient appointments

With the development of information and communication technologies, all public tertiary hospitals in China began to use online outpatient appointment systems. However, the phenomenon of patient no-shows in online outpatient appointments is becoming more serious. The objective of this study is to design a prediction model for patient no-shows, thereby assisting hospitals in making relevant decisions, and reducing the probability of patient no-show behavior. We used 382,004 original online outpatient appointment records, and divided the data set into a training set (N1 ​= ​286,503), and a validation set (N2 ​= ​95,501). We used machine learning algorithms such as logistic regression, k-nearest neighbor (KNN), boosting, decision tree (DT), random forest (RF) and bagging to design prediction models for patient no-show in online outpatient appointments. The patient no-show rate of online outpatient appointment was 11.1% (N ​= ​42,224). From the validation set, bagging had the highest area under the ROC curve and AUC value, which was 0.990, followed by random forest and boosting models, which were 0.987 and 0.976, respectively. In contrast, compared with the previous prediction models, the area under ROC and AUC values of the logistic regression, decision tree, and k-nearest neighbors were lower at 0.597, 0.499 and 0.843, respectively. This study demonstrates the possibility of using data from multiple sources to predict patient no-shows. The prediction model results can provide decision basis for hospitals to reduce medical resource waste, develop effective outpatient appointment policies, and optimize operations.

Outpatient regulation system in health management: economic benefits of technological innovations

Purpose The purpose of this paper is to evaluate the economic benefits of managing an outpatient appointments system with technological innovations. Design/methodology/approach This study uses a quantitative methodological procedures aiming to evaluate the cost-benefit relation and also the payback of the management and operation of an outpatient appointments system with technological innovations. Findings This study found a great benefit-cost relation of 30.6 showing the great economic value and social impact of managing an outpatient appointments regulation system with technological innovations. Research limitations/implications This study presents contribution to the literature discussion about the economic evaluation of the benefits of managing and operating more effective outpatient appointments systems because of important technological innovations. Practical implications This paper presents and discusses the most important and commonly used strategies and technological innovations to deal with and to manage an outpatient appointment regulation system aiming to reduce the patient no-show rates. Social implications The findings of this study show a great benefit-cost relation of about 30.6 which is being reverted to the society. Originality/value There not exist many similar studies in the pertinent literature, mostly with the Brazilian contexts.

Transition to telephone genetic counseling services during the COVID-19 pandemic.

The COVID-19 pandemic has significantly disrupted the delivery of healthcare services, including oncology. To ensure continuity of cancer genetic counseling at a large academic medical center while also promoting the safety of patients and staff, our team transitioned to fully remote telephone genetic counseling and testing services within 48hr. We compare differences in the six weeks following the shift to telephone genetic counseling (post-COVID) to the six weeks preceding the pandemic (pre-COVID). We maintained 99% of our total visit capacity and saw a decrease in patient no-show rate from 9.5% to 7.3%. Of all patients who received telephone genetic counseling, fewer consented to genetic testing as compared to patients seen in-person prior to the pandemic (79% pre-COVID v. 72% post-COVID; p=.012). Four weeks after this cohort was closed for analysis, 96 out of 303 samples (32%) had not been received by the genetic testing laboratory, despite at least one reminder phone call to the patient. In 13 reported instances, a second sample was required (quality not sufficient, lost or mislabeled sample), thus delaying test results. We conclude that a rapid transition to remote genetic counseling and testing allowed uninterrupted access to cancer genetics services during to the COVID-19 pandemic. Patient compliance with sample return and higher rates of sample failure emerge as potential barriers to timely genetic testing under this service delivery model.

Socioeconomic factors that impact patient no-shows in the ambulatory urology clinic

This study seeks to identify patient socioeconomic factors that may be associated with no-shows to our academic urology clinics. This was a retrospective analysis of patient attendance to appointments scheduled at the main urology clinics of our academic center between April 2018 and April 2019. Attendance to scheduled appointments was computed and analyzed with respect to patients’ demographics and socioeconomic status, along with clinic site, type of provider scheduled with, and reason for visit. Chi-square test and simple and multiple logistic regression were performed in order to determine the relationship between patient no-show rates and the variables for appointment characteristics. The cohort of patients at highest risk for no-show was associated with those who were uninsured (OR = 30.00, 95% CI: 23.73-–7.92; p < 0.0001) and from our county’s urban sector (OR = 1.96, 95% CI:1.63–2.37; p < 0.0001), in which the population reported a household income of 30% below poverty level and was only one-third high-school educated. Further studies of this at-risk population who are likeliest to miss their urology clinic appointment may appropriately address these patients’ needs and thus curtail the number of no-shows to future outpatient appointments.

Patient Retention and Continuity of Care of an Underserved Oncology Population within a Public Safety-Net Health System

Cancer patients in underserved populations are at high risk for patient attrition and treatment delay. Infrequent appointment recording often results in patients who are lost to follow-up. We hypothesized that the utilization of Lean Six Sigma methodologies would increase patient scheduling for radiation oncology appointments thereby increasing patient retention within a large urban safety-net health system. The entire clinic staff collaborated to improve electronic health record (EHR) appointment scheduling using the Lean Six Sigma DMAIC approach (Define, Measure, Analyze, Improve and Control). Appointment data was collected for three months prior to and during the pilot study period from the EHR and the radiation oncology record and verify system. We determined the root causes for delinquent scheduling, including lack of staff availability to schedule patients into two electronic systems and to call no-show patients to reschedule. Interventions included implementation of an electronic order for follow-up scheduling and blocked time for personnel to contact no-show patients and verify scheduling. After the formation of a novel process map, control plan, and Failure Modes Effects Analysis (FMEA), the pilot study commenced for a planned six-month period. We report on the first two months of our pilot study. Follow-up appointment scheduling into the EHR improved from baseline 2% to 98% (p<0.01). No-show appointment rescheduling into the EHR improved from baseline 0% to 64%(p<0.01). A novel electronic scheduling intervention was added after the first month when no-show rescheduling into the EMR was at 43% with subsequent no-show rescheduling after the second month at 87% (p = 0.01). The patient no-show rate declined from 19% for the pre-intervention period to 17% for the first pilot month and to 15% for the second pilot month (p = 0.3). Utilization of the Lean Six Sigma methodology resulted in a significant increase in appointment scheduling within the radiation oncology department. While not yet significant, the no-show rate appears to be trending downward as a result of this scheduling improvement, and we expect the no-show rate to continue to decline as the study matures. These findings suggest that optimized appointment scheduling may improve patient retention and continuity of care in an at-risk population. Future directions include evaluating cancer outcomes and healthcare cost savings as a result of higher patient retention.

Improving patient retention by optimizing appointment scheduling in an underserved oncology population within a public safety-net health system.

193 Background: Cancer patients in underserved populations are at high risk for patient attrition and treatment delay. We theorized that decreased appointment scheduling was contributing to a high no-show rate within an urban safety-net hospital. Clinic appointments were being scheduled in the department-specific record and verify system rather than the hospital-wide electronic health record (EHR). We sought to adopt EHR scheduling with the long-term goal of decreasing the no-show rate. Our aim was to utilize the Lean Six Sigma (LSS) methodologies to increase EHR scheduling of follow-up and rescheduling of no-show appointments to 50%. Methods: While involving the entire clinic staff, we utilized the Lean Six Sigma DMAIC model: define, measure, analyze, improve and control, to improve EHR appointment scheduling. Appointment data was collected for the 3 months prior to and during the intervention time period. We determined the root causes for delinquent scheduling, including lack of staff availability to schedule patients into two electronic systems and to call no-show patients. Interventions included implementation of an electronic order for follow-up scheduling and blocked time for personnel to contact no-show patients and verify scheduling. After the formation of a novel process map, control plan, and Failure Modes Effects Analysis (FMEA), the pilot study ran for 2 months. Results: Follow-up appointment scheduling into the EHR improved from 2% to 98% (p &lt; 0.01). After the first intervention month, the no-show rescheduling rate improved from 0% to 43% (p &lt; 0.01): below goal. The team revised the process map by substituting a no-show EHR order in place of the calendar intervention. This constituted the beginning of month 2 and no-show appointment rescheduling subsequently improved from 43% to 87% (p &lt; 0.01). The patient no-show rate was 19% during the pre-intervention period, 17% for the first intervention cycle and 15% for the second intervention cycle (p = 0.3). Conclusions: Utilization of LSS allowed for successful adoption of EHR appointment scheduling within our department. While not yet significant, the no-show rate appears to be trending downward as a result of improved scheduling, and we expect the no-show rate to continue to decline as the study matures. These findings suggest that optimized appointment scheduling may decrease patient retention in an at-risk population. Future directions include evaluating cancer outcomes and decreased healthcare costs as a result of higher patient retention.

American Dental Association: Advancing Health Literacy Within and by the Dental Profession

Poor oral health literacy is associated with poorer oral health knowledge, increased dental caries severity and increased rates of patient no-show rates. The American Dental Association has made health literacy a priority since 2006 with the development of the National Advisory Committee on Health Literacy in Dentistry and an action plan to guide its work. This article summarizes the ADA’s efforts to improve the oral health literacy of the public and current and future dental professionals.

MON-001 Assessment of Endocrine Clinic Attendance Rates to Guide Interventions to Reduce Patient No-Show Rates

As a part of our endocrine fellowship quality improvement training, we sought to improve patient access to endocrine outpatient clinic appointments at our institution. From the physician perspective, patients who do not show up for a 30-45 minute new patient visit slot disrupt clinic flow and limits access for other patients. Therefore, we decided to focus our attention on reducing the new patient no-show rate. Here, we assessed the efficacy of our current scheduling system from 2014-2016. This analysis was performed as part of a quality improvement project to inform future interventions. Previously, only a few studies have attempted to identify baseline no-show rates (1,2). Our institution utilizes an automated telephone appointment reminder system (Televox) which calls patients three to four business days prior to a scheduled appointment and gives the patient an opportunity to confirm or cancel the appointment. Televox data for 7,556 patients over 34 months (1/2014-10/2016) revealed a 95% show frequency for patients who confirmed their appointment, 82% show frequency for patients for which there was no response to the Televox reminder, and 78% show frequency of patients who were not reached (Televox call not made). P-Chart of the show frequencies of patients who did not respond or were not reached fluctuated between 70-90% over this period. This analysis shows that new patients who did not respond or were not reached are a potential target for intervention. By identifying this cohort early, we will be able to implement intervention and reschedule patients in a timely manner to improve clinic flow and clinic access.

Reengineering the Communication Process to Reduce Patient No-Show Rates in Hospital Outpatient Clinics

Abstract Background: Patient “no-shows” cause significant concern for healthcare organizations as it affects continuity of care provided to the patient, quality of care, waiting times for new patients, and wastes clinic resources. This project aimed to reduce the rate of no-shows in a general pediatric clinic to less than 10% to be consistent with the international benchmark range of 5%–10% of primary care clinics. Materials and Methods: A multidisciplinary team was formed to address the rate of no-shows using a quality improvement–driven approach in August 2017. Retrospective data analysis showed that 33% of patients who were referred to the general pediatric clinic at King Abdullah Specialist Children Hospital did not show up for their appointments in March 2017. Results: The impact of reengineering the patients' appointment communication process led to a noticeable reduction in the rate of no-shows in the general pediatric clinic, reaching 14% in 1 month (November to December 2017) compared with the baseline of 33%. Data analysis for the post-intervention period showed a progressive decline in the no-show rate, reaching below 10% in the general pediatric clinic, indicating a big shift in the rate of no-shows among the patients attending the general pediatric clinic. Conclusion: Reengineering the communication process and increasing awareness to update contact information are effective strategies for improving communication with patients and reducing the rate of no-shows for scheduled appointments. The next step is to share project findings with healthcare workers and leaders to sustain the improvement.

Modelling the impact of new patient visits on risk adjusted access at 2 clinics.

To evaluate the effect new outpatient clinic visits has on the availability of follow-up visits for established patients when patient visit frequency is risk adjusted. Diagnosis codes for patients from 2 Internal Medicine Clinics were extracted through billing data. The HHS-HCC risk adjusted scores for each clinic were determined based upon the average of all clinic practitioners' profiles. These scores were then used to project encounter frequencies for established patients, and for new patients entering the clinic based on risk and time of entry into the clinics. A distinct mean risk frequency distribution for physicians in each clinic could be defined providing model parameters. Within the model, follow-up visit utilization at the highest risk adjusted visit frequencies would require more follow-up slots than currently available when new patient no-show rates and annual patient loss are included. Patients seen at an intermediate or lower visit risk adjusted frequency could be accommodated when new patient no-show rates and annual patient clinic loss are considered. Value-based care is driven by control of cost while maintaining quality of care. In order to control cost, there has been a drive to increase visit frequency in primary care for those patients at increased risk. Adding new patients to primary care clinics limits the availability of follow-up slots that accrue over time for those at highest risk, thereby limiting disease and, potentially, cost control. If frequency of established care visits can be reduced by improved disease control, closing the practice to new patients, hiring health care extenders, or providing non-face to face care models then quality and cost of care may be improved.