Increasing Patient Participation in the Medication Reconciliation Process

It is estimated, that ambulatory care settings have a 25% adverse drug events (ADEs) rate, and 39% of those event were preventable errors (Taché, Sönnichsen, and Ashcroft, 2011). Considering many adverse drug events are related to medication errors, preventing medication errors is fundamental to improving patient safety and outcomes. Medication reconciliation is the process of identifying and resolving medication discrepancies that occur, during transitions in care. Patient participation is a key component to the medication reconciliation process. With the intent to improve patient participation, a patient awareness intervention was implemented in the cardiology outpatient clinic. Data was collected using microsystem assessments, staff /patient medication reconciliation questionnaires. The intervention includes the use of patient posters, brochures and pre-appointment phone call reminders to bring in their medications. The barriers to implementing the patient awareness intervention in this clinic were in part related to resistance to change and lack of understanding of the medication reconciliation process. The barriers to this process will be further discussed, in this paper. The patient pre-appointment phone calls resulted in a 7% increase in patients bringing in their medications. As a result, the care providers were able to verify and reconcile the patient medications at the appointment

Data Analytics and Modeling for Appointment No-show in Community Health Centers

Objectives: Using predictive modeling techniques, we developed and compared appointment no-show prediction models to better understand appointment adherence in underserved populations. Methods and Materials: We collected electronic health record (EHR) data and appointment data including patient, provider and clinical visit characteristics over a 3-year period. All patient data came from an urban system of community health centers (CHCs) with 10 facilities. We sought to identify critical variables through logistic regression, artificial neural network, and naïve Bayes classifier models to predict missed appointments. We used 10-fold cross-validation to assess the models’ ability to identify patients missing their appointments. Results: Following data preprocessing and cleaning, the final dataset included 73811 unique appointments with 12,392 missed appointments. Predictors of missed appointments versus attended appointments included lead time (time between scheduling and the appointment), patient prior missed appointments, cell phone ownership, tobacco use and the number of days since last appointment. Models had a relatively high area under the curve for all 3 models (e.g., 0.86 for naïve Bayes classifier). Discussion: Patient appointment adherence varies across clinics within a healthcare system. Data analytics results demonstrate the value of existing clinical and operational data to address important operational and management issues. Conclusion: EHR data including patient and scheduling information predicted the missed appointments of underserved populations in urban CHCs. Our application of predictive modeling techniques helped prioritize the design and implementation of interventions that may improve efficiency in community health centers for more timely access to care. CHCs would benefit from investing in the technical resources needed to make these data readily available as a means to inform important operational and policy questions

Reducing Same Day Missed Appointments

Radiology Associates (RA) is a diagnostic imaging center that offers high-quality, digital medical imaging and interventional radiology services for patients, physicians and healthcare organizations across the Central Coast. They are an ongoing problem that involves a considerable portion of their patients not showing up for their appointments Our project aims to reduce same day missed appointments at Radiology Associates. Radiology Associates currently has a no-show rate of 13.48%. They lose approximately $240 for every same day missed appointment. Our goal was to find new ways to reduce their no-show rate to 8$39,285.35 by reducing the no-show percentage by 5.5%. We researched literature on causes of no-shows and alternative scheduling methods. We then mapped out the scheduling process and analyzed data on no-shows. After discovering some potential causes for the high no-show rate, we constructed solutions and created standard operating procedures

Impact of Advanced Access Scheduling on Missed Appointment Rates in Primary Care

A major problem encountered within outpatient physician offices are missed appointments. Missed appointment research revealed how no-show rates remain a focus for healthcare administrators as decreasing no-show rates may reverse harmful health consequences. The purpose of this study, which also addressed the research gap, was to determine if there was an association between advanced access scheduling and no-show rates for patients scheduled with preferred primary care physicians versus nonpreferred primary care physicians. The health belief model was the conceptual framework as missing a prescheduled appointment is a health behavior. The 1st and 2nd research questions examined whether there was a statistically significant mean proportion difference between the national no-show rate and the study no-show rates. The 3rd research question examined the association between the preferred and nonpreferred primary care physicians and no-show visit status. Historic primary care prescheduled visit data were electronically obtained for patients over the age of 18. Utilizing SPSS software, 4,815 visits were analyzed using z test of proportion and Chi Square test for association. Study results demonstrated a statistically significant difference between the national no-show rate and this study and a significant association between physician type and visit status. Results indicated the potential for improved appointment compliance if patients are scheduled with their preferred primary care physician. This study may promote positive social change by providing healthcare administrators with an understanding of the significance surrounding advanced access scheduling and patient no-show behaviors, thus decreasing missed appointment rates in primary care

Exploring Strategies for Reducing Patient Failure to Keep Scheduled Appointments

High no-show rates in the ambulatory setting lead to underutilized resources, decreased clinic revenue, and lower productivity. The purpose of this single case study was to explore strategies that administrators used to maintain acceptable no-show rates and maintain the sustainability of the healthcare practice. The target population for this study included local chapter members of a professional healthcare organization that provided access to practice managers and administrators in the Las Vegas, Nevada regional area where there are a large number of practices that are not part of a health system; the sustainability of these practices is dependent on allocation of adequate resources. The conceptual framework for this study was Kotter\u27s 8-step change management model that uses 8 steps for successfully managing change within the organization and developing quality improvement initiatives. Data collection included semistructured interviews with 2 practice leaders, observation of the organization\u27s practice management and appointment scheduling systems, and a review of internal reports related to appointment trends and no-show rates. Based on the data analysis using deductive and open coding techniques, 3 distinctive themes emerged from the data: appointment booking strategies, appointment reminder strategies, and provider flexibility. The results of this study might positively affect positive social change by helping administrators improve access to care in an outpatient setting through improved appointment utilization and improve patient care outcomes with more appointment availability

Revving Up Revenue: Unlocking the Power of Cancellation Policies on Booking Intentions

Over the years, last-minute cancellations and no-shows have presented difficulties for the hospitality sector, negatively affecting hotel operations and revenue. The cancellation policies of hotels have changed significantly as a result. These rules have been modified to act as "price fences" as a result of revenue management strategies used in the hotel industry. This study aims to find out how various cancellation policies impact consumers' perceptions of risk and intent to reserve hotel rooms at various times. The study provides insight into the factors that affect consumer behavior and choices for hospitality goods and services by examining these elements. The results of the study offer some understanding into how hotels and other hospitality businesses can use cancellation policies to customize their marketing strategies to meet consumer needs and preferences while successfully managing revenue and profitability

Simulation and Modeling for Improving Access to Care for Underserved Populations

Indiana University-Purdue University Indianapolis (IUPUI)This research, through partnership with seven Community Health Centers (CHCs) in Indiana, constructed effective outpatient appointment scheduling systems by determining care needs of CHC patients, designing an infrastructure for meaningful use of patient health records and clinic operational data, and developing prediction and simulation models for improving access to care for underserved populations. The aims of this study are 1) redesigning appointment scheduling templates based on patient characteristics, diagnoses, and clinic capacities in underserved populations; 2) utilizing predictive modeling to improve understanding the complexity of appointment adherence in underserved populations; and 3) developing simulation models with complex data to guide operational decision-making in community health centers. This research addresses its aims by applying a multi-method approach from different disciplines, such as statistics, industrial engineering, computer science, health informatics, and social sciences. First, a novel method was developed to use Electronic Health Record (EHR) data for better understanding appointment needs of the target populations based on their characteristics and reasons for seeking health, which helped simplify, improve, and redesign current appointment type and duration models. Second, comprehensive and informative predictive models were developed to better understand appointment non-adherence in community health centers. Logistic Regression, Naïve Bayes Classifier, and Artificial Neural Network found factors contributing to patient no-show. Predictors of appointment non-adherence might be used by outpatient clinics to design interventions reducing overall clinic no-show rates. Third, a simulation model was developed to assess and simulate scheduling systems in CHCs, and necessary steps to extract information for simulation modeling of scheduling systems in CHCs are described. Agent-Based Models were built in AnyLogic to test different scenarios of scheduling methods, and to identify how these scenarios could impact clinic access performance. This research potentially improves well-being of and care quality and timeliness for uninsured, underinsured, and underserved patients, and it helps clinics predict appointment no-shows and ensures scheduling systems are capable of properly meeting the populations’ care needs.2021-12-2

Managerial Intervention Strategies to Reduce Patient No-Show Rates

High patient no-show rates increase health care costs, decrease healthcare access, and reduce the clinical efficiency and productivity of health care facilities. The purpose of this exploratory qualitative single case study was to explore and analyze the managerial intervention strategies healthcare administrators use to reduce patient no-show rates. The targeted research population was active American College of Healthcare Executives (ACHE), Hawaii-Pacific Chapter healthcare administrative members with operational and supervisory experience addressing administrative patient no-show interventions. The conceptual framework was the theory of planned behavior. Semistructured interviews were conducted with 4 healthcare administrators, and appointment cancellation policy documents were reviewed. Interpretations of the data were subjected to member checking to ensure the trustworthiness of the findings. Based on the methodological triangulation of the data collected, 5 common themes emerged after the data analysis: reform appointment cancellation policies, use text message appointment reminders, improve patient accessibility, fill patient no-show slots immediately, and create organizational and administrative efficiencies. Sharing the findings of this study may help healthcare administrators to improve patient health care accessibility, organizational performance and the social well-being of their communities

Transition and Thriving in University: A Grounded Theory of the Transition Experiences and Conceptions of Thriving of a Selection of Undergraduate Students at Western University

Abstract The transition from high school to university has been associated with decreases in health and wellbeing for some students. The purpose of this qualitative research was to explore the transitional needs and experiences of students leaving high school and entering Western University, to explore how students conceptualize thriving, and to develop a substantive theory of transition and thriving for Western University students. A total of 42 students and 21 staff members from Western University participated in this study. Data were collected through focus groups and individual interviews. Utilizing grounded theory data analysis methods two conceptual models were developed. The first model outlines students’ transition experiences, as well as their conceptualization of thriving at university. The data suggest that students tend to be unprepared for the transition to university. The majority of students reported that their transition to university consisted of mostly negative experiences. The second model uses the data and the theoretical frameworks that guided the study to explain the transitional experiences described by students and staff. The model shows that when students transition to university, they actually experience multiple transitions within a short period of time. The data included in the model also show that there are several person-environment tensions and interactions that affect students’ transition experiences and thriving outcomes. This study elucidates the factors that affect students as they transition from high school to Western University. The substantive theory generated from the data explains that students enter university with inadequate skills, and with inaccurate knowledge and expectations about university life. As a result of their inadequate preparation students face numerous challenges, the most difficult challenges tend to be time management, making friends, and managing the increased workload. Thriving was conceptualized as achieving academic success, employing effective coping skills, having a positive perspective, engaging in healthy behaviours, gaining connectedness, and occupational participation. This theory is preliminary and further research is needed to validate the theory for generalization to other Canadian universities. The results, however, provide valuable information to guide assessment and further development of potential support services and programs to assist students transitioning to university. Keywords Transition, thriving, high school, university, students, experiences, grounded theor

Essays in Appointment Management

Patients who no-show or who cancel their outpatient clinic appointments can be disruptive to clinic operations. Scheduling strategies, such as slot overbooking or servicing patients during overtime slots, may assist with mitigating such disruptions. In the majority of scheduling models, no-shows and cancellations are considered together, or cancellations are not considered at all. In this dissertation, I propose novel prediction models to forecast the probability of no-show and cancellation for patients. I present analyses to show that no-shows and cancellations are two different types of patient behavior, and should be treated separately when scheduling a patient. Additionally, I develop a multi-day, online, overbooking model that incorporates no-show and cancellation probabilities, and outlines how patients should be optimally overbooked in an outpatient clinic schedule to increase clinic service reward. I find that past history is an indicator of future no-show behavior for patients attending outpatient clinics, and that only a limited look-back window is needed in order to gain insight into patient’s future behavior. Advance appointment cancellations are more challenging to predict, and tend to occur at the beginning or at the end of an appointment’s lifecycle. The optimal overbooking strategy is a function of both the no-show and the cancellation probabilities, and affects both the day on which an overbooking may occur, and the appointment slot in which the patient is overbooked