Patient-Centered Appointment Scheduling Using Agent-Based Simulation

Enhanced access and continuity are key components of patient-centered care. Existing studies show that several interventions such as providing same day appointments, walk-in services, after-hours care, and group appointments, have been used to redesign the healthcare systems for improved access to primary care. However, an intervention focusing on a single component of care delivery (i.e. improving access to acute care) might have a negative impact other components of the system (i.e. reduced continuity of care for chronic patients). Therefore, primary care clinics should consider implementing multiple interventions tailored for their patient population needs. We collected rapid ethnography and observations to better understand clinic workflow and key constraints. We then developed an agent-based simulation model that includes all access modalities (appointments, walk-ins, and after-hours access), incorporate resources and key constraints and determine the best appointment scheduling method that improves access and continuity of care. This paper demonstrates the value of simulation models to test a variety of alternative strategies to improve access to care through scheduling

A Multidimensional Data Warehouse for Community Health Centers

Community health centers (CHCs) play a pivotal role in healthcare delivery to vulnerable populations, but have not yet benefited from a data warehouse that can support improvements in clinical and financial outcomes across the practice. We have developed a multidimensional clinic data warehouse (CDW) by working with 7 CHCs across the state of Indiana and integrating their operational, financial and electronic patient records to support ongoing delivery of care. We describe in detail the rationale for the project, the data architecture employed, the content of the data warehouse, along with a description of the challenges experienced and strategies used in the development of this repository that may help other researchers, managers and leaders in health informatics. The resulting multidimensional data warehouse is highly practical and is designed to provide a foundation for wide-ranging healthcare data analytics over time and across the community health research enterprise

Predictive Modeling for Appointment No-show in Community Health Centers

Reducing no-show rates is one of the most important measures of access to care in Community Health Centers (CHCs). We used EMR and scheduling data to develop no-show prediction models to help design effective scheduling processes and system redesign for greater access in CHCs. Patient and provider characteristics and visit features are key elements for predicting patient adherence with an 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

The impact of interventions on appointment and clinical outcomes for individuals with diabetes: a systematic review

BACKGROUND: Successful diabetes disease management involves routine medical care with individualized patient goals, self-management education and on-going support to reduce complications. Without interventions that facilitate patient scheduling, improve attendance to provider appointments and provide patient information to provider and care team, preventive services cannot begin. This review examines interventions based upon three focus areas: 1) scheduling the patient with their provider; 2) getting the patient to their appointment, and; 3) having patient information integral to their diabetes care available to the provider. This study identifies interventions that improve appointment management and preparation as well as patient clinical and behavioral outcomes. METHODS: A systematic review of the literature was performed using MEDLINE, CINAHL and the Cochrane library. Only articles in English and peer-reviewed articles were chosen. A total of 77 articles were identified that matched the three focus areas of the literature review: 1) on the schedule, 2) to the visit, and 3) patient information. These focus areas were utilized to analyze the literature to determine intervention trends and identify those with improved diabetes clinical and behavioral outcomes. RESULTS: The articles included in this review were published between 1987 and 2013, with 46 of them published after 2006. Forty-two studies considered only Type 2 diabetes, 4 studies considered only Type 1 diabetes, 15 studies considered both Type 1 and Type 2 diabetes, and 16 studies did not mention the diabetes type. Thirty-five of the 77 studies in the review were randomized controlled studies. Interventions that facilitated scheduling patients involved phone reminders, letter reminders, scheduling when necessary while monitoring patients, and open access scheduling. Interventions used to improve attendance were letter reminders, phone reminders, short message service (SMS) reminders, and financial incentives. Interventions that enabled routine exchange of patient information included web-based programs, phone calls, SMS, mail reminders, decision support systems linked to evidence-based treatment guidelines, registries integrated with electronic medical records, and patient health records. CONCLUSIONS: The literature review showed that simple phone and letter reminders for scheduling or prompting of the date and time of an appointment to more complex web-based multidisciplinary programs with patient self-management can have a positive impact on clinical and behavioral outcomes for diabetes patients. Multifaceted interventions aimed at appointment management and preparation during various phases of the medical outpatient care process improves diabetes disease management

No-Shows to Primary Care Appointments: Subsequent Acute Care Utilization among Diabetic Patients

Background Patients who no-show to primary care appointments interrupt clinicians’ efforts to provide continuity of care. Prior literature reveals no-shows among diabetic patients are common. The purpose of this study is to assess whether no-shows to primary care appointments are associated with increased risk of future emergency department (ED) visits or hospital admissions among diabetics. Methods A prospective cohort study was conducted using data from 8,787 adult diabetic patients attending outpatient clinics associated with a medical center in Indiana. The outcomes examined were hospital admissions or ED visits in the 6 months (182 days) following the patient’s last scheduled primary care appointment. The Andersen-Gill extension of the Cox proportional hazard model was used to assess risk separately for hospital admissions and ED visits. Adjustment was made for variables associated with no-show status and acute care utilization such as gender, age, race, insurance and co-morbid status. The interaction between utilization of the acute care service in the six months prior to the appointment and no-show was computed for each model. Results The six-month rate of hospital admissions following the last scheduled primary care appointment was 0.22 (s.d. = 0.83) for no-shows and 0.14 (s.d. = 0.63) for those who attended (p \u3c 0.0001). No-show was associated with greater risk for hospitalization only among diabetics with a hospital admission in the prior six months. Among diabetic patients with a prior hospital admission, those who no-showed were at 60% greater risk for subsequent hospital admission (HR = 1.60, CI = 1.17–2.18) than those who attended their appointment. The six-month rate of ED visits following the last scheduled primary care appointment was 0.56 (s.d. = 1.48) for no-shows and 0.38 (s.d. = 1.05) for those who attended (p \u3c 0.0001); after adjustment for covariates, no-show status was not significantly related to subsequent ED utilization

A problem space genetic algorithm in multiobjective optimization

In this study, a problem space genetic algorithm (PSGA) is used to solve bicriteria tool management and scheduling problems simultaneously in ¯exible manufacturing systems. The PSGA is used to generate approximately ef®cient solutions minimizing both the manufacturing cost and total weighted tardiness. This is the ®rst implementation of PSGA to solve a multiobjective optimization problem (MOP). In multiobjective search, the key issues are guiding the search towards the global Pareto-optimal set and maintaining diversity. A new ®tness assignment method, which is used in PSGA, is proposed to ®nd a well-diversi®ed, uniformly distributed set of solutions that are close to the global Pareto set. The proposed ®tness assignment method is a combination of a nondominated sorting based method which is most commonly used in multiobjective optimization literature and aggregation of objectives method which is popular in the operations research literature. The quality of the Pareto-optimal set is evaluated by using the performance measures developed for multiobjective optimization problems