No-shows in appointment scheduling - a systematic literature review.

In the mid-1980s, Heckhausen and Gollwitzer set out to analyze how people control their actions (see Heckhausen, Gollwitzer, & Weinert, 1987). They quickly realized that breaking action control down into different phases greatly benefited its understanding. Heckhausen and Gollwitzer’s analysis was heavily influenced by the work of Kurt Lewin (e.g., Lewin et al., 1944), for whom there was never any doubt that motivational phenomena can only be properly understood and analyzed from an action perspective that distinguishes the processes of goal setting from those of goal striving, an insight that went unheeded for several decades. Accordingly, Heckhausen and Gollwitzer (1987) proposed the “Rubicon”model of action phases, which describes the course of action as a temporal, linear path starting with a person’s wishes or desires and ending with the evaluation of the action outcomes achieved. The model was designed to raise and help answer the following questions: How do people select their goals? How do they plan the execution of goal striving? How do they enact these plans? Moreover, how do they evaluate their accomplishments? According to the Rubicon model, a course of action involves a phase of deliberating the desirability and feasibility of one’s wishes at the outset in order to arrive at a binding decision regarding which of them one wants to pursue as a goal (pre-decisional phase), a phase of planning concrete strategies for achieving this goal Practical Summary

Changing Behavior Using the Model of Action Phases

Background: Patient no-shows in outpatient delivery systems remain problematic. The negative impacts include underutilized medical resources, increased healthcare costs, decreased access to care, and reduced clinic efficiency and provider productivity. Objective: To develop an evidence-based predictive model for patient no-shows, and thus improve overbooking approaches in outpatient settings to reduce the negative impact of no-shows. Methods: Ten years of retrospective data were extracted from a scheduling system and an electronic health record system from a single general pediatrics clinic, consisting of 7,988 distinct patients and 104,799 visits along with variables regarding appointment characteristics, patient demographics, and insurance information. Descriptive statistics were used to explore the impact of variables on show or no-show status. Logistic regression was used to develop a no-show predictive model, which was then used to construct an algorithm to determine the no-show threshold that calculates a predicted show/no-show status. This approach aims to overbook an appointment where a scheduled patient is predicted to be a no-show. The approach was compared with two commonly-used overbooking approaches to demonstrate the effectiveness in terms of patient wait time, physician idle time, overtime and total cost. Results: From the training dataset, the optimal error rate is 10.6% with a no-show threshold being 0.74. This threshold successfully predicts the validation dataset with an error rate of 13.9%. The proposed overbooking approach demonstrated a significant reduction of at least 6% on patient waiting, 27% on overtime, and 3% on total costs compared to other common flat-overbooking methods. Conclusions: This paper demonstrates an alternative way to accommodate overbooking, accounting for the prediction of an individual patient’s show/no-show status. The predictive no-show model leads to a dynamic overbooking policy that could improve patient waiting, overtime, and total costs in a clinic day while maintaining a full scheduling capacity. Citation: Huang Y, Hanauer D.A. Patient no-show predictive model development using multiple data sources for an effective overbooking approach. Appl Clin Inf 2014; 5: 836–860 http://dx.doi.org/10.4338/ACI-2014-04-RA-0026

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Patient No-Show Predictive Model Development using Multiple Data Sources for an Effective Overbooking Approach

The Handbook of Behaviour Change

Importance Transportation barriers contribute to missed primary care appointments for patients with Medicaid. Rideshare services have been proposed as alternatives to nonemergency medical transportation programs because of convenience and lower costs. Objective To evaluate the association between rideshare-based medical transportation and missed primary care appointments among Medicaid patients. Design, Setting, and Participants In a prospective clinical trial, 786 Medicaid beneficiaries who resided in West Philadelphia and were established primary care patients at 1 of 2 academic internal medicine practices located within the same building were included. Participants were allocated to being offered complimentary ride-sharing services (intervention arm) or usual care (control arm) based on the prescheduled day of their primary care appointment reminder. Those scheduled on even-numbered weekdays were in the intervention arm and on odd-numbered weekdays, the control arm. The primary study outcome was the rate of missed appointments, estimated using an intent-to-treat approach. All individuals receiving a phone call reminder were included in the study sample, regardless of whether they answered their phone. The study was conducted between October 24, 2016, and April 20, 2017. Interventions A model of providing rideshare-based transportation was designed. As part of usual care, patients assigned to both arms received automated appointment phone call reminders. As part of the study protocol, patients assigned to both arms received up to 3 additional appointment reminder phone calls from research staff 2 days before their scheduled appointment. During these calls, patients in the intervention arm were offered a complimentary ridesharing service. Research staff prescheduled rides for those interested in the service. After their appointment, patients phoned research staff to initiate a return trip home. Main Outcomes and Measures Missed appointment rate (no shows and same-day cancellations) in the intervention compared with control arm. Results Of the 786 patients allocated to the intervention or control arm, 566 (72.0%) were women; mean (SD) age was 46.0. (12.5) years. Within the intervention arm, 57 among 288 (19.8%) participants who answered the phone call used ridesharing. The missed appointment rate was 36.5% (144 of 394) for the intervention arm and 36.7% (144 of 392) for the control arm ( P  = .96). Conclusions and Relevance The uptake of ridesharing was low and did not decrease missed primary care appointments. Future studies trying to reduce missed appointments should explore alternative delivery models or targeting populations with stronger transportation needs. Trial Registration clinicaltrials.gov Identifier:NCT02955433

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Association of Rideshare-Based Transportation Services and Missed Primary Care Appointments: A Clinical Trial

Background: Missed primary care appointments lead to poor disease control and later presentation to care. No-show rates are higher in clinics caring for underserved populations and may contribute t...

https://journals.sagepub.com/doi/pdf/10.1177/2150131913498513

Emma Kaplan-Lewis

Papers

No-Show to Primary Care Appointments: Why Patients Do Not Come