The Usefulness of Patients?? Individual Characteristics in Predicting No-Shows in Outpatient Clinics

Dove, Henry G.; Schneider, Karen

doi:10.1097/00005650-198107000-00004

Cited by 53 publications

(25 citation statements)

References 5 publications

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“…We are not aware of any studies investigating a role for NLP in measuring appointment adherence. [19][20][21][32][33][34][35][36][37][38][39][40] Thus, our work highlights a novel method using NLP for this purpose.…”

Section: Discussionmentioning

confidence: 99%

Predicting Non-Adherence with Outpatient Colonoscopy Using a Novel Electronic Tool that Measures Prior Non-Adherence

et al. 2015

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BACKGROUND: Accurately predicting the risk of no-show for a scheduled colonoscopy can help target interventions to improve compliance with colonoscopy, and thereby reduce the disease burden of colorectal cancer and enhance the utilization of resources within endoscopy units. OBJECTIVES: We aimed to utilize information available in an electronic medical record (EMR) and endoscopy scheduling system to create a predictive model for no-show risk, and to simultaneously evaluate the role for natural language processing (NLP) in developing such a model. DESIGN: This was a retrospective observational study using discovery and validation phases to design a colonoscopy non-adherence prediction model. An NLPderived variable called the Non-Adherence Ratio ("NAR") was developed, validated, and included in the model. PARTICIPANTS: Patients scheduled for outpatient colonoscopy at an Academic Medical Center (AMC) that is part of a multi-hospital health system, 2009 to 2011, were included in the study. MAIN MEASURES: Odds ratios for non-adherence were calculated for all variables in the discovery cohort, and an Area Under the Receiver Operating Curve (AUC) was calculated for the final non-adherence prediction model. KEY RESULTS: The non-adherence model included six variables: 1) gender; 2) history of psychiatric illness, 3) NAR; 4) wait time in months; 5) number of prior missed endoscopies; and 6) education level. The model achieved discrimination in the validation cohort (AUC= =70.2 %). At a threshold non-adherence score of 0.46, the model's sensitivity and specificity were 33 % and 92 %, respectively. Removing the NAR from the model significantly reduced its predictive power (AUC = 64.3 %, difference = 5.9 %, p<0.001). CONCLUSIONS: A six-variable model using readily available clinical and demographic information demonstrated accuracy for predicting colonoscopy non-adherence. The NAR, a novel variable developed using NLP technology, significantly strengthened this model's predictive power.

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Section: Discussionmentioning

confidence: 99%

Predicting Non-Adherence with Outpatient Colonoscopy Using a Novel Electronic Tool that Measures Prior Non-Adherence

et al. 2015

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“…In this study they found age (p < .0001), race (p < .0001), physician identified psychosocial problems (p < .01), and the previous appointment keeping behavior (p < .0001) to be independently correlated with no-show behavior (Goldman et al, 1981).…”

Section: Problem Statementmentioning

confidence: 61%

“…By adjusting schedules to anticipate the number of patients who miss their appointment, clinics have been able to achieve greater efficiency through demand forecasting (Dove and Schneider, 1981). In Dove and Schneider's study of 756 patients with scheduled appointments they found that patient's age, appointment interval, travel distance and previous no-show record were the strongest predictors of no-show behavior, achieving statistical significance at the .05…”

Section: Problem Statementmentioning

confidence: 99%

“…Lending further creditability to the predictive value of patient specific characteristics, Goldman, Freidin, Cook, Eigner and Grich (1981) found the predictive value of no-show behavior could be used to guide changes in scheduling templates or identify particular groups of patients for which targeted interventions may have a positive impact on behavior. In this study they found age (p < .0001), race (p < .0001), physician identified psychosocial problems (p < .01), and the previous appointment keeping behavior (p < .0001) to be independently correlated with no-show behavior (Goldman et al, 1981).…”

Section: Problem Statementmentioning

confidence: 99%

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Analysis of the Relationship Between Predictors of No-Show Appointment Behavior and the Benefit of Automated Patient Reminders

O'Haver¹

2002

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. REPORT DATE MAY 20022. REPORT TYPE Appointments that are not kept can potentially impact both the personal health status of the patient and the financial health of the Military Healthcare System. This report studied nine predictors of appointment non-adherence using both univariate and multivariate analyses to show which predictors have the greatest effect on patient appointment keeping behavior and the resultant benefit of automated telephone reminder technology. Univariate analysis revealed the following eight significant relationships with appointment keeping behavior: age, marital status, beneficiary category, Tricare Prime enrollment, proximity to the facility, branch of service, appointment day of theweek and call to appointment interval. However, multivariate analysis revealed that age, beneficiary category, sponsors branch of service and marital status were the only variables that contributed to the statistical power of the predictive model, which produced an R2 value of 0.011 (p < .001). The study went on to find that implementation of an automated appointment reminder system yielded a statistically significant reduction in the overall clinic no-show rate. The reduction from 8.65% in FY00 to 7.60% in FY01 resulted in a X2 (1) = 7.24, p< .05. This finding demonstrates the usefulness of this technology as a means for improving overall clinic efficiency. The study went on to find that implementation of an automated appointment reminder system yielded a statistically significant reduction in the overall clinic no-show rate. The reduction from 8.65% in FY00 to 7.60% in FY01 resulted in a X 2 (1) = 7.24, p < .05. This finding demonstrates the usefulness of this technology as a means for improving overall clinic efficiency.

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“…A few studies also considered the effect of personal issues such as oversleeping or forgetting, health status, presence of a sick child or relative, and lack of transportation on missing appointments [6,7]. We will consider many of these factors in our proposed model and also study the effect of personal behavior such as previous appointment-keeping pattern [12] in predicting no-shows.…”

Section: Factors Affecting No-showsmentioning

confidence: 99%

A probabilistic model for predicting the probability of no-show in hospital appointments

Alaeddini

Yang

Reddy

et al. 2011

Health Care Manag Sci

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The number of no-shows has a significant impact on the revenue, cost and resource utilization for almost all healthcare systems. In this study we develop a hybrid probabilistic model based on logistic regression and empirical Bayesian inference to predict the probability of no-shows in real time using both general patient social and demographic information and individual clinical appointments attendance records. The model also considers the effect of appointment date and clinic type. The effectiveness of the proposed approach is validated based on a patient dataset from a VA medical center. Such an accurate prediction model can be used to enable a precise selective overbooking strategy to reduce the negative effect of no-shows and to fill appointment slots while maintaining short wait times.

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The Usefulness of Patients?? Individual Characteristics in Predicting No-Shows in Outpatient Clinics

Cited by 53 publications

References 5 publications

Predicting Non-Adherence with Outpatient Colonoscopy Using a Novel Electronic Tool that Measures Prior Non-Adherence

Predicting Non-Adherence with Outpatient Colonoscopy Using a Novel Electronic Tool that Measures Prior Non-Adherence

Analysis of the Relationship Between Predictors of No-Show Appointment Behavior and the Benefit of Automated Patient Reminders

A probabilistic model for predicting the probability of no-show in hospital appointments

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