Non-attendance at Outpatient Clinics: Is It Related to the Referral Process?

Lloyd, Margaret; Bradford, Caroline; Webb, Sarah

doi:10.1093/fampra/10.2.111

Cited by 51 publications

(48 citation statements)

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“…Telephone reminders have been found useful in improving attendance at outpatient clinics [19]. Some authors have also demonstrated that good communication between the patient and the referral physician, with a thorough explanation of the procedure, is one of the most important reasons for good attendance at outpatient clinics [20][21][22]. This finding emphasizes the need for continued improvement in physician-patient communication when first planning the procedure.…”

Section: Discussionmentioning

confidence: 97%

Factors Contributing to Patient Nonattendance at Open-access Endoscopy

et al. 2006

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Patients who miss endoscopy appointments cause inefficient utilization of medical resources. Because national nonattendance rates are as high as 27% and reasons for nonattendance have not been well studied, we sought to quantitate nonattendance at our tertiary care institution. We conducted a retrospective records review of the institutional database to identify patients who did not attend a scheduled endoscopy appointment between January 2000 and December 2003. Nonattendance was defined as either not showing up for an appointment or canceling it on the day it was scheduled. At our institution, patient care assistants contact such patients to document their reasons in the database. Of 36,480 patients scheduled for outpatient endoscopy, 1,490 (4.1%) did not show up because of either facility-related (44.3%; e.g., scheduling errors) or patient-related (55.7%; e.g., noncancellation, illness, or hospitalization) reasons. Our 4.1% nonattendance rate over 4 years is considerably lower than that reported by other endoscopy centers.

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Section: Discussionmentioning

confidence: 97%

Factors Contributing to Patient Nonattendance at Open-access Endoscopy

et al. 2006

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“…This suggests that three-quarters of a surgeon's RTT rate for HR and half their rate for KR are explained by factors other than patient factors or the hospital at which they operate. 75,76 Previous unsatisfactory experience of health care, such as long waiting times and unhelpful staff with poor communication skills, also has an impact, 77,78 as do not being involved in the referral decision-making process 69,71 and not understanding the reasons for the appointment. 79 Previous non-attendance is a strong predictor for future behaviour related to attendance, 78 with the probability of missing a second appointment being much higher than the probability of not turning up to the first.…”

Section: Return-to-theatre Metrics For Orthopaedicsmentioning

confidence: 99%

“…With age, it is likely that non-attendance rates have a bimodal distribution, with young adults and teenagers as well as the elderly being those most likely to miss their appointments. 71 It has been reported that alcoholics, intravenous drug users and those who are pregnant have higher non-attendance rates, 75 as well as past and current smokers. 81,82 Patients who are single parents or have children living at home are more likely to be non-attenders.…”

Section: Return-to-theatre Metrics For Orthopaedicsmentioning

confidence: 99%

Can valid and practical risk-prediction or casemix adjustment models, including adjustment for comorbidity, be generated from English hospital administrative data (Hospital Episode Statistics)? A national observational study

Bottle

Gaudoin

Goudie

et al. 2014

Health Services and Delivery Research

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Background NHS hospitals collect a wealth of administrative data covering accident and emergency (A&E) department attendances, inpatient and day case activity, and outpatient appointments. Such data are increasingly being used to compare units and services, but adjusting for risk is difficult. Objectives To derive robust risk-adjustment models for various patient groups, including those admitted for heart failure (HF), acute myocardial infarction, colorectal and orthopaedic surgery, and outcomes adjusting for available patient factors such as comorbidity, using England’s Hospital Episode Statistics (HES) data. To assess if more sophisticated statistical methods based on machine learning such as artificial neural networks (ANNs) outperform traditional logistic regression (LR) for risk prediction. To update and assess for the NHS the Charlson index for comorbidity. To assess the usefulness of outpatient data for these models. Main outcome measures Mortality, readmission, return to theatre, outpatient non-attendance. For HF patients we considered various readmission measures such as diagnosis-specific and total within a year. Methods We systematically reviewed studies comparing two or more comorbidity indices. Logistic regression, ANNs, support vector machines and random forests were compared for mortality and readmission. Models were assessed using discrimination and calibration statistics. Competing risks proportional hazards regression and various count models were used for future admissions and bed-days. Results Our systematic review and empirical analysis suggested that for general purposes comorbidity is currently best described by the set of 30 Elixhauser comorbidities plus dementia. Model discrimination was often high for mortality and poor, or at best moderate, for other outcomes, for example c = 0.62 for readmission and c = 0.73 for death following stroke. Calibration was often good for procedure groups but poorer for diagnosis groups, with overprediction of low risk a common cause. The machine learning methods we investigated offered little beyond LR for their greater complexity and implementation difficulties. For HF, some patient-level predictors differed by primary diagnosis of readmission but not by length of follow-up. Prior non-attendance at outpatient appointments was a useful, strong predictor of readmission. Hospital-level readmission rates for HF did not correlate with readmission rates for non-HF; hospital performance on national audit process measures largely correlated only with HF readmission rates. Conclusions Many practical risk-prediction or casemix adjustment models can be generated from HES data using LR, though an extra step is often required for accurate calibration. Including outpatient data in readmission models is useful. The three machine learning methods we assessed added little with these data. Readmission rates for HF patients should be divided by diagnosis on readmission when used for quality improvement. Future work As HES data continue to develop and improve in scop...

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“…По данным Lloyd M. и со-авт., пациенты, которые не могли доверительно обсу-дить свои проблемы с врачом, чаще пропускали прие-мы [31].…”

Section: факторы связанные с врачомunclassified