A comparative survey of missed initial and follow-up appointments to psychiatric specialties in the United Kingdom
ABSTRACT Missed appointments are common in psychiatry. Nonattendance at the initial appointment may have different prognostic significance than nonattendance at subsequent appointments. This study examined the frequency of missed appointments among 9,511 initial outpatient appointments and 7,700 follow-up appointments across ten psychiatric subspecialties in a publicly funded mental health service in the United Kingdom.
The pooled missed appointment rate was 15.9%, higher than in previous studies on primary and secondary care attendance in the United Kingdom. Nonattendance was lowest on Fridays, in winter months, and in geriatric psychiatry and highest for substance abuse services and in community psychiatry. In most services, attendance improved after the initial appointment, but in psychosomatic medicine and geriatric psychiatry this pattern was reversed.
There was a low rate of missed appointments in geriatric psychiatry, rehabilitation psychiatry, cognitive-behavioral therapy, and psychosocial medicine. A high nonattendance rate was found among persons with drug and alcohol difficulties and to a lesser extent in general adult psychiatry. Future studies should consider initial and follow-up appointments as distinct.
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ABSTRACT: Short message service (SMS), commonly referred to as text messaging, allows instantaneous communication between mobile telephones and other systems via 160-character messages. SMS has a wide reach, and thus researchers and public health officials have considered using this platform for health-related communication. Today, as our society continues to integrate components of automation in various forms and levels of human interaction, and with the increasing ubiquity of mobile technology in health care, technology-delivered health interventions such as SMS offer a creative alternative that can be a valuable tool to assist mental health patients in their own treatment and recovery and for the mental health clinicians who are responsible for providing care and its delivery. © The Author(s) 2015.Journal of the American Psychiatric Nurses Association 01/2015; 21(1):31-33. DOI:10.1177/1078390314566883
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ABSTRACT: A no-show occurs when a scheduled patient neither keeps nor cancels the appointment. A cancellation happens when individuals contact the clinic and cancel their scheduled appointments. Such disruptions not only cause inconvenience to hospital management, they also have a significant impact on the revenue, cost and resource utilization for almost all of the healthcare systems. In this paper, we develop a hybrid probabilistic model based on multinomial logistic regression and Bayesian inference to predict accurately the probability of no-show and cancellation in real-time. First, a multinomial logistic regression model is built based on the entire population’s general social and demographic information to provide initial estimates of no-show and cancellation probabilities. Next, the estimated probabilities from the logistic model are transformed into a bivariate Dirichlet distribution, which is used as the prior distribution of a Bayesian updating mechanism to personalize the initial estimates for each patient based on his/her attendance record. In addition, to further improve the estimates, prior to applying the Bayesian updating mechanism, each appointment in the database is weighted based on its recency, weekday of occurrence, and clinic type. The effectiveness of the proposed approach is demonstrated using healthcare data collected at a medical center. We also discuss the advantages of the proposed hybrid model and describe possible real-world applications. Keywords: Multinomial logistic regression, Dirichlet distribution, Bayesian inference, healthcare operations improvement, no-show and cancellation prediction03/2015; 5(1):14-32. DOI:10.1080/19488300.2014.993006
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ABSTRACT: This study aimed to examine the factors related to and the outcomes of schizophrenic patients with co-occurring methamphetamine use disorders (MUDs). All schizophrenic patients discharged from a psychiatric hospital between January 1, 2006, and December 31, 2006, were monitored. This study compared the important demographic and clinical variables between patients with co-occurring MUDs and those without, and postdischarge measured time to rehospitalization during a 1-year period. Seven hundred fifty-six patients were included in this study. Of these patients, 88 (11.6%) reported the use of methamphetamine. Univariate analyses indicated that male sex, low educational level, discharge against medical advice, missed first appointment after discharge, co-occurring other illicit substance use disorder, age (younger), diazepam equivalents prescribed at discharge (higher), number of previous admissions within the past 5 years (higher), and length of hospital stay (longer) were predictive of patients with co-occurring MUDs. There were also significant differences in time to rehospitalization between these two groups during the follow-up periods. Many factors can be identified in schizophrenic patients with co-occurring MUDs. Furthermore, schizophrenic patients with co-occurring MUDs were more likely to be rehospitalized. Future studies in many different mental health systems are needed before these findings can be generalized.Journal of Nervous & Mental Disease 09/2014; 202(11). DOI:10.1097/NMD.0000000000000197 · 1.81 Impact Factor