A predictive model for decreasing clinical no-show rates in a primary care setting

Ahmad, Mahmood; Zhang, Angie; Mhaskar, Rahul

doi:10.1080/20479700.2019.1698864

Cited by 18 publications

(20 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The systematic review conducted by Carreras et al showed that at least half of the studies on no-show prediction identified age, gender, distance from home to the healthcare center, weekday, visit time, lead time, and history of previous attendance as predictors of non-attendance; marital status and visit type (first or successive) were also frequently used [14]. Our findings were mostly in line with the results reported by Carreras et al, although we did not find an association between gender and non-attendance, as reported elsewhere [17], [18]. Other studies described that nonattendance was associated with the number of previous appointments [19], [20], the status of the last appointment [21], [22], and the treatment category (e.g., surgery) [23], which was also consistent with our results.…”

Section: Discussionsupporting

confidence: 92%

Preventing non-attendance in outpatient appointments: predictive model development, validation, and clinical assessment

Valero-Bover¹,

González²,

Carot-Sans³

et al. 2021

Preprint

View full text Add to dashboard Cite

Objective: To develop and validate an algorithm for predicting non-attendance to outpatient appointments. Results: We developed two decision tree models for dermatology and pneumology services (trained with 33,329 and 21,050 appointments, respectively). The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and a balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) - 65.53% for pneumology, respectively. When using the algorithm for identifying patients at high risk of non-attendance in the context of a phone-call reminder program, the non-attendance rate decreased 50.61% (P<.001) and 39.33% (P=.048) in the dermatology and pneumology services, respectively. Conclusions: A machine learning model can effectively identify patients at high risk of non-attendance based on information stored in electronic medical records. The use of this model to prioritize phone call reminders to patients at high risk of non-attendance significantly reduced the non-attendance rate.

show abstract

Section: Discussionsupporting

confidence: 92%

Preventing non-attendance in outpatient appointments: predictive model development, validation, and clinical assessment

Valero-Bover¹,

González²,

Carot-Sans³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In these studies, AUCs of 0.81 and 0.886 were reported, respectively. Another approach that has been considered to predict no-show is probit regression in Ahmad et al [ 40 ], obtaining an AUC of 0.7. Other research works used LR in 2019, like those of Chua and Chow [ 41 ] and Dantas et al [ 42 ].…”

Section: Resultsmentioning

confidence: 99%

“…This limitation makes it very difficult to predict missing attendance on the first visit. Different authors have addressed this problem by means of different techniques such as not including in the analysis patients who do not have a certain number of previous visits [ 5 , 28 ], not including the first appointment in the study [ 7 , 15 , 16 , 21 , 30 , 44 , 53 ], or including a variable that indicates whether the appointment corresponds to the first visit [ 14 , 17 , 20 , 23 , 27 , 29 , 32 , 34 , 40 , 41 , 42 , 43 , 48 , 50 , 51 , 52 , 56 , 58 , 59 , 60 ].…”

Section: Discussionmentioning

confidence: 99%

Patient No-Show Prediction: A Systematic Literature Review

Carreras-García

Delgado-Gómez

Llorente

et al. 2020

Entropy

View full text Add to dashboard Cite

Nowadays, across the most important problems faced by health centers are those caused by the existence of patients who do not attend their appointments. Among others, these patients cause loss of revenue to the health centers and increase the patients’ waiting list. In order to tackle these problems, several scheduling systems have been developed. Many of them require predicting whether a patient will show up for an appointment. However, obtaining these estimates accurately is currently a challenging problem. In this work, a systematic review of the literature on predicting patient no-shows is conducted aiming at establishing the current state-of-the-art. Based on a systematic review following the PRISMA methodology, 50 articles were found and analyzed. Of these articles, 82% were published in the last 10 years and the most used technique was logistic regression. In addition, there is significant growth in the size of the databases used to build the classifiers. An important finding is that only two studies achieved an accuracy higher than the show rate. Moreover, a single study attained an area under the curve greater than the 0.9 value. These facts indicate the difficulty of this problem and the need for further research.

show abstract

“…The years of publication in the included studies range from 1996 to 2022 with 10 studies published between 2017 and 2022 [ 15 , 16 , 18 – 24 , 26 ]. Collectively, the studies cross eight countries (USA, Spain, Canada, Great Britain, Poland, Portugal, Australia, and New Zealand) with seven studies originating primarily from the USA [ 15 , 16 , 19 – 22 , 26 ]. Eight of the studies are journal articles [ 15 – 18 , 21 , 22 , 24 , 26 ] and four studies are conference papers [ 19 , 20 , 23 , 25 ].…”

Section: Resultsmentioning

confidence: 99%

Machine learning in general practice: scoping review of administrative task support and automation

et al. 2023

View full text Add to dashboard Cite

Background Artificial intelligence (AI) is increasingly used to support general practice in the early detection of disease and treatment recommendations. However, AI systems aimed at alleviating time-consuming administrative tasks currently appear limited. This scoping review thus aims to summarize the research that has been carried out in methods of machine learning applied to the support and automation of administrative tasks in general practice. Methods Databases covering the fields of health care and engineering sciences (PubMed, Embase, CINAHL with full text, Cochrane Library, Scopus, and IEEE Xplore) were searched. Screening for eligible studies was completed using Covidence, and data was extracted along nine research-based attributes concerning general practice, administrative tasks, and machine learning. The search and screening processes were completed during the period of April to June 2022. Results 1439 records were identified and 1158 were screened for eligibility criteria. A total of 12 studies were included. The extracted attributes indicate that most studies concern various scheduling tasks using supervised machine learning methods with relatively low general practitioner (GP) involvement. Importantly, four studies employed the latest available machine learning methods and the data used frequently varied in terms of setting, type, and availability. Conclusion The limited field of research developing in the application of machine learning to administrative tasks in general practice indicates that there is a great need and high potential for such methods. However, there is currently a lack of research likely due to the unavailability of open-source data and a prioritization of diagnostic-based tasks. Future research would benefit from open-source data, cutting-edge methods of machine learning, and clearly stated GP involvement, so that improved and replicable scientific research can be done.

show abstract

A predictive model for decreasing clinical no-show rates in a primary care setting

Cited by 18 publications

References 49 publications

Preventing non-attendance in outpatient appointments: predictive model development, validation, and clinical assessment

Preventing non-attendance in outpatient appointments: predictive model development, validation, and clinical assessment

Patient No-Show Prediction: A Systematic Literature Review

Machine learning in general practice: scoping review of administrative task support and automation

Contact Info

Product

Resources

About