Predictors of outpatients’ no-show: big data analytics using apache spark

Daghistani, Tahani; AlGhamdi, Huda; Alshammari, Riyad; AlHazme, Raed H.

doi:10.1186/s40537-020-00384-9

Cited by 19 publications

(10 citation statements)

References 41 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…14 This finding provides an additional important feature to consider in the development of prediction models for missed appointments, as it represents a previously un-assessed predictor in such models. [43][44][45] Because of the crosssectional nature of our data, though, we are unable to draw temporal relationships between being a portal user and no-shows. An area of future research could be evaluating differences in no shows between appointments made through the portal compared to other means.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Characteristics of Adult Primary Care Patients Who Use the Patient Portal: A Cross-Sectional Analysis

et al. 2022

View full text Add to dashboard Cite

Background: The patient portal allows patients to engage with their healthcare team beyond the clinical encounter. While portals can improve patient outcomes, there may be disparities in which patients access the portal by sociodemographic factors. Understanding the characteristics of patients who use the portal could help design future interventions to expand portal adoption. Objectives: This study aimed to (1) examine the socioeconomic factors, comorbid conditions, and healthcare utilization among patients of a large academic primary care network who are users and non-users of the patient portal; and (2) describe the portal functions most frequently utilized. Methods: We included all adult patients at Atrium Health Wake Forest Baptist who had at least two primary care visits between 2018 and 2019. Patients' demographics, comorbidities, healthcare utilization, and portal function usage were extracted from the electronic health record and merged with census data (income, education, and unemployment) from the American Community Survey. A myWakeHealth portal user was defined as a patient who used a bidirectional portal function at least once during the study period. We used multivariable logistic regression to determine which patient characteristics were independently associated with being a portal user. Results: Of the 178,720 patients who met inclusion criteria, 32% (N=57,122) were users of myWakeHealth. Compared to non-users, users were more likely to be 18-64 years of age, female, non-Hispanic white, married, commercially insured, have higher disease burden, and have lower healthcare utilization. Patients residing in areas with the highest educational attainment had 51% higher odds of being a portal user than the lowest (p<0.001). Among portal users, the most commonly used function was messaging clinic providers. Conclusions: We found that patient demographics and area socioeconomic factors were associated with patient portal adoption. These findings suggest that efforts to improve portal adoption should be targeted at vulnerable patients.

show abstract

Section: Accepted Manuscriptmentioning

confidence: 99%

Characteristics of Adult Primary Care Patients Who Use the Patient Portal: A Cross-Sectional Analysis

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The high performance of Spark enables it to deal with data in a real-time streaming module and by using distributed workers, which is caching the result partially in memory. Moreover, it is very efficient such that it outperforms the Hadoop MapReduce framework [19] while preserving the fault tolerance and scalability of MapReduce, which is up to 10-times faster for interactive machine learning workloads [12].…”

Section: Spark Platformmentioning

confidence: 99%

Improved k-Means Clustering Algorithm for Big Data Based on Distributed SmartphoneNeural Engine Processor

Awad

Hamad

2022

Electronics

View full text Add to dashboard Cite

Clustering is one of the most significant applications in the big data field. However, using the clustering technique with big data requires an ample amount of processing power and resources due to the complexity and resulting increment in the clustering time. Therefore, many techniques have been implemented to improve the performance of the clustering algorithms, especially for k-means clustering. In this paper, the neural-processor-based k-means clustering technique is proposed to cluster big data by accumulating the advantage of dedicated machine learning processors of mobile devices. The solution was designed to be run with a single-instruction machine processor that exists in the mobile device’s processor. Running the k-means clustering in a distributed scheme run based on mobile machine learning efficiently can handle the big data clustering over the network. The results showed that using a neural engine processor on a mobile smartphone device can maximize the speed of the clustering algorithm, which shows an improvement in the performance of the cluttering up to two-times faster compared with traditional laptop/desktop processors. Furthermore, the number of iterations that are required to obtain (k) clusters was improved up to two-times faster than parallel and distributed k-means.

show abstract

“…o-show patients are those who fail to show up for booked appointments or cancel at the last minute, leaving the health facility unable to fill the appointment time. 1,2 No-show appointments cause health centers to lose time and money while also disrupting the continuity of care for the patients. 3 No-shows by patients result in inefficient clinic operations, high clinic expenses, and low patient satisfaction.…”

mentioning

confidence: 99%

“…While the tolerable level of no-shows can vary depending on the specific operational and environmental contexts of different clinics, our research aims to shed light on the prevalence and impacts of such occurrences. 1,2 In many settings, a certain degree of no-shows is anticipated and deemed acceptable, although healthcare providers endeavor to minimize this to ensure optimal operational efficiency and patient care. 3,5 Our research not only explores the existing norms, but also attempts to identify the distinctive challenges posed by the geographical and demographic context of our study area.…”

mentioning

confidence: 99%

Distance Matters: Investigating No-Shows in a Large Rural Provider Network

Shour,

Onitilo

2023

Clin Med Res

View full text Add to dashboard Cite

Background/Objective: No-shows have a negative effect on healthcare outcomes. It is unclear, however, whether patients' distance from the clinic is associated with higher no-show rates. To fill this knowledge gap, we examined the relationship between patients' distance from the clinic and no-shows in a rural provider network.Methods: Data from Marshfield Clinic Health System's scheduling system, including 263,464 recent patient appointments in 2021 were analyzed. The outcome was no-shows, defined as when patients missed an appointment (categorized as yes/no). The exposure was the distance to the clinic, measured in miles as a straight-line distance from the clinic in the patient's zip code to the facility where the appointment was held (classified as <5 miles, 5-10, 10-20; >20, and used as continuous). Covariates were patient demographics, appointments, providers, and insurance status. Chi-square and logistic regression were used with p-values ≤.05 considered statistically significant. Results:The no-show rate was 8.0%. Patients who lived <5 miles (8.3%) and >20 miles (8.2%) from the clinic had higher no-show rates than those who lived between 10-20 miles (8.0%) and 5-10 miles (7.6%), at P=0.001. In the adjusted model, the odds of no-show were similar between patients who did not show and those who did (OR:1.00,95%CI:1.00-1.00). No-shows were more likely among male patients compared to females (

show abstract

Predictors of outpatients’ no-show: big data analytics using apache spark

Cited by 19 publications

References 41 publications

Characteristics of Adult Primary Care Patients Who Use the Patient Portal: A Cross-Sectional Analysis

Characteristics of Adult Primary Care Patients Who Use the Patient Portal: A Cross-Sectional Analysis

Improved k-Means Clustering Algorithm for Big Data Based on Distributed SmartphoneNeural Engine Processor

Distance Matters: Investigating No-Shows in a Large Rural Provider Network

Contact Info

Product

Resources

About