scholarly journals What Yelp Effect? An Analysis of Hundreds of Thousands of Online Reviews of Urgent Cares (Preprint)

2021 ◽  
Author(s):  
Dian Hu ◽  
Cindy Meng-Hsin Liu ◽  
Rana Hamdy ◽  
Michael Cziner ◽  
Melody Fung ◽  
...  
Keyword(s):  
Application Programming Interface ◽  
Online Reviews ◽  
Management Plan ◽  
Machine Learning Algorithms ◽  
Urgent Care ◽  
Antibiotic Prescribing ◽  
George Washington ◽  
Patient Dissatisfaction ◽  
Institutional Review ◽  
Application Programming

BACKGROUND Providers of on-demand care, such as in urgent care, may prescribe antibiotics unnecessarily because they fear receiving negative reviews online from unsatisfied patients – the so-called “Yelp Effect”. This effect is hypothesized to be a significant driver of inappropriate antibiotic prescribing, exacerbating antibiotic resistance. OBJECTIVE In this study, we aim to determine the frequency with which patients left negative reviews online after having expected, but not received, antibiotics in an urgent care setting. METHODS We obtained a list of 8662 urgent care facilities from the Yelp Application Programming Interface (API). Using this list, we automatically collected 481825 online reviews from Google Maps between January 21st, and Feb 10th, 2019. We used machine learning algorithms to summarize the contents of these reviews. Additionally, 200 randomly sampled reviews were analyzed by four annotators to verify the types of messages present and whether they were consistent with the “Yelp Effect”. RESULTS We collected 481825 reviews, of which 1696 (95% CI: 1240-2152) exhibited the “Yelp effect”. Instead, negative reviews primarily identified operations issues: wait times, rude staff, billing, and communication. CONCLUSIONS Urgent care patients rarely express expectation for antibiotics in negative online reviews. Thus, our findings do not support an association between lack of antibiotic prescriptions and negative online reviews. Rather, patient dissatisfaction in urgent care was most strongly linked to operations issues that are not related to the clinical management plan. CLINICALTRIAL This research was approved by The George Washington University Committee on Human Research, Institutional Review Board (IRB), FWA00005945 (IRB #180804).

scholarly journals Questioning the Yelp Effect: Mixed Methods Analysis of Web-Based Reviews of Urgent Cares

10.2196/29406 ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. e29406
Author(s):  
Dian Hu ◽  
Cindy Meng-Hsin Liu ◽  
Rana Hamdy ◽  
Michael Cziner ◽  
Melody Fung ◽  
...  
Keyword(s):  
Application Programming Interface ◽  
Management Plan ◽  
Machine Learning Algorithms ◽  
Urgent Care ◽  
Antibiotic Prescribing ◽  
Web Based ◽  
Care Facilities ◽  
Application Programming ◽  
To Receive ◽  
Urgent Care Setting

Background Providers of on-demand care, such as those in urgent care centers, may prescribe antibiotics unnecessarily because they fear receiving negative reviews on web-based platforms from unsatisfied patients—the so-called Yelp effect. This effect is hypothesized to be a significant driver of inappropriate antibiotic prescribing, which exacerbates antibiotic resistance. Objective In this study, we aimed to determine the frequency with which patients left negative reviews on web-based platforms after they expected to receive antibiotics in an urgent care setting but did not. Methods We obtained a list of 8662 urgent care facilities from the Yelp application programming interface. By using this list, we automatically collected 481,825 web-based reviews from Google Maps between January 21 and February 10, 2019. We used machine learning algorithms to summarize the contents of these reviews. Additionally, 200 randomly sampled reviews were analyzed by 4 annotators to verify the types of messages present and whether they were consistent with the Yelp effect. Results We collected 481,825 reviews, of which 1696 (95% CI 1240-2152) exhibited the Yelp effect. Negative reviews primarily identified operations issues regarding wait times, rude staff, billing, and communication. Conclusions Urgent care patients rarely express expectations for antibiotics in negative web-based reviews. Thus, our findings do not support an association between a lack of antibiotic prescriptions and negative web-based reviews. Rather, patients’ dissatisfaction with urgent care was most strongly linked to operations issues that were not related to the clinical management plan.

scholarly journals Cross-Method-Based Analysis and Classification of Malicious Behavior by API Calls Extraction

2019 ◽  
Vol 9 (2) ◽  
pp. 239 ◽  
Author(s):  
Bruce Ndibanje ◽  
Ki Kim ◽  
Young Kang ◽  
Hyun Kim ◽  
Tae Kim ◽  
...  
Keyword(s):  
Application Programming Interface ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Public Security ◽  
Detection Accuracy ◽  
Malicious Behavior ◽  
Application Programming ◽  
Programming Interface ◽  
Method Show

Data-driven public security networking and computer systems are always under threat from malicious codes known as malware; therefore, a large amount of research and development is taking place to find effective countermeasures. These countermeasures are mainly based on dynamic and statistical analysis. Because of the obfuscation techniques used by the malware authors, security researchers and the anti-virus industry are facing a colossal issue regarding the extraction of hidden payloads within packed executable extraction. Based on this understanding, we first propose a method to de-obfuscate and unpack the malware samples. Additional, cross-method-based big data analysis to dynamically and statistically extract features from malware has been proposed. The Application Programming Interface (API) call sequences that reflect the malware behavior of its code have been used to detect behavior such as network traffic, modifying a file, writing to stderr or stdout, modifying a registry value, creating a process. Furthermore, we include a similarity analysis and machine learning algorithms to profile and classify malware behaviors. The experimental results of the proposed method show that malware detection accuracy is very useful to discover potential threats and can help the decision-maker to deploy appropriate countermeasures.

scholarly journals Disease Prediction and Drug Recommendation Android Application using Data Mining (Virtual Doctor)

2019 ◽  
Vol 8 (3) ◽  
pp. 6996-7001
Keyword(s):  
Data Mining ◽  
Research Work ◽  
Application Programming Interface ◽  
Machine Learning Algorithms ◽  
Useful Knowledge ◽  
Medical Databases ◽  
Application Programming ◽  
Mining Procedure ◽  
Using Data ◽  
Programming Interface

Data Mining is a method that requires analyzing and exploring large blocks of data to glean meaningful trends and patterns. In today’s period, every person on earth relies on allopathic treatments and medicines. Data mining techniques can be applied to medical databases that have a vast scope of opportunity for textual as well as visual data. In medical services, there are myriad obscure data that needs to be scrutinized and data mining is the key to gain useful knowledge from these data. This paper provides an application programming interface to recommend drugs to users suffering from a particular disease which would also be diagnosed by the framework through analyzing the user's symptoms by the means of machine learning algorithms. We utilize some insightful information here related to mining procedure to figure out most precise sickness that can be related with symptoms. The patient can without much of a stretch recognize the diseases. The patients can undoubtedly recognize the disease by simply ascribing their issues and the application interface produces what malady the user might be tainted with. The framework will demonstrate complaisant in critical situations where the patient can't achieve a doctor's facility or when there are situations, when professional are accessible in the territory. Predictive analysis would be performed on the disease that would result in recommending drugs to the user by taking into account various features in the database. The experimental results can also be used in further research work and for Healthcare tools.

scholarly journals Application Programming Interface for a Customer Experience Analysis Tool

2021 ◽  
Author(s):  
George Kopsiaftis ◽  
Ioannis Georgoulas ◽  
Ioannis Rallis ◽  
Ioannis Markoulidakis ◽  
Kostis Tzanettis ◽  
...  
Keyword(s):  
Graphical Representation ◽  
Application Programming Interface ◽  
Customer Experience ◽  
Machine Learning Algorithms ◽  
Analysis Tool ◽  
Net Promoter Score ◽  
Application Programming ◽  
Net Promoter ◽  
User Friendly ◽  
Programming Interface

This paper analyzes the architecture of an application programming interface (API) developed for a novel customer experience tool. The CX tool aims to monitor the customer satisfaction, based on several experience attributes and metrics, such as the Net Promoter Score. The API aims to create an efficient and user-friendly environment, which allow users to utilize all the available features of the customer experience system, including the exploitation of state-of-the-art machine learning algorithms, the analysis of the data and the graphical representation of the results.

Sistem Pengawasan Aktifitas Penggunaan Smartphone Android

2018 ◽  
Vol 9 (1) ◽  
pp. 24-31
Author(s):  
Rudianto Rudianto ◽  
Eko Budi Setiawan
Keyword(s):  
Application Programming Interface ◽  
Third Party ◽  
Image Content ◽  
Image File ◽  
Application Programming ◽  
Index Terms ◽  
Programming Interface

Availability the Application Programming Interface (API) for third-party applications on Android devices provides an opportunity to monitor Android devices with each other. This is used to create an application that can facilitate parents in child supervision through Android devices owned. In this study, some features added to the classification of image content on Android devices related to negative content. In this case, researchers using Clarifai API. The result of this research is to produce a system which has feature, give a report of image file contained in target smartphone and can do deletion on the image file, receive browser history report and can directly visit in the application, receive a report of child location and can be directly contacted via this application. This application works well on the Android Lollipop (API Level 22). Index Terms— Application Programming Interface(API), Monitoring, Negative Content, Children, Parent.

Sign in / Sign up

Export Citation Format

Share Document