Reducing the outpatient waiting time based on CBR algorithm using smartphone

Liu, Yuh-Tzong; Chen, Yihan; Wang, Nei-Phon; Lin, Chun-Jung; Chen, Yun-An

doi:10.1109/icmlc.2014.7009659

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…More importantly, the system has improved patient safety by enabling pharmacists to review all prescriptions and by reducing drug-packing errors (IMDA, 2016). The RFID and barcode applications help to manage services and improve quality and patient safety (Liu et al, 2014).…”

Section: Six Sigma Methodsmentioning

confidence: 99%

Reducing pharmacy patient waiting time

Alam

Osama

Iqbal

et al. 2018

IJHCQA

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Purpose Pharmacy services start right from prescribing medicines and continue as the medication’s effect is monitored. Hospital and community pharmacy staff promote rational prescribing and medicine use. Consequentially, pharmacy is a complex and busy field. Often there are peak workload hours when patients must wait, which is associated with patient dissatisfaction that may negatively affect patient experience and the organisation’s reputation. The purpose of this paper is to enlist techniques, methods and technological advancements that have been successfully employed to reduce patient waiting time. Design/methodology/approach A database search was conducted in 2017 to locate articles addressing methods and technologies that reduce pharmacy waiting time. The literature revealed various techniques and technologies like queuing theory, tele-pharmacy, evidence-based pharmacy design, automated pharmacy systems (robotics), system modelling and simulation and the Six Sigma method for identifying potential problems associated with increased wait time. Findings The authors conclude that various techniques and methods, including automated queuing technology, tele-pharmacy, automated pharmacy devices/machines for quick and accurate filling and dispensing, computer simulation modelling, evidence-based pharmacy infrastructure for smooth workflow and Six Sigma can maintain customer satisfaction, reduce waiting time, attract new customers, decrease workload and improve the organisation’s reputation. Practical implications The authors conclude that various techniques and methods, including automated queuing technology, tele-pharmacy, automated pharmacy devices/machines for quick and accurate filling and dispensing, computer simulation modelling, evidence-based pharmacy infrastructure for smooth workflow and Six Sigma methodology can maintain customer satisfaction, reduce waiting time, attract new customers, decrease workload and improve the organisation’s reputation. Originality/value The authors carried out a literature search and identified the techniques that have been successfully implemented to reduce pharmacy patient waiting time and methods that can identify potential process behind medication dispensation delays.

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Section: Six Sigma Methodsmentioning

confidence: 99%

Reducing pharmacy patient waiting time

Alam

Osama

Iqbal

et al. 2018

IJHCQA

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“…Patients' wait times are lengthened when a facility's capacity is exceeded by the demand for care. Outpatient clinic waiting times are universally recognized as one of the most important issues in healthcare all over the world [11]. There are two levels to it: real and perceived waiting times.…”

Section:  Issn: 2252-8806mentioning

confidence: 99%

“…The justification of sample size and a clear explanation of treatments were evaluated, as were the statistical analysis methodologies utilized in the various research. [2], [11], [16], [18], [19], [21], [24], [31]- [33], [36]- [47].…”

Section: Quality Assessmentmentioning

confidence: 99%

A systematic review of artificial intelligence-based methods in healthcare

Apio

Kissi

Achampong

2023

IJPHS

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Artificial intelligence (AI) in healthcare has enormous potential for transforming healthcare. AI is the ability of machines to learn and exhibit close to human levels of cognition in various specific ways. Leveraging AI software to support activities will improve patient satisfaction which is inextricably tied to the length of time patients spend in waiting queues. Literature searches were conducted in PubMed, Research Gate, BMC Health Services Research, JMIR Publications and Cochrane Central to find related documentation that was published between January 2011 and April 2021. The studies featured and reported on AI technologies that had been used in primary, secondary, or tertiary healthcare situations directed towards reducing waiting times. A total of 22 articles were primarily used, including 8 retrospective studies, 4 prospective studies and 3 case-control studies. AI technologies have enormous potential in the creation of a future with more reliable healthcare systems. It is however clear that more studies in the field are required to validate the existing evidence of its potential. AI in healthcare is crucial to reducing patients' time at healthcare facilities. The use of AI can also help improve patient outcomes and more research should be geared toward that.

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“…The results helped the hospital management to be well aware of the risk of overcrowding and long waits [23]. In [24], Liu proposed a smart phone-based prediction system using a case-based reasoning algorithm (CBR). The application helped the patients to reach the hospital at the specified time, therefore reducing the wait time.…”

Section: B Contributionsmentioning

confidence: 99%

A Novel Data-Driven Adaptive Technique to Generate a Physician Visiting Schedule for Better Patient Experience

Safdarid¹,

Khan

Shaukat

2022

IEEE Access

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This paper proposes an adaptive data-driven novel technique for generating physician's schedules exploring the trade-off between physician waiting time and patient experience. Generally, in hospitals fix time slots are assigned to the patients, without differentiating patients into different categories. Some patients are referral patients who need less time, others are first-time visitors who need more time than referral patients. Some are serious patients who take even more time, so a fixed time slot can increase the waiting time both at the doctor and the patient level. So, there is a need to categorize patients according to their treatment which could help in improving the waiting time of doctors and as well of the patients. This technique trains the patient's data according to their specific category based on the time doctors usually spend on them. This can help in generating a schedule that will minimize the waiting of patients and at the same time improve the hospital performance by scheduling maximum patients ensuring that the same patient's get the best experience. To achieve this a data-driven scheduling algorithm (DDSA) is proposed which uses probability distribution, clustering and doctor's scheduling algorithm to classify patients into different categories. The data is collected through the RFID machines. The patients are given cluster head time for the treatment. Different patient categories are given different treatment times. Then patient and doctor commutative waiting times are calculated. The system applies different set-up times to allow hospital management to trade-off between doctor and patient waiting times. The average waiting time for each patient using DDSA comes out to be seven minutes. Hence, this technique can help hospitals across the globe in improving their performance and patient experience.

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Reducing the outpatient waiting time based on CBR algorithm using smartphone

Cited by 3 publications

References 8 publications

Reducing pharmacy patient waiting time

Reducing pharmacy patient waiting time

A systematic review of artificial intelligence-based methods in healthcare

A Novel Data-Driven Adaptive Technique to Generate a Physician Visiting Schedule for Better Patient Experience

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