Patient Queue Systems in Hospital Using Patient Treatment Time Prediction Algorithm

Handayani, Dwi; Mustafid, Mustafid; Surarso, Bayu

doi:10.22219/kinetik.v5i1.1001

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…According to the results of the developed system , it is observed that age plays an important role in treatment time. Early ages have less treatment time than the old ages due to morbidity issues [18]. Javadifard et al, discussed the issues of long waiting times in the phlebotomy units, as all the patients from other outpatient departments visits this unit for their tests.…”

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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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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“…Long queues happen in various sectors, i.e., hospitals, banks, and retail stores [2]. In healthcare, taking a long time to book an appointment, get treatment, or take medicine can negatively impact patients' satisfaction and safety [1,[3][4][5][6][7][8]. It is essential to have proper solutions for those long queues in healthcare organizations, where these solutions help to manage the queues along with their related statistics.…”

Section: Introductionmentioning

confidence: 99%

Medical data integration using HL7 standards for patient’s early identification

2021

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Integration between information systems is critical, especially in the healthcare domain, since interoperability requirements are related to patients’ data confidentiality, safety, and satisfaction. The goal of this study is to propose a solution based on the integration between queue management solution (QMS) and the electronic medical records (EMR), using Health Level Seven (HL7) protocols and Extensible Markup Language (XML). The proposed solution facilitates the patient’s self-check-in within a healthcare organization in UAE. The solution aims to help in minimizing the waiting times within the outpatient department through early identification of patients who hold the Emirates national ID cards, i.e., whether an Emirati or expatriates. The integration components, solution design, and the custom-designed XML and HL7 messages were clarified in this paper. In addition, the study includes a simulation experiment through control and intervention weeks with 517 valid appointments. The experiment goal was to evaluate the patient’s total journey and each related clinical stage by comparing the “routine-based identification” with the “patient’s self-check-in” processes in case of booked appointments. As a key finding, the proposed solution is efficient and could reduce the “patient’s journey time” by more than 14 minutes and “time to identify” patients by 10 minutes. There was also a significant drop in the waiting time to triage and the time to finish the triage process. In conclusion, the proposed solution is considered innovative and can provide a positive added value for the patient’s whole journey.

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The Queue System of Kemlagi Community Health Center Using Decision Tree Method Based On a Website

Mukarrom,

Aswoyo,

Budikarso

et al. 2024

2024 International Electronics Symposium (IES)

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Patient Queue Systems in Hospital Using Patient Treatment Time Prediction Algorithm

Cited by 4 publications

References 15 publications

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

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

Medical data integration using HL7 standards for patient’s early identification

The Queue System of Kemlagi Community Health Center Using Decision Tree Method Based On a Website

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