A Queuing model for Dealing with Patients with Severe Disease

This paper suggests a proposed single server queueing model for severe diseases especially in Out-patient Department. The Outpatient Department of a hospital is visited by patients of all types ofdisease. Some of these diseases require immediate medical attention as severe complications may ariseif treatment is delayed. The goal of the study was to develop a queueing model considering patientswith severe disease and to study the improvement in the service time using the model. The singleserver queueing model was modied and analyzed. The eciency of the model was tested by usingoutpatient medical service, arrivals and departure of patients over a period of one year of a localhospital in Guwahati. The result indicated the average outpatient medical service response times forservice improve over the general model

An Integrated Model of Patient and Staff Satisfaction Using Queuing Theory.

This paper investigates the connection between patient satisfaction, waiting time, staff satisfaction, and service time. It uses a variety of models to enable improvement against experiential and operational health service goals. Patient satisfaction levels are estimated using a model based on waiting (waiting times). Staff satisfaction levels are estimated using a model based on the time spent with patients (service time). An integrated model of patient and staff satisfaction, the effective satisfaction level model, is then proposed (using queuing theory). This links patient satisfaction, waiting time, staff satisfaction, and service time, connecting two important concepts, namely, experience and efficiency in care delivery and leading to a more holistic approach in designing and managing health services. The proposed model will enable healthcare systems analysts to objectively and directly relate elements of service quality to capacity planning. Moreover, as an instrument used jointly by healthcare commissioners and providers, it affords the prospect of better resource allocation.The authors acknowledge the North West London Hospitals NHS Trust for supporting this research. We also give special thanks to the following persons for their various contributions to this work: Justin Gore for being a cosupervisor of the project; Professors Lorraine De Sousa of Brunel University and Janet Smart of Oxford University for their many constructive critiques of the initial work. Finally, the research was in part supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care East of England (CLAHRC EoE) at Cambridge and Peterborough NHS Foundation Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.This is the final published version of the article, originally published in IEEE Journal of Translational Engineering in Health and Medicine, 3, 2015, DOI: 10.1109/JTEHM.2015.240043

The Clinical Information System That Effects The Patients' Satisfaction Of The Healthcare Services

This research intends to understand and investigate how to increase patients' satisfaction by implementing the clinical information system and by looking at the service quality, green environment, and patients' perceptions to provide better patient satisfaction concerning healthcare services. The data collection is done through the distribution of questionnaires in Jakarta, Indonesia. This research is based on descriptive and verification methods; the sample is determined through Purposive Sampling, and the method analysis technique is Partial Least Square (PLS). The research results show that patients' perception significantly influences clinical system information compared to quality services and a green environment to achieve patient satisfaction. Healthcare clinics need to implement the clinical system information to assist the healthcare workers in providing quality patient services

Factors Affecting Surgical Waiting Time in Cancer Patients at Referral Hospitals of West Java Province

A challenge for hospitals in facing the high number of patient visits is to provide quality services. One of the vital services in dealing with patients, especially those who will have cancer surgery considering the high rate of mortality cancer, is an improvement in waiting time (WT). Waiting time for elective surgery is one indicator of service quality with a standard of ≤2 days. This research aimed to determine the average WT for surgery, influencing factors, and optimal queuing models. The method used was quantitative and qualitative methods applied to 207 samples with consecutive sampling at West Java Provincial Al-Ihsan Regional General Hospital Bandung from October to December 2016. The analysis used partial least squares (PLS). The results of the study showed that the average WT for surgery was 32 days. Factors that influence WT were inpatient rooms, number of medical personnel, condition of patients, and health insurance. The optimal queue model to reduce surgical waiting time are adding inpatient beds, oncologist doctor, and creating an online system for registration and confirmation of inpatient rooms and operating.   FAKTOR YANG MEMENGARUHI WAKTU TUNGGU OPERASI PASIEN KANKER DI RUMAH SAKIT RUJUKAN JAWA BARAT Tantangan bagi rumah sakit dalam menghadapi jumlah kunjungan pasien yang tinggi adalah mampu memberikan pelayanan berkualitas. Salah satu pelayanan signifikan bagi pasien kanker yang akan menjalani operasi adalah perbaikan waktu tunggu karena mortalitas pasien kanker yang tinggi. Waktu tunggu operasi elektif merupakan salah satu indikator mutu pelayanan dengan standar ≤2 hari. Penelitian bertujuan mengetahui waktu tunggu operasi rerata, faktor yang memengaruhi, dan model antrean yang optimal. Metode yang digunakan adalah kuantitatif dan kualitatif yang diterapkan pada 207 sampel secara consecutive sampling di RSUD Al-Ihsan Provinsi Jawa Barat Bandung dari Oktober hingga Desember 2016. Analisis menggunakan partial least squares (PLS). Hasil penelitian menunjukkan bahwa waktu tunggu operasi rerata adalah 32 hari. Faktor yang berpengaruh terhadap waktu tunggu operasi adalah ruang rawat inap, jumlah tenaga medis, kondisi pasien, dan jaminan kesehatan. Model antrean yang optimal untuk menurunkan waktu tunggu operasi adalah penambahan tempat tidur rawat inap, penambahan dokter spesialis bedah onkologi, serta pembuatan sistem daring untuk pendaftaran dan konfirmasi kesiapan ruang rawat inap dan ruang operasi

Logistics performances of health care system using queue analysis

As there is a very high demand for health service that exceeds the available capacity, the public healthcare centers are overwhelmed with the long queues or they are delivering the service with relatively very low consultation time. In the existing conditions, patients go as early as they can to the healthcare facilities, waiting in queue, even before the opening and had to wait long time for examination, consultation and diagnosis. However, due to high number of patients at the outpatient departments relative to the number of physicians, it results in an increased workload on the physicians and it shortens the patient consultation time, which has an impact on the patients’ health. The main objective of this research was to study the logistic performances of the healthcare system using queuing analysis. This research used three key performance indicators namely, patient queue length, patient waiting time and consultation time length. The performance evaluation was conducted based on data from patients who visited 69 clinical, surgical and diagnosis departments at the outpatient clinics of the hospital. Queue analysis was performed to determine the operational characteristics using a queue scenario with Poisson arrival, exponential service, infinite population, First Comes First Served (FCFS) discipline and multiple server arrangement. The study showed that the patients’ arrival rate highly exceeded the service rate, in each respective clinical department. The outpatient clinics at the SPHMMC achieved an average total waiting time of 92 minutes to get consultation and nearly 70% of the patients waited for more than 95 minutes. The consultation time was as low as 5.71 minute at the Medical clinic and 6.16 minute at the Ophthalmology clinic and around 60% of the patients saw the doctor for a time less than 10 minutes. Therefore, this research recommends addressing the gaps in human resources and logistical supplies, to implement and enforce a staggered patient scheduling and appointment system and to have serious intervention and control on the dual practice, to ensure a smooth clinic process and to reduce waiting times

Reflexiones para optimizar el triaje en cirugía

Introducción: La categorización de las urgencias quirúrgicas es una necesidad en razón al continuo desbalance entre la oferta y la demanda de servicios quirúrgicos en la mayoría de las instituciones donde se encuentra habilitada la prestación del servicio. Hay abordajes en el tema, con estrategias de priorización de los casos quirúrgicos, que consideran escalas y flujogramas, pero su ausente validez externa y las particularidades de las instituciones y aseguradores, han limitado una generalización de los resultados. Métodos: Se efectúa una conceptualización del triaje de las urgencias quirúrgicas con planteamientos críticos y reflexivos soportados en la evidencia. Se identifican, asimismo, las posibles oportunidades para la investigación. Discusión: Los beneficios potenciales de un triaje quirúrgico en situaciones de urgencia, son extensivos a todos los actores del sistema de salud, disminuyen la posibilidad de desenlaces y repercusiones económicas negativas para las instituciones y los aseguradores. La teoría de las colas ofrece el soporte para un entendimiento del tema y contribuye en las soluciones. Su adopción es escasa como parte de una estrategia local de priorización quirúrgica en un contexto de urgencia. Conclusión: La creación de estrategias que establezcan el triaje para el paciente con una urgencia quirúrgica están influenciadas por la participación continua y efectiva de los actores involucrados en el proceso y en su impacto en los desenlaces clínicos