Elective pathway modelling
Başlık çevirisi mevcut değil.
- Tez No: 403189
- Danışmanlar: Dr. JENNIFER MORGAN
- Tez Türü: Yüksek Lisans
- Konular: Matematik, Mathematics
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2016
- Dil: İngilizce
- Üniversite: Cardiff University (Prifysgol Caerdydd)
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 105
Özet
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Özet (Çeviri)
Aims The specific aim of this study was to model the flow of patients through the steps of the elective pathway within the Gynaecology service in Cardiff & Vale University Health Board in order to allow service managers to predict demand. The development of a simulation model was intended to enable the identification of delays and long waiting times in the system (bottlenecks), testing scenarios to solve these problems and evaluating the best scenarios. Background Elective treatment is defined as planned in advance treatment. Typically, elective treatment consists of outpatient appointments, admissions (such as a day case operation or inpatient admission), follow-up appointments and other procedures which are related to outpatient treatment. The Gynaecology service consists of main three pathways which are Parent Gynaecology (Urgent-Routine), Gynaecology-Oncology and Urodynamic pathways according to information which is attained from service administrator and dataset. Methods and Results Discrete event simulation was used to model the Gynaecology service. The model development process was carried out in two parallel stages: data analysis and simulation model building. In the data analysis stage, data was organized by utilising some data mining and data grouping techniques. Descriptive statistics were attained and distributions and quantification of transitions between stages in the model were identified through the analysis. Additional data analysis which helped to convey the understanding of the system obtained, but not pertinent to the simulation model, was presented to the stakeholder during the project. In the other stage (simulation model building), a model was built by using Simul8 software and populated with the data which was found in the data analysis stage. Finally, a verified and validated model was achieved following testing of the model and discussions with stakeholders. Four scenarios are tested to decrease waiting times of patients and to identify potential improvements to the Gynaecology Department. Scenario 1 tested the impact of including an additional clinic for new patients only and for new and follow-up patients. II Increasing 'new' patient capacity leads to more patients in the follow-up cycle which the department is already struggling to see in a timely manner. Therefore, adding a clinic for new outpatients needs to be balanced with an additional follow-up clinic or a clinic which is added to system should be served for both new and follow up patients to improve the system. In scenario 2, the effect of treatment durations are tested and the treatment durations of new and follow-up patients were decreased. The results shows that decreasing treatment durations helped to shorten waiting times and delays and it made the system very effective. In scenario 3, some follow-up clinics were shifted as virtual review clinics (which have shorter treatment durations) in order to see the impact of virtual review clinics. It significantly decreased delays in all pathways. Moreover, not only was a reduction in follow up delays observed, but also a reduction in waiting times for new outpatient appointments in Gynae-Oncology and Urodynamic pathways as resources are shared based on the volume of new and follow appointments in these pathways. Finally, scenario 4 tested the impact of adding one nurse led clinic (which serves for all pathways). As a result of this scenario, nurse led clinic was found very useful to cope up with delays. In addition, all follow-up delays and waiting times for new outpatient appointments reduced in Gynae-Oncology and Urodynamic similar to scenario 3. Findings The total number of new outpatients which explains the total demand for Gynaecology service in 3 years was found as 53,250. Moreover, it has been seen that most patients who are required follow-up appointment are accepted in 'less than 1 month' time period for their follow up appointments in urgent and gynae-oncology pathways. However, in routine and urodynamic pathways, most patients are accepted 'between 1 and 3 months' time period for their follow-up appointments. The number of inpatients was found as 9,586. Furthermore, total 15,856 patients disappeared in the system because of the reasons such as“did not attend (more than once)”or“transferred to another H\C professional”and total 32,958 patients were discharged in 3 years. Waiting times of new outpatient appointments are another component to evaluate demand for services at each stage. According to simulation model outcomes, new outpatients wait average 27 weeks for their first outpatient appointments in Routine pathway. However, there is no significant delay for follow up appointments which are in different time periods in this pathway. In Urgent-Parent Gynaecology pathway, new III outpatients wait average 7.5 weeks for their first outpatient appointments. Moreover, it has been found that overdue rate (the delay represented as percentage of the Pause which is the predetermined waiting time) of 'less than 1 month' follow-up time period is quite high, but the total waiting times of patients for this time period do not go beyond 1 month. On the other hand, the overdue rates for other time periods in the urgent pathway are less than 1 percent. The waiting times for new outpatient appointments is found as average 2 weeks for Gynae-Oncology and 8 weeks for Urodynamic pathway. Moreover, delays of follow-up appointment time periods are less than one week in both pathways. Additionally, all scenarios facilitated to improve the current system, though decreasing treatment durations (scenario 2.1) was found as the best simulated scenario. However, this scenario may not be wholly achievable and was used to illustrate the perfect situation and understand the importance of treatment durations. On the other hand, shifting some clinics as virtual review clinics (scenario 3) was determined as the most feasible scenario in terms of eliminating follow-up appointment delays and adding one extra clinic to the system(scenario 1.2) was seen as one of the best scenarios to shorten waiting times of new outpatients in the Gynaecology department. Conclusion The simulation model and results were found to be considerably consistent and met the expectations of the stakeholder. It can be said that the Gynaecology department is close to meet government waiting time targets, especially for non-routine pathways. However, waiting time for follow-up appointments are longer than planned waiting time. Developing some strategies which focus on Urgent Parent Gynaecology pathway as it is the busiest pathway and solve bottlenecks in the follow-up appointment time periods which is less than 1 month as it is the busiest time period will be more useful to increase efficiency of department. Moreover, the scenarios which were produced in this study can be applied to make the system more efficient. Furthermore, the scenarios can be combined with each other in order to solve different problems on the system. Consequently, the study illustrates the bottlenecks and patient flows in an elective pathway model which is rarely seen in literature. In addition, the study presents a model which can be used as a tool by the service managers to set a course for the department.
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