Impact of the Problem on the Patient
Patients are not only recipients of the services of medical organizations, but they also take a direct part in creating the day-to-day work of these organizations. Regarding appointment scheduling, patients can be irresponsible by not showing up for appointments and not giving notice of no-shows. Such behavior creates problems for other participants and must therefore be addressed organizationally. For the safety and comfort of all patients, including those who follow the rules and those who do not follow the rules, hospitals, polyclinics, and outpatient polyclinic institutions create schedule management models and implement them as technical support tools for service delivery.
Impact of the Problem on the Organization
Non-appearance of patients, their tardiness, the arrival of two patients at the same time, and lateness of doctors – these life situations lead to inefficient use of time resources and professional skills of doctors. Since these two resources are the main elements of the clinic’s work, inefficiency in planning directly affects the economic success of the clinic and its ability to provide high-quality services. Inadequate planning in the outpatient, polyclinic and outpatient-polyclinic institutions is a problem that requires a comprehensive solution for the benefit of organizations and their patients.
Identify the PICO components
P – no-shows and lateness to appointments in the outpatient, polyclinic, and outpatient-polyclinic institutions
I – development of software and managerial practices for optimization of outpatient appointment scheduling
C – before and after the introduction of optimization practices
O – the increased efficiency of workflows
Evidence-Based Practice Question
Will the introduction of practices for optimization of outpatient appointment scheduling with the use of software and managerial solutions lead to better efficiency of workflows?
Research Article (Title of Article)
Srinivas, S., & Salah, H. (2021). Consultation length and no-show prediction for improving appointment scheduling efficiency at a cardiology clinic: a data analytics approach. International Journal of Medical Informatics, 145, 104290.
Background Introduction
The authors present reflections on counseling processes in clinics. In their opinion, such counseling can be divided into two types – with zero service time due to the patient’s non-appearance, and one characterized by a positive value and high dispersion, that is, high variability from the prognostic point of view. In addition, the authors point out that these two variables are volatile, and it is difficult for planners to make predictions about the duration of consultations. The duration of consultations, in turn, should be clearly defined for timely access to help.
Methodology
The authors conducted a study in a cardiology clinic using two years of data obtained from electronic medical records. The scientists developed 16 predictors for the patient, destination, and physician. They developed a two-component approach based on machine learning to handle semi-continuous consultation durations. Controlled classification models were used to predict no-shows and classify consultations according to the zero-positive principle. Regression algorithms were developed to estimate the non-zero duration of the consultation.
Three types of algorithms – random forests, stochastic gradient boosting, and deep neural networks – were used to classify no-shows and to positively predict consultation duration. A full two-part model was developed using classification and regression models. The prediction error on the new data was compared with the clinic’s current performance. The scientists used unique evaluation metrics for the classification models – the area under the receiver operating characteristic curve (AUC-ROC) and the area under the precision-recall curve (AUC-PR). Scientists used the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) to evaluate the prediction of the regression algorithms. Scientists adopted a simulation modeling approach to evaluate the effectiveness of using predictions in the process of machine learning to plan decisions.
Level of Evidence
Evidence presented in the article is based on the development of analytical models that used data for two years obtained from electronic medical records of a cardiology clinic. This is the Level III evidence since the study presented is a non-experimental qualitative study that uses analytical models to make a prognostic model that will be used for outpatient appointment scheduling.
Data Analysis
The scientists analyzed the data following the assessment of the performance of the proven classification models. Stochastic Gradient Classification Tree (SGBCT) showed the best performance (AUC-ROC = 0.85, AUC-PR = 0.64). The deep neural network regressor (DNNR) produced the lowest error for positive prediction of consultation duration (MAE = 8.55, RMSE = 6.88, MAPE = 12.24). The use of the full two-part SGBCT + DNNR model showed excellent results that outperformed the current clinic approach to estimating consultation duration with a 56% reduction in RMSE errors and a 52% reduction in MAE errors.
Ethical Considerations
Ethical considerations were not presented since the study used anonymous data of clinic visits, which are not ethically sensitive. The research committee received permission to process the data from the clinic’s management.
Quality Rating
The study under consideration is the Quality A study, as it shows consistent results, and uses a sufficient sample size of data. It was adequately controlled, and the authors showed definitive conclusions; the authors also used support from their colleagues, presenting ideas and arguments of other researchers.
Analysis of the Results / Conclusions
The use of predictions in the process of machine learning for planning decisions on outpatient appointment scheduling should become an alternative to the actual organization of processes in the cardiology clinic. According to the results of data analysis, further application of the IT model developed by scientists for appointment planning will reduce the patient’s waiting time and the doctor’s idle time by 56% and 52%. The scientists emphasize that the clinical tasks of predicting no-shows and consultation duration can be effectively and accurately determined using algorithms of machine learning integrated into the clinical planning system. This will reduce patient waiting times and improve resource utilization.
Non-Research Article (Title of Article)
Kuiper, A., de Mast, J., & Mandjes, M. (2021). The problem of appointment scheduling in outpatient clinics: a multiple case study of clinical practice. Omega, 98, 102122.
Background Introduction
Scientists note the insufficiency in the variability of research directions on the topic of optimization of appointment scheduling. They believe that the mathematical optimization approach is insufficient, as this approach works with the array of appointments without analyzing it qualitatively, only quantitatively. The authors believe that a qualitative analysis of the types of appointments in real health care programs will allow a more complete understanding of the goals and limitations when planning the schedules of visits to outpatient clinics. Therefore, they decided to apply a multiple case study approach to reconstruct the structure of the problem. Scientists developed a research model using the theory of operational management. In this way, scientists have opened a new way to plan meetings using the theory of operations management.
Type of Evidence
The presented study is a multiple case study of clinical practice.
Level of Evidence
The study has Level V evidence since it is based on experimental, non-researched evidence, using multiple cases, and the proposed model requires testing and refinement.
Quality Rating
This is a Quality B study, as it shows consistent results, but does not provide meta-analysis. The authors show definitive conclusions and use support from scholarly literature.
Author’s Recommendations
The authors believe that mathematical concepts that describe the service process as repetitive with limited variety are not sufficient. They believe that the practice is more complex and advise applying workflow management approaches, which they believe allows for greater variability and flexibility in planning visit schedules.
Recommended Practice Change
Key Stakeholders
In light of the information presented in the two articles, practice changes should focus on implementing the processes of machine learning and managing work processes. Key stakeholders are management, doctors, nurses, and patients of clinics.
Barrier to Implementation
Barriers to implementation may include a lack of appropriate technology and software. Another barrier is the lack of tested management practices aimed at optimizing the planning of visit schedules.
Strategy to Overcome the Implementation Barrier
To overcome the implementation barrier, clinics should buy and establish the adequate use of technology and introduce training for managers.
Indicators to Measure the Outcome
To measure the outcome, the managers responsible for the strategy implementation should use some meta-analysis tools to measure how the effectiveness of appointment scheduling improved after the suggested strategy was introduced.
References
Kuiper, A., de Mast, J., & Mandjes, M. (2021). The problem of appointment scheduling in outpatient clinics: a multiple case study of clinical practice. Omega, 98, 102122.
Srinivas, S., & Salah, H. (2021). Consultation length and no-show prediction for improving appointment scheduling efficiency at a cardiology clinic: a data analytics approach. International Journal of Medical Informatics, 145, 104290.