The behaviour of patients No-Show in Online Medical Consultation System: A Systematic Literature Review
Keywords:
Online Medical Consultation (OMC), Patient no-shows, Machine Learning Algorithms, Digital Technology, COVID-19, Behaviour of PatientsAbstract
Online healthcare consultation is one of the advents in information and communication technologies (ICTs). Through this, the patient can interact with doctors and attain medical care, including information on community forums, consultations, health records, etc. Over the past few years, patients' popularity for online consultation has increased because of reduced effort. Online medical consultation provides various benefits to patients as compared with face-to-face consultation. Especially, it mitigates several problems hospitals face, such as geographical inconvenience, reduced capacity and long queues. Thus, online medical appointments and consultations highly assist the patient's self-health management. However, the patient no-shows in online appointments can directly influence the services of the healthcare sector. Different related works performed by various authors are summarised in the literature review by referring to several research papers related to the no-show prediction, risk categorisation and online consultation methodologies. A detailed description of different works is provided based on the enhancement of the balancing process to maximise the data handling efficiency and accuracy and to minimise the error rates, model complexities and training time. Also, diverse optimisation strategies are reviewed to enhance the feature selection process with better convergence and prominent feature consideration. By conducting the literature survey, the significance of techniques, the performance obtained, and the drawbacks can be analysed. Through this survey, novel methodologies can be proposed to consider the existing drawbacks to overcome with future directions.
This systematic literature review seeks to improve the thoroughness and transparency of the review process by following a method aligned with the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. In accordance with the PRISMA guidelines, this review provides a comprehensive analysis of the existing literature on the behaviour of patients No-Show in Online Medical Consultation Systems. The methodology for the review incorporates a systematic search strategy, stringent selection criteria, meticulous data extraction, and a critical analysis of the findings. The results highlight key trends, themes, and gaps in the literature, while the discussion provides insights, implications, and future research directions. In summary, employing PRISMA in this comprehensive systematic literature review enhances the validity and reliability of the findings, thereby contributing to the progression of knowledge in the domain of patient No-Show behaviour within Online Medical Consultation Systems. With the rising growth of information and communication technologies, an online appointment system is enhanced in several hospitals over the globe. However, an online outpatient appointment system faces various challenges like health, financial, scheduling, and time management problems due to a rising incidence of patient no-shows, referring to patients who do not attend their scheduled appointments. Thus, to assist the hospitals in generating proper decisions and minimise the rate of patient no-show behaviour. The performance metrics are highly utilised to prove the superiority of the machine learning models to predict no-show behaviour in online medical consultation systems.
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