ALGORITHM AND SOFTWARE OF MEDICAL PERSONNEL SELECTION SYSTEM

There is a lot of routine work in any organization, including in recruitment agencies. Effective management organization and automation of activities of employees of recruiting agencies is not an easy task. The system should automate the routine actions of workers of recruiting agencies and be convenient for their clients. This paper proposes an approach to automating the selection of necessary medical staff. Not all information systems used by recruiting agencies can compare candidates and generate results that include several of the best candidates. Based on the analysis of the subject area, groups of parameters that significantly affect the choice of medical personnel were determined. The proposed approach is to analyze the request from the client, and then in the system find requests of other clients similar to it in terms of parameters, for which a candidate has already been found. The next step is to take the profiles of healthcare professionals that have been suggested for these requests (they act as benchmarks) to further compare them with existing candidates. Each employee profile parameter has its own similarity function. Available candidates will receive scores and will be ranked. We also additionally adjust the assessment by comparing candidates with the current request. Software was developed to automate the selection of medical personnel. For its implementation, a three-level client-server architecture is proposed. MVC (Model View Controller) architecture was chosen for the server part. The Single Page Application architectural template is used for the client part. The server part is divided into three layers, which further demarcate and structure the responsibilities of the system components. .NET technologies are used to implement business logic. SQL Server is used for the server and database provider. The use of the software implementation of the developed system demonstrated quite good results. The average time for selecting the 10 best candidates out of 500 is 0.4 seconds, and the processing of only 1 resume by a person takes several minutes

A doctor recommender system based on collaborative and content filtering

The volume of healthcare information available on the internet has exploded in recent years. Nowadays, many online healthcare platforms provide patients with detailed information about doctors. However, one of the most important challenges of such platforms is the lack of personalized services for supporting patients in selecting the best-suited doctors. In particular, it becomes extremely time-consuming and difficult for patients to search through all the available doctors. Recommender systems provide a solution to this problem by helping patients gain access to accommodating personalized services, specifically, finding doctors who match their preferences and needs. This paper proposes a hybrid content-based multi-criteria collaborative filtering approach for helping patients find the best-suited doctors who meet their preferences accurately. The proposed approach exploits multi-criteria decision making, doctor reputation score, and content information of doctors in order to increase the quality of recommendations and reduce the influence of data sparsity. The experimental results based on a real-world healthcare multi-criteria (MC) rating dataset show that the proposed approach works effectively with regard to predictive accuracy and coverage under extreme levels of sparsity

Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review

A health recommender system (HRS) provides a user with personalized medical information based on the user’s health profile. This scoping review aims to identify and summarize the HRS development in the most recent decade by focusing on five key aspects: health domain, user, recommended item, recommendation technology, and system evaluation. We searched PubMed, ACM Digital Library, IEEE Xplore, Web of Science, and Scopus databases for English literature published between 2010 and 2022. Our study selection and data extraction followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. The following are the primary results: sixty-three studies met the eligibility criteria and were included in the data analysis. These studies involved twenty-four health domains, with both patients and the general public as target users and ten major recommended items. The most adopted algorithm of recommendation technologies was the knowledge-based approach. In addition, fifty-nine studies reported system evaluations, in which two types of evaluation methods and three categories of metrics were applied. However, despite existing research progress on HRSs, the health domains, recommended items, and sample size of system evaluation have been limited. In the future, HRS research shall focus on dynamic user modelling, utilizing open-source knowledge bases, and evaluating the efficacy of HRSs using a large sample size. In conclusion, this study summarized the research activities and evidence pertinent to HRSs in the most recent ten years and identified gaps in the existing research landscape. Further work shall address the gaps and continue improving the performance of HRSs to empower users in terms of healthcare decision making and self-management

Factores asociados a la falta de adherencia al tratamiento en pacientes diabéticos atendidos por un prestador de salud, Ecuador 2022

Este trabajo investigativo tiene el propósito de analizar los factores asociados a la falta de adherencia al tratamiento en pacientes diabéticos atendidos por un prestador de salud, 2022. Por lo cual, se adoptó dentro de la metodología el enfoque cuantitativo, diseño no experimental, correlacional y transversal, la muestra fue de 325 personas seleccionadas a través de un muestreo aleatorio simple, siendo ellas a quienes se les aplicó la encuesta usando un cuestionario. Los hallazgos más relevantes que se obtuvieron en el estudio acerca de los factores asociados a la no adherencia fueron el paciente - terapia 56.0%, la relación profesional - paciente 46.8% y el estilo de vida 43.7%, cuyo nivel fue catalogado como alto. En cambio, el grado de cumplimiento terapéutico fue medio 56.9%, lo que se debió a las dimensiones evita conductas que potencien la patología 60.0%, sigue la dieta estricta 54.8% y supervisa los efectos terapéuticos 53.8%. Al final se concluyó que los factores asociados se relacionan de manera positiva y alta con la falta de adherencia al tratamiento al conseguir un Rho 0.932 y una significancia (p=0.001). Inclusive, todos los factores estudiados mostraron una asociación positiva y alta con el incumplimiento de la terapia