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Various care processes have been affected by COVID-19. One of the most dramatic has been the care of chronic patients under medical supervision. According to the World Health Organization (WHO), a chronic patient has one or more long-term illnesses, and must be permanently monitored by the health team.. In fact, and according to the Chilean Ministry of Health (MINSAL), 7 out of 10 chronic patients have suspended their medical check-ups, generating critical situations, such as a more significant number of visits to emergency units, expired prescriptions, and a higher incidence in hospitalization rates. For this problem, health services in Chile have had to reschedule their scarce medical resources to provide care in all health processes. One element that has been considered is caring through telemedicine and patient prioritization. In the latter case, the aim was to provide timely care to those critical patients with high severity and who require immediate clinical attention. For this reason, in this work, we present the following methodological contributions: first, an unsupervised algorithm that analyzes information from anonymous patients to classify them according to priority levels; and second, rules that allow health teams to understand which variable(s) determine the classification of patients. The results of the proposed methodology allow classifying new patients with 99.96% certainty using a three-level decision tree and five classification rules.

Currently, in Chile, more than a quarter-million of patients are waiting for an elective surgical intervention. This is a worldwide reality, and it occurs as the demand for healthcare is vastly superior to the clinical resources in public systems. Moreover, this phenomenon has worsened due to the COVID-19 sanitary crisis. In order to reduce the impact of this situation, patients in the waiting lists are ranked according to a priority. However, the existing prioritization strategies are not necessarily systematized, and they usually respond only to clinical criteria, excluding other dimensions such as the personal and social context of patients. In this paper, we present a decision-support system designed for the prioritization of surgical waiting lists based on biopsychosocial criteria. The proposed system features three methodological contributions, first, an ad-hoc medical record form that captures the biopsychosocial condition of the patients, second, a dynamic scoring scheme that recognizes that patients’ conditions evolve differently while waiting for the required elective surgery, and third, a methodology for prioritizing and selecting patients based on the corresponding dynamic scores and additional clinical criteria. The designed decision-support system was implemented in the otorhinolaryngology unit in the Hospital of Talca, Chile, in 2018. When compared to the previous prioritization methodology, the results obtained from the use of the system during 2018 and 2019 show that this new methodology outperforms the previous prioritization method quantitatively and qualitatively. As a matter of fact, the designed system allowed a decrease, from 2017 to 2019, in the average number of days in the waiting list from 462 to 282 days.

Although there is a great number of maturity models proposed for Information Systems, most of them have three limitations: (1) they are focused on a single or small subset of companies, (2) they do not address the evolution of enterprise systems and e-business applications, simultaneously, (3) they are only focused on developed countries and do not consider emerging economies. We developed a maturity model of Information Systems that addresses these limitations through a data mining approach. The results showed that clustering analysis was an effective method for discovering similar groups of companies according to the set of enterprise systems and e-business applications that they adopted. Two major conclusions can be outlined: Unlike previous models, it has been shown that companies can be grouped only in three stages of maturity. Furthermore, the evolutionary pattern of systems adopted by companies follows a path oriented to obtain greater efficiencies at the expense of those that strengthen the relationship with customers. The results are relevant to practitioners, researchers and policy makers in emerging economies.