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Análise preditiva de dados em cuidados de saúde: otimização de resultados pela monitorização da progressão da doença
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O aumento do custo de vida, o envelhecimento da população e a prevalência de doenças crónicas estão a mudar a saúde tal como a conhecemos hoje. Para responder a estas mudanças, o sector da saúde tem vindo a sofrer várias transformações, quer ao nível de novos processos, quer ao nível da implementação de novos sistemas e softwares. A revolução digital trouxe uma necessidade acrescida ao nível da gestão e análise dos dados deste setor, uma vez que o foco está agora centrado no doente. O principal objetivo desta análise é melhorar os cuidados prestados, quer em termos de qualidade, eficiência e mesmo de custo. Desde a existência de uma maior evidência clínica para apoiar a tomada de decisões, rapidez na identificação de doenças de alto risco, emissão de alertas em tempo real para os profissionais de saúde, tudo isto permite uma assistência médica rápida e eficaz, bem como cuidados pessoais e informais.
Em particular, a doença de Parkinson é extremamente complexa e heterogénea, dado que cada doente tem um ritmo de progressão e uma combinação de sintomas totalmente diferentes, levando assim à existência de muitas lacunas na sua compreensão, tanto a nível científico como na prática médica. Assim, a única forma de resolver estas carências e chegar a conclusões cientificamente corretas é recolher dados sobre os sinais e sintomas, mais especificamente da sua intensidade e progressão.
De modo a responder a esta necessidade, neste trabalho realizamos uma revisão da literatura e propomos o desenvolvimento de um software cujas funcionalidades permitem a monitorização dos sintomas destes doentes. Através da aplicação proposta, será possível recolher dados de diversos pacientes, e monitorizar a doença com vista a retardar a sua progressão.
Depois disto, e como aplicação direta desta recolha, devemos trabalhar no desenvolvimento de um algoritmo que através da monitorização dos sintomas auxilia a gestão do tratamento, para qualquer doente. Este software deve sempre ter em conta as particularidades de cada doente, sobretudo a sua idade, comorbilidades, duração e intensidade dos sintomas, causa dos sintomas, e medicação concomitante.
The rising cost of living, an aging population, and the prevalence of chronic diseases is changing health as we know today. To respond to these changes, the health sector has been undergoing several transformations, both in terms of new processes and the implementation of new systems and softwares. The digital revolution has brought an increased need for the correct management and analysis of data from this sector since the focus is now centered on the patient. The main goal of this analysis is to improve the care provided, both in terms of quality, efficiency, and cost. From the existence of greater clinical evidence to support decision-making, speed of identification of high-risk diseases, delivery of real-time alerts to health professionals, all this then allows for fast and effective medical assistance, self, and informal care. In particular, Parkinson's disease is extremely complex and heterogeneous, since each patient has a different rate of progression and combination of symptoms, thus leading to many gaps in its understanding, both scientifically and in medical practice. Therefore, the only way to address these gaps and reach scientifically correct conclusions is to collect data on the signs and symptoms, in particular their intensity and progression. To meet this need, in this study, we conducted a literature review and proposed the development of a software application whose functionalities allow the monitoring of these patients' symptoms. Through the proposed software, it will be possible to collect data from several patients and monitor the disease with the intent of slowing its progression. After this, and as a direct application of the gathering of this information, we should work on the development of an algorithm that, through symptom monitoring, helps manage treatment for any patient. Such software should always consider each patient's particularities, especially in terms of age, comorbidities, duration, and intensity of symptoms, cause of symptoms, and concomitant medication.
The rising cost of living, an aging population, and the prevalence of chronic diseases is changing health as we know today. To respond to these changes, the health sector has been undergoing several transformations, both in terms of new processes and the implementation of new systems and softwares. The digital revolution has brought an increased need for the correct management and analysis of data from this sector since the focus is now centered on the patient. The main goal of this analysis is to improve the care provided, both in terms of quality, efficiency, and cost. From the existence of greater clinical evidence to support decision-making, speed of identification of high-risk diseases, delivery of real-time alerts to health professionals, all this then allows for fast and effective medical assistance, self, and informal care. In particular, Parkinson's disease is extremely complex and heterogeneous, since each patient has a different rate of progression and combination of symptoms, thus leading to many gaps in its understanding, both scientifically and in medical practice. Therefore, the only way to address these gaps and reach scientifically correct conclusions is to collect data on the signs and symptoms, in particular their intensity and progression. To meet this need, in this study, we conducted a literature review and proposed the development of a software application whose functionalities allow the monitoring of these patients' symptoms. Through the proposed software, it will be possible to collect data from several patients and monitor the disease with the intent of slowing its progression. After this, and as a direct application of the gathering of this information, we should work on the development of an algorithm that, through symptom monitoring, helps manage treatment for any patient. Such software should always consider each patient's particularities, especially in terms of age, comorbidities, duration, and intensity of symptoms, cause of symptoms, and concomitant medication.
Descrição
Trabalho Final de Mestrado Integrado, Ciências Farmacêuticas, 2023, Universidade de Lisboa, Faculdade de Farmácia.
Palavras-chave
Parkinson’s disease Symptoms Disease management Monitoring of the symptoms Mobile applications Mestrado Integrado - 2023