Planeamento da alta : impacto nos indicadores de desempenho hospitalar

RESUMO - Planeamento da Alta: Impacto nos Indicadores de Desempenho Hospitalar O problema do presente trabalho reside na percentagem de hospitais do SNS que não tem um processo de planeamento de alta standartizado. Norteada pela questão de investigação: Qual o impacto do Planeamento das Altas nos indicadores de desempenho dos hospitais do SNS?; pelas hipóteses de que os hospitais com um processo de Planeamento da Alta standartizado têm uma Taxa de Reinternamento e uma Demora Média menor; e pelo objetivo geral: Conhecer o impacto do planeamento da alta standartizado nos indicadores de desempenho dos hospitais do SNS, enveredou-se pelo paradigma quantitativo e foi realizada uma análise comparativa dos indicadores de desempenho de dois grupos hospitalares. Estudou-se uma amostra da população acessível, constituída por 57 estabelecimentos hospitalares do SNS e destes foram criados dois grupos de hospitais, que diferiam no facto de ter um processo de planeamento de alta standartizado. No final obteve-se uma amostra, não probabilística, de 12 hospitais, seis que possuíam um processo standartizado e os outros seis não. A recolha de dados foi efetuada através dos sítios da ACSS, DGS e das entidades hospitalares. Os dados foram tratados através da estatística descritiva e indutiva, com recurso a programas informáticos. Da análise emergiram como principais resultados: A existência de um processo de planeamento de alta hospitalar standartizado não tem um impacto significativo nos indicadores de desempenho hospitalar, nomeadamente na redução das Taxas de Reinternamento e da Demora Média hospitalar.

Scaling In-Memory databases on multicores

Current computer systems have evolved from featuring only a single processing unit and limited RAM, in the order of kilobytes or few megabytes, to include several multicore processors, o↵ering in the order of several tens of concurrent execution contexts, and have main memory in the order of several tens to hundreds of gigabytes. This allows to keep all data of many applications in the main memory, leading to the development of inmemory databases. Compared to disk-backed databases, in-memory databases (IMDBs) are expected to provide better performance by incurring in less I/O overhead. In this dissertation, we present a scalability study of two general purpose IMDBs on multicore systems. The results show that current general purpose IMDBs do not scale on multicores, due to contention among threads running concurrent transactions. In this work, we explore di↵erent direction to overcome the scalability issues of IMDBs in multicores, while enforcing strong isolation semantics. First, we present a solution that requires no modification to either database systems or to the applications, called MacroDB. MacroDB replicates the database among several engines, using a master-slave replication scheme, where update transactions execute on the master, while read-only transactions execute on slaves. This reduces contention, allowing MacroDB to o↵er scalable performance under read-only workloads, while updateintensive workloads su↵er from performance loss, when compared to the standalone engine. Second, we delve into the database engine and identify the concurrency control mechanism used by the storage sub-component as a scalability bottleneck. We then propose a new locking scheme that allows the removal of such mechanisms from the storage sub-component. This modification o↵ers performance improvement under all workloads, when compared to the standalone engine, while scalability is limited to read-only workloads. Next we addressed the scalability limitations for update-intensive workloads, and propose the reduction of locking granularity from the table level to the attribute level. This further improved performance for intensive and moderate update workloads, at a slight cost for read-only workloads. Scalability is limited to intensive-read and read-only workloads. Finally, we investigate the impact applications have on the performance of database systems, by studying how operation order inside transactions influences the database performance. We then propose a Read before Write (RbW) interaction pattern, under which transaction perform all read operations before executing write operations. The RbW pattern allowed TPC-C to achieve scalable performance on our modified engine for all workloads. Additionally, the RbW pattern allowed our modified engine to achieve scalable performance on multicores, almost up to the total number of cores, while enforcing strong isolation.