385 research outputs found
Performance Evaluation of Smart Decision Support Systems on Healthcare
Medical activity requires responsibility not only from clinical knowledge and skill but
also on the management of an enormous amount of information related to patient care. It is
through proper treatment of information that experts can consistently build a healthy wellness
policy. The primary objective for the development of decision support systems (DSSs) is
to provide information to specialists when and where they are needed. These systems provide
information, models, and data manipulation tools to help experts make better decisions in a
variety of situations.
Most of the challenges that smart DSSs face come from the great difficulty of dealing
with large volumes of information, which is continuously generated by the most diverse types
of devices and equipment, requiring high computational resources. This situation makes this
type of system susceptible to not recovering information quickly for the decision making. As a
result of this adversity, the information quality and the provision of an infrastructure capable
of promoting the integration and articulation among different health information systems (HIS)
become promising research topics in the field of electronic health (e-health) and that, for this
same reason, are addressed in this research. The work described in this thesis is motivated
by the need to propose novel approaches to deal with problems inherent to the acquisition,
cleaning, integration, and aggregation of data obtained from different sources in e-health environments,
as well as their analysis.
To ensure the success of data integration and analysis in e-health environments, it
is essential that machine-learning (ML) algorithms ensure system reliability. However, in this
type of environment, it is not possible to guarantee a reliable scenario. This scenario makes
intelligent SAD susceptible to predictive failures, which severely compromise overall system
performance. On the other hand, systems can have their performance compromised due to the
overload of information they can support.
To solve some of these problems, this thesis presents several proposals and studies
on the impact of ML algorithms in the monitoring and management of hypertensive disorders
related to pregnancy of risk. The primary goals of the proposals presented in this thesis are
to improve the overall performance of health information systems. In particular, ML-based
methods are exploited to improve the prediction accuracy and optimize the use of monitoring
device resources. It was demonstrated that the use of this type of strategy and methodology
contributes to a significant increase in the performance of smart DSSs, not only concerning precision
but also in the computational cost reduction used in the classification process.
The observed results seek to contribute to the advance of state of the art in methods
and strategies based on AI that aim to surpass some challenges that emerge from the integration
and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to
quickly and automatically analyze a larger volume of complex data and focus on more accurate
results, providing high-value predictions for a better decision making in real time and without
human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento
e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações
relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações
que os especialistas podem consistentemente construir uma política saudável de bem-estar. O
principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações
aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações,
modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores
decisões em diversas situações.
A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade
de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos
tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação
torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a
tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão
de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas
de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde
eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho
descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar
com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de
diferentes fontes em ambientes de e-saúde, bem como sua análise.
Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é
importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade
do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário
totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas
de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os
sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que
podem suportar.
Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e
estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos
relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta
tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os
métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o
uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo
de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD
inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional
utilizado no processo de classificação.
Os resultados observados buscam contribuir para o avanço do estado da arte em métodos
e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que
advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados
em inteligência artificial é possível analisar de forma rápida e automática um volume maior de
dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana
MS
thesisDelivery of high quality health care requires access to complete and accurate patient information. Variation in data context and content across disparate clinical systems adversely affects the integration of information needed for effective patient care and outcomes research. This study detects the extent and nature of data variation across three disparate clinical systems used along different points of the perinatal care continuum at Intermountain Health Care (IHC). Three analytical methods were used to examine data variation: data structure analysis; clinician perception of missing data elements; and patient record review of key data values. Knowledge acquisition techniques and consensus among clinical domain experts were used to select sample data elements for the data structure analysis. Findings revealed only 17% of the sample data elements had ompatible structure and meaning across the prenatal, labor and delivery (L&D), and newborn intensive care (NICU) clinical data systems. Impact on clinician efficiency from missing and contradicting information in nonintegrated perinatal systems was captured and analyzed using a Critical Incident Technique-based clinician survey. In a 1-month period, 75% of responding clinicians reported missing data and 34% reported contradicting data. The time taken to resolve problems from 1 month's missing data was estimated to be 231 hours for 23 clinicians. Data values from patient records for eight laboratory results were compared across the three perinatal systems. The best match across any two systems was 88% (blood type) and the worst was 0% (antibody screen, chlamydia). The highest incidence of contradicting data was 2.5% for blood type. Comparing agreement of the three methods, triangulation,"" gave additional insight into IHC's data variation problem. The data model study and the patient record review study showed missing data element problems beyond what clinicians perceived. In all, the consistency of data capture in the three perinatal systems at IHC is worse than expected. The data necessary to computationally execute the logic of the perinatal care process models is intermittent and unreliable. Rework of the perinatal applications based on a uniform data model and standard terminologies will provide an infrastructure to achieve IHC's vision of interdisciplinary care."
Clinical foundations and information architecture for the implementation of a federated health record service
Clinical care increasingly requires healthcare professionals to access patient record information that
may be distributed across multiple sites, held in a variety of paper and electronic formats, and
represented as mixtures of narrative, structured, coded and multi-media entries. A longitudinal
person-centred electronic health record (EHR) is a much-anticipated solution to this problem, but
its realisation is proving to be a long and complex journey.
This Thesis explores the history and evolution of clinical information systems, and establishes a set
of clinical and ethico-legal requirements for a generic EHR server. A federation approach (FHR) to
harmonising distributed heterogeneous electronic clinical databases is advocated as the basis for
meeting these requirements.
A set of information models and middleware services, needed to implement a Federated Health
Record server, are then described, thereby supporting access by clinical applications to a distributed
set of feeder systems holding patient record information. The overall information architecture thus
defined provides a generic means of combining such feeder system data to create a virtual
electronic health record. Active collaboration in a wide range of clinical contexts, across the whole
of Europe, has been central to the evolution of the approach taken.
A federated health record server based on this architecture has been implemented by the author
and colleagues and deployed in a live clinical environment in the Department of Cardiovascular
Medicine at the Whittington Hospital in North London. This implementation experience has fed
back into the conceptual development of the approach and has provided "proof-of-concept"
verification of its completeness and practical utility.
This research has benefited from collaboration with a wide range of healthcare sites, informatics
organisations and industry across Europe though several EU Health Telematics projects: GEHR,
Synapses, EHCR-SupA, SynEx, Medicate and 6WINIT.
The information models published here have been placed in the public domain and have
substantially contributed to two generations of CEN health informatics standards, including CEN
TC/251 ENV 13606
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