Patient portals are web-based tools that provide patients with access to their health records and enhance communication with providers. Despite the efforts in expanding their use and patients interest in using them, patient portal usage remains low. Higher use of portals is associated with greater patient engagement and better healthcare quality and outcomes. This study investigated the impact of perceived usefulness (PU) and perceived ease of use (PEU) on patient portal usage. The conceptual framework was based on the Technology Acceptance Model, which suggests that PU and PEU of a system affect attitude and behavioral intention toward using the system, and ultimately the use of the system. The research questions focused on whether PU and PEU significantly affect portal usage. Participants included a convenience sample of 432 patients of Abington Health, located in Abington, PA, who had access to Abington's eClinicalWorks patient portal. Cross-sectional data collected from the completed online surveys included responses to Davis' PU and PEU measurement scale, self-reported portal login frequency and login duration, and some patient demographics. Data was analyzed by using chi-square test of independence and multinomial logistic regression. The study found that a significant relationship exists between PU and login frequency, PU and login duration, and PEU and login duration; however, the impact of PU and PEU on portal usage was not significant. The study could be repeated among a different population using a different patient portal. This study helps understand the relationship between PU/PEU and portal usage, something healthcare providers can capitalize upon when promoting portal use, and ultimately, encouraging greater patient engagement in their own health.

The aim of this project was to investigate what information is critical to convey to nurses when performing digital triage. In addition, the project aimed to investigate how such information could be visualized. This was done through a combined user research and machine learning approach, which enabled for a more nuanced and thorough investigation compared to only making use of one of the two fields. There is sparse research investigating how digital triaging can be improved and made more efficient. Therefore, this study has contributed with new and relevant insights. Three machine learning algorithms were implemented to predict the right level of care for a patient. Out of these three, the random forest classifier proved to have the best performance with an accuracy of 69.46%, also having the shortest execution time. Evaluating the random forest classifier, the most important features were stated to be the duration and progress of the symptoms, allergies to medicine, chronic diseases and the patient's own estimation of his/her health. These factors could all be confirmed by the user research approach, indicating that the results from the approaches were aligned. The results from the user research approach also showed that the patients' own description of their symptoms was of great importance. These findings served as a basis for a number of visualization decisions, aiming to make the triage process as accurate and efficient as possible.

Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2017-04-19T16:23:48Z No. of bitstreams: 1 Vitor Secretti Bertoncello_.pdf: 8707879 bytes, checksum: 8c444504769b11f83390c5d057d72e7c (MD5) / Made available in DSpace on 2017-04-19T16:23:48Z (GMT). No. of bitstreams: 1 Vitor Secretti Bertoncello_.pdf: 8707879 bytes, checksum: 8c444504769b11f83390c5d057d72e7c (MD5) Previous issue date: 2017-02-21 / Nenhuma / O crescimento constante da expectativa de vida no Brasil trouxe à tona um desafio para a rede de atendimento de saúde. A maior longevidade da população resulta diretamente no aumento do registro de doenças crônicas, elevando a procura por atendimento médico. A superação desse desafio passa pela mudança no modelo de atendimento de saúde, cujo objetivo é inserir o paciente como membro ativo no cuidado ao próprio bem-estar. Esse novo modelo gerou demanda por novas soluções de tecnologia de informação e comunicação capazes de atender satisfatoriamente ao paciente, como soluções em computação móvel e ubíqua. Assim, o presente trabalho propõe o modelo chamado AllHealthcare, consistindo em uma solução de registro de saúde pessoal (PHR), em que o próprio paciente é capaz de construir um Perfil de Acompanhamento Dinâmico (PAD) para seus cuidados de saúde, de acordo com suas necessidades, e alterá-lo conforme novas demandas ou focos venham a ser considerados. Tal dinamismo na construção de um perfil personalizado é alcançado devido ao modelo proposto se basear em arquétipos do padrão OpenEHR, o que também atribui a característica de interoperabilidade semântica à presente proposta. Em revisão de pesquisas com foco em PHR, grande parte das soluções encontradas são concebidas e focadas para tratamento de determinada doença, ou seus modelos não atendem a um conjunto de requisitos operacionais importantes e atuais, como a mobilidade do cuidado de saúde e a interoperabilidade semântica das informações de saúde. Do modelo proposto, foi implementado um protótipo funcional, o qual passou por três etapas de avaliação. Nas três etapas de avaliação os resultados foram positivos e e os participantes apontaram um conjunto de possíveis melhorias, mesmo assim, aplicando-se o modelo de aceitação de tecnologia (TAM) a 22 usuários do protótipo, obteve uma aceitação média de 86,6%. / The constant growth of life expectancy in Brazil brought to the focus a challenge for the healthcare systems. The increased longevity of the population directly results in increased registration of chronic diseases, increasing the demand for healthcare. Overcoming this challenge is to change the health care model, whose goal is to insert the patient as an active member in the care of own welness. This new model has generated demand for new information and communication technologies solutions able to satisfactorily meet the patient, as solutions in mobile and ubiquitous computing. Thus, this paper proposes the model called AllHealthcare, consisting of a personal health record (PHR) solution that the patient is able to build a Dynamic Accompanying Profile (PAD) for their health care, according to their needs, and change it as new demands or focus may be considered. This dynamism in building a custom profile is achieved due to the proposed model is based on openEHR standard archetypes, which also provides the feature of semantic interoperability to this proposal. In a review research focusing on PHR, most of the solutions founded are designed and focused for treatment of a particular disease or their models do not fulfill a number of important and current operational requirements, such as healthcare mobility and semantic interoperability of health information. From the proposed model, a functional prototype was implemented, underwent through three stages of evaluation. In the three evaluation stages, the results were positive and the participants pointed out a set of possible improvements, even though applying the technology acceptance model (TAM) to 22 prototype users, obtained an average acceptance of 86.6%.

M. Tech. (Department of Process Control and Computer System, Faculty of Engineering and Technology), Vaal University of Technology. / Personal health record system (PHRS) is a tool that is used in maintaining the health and wellness of an individual as well as helping with illness of an individual. PHRS gives the individual access to a wide range of credible health information, data, and knowledge. Individuals can use that access to improve their health and manage their diseases. Patients with chronic illness can track and manage their health in conjunction with their health provider, this will promote early intervention when they encounter an abnormality or complications. Continuous monitoring and storing of health information has been a challenge for patients and health providers. Personal health data monitoring and retrieval system for health providers was designed and implemented. The main aim of this study was to develop an accurate, secure and flexible personal health data monitoring and retrieval system for health providers (PHRS). This was achieved. The research showed that when a patient health information (Electrocardiogram (ECG) and temperature) was monitored, the heartbeat (ECG) sensor output the value as an analog value to the signal processing (NodeMCU) which was then converted to a digital value. The temperature sensor used in this research is a digital sensor which lowers the amount of ADC conversion that was done. The digital values are displayed on LCD, its send to a personal health data storage system which was designed for health information storage purposes. It also sends the value to a retrieval system which is an android application that is connected to the data storage system to display health information to the health providers from a remote location. Personal health data storage and the retrieval system are secure due to the Message queuing telemetry transport (MQTT) protocol used in the application layer. The MQTT protocol is secure due to the secure socket layer (SSL) which is an encryption based on presentation layer embedded inside the MQTT protocol. After the comparison of the software simulation and the prototype test, the differences in the value for the heartbeat and temperature sensor indicate 0.04 BPM and 0.04 oC which shows the accuracy of the sensing circuit. Personal health data monitoring and retrieval system developed is applicable and useful to multiple entities in and around South Africa such as; Ministry of health, hospitals, sport and recreations.

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