Os Sistemas de Registro Eletrônico em Saúde (S-RES) permitem manipular e analisar um grande volume de dados e informações de saúde. O desenvolvimento, disponibilização e uso de funcionalidades para S-RES pode beneficiar tanto os profissionais de saúde como os pacientes. Estes sistemas devem ser próprios para o contexto onde serão utilizados, podendo estar voltados a diferentes áreas da saúde, assim como para diferentes níveis de atenção à saúde. Para o usuário final, a adequação do S-RES é avaliada pela qualidade em uso que resulta, principalmente, da funcionalidade, confiabilidade, usabilidade e eficiência do sistema. O termo funcionalidade designa o aspecto do sistema computacional que retrata as funções necessárias para a resolução de problemas dentro de um determinado contexto de uso. A funcionalidade se refere àquilo que um programa faz e, no caso de software interativo, o que ele deve oferecer para seus usuários. Frente a este contexto, a presente dissertação se propõe a identificar, através da literatura e de documentos de referência sobre o tema, as funcionalidades para os S-RES com potencial de apoiar os profissionais de saúde na prestação do cuidado ao paciente na Atenção Primária à Saúde (APS). Nenhuma das listas de funcionalidades existentes na literatura é específica para S-RES para APS. Foi realizada uma revisão da literatura nas principais bases de dados da área da saúde. Para a extração das funcionalidades, foram selecionados os documentos mais relevantes e que eram referência para os demais materiais consultados. As funcionalidades apresentadas pelos documentos foram compiladas e formatadas em uma planilha eletrônica de maneira que pudessem ser utilizadas para seleção de funcionalidades para um S-RES para APS. As funcionalidades identificadas foram categorizadas e agrupadas por similaridade de aplicação em sete categorias relacionadas ao contexto de trabalho na APS. Três documentos foram utilizados para a seleção das funcionalidades. A análise das funcionalidades identificadas evidenciou a predominância de funcionalidades relacionadas a aspectos clínicos da prestação do cuidado dos pacientes. De um total de 145 funcionalidades, 91 (62,8%) foram classificadas como de “manejo clínico do paciente”, grande parte dessas voltadas para o diagnóstico e tratamento clínico, como também para o apoio à decisão clínica. O conjunto de funcionalidades relacionadas à “prevenção” e às classificadas como de “educação em saúde e comunicação com o paciente” representaram juntas apenas 20% do total, com respectivamente 11,7% e 8,3% do total de funcionalidades identificadas. Importantes funcionalidades para S-RES de APS que consideram as perspectivas e preferências do paciente e de sua família em relação à saúde, e ainda, o relacionamentos entre os sujeitos, foram classificadas como “aspectos subjetivos e familiares” e representaram 4,8% do total de funcionalidades de APS. Por fim, é possível reconhecer que a maioria das funcionalidades para S-RES adequadas ao contexto da APS está direcionada ao manejo clínico dos pacientes. São poucas as funcionalidades que contemplam as demais dimensões do trabalho em APS e que favorecem uma compreensão da pessoa de modo integral. / Electronic Health Records (EHR) systems allow to manipulate and analyze large volumes of data and health information. The development , availability and use of features for EHR systems can benefit both health professionals and patients. These systems shall be suitable to the context where they will be used, or can be directed to different areas of health, as well as different levels of health care. For the end user, the adequacy of the EHR systems is evaluated for quality in use which results mainly from the: functionality, reliability, usability and system efficiency. Functionality refers to the aspect of the computer system that represents the functions required to solve problems within a specified context of use. Functionality refers to what a program does and, in the case of interactive software, what it must offer to its users. Facing this context, this thesis aims to identify, through literature and reference documents on the subject, the functionality for the EHR systems with the potential to support health professionals in the provision of patient care in Primary health Care (PHC). None of functionalities lists existing in the literature is specific to EHR systems for PHC. A literature review was conducted in the main bases of health care data. For the extraction of functionalities, the most relevant documents were selected and they were reference for other found materials. The functionalities presented by the documents were compiled and formatted in a electronic spreadsheet. So it could be used for selection of functionalities for an EHR systems for PHC. The identified functionalities were categorized and grouped by similarity application in seven categories related to the work context in PHC. Three documents were used for selection of functionalities. The analysis of the identified functionalities showed the predominance of functionalities related to clinical aspects of the provision of patient care. The total of 145 functionalities, 91 (62.8%) were classified as "clinical management of patients", most of these focused on the diagnosis and treatment, but also to clinical decision support. The group of functionalities related to "prevention" and classified as "health education and communication with the patient" together accounted for only 20% of the total, respectively 11.7% and 8.3% of the identified functionalities. Important functionalities to EHR systems for Primary Healh Care which regard the perspectives and preferences of patients and their families in relation to health, and also the relationships between the subjects were classified as "subjective and family aspects" and represented 4.8% of total PHC functionalities. Finally, it is possible to recognize that most of the appropriate EHR systems functionalities to the context of Primary Health Care is directed to the clinical management of patients. There are few functionalities that contemplate other dimensions of Primary Health Care work and support a comprehension of the person as a whole.

Os Sistemas de Registro Eletrônico em Saúde (S-RES) permitem manipular e analisar um grande volume de dados e informações de saúde. O desenvolvimento, disponibilização e uso de funcionalidades para S-RES pode beneficiar tanto os profissionais de saúde como os pacientes. Estes sistemas devem ser próprios para o contexto onde serão utilizados, podendo estar voltados a diferentes áreas da saúde, assim como para diferentes níveis de atenção à saúde. Para o usuário final, a adequação do S-RES é avaliada pela qualidade em uso que resulta, principalmente, da funcionalidade, confiabilidade, usabilidade e eficiência do sistema. O termo funcionalidade designa o aspecto do sistema computacional que retrata as funções necessárias para a resolução de problemas dentro de um determinado contexto de uso. A funcionalidade se refere àquilo que um programa faz e, no caso de software interativo, o que ele deve oferecer para seus usuários. Frente a este contexto, a presente dissertação se propõe a identificar, através da literatura e de documentos de referência sobre o tema, as funcionalidades para os S-RES com potencial de apoiar os profissionais de saúde na prestação do cuidado ao paciente na Atenção Primária à Saúde (APS). Nenhuma das listas de funcionalidades existentes na literatura é específica para S-RES para APS. Foi realizada uma revisão da literatura nas principais bases de dados da área da saúde. Para a extração das funcionalidades, foram selecionados os documentos mais relevantes e que eram referência para os demais materiais consultados. As funcionalidades apresentadas pelos documentos foram compiladas e formatadas em uma planilha eletrônica de maneira que pudessem ser utilizadas para seleção de funcionalidades para um S-RES para APS. As funcionalidades identificadas foram categorizadas e agrupadas por similaridade de aplicação em sete categorias relacionadas ao contexto de trabalho na APS. Três documentos foram utilizados para a seleção das funcionalidades. A análise das funcionalidades identificadas evidenciou a predominância de funcionalidades relacionadas a aspectos clínicos da prestação do cuidado dos pacientes. De um total de 145 funcionalidades, 91 (62,8%) foram classificadas como de “manejo clínico do paciente”, grande parte dessas voltadas para o diagnóstico e tratamento clínico, como também para o apoio à decisão clínica. O conjunto de funcionalidades relacionadas à “prevenção” e às classificadas como de “educação em saúde e comunicação com o paciente” representaram juntas apenas 20% do total, com respectivamente 11,7% e 8,3% do total de funcionalidades identificadas. Importantes funcionalidades para S-RES de APS que consideram as perspectivas e preferências do paciente e de sua família em relação à saúde, e ainda, o relacionamentos entre os sujeitos, foram classificadas como “aspectos subjetivos e familiares” e representaram 4,8% do total de funcionalidades de APS. Por fim, é possível reconhecer que a maioria das funcionalidades para S-RES adequadas ao contexto da APS está direcionada ao manejo clínico dos pacientes. São poucas as funcionalidades que contemplam as demais dimensões do trabalho em APS e que favorecem uma compreensão da pessoa de modo integral. / Electronic Health Records (EHR) systems allow to manipulate and analyze large volumes of data and health information. The development , availability and use of features for EHR systems can benefit both health professionals and patients. These systems shall be suitable to the context where they will be used, or can be directed to different areas of health, as well as different levels of health care. For the end user, the adequacy of the EHR systems is evaluated for quality in use which results mainly from the: functionality, reliability, usability and system efficiency. Functionality refers to the aspect of the computer system that represents the functions required to solve problems within a specified context of use. Functionality refers to what a program does and, in the case of interactive software, what it must offer to its users. Facing this context, this thesis aims to identify, through literature and reference documents on the subject, the functionality for the EHR systems with the potential to support health professionals in the provision of patient care in Primary health Care (PHC). None of functionalities lists existing in the literature is specific to EHR systems for PHC. A literature review was conducted in the main bases of health care data. For the extraction of functionalities, the most relevant documents were selected and they were reference for other found materials. The functionalities presented by the documents were compiled and formatted in a electronic spreadsheet. So it could be used for selection of functionalities for an EHR systems for PHC. The identified functionalities were categorized and grouped by similarity application in seven categories related to the work context in PHC. Three documents were used for selection of functionalities. The analysis of the identified functionalities showed the predominance of functionalities related to clinical aspects of the provision of patient care. The total of 145 functionalities, 91 (62.8%) were classified as "clinical management of patients", most of these focused on the diagnosis and treatment, but also to clinical decision support. The group of functionalities related to "prevention" and classified as "health education and communication with the patient" together accounted for only 20% of the total, respectively 11.7% and 8.3% of the identified functionalities. Important functionalities to EHR systems for Primary Healh Care which regard the perspectives and preferences of patients and their families in relation to health, and also the relationships between the subjects were classified as "subjective and family aspects" and represented 4.8% of total PHC functionalities. Finally, it is possible to recognize that most of the appropriate EHR systems functionalities to the context of Primary Health Care is directed to the clinical management of patients. There are few functionalities that contemplate other dimensions of Primary Health Care work and support a comprehension of the person as a whole.

eHealth Strategies in countries have shown a trend that countries are moving to Electronic Health Records(EHR). EHR implementation is expected to produce benefits for patients, professionals, organisations, and the population as a whole. The use of some format of an Electronic Health Record is used by many countries and others are in the implementation or planning phases. South Africa has kicked of the project to implement a national EHR as part of the national eHealth Strategy. This study aims to analyse the key success factors from other EHR implementation projects and evaluate if South Africa is ready to implement an EHR.

The study of organisations’ information security cultures has attracted scholars as well as healthcare services industry to research the topic and find appropriate tools and approaches to develop a positive culture. The vast majority of studies in Saudi national health services are on the use of technology to protect and secure health services information. On the other hand, there is a lack of research on the role and impact of an organisation’s cultural dimensions on information security. This research investigated and analysed the role and impact of cultural dimensions on information security in Saudi Arabia health service. Hypotheses were tested and two surveys were carried out in order to collect data and information from three major hospitals in Saudi Arabia (SA). The first survey identified the main cultural-dimension problems in SA health services and developed an initial information security culture framework model. The second survey evaluated and tested the developed framework model to test its usefulness, reliability and applicability. The model is based on human behaviour theory, where the individual’s attitude is the key element of the individual’s intention to behave as well as of his or her actual behaviour. The research identified a set of cultural and sub-cultural dimensions in SA health information security and services.

eHealth Strategies in countries have shown a trend that countries are moving to Electronic Health Records(EHR). EHR implementation is expected to produce benefits for patients, professionals, organisations, and the population as a whole. The use of some format of an Electronic Health Record is used by many countries and others are in the implementation or planning phases. South Africa has kicked of the project to implement a national EHR as part of the national eHealth Strategy. This study aims to analyse the key success factors from other EHR implementation projects and evaluate if South Africa is ready to implement an EHR.

A Thesis submitted to The University of Arizona College of Medicine - Phoenix in partial fulfillment of the requirements for the Degree of Doctor of Medicine. / BACKGROUND: In order to improve the quality and efficacy of healthcare while reducing the overall cost to deliver that healthcare, it has become increasingly important to manage utilization of services for populations of patients. Healthcare systems are aggressively working to identify patients at risk for hospital readmissions. Although readmission rates have been studied before, parameters for identifying patients at risk for readmission appear to vary depending the patient population. We will examine existing Electronic Health Record (EHR) data at Banner Health to establish what parameters are clinical indicators for readmission risk. Three conditions were identified by the CMS to have high and costly readmissions rates; heart failure (HF), acute myocardial infarction (AMI), and pneumonia. This study will focus on attempting to determine the primary predictive variables for these three conditions in order to have maximum impact on cost savings. METHODS: A literature review was done and 68 possible risk variables were identified. Of these, 30 of the variables were identifiable within the EHR system. Inclusion criteria for individual patient records are that they had an index admission secondary to AMI, heart failure, or pneumonia and that they had a subsequent readmission within 30 days of the index admission. Pediatric populations were not studied since they have unique factors for readmission that are not generalizable. Logistics regression was applied to all data including data with missing data rows. This allowed all coefficients to be interpreted for significance. This model was termed the full model. Variables that were determined to be insignificant were subsequently removed to create a new reduced model. Chi square testing was then done to compare the reduced model to the full model to determine if any significant differences existed between the two. RESULTS: Several variables were determined to be the significant predictors of readmission. The final reduced model had 19 predictors. When analyzed using ROC analysis, the area under the curve (AUC) was 0.64. CONCLUSION: Several variables were identified that could be significant contributors to readmission risk. The final model had an AUC on it ROC of 0.64 suggesting that it would only have poor to moderate clinical value for predicting readmission.

The ongoing digitization of healthcare, which has been much accelerated by the widespread adoption of electronic health records, generates unprecedented amounts of clinical data in a readily computable form. This, in turn, affords great opportunities for making meaningful secondary use of clinical data in the endeavor to improve healthcare, as well as to support epidemiology and medical research. To that end, there is a need for techniques capable of effectively and efficiently analyzing large amounts of clinical data. While machine learning provides the necessary tools, learning effective predictive models from electronic health records comes with many challenges due to the complexity of the data. Electronic health records contain heterogeneous and longitudinal data that jointly provides a rich perspective of patient trajectories in the healthcare process. The diverse characteristics of the data need to be properly accounted for when learning predictive models from clinical data. However, how best to represent healthcare data for predictive modeling has been insufficiently studied. This thesis addresses several of the technical challenges involved in learning effective predictive models from electronic health records. Methods are developed to address the challenges of (i) representing heterogeneous types of data, (ii) leveraging the concept hierarchy of clinical codes, and (iii) modeling the temporality of clinical events. The proposed methods are evaluated empirically in the context of detecting adverse drug events in electronic health records. Various representations of each type of data that account for its unique characteristics are investigated and it is shown that combining multiple representations yields improved predictive performance. It is also demonstrated how the information embedded in the concept hierarchy of clinical codes can be exploited, both for creating enriched feature spaces and for decomposing the predictive task. Moreover, incorporating temporal information leads to more effective predictive models by distinguishing between event occurrences in the patient history. Both single-point representations, using pre-assigned or learned temporal weights, and multivariate time series representations are shown to be more informative than representations in which temporality is ignored. Effective methods for representing heterogeneous and longitudinal data are key for enhancing and truly enabling meaningful secondary use of electronic health records through large-scale analysis of clinical data.

Health care today harms too frequently and routinely fails to deliver its potential benefits. Significant evidence suggests that high quality primary care can positively affect health outcomes. I explored three related topics mentioned frequently in current United States health reform €“ Electronic Health Records (EHR), physician profiling and Medical Home. An investment in these areas is expected to significantly improve quality of care and efficiency; however, there is only a patchwork of evidence supporting such claims. To achieve EHR promises, my research employed a standardization lens to study the dynamics between EHR embedded structures and primary care processes. Using grounded theory, a standardization dynamics model was created describing the influencers, conditions and consequences of the process state. A matrix of two conditions, information exchange and patient complexity, identified four distinct pathways that require a different balance between standardization and flexibility. The value of such pathways is that they frame choices about how to use embedded IT structures to support effective delivery processes. Physician profiling is an emerging methodology used in health care quality improvement programs. Efforts to measure performance at the individual physician level face a number of challenges, including the need for sufficient sample size to support reliable measurement. A process for creating a physician profiling model was developed, and a model designed for a case study site. Results indicate that reliable physician profiling is possible across care domains using a hierarchical composite model. Patient-Centered Medical Home (PCMH) is a new care delivery approach for providing comprehensive primary care that seeks to strengthen the physician-patient relationship. This exploratory study utilizes Pearson correlation coefficients to test four hypotheses about relationships between two sources of data: (1) PPC-PCMH Survey results that measure adoption of PCMH structures and (2) patient experience data from Massachusetts Health Quality Partners (MHQP). The results showed that the PPC-PCMH structures of access and communication were negatively correlated with the related patient experience measure. This study contributes to the literature by addressing deficiencies in how EHR-enabled processes, physician profiling models and Medical Home constructs are measured, to support improved outcomes.

The United States health-care system is considered to be unsustainable due to its unbearably high cost. Many of the resources are spent on acute conditions rather than aiming at preventing them. Preventive medicine methods, therefore, are viewed as a potential remedy since they can help reduce the occurrence of acute health episodes. The work in this dissertation tackles two distinct problems related to the prevention of acute disease. Specifically, we consider: (1) early detection of incorrect or abnormal postures of the human body and (2) the prediction of hospitalization due to heart related diseases. The solution to the former problem could be used to prevent people from unexpected injuries or alert caregivers in the event of a fall. The latter study could possibly help improve health outcomes and save considerable costs due to preventable hospitalizations. For body posture detection, we place wireless sensor nodes on different parts of the human body and use the pairwise measurements of signal strength corresponding to all sensor transmitter/receiver pairs to estimate body posture. We develop a composite hypothesis testing approach which uses a Generalized Likelihood Test (GLT) as the decision rule. The GLT distinguishes between a set of probability density function (pdf) families constructed using a custom pdf interpolation technique. The GLT is compared with the simple Likelihood Test and Multiple Support Vector Machines. The measurements from the wireless sensor nodes are highly variable and these methods have different degrees of adaptability to this variability. Besides, these methods also handle multiple observations differently. Our analysis and experimental results suggest that GLT is more accurate and suitable for the problem. For hospitalization prediction, our objective is to explore the possibility of effectively predicting heart-related hospitalizations based on the available medical history of the patients. We extensively explored the ways of extracting information from patients' Electronic Health Records (EHRs) and organizing the information in a uniform way across all patients. We applied various machine learning algorithms including Support Vector Machines, AdaBoost with Trees, and Logistic Regression adapted to the problem at hand. We also developed a new classifier based on a variant of the likelihood ratio test. The new classifier has a classification performance competitive with those more complex alternatives, but has the additional advantage of producing results that are more interpretable. Following this direction of increasing interpretability, which is important in the medical setting, we designed a new method that discovers hidden clusters and, at the same time, makes decisions. This new method introduces an alternating clustering and classification approach with guaranteed convergence and explicit performance bounds. Experimental results with actual EHRs from the Boston Medical Center demonstrate prediction rate of 82% under 30% false alarm rate, which could lead to considerable savings when used in practice.

Nos últimos anos, a ciência e a tecnologia proporcionaram uma larga gama de ferramentas aos profissionais de saúde. Em especial, as Tecnologias da Informação, pois favorecem o aprimoramento considerável da qualidade dos serviços de saúde prestados à população, quando gerenciadas adequadamente. O objetivo deste estudo foi identificar a relação entre risco de desenvolvimento de lesão por pressão e complexidade assistencial em pacientes críticos internados na unidade de terapia intensiva de um hospital universitário por meio dos registros eletrônicos de saúde. Trata-se de estudo correlacional, longitudinal e descritivo, com abordagem quantitativa. A coleta de dados foi conduzida durante 120 dias, a amostra foi composta por 74 pacientes que atenderam aos critérios de inclusão da pesquisa. Em relação às características sociodemográficas e clínicas, foi observado maioria do sexo masculino (56,8%), brancos (73%), na faixa etária de 60 a 79 anos (40,5%) e o tempo médio de internação nessa unidade correspondeu a 10,5 dias. A maioria dos indivíduos apresentou risco elevado para a lesão por pressão com média de 11,7%, complexidade assistencial média foi de 84,7% e frequência média diária de 5,5% reposicionamentos, registrados no sistema de informação hospitalar. Quanto ao desfecho dos pacientes, 28 (37,8%) apresentaram lesão por pressão notificada no sistema de informação hospitalar, 27 (36,5%) evoluíram para óbito na Unidade de Terapia Intensiva e 15 (20,3%) evoluíram a óbito e desenvolveram lesão por pressão, mostrando uma associação estatisticamente significante (p= 0,017). Foi observado significância estatística (p<0,001) e relação inversa para a complexidade assistencial e risco para desenvolvimento. As variáveis complexidade assistencial, risco para desenvolvimento de lesão por pressão, posições observadas foram registradas e também frequência de reposicionamento foram coletadas 776 vezes e observou-se que 605 (78%) da amostra em relação ao escore de complexidade assistencial foram registradas. Em 50% dos dias que os profissionais de enfermagem foram escalados com um paciente identificou-se que não foi atingida a capacidade máxima de trabalho do mesmo. No entanto, foi possível identificar que a capacidade máxima foi ultrapassada quando os profissionais assumiram o segundo paciente, ocorrendo uma possível sobrecarga de trabalho em 75% dos dias. Foi possível identificar diariamente os registros inexistentes dos escores relacionados à complexidade assistencial, risco para o desenvolvimento de lesão por pressão e reposicionamento. Portanto, esta pesquisa evidencia a relevância dos dados e informações produzidas pela equipe de enfermagem para identificar os pacientes em risco, estabelecer medidas preventivas para os mesmos e consequentemente melhorar os indicadores de qualidade por meio dos registros eletrônicos e, assim, superar os desafios relacionados a segurança, qualidade e efetividade da assistência de enfermagem / In recent years, science and technology have provided a wide range of tools to health professionals. In particular, information technology, because they favor the improvement of quality of considerable health care provided to the population, when properly managed. The aim of this study was to identify the relationship between risk of pressure injury development and complexity care in critically ill patients admitted to the intensive care unit of a university hospital through electronic records. This is a longitudinal and correlational descriptive study with quantitative approach. Data collection was conducted for 120 days; the sample was composed of 74 patients who met the inclusion criteria. In relation to the sociodemographic and clinical characteristics, it was observed mostly male (56.8%), white (73%), aged 60 to 79 years (40.5%) and the average time of staying in this unit was 10.5 days. The majority of individuals presented a high risk for pressure injury with an average of 11.7%, average complexity care was 84.7% and average daily frequency of replacement registered was 5.5%, on the hospital information system. As for the outcome of patients, 28 (37.8%) had notified pressure injury in the hospital information system, 27 (36.5%) evolved to death in the intensive care unit and 15 (20.3%) evolved to death and developed pressure injury, showing a statistically significant association (p=0.017). Statistical significance was observed (p < 0.001) and inverse relationship to the complexity and risk to development assistance. The variables care complexity, risk for pressure injury development, positions observed, recorded and also repositioning frequency were collected 776 times and it was observed that 605 (78%) of the sample in relation to the care complexity scores were recorded. In 50% of the days that the nursing professionals have been scaled with a patient identified that was not achieved the maximum working capacity of the same. However, it was possible to identify the maximum capacity was exceeded when the professionals took the second patient, a possible overload of work in 75% of the days. It was possible to identify daily non-existent records of scores related to complexity, risk for pressure injury development and repositioning. Therefore, this research highlights the importance of data and information produced by the nursing staff to identify patients at risk, establish preventive measures to the same and consequently improve the quality indicators by means of electronic records and thus overcome the challenges related to safety, quality and effectiveness of nursing care