The implementation of Electronic Medical Records (EMRs) may lead to improved quality of primary health care. To investigate this, we conducted a mixed methods study of eighteen Toronto family physicians who implemented EMRs in 2006 and nine comparison family physicians who continued to use paper records. We used a controlled before-after design and two focus groups. We examined five preventive services with Pay for Performance incentives: Pap smears, screening mammograms, fecal occult blood testing, influenza vaccinations and childhood vaccinations. There was no difference between the two groups: after adjustment, combined preventive services for the EMR group increased by 0.7% less than for the non-EMR group (p=0.55, 95% CI -2.8, 3.9). Physicians felt that EMR implementation was challenging.

The purpose of this research was to determine if patient journey process modeling could act as a change management tool to support electronic health record (EHR) adoption, at a tertiary-care mental health centre. This research study was based on a pre/post design, which evaluated the attitudes of clinicians??? pre and post implementation of the EHR. A survey was used to assess the attitudes of various healthcare professionals, such as physicians, nurses and a spectrum of allied health disciplines, at various phases of the planning and implementation process. In addition to the surveys, current and future state PaJMa (patient journey modeling architecture) models representing technology use and process flows of all units were created by observational studies, and served as change management tools. These PaJMa models were then presented as part of an intervention that was held in the form of an educational session to highlight the benefits of technology, and to address the common concerns identified from the initial survey results. The centre for mental health sciences facility was used as the case study to apply the PaJMa model and assess its change management functionality. Since, the organization was moving from paper to electronic based patient charts it was an ideal choice for this research. It was predicted that the attitudes and opinions of clinicians towards the EHR implementation, and EHRs in general, would change and become more positive with increased knowledge and education. This in-turn would increase EHR adoption and hence lead to a successful implementation.

Narrative information in Electronic Health Records (EHRs) contains a wealth of clinical information about treatments, diagnosis, medication and family history. In addition, the scientific literature represents a rich source of information that summarises the latest results and new research findings relevant to different diseases. These two textual sources often contain different types of valuable phenotypic information that may be complementary to each other. Combining details from each source thus has the potential to be useful in uncovering new disease-phenotypic associations. In turn, these associations can help to identify patients with high risk factors, and they can be useful in developing solutions to control the causes responsible for the development of different diseases. However, clinicians at the point of care have limited time to review the large volume of potentially useful information that is locked away in unstructured text format. This in turn limits the utility of this “raw” information to clinical practitioners and computerised applications. Accordingly, the provision of automated and efficient means to extract, combine and present phenotype information that may be scattered amongst a large number of different textual sources in an easily digestible format is a prerequisite to the effective use and comprehensive understanding of details contained within both the records and the literature. The development of such facilities can in turn help in deriving information about disease correlations and supporting clinical decisions. This thesis is the first comprehensive study focussing on extracting and integrating phenotypic information from two different biomedical sources using Text Mining (TM) techniques. In this research, we describe our work on (1) extracting phenotypic information from both EHRs and the biomedical literature; (2) extracting the relations between phenotypic information and distilling them from EHRs using an event-based approach; and (3) using normalisation methods to link the phenotypic information found in EHRs with associated mentions found in the literature as a first step towards the automatic integration of information from these heterogeneous sources.

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

Information security has significant role in Healthcare organizations. The Electronic Health Record (EHR) with patient’s information is considered as very sensitive in Healthcare organization. Sensitive information of patients in healthcare has to be managed such that it is safe and secure from unauthorized access. The high-level quality care to patients is possible if healthcare management system is able to provide right information in right time to right place. Availability and accessibility are significant aspects of information security, where applicable information needs to be available and accessible for user within the healthcare organization as well as across organizational borders. At the same time, it is essentials to protect the patient security from unauthorized access and maintain the appropriate level in health care regarding information security. The aim of this thesis is to explore current management of information security in terms of Electronic Health Records (EHR) and how these are protected from possible security threats and risks in healthcare, when the sensitive information has to be communicated among different actors in healthcare as well as across borders. The Blekinge health care system was investigated through case study with conduction of several interviews to discover possible issues, concerning security threats to management of healthcare. The theoretical work was the framework and support for possible solutions of identified security risks and threats in Blekinge healthcare. At the end after mapping, the whole process possible guidelines and suggestions were recommended for healthcare in order to prevent the sensitive information from unauthorized access and maintain information security. The management of technical and administrative bodies was explored for security problems. It has main role to healthcare and in general, whole business is the responsibility of this management to manage the sensitive information of patients. Consequently, Blekinge healthcare was investigated for possible issues and some possible guidelines and suggestions in order to improve the current information security with prevention of necessary risks to healthcare sensitive information. / muqadas@gmail.com

Cloud Computing today, is an evolving technology which features large Data Storage and ready-to-access from any device. The Healthcare Industry stores large Databases of patient’s records, considering the advantages of Cloud Computing it is looking forward to moving on from the traditional, proprietary Database Management Model into an Open Source Cloud DBMS Model. To complete this transition, it is of primary importance to provide Privacy and Security for Electronic Medical Record / Electronic Health Record. There are several kinds of research being done on how to mitigate these privacy issues using algorithms like Attribute Based Encryption and Identity-Based Encryption. In this study, we compare the performance of these two attribute based encryption methods. This thesis compares the performance of the state-of-the-art Attribute Based Encryption Schemas for Electronic Medical Record / Electronic Health Record Systems. Performance evaluation is conducted in local and cloud environments. A Literature Review has been performed to identify the existing Cloud-based Electronic Health Record Systems which uses the attribute based encryption as a mechanism to mitigate the privacy issues and realization in Cloud. Two algorithms have been selected by performing snowballing from the IEEE Research Articles. Experimentation was performed on the two algorithms in a local machine and on Amazon Web Services Cloud Platform to compare the performance. Verification of performance in each stage of the execution of the algorithms, in both local machine and Cloud environment, was done.

Clinical intelligence about a patient's risk of future adverse health events can support clinical decision making in personalized and preventive care. Healthcare predictive analytics using electronic health records offers a promising direction to address the challenging tasks of risk profiling. Patients with chronic diseases often face risks of not just one, but an array of adverse health events. However, existing risk models typically focus on one specific event and do not predict multiple outcomes. To attain enhanced risk profiling, we adopt the design science paradigm and propose a principled approach called Bayesian multitask learning (BMTL). Considering the model development for an event as a single task, our BMTL approach is to coordinate a set of baseline models-one for each event-and communicate training information across the models. The BMTL approach allows healthcare providers to achieve multifaceted risk profiling and model an arbitrary number of events simultaneously. Our experimental evaluations demonstrate that the BMTL approach attains an improved predictive performance when compared with the alternatives that model multiple events separately. We also find that, in most cases, the BMTL approach significantly outperforms existing multitask learning techniques. More importantly, our analysis shows that the BMTL approach can create significant potential impacts on clinical practice in reducing the failures and delays in preventive interventions. We discuss several implications of this study for health IT, big data and predictive analytics, and design science research.

The purpose of this study was to identify the characteristics of cancer patients and the most frequently chosen nursing diagnoses, outcomes and interventions chosen for care plans from a large Midwestern acute care hospital. In addition the patients' outcome change scores and length of stay from the four oncology specialty units are investigated. Donabedian's structure-process-outcome model is the framework for this study. This is a descriptive retrospective study. The sample included a total of 2,237 patients admitted on four oncology units from June 1 to December 31, 2010. Data were retrieved from medical records, the nursing documentation system, and the tumor registry center. Demographics showed that 63% of the inpatients were female, 89% were white, 53 % were married and 26% were retired. Most patients returned home (82%); and 2% died in the hospital. Descriptive analysis identified that the most common nursing diagnoses for oncology inpatients were Acute Pain (78%), Risk for Infection (31%), and Nausea (26%). Each cancer patient had approximately 3.1 nursing diagnoses (SD=2.5), 6.3 nursing interventions (SD=5.1), and 3.7 nursing outcomes (SD=2.9). Characteristics of the patients were not found to be related to LOS (M=3.7) or outcome change scores for Pain Level among the patients with Acute Pain. Specifically, 88% of patients retained or improved outcome change scores. The most common linkage of NANDA-I, NOC, and NIC (NNN), a set of standardized nursing terminologies used in the study that represents nursing diagnoses, nursing-sensitive patient outcomes and nursing interventions, prospectively, was Acute Pain--Pain Level--Pain Management. Pain was the dominant concept in the nursing care provided to oncology patients. Risk for Infection was the most frequent nursing diagnosis in the Adult Leukemia and Bone Transplant Unit. Patients with both Acute Pain and Risk for Infection may differ among units; while the traditional study strategies rarely demonstrate this finding. Identifying the pattern of core diagnoses, interventions, and outcomes for oncology nurses can direct nursing care in clinical practice and provide direction for future research tot targets areas of high impact and guide education and evaluation of nurse competencies.

Indiana University-Purdue University Indianapolis (IUPUI) / In the age of big data, better use of large, real-world datasets is needed, especially ultra-large databases that leverage health information exchange (HIE) systems to gather data from multiple sources. Promising as this process is, there have been challenges analyzing big data in healthcare due to big data attributes, mainly regarding volume, variety, and velocity. Thus, these health data require not only computational approaches but also context-based controls.In this research, we systematically examined associations among select neural and dermal conditions in an ultra-large healthcare database derived from an HIE, in which computational approaches with epidemiological measures were used. After a systematic cleaning, a binary logistic model-based methodology was used to search for associations, controlling for race and gender. Age groups were chosen using an algorithm to find the highest incidence rates for each condition pair. A binomial test was conducted to check for significant temporal direction among conditions to infer cause and effect. Gene-disease association data were used to evaluate the association among the conditions and assess the shared genetic background. The results were adjusted for multiple testing, and network graphs of significant associations were created. Findings among methodologies were compared to each other and with prior studies in the literature. In the results, seemingly distant neural and dermal conditions had an extensive number of associations. Controlling for race and gender tightened these associations, especially for racial factors affecting dermal conditions, like melanoma, and gender differences on conditions like migraine. Temporal and gene associations helped explain some of the results, but not all. Network visualizations summarized results, highlighting central conditions and stronger associations. Healthcare data are confounded by many factors that hide associations of interest. Triangulating associations with separate analyses helped with the interpretation of results. There are still numerous confounders in these data that bias associations. Aside from what is known, our approach with limited variables may inform hypothesis generation. Using additional variables with controlled-computational methods that require minimal external input may provide results that can guide healthcare, health policy, and further research.

Medication errors are among the most common causes of unintended harm to patients and have led to many deaths. Some categories of medication errors include; medications administered to the wrong person; medications administered at the wrong time, through the wrong route; administration of the wrong medication and/or dose; and the omission of medications. Guided by the logic model, the just culture model, and the Knowles theory of andragogy, the purpose of the project was to determine if providing information related to evidence-based strategies to reduce medication errors would result in safer medication administration practices and improved patient outcomes A survey was administered to 11 medical and nursing staff at an outpatient internal medical clinic to determine their knowledge about medications errors prior to providing evidence-based information on strategies to reduce medication errors. After the educational session, a survey was conducted to determine staff members' retention of knowledge. A significant increase in the percent of correct responses to the survey from 68% to 100% after the educational session (t = -3.9; p = 0.001)) shows that the educational in-service had a positive outcome in increasing staff members' knowledge about reducing medication errors in an out-patient internal medicine clinic. Improving clinic staff knowledge and behaviors regarding medication administration has the potential to bring about social change by decreasing medication errors, improving patient safety, and improving health outcomes.