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Electronic Health Record (EHR) creates a huge number of databases which are being updated dynamically. Major goal of interoperability in healthcare is to facilitate the seamless exchange of healthcare related data and an environment to supports interoperability and secure transfer of data. The health care organisations face difficulties in exchanging patient’s health care information and laboratory reports etc. due to a lack of semantic interoperability. Hence, there is a need of semantic web technologies for addressing healthcare interoperability problems by enabling various healthcare standards from various healthcare entities (doctors, clinics, hospitals etc.) to exchange data and its semantics which can be understood by both machines and humans. Thus, a framework with a similarity analyser has been proposed in the thesis that dealt with semantic interoperability. While dealing with semantic interoperability, another consideration was the use of word embedding and ontology for knowledge discovery. In medical domain, the main challenge for medical information extraction system is to find the required information by considering explicit and implicit clinical context with high degree of precision and accuracy. For semantic similarity of medical text at different levels (conceptual, sentence and document level), different methods and techniques have been widely presented, but I made sure that the semantic content of a text that is presented includes the correct meaning of words and sentences. A comparative analysis of approaches included ontology followed by word embedding or vice-versa have been applied to explore the methodology to define which approach gives better results for gaining higher semantic similarity. Selecting the Kidney Cancer dataset as a use case, I concluded that both approaches work better in different circumstances. However, the approach in which ontology is followed by word embedding to enrich data first has shown better results. Apart from enriching the EHR, extracting relevant information is also challenging. To solve this challenge, the concept of analogy has been applied to explain similarities between two different contents as analogies play a significant role in understanding new concepts. The concept of analogy helps healthcare professionals to communicate with patients effectively and help them understand their disease and treatment. So, I utilised analogies in this thesis to support the extraction of relevant information from the medical text. Since accessing EHR has been challenging, tweets text is used as an alternative for EHR as social media has appeared as a relevant data source in recent years. An algorithm has been proposed to analyse medical tweets based on analogous words. The results have been used to validate the proposed methods. Two experts from medical domain have given their views on the proposed methods in comparison with the similar method named as SemDeep. The quantitative and qualitative results have shown that the proposed analogy-based method bring diversity and are helpful in analysing the specific disease or in text classification.

Longitudinal personalized electronic health record (LPHR) provides a holistic view of health records for individuals and offers a consistent patient-controlled information system for managing the health care of patients. Except for the patients in Veterans Affairs health care service, however, no LPHR is available for the general population in the U.S. that can integrate the existing patients' electronic health records throughout life of care. Such a gap may be contributed mainly by the fact that existing patients' electronic health records are scattered across multiple health care facilities and often not shared due to privacy and security concerns from both patients and health care organizations. The main objective of this dissertation is to address these roadblocks by designing a scalable and interoperable LPHR with patient-controlled and mutually-trusted security and privacy. Privacy and security are complex problems. Specifically, without a set of access control policies, encryption alone cannot secure patient data due to insider threat. Moreover, in a distributed system like LPHR, so-called race condition occurs when access control policies are centralized while decisions making processes are localized. We propose a formal definition of secure LPHR and develop a blockchain-enabled next generation access control (BeNGAC) model. The BeNGAC solution focuses on patient-managed secure authorization for access, and NGAC operates in open access surroundings where users can be centrally known or unknown. We also propose permissioned blockchain technology - Hyperledger Fabric (HF) - to ease the shortcoming of race condition in NGAC that in return enhances the weak confidentiality protection in HF. Built upon BeNGAC, we further design a blockchain-enabled secure and trusted (BEST) LPHR prototype in which data are stored in a distributed yet decentralized database. The unique feature of the proposed BEST-LPHR is the use of blockchain smart contracts allowing BeNGAC policies to govern the security, privacy, confidentiality, data integrity, scalability, sharing, and auditability. The interoperability is achieved by using a health care data exchange standard called Fast Health Care Interoperability Resources. We demonstrated the feasibility of the BEST-LPHR design by the use case studies. Specifically, a small-scale BEST-LPHR is built for sharing platform among a patient and health care organizations. In the study setting, patients have been raising additional ethical concerns related to consent and granular control of LPHR. We engineered a Web-delivered BEST-LPHR sharing platform with patient-controlled consent granularity, security, and privacy realized by BeNGAC. Health organizations that holding the patient's electronic health record (EHR) can join the platform with trust based on the validation from the patient. The mutual trust is established through a rigorous validation process by both the patient and built-in HF consensus mechanism. We measured system scalability and showed millisecond-range performance of LPHR permission changes. In this dissertation, we report the BEST-LPHR solution to electronically sharing and managing patients' electronic health records from multiple organizations, focusing on privacy and security concerns. While the proposed BEST-LPHR solution cannot, expectedly, address all problems in LPHR, this prototype aims to increase EHR adoption rate and reduce LPHR implementation roadblocks. In a long run, the BEST-LPHR will contribute to improving health care efficiency and the quality of life for many patients. / Doctor of Philosophy / Longitudinal personalized electronic health record (LPHR) provides a holistic view of health records for individuals and offers a consistent patient-controlled information system for managing the health care of patients. Except for the patients in Veterans Affairs health care service, however, no LPHR is available for the general population in the U.S. that can integrate the existing patients' electronic health records throughout life of care. Such a gap may be contributed mainly by the fact that existing patients' electronic health records are scattered across multiple health care facilities and often not shared due to privacy and security concerns from both patients and health care organizations. The main objective of this dissertation is to address these roadblocks by designing a scalable and interoperable LPHR with patient-controlled and mutually-trusted security and privacy. We propose a formal definition of secure LPHR and develop a novel blockchain-enabled next generation access control (BeNGAC) model, that can protect security and privacy of LPHR. Built upon BeNGAC, we further design a blockchain-enabled secure and trusted (BEST) LPHR prototype in which data are stored in a distributed yet decentralized database. The health records on BEST-LPHR are personalized to the patients with patient-controlled security, privacy, and granular consent. The unique feature of the proposed BEST-LPHR is the use of blockchain technology allowing BeNGAC policies to govern the security, privacy, confidentiality, data integrity, scalability, sharing, and auditability. The interoperability is achieved by using a health care data exchange standard. We demonstrated the feasibility of the BEST-LPHR design by the use case studies. Specifically, a small-scale BEST-LPHR is built for sharing platform among a patient and health care organizations. We engineered a Web-delivered BEST-LPHR sharing platform with patient-controlled consent granularity, security, and privacy realized by BeNGAC. Health organizations that holding the patient's electronic health record (EHR) can join the platform with trust based on the validation from the patient. The mutual trust is established through a rigorous validation process by both the patient and built-in blockchain consensus mechanism. We measured system scalability and showed millisecond-range performance of LPHR permission changes. In this dissertation, we report the BEST-LPHR solution to electronically sharing and managing patients' electronic health records from multiple organizations, focusing on privacy and security concerns. While the proposed BEST-LPHR solution cannot, expectedly, address all problems in LPHR, this prototype aims to increase EHR adoption rate and reduce LPHR implementation roadblocks. In a long run, the BEST-LPHR will contribute to improving health care efficiency and the quality of life for many patients.

This dissertation develops explainable and sparse predictive models applied to two main healthcare applications: reproductive health and oncology. Through the application of advanced machine learning techniques and survival analysis, we aim to enhance predictive accuracy and provide actionable insights in these critical areas. The thesis is structured into four distinct problems, each focusing on a particular research question. The first problem concerns the prediction of the probability of conception among couples actively trying to conceive. Using self-reported health data from a North American preconception cohort study, we analyzed factors such as sociodemographics, lifestyle, medical history, diet quality, and specific male partner characteristics. Machine learning algorithms were employed to predict the probability of conception demonstrating improved discrimination and potential clinical utility. The second problem explores the application of machine learning algorithms to electronic health record (EHR) data for identifying predictor variables associated with polycystic ovarian syndrome (PCOS) diagnosis. Employing gradient boosted trees and feed-forward multilayer perceptron classifiers, we developed a scoring system that improved the model's performance, providing a valuable tool for early detection and intervention. The third problem focuses on predicting the risk of miscarriage among female participants who conceived during the study period. Utilizing both static and survival analysis, including Cox proportional hazard models, we developed predictive models to assess miscarriage risk. The study revealed that most miscarriages were due to random genetic errors during early pregnancy, indicating that miscarriage is not easily predicted based on preconception sociodemographic and lifestyle characteristics. Finally, the fourth problem focuses on the development of predictive models for managing Chronic Myeloid Leukemia (CML) patients. We developed models to predict whether patients will achieve deep molecular response (DMR) at later treatment stages and maintaining this status up to 60 months post-treatment initiation. These models offer insights into treatment effectiveness and patient management, aiming to support clinical decision-making and improve long-term patient outcomes. By emphasizing the explainability of these models, this dissertation not only aims to provide accurate predictions but also to ensure that the results are interpretable and actionable for healthcare professionals. Overall, this thesis showcases the potential of predictive modeling to improve reproductive health and oncology-related outcomes. The development and validation of various models in these contexts underscore the value of machine learning algorithms in healthcare research, analysis of epidemiologic data, and prediction of critical health events. The findings have significant implications for enhancing patient care, informing clinical practices, and guiding healthcare policy decisions.

Aim: The overall aim of this thesis is to explore School health nursing through school nurses’ descriptions of school children’s health and to analyse factors influencing the recording of schoolchildren’s health in the School Health Record (SHR). An additional aim is to evaluate family nursing interventions as a tool for the school nurses in the School Health Service (SHS).Methods: The thesis comprises four papers. A combination of qualitative and quantitative methods was used through individual interviews with a strategic sample of school nurses (n=12) (PI), a national survey to a representative sample of school nurses (n=129) (PII, III) and the implementation of family nursing models developed in Canada with girls in their early adolescence with recurrent health complaints and their families (n=4) in co-operation with their school nurses (n=2) (PIV). The Strengths and Difficulties Questionnaire (SDQ) was used as pre and post test. Evaluation interviews were conducted with the families and the nurses separately. Qualitative content analyses were used to analyze the interview text with the school nurses and the families. Manifest content analysis was used to analyze the free text answers of the survey and the evaluation interview with the school nurses. Descriptive statistical analyses were used to describe demographic data in all four papers. The SDQ was hand-scored statistically.Findings: The findings showed that nurses judged the schoolchildren’s mental health as deteriorated, especially in socially disadvantaged areas and more generally among girls expressed as psychosomatic symptoms. Individual factors related to lifestyle affected the schoolchildren’s physical health, and the mental health was, to a large extent, affected by the school environment and family relations. The latter seemed to be the most important factor affecting schoolchildren’s mental health. The basis for the school nurses judgement of the physical health was health check-ups and the health dialogues. Spontaneous visits were more commonly used to judge the mental health. Recording schoolchildren’s mental health was a challenge for school nurses. Difficulties were related to ethical considerations, tradition, lack of time and the improper structure of the SHR. Fears of marking the schoolchild for life related to the schoolchild itself, the parents or to other authorities/successive caregivers were brought up as hinders for recording mental and social health. Family sessions may be useful within the profession when handling recurrent health complaints among adolescence girls. The girls and their families experienced relief, they felt confirmed and that their feelings and reactions were normal in that situation. The families became aware of their own strengths and possibilities and this was supported by the SDQ which showed an increased well-being. The school nurses valued this way of working and meant that the sessions seemed to start a changing process within the families.Conclusions: The results indicate that school nurses have a deep knowledge about schoolchildren’s health which is not used to its full potential in a public health perspective. However, the experienced difficulties recording schoolchildren’s mental health seem obvious, which would demand developing the SHR for the needs of today. Family sessions in SHS with the school nurse as a collaborator with the family seemed useful and may be transferable to other health problems expressed by the schoolchildren. Bronfenbrenner’s ecological systems theory and other models for health determinants are used to illustrate the school nurse as a mediator working on the bridge over different health streams with schoolchildren’s health on an individual and a population level. / Syfte: Avhandlingens övergripande syfte är att undersöka skolsköterskors uppfattning om skolbarns hälsa och att analysera faktorer som påverkar dokumentation av skolbarns hälsa i skolhälsovårdsjournalen. Ett ytterligare syfte är att utvärdera modeller för familjeinterventioner som redskap för skolsköterskor i skolhälsovård.Metoder: Avhandlingen består av fyra delarbeten. En kombination av kvalitativa och kvantitativa metoder användes. I delarbete I genomfördes intervjuer med ett strategiskt urval av skolsköterskor (n=12). Intervjuerna analyserades med kvalitativ innehållsanalys. I delarbeten II och III distribuerades en nationell enkät till ett representativt urval av Sveriges skolsköterskor (n=129). Manifest innehållsanalys användes vid analys av de öppna frågorna. I delarbete IV genomfördes en interventionsstudie med familjesamtal, inspirerad av modeller för familjefokuserad omvårdnad utvecklade i Kanada. Skolflickor i tidig adolescens med återkommande subjektiva hälsoproblem (n=4) och deras föräldrar i samarbete med deras verksamma skolsköterskor (n=2) ingick i studien. Separata utvärderingsintervjuer genomfördes med familjer och skolsköterskor. Intervjuerna analyserades med kvalitativ och manifest innehållsanalys respektive. The Strengths and Difficulties Questionnaire (SDQ) användes som före/efter test vid interventionen och resultatet bearbetades statistiskt manuellt. Deskriptiv statistik användes för analys av demografisk data i samtliga delarbeten.Resultat: Resultatet visade att skolsköterskorna bedömde skolbarnens mentala hälsa som försämrad särskilt bland flickor och i socioekonomiskt utsatta områden. Individuella livsstilsfaktorer påverkade skolbarnens fysiska hälsa och den mentala hälsan var i stor utsträckning påverkad av skolmiljö och familjerelationer. Det sistnämnda verkade vara den mest betydelsefulla påverkansfaktorn för skolbarnens mentala hälsa. Bedömningen av den fysiska hälsan baserades på hälsokontroller och hälsosamtal medan spontana besök var vanligare för bedömning av den mentala hälsan. Dokumentation av mentala hälsa var en utmaning för skolsköterskorna. Svårigheterna kunde relateras till etiska överväganden, tradition och tidsbrist samt till skolhälsovårdsjournalens struktur som inte ansågs uppfylla dagens krav. Skolsköterskorna uttryckte en rädsla för att journalanteckningarna skulle märka skolbarnet för livet. Framtida tolkningar relaterade till skolbarnet själv, föräldrar eller andra/påföljande vårdgivare uttrycktes som hinder för att dokumentera mental och social hälsa. Familjesamtal visade sig vara användbara i skolhälsovården. De medverkande flickorna och deras familjer kände sig bekräftade i att deras känslor och reaktioner var normala. De sade sig bli medvetna om egna styrkor och möjligheter vilket styrktes av SDQ som visade ett ökat välbefinnande efter sammankomsterna, både hos skolbarnen och hos föräldrarna. Skolsköterskorna var positiva till att arbeta med familjesamtal och upplevde sig mer som samverkanspartner än som expert. Samtalen ledde till att en förändringsprocess startade i familjerna enligt skolsköterskorna.Slutsatser: Resultatet indikerar att skolsköterskor har en djup kunskap om skolbarns hälsa som sannolikt kunde tas tillvara på ett bättre sätt ur ett folkhälsoperspektiv på såväl nationell som lokal nivå. Behovet av att utveckla skolhälsovårdsjournalen efter dagens behov och fördjupad kunskap om de upplevda svårigheterna att dokumentera skolbarns mentala hälsa är uppenbar. Familjesamtal, där skolsköterskan intar en roll som samverkanspartner, visade sig användbara och kan sannolikt överföras till andra hälsoproblem bland skolbarn. Bronfenbrenners utvecklingsekologiska systemteori och andra modeller för hälsodeterminanter används för att illustrera skolsköterskans arbete med skolbarns hälsa på såväl en individuell nivå som folkhälsonivå.

Unintended consequences of electronic health records represent undesired effects on individuals or systems, which may contradict initial goals and impact patient care. The purpose of this study was to determine the extent to which a new quantitative measure called the Carrington-Gephart Unintended Consequences of Electronic Health Record Questionnaire (CG-UCE-Q) was valid and reliable. Then, it was used to describe acute care nurses' experience with unintended consequences of electronic health records and relate them to the professional practice environment. Acceptable content validity was achieved for two rounds of surveys with nursing informatics experts (n = 5). Then, acute care nurses (n = 144) were recruited locally and nationally to complete the survey and describe the frequency with which they encounter unintended consequences in daily work. Principal component analysis with oblique rotation was applied to evaluate construct validity. Correlational analysis with measures of the professional practice environment and workarounds was used to evaluate convergent validity. Test-retest reliability was measured in the local sample (N = 68). Explanation for 63% of the variance across six subscales (patient safety, system design, workload issues, workarounds, technology barriers, and sociotechnical impact) supported construct validity. Relationships were significant between subscales for electronic health record-related threats to patient safety and low autonomy/leadership (P < .01), poor communication about patients (P < .01), and low control over practice (P < .01). The most frequent sources of unintended consequences were increased workload, interruptions that shifted tasks from the computer, altered workflow, and the need to duplicate data entry. Convergent validity of the CG-UCE-Q was moderately supported with both the context and processes of workarounds with strong relationships identified for when nurses perceived a block and altered process to work around it to subscales in the CG-UCE-Q for electronic health record system design (P < .01) and technological barriers (P < .01).

Despite a Presidential Order in 2004 that launched national incentives for the use of health information technology, specifically the Electronic Health Record (EHR), adoption of the EHR has been slow. This study attempts to quantify factors associated with adoption of the EHR and Computerized Provider Order Entry (CPOE) by combining multiple organizational theories and empirical studies. The study is conducted in two phases. The primary phase of this study identifies and evaluates the effects of external environmental and internal organizational factors on healthcare organizations to adopt the EHR. From secondary data, twelve IVs (df=19) are chosen based on existing models and literature. Logistic regression is used to determine the association between the environmental factors and EHR adoption. The secondary phase of this study examines the adoption of five variations of CPOE using the same IVs from phase one. This EHR component of CPOE is chosen due to its promotion as a solution to help cross the quality chasm (IOM, 2001). Secondary data are analyzed and logistic regression is used to quantify the association between the factors of EHR adoption and CPOE adoption. Eleven of the twelve IVs are significant between the two phases (p<.1). This study uses data from 2009 because the HITECH Act was passed that year and significant government incentives were offered for those health care organizations (HCOs) that meet the qualifications of meaningful use. This study serves as a baseline for future studies, extends the work of other empirical studies, and fills a gap in the literature concerning factors associated with the adoption of the EHR and specific dimensions of CPOE. The Kruse Theory developed is strongly based in literature and reflects complexity commensurate with the health care industry.

Les informations médicales recueillies dans le cadre du soin doivent être utilisables pour répondre à d’autres objectifs plus collectifs. Dans ce contexte de réutilisation des données d’un système d’information clinique pour de la recherche en épidémiologie, l’objectif de ce travail est d’étudier l’apport de l'intéropérabilité sémantique à travers un certain nombre de situations concrêtes que nous avons rencontrées et étudiées et qui illustrent la nature des problèmes de cohérence sémantiques liés au traitement des données médicales et de santé.La coexistence, à un moment donné, de plusieurs référentiels sémantiques ne doit pas être considéré comme un obstacle à l'interopérabilité. Des outils génériques peuvent être conçus et développés pour passer de façon transparente d'un composant à un autre avec aussi peu de perte d’information que possible. L’Unified Medical Language System (UMLS) est un de ses outils d’intégration sémantique. Son usage dans le cadre de ces travaux montre le caractère général de cette méthode et son potentiel pour résoudre cette classe de problèmes d’intéropérabilité sémantique.La richesse de chacune des terminologies permet, lorsqu’elles sont associées dans un même référentiel sémantique pivot, d’enrichir l’ensemble des terminologies prises individuellement pour une meilleur représentation des connaissances.L’interopérabilité sémantique améliore la disponibilité et la qualité des données réutilisables pour des recherches en santé publique. Elle permet d’enrichir les données existantes. Elle fournit les moyens d'accéder à de nouvelles sources de données, agrégées de manière valide, permettant des analyses comparatives ou des analyses plus riches. / Medical information collected during clinical care must be re-used to address other more collective goals. In this context of re-using data from a clinical information system for epidemiological research, the objective of this work is to study the contribution of semantic interoperability across a number of practical situations we have met and discussed which illustrate the nature of semantic consistency problems associated with processing of medical data.Coexistence at a given time, of several semantic repositories should not be considered as an obstacle to interoperability. Generic tools can be designed and developed to move seamlessly from one component to another with as little loss of information as possible. The Unified Medical Language System (UMLS) is one of the semantic integration tools. Its use in this work shows the generality of this method and its potential for solving this class of semantic interoperability problems.The richness of each of the terminology can, when combined into a single pivot semantic repository, enrich the set of terminologies individually for a better representation of knowledge.Semantic interoperability improves the availability and quality of reusable data for public health research. It also enriches existing data. It provides access to new sources of data, aggregated in a valid manner, allowing benchmarking or richer analysis.