Development of a Provisional Domain Model for the Nursing Process for Use within the Health Level 7 Reference Information Model

Objective: Since 1999, the Nursing Terminology Summits have promoted the development, evaluation, and use of reference terminology for nursing and its integration into comprehensive health care data standards. The use of such standards to represent nursing knowledge, terminology, processes, and information in electronic health records will enhance continuity of care, decision support, and the exchange of comparable patient information. As part of this activity, working groups at the 2001, 2002, and 2003 Summit Conferences examined how to represent nursing information in the Health Level 7 (HL7) Reference Information Model (RIM). Design: The working groups represented the nursing process as a dynamic sequence of phases, each containing information specific to the activities of the phase. They used Universal Modeling Language (UML) to represent this domain knowledge in models. An Activity Diagram was used to create a dynamic model of the nursing process. After creating a structural model of the information used at each stage of the nursing process, the working groups mapped that information to the HL7 RIM. They used a hierarchical structure for the organization of nursing knowledge as the basis for a hierarchical model for "Findings about the patient.” The modeling and mapping reported here were exploratory and preliminary, not exhaustive or definitive. The intent was to evaluate the feasibility of representing some types of nursing information consistently with HL7 standards. Measurements: The working groups conducted a small-scale validation by testing examples of nursing terminology against the HL7 RIM class "Observation.” Results: It was feasible to map patient information from the proposed models to the RIM class "Observation.” Examples illustrate the models and the mapping of nursing terminology to the HL7 RIM. Conclusion: It is possible to model and map nursing information into the comprehensive health care information model, the HL7 RIM. These models must evolve and undergo further validation by clinicians. The integration of nursing information, terminology, and processes in information models is a first step toward rendering nursing information machine-readable in electronic patient records and messages. An eventual practical result, after much more development, would be to create computable, structured information for nursing documentatio

A Model for Setting Optimal Data-Acquisition Policy and its Application with Clinical Data

Manual data acquisition is often subject to incompleteness – data attributes that are missing due to time and data-availability constraints, which might damage data usability for analyses and decision making. This study introduces a novel optimization model for setting mandatory versus voluntary attributes in a dataset. This model may direct the decision of whether or not to enforce the acquisition of certain attributes, given certain constraints and dependencies. The feasibility and the potential contribution of the proposed model were evaluated with a clinical dataset that reflects Colonoscopy procedures performed in a large hospital over a 4-year period. The evaluation demonstrated that the model can be reasonably estimated within the given context, and that its implementation may contribute important insight toward improving data quality. The current data-acquisition setup was shown to be sub-optimal, and some further evaluation identified factors that influence incompleteness and may require revisions to current data acquisition policies

METADATA MANAGEMENT FOR CLINICAL DATA INTEGRATION

Clinical data have been continuously collected and growing with the wide adoption of electronic health records (EHR). Clinical data have provided the foundation to facilitate state-of-art researches such as artificial intelligence in medicine. At the same time, it has become a challenge to integrate, access, and explore study-level patient data from large volumes of data from heterogeneous databases. Effective, fine-grained, cross-cohort data exploration, and semantically enabled approaches and systems are needed. To build semantically enabled systems, we need to leverage existing terminology systems and ontologies. Numerous ontologies have been developed recently and they play an important role in semantically enabled applications. Because they contain valuable codified knowledge, the management of these ontologies, as metadata, also requires systematic approaches. Moreover, in most clinical settings, patient data are collected with the help of a data dictionary. Knowledge of the relationships between an ontology and a related data dictionary is important for semantic interoperability. Such relationships are represented and maintained by mappings. Mappings store how data source elements and domain ontology concepts are linked, as well as how domain ontology concepts are linked between different ontologies. While mappings are crucial to the maintenance of relationships between an ontology and a related data dictionary, they are commonly captured by CSV files with limits capabilities for sharing, tracking, and visualization. The management of mappings requires an innovative, interactive, and collaborative approach. Metadata management servers to organize data that describes other data. In computer science and information science, ontology is the metadata consisting of the representation, naming, and definition of the hierarchies, properties, and relations between concepts. A structural, scalable, and computer understandable way for metadata management is critical to developing systems with the fine-grained data exploration capabilities. This dissertation presents a systematic approach called MetaSphere using metadata and ontologies to support the management and integration of clinical research data through our ontology-based metadata management system for multiple domains. MetaSphere is a general framework that aims to manage specific domain metadata, provide fine-grained data exploration interface, and store patient data in data warehouses. Moreover, MetaSphere provides a dedicated mapping interface called Interactive Mapping Interface (IMI) to map the data dictionary to well-recognized and standardized ontologies. MetaSphere has been applied to three domains successfully, sleep domain (X-search), pressure ulcer injuries and deep tissue pressure (SCIPUDSphere), and cancer. Specifically, MetaSphere stores domain ontology structurally in databases. Patient data in the corresponding domains are also stored in databases as data warehouses. MetaSphere provides a powerful query interface to enable interaction between human and actual patient data. Query interface is a mechanism allowing researchers to compose complex queries to pinpoint specific cohort over a large amount of patient data. The MetaSphere framework has been instantiated into three domains successfully and the detailed results are as below. X-search is publicly available at https://www.x-search.net with nine sleep domain datasets consisting of over 26,000 unique subjects. The canonical data dictionary contains over 900 common data elements across the datasets. X-search has received over 1800 cross-cohort queries by users from 16 countries. SCIPUDSphere has integrated a total number of 268,562 records containing 282 ICD9 codes related to pressure ulcer injuries among 36,626 individuals with spinal cord injuries. IMI is publicly available at http://epi-tome.com/. Using IMI, we have successfully mapped the North American Association of Central Cancer Registries (NAACCR) data dictionary to the National Cancer Institute Thesaurus (NCIt) concepts

Patient Safety and Quality: An Evidence-Based Handbook for Nurses

Compiles peer-reviewed research and literature reviews on issues regarding patient safety and quality of care, ranging from evidence-based practice, patient-centered care, and nurses' working conditions to critical opportunities and tools for improvement

Developing a Tool to Support Decisions on Patient Prioritization at Admission to Home Health Care

Background and aims: Millions of Americans are discharged from hospitals to home health every year and about third of them return to hospitals. A significant number of rehospitalizations (up to 60%) happen within the first two weeks of services. Early targeted allocation of services for patients who need them the most, have the potential to decrease readmissions. Unfortunately, there is only fragmented evidence on factors that should be used to identify high-risk patients in home health. This dissertation study aimed to (1) identify factors associated with priority for the first home health nursing visit and (2) to construct and validate a decision support tool for patient prioritization. I recruited a geographically diverse convenience sample of nurses with expertise in care transitions and care coordination to identify factors supporting home health care prioritization. Methods: This was a predictive study of home health visit priority decisions made by 20 nurses for 519 older adults referred to home health. Variables included sociodemographics, diagnosis, comorbid conditions, adverse events, medications, hospitalization in last 6 months, length of stay, learning ability, self-rated health, depression, functional status, living arrangement, caregiver availability and ability and first home health visit priority decision. A combination of data mining and logistic regression models was used to construct and validate the final model. Results: The final model identified five factors associated with first home health visit priority. A cutpoint for decisions on low/medium versus high priority was derived with a sensitivity of 80% and specificity of 57.9%, area under receiver operator curve (ROC) 75.9%. Nurses were more likely to prioritize patients who had wounds (odds ratio [OR]=1.88), comorbid condition of depression (OR=1.73), limitation in current toileting status (OR= 2.02), higher numbers of medications (increase in OR for each medication =1.04) and comorbid conditions (increase in OR for each condition =1.04). Discussion: This dissertation study developed one of the first clinical decision support tools for home health, the PREVENT - Priority for Home Health Visit Tool. Further work is needed to increase the specificity and generalizability of the tool and to test its effects on patient outcomes

A modular, open-source information extraction framework for identifying clinical concepts and processes of care in clinical narratives

In this thesis, a synthesis is presented of the knowledge models required by clinical informa- tion systems that provide decision support for longitudinal processes of care. Qualitative research techniques and thematic analysis are novelly applied to a systematic review of the literature on the challenges in implementing such systems, leading to the development of an original conceptual framework. The thesis demonstrates how these process-oriented systems make use of a knowledge base derived from workflow models and clinical guidelines, and argues that one of the major barriers to implementation is the need to extract explicit and implicit information from diverse resources in order to construct the knowledge base. Moreover, concepts in both the knowledge base and in the electronic health record (EHR) must be mapped to a common ontological model. However, the majority of clinical guideline information remains in text form, and much of the useful clinical information residing in the EHR resides in the free text fields of progress notes and laboratory reports. In this thesis, it is shown how natural language processing and information extraction techniques provide a means to identify and formalise the knowledge components required by the knowledge base. Original contributions are made in the development of lexico-syntactic patterns and the use of external domain knowledge resources to tackle a variety of information extraction tasks in the clinical domain, such as recognition of clinical concepts, events, temporal relations, term disambiguation and abbreviation expansion. Methods are developed for adapting existing tools and resources in the biomedical domain to the processing of clinical texts, and approaches to improving the scalability of these tools are proposed and evalu- ated. These tools and techniques are then combined in the creation of a novel approach to identifying processes of care in the clinical narrative. It is demonstrated that resolution of coreferential and anaphoric relations as narratively and temporally ordered chains provides a means to extract linked narrative events and processes of care from clinical notes. Coreference performance in discharge summaries and progress notes is largely dependent on correct identification of protagonist chains (patient, clinician, family relation), pronominal resolution, and string matching that takes account of experiencer, temporal, spatial, and anatomical context; whereas for laboratory reports additional, external domain knowledge is required. The types of external knowledge and their effects on system performance are identified and evaluated. Results are compared against existing systems for solving these tasks and are found to improve on them, or to approach the performance of recently reported, state-of-the- art systems. Software artefacts developed in this research have been made available as open-source components within the General Architecture for Text Engineering framework

Development of a Web Platform for Surgical Oncologists in Portugal

In an age of enormous access to clinical data and rapid technological development, ensuring that physicians have computational tools to navigate a sea of information and improve health outcomes is vital. A major advance in medical practice is the incorporation of Clinical Decision Support Systems (CDSSs) to assist and support the healthcare team in clinical decision making, thus improving the quality of decisions and overall patient care, while minimizing costs. Postsurgical complications of cancer surgery are hard to predict, although there are several traditional risk scores available. However, there is an urgent need to improve perioperative risk assessment to reduce the growing postoperative burden in the Portuguese population. Understanding the individual risks of performing surgical procedures is essential to customizing preparatory, intervention, and aftercare protocols to minimize post-surgical complications. This knowledge is essential in oncology, given the nature of the interventions, the fragile profile of patients with comorbidities and drug exposure, and the possible recurrence of cancer. This thesis aims to develop an user-friendly web platform to support the collaboration and manage clinical data among oncologists at the Portuguese Institute of Oncology, Porto. The work integrates both a database to register/store the clinical data of cancer patients in a structured format, visualization tools and computational methods to calculate a specific risk score of postoperative outcomes for the Portuguese population. The platform named IPOscore will not only to manage the clinic data but also offer a predictive healthcare system, as an valuable instrument for the oncologists.Numa época de grande acesso a dados e rápido desenvolvimento tecnológico, garantir que os médicos tenham as ferramentas de apoio à decisão clínica para se deslocar em um mar de informação para encontrar o que é mais relevante para as necessidades dos pacientes é vital para otimizar os resultados de saúde. Um grande avanço na prática médica é a incorporação de Sistemas de Apoio à Decisão Clínica (CDSSs) para auxiliar e apoiar a equipe de saúde na tomada de decisão clínica, melhorando assim a qualidade das decisões e o atendimento geral ao paciente, minimizando custos. As complicações pós-operatórias da cirurgia do cancro ainda são difíceis de prever, embora existam muitos scores de risco destinados a fazer tais previsões. Compreender os riscos individuais de realizar procedimentos cirúrgicos é essencial para personalizar os protocolos preparatórios, de intervenção e pós-atendimento para minimizar as complicações pós-cirúrgicas. Esse conhecimento é fundamental em oncologia, dada a natureza das intervenções, o perfil frágil dos pacientes com comorbidades e exposição a drogas e a possível recorrência do cancro. Este trabalho propõe a construção duma plataformaweb de fácil utilização para apoiar a colaboração e dispor uma gestão de dados clínicos entre oncologistas. O trabalho integra uma base de dados para registrar / armazenar os dados clínicos, fisiológicos e biopatológicos de pacientes com cancro num formato estruturado e métodos computacionais para calcular um grau de risco específico de complicações pós-operatórias para a população portuguesa. A plataforma denominada IPOscore servirá para a gestão de dados clinicos, mas também oferecerá um sistema preditivo e preventivo, como uma ferramenta de apoio à decisão médica no contexto clínico diário

Exploring the Experiences of Injecting Drug Users Living with Leg Ulceration: a Qualitative Design

There is a paucity of scientific evidence into the lived experience of people who have a history of injecting drug use and are living with leg ulceration. Portraying the true voice of injecting drug users (IDUs) through narrative means is a novelty in contemporary literature. The representation of the life and the person behind the leg ulcer, having experienced addiction, is original from a purist narrative perspective. This study, led from the perspective of a nurse-researcher leading in the field of wound management, offers a unique opportunity to gain a rare glimpse into the daily life of IDUs, as reported in their own words. The aim of this study was to explore the experience of injecting drug users living with leg ulceration using qualitative methodology. A naturalistic paradigm framed the design by allowing participants to control the data in an unrestricted an open manner without direct intrusion form the researcher. Qualitative methodology was central to collecting data on life experience and feelings. The ethics process detailed a rigorous application to explore the professional, ethical virtues from the perspective of an insider-outsider working with sensitive data in a marginalised population. Diaries were kept and recorded by participants over four weeks in their routine daily life; this was followed by semi-structured interviews. The diaries allowed a unique insight into the past, present and future of IDUs and how their ulcer affected their lives. The diaries also facilitated a means of reflection on themselves and their wounded body. The interviews offered an opportunity to explore in detail the diary entries and other stories participants wished to share. The study recruited twelve participants from leg ulcer clinics set in London; three women and nine men older than 18 years of age (median age of 52 years; range 35 - 62 years). Ten completed the data collection process; two of the participants, aged 61 and 62 years, were married. Gatekeepers working with IDUs with leg ulceration were central to the process of engagement and recruitment. Participants welcomed the design as an opportunity to voice and share their journey of living with an open wound. The findings revealed the detailed suffering participants endured living with their ulcer: pain, shame and stigma were clearly voiced in their narratives. The majority of participants had experienced some form of stigma during their life and this was exacerbated as they were drug users. The self-blame and punishment triggered by this felt stigma was a detriment to the health of participants. Those in contact with specialist wound care services saw a significant improvement in wound healing and this had a positive impact on their wellbeing and their overall outlook on life. Participants also voiced enacted-stigma experienced from encounters in health practice. These negative experiences exacerbated the self-stigma. Findings also portrayed the multiple characteristics and talents of participants including humour, art and resilience. This research contributes to science and practice by understanding the lives of IDUs living with leg ulceration. It provides a platform from which to engage both generalists and specialists who care for these patients and has the potential to influence medical and social policy-making and clinical practice in this field. By means of narrative inquiry, this study may challenge the conventional social stereotypes, the taboos and the stigma still experienced by this patient group in health care

An interoperable electronic medical record-based platform for personalized predictive analytics

Indiana University-Purdue University Indianapolis (IUPUI)Precision medicine refers to the delivering of customized treatment to patients based on their individual characteristics, and aims to reduce adverse events, improve diagnostic methods, and enhance the efficacy of therapies. Among efforts to achieve the goals of precision medicine, researchers have used observational data for developing predictive modeling to best predict health outcomes according to patients’ variables. Although numerous predictive models have been reported in the literature, not all models present high prediction power, and as the result, not all models may reach clinical settings to help healthcare professionals make clinical decisions at the point-of-care. The lack of generalizability stems from the fact that no comprehensive medical data repository exists that has the information of all patients in the target population. Even if the patients’ records were available from other sources, the datasets may need further processing prior to data analysis due to differences in the structure of databases and the coding systems used to record concepts. This project intends to fill the gap by introducing an interoperable solution that receives patient electronic health records via Health Level Seven (HL7) messaging standard from other data sources, transforms the records to observational medical outcomes partnership (OMOP) common data model (CDM) for population health research, and applies predictive models on patient data to make predictions about health outcomes. This project comprises of three studies. The first study introduces CCD-TOOMOP parser, and evaluates OMOP CDM to accommodate patient data transferred by HL7 consolidated continuity of care documents (CCDs). The second study explores how to adopt predictive model markup language (PMML) for standardizing dissemination of OMOP-based predictive models. Finally, the third study introduces Personalized Health Risk Scoring Tool (PHRST), a pilot, interoperable OMOP-based model scoring tool that processes the embedded models and generates risk scores in a real-time manner. The final product addresses objectives of precision medicine, and has the potentials to not only be employed at the point-of-care to deliver individualized treatment to patients, but also can contribute to health outcome research by easing collecting clinical outcomes across diverse medical centers independent of system specifications