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Connie Delaney

Publications and source records attributed to Connie Delaney.

9 recordsLinked to original sources
PubMed2006

Building a personal health record from nursing perspective.

A complex set of needs-the increased accessibility to and interest in health information, more respected patients' rights, advanced information technologies, and pressure to seek a cost-effective healthcare delivery-introduced the Personal Health Record (PHR). Nursing care, as essential to healthcare, must be represented in a PHR, but few studies have explored the nurses' role in the development and expansion of the PHR. Our 'IOWA PERSONAL HEALTH RECORD (IowaPHR)' would be a pioneer to demonstrate how nursing can be integrated in the PHR. IowaPHR is attributed with these innovations: (1) information that embraces main community health concerns; (2) transformation of a standardized nursing language into questions adjusted for consumer vocabulary level; and (3) a user-friendly interface equipped with trending of health conditions and a diary function. The usability of this PHR is validated by experts in terms of content, ease of navigation, time needed to complete tasks, ability to find desired information, and site presentation. This work will provide a way for nursing informatics to make a difference in health informatics.

Humans↗
PubMed2006

Knowledge discovery: Detecting elderly patients with impaired mobility.

Immobility is an important health concern for the elderly patients and healthcare providers who care for the elderly. The purpose of this study was to test a knowledge discovery method to detect elderly patients with impaired mobility in a large clinical dataset. The research method applied an exploratory design and a data mining classification method (cost sensitive Decision Tree J48 from WEKA) to classify patients. Important factors were identified by the Feature Selection method. The Decision Tree algorithm classified patients in the dataset with 65% sensitivity and 72% specificity for a reduced model. The results were evaluated by 10-fold cross validation. Examples of decision rules were also extracted. The study can be applied to classify different health problems in different populations and serves as a foundation for the development of healthcare decision support systems.

Aged↗
PubMed2005

Bedside, classroom and bench: collaborative strategies to generate evidence-based knowledge for nursing practice.

The rise of evidence-base practice (EBP) as a standard for care delivery is rapidly emerging as a global phenomenon that is transcending political, economic and geographic boundaries. Evidence-based nursing (EBN) addresses the growing body of nursing knowledge supported by different levels of evidence for best practices in nursing care. Across all health care, including nursing, we face the challenge of how to most effectively close the gap between what is known and what is practiced. There is extensive literature on the barriers and difficulties of translating research findings into practical application. While the literature refers to this challenge as the "Bench to Bedside" lag, this paper presents three collaborative strategies that aim to minimize this gap. The Bedside strategy proposes to use the data generated from care delivery and captured in the massive data repositories of electronic health record (EHR) systems as empirical evidence that can be analysed to discover and then inform best practice. In the Classroom strategy, we present a description for how evidence-based nursing knowledge is taught in a baccalaureate nursing program. And finally, the Bench strategy describes applied informatics in converting paper-based EBN protocols into the workflow of clinical information systems. Protocols are translated into reference and executable knowledge with the goal of placing the latest scientific knowledge at the fingertips of front line clinicians. In all three strategies, information technology (IT) is presented as the underlying tool that makes this rapid translation of nursing knowledge into practice and education feasible.

Cooperative Behavior↗
PubMed2004

Nursing process outcome linkage research: issues, current status, and health policy implications.

BACKGROUND: The use of large clinical datasets to assess the effectiveness of health care is of growing interest in continuing efforts to understand the impact of healthcare costs on quality. Correspondingly, there is a greater need to define and measure outcomes that are sensitive to nursing interventions. However, concerns exist about the ability to amass and use large clinical nursing datasets to assess the effectiveness of nursing interventions. Some nursing studies have used large clinical datasets to examine patterns of nursing diagnoses, interventions, and outcomes. Among patient populations, however, systematic effectiveness studies of nursing process and outcome linkages at the individual nurse and patient level of analysis are essentially nonexistent. This is largely the result of slow development of nursing classifications, reference terminologies, and reference information standards. Nursing information systems have an unprecedented potential for documentation of nursing practice, as well as the accumulation and analysis of large clinical datasets, to improve nursing performance, increase nursing knowledge, and provide data and information necessary for nursing to participate in the formulation of healthcare policy. OBJECTIVES: A literature search shows that a common framework is beginning to evolve that represents nursing's essential information, eg, the Nursing Minimum Data Set, Management Minimum Data Set, and several standardized nursing languages. Extensive research and other initiatives have produced 1) nursing languages and reference terminologies that span healthcare settings; 2) information models; and 3) standards for datasets supporting information systems. A number of issues remain, however, that concern the development of uniform nursing datasets, definitions of outcomes, quality of nursing data, information system design, and methods of data analysis. We review nursing process outcome research, clarify issues inherent in nursing effectiveness research, and discuss implications for nursing and health policy.

Health Policy↗
PubMed2004

Translation and validation of the Nursing Outcomes Classification labels and definitions for acute care nursing in Iceland.

BACKGROUND: The Nursing Outcomes Classification (NOC), second edition, consists of 260 patient outcomes with definitions. This has been translated into five languages, but has not been clinically validated outside the United States of America (USA). AIM: The aim of this paper is to describe the translation of the labels and definitions from the NOC, second edition from English to Icelandic and validation for acute-care nursing in Iceland. METHOD: A survey that has been designed to identify nurses' perception of the percentage of patients' for whom each NOC outcome is relevant in clinical nursing practice was used for clinical validation in this study. The translation procedure involved source to target language translation, parallel comparison, pilot test and field test. Validation included test-retest to measure the reliability for each of the 260 outcome variables. Data collected from 140 clinical nurses from 54 departments within 13 nursing specialties at Landspitali University-Hospital in Iceland, in November 2001, were analysed to establish construct validity by confirmatory factor analysis. Internal consistency was calculated. RESULTS: Translation was successful. Test-retest showed that 181 of the 260 NOC outcomes were significant (P < 0.05) and moderately or highly correlated (r > 0.50) (Pearson's correlation). The confirmatory factor analysis showed that 22 of the 29 NOC classes had only one factor at the loading criteria > or =0.30. Of the 260 outcomes, 244 had loading on one factor (> or =0.30) within its class. Internal consistency was >0.80 (Cronbach's alpha). LIMITATIONS: Low response rate was a limitation. The indicators of each NOC outcome were not addressed. CONCLUSION: The Icelandic version of the NOC survey is a comprehensive tool that can be applied to nursing in acute-care for research purposes as well as to prepare for the implementation of NOC in clinical information systems.

Humans↗
PubMed2004

Using a Nursing Minimum Data Set with older patients with dementia in an acute care setting.

BACKGROUND: Many older people with dementia are admitted to acute care settings suffering from comorbidities. These and their treatments can lead to confusion in these patients, adding to their existing cognitive deficits, and this may not be recognized by care staff. The care of such patients is complex and requires multidisciplinary team input. The purposes of the Nursing Minimum Data Set are to describe the nursing care of patients in a variety of settings and to establish comparability of nursing data across clinical populations, settings and time. AIMS: This paper reports a study to describe the characteristics of hospitalized older patients with dementia and nursing diagnoses and nursing interventions for these patients, and to identify trends in the nursing care provided over a 3-year period using a Nursing Minimum Data Set from a community hospital in the United States of America. METHODS: Secondary data analysis was conducted in 2000 on a large clinical discharge data set containing Nursing Minimum Data Set elements. The sample included 597 elders with dementia among a total of 7772 older patients who were discharged between 1996 and 1998. RESULTS: The most common comorbidity was hypertension (n = 123, 21%), followed by cardiac dysrhythmias (n = 80, 13%). The most frequent nursing diagnoses were altered health maintenance (n = 419, 84%), knowledge deficit (n = 357, 71%), potential for injury (n = 242, 48%), potential for infection (n = 230, 46%), pain (n = 184, 37%), impaired physical mobility (n = 169, 34%), and altered thought process (n = 144, 29%). The most frequent interventions were discharge planning (n = 340, 68%), surveillance safety (n = 195, 39%), fall prevention (n = 175, 35%), teaching: disease process (n = 166, 33%), learning facilitation (n = 148, 30%), and infection protection (n = 147, 29%). CONCLUSIONS: The results provide a description of nursing diagnoses and interventions for elders with dementia in an acute care setting using the Nursing Minimum Data Set framework. They identify the need to develop staff education programmes for individualized care of older patients with dementia. In addition, they support the need for continued work on linkage of the nursing care elements of the Nursing Minimum Data Set, including nursing diagnoses, nursing interventions, and nursing-sensitive outcomes.

Aged↗
PubMed2004

Modeling, the essential step to consolidate and integrate a national NMDS.

How to develop a nursing minimum data set (NMDS) as a part of a nursing information system, which is itself a part of a health information system, this is the challenge presented in this paper and realized in Switzerland through the NURSING data project. The development of the CH-NMDS is not just a selection of data but a real conceptual work consisting in making the link between the information system and the terminology system and this for any level of aggregation. The building of a conceptual coherence between the collection of the data, their storage and their utilization for decision making was made by using the object modeling language, not for programming aims but statistical purposes. For example, the nursing phenomena and the nursing interventions are considered as classification concepts as well as sets of data.

Data Collection↗
PubMedDate not supplied

Standardized nursing language in the systematized nomenclature of medicine clinical terms: A cross-mapping validation method.

Many standardized healthcare languages have been mapped to the Systematized Nomenclature of Medicine Clinical Terms known as SNOMED CT, which was developed by the College of American Pathologists. This study describes a methodology for detecting misassigned concepts from source systems to SNOMED CT and presents the results of applying this methodology to a subset of concepts from two standardized nursing languages, the Nursing Interventions Classification and the Nursing Outcomes Classification. The methodology is based on comparing the knowledge representations of a set of nursing concepts between source systems (nursing languages) and SNOMED CT. If any nursing concept differs in knowledge representation in the target system compared with the source system, editorial misassignment of the concept was declared and recommendations for target system developers were made. In a total of 75 nursing concepts used to test this method, five misassigned concepts(6.6%) were found in SNOMED CT. This method can be used to validate other healthcare languages.

Clinical Medicine↗