Predictive validity of the Nursing Severity Index in patients with musculoskeletal disease. Nurses of University Hospitals of Cleveland

Prevalence of nursing diagnoses as a measure of nursing complexity in a hospital setting

AIMS

To describe the prevalence of nursing diagnoses on admission among inpatient units and medical diagnoses and to analyse the relationship of nursing diagnoses to patient characteristics and hospital outcomes.

BACKGROUND

Nursing diagnoses classify patients according to nursing dependency and can be a measure of nursing complexity. Knowledge regarding the prevalence of nursing diagnoses on admission and their relationship with hospital outcomes is lacking.

DESIGN

Prospective observational study.

METHODS

Data were collected for 6 months in 2014 in four inpatient units of an Italian hospital using a nursing information system and the hospital discharge register. Nursing diagnoses with prevalence higher or equal to 20% were considered as 'high frequency.' Nursing diagnoses with statistically significant relationships with either higher mortality or length of stay were considered as 'high risk.' The high-frequency/high-risk category of nursing diagnoses was identified.

RESULTS

The sample included 2283 patients. A mean of 4·5 nursing diagnoses per patient was identified; this number showed a statistically significant difference among inpatient units and medical diagnoses. Six nursing diagnoses were classified as high frequency/high risk. Nursing diagnoses were not correlated with patient gender and age. A statistically significant perfect linear association (Spearman's correlation coefficient) was observed between the number of nursing diagnoses and both the length of stay and the mortality rate.

CONCLUSION

Nursing complexity, as described by nursing diagnoses, was shown to be associated with length of stay and mortality. These results should be confirmed after considering other variables through multivariate analyses. The concept of high-frequency/high-risk nursing diagnoses should be expanded in further studies.

Impact of nursing diagnoses on patient and organisational outcomes: a systematic literature review

AIMS AND OBJECTIVES

To investigate the impact of nursing diagnoses on patient and organisational outcomes in any field of health care where nurses are involved.

BACKGROUND

In healthcare systems, descriptions of patient complexity and outcomes and payment criteria are primarily based on medical diagnoses and procedures. Other aspects of patient care are rarely considered. Nursing diagnoses are believed to be related to healthcare outcomes, but comprehensive evidence for this association is missing.

DESIGN

Systematic literature review.

METHODS

The search was conducted in PubMed, CINAHL and Scopus databases without year or language limitations. The studies were categorised according to their methodological quality (low, good or high) and classified based on their levels of evidence on a scale of 1 (strongest evidence) to 5 (weakest evidence).

RESULTS

Seventeen of 3426 potentially relevant studies met the eligibility criteria. Eleven studies were classified as low, five as good and one as high quality. The levels of evidence were rated as 2 for one study, three for two studies, four for nine studies and five for five studies. Nursing diagnoses were found to predict patient (quality of life, mortality) and organisational (length of hospital stay, hospital charges, amount of nursing care, discharge dispositions) outcomes. Patient care plans based on nursing diagnoses improved sleep quality, quality of life and glycaemic control. When added to information from disease-based classification systems (e.g. diagnosis-related groups), nursing diagnoses improved the predictions of the above outcomes.

CONCLUSIONS

Nursing diagnoses have a great potential to predict patient and organisational outcomes. High-quality research is required to better investigate the existence and strength of these relationships.

RELEVANCE TO CLINICAL PRACTICE

The systematic use of nursing diagnoses in clinical practice, as well as the sharing of high-quality nursing data in large databases, may provide a considerable boost to the contribution of nursing to healthcare outcomes.

The basis for improving and reforming long-term care. Part 4: identifying meaningful improvement approaches (segment 2)

While many aspects of nursing home care have improved over time, numerous issues persist. Presently, a potpourri of approaches and a push to "fix" the problem have overshadowed efforts to correctly define the problems and identify their diverse causes. This fourth and final article in the series (divided between last month's issue and this one) recommends strategies to make sense of improvement and reform efforts. This month's concluding segment covers additional proposed approaches. Despite the challenges of the current environment, all of the proposed strategies could potentially be applied with little or no delay. Despite having brought vast increases in knowledge, the research effort may be losing its traction as a formidable force for meaningful change. It is necessary to rethink the questions being asked and the scope of answers being sought. A shift to overcoming implementation challenges is needed. In addition, it is essential to address issues of jurisdiction (the apparent "ownership" of assessment and decision making over patient problems or body parts) and reductionism (the excessive management of these issues and problems without proper context) that result in fragmented and problematic care. Issues of knowledge and skill also need to be addressed, with greater emphasis on key generic and technical competencies of staff and practitioners, in addition to factual knowledge. There is a need to rethink the approach to measuring performance and trying to improve quality of care and services. There are significant limits to trying to use quality measures to improve outcomes and performance. Ultimately, vast improvement is needed in applying care principles and practices, independent of regulatory sources. Reimbursement needs to be revamped so that it helps promote care that is consistent with human biology and other key concepts. Finally, improving long-term care will require a coordinated societal effort. All social institutions and health care settings need to address their own shortcomings and contribute constructively in order to improve and reform nursing homes and health care generally. It is not helpful to scapegoat nursing homes for what are far more universal problems of care, practice, and performance.

Nursing Diagnoses, Diagnosis-Related Group, and Hospital Outcomes

BACKGROUND AND OBJECTIVE

There are no nursing centric data in the hospital discharge abstract. This study investigates whether adding nursing data in the form of nursing diagnoses to medical diagnostic data in the discharge abstract can improve overall explanation of variance in commonly studied hospital outcomes.

METHOD

A retrospective analyses of 123,241 sequential patient admissions to a university hospital in a Midwestern city was performed. Two data sets were combined: (1) a daily collection of patient assessments by nurses using nursing diagnosis terminology (NDX); and (2) the summary discharge information from the hospital discharge abstract including diagnosis-related group (DRG) and all payer refined DRG (APR-DRG). Each of 61 daily NDX observations were collapsed as frequency of occurrence for the hospital stay and inserted into the discharge abstract. NDX was then compared to both DRG and APR-DRG across 5 hospital outcome variables using multivariate regression or logistic regression.

RESULTS AND CONCLUSIONS

In all statistical models, DRG, APR-DRG, and NDX were significantly associated with the 5 hospital outcome variables (P <.0001). When NDX was added to models containing either the DRG or the APR-DRG, explanatory power (R2) and model discrimination (c statistic) improved by 30% to 146% across the outcome variables of hospital length of stay, ICU length of stay, total charges, probably of death, and discharge to a nursing home (P <.0001). The findings support the contention that nursing care is an independent predictor of patient hospital outcomes. These nursing data are not redundant with the medical diagnosis, in particular, the DRG. The findings support the argument for including nursing care data in the hospital discharge abstract. Further study is needed to clarify which nursing data are the best fit for the current hospital discharge abstract data collection scheme.

Contexts for the Study of the Economic Influence of Nursing Diagnoses on Patient Outcomes. Contextos para o estudo da influencia economica dos diagnosticos de enfermagem sobre os resultados do paciente. Contextos para el estudio de la influencia economica de los diagnosticos de enfermeria en los resultados de pacientes

PURPOSE

To examine the historical, legal/ organizational, informatics, clinical, economic, and policy contexts underlying economic consequences of nursing diagnoses on patient outcomes.

DATA SOURCES

Published literature, online material.

DATA SYNTHESIS

Nursing diagnoses influence diagnostic-specific patient outcomes and other outcome variables such as length of hospital stay, morbidity, and mortality.

CONCLUSIONS

Examination of the economic ramifications of nursing diagnosis on patient outcomes can be facilitated using standardized language and databases containing nursing-sensitive measures.

The future of subacute care

Subacute care is a program of care for individuals with recent or current illness or injury. Currently, the services received by patients in subacute care vary considerably among sites, partly because these sites may specialize in certain treatments and partly because providers often define subacute care according to their own areas of expertise. Certain approaches to patient care, however, are universal regardless of diagnosis, and care that is given should be based on essential geriatric principles. In setting a standard for care in the subacute care setting, certain parameters must be clarified, including (1) defining subacute care, including what it is and what it is not; (2) selecting the right patient to receive subacute care; (3) making sure that care is centered on patients rather than sites or providers; and (4) ensuring that care is reimbursed adequately and appropriately. These issues are addressed, and guidelines on how to accomplish the goal of standardizing subacute care are provided.

Using admission characteristics to predict return to the community from a post-acute geriatric evaluation and management unit

OBJECTIVE

To compare the Cumulative Illness Rating Scale (CIRS) and the Nursing Severity Index (NSI) as independent predictors of discharge outcome from a post-acute GEM unit and to define a multivariate model for predicting the same outcome.

DESIGN

Retrospective chart review for the entire sample. The sample was split into two cohorts, a derivation cohort (n = 298) and a validation cohort (n = 154).

SETTING

A 20-bed, post-acute GEM unit in a nonproprietary skilled nursing facility.

PARTICIPANTS

All 452 patients admitted to the GEM from the unit's inception in December 1994 until January 1998.

MEASUREMENT

Demographics, CIRS, NSI, functional status, and social support variables were measured using data available on admission to the GEM unit. The discharge outcome was dichotomized as return to the community or not.

RESULTS

A total of 99.7% of the individuals in the derivation cohort were living in the community before the index hospitalization; 75.8% of patients in the derivation cohort returned to the community. The NSI, individual "severe" items from the CIRS, age, and social support were in the final logistic regression model fitted to the derivation cohort. A total of 87.7% of the observed discharge outcomes were predicted when the model was applied to the validation cohort and the calculated probability of return to the community.

CONCLUSIONS

Variables for severity of illness, function, social support, and age combined into a logistic regression equation that predicted more than 80% of the dichotomized discharge outcome in the derivation cohort. The proportion of discharge outcomes that were predicted with the validation cohort remained high at 87.7%. The NSI and CIRS were each important to a model that is anticipated to refine the selection of geriatric patients for post-acute services.

Adverse outcomes and variations in organization of care delivery

OBJECTIVES

This article evaluates the state of the science with respect to morbidity, mortality, and adverse effects as outcomes indicative of variations in organizational variables in care delivery systems.

METHODS

Eighty-one research papers research examining relations among organizational structures or processes and mortality/adverse effects were reviewed, assembled from electronic and manual searches of the biomedical and health services research literature.

RESULTS

Most research relating mortality and other adverse outcomes to organizational variables has been conducted in acute care hospitals since 1990, with these outcome indicators linked more frequently to organizational structures than to organizational or clinical processes. There is support in some studies, but not in others, that nursing surveillance, quality of working environment, and quality of interaction with other professionals distinguish hospitals with lower mortality and complications from those with higher rates of these adverse effects. Increasing sophistication of risk adjustment methods suggests that variations in mortality and complications are influenced by patient variables more than by organizational variables. Adverse events may be a more sensitive marker of differences in organizational quality in acute care hospitals and long-term care.

CONCLUSIONS

Taken together, the acute care studies are not conclusive regarding the extent to which the organizational features of care delivery systems positively influence such bottom-line outcomes as mortality. As severity-adjustment methods become more refined for hospital patients, many of the small differences currently seen in mortality and complications may disappear. However, given that adverse events appear more closely related to organizational factors than in mortality, researchers need to refine and better define such events that are logically related to the coordinative organizational processes among caregivers. Finally, researchers need to go much beyond mortality, morbidity, and adverse events in evaluating the linkage between the organization of care and outcomes.