Predictive factors of a prolonged length of stay in a community Nursing-Led unit: A retrospective cohort study.

AIMS AND OBJECTIVES: To describe the care provided to patients admitted into a community Nursing-Led inpatient unit and to identify factors predicting a length of stay exceeding an established threshold. BACKGROUND: Few studies have been conducted to describe the care provided in a Nursing-Led unit. No studies have investigated factors affecting length of stay in these services. DESIGN: Retrospective cohort study. METHODS: Consecutive patients admitted to a community Nursing-Led unit between 2009-2015 were enrolled. Sociodemographic, medical and nursing care (diagnoses and activities) variables were collected from electronic health records. Descriptive analysis and a backward stepwise logistic regression model were applied. The study followed the STROBE guidelines. RESULTS: The study enrolled 904 patients (mean age: 77.7 years). The most frequent nursing diagnoses were bathing self-care deficit and impaired physical mobility. The nursing activities most provided were enteral medication administration and vital signs measurement. Approximately 37% of the patients had a length of stay longer than the established threshold. Nine covariates, including being discharged to home, having an impaired memory nursing diagnosis or being treated for advanced wound care, were found to be independent predictors of prolonged length of stay. Variables related to medical conditions did not affect the length-of-stay threshold. CONCLUSIONS: The length of stay in the community Nursing-Led unit was mainly predicted by conditions related to sociodemographic factors, nursing complexity and functional status. This result confirms that the medical and nursing needs of a community Nursing-Led unit population substantively differ from those of hospitalised acute patients. RELEVANCE TO CLINICAL PRACTICE: The nursing complexity and related nursing care to be provided may be adopted as a criterion to establish the appropriate length of stay in the community Nursing-Led unit for each individual patient.

Exploring Inpatient Medication Patterns: A Big Data and Multilevel Approach.

PURPOSE: Exploratory study to examine inpatient medication administration patterns. METHODS: Data from multiple sources were utilized for this study. The outcome was time difference between medication schedule and administration. A 3-level hierarchical linear regression approach, both unadjusted and adjusted, was considered for this study where medication administration events are nested within patients nested within nurses or units. Intraclass correlation coefficients (ICCs) were calculated and compared. RESULTS: On average, medications were delayed by 12 (SD, 48.8) minutes. From the full model, patient ICCs decreased when "unit" replaced "nurse" as the 3rd level (0.541 vs 0.444). Patients who spoke Spanish had a significant 2.3- to 4.2-minute delay in medication administration. Certified nurses significantly give medications earlier compared with noncertified nurses by 1.6 minutes. DISCUSSION: Optimal medication administration is a multifactorial concern with nurses playing a role. Nursing leaders should also consider patient demographics and unit conditions, such as culture, for medication administration optimization.

Enhancing the performance of predictive models for Hospital mortality by adding nursing data.

BACKGROUND: Mortality is the most considered outcome for assessing the quality of hospital care. However, hospital mortality depends on diverse patient characteristics; thus, complete risk stratification is crucial to correctly estimate a patient's prognosis. Electronic health records include standard medical data; however, standard nursing data, such as nursing diagnoses (which were considered essential for a complete picture of the patient condition) are seldom included. OBJECTIVE: To explore the independent predictive power of nursing diagnoses on patient hospital mortality and to investigate whether the inclusion of this variable in addition to medical diagnostic data can enhance the performance of risk adjustment tools. METHODS: Prospective observational study in one Italian university hospital. Data were collected for six months from a clinical nursing information system and the hospital discharge register. The number of nursing diagnoses identified by nurses within 24 h after admission was used to express the nursing dependency index (NDI). Eight logistic regression models were tested to predict patient mortality, by adding to a first basic model considering patient's age, sex, and modality of hospital admission, the level of comorbidity (CCI), and the nursing and medical condition as expressed by the NDI and the All Patient Refined-Diagnosis Related Group weight (APR-DRGw), respectively. RESULTS: Overall, 2301 patients were included. The addition of the NDI to the model increased the explained variance by 20%. The explained variance increased by 56% when the APR-DRGw, CCI, and NDI were included. Thus, the latter model was nearly highly accurate (c = 0.89, 95% confidence interval: 0.87-0.92). CONCLUSION: Nursing diagnoses have an independent power in predicting hospital mortality. The explained variance in the predictive models improved when nursing data were included in addition to medical data. These findings strengthen the need to include standardized nursing data in electronic health records.

Nursing Value User Stories: A Value Measurement Method for Linking Nurse Contribution to Patient Outcomes.

The use of nursing big data sets for value-based measurement is novel. Nursing value measurement depends on the availability of essential data attributes in the electronic health record related to nursing care delivered (what happened, when, and the result seen). Key in measuring value is a standardized structure and format of these attributes for enabling uniform consistent analysis, along with data sets that are sharable and comparable across individuals and groups, time, organization, and practice focus. The foundation of such sharable and comparable data sets would represent at a minimum individual essential nurse care actions and the resulting patient outcome(s). While nurses generate an extraordinary amount of health-related data, healthcare information systems are not designed to collect structured data that reflect the unique attributes of nursing care or support nursing analytic activities that would measure value. More important, the multidimensional features of the nursing process are difficult to untangle and differentiate from other healthcare workers and nonnursing care activities. The complexity of nursing knowledge work has limited the development of nursing data science methods like value measurement and discouraged value versus cost discussions. This article sets out to describe nursing value measurement and an approach that nurse scientists are maximizing through methods adapted from agile project management, including user stories, and business analysis processes to recognize nurses as primary contributors to patient outcomes and value generation. Nursing Value User Story methods deconstruct complex nursing scenarios into user stories that capture nursing actions as standardized data that can be mapped to a common nursing data model. Methods described here are being used in pilot research at Los Angeles Children's Hospital, and results will be available in 2019.

Nursing Diagnoses, Interventions, and Activities as Described by a Nursing Minimum Data Set: A Prospective Study in an Oncology Hospital Setting.

BACKGROUND: Oncological diseases affect the biopsychosocial aspects of a person's health, resulting in the need for complex multidisciplinary care. The quality and outcomes of healthcare cannot be adequately assessed without considering the contribution of nursing care, whose essential elements such as the nursing diagnoses (NDs), nursing interventions (NIs), and nursing activities (NAs) can be recorded in the Nursing Minimum Data Set (NMDS). There has been little research using the NMDS in oncology setting. OBJECTIVE: The aim of this study was to describe the prevalence and distribution of NDs, NIs, and NAs and their relationship across patient age and medical diagnoses. METHODS: This was a prospective observational study. Data were collected between July and December 2014 through an NMDS and the hospital discharge register in an Italian hospital oncology unit. RESULTS: On average, for each of 435 enrolled patients, 5.7 NDs were identified on admission; the most frequent ND was risk for infection. During the hospital stay, 16.2 NIs per patient were planned, from which 25.2 NAs per day per patient were delivered. Only a third of NAs were based on a medical order, being the highest percentage delivered on nursing prescriptions. The number of NDs, NIs, and NAs was not related to patient age, but differed significantly among medical diagnoses. CONCLUSIONS: An NMDS can depict patient needs and nursing care delivered in oncology patients. Such data can effectively describe nursing contribution to patient care. IMPLICATIONS FOR PRACTICE: The use of an NMDS raises the visibility of nursing care in the clinical records. Such data enable comparison and benchmarking with other healthcare professions and international data.

Nursing Diagnoses as Predictors of Hospital Length of Stay: A Prospective Observational Study.

PURPOSE: To investigate whether the number of nursing diagnoses on hospital admission is an independent predictor of the hospital length of stay. DESIGN: A prospective observational study was carried out. A sample of 2,190 patients consecutively admitted (from July to December 2014) in four inpatient units (two medical, two surgical) of a 1,547-bed university hospital were enrolled for the study. METHODS: Data were collected from a clinical nursing information system and the hospital discharge register. Two regression analyses were performed to investigate if the number of nursing diagnoses on hospital admission was an independent predictor of length of stay and length of stay deviation after controlling for patients' sociodemographic characteristics (age, gender), clinical variables (disease groupers, disease severity morbidity indexes), and organizational hospital variables (admitting inpatient unit, modality of admission). FINDINGS: The number of nursing diagnoses was shown to be an independent predictor of both the length of stay (ß = .15; p < .001) and the length of stay deviation (ß = .19; p < .001). CONCLUSIONS: The number of nursing diagnoses is a strong independent predictor of an effective hospital length of stay and of a length of stay longer than expected. CLINICAL RELEVANCE: The systematic inclusion of standard nursing care data in electronic health records can improve the predictive ability on hospital outcomes and describe the patient complexity more comprehensively, improving hospital management efficiency.

Evidence based practice readiness: A concept analysis.

AIM: To analyse and define the concept "evidence based practice readiness" in nurses. BACKGROUND: Evidence based practice readiness is a term commonly used in health literature, but without a clear understanding of what readiness means. Concept analysis is needed to define the meaning of evidence based practice readiness. METHOD: A concept analysis was conducted using Walker and Avant's method to clarify the defining attributes of evidence based practice readiness as well as antecedents and consequences. A Boolean search of PubMed and Cumulative Index for Nursing and Allied Health Literature was conducted and limited to those published after the year 2000. Eleven articles met the inclusion criteria for this analysis. RESULTS: Evidence based practice readiness incorporates personal and organisational readiness. Antecedents include the ability to recognize the need for evidence based practice, ability to access and interpret evidence based practice, and a supportive environment. CONCLUSION: The concept analysis demonstrates the complexity of the concept and its implications for nursing practice. The four pillars of evidence based practice readiness: nursing, training, equipping and leadership support are necessary to achieve evidence based practice readiness. IMPLICATIONS FOR NURSING MANAGEMENT: Nurse managers are in the position to address all elements of evidence based practice readiness. Creating an environment that fosters evidence based practice can improve patient outcomes, decreased health care cost, increase nurses' job satisfaction and decrease nursing turnover.

Using Time-Referenced Data to Assess Medication Administration Performance and Quality.

OBJECTIVE: This study tests the feasibility of using a large (big) clinical data set to test the ability to extract time-referenced data related to medication administration to identify late doses and as-needed (PRN) administration patterns by RNs in an inpatient setting. METHODS: The study is a secondary analysis of a set of data using bar-code medication administration time stamps (n = 3043812) for 50883 patients admitted to a single, urban, 525-bed hospital in 11 inpatient units by 714 nurses between April 1, 2013, and March 31, 2015. RESULTS: The large majority of scheduled medications (43.3%) were administered between 9 to 10 AM and 9 to 10 PM accounting for the most amount of delayed doses. On average, patients received 8.9 medications per day, and nurses administered 19.7 medications per shift. The average full-time nurse administered 3414 medications per year. CONCLUSIONS: The findings support use of time-referenced data to identify clinical processes and performance in administering scheduled and PRN medications.

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.

Using a Data Quality Framework to Clean Data Extracted from the Electronic Health Record: A Case Study.

OBJECTIVES: We examine the following: (1) the appropriateness of using a data quality (DQ) framework developed for relational databases as a data-cleaning tool for a data set extracted from two EPIC databases, and (2) the differences in statistical parameter estimates on a data set cleaned with the DQ framework and data set not cleaned with the DQ framework. BACKGROUND: The use of data contained within electronic health records (EHRs) has the potential to open doors for a new wave of innovative research. Without adequate preparation of such large data sets for analysis, the results might be erroneous, which might affect clinical decision-making or the results of Comparative Effectives Research studies. METHODS: Two emergency department (ED) data sets extracted from EPIC databases (adult ED and children ED) were used as examples for examining the five concepts of DQ based on a DQ assessment framework designed for EHR databases. The first data set contained 70,061 visits; and the second data set contained 2,815,550 visits. SPSS Syntax examples as well as step-by-step instructions of how to apply the five key DQ concepts these EHR database extracts are provided. CONCLUSIONS: SPSS Syntax to address each of the DQ concepts proposed by Kahn et al. (2012)1 was developed. The data set cleaned using Kahn's framework yielded more accurate results than the data set cleaned without this framework. Future plans involve creating functions in R language for cleaning data extracted from the EHR as well as an R package that combines DQ checks with missing data analysis functions.

Measuring Nursing Value from the Electronic Health Record.

We report the findings of a big data nursing value expert group made up of 14 members of the nursing informatics, leadership, academic and research communities within the United States tasked with 1. Defining nursing value, 2. Developing a common data model and metrics for nursing care value, and 3. Developing nursing business intelligence tools using the nursing value data set. This work is a component of the Big Data and Nursing Knowledge Development conference series sponsored by the University Of Minnesota School Of Nursing. The panel met by conference calls for fourteen 1.5 hour sessions for a total of 21 total hours of interaction from August 2014 through May 2015. Primary deliverables from the bit data expert group were: development and publication of definitions and metrics for nursing value; construction of a common data model to extract key data from electronic health records; and measures of nursing costs and finance to provide a basis for developing nursing business intelligence and analysis systems.

Rationale for Quantification in Nursing Services Using the International Council of Nurses Nurse-Patient Summary.

This review provides evidence that new data from nurses meets criteria that explains variation in hospital charges, length of hospital stay and end results of hospital care compared with ICD data; that nurses' data can be used to evaluate assignments of nurses to patients; that new data properly distinguishes patients' human needs within ICD categories. These new data are derived from the professional literature indexed and synthesized by Henderson. It is proposed to adopt the ICN-NPSum to standardize quantification in nursing services.

Measuring Nursing Care Value.

The value of nursing care as well as the contribution of individual nurses to clinical outcomes has been difficult to measure and evaluate. Existing health care financial models hide the contribution of nurses; therefore, the link between the cost and quality o nursing care is unknown. New data and methods are needed to articulate the added value of nurses to patient care. The final results and recommendations of an expert workgroup tasked with defining and measuring nursing care value, including a data model to allow extraction of key information from electronic health records to measure nursing care value, are described. A set of new analytic metrics are proposed.

Nurses and the Ethics of Big Data.

As we move toward a value-based health care system and payment models based on individual performance of providers, nurses are faced with a dilemma. Should we as a profession actively pursue the development of individual nurse performance metrics, analysis, benchmarks, and practice standards, similar to those being implemented for physicians? Or should we wait until these metrics are imposed by payers and policymakers with little or no input from nurses?

Diagnostic Imaging Services in Magnet and Non-Magnet Hospitals: Trends in Utilization and Costs.

PURPOSE: The purpose of this study was to better understand trends in utilization and costs of diagnostic imaging services at Magnet hospitals (MHs) and non-Magnet hospitals (NMHs). METHODS: A data set was created by merging hospital-level data from the American Hospital Association's annual survey and Medicare cost reports, individual-level inpatient data from the Healthcare Cost and Utilization Project, and Magnet recognition status data from the American Nurses Credentialing Center. A descriptive analysis was conducted to evaluate the trends in utilization and costs of CT, MRI, and ultrasound procedures among MHs and NMHs in urban locations between 2000 and 2006 from the following ten states: Arizona, California, Colorado, Florida, Iowa, Maryland, North Carolina, New Jersey, New York, and Washington. RESULTS: When matched by bed size, severity of illness (case mix index), and clinical technological sophistication (Saidin index) quantiles, MHs in higher quantiles indicated higher rates of utilization of imaging services for MRI, CT, and ultrasound in comparison with NMHs in the same quantiles. However, average costs of MRI, CT, and ultrasounds were lower at MHs in comparison with NMHs in the same quantiles. CONCLUSIONS: Overall, MHs that are larger in size (number of beds), serve more severely ill patients (case mix index), and are more technologically sophisticated (Saidin index) show higher utilization of diagnostic imaging services, although costs per procedure at MHs are lower in comparison with similar NMHs, indicating possible cost efficiency at MHs. Further research is necessary to understand the relationship between the utilization of diagnostic imaging services among MHs and its impact on patient outcomes.

Hospital Nursing Workforce Costs, Wages, Occupational Mix,and Resource Utilization.

BACKGROUND: The objective of the study was to better understand how hospitals use different types of RNs, LPNs, and nurse aides in proprietary (for-profit), nonprofit, and government-owned hospitals and to estimate the wages, cost, and intensity of nursing care using a national data set. METHOD: This is a cross-sectional observational study of 3,129 acute care hospitals in all 50 states and District of Columbia using data from the 2008 Occupational Mix Survey administered by the Centers for Medicare &Medicaid Services (CMS). Nursing skill mix, hours, and labor costs were combined with other CMS hospital descriptive data, including type of hospital ownership, urban or rural location, hospital beds, and case-mix index. RESULTS: RN labor costs make up 25.5% of all hospital expenditures annually, and all nursing labor costs represent 30.1%, which is nearly a quarter trillion dollars ($216.7 billion) per year for inpatient nursing care. On average, proprietary hospitals employ 1.3 RNs per bed and 1.9 nursing personnel per bed in urban hospitals compared with 1.7 RNs per bed and 2.3 nursing personnel per bed for nonprofit and government-owned hospitals (P G .05). States with higher ratios of RN compared with LPN licenses used fewer LPNs in the inpatient setting. CONCLUSION: The findings from this study can be helpful in comparing nursing care across different types of hospitals, ownership, and geographic locations and used as a benchmark for future nursing workforce needs and costs.