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.

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.

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.

Risk-Adjusted Staffing to Improve Patient Value.

The presence of hospital-acquired conditions, infections, or other adverse events are a reflection of inadequate patient safety and can have short and long-term impacts of quality of life for patients as well as financial implications for the hospital. Using unit-level information to develop a tool, the Patient Risk Assessment Profile, nurses on an inpatient surgical unit proactively assessed patient risk to guide staffing decisions and nurse-patient assignment with the goal to improve patient value, reduce adverse events, and avoid unnecessary hospital costs. Findings showed decreased adverse event rates for patient falls, catheter-acquired urinary tract infection, central line-acquired blood stream infection, and pressure ulcer prevalence after the intervention was implemented. In addition, end-of-shift over-time and patient cost per case decreased as well yielding an operational impact in hospital financial performance.

Nursing Care Value-Based Financial Models.

Nursing care makes up one of the largest expenditures in the health care system, yet patient-level nursing intensity and costs are essentially unknown. Prior efforts to define nursing care value have been stymied by a lack of available data; however, emerging information from electronic health records provide an opportunity to measure nursing care in ways that have not been possible. New metrics using these data will allow improved measurement of cost, quality, and intensity at the level of each nurse and patient across many different settings which can be used to inform operational and clinical decision making. In this article, the initial results and recommendations of an expert panel tasked with defining and measuring nursing care value as part of a larger effort to address evolving issues related to big data and nursing knowledge development are described.

Effects of P6 stimulation on postoperative nausea and vomiting in laparoscopic cholecystectomy patients.

PURPOSE: Postoperative nausea and vomiting (PONV) remains one of the most common postsurgical complications after anesthesia and surgery. Pericardium 6 (P6) stimulation is believed to prevent PONV and is a potential adjunctive treatment with pharmacologic agents. The purpose of this study was to compare the effects of P6 stimulation on PONV occurrence to a control group not receiving the P6 stimulation in sequential female patients undergoing laparoscopic cholecystecomy at a community hospital in central Florida between November 2010 and March 2013. DESIGN: This study is a double-blinded randomized controlled trial. METHODS: PONV was measured on admission to the postanesthesia care unit (PACU), at 30 and 60 minutes, at discharge from the PACU to home and at two points at home up to 6 hours and between 6 and 24 hours. FINDINGS: Of the 56 total patients, those in the P6 group (n = 26) had statistically significant lower incidence of PONV, 0%, vs 14.3% in the control group (n = 27; P < .05) on admission to the PACU, but at all other time points, there was no significant difference in PONV. Thirty-one percent of the patients in the P6 group had PONV in PACU or at home compared with 51.9% in the control group. CONCLUSIONS: The results of the study demonstrate that the use of P6 stimulation in the perioperative arena is clinically meaningful; however, more research is needed with a larger sample size.

Is there a business case for magnet hospitals? Estimates of the cost and revenue implications of becoming a magnet.

BACKGROUND: Although Magnet hospitals (MHs) are known for their better nursing care environments, little is known about whether MHs achieve this at a higher (lower) cost of health care or whether a superior nursing environment yields higher net patient revenue versus non-MHs over an extended period of time. OBJECTIVE: To examine how achieving Magnet status is related to subsequent inpatient costs and revenues controlling for other hospital characteristics. DATA AND METHODS: Data from the American Hospital Association Annual Survey, Hospital Cost Reporting Information System reports collected by Centers for Medicare & Medicaid Services, and Magnet status of hospitals from American Nurses Credentialing Center from 1998 to 2006 were combined and used for the analysis. Descriptive statistics, propensity score matching, fixed-effect, and instrumental variable methods were used to analyze the data. RESULTS: Regression analyses revealed that MH status is positively and significantly associated with both inpatient costs and net inpatient revenues for both urban hospitals and all hospitals. MH status was associated with an increase of 2.46% in the inpatient costs and 3.89% in net inpatient revenue for all hospitals, and 2.1% and 3.2% for urban hospitals. CONCLUSIONS: Although it is costly for hospitals to attain Magnet status, the cost of becoming a MH may be offset by higher net inpatient income. On average, MHs receive an adjusted net increase in inpatient income of $104.22-$127.05 per discharge after becoming a Magnet which translates to an additional $1,229,770-$1,263,926 in income per year.

Adoption of national quality forum safe practices by Magnet® hospitals.

BACKGROUND: Magnet® hospitals (MHs) are known for their high retention rates of nurses and positive work environment, yet little is known about whether MHs also have higher levels of safe practice adoption rates compared with non-Magnet hospitals (NMHs). METHODS: In this study, we investigate adoption of National Quality Forum (NQF) Safe Practices in 34 regions during 2004 to 2006 that were part of the Leapfrog Group initiative to improve quality of hospital care. We conducted a secondary data analysis by combining multiple data sets from the American Hospital Association Annual Survey,Healthcare Cost Reports Information System, and Leapfrog Group Annual Hospital Survey. A composite safe practice score (CSPS) was constructed from the Leapfrog annual survey and ranged from 0 (no adoption) to 1,000 (complete adoption) of the 30 NQF Safe Practices. A descriptive analysis and a regression with Heckman correction to control for selection bias were used to determine the effect of Magnet status and other hospital and market characteristics on differences in CSPS over the 3-year period. RESULTS: There were 140 MHs and 1,320 NMHs reporting data for the CSPS. In 2004, MHs had a mean CSPS of 865 versus 774 for NMHs (P < .001). By 2006, NMHs improved their CSPS from 774 to 872 (98 points), whereas MHs improved their CSPS from 865 to 925 (60 points, P < .001). Regression analysis showed a positive and significant effect of Magnet status of hospitals on the adoption rates of NQF Safe Practices as measured by the CSPS. Our results also indicated that smaller hospitals (in bed size), hospitals with larger share of Medicare patients, higher nurse intensity levels (mean hours of nursing care per day), and higher levels of competition among hospitals in Leapfrog rollout regions were associated with higher CSPS. CONCLUSION: Magnet hospitals in the urban areas of 34 Leapfrog rollout regions were more likely to have higher adoption rates of NQF Safe Practices in comparison to NMHs in the same demographic areas during the time frame of the study, but other hospitals nearly closed the gap by 2006.

Adoption of national quality forum safe practices by Magnet® hospitals.

BACKGROUND: : Magnet hospitals (MHs) are known for their high retention rates of nurses and positive work environment, yet little is known about whether MHs also have higher levels of safe practice adoption rates compared with non-Magnet hospitals (NMHs). METHODS: : In this study, we investigate adoption of National Quality Forum (NQF) Safe Practices in 34 regions during 2004 to 2006 that were part of the Leapfrog Group initiative to improve quality of hospital care. We conducted a secondary data analysis by combining multiple data sets from the American Hospital Association Annual Survey, Healthcare Cost Reports Information System, and Leapfrog Group Annual Hospital Survey. A composite safe practice score (CSPS) was constructed from the Leapfrog annual survey and ranged from 0 (no adoption) to 1,000 (complete adoption) of the 30 NQF Safe Practices. A descriptive analysis and a regression with Heckman correction to control for selection bias were used to determine the effect of Magnet status and other hospital and market characteristics on differences in CSPS over the 3-year period. RESULTS: : There were 140 MHs and 1,320 NMHs reporting data for the CSPS. In 2004, MHs had a mean CSPS of 865 versus 774 for NMHs (P < .001). By 2006, NMHs improved their CSPS from 774 to 872 (98 points), whereas MHs improved their CSPS from 865 to 925 (60 points, P < .001). Regression analysis showed a positive and significant effect of Magnet status of hospitals on the adoption rates of NQF Safe Practices as measured by the CSPS. Our results also indicated that smaller hospitals (in bed size), hospitals with larger share of Medicare patients, higher nurse intensity levels (mean hours of nursing care per day), and higher levels of competition among hospitals in Leapfrog rollout regions were associated with higher CSPS. CONCLUSION: : Magnet hospitals in the urban areas of 34 Leapfrog rollout regions were more likely to have higher adoption rates of NQF Safe Practices in comparison to NMHs in the same demographic areas during the time frame of the study, but other hospitals nearly closed the gap by 2006.