A Systematic Review for Nurse-Patient Ratio and Patient Outcome

INTRODUCTION

Fatal problems which seemingly plagues many hospitals and other medical facilities at a national and global level is related to staffing issues. Inadequate staffing of nurses includes improper ratios of nurses to the level of patient acuity and improper mixture of skilled experienced nurse’s verses inexperienced younger but more educated nurses. For example, consider a 62 bed Med-Surge unit with staffing ratios of 1:6 on nights, 1:4 on days and four patient care technicians verses routine staffing ratios of 1:7 on nights and 1:6 on days with two patient care technicians. The Labor Management Institute and the National Data base for Nursing Quality Indicators guide for staffing suggest ratios of 1:4 or 1:5 on medical-surgical units, 1:3 or 1:4 on intermediate units and 1:2 in intensive care units (AORN, 2013).

Research demonstrates statistically how inadequate nursing staffed unit’s results in adverse events, such as: falls, medication errors, needle sticks and injuries to nursing staff (Loan, Patrician, & West, 2012). Realistic cases were presented to illustrate how staffing inadequacy is a potential and hazardous threat. The threat is towards patients and nursing staff alike. The evidence can attest to the fact that to improve or reduce these adverse events, hospitals must provide adequate staffing. They must utilize the right mix of expertise and experience to appropriate patient acuity when assigning care. The most challenging sick patients should be assigned to nurses more equipped to manage such acuity of illness (Loan, Patrician, & West, 2012).

A Systematic Review for Nurse-Patient Ratio and Patient Outcome PICOT Question

Population: Inappropriate nurse-patient staffing ratio and Hospital In-Patient safety.

Intervention: According to AORN (2013) Nurse Staffing Ratios, the best practice to ensure adequate nurse patient ratio is:

1) Conduct a failure mode effect analysis on nurse staffing to develop strategies to use when staffing levels are not adequate 2) Creating an internal resource pool for flexibility and census adjustment 3) Communicating all action plans to staff nurses and administrative stakeholders;” 4) Empowering staffing ratios annually with other facilities 5) Correlating staffing with patient outcomes, adverse events, and root causes 6) Evaluating patient satisfaction feedback.

: A daily follow-up should be done on all shifts to assess the nurse/patient-ratio and shift performance. Any adverse events such as: patient’s falls, neglect, unrecognize decline in patient condition leading to codes; nursing staff issues, missing breaks or lunch, missed patient orders overwhelm staff, or nursing staffing remaining after punch out time to chart should be addressed (Loan, Patrician, & West, 2012).

Outcome: Is the care of patient symptoms and treatment impacted by an inadequate staffing, working environment or nursing workload? Do patients receive inadequate care due to poor nurse–patient staffing ratios? Does nurse staffing ratios impact patient mortality and morbidity? Are staff members taking shortcuts thus causing injury to themselves and negatively impacting patient care? How does nurse–patient staffing ratios affect patient readmissions?

Outcomes should be measured from first patient contact at registration in the emergency room, the length of stay, and discharge. Daily monitoring helps to determine patient satisfaction. Areas such as staff interactions, treatment regime, ADLs and other concerns can be addressed. In addition, discharged follow-up surveys can be completed in efforts to direct future care measures, to measure the effectiveness of provided care and the total outcome (Loan, Patrician, & West, 2012).

METHODS

A systematic review of nurse-patient ratio and its impact on patient’s outcome was explored using Systematic Review, Meta-Analyses and Evidence Base Practices with Protocols and standards designed by the Joint Commission and Center for Medicare and Medicaid Services (AORN, 2013). Mixed-effects regression models with sample data from multiple states that incorporates the Agency for Healthcare Research and quality (AHRQ), plus the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) collected for 12 months on patient satisfaction. (Abrahamson, Hass, Morgan, Fulton, & Ramanujam, 2016). Also initiated was a four-point Likert scale-type question for measuring job satisfaction, and a nine-item emotional exhaustion subscale of Maslach for measuring burnout; scores above twenty-seven was indication for emotional exhaustion (Aitken, Sloane, & Stimpfel, 2012 ).

Search Strategy

Medline/PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Ovid Online, ProQuest (a dissertation database), Cochrane Library, and Virginia Henderson International Nursing Library were used to search the literature. Search terms includes Nurse patient-ratio or under-staffing, nursing staff and quality of care, patient safety and nursing staff. Review was limited to North America and in English. Most review material were within the last five years with the exception of two articles. Reports published prior to 2011 were excluded from the review.

Literature Selection

Relevant articles and data was collected from reports to the Department of Health and Human Services Centers for Medicare and Medicaid. Extracted data were formatted into tables. Other reports were included in the studies. The studies were inclusive of writer’s name, date, year publish, study design, sample sizes and description, location of study and instrumentation used for data collection with results, recommendation for future research, and Limitations. Cross-sectional design (Aitken, Sloane, Stimpfel, 2012) and longitudinal models were used (Rochefort, Buckeridge, & Abrahamowicz, 2015).

Quality Assessment

In 2010, The Center for Medicare & Medicaid (CMS) initiated a value based policy which links reimbursement to hospitals with quality patient care; incentives are awarded to facilities for given higher quality care. The quality of care are clinical processes of patient care whereas HCAPS indicates scores and rewards by quality of care. Patient quality of care was measured via the assessment of HCAPS legitimate vendor PRESS Ganey Associates with English and Spanish Language (Abrahamson et al., 2016). Four adverse quality measures were also taken because of their hypothesized relation to nurse staffing levels, patient mortality, morbidity and cost. The four are Hospital Acquired Pneumonia, Ventilated Acquired Pneumonia, and Deep Vein Thrombosis of lower and upper extremities (Rochefort et al., 2015).

Results

Study Characteristics, Samples, Settings

Samples included all adult medical, surgical and intensive care patient admitted to Mc Gill University Health Center during Januray1, 2010 and December 31, 2015. Patient were studied and assessed for adverse events during their inpatient stay and 7-days post discharge (Rochefort et al., 2015). In addition, 12 databases were queried from entrance to the hospital, including any procedure, incident reports and patrolled. Nurse staffing intensity was measured for each shift via payroll records (Rochefort et al., 2015). The data gleaned from sampling hospitals which were dispersed geographically (Abrahamson et al., 2016).

In this study a sample of 22,275 registered nurses were taken from multiple states of patient conditions and nursing care. Nurses were from 577 hospitals in California, New Jersey, Pennsylvania and Florida with an average of 10 nurses per hospital. The sample was limited to medical, surgical and intensive care. All other units were excluded (Aitken, Sloane, & Stimpfel, 2012).

Major Variables

Variables from the response by survey were measured at the at hospital and unit level. Major correlated variables in HCAPS and AHRQ were teamwork, adequate staffing, and positive patient experience (Abrahamson et al., 2016). Variables used are organization features and hospital structural characteristics which are related to outcome (Aitken, Sloane, & Stimpfel, 2012 ).

Discussion

One Study generated information which was evidence based and facilitated leaders, managers, managing care resources, identifying staffing patterns, and risk reduction of adverse events (Rochefort et al., 2015). Three crucial factors which influenced nursing experiences were teamwork, adequate staffing and the learning culture. Positive patient outcomes has a direct correlation between care rendered and the nurse’s approach. Nurses leaves an impression on the patient experience. When nurses are perceived as being in control, having adequate support and staffing then they can be the prime movers for safety and safely render adequate and cultural care (Abrahamson, et al., 2016). The longer the shift worked by nurses, the worst the patient outcome; the longer the hours worked by nurses, the more likely they are to leave the job (Aitken, Sloane, & Stimpfel, 2012). Staffing level is directly related to improved HCAPS levels; hospitals with better staffing has higher patient satisfaction in all areas (Martsolf, et al., 2016).

Limitations

In one study, limitation was the method for measuring adverse events, it hampered the ability to identify policies related to nursing staff and the reduction of adverse events (Rochefort et al., 2015). In this study the sample was not a national representation. There was the likelihood of error due to the use of averages in the unit as oppose to individual measurement, and the exclusion of nurse’s job satisfaction (Abrahamson, et al., 2016).

The cross-sectional study limited the study to make inferences about nurse working hours verses patient outcomes. The sample from four states represented only a quarter of the US population and did not represent every hospital (Aitken, Sloane, & Stimpfel, 2012). Although multiple states were included in the study, 80 percent of data came from the state of California. This was related to the hospitals’ level analysis and the fact that nursing staff level couldn’t be cemented to an individual study. Therefore, temporal changes in nursing staff and patient outcome couldn’t be established due to static models used in the study (Martsolf et al., 2016).

Future Research

Future studies can expand to the present by including a combined unit mix with the inclusion of hospital magnet status influences on patient experience and nurse perception of adequate workloads (Abrahamson et al., 2016).

Clinical Relevance

This is the first study which modeled the strategies of nurse staffing as it related to the risk of adverse events with dynamic time exposure. The study identifies nurse patient ratio with the greatest risk and enable mangers to minimize risk while optimizing resources (Rochefort et al., 2015). The study will aid nurse mangers to adjust staffing to compensate for a dynamic work load; enhancement in staff assignment will improve communication, teamwork and a reduction in staff turnover (Abrahamson et al., 2016). The result of the study reveals to policy makers the need for change at national and local levels. The study suggests the need for hour reduction for working nurses and an improvement in staffing condition by accrediting bodies and Joint Commission. In 2011, the Joint Commission instituted nine evidence-based actions for shift hours and staff input in the implementation of work schedules (Aitken, Sloane, & Stimpfel, 2012). The study can be used by policymakers to make improvements in laws and policies, for maintaining optimum staffing and enhancing HCAPS scores (Martsolf et al., 2016).

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Conclusion

According to Meyer & Clarke (2011) all patients risk of death increased by 2-5% for each shift where the patient’s unit was understaffed and by 4-7% for each shift with high turnover. In an article by Martin (2015) nursing job dissatisfaction was 4 times higher than the national average. Not only are nurses unhappy and willing to quit their jobs; their practicing ethics is questionably (Martin, 2015). It is believed that understaffing and nursing shortage impacts the principle of nursing practice. In June of 2010 the Senate and House passed the “Registered Nurse Safe Staffing Act” (Martin, 2015). The bill allowed nurses to voice concerns to the Courts about unsafe staffing, this protecting the nurse. A safe staffing ratio would increase patient satisfaction and reduce staff turnover. The prevailing thought among administrators is by increasing staff it would decrease profit. But contrarily, studies have shown that even though cost is increase, profitability is not affected due to the added benefit of reducing adverse event (Martin, 2015).

References

Abrahamson, K., Hass, Z., Morgan, K., Fulton, B., & Ramanujam, R. (2016). The relationship between nurse-reported safety culture and the patient experience. Journal of Nursing Administration, 46(12). 662-668.

Conley, S., & Redeker, N. (2016). A Systematic Review of Self-Management Interventions for Inflammatory Bowel Disease. Journal of Nursing Scholarship, 48(2), 118-127 110p. doi: 10.1111/jnu.12189.

Loan, L., Patrician, P., & West, G. (2012). Staffing matters-every shift. American Journal of Nursing, 112(12), 22-27. doi:10.1097/01.NAJ.0000423501.155

Martin, C. J. (2015). The effects of nurse staffing on quality of care. MEDSURG Nursing, 24(2), 4-6.

Martsolf, G. R., Gibson, T. B., Benevent, R., Jiang, H. J., Stocks, C., Ehrlich, E. D., Kandrack, R., & Auerbach, D. I. (2016). An examination of hospital nurse staffing and patient experience with care: Differences between cross-sectional and longitudinal estimates. Health Service Research and Educational Trust, 51, 2221–2241. doi:10.1111/1475-6773.12462

Melnyk, B. M. & Fineout-Overholt, E. (2015). Evidence-based practice in nursing & healthcare: A guide to best practice (3rd ed.). Philadelphia: Wolters Kluwer Health

Meyer, R. M., & Clarke, S. P. (2011). Shifts with nurse understaffing and high patient churn linked to heightened inpatient mortality risk in a single site study. Evidence Based Nursing, 14(4), 122-123. doi:10.1136/ebn.2011.100052

Needleman, J., Buerhaus, P., Pankratz, V. S., Leibson, C. L., Stevens, S. R. & Harris, M. (2011). Nurse staffing and inpatient hospital mortality. The New England Journal of Medicine, 364(11), 1037-45. doi:http://dx.doi.org.ezproxy.fau.edu/10.1056/NEJMsa1001025

Nurse Staffing Ratios. (2013). AORN Journal, 97(5), 604-538. doi:10.1016/j.aorn.2013 .02.011.

Rochefort, C. M., Buckeridge, D. L., & Abrahamowicz, M. (2015). Improving patient safety by optimizing the use of nursing human resources. Implementation Science, 10(1), 89. doi:10.1186/s13012-015-0278-1

Witkoski Stimpfel, A., Sloane, D. M., & Aiken, L. (2012). The longer the shifts for hospital nurses, the higher the levels of burnout and patient dissatisfaction. Health Aff (Millwood), 31(11), 2501-2509. doi:10.1277/hlthaff.2011.1377.

Table 1. Evaluation Table
Citation	Conceptual Framework	Design/Methd	Sample/Setting	Major Variables & Definitions	Measurement of Variables	Data Analysis	Findings	Appraisal of Worth
Abrahamson, et al., 2016, The relationship between nurse-reported safety culture and the patient experience	To obtain a better understanding of the relationship between nurse-reported safety culture and patient experience	Regression model identified by hospital samples obtain from AHRQ Survey and HCAHPS patient satisfaction survey over a period of 12-months from units and hospital	Surveys were sent to patient by mailed and returned to PRESS GANEY/ A random sample was collected from each hospital The sample comprises of 136 unit and 45 hospitals	Major variables were HCAHPS nursing communication subscales, patient recommendation to hospital, patient ratings of their hospital experienced Independent Variables: teamwork, feedback, safety etc.	The 7-unit scales of safety of safety was used at the unit level with Alpha ranges from 0.63 to 0.83 and 3 scales for measurement at the hospital level with an alpha range of 0.8 to 0.83	The mean number of units/service line/hospital was 3 with a range from 1 to 5 The 3 outcome variables used the nurse safety culture scales as predictor variable and controlling from hospital and unit	The findings reveal the relationship between teamwork, adequate staffing, organizational learning impacting a positive patient outcome	Sample not a national representation The likelihood of error due to the use of averages in the unit as oppose to individual measurement The exclusion of nurse’s job satisfaction USPSTF “A”
Loan, Patrician, & West, 2012, Staffing matters-every shift	To show that inadequately staffed shifts lends to adverse event such as pt. falls, mediation error and needle sticks for nurses	From 1996 to 2009 data from 111,500 shifts on 56 inpatients units in 13 U.S Army, Navy and Air force hospital collect and analyzed data from falls, medication errors and needle stick errors on quarterly basis	The number mix consisted of nursing personnel experiences on shift associated injuries like needle sticks	Variables used were work environment, nurse experience, education level, physician- nurse relationship	The percentage calculated from odd ratios from the Bayesian hierarchical logistic regression models	The probabilities were calculated using the base rate of 0.24 falls, o. o5 falls with injury and 0.67 medication errors	The MillNod model shows a 36% increased probability of a fall with injury because of a 10% decrease in RN on the shift	Difficult to generalized military data to a civilian hospital Data from bargaining units was absent in study Some variables may have to be adjusted when staffing adjusted but outcome remains the same USPSTF “A”
Martsolf, et al., 2016, An examination of hospital nurse staffing and patient experience with care	To study the relationship between hospital staffing and HCHPS	Cross-sectional and longitudinal models to determine staffing levels, skill mix and HCHPS measure	Hospital data collected from 2009 to 2011 from 341 hospital in 3 states	Control variables were discharged data Dependent variables consisted of 7 HCHPS measures	A sensitivity analysis was used	A cross-sectional regression analysis was used	Nursing staff levels is associated with HCAHPS scores	The limitations, only 3 states were used Nurse staffing levels could not be tied to individual patient The Model could not control for unobservable time USPSTF “B”
Meyer, & Clarke, 2011, Shifts with nurse understaffing and high patient churn linked to heightened inpatient mortality risk in a single site study	To show increased risk of death due to unit conditions, nurse staffing and turn overs	An Observational study of unit characteristics and staffing levels	Data collected for the years from 2003 to 2006 197,961 patients with 51.4 % males With a mean age of 60.2 Pediatrics excluded 80 % of population not local	Variables included understaffing and high turnovers	Cox proportional-hazards regression models with the time from hospital admission as the time scale and in-hospital death as the outcome	An analysis was done between mortality levels of RN staffing a d other variables using COX Variables ere pt. age, death, sex payer type, Units Shifts turnovers	Risk of death increased 2-5% for each shift where the patient’s unit was understaffed by 4-7% for each shift turnover High patient turnover and Under target Nursing staff is related to death risk The study can help shape payment system to increase nursing staff Having adequate staffing and any factors impacting workload should be active in streamlining nursing care	IT was an observation study with confounding possibilities Care delivery models excluded Staff exclusive of RNs was excluded Units Characteristics excluded Factors contributing to death after the first 90 days of admission or death outside the hospital was excluded Further studies needed to examine the relationship between RNs and other staff USPSTF “A”
AORN, 2013, Nurse Staffing Ratios	To reveal inappropriate nursing skill, mix and RN ratio can impact patient safety	THE information gleaned from a case study in the ICU with a male patient diagnose with COPD	Adapted from AHRQ website	Patient outcome And nursing staff		A report from AHQR	Recommendation, rules and protocols were implanted by the Institute of labor, ANA, JACHO, AND CMS about best nursing practice to perform the optimum results in patient outcome	USPSTF “A”
Rochefort, Buckeridge, & Abrahamowicz, 2015, Improving patient safety by optimizing the use of nursing human resources	To determine the optimum nurse patient care to reduce Adverse Events	Data collected from 2010 to 2015 from medical and icu units all adult To assess for AE from admission to 7 days after discharge	Data extracted from 12 data bases from admission to payroll	Variables used were RN overtime hours, Mean RN work experience	Cox proportional hazards regression models are used The Natural language processing techniques was used to identify AEs Statistical NLP used probability to classify documents	Descriptive statistics used to summarize staffing and relationship between Staffing ratios and HAP, VAP, and VTE P< .001	Evidence base information was produce to assist Mangers In having the Ideal staffing ration to reduce AEs	Investigation of nursing policies impacting AEs was neglected Policies USPSTF “A”
Stimpfel, Sloane, & The longer the shifts for hospital nurses, the higher the levels of burnout and patient dissatisfaction Aiken, 2012,	To show the relationship between extensive working hours for RNs, RNs burnout and patient outcome	A cross-sectional study from three sources using surveyed data from four states from 2005 to 2008	A sample of 22, 275 RNs from 4 state with a total of 577 hospital Nurses not working directly with patients was deleted	Control variables Were RNs age, sex, reasons for leaving control for hospital type and difference in nursing care established	Subscales of the Practice Environmental Scale was used Practice Environment scale of the Nursing Work Index was used	Data analyses by using the Statistical software SAS, version 9.3. Significance level was P<0.5 for a two-tailed test Stats were shift length and nurse outcome, nurses, hospital type and RNs satisfaction	Findings correlate longer shift work with poor patient outcomes and nurse’s burnout The importance of the finding important due to CMS incentive to healthcare institution for better outcome	The cross-sectional approach limits the study inference ability Study is limited to four states Can contribute to Changes by regulating and accrediting bodies The study Study approve by Institutional review board of Pennsylvania with no harm to patient USPSTF “A”

LEGEND: AHRQ – Agency for Healthcare Research and Quality

ANA – American Nursing Association

CMS – Centers for Medicare and Medicaid

JACHO- Joint Commission on the Accreditation of Healthcare   Organization

HCAHPS – Hospital Consumer Assessment of Healthcare Providers and System

Pt. – Patients

MillNod – Military Nursing Outcomes Database

CMS- Center for Medicare and Medicaid

AE- Adverse events

NLP- Natural Order Processing

VTE –VENOUS THROMBOEMBOLISM

VAP –VENTILLATED ACQURED PNEUMONIA

DVT – DEEP VAIN THROMBOSIS

Melnyk, B. & Fineout-Overholt, E. (2015). Evidence-based practice in nursing & healthcare: A guide to best practice (3rd ed.). Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins. ISBN 9781451190946