Introduction into Nursing Research
Carol S. Coose
Needleman et al., (2011) in Nurse Staffing Ratios and Hospital Mortality looked at the ratio of nurses to patients care, and found that there was the need to match staffing with patients’ needs for nursing care. Taking cross-sectional studies of hospital-level administrative data, they were able to justify the association between lower levels of staffing of registered nurses (RNs) and increased patient mortality. The reason for this is the unavailability of registered nurses. In an evaluation of patients of a particular hospital during the first 30 days of their admission, which included 90 RN shifts, it was found “31.9% stayed in wards that had shifts with actual staff levels of 8 hours or more below target, while 34.6% stayed in wards that had three or more shifts with below-target staffs. 39.7% of patients did not get any high-turnover shifts, and 12.6% patients were exposed to three or more shifts with high turnover” (Needleman et al., p.1040). This clearly showed that the majority of patients had less care, because of which, their mortality rate was abnormally high.
Registered Nurses, Mortality, Staffing, Patients, Below-Target, High-turnover
Research Issue and Purpose:
In this retrospective observational study, attention is drawn to the inappropriate difference in nurses-to-patients ratio because of which, a high mortality rate among patients is witnessed. The research endeavors to improve statistical controls and narrow the gaps of past researches in order to show the direct connection that many critics of similar research have affirmed as possible variables of why there is a correlation between quality measures, staffing ratios, and mortality rates. The following analysis included the counting of below-target and high-turnover shifts occurring within the first 5 days of a patient’s stay in the hospital; the inclusion of patients who stayed in general wards, step down units, and ICU, and those patients who were exposed to below-target and high-turnover shifts in a rolling window of six shifts (2 days) before the next shift.
While this research has taken into account and consideration the possibilities of certain variations in ratio count, there continues to be speculation from certain sections of the industry that the findings may be inappropriate. The reason for such speculation comes from the fact that such studies do not show the direct link between the level of staffing and individual patient experiences and have not included sufficient statistical controls. Since this is a retrospective observational study, the study takes a look at how RNs manage patient care over 30 days in 90 RN shifts.
Results from previous researches showed that a link existed between quality measures, patient-to-RN ratios in hospitals, and adverse outcomes. Statistical data from insurance companies such as Medicare and Medicaid were gathered, which were reasonably authentic. Both Medicare and Medicaid keep track of patients’ treatments, and disburse compensation as and when required. These insurance agencies monitor and investigate cases where they feel there is a dereliction of duty by the hospital or its staff which has led to patients getting ‘hospital- acquired preventable diseases or conditions’ such as, Urinary Tract Infection (UTI) resulting from faulty Foley catheter placement. The hospitals try and abdicate their responsibilities from such situations saying that they were not at fault, and put the blame squarely on the patient’s physical condition. Therefore, insurance agencies such as Medicare and Medicaid, always study their patients medical history before any form of claim is reimbursed. Thus, when such agencies are approached for data, they are definitely reliable. The impact of non-payment of claims made by patients to hospitals is changing the face of treatment today. There has been a dip in the quality of treatment, and a rise in the turnout of registered nurses for duty. In the past, direct correlation between mortality and RN staffing ratios has been ambiguous with failure to account for supporting variables.
It is obvious that in labor sensitive and intensive organizations such as hospitals, there is a competition for quality service, especially when there is a dearth in the number of qualified personnel available to cater to the demand. Studies involving RN staffing in hospitals showed that when “the nursing workload is high, nurses’ surveillance of patients is impaired, and the risk of adverse events increases” (Needleman et al., p.1038). This staffing shortfall resulted in substantial overtime which, in turn, created even higher turnover. When the over-burdened RNs are allowed overtime, most of the senior RNs declined overtime work, and when they did, their quality of service declined. When senior RNs abstained from working overtime, the new RNs were forced to work overtime, especially during the undesirable periods, as a result of which, they couldn’t withstand the pressure and made mistakes. In order to prove the RNs exposure to high workload shifts, Needleman et al., (2011, p.1038) constructed “measures of below-target staffing and high turnover, each of which increased the workload for nurses.” This was done using a well structured and well calibrated and audited commercial patient-classification system.
Literature Review Quality:
Needleman et al (2011) excluded data of those patients who were reluctant to publicize their medical history for the research. The final figure of data of patients who allowed their medical history to be included in the research stood at 197,961 admissions. Data was collected from electronic discharge abstracts, which gave the data the authenticity it required to form a non-biased judgment. Observations were made by targeting shift-by-shift turnout of RNs based on the units where patients were located, and then merging these units and staffing data to understand the strength ratio. The merging of data resulted in the creation of 3,227,457 separate records with information for each patient for each shift during which they were hospitalized (Needleman et al., p.1038).
Theoretical or Conceptual Framework:
Patient turnover is a high-risk period for medical practitioners because of its impact on the limited resources (RNs) available in hospitals. Below-target staffing and high turnover increases the workload on nurses, and when nurses are forced to work overtime, or become lethargic due to fatigue and exhaustion, it will have a direct impact on the quality of treatment. Therefore, in order to assess its effect, Needleman et al (2011) designed specific measures for each shift, so that they equaled the sum of ward admissions, transfers, and discharges, and the turnover equaled 100%. A shift was considered to be of 8 hours, and would be considered to be of high turnover if it exceeded this. Patient mortality was based on a patient’s diagnosis. .
Participants and sampling:
The data for this study came from a tertiary magnet hospital. “197,961 patient admissions and 176,696 nursing shifts of RNs of 8 hours each in 43-hospital units, with an average age of 60.2 years, made up of pediatric patients,” Needleman et al (2011) were not included in this study. “Of the available data, half were men at 51.4%, two thirds were general care patients, and the remaining one third was split between critical care and step down units. 80% of the population was outside the local area. The data used was from 2003 through 2006, retrieved from electronic data systems of the medical center” Needleman et al (p.1038). Only those in intensive care, step-down care, and general care who required medical or surgical treatment were considered.
Steps taken to protect human research subjects:
Patient who didn’t want to disclose their medical history were not included in the study, and the data collected did not allow the researchers to identify patients who had do-not-resuscitate orders.
Conflicts of interest:
Grant money came from the agency of healthcare and research and quality. Dr. Buerhaus reported having served as an unpaid member of the advisory board for Johnson and Johnson’s campaign for the future of nursing, and that the institution has received monies from Johnson and Johnson on his behalf.
A Cox proportional model was used to estimate the risk of death. The model accounted for the use of variable characteristics of patients and hospital units, before making appropriate modifications. The model examined the coefficients of all explanatory variables, before giving it a hazardous score; the higher the value of a variable, the more hazardous it is for the patient. Correspondingly, a negative regression coefficient gave the patients a healthier projection. This research study used a tertiary academic center with magnet status having a lower than average mortality rate. All statistical analysis was done using the SAS model. “Patient-level measures to calculate the risk of death, patient’s age and sex, payment mode, type of admission, and other coexisting conditions included in the Elixhauser algorithm” were employed (Needleman et al., 2011, p.1039).
As in all researches, this one too was confounding for a number of reasons. A lot of compromise had to be made in including information on care delivery models, and the availability of staff members aside from RNs. There was also no possibility of continuing the observation of factors influencing mortality after the research was completed. There was also the limitation on the usage of data, and the data did not allow the researchers identify patients who had do-not-resuscitate orders. Given these limitations, further research will be necessary to understand the complex interplay among staffing of nurses, patient preferences, work processes, and clinical outcomes, among others, says (Needleman et al., 2011, p.1044).
Findings and Implications for policy:
The findings of this study are troubling. This study was conducted in a magnet hospital with ideal 8 hours shifts for nurses. Multiple variables were established to find the elements that contributed to a patient’s mortality in below-target and high turnover RNs shifts. The research clearly showed a clear increase in mortality when RNs-to-patients ratios are low. Additionally, mortality increases when a high level of traffic with admissions, transfers, and discharges occur.
Needleman, J., Buerhaus, P., Pankratz, V., Leibson, C. L., Stevens, S. R., and Harris, M. (2011), Nurse Staffing and Inpatient Hospital Mortality.[Special issue].New England Journal Of Medicine, 364(11), 1037-1045, doi:10.1056/NEJMsa1001025