Nursing intensity and costs of nurse staffing demonstrated by the RAFAELA system: liver vs. kidney transplant recipients
Abstract
Aim
To compare nursing intensity and nurse staffing costs for liver transplant (LTx) vs. kidney transplant (KTx) patients through the use of the RAFAELA system (the OPCq instrument).
Background
High-quality patient care correlates with the correct allocation of nursing staff. Valid systems for obtaining data on nursing intensity, in relation to actual patient care needs, are needed to ensure correct staffing.
Methods
A prospective, comparative study of 85 liver and 85 kidney transplant patients. Nursing intensity was calculated using the Oulu Patient Classification (OPCq) instrument. The cost per nursing intensity point was calculated by dividing annual total nursing wage costs with annual total nursing intensity points.
Results
The results showed significantly higher nursing intensity per day for liver transplant patients compared to kidney transplant patients. The length of stay was the most important variable in relation to nursing intensity points per day.
Conclusions
The study demonstrated differences in nursing intensity and nurse staffing costs between the two patient groups.
Implications for Nursing Management
When defending nurse staffing decisions, it is essential that nurse managers have evidence-based knowledge of nursing intensity and nurse staffing costs.
Introduction
High-quality patient care correlates strongly with the correct allocation of nursing staff. Nurses’ contribution to the treatment and care of patients, can be measured through nursing intensity (NI). Nursing intensity is defined as patients’ need for care and the nursing interventions needed to ensure good care, and has a direct impact on quality and outcome (Rafferty et al. 2007, Needleman et al. 2011, Keogh 2013, McHugh et al. 2013, Aiken et al. 2014, Fagerström et al. 2014). Still, given that for most hospitals the nursing budget is one of the highest costs (Chiang 2009, Welton et al. 2009, Aiken et al. 2014, Fagerström et al. 2014), when expenditures need to be reduced a cutback in nursing resources is often suggested.
Diagnosis-related groups (DRGs) were introduced in the 1980s in an attempt to classify the care products that hospitals provide and predetermine the costs and resources needed to ensure good care (Mitchell et al. 2003, Welton et al. 2006). Still, when costs are predetermined DRGs do not take patients’ care needs or NI into account and do not reflect the actual nursing workload. As DRGs do not allow for the reimbursement of ‘extra’ costs, when such systems have been used nurses have been challenged to provide evidence-based knowledge on nursing workload in order to justify their existence and associated (‘extra’) costs.
As more cost-effective health services are sought, it is important that nurse managers present solid arguments that support nurse staffing decisions. How nursing resources are to be allocated is greatly debated, and, recently, many hospitals have instituted patient-to-nurse ratios. However, the decision-making behind such patient-to-nurse ratios is generally not evidence or research-based but instead reflects the minimum level of staffing that a hospital must maintain – regardless of the type or intensity of care needed. Such ratios do not take into account the constantly changing nature or a variety of patients’ care needs (Welton 2007). The use of validated tools that define optimum levels of nurse staffing provides a more accurate way of balancing care needs with nursing resources and estimating nursing costs.
The RAFAELA system, including the Oulu Patient Classification instrument (OPCq), was designed to measure NI and nursing staff allocation (Rauhala & Fagerström 2004, Pusa 2007). The system, previously described in detail elsewhere (Rauhala & Fagerström 2004), was developed in Finland during the 1990s and is used in almost all hospitals in Finland. Since 2010, Iceland, Norway, Sweden and the Netherlands have also become RAFAELA users. The system has been tested concerning psychometric properties several times in Finland (Fagerström et al. 2000, Rauhala & Fagerström 2004, Frilund & Fagerström 2009), and in Norway (Andersen et al. 2014), with satisfactory results in both countries.
The aim of this study, which is the third sub-study part of a larger Norwegian research project, was to compare daily NI and nurse staffing costs related to the care of patients undergoing a liver transplant (LTx) and patients undergoing a kidney transplant (KTx) through the use of the RAFAELA system (specifically the OPCq instrument). Furthermore, the variables impacting the NI for the two groups were explored. The measurements of NI used in this study were calculated through use of the OPCq instrument. A priori, we hypothesized a significant higher NI for Ltx patients compared to KTx patients.
Methods
Design
A prospective, comparative design was used to investigate the differences between the two patient groups. Data were prospectively collected from May 2011 through to May 2012. There were no participant selection criteria. Instead, patients who had given their written, informed consent were consecutively enrolled in the study. From a total of 170 patients, 3315 classifications were elicited. The data on nurse staffing costs were derived from the participating hospital's internal financial reports. Additional data such as patient characteristics, stay in an intensive care unit (ICU) and in-hospital stays at the Transplant Surgery or Gastroenterology and Transplant Medicine units were obtained from patient records. Two researchers manually checked the accuracy of the data.
Setting and sample
Data were obtained from two units specialised in organ transplantation, the Transplant Surgery unit (a surgical unit) and the Gastroenterology and Transplant Medicine unit (a medical unit) at Oslo University Hospital, Oslo, Norway (Table 1). Daily measurements (classifications) of NI were obtained through use of the OPCq instrument. Before the start of the study, the responsible researchers provided training in the use of the OPCq instrument to those nurses at the units included in the study; the nurses on call on each unit realised the classifications. A total of 85 patients undergoing LTx and 85 undergoing KTx were included in the study.
| Number of beds | Number of registered nurses | Number of nursing assistants | |
|---|---|---|---|
| Surgical unit | 27 | 43 | 0 |
| Medical unit | 25 | 34 | 10 |
Patient pathways
Before surgery, nurses coordinated the arrival of LTx and KTx patients at the hospital and helped patients with admission, which may occur at any time of the day or night and which includes a pre-operative medical workup. The initial medical condition of the patients upon admission varied: LTx patients may walk in unassisted or be transferred by ambulance from another hospital whereas KTx patients, in general, walk in unassisted. The time from arrival until the moment that an operation is scheduled to begin is limited, and nurses help the patients with the various admission procedures, collecting and giving information.
After surgery, LTx patients remained in ICU for a period varying from 6 hours to 1–2 weeks whereas KTx patients were transferred immediately to the surgical unit for post-operative care. Both groups were kept under observation and their medical conditions continually evaluated, especially regarding the medication given and possible signs of organ rejection. Both groups also required extensive help and nursing care in regards to basic needs. Transplant surgery is considered major surgery, and for certain patients the risk for complications is high: many become chronically ill whereas some require further surgery as a result of post-operative complications.
After their stay at the ICU, LTx patients typically continued their recovery at the surgical unit before moving to the medical unit about 10 days post transplantation. Some LTx patients were discharged from the surgical unit directly to a local hospital. KTx patients were typically discharged after about a week at the surgical unit and were offered regular follow-ups at an outpatient clinic for about 8 weeks. Some KTx patients experienced difficulties in managing their new life or experience medical complications and could, therefore, be transferred to the medical unit for a period of time before being discharged or admitted to a local hospital.
Measuring NI with the RAFAELA system (the OPCq instrument)
Part of the RAFAELA system, the OPCq instrument, measures six sub-areas of patient needs and associated nursing interventions: (i) planning and coordination of care; (ii) breathing, blood circulation and symptoms of disease; (iii) nutrition and medication; (iv) personal hygiene and excretion; (v) activity/movement, sleep and rest, and (vi) teaching, guidance and follow-up in care and emotional support. When using the OPCq, the nurse taking care of a patient during a day shift classifies the patient once per calendar day, with the classification being based on how the patient's needs have been met through actual nurse interventions during the last 24-hour period. For example, sub-area one (planning and coordination of care) relates to multidisciplinary cooperation in which negotiations are undertaken between staff to plan and implement nursing activities appropriate for each patient. Sub-area six (teaching, guidance and follow-up in care and emotional support) relates to the patient's and his/her family members’ need for education, advice and/or support related to illness, crisis management or mourning (Fagerström et al. 2000).
As measured using the OPCq, NI can vary for each sub-area between one and four points: A = 1 point, B = 2 points, C = 3 points and D = 4 points. The points given represent care levels (A–D), which are used to describe the patient's general and immediate care needs. Level A describes a patient who manages relatively well on his/her own, B describes a patient who occasionally needs care, C describes a patient who needs repeated care and D describes a patient who cannot manage unattended. The NI points are added up to a total score, ranging from 6 to 24 points per patient. On this basis, the patients are classified into five different patient categories, from minimum to intensive care needs (Rauhala & Fagerström 2004).
The OPCq instrument was translated into Norwegian by an expert group consisting of three clinical nurse specialists and one Master nurse. The head researcher for this study (M.H.A.) led this group. Established principles for good practice in translation were followed (Wild et al. 2005). In short, this process included forward translation, reconciliation and back translation, with harmonisation discussions throughout all stages.
Statistical analysis
The NI points per day were calculated using the OPCq instrument. The cost per NI point was calculated by dividing the total nursing wage costs for 2012 for the hospital with the total number of NI points for 2012 for each of the units. To facilitate understanding of the costs involved, costs were converted from Norwegian kroner (NOK) to United States dollars (USD; 1 USD = 7.87 NOK). The year 2012 was chosen because this was the first year a complete data set of NI points was available.
To discern differences between the groups, a minimum of 60 patients per group was required. To treat for missing patient pathway classifications, we nonetheless decided to include 85 patients in each group. No participant selection criteria were applied. All patients had their NI consecutively classified by nurses on the respective units. A Student's t-test (for independent samples) was used to analyse the differences in NI per day between the two groups. A paired-samples t-test was used to analyse the differences in NI per day between the two units. Multiple linear regression analysis was used to predict the factors affecting the NI points per day. Independent variables were patient groups, age, gender, length of stay at hospital units, whether an extra ICU stay was needed and whether patients were transferred from a surgical to a medical unit. A P- value of less than 0.05 was considered statistically significant. The spss statistical package version 21 (SPSS, Chicago, IL, USA) was used for all statistical analyses.
Results
A total of 170 patients (113 men and 57 women) were included in the study. Patient characteristics are presented in Table 2. The data demonstrated well-balanced groups concerning age. There were significantly more males in the KTx group compared to the LTx group. The LTx patients had significantly longer lengths of stay at both the surgical and medical unit compared to the KTx patients. Significantly more LTx patients needed an extra ICU stay compared to the KTx patients. As many as 88.2% of KTx patients and 38.8% of LTx patients were transferred from the surgical unit to the medical unit.
| LTx patients | KTx patients | P-value | |
|---|---|---|---|
| N (%) | 85 (50) | 85 (50) | |
| Age | 52 (12.3) | 55 (14.9) | 0.147a |
| Male patients | 48 (56.5) | 65 (76.5) | 0.006b |
| Length of stay at surgical unit (days) | 11.5 (3.0) | 7.8 (3.4) | 0.000a |
| Length of stay at medical unit (days) | 8.6 (5.5) | 3.1 (5.8) | 0.000a |
| N = patients in need of extra ICU stay | 15 (17.6) | 5 (5.9) | 0.021a |
| N = patients transferred from surgical to medical unit | 75 (88.2) | 33 (38.8) |
- Values are given as mean (SD) or as counts (%).
- a Student's t test.
- b Pearson's chi-square.
- LTx, liver transplant; KTx, kidney transplant.
The results also showed significantly higher NI points per day for the LTx patients while at the medical unit compared to KTx patients (Table 3).
| LTx patients | KTx patients | P-value | |
|---|---|---|---|
| N (%) | 85 (50) | 85 (50) | |
| Surgical unit, NI points per day | 17.5 (2.9) | 17.0 (2.8) | 0.316a |
| N (%) | 74 (69.2) | 33 (30.8) | |
| Medical unit, NI points per day | 13.1 (2.7) | 11.8 (1.4) | 0.015a |
- Values are given as the mean (SD) or as counts (%).
- a Student's t-test.
- NI, nursing intensity; LTx, liver transplant; KTx, kidney transplant.
As seen in Table 4, there was a statistically significant difference in NI points per day concerning patients’ stays at the surgical unit (mean score = 17.5) and the medical unit (mean score = 12.7).
| Surgical unit | Medical unit | P-value | |
|---|---|---|---|
| NI points per day | 17.5 (2.6) | 12.7 (2.5) | <0.000a |
- Values are given as mean (SD).
- a Paired-samples t-test.
- NI, nursing intensity.
The most significant predictor variable impacting NI points per day for patients’ total stay was the length of stay. The most significant predictor variable impacting NI points per day at the surgical unit was a stay at the ICU. The most significant predictor variable impacting NI points per day at the medical unit was patient group (Table 5).
| NI points per day – total stay n = 70 | NI points per day – surgical unit n = 70 | NI points per day – medical unit n = 70 | |
|---|---|---|---|
| Age at Tx | |||
| B | 0.05 | .14 | −0.08 |
| P | 0.48 | 0.07 | 0.44 |
| CI | −0.03 | −0.05 | −0.00 |
| 0.05 | 0.02 | 0.06 | |
| Gender | |||
| B | 0.14 | 0.05 | 0.06 |
| P | 0.07 | 0.55 | 0.54 |
| CI | −0.08 | −0.70 | −0.63 |
| 2.18 | 1.33 | 1.17 | |
| Patient group | |||
| B | 0.02 | 0.12 | 0.27 |
| P | 0.85 | 0.23 | 0.03 |
| CI | −0.12 | −2.55 | −1.75 |
| 1.50 | −0.10 | 0.42 | |
| Stay at ICU | |||
| B | 0.09 | 0.30 | 0.02 |
| P | 0.71 | 0.00 | 0.87 |
| CI | −0.66 | −1.45 | 1.49 |
| 2.88 | 1.71 | 4.29 | |
| Length of hospital stay | |||
| B | −0.29 | −0.14 | −0.10 |
| P | <0.00 | 0.13 | 0.41 |
| CI | −0.22 | −0.11 | −0.12 |
| −0.05 | 0.05 | 0.02 | |
- B, the standardized regression coefficient; CI, 95% upper and lower confidence interval; ICU, intensive care unit.
As seen in Table 6, the cost of one NI point at the surgical unit was higher (44.20 USD) compared to the medical unit (40.00 USD).
| Surgical unit | Medical unit | |
|---|---|---|
| Total nursing wage costs, 2012 | 4 232 775.20 USD | 3 319 044.50 USD |
| Total amount of NI points per unit, 2012 | 95823 | 83044 |
| Cost of one NI point, 2012 | 44.20 USD | 40.00 USD |
- NI, nursing intensity.
Discussion
Nursing continues to be one of the most important contributions to healthcare services all over the world. It is crucial that valid systems for obtaining data on nursing workload in relation to actual patient care needs be developed, to support and strengthen nurse managers in their provision of high-quality care. Recent research has shown that current systems are insufficient (Birmingham 2010) or that the instruments used to measure nursing workload are imprecise (Kane et al. 2007)). The aim of this study was to compare daily NI and nurse staffing costs related to the care of LTx and KTx patients through the use of the RAFAELA system (the OPCq instrument) in a Norwegian hospital setting and to analyse the patient-related variables that impact NI. The results indicate that the OPCq instrument provides nurse managers and hospital leaders with valid information that describes the differences in NI between patient groups and units. By combining data obtained through the OPCq instrument with cost data from the hospital's internal financial reports, significant differences between the cost of NI per unit were demonstrated. We conclude that the OPCq instrument, and thereby the RAFAELA system, facilitates the processing of valid and precise information also when used outside of Finland.
The international relevance of the RAFAELA system within clinical nursing practice has been demonstrated in this study. In a study in which current methodologies were reviewed in relation to the ability of systems to estimate nursing costs, Chiang (2009) refers to the RAFAELA system as a viable methodology and system, which correlates with our findings. As NI can be defined in many different ways (Morris et al. 2007), it is important to stress that the RAFAELA system focuses on patient-related, and not unit-related, NI (Fagerström et al. 2014).
Our data showed significantly higher NI points per day for the LTx patients compared to the KTx patients while at the medical unit. NI per day was also higher, but not significantly so, for the LTx patients while at the surgical unit. Four patients stayed for more than 20 days at the surgical unit and another four for more than 20 days at the medical unit. Even when these patients were removed from the analyses, the results concerning NI points per day remained unchanged. Thus, the findings of our study indicate that LTx patients need extra nursing compared to KTx patients. LTx surgery is considered more complex and requires a longer operative time than KTx surgery. The risk of pre- and post-operative complications is also higher for LTx surgery compared to KTx surgery.
The reason the NI per day was not significantly higher for the LTx patients while at the surgical unit probably has to do with the different post-operative pathways offered to LTx and KTx patients at the hospital. LTx patients remain in ICU whereas KTx patients are immediately transferred to the surgical unit for post-operative care. We were unable to capture OPCq data from the LTx patients’ stay at ICU because the RAFAELA system was not integrated into the ICU. Thus, NI data for LTx patients’ most demanding postoperative period was not measured. It is reasonable to believe that had we been able to capture such data for the LTx patients during this time, a significant difference would have been seen between the two groups.
Comparing NI for the groups as a whole, we found NI points per day to be significantly higher at the surgical unit compared to the medical unit. This may reflect the fact that patients’ care needs decrease after the first post-operative week, coinciding with when patients are transferred from the surgical unit to the medical unit. While at the surgical unit, patients need extensive help during the initial post-operative period. This can be compared to the medical unit, where many patients can take care of the majority of their basic needs themselves. A further factor is that an intensive, individual education programme was offered to patients 2–3 days after surgery while they were in the surgical unit, which may also help explain the significantly higher NI at the surgical unit.
Multiple regression analysis indicated that the most significant predictor variable impacting NI points per day for the total hospital stay was the length of the hospital stay. It is reasonable to suppose that a longer than average hospital stay reflects the medical complexity of a patient's situation and thereby correlates with higher NI, which is also seen in previous research (Mak et al. 2012). The most significant predictor variable impacting NI points per day for a surgical unit stay was a stay in the ICU. This is comparable to the results from a recent study where a stay in the ICU was determined to be an important factor in relation to patients’ care intensity (van Oostveen et al. 2015). Patients undergoing surgery who require a post-operative stay in the ICU have complex care needs and require extensive nursing care even after being transferred to a surgical unit. The most significant predictor variable impacting NI points per day for a medical unit stay was patient group. Most KTx patients were discharged directly from a surgical unit to an outpatient clinic, whereas most LTx patients were transferred to a medical unit. For LTx patients, their stay in a medical unit included careful observation, systematic pain management and an intensive care approach, all designed to help decrease post-operative complications. This may explain why patient group was the most significant predictor variable for a medical unit stay.
The total length of hospital stay, a stay in the ICU and patient group are significant factors that affect NI, and they all reflect patients’ various medical conditions and associated care needs. This is an important finding and may contribute to nurse managers being able to balance positively actual patient care needs with nursing resources.
The highest cost per NI point was seen for the surgical unit. This may be explained by the emergency status of the unit: 60–70% of the unit's activity is not planned (The Norwegian Renal Registry 2010, Scandiatransplant 2011). Transplant patients arrived at the unit at all hours and must undergo a pre-operative medical workup, which the unit's nurses were responsible for. The unit's nurses also provided post-operative care for both LTx and KTx patients, the latter also during the immediate post-operative period. The number of registered nurses required to staff the unit reflects its status; a minimum of eight nurses were required during the evening shift and four during the night shift.
Our findings highlight the need to connect DRGs to measurements of NI, to more closely reflect true patient needs. Previous researchers also share this view (Ruland & Ravn 2003, Lang et al. 2004, Spence et al. 2006, Fagerström & Rauhala 2007, Brown et al. 2010, Andreasson et al. 2015).
The strength of our study lies in its prospective design and sample size and our use of a valid instrument. The data collection period lasted for an entire year, and no selection criteria were applied. The patient characteristics were well balanced across the groups (Table 2) and reflected the characteristics of the Norwegian LTx and KTx patient populations (The Norwegian Renal Registry 2010, Scandiatransplant 2011).
Limitations of the study
We were unable to capture NI data from patients’ stays at the ICU because the RAFAELA system was not integrated into this unit. This is a limitation as the base for benchmarking should be comparable data. Nevertheless, we maintain that if data from the ICU stay for LTx patients were available, the statistical differences seen between the groups would increase, which would serve to confirm further our findings. Another limitation was that nurse competency levels were not included in our study because this variable is not integrated into the RAFAELA system, a well-known weakness of the system. Integrating nurse competency levels into the RAFAELA system is essential for its further development (Fagerström et al. 2014). Further limitations are that the RAFAELA system is being evaluated at only one hospital in Norway (a single setting) and that the study sample only included data from one surgical and one medical ward. Caution should be used when applying the conclusions from our findings to other hospital settings.
Conclusions
Significant differences in NI points per day were seen in two different patient groups and between two different units. It should become standard to measure the NI used for in-hospital patients, to realise valid information on patient care needs, the allocation of nursing staff and nursing costs.
Implications for Nursing Management
A scientific contribution was made regarding the usefulness of the measurement of NI in two different patient groups in relation to nurse staffing costs. The study findings also revealed important factors that impact NI. It is essential that nurse managers have access to such information when realising and defending nurse staffing decisions.
Source of Funding
This research was funded by South-Eastern Regional Health Authority in Norway.
Ethical approval
The study was conducted in accordance with the Helsinki declaration and assessed by the Regional Committees for Medical and Health Research Ethics in Norway in 2010. The project did not directly affect patients or their care. Approval was obtained from the institutional review board at Oslo University Hospital (number #2010/27572). All patients who had their NI classified for the study signed informed consent forms, were provided with oral and written information about the study and were guaranteed anonymity, confidentiality and the right to withdraw from the study at any time. The RAFAELA system is owned by the Association of Finnish Local and Regional Authorities, and its use is managed by the FCG Finnish Consulting Group Ltd. The actual study was initiated by Oslo University Hospital. Hence the first author of this paper, M.H.A., made the first contact with FCG. The license to use the system was acquired through a standard agreement between Oslo University Hospital and FCG. The authors declare no conflict of interests.
