⚡ The Importance Of Fall In Acute Care

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The Importance Of Fall In Acute Care



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Critical Thinking for Fall Injury Prevention: AHRQ Preventing Falls in Hospitals Toolkit

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A preliminary analysis on the within-unit trend and seasonality of fall and pressure rates was conducted. There were units with complete fall data 36 quarters in — For each of these units, a linear model was used to test the trend time and seasonality three dummy variables indicating four quarters in the fall rate based on the 36 quarterly observations. The proportions of units with significant trend and significant seasonality among the units were calculated. A similar analysis was conducted for the pressure ulcer rate based on units with complete pressure ulcer data. Our primary analyses were done based on variables aggregated over units within groups.

So units were divided into groups based on their data structures and all variables were aggregated across units within groups then the analyses were conducted at group level. A grouping mechanism was developed to include all units in analyses. Units had data available at different time points due to various reasons. Sometimes units missed the deadlines of data reporting or failed to report for some other reasons, which created gaps in the longitudinal data. We created 9 indicator variables, one for each year of the study — , to indicate whether each unit reported complete data for the study year or not.

When we grouped units with identical values on these indicator variables, unique groups for falls and unique groups for pressure ulcers were identified. These groups were of various sizes: the largest ones had more than units while the smallest ones comprised a single unit; most groups had less than units. For the analyses on falls, 42 final groups were built based on a total of , unit-level quarterly observations; for the analyses on pressure ulcers, 41 groups were built based on a total of , unit-level quarter observations see Additional file 1 : Figure S1, flowcharts of data preparation. All variables were aggregated over units within groups at each quarter. To determine the fall rate, the total numbers of falls and patients days were summed over units within groups first, then the quarterly rates of falls per patient days were calculated for each group.

To determine the pressure ulcer rate, the total numbers of patients with pressure ulcers and patients assessed for pressure ulcers were summed over units within groups first, then the quarterly proportions of patients with pressure ulcers were calculated for each group. Staffing variables were determined in a similar way. All outcome and staffing variables used for analysis were change scores from the first quarter with data in this study for each group baseline. The outcome variables were changes in fall rate the number of falls per patient days and changes in pressure ulcer rate the proportion of patients with hospital-acquired pressure ulcers from baseline.

Changes in rates of injurious falls and pressure ulcers of stage III or above i. Staffing variables included change scores from baseline of total nursing hours per patient day total HPPD and percent of nursing hours provided by registered nurses RN skill-mix. To separate the associations between staffing and patient outcomes at the trend level from those at the seasonal level, each of the staffing variables was decomposed into two components: the annual mean and the quarterly difference from the annual mean. Computational formulas are shown below. Unit type and hospital characteristics were not considered in the analyses as these characteristics were inseparable after aggregation.

Also, there was no control for patient risk, however, we would expect risk to average out to a large extent in aggregation. The analyses were designed to examine temporal associations between nurse staffing and patient outcomes in aggregate, not at hospital or unit level. Weighted linear mixed models were used for group-level analysis. Linear mixed models with AR 1 correlation structure were chosen to accommodate the correlation among the repeated measures of patient outcomes within group. Weighted linear mixed models are linear mixed models with different weights applied to different subjects.

For this study, the weight assigned to each group was its size ie, number of units so that groups with more units carried more weights in the models. Stata package mixed was used for modeling, and weighting was fulfilled with option pweight group size available for STATA 12 and later versions. Table 1 shows hospital and unit characteristics at baseline for all units included in the final analyses of falls and pressure ulcers. The characteristics of the samples for falls and pressure ulcers were similar.

Note that baselines for different groups could be in different calendar years as groups were determined by the data reporting pattern. Seasonality in fall rate and pressure ulcer rate was observable at the group level but was not detectable in our preliminary analyses of single units. Figure 1 shows the change in fall rate and pressure ulcer rate aggregated over units with complete data 36 quarters , as well as these rates with annual trends removed. A seasonal pattern can be seen for both outcomes Fig. At the single unit level, a statistically significant time trend in fall rate Fig. For the pressure ulcer rate Fig. Aggregated quarterly fall rates and pressure ulcer rates. Quarterly fall rates were aggregated over units with all 36 quarters of fall data in — and pressure ulcer rates were aggregated over units with all 36 quarters of pressure ulcer data.

Top plots are change scores from Quarter 1 and bottom plots are the change scores with the trend removed minus annual means. Figures 2 and 3 show the group-level fall and pressure ulcer rates separated into annual means and seasonal variations see Methods section for the decomposition method. The decreasing trends are strong and consistent across groups for both falls Fig. The seasonal pattern is more consistent across groups for pressure ulcer rates Fig. It seems that pressure ulcers were most likely to occur in Quarter 1 in a year and falls were more likely to occur in Quarter 1 and Quarter 4. For injurious falls, no seasonality is observable in the figure. Trend and seasonal variation in fall rates. Trend is represented by the trajectory of annual mean rate and seasonal variation is represented by the trajectory of quarterly rate — annual mean rate ; rates were aggregated over units within 42 groups.

The thick line in each plot is for the group of units with all 36 quarters of data in — Each vertical dash line denotes the fourth quarter of a year. Trend and seasonal variation in rates of hospital-acquired pressure ulcers HAPUs. Trend is represented by the trajectory of annual mean rate and seasonal variation is represented by the trajectory of quarterly rate — annual mean rate ; rates were aggregated over units within 41 groups. Table 2 shows the results of weighted linear mixed models using changes in total HPPD and RN skill-mix as covariates to model changes in fall and pressure ulcer rates.

Based on aggregated data, highly significant associations between nurse staffing and patient outcomes were found at both trend and seasonal levels. At trend level, increases in total nurse staffing and increases in the proportion of RN staffing were both associated with decreases in falls and pressure ulcers. At seasonal level, increases in total nurse staffing remained associated with decreases in falls and pressure ulcers, but findings were mixed for the proportion of RN staffing.

To the best of our knowledge, this is the first study designed to examine the longitudinal associations between nurse staffing and patient outcomes at both trend and seasonal levels for US hospitals. Using a grouping and aggregation approach, we were able to capture significant associations of staffing and patient outcomes at both trend and seasonal levels while including units with various missing data structures into analyses. The large within-unit variability noise may too large for studies on patient outcomes like falls and pressure ulcers to capture meaningful associations signal. Due to such variability, seasonality was undetectable in fall or pressure ulcers rates at single unit level; with all units included, seasonal associations between staffing and falls and pressure ulcers were undetectable when we used hierarchical generalized linear model to conduct unit-level analyses results not shown.

On the other hand, a single hospital or unit may not immediately see better results in patient outcomes after implementing quality-improvement strategies for the same reason, especially when the outcomes are measured at the monthly or quarterly level. The trend-level inverse associations between changes in nurse staffing total HPPD and RN skill-mix and all four outcomes are consistent with the fact that RN staffing increased and rates of adverse outcomes decreased from to [ 4 , 11 , 16 , 17 ]. The inverse associations between RN skill-mix and fall and pressures ulcer rates at trend level remained significant with control for total HPPD, suggesting that the composition of nursing staff matters.

Annual non-RN staffing level was relatively stable except for a sudden drop around [ 4 ]. This drop was consistent across groups in this study see Additional file 2 : Figure S2, which demonstrates time trends in staffing variables. At about the same time, the rate of pressure ulcers of stage III or above also dropped consistently across groups Fig. The possible connection between these two sudden changes needs further examination in the future. The CMS rule change may provide some explanation for these study results. It is expected that hospitals would initiate mechanisms to better track these POA conditions and take actions to prevent falls and pressure ulcers in order to receive full reimbursement from CMS.

Interpretations of the seasonal associations of staffing and patient outcomes are less straightforward. Total HPPD overall increased from Quarter 1 to Quarter 4 in a year, whereas RN skill-mix tended to decrease from Quarter 1 to Quarter 2 and increase from Quarter 3 to Quarter 4 see Additional file 3 : Figure S3, which demonstrates seasonal patterns in staffing variables. RN skill-mix varied across seasons because RN and non-RN staffing changed differently across seasons. RN HPPD kept increasing from year to year and from quarter to quarter except that it tended to plummet in Quarter 1 of each year when patient volume tended to peak [ 11 ].

Non-RN HPPD was overall stable at trend level except for the sudden drop around but changed regularly at seasonal level, likely due to the seasonal change in patient volume. Quarter 1 and Quarter 4 in a year were more likely to have lower staffing, but RN skill-mix tended to be higher in these quarters. Thus, it is not surprising to see positive association of RN skill-mix with falls and pressure ulcers at seasonal level.

Seasonal nursing shortage could have contributed to seasonality in patient outcomes. In winter, staffing level tended to be lower than other seasons even though the total staffing hours were higher because the increases in patient volumes were much greater [ 11 ]. Winter peak in overall or disease-specific hospital admissions has been reported in many other countries, such as Bangladesh [ 22 ], Canada [ 23 ], Denmark [ 24 ], Israel [ 25 ], Japan [ 26 ], and United Kingdom [ 27 ] etc. Meanwhile, nursing shortage was considered a global issue that International Council of Nurses ICN and other international nursing institutes initiated a global review in to identify the policy and practice issues and solutions [ 28 ].

What found in this study based on US hospitals is consistent with the global situation. This study is limited from several aspects, for which the conclusions may be drawn from the findings are restricted. More rigorous future studies are needed to confirm the findings. The analysis approach was limited to the existing database. No patient information is available for patient-level analysis as well as reducing unexplained variation noise ; data aggregation across units reduces noise but forsakes the direct association between staffing and outcomes at unit level.

Furthermore, inconsistent reporting patterns across units led to data aggregation based on data missing patterns that has little meaningful interpretations. The association between nurse staffing and patient outcomes found in time trend cannot tells us if or how much of the improvements in patient outcomes was due to the increases in nurse staffing. Various factors may have contributed to the improvement in patient outcomes, such as changes in nursing practice, new technology, and other unit- or hospital-level quality improvement efforts.

There was no control for these potential confounders in our study. If researchers plan to conduct an observational longitudinal study in the future, it is not clear how these factors can be effectively controlled given the difficulty of identifying and measuring these factors all across units and hospitals. Botsford said health systems looking to develop a proactive care transitions program should start with the following three steps: establish multi-disciplinary planning teams, use technology to support informed decisions and build high-quality, post-acute care networks.

To further highlight this change management process, Dr. LaBine shared Spectrum Health's journey in revamping its care transition program. The health system saw huge variations in both care and medical spend for the skilled nursing facility network, but had no way of measuring the financial and clinical outcomes in the post-acute care setting, according to Dr. Spectrum Health developed a comprehensive RN care management transition program that went from a telephonic model to boots on the ground in the acute care facility and skilled nursing facilities.

The health system also implemented naviHealth's LiveSafe data-driven tool known today as nH Predict to keep track of the financial and clinical outcomes at each post-acute care site. Prior to implementing this program, the health system reported skilled nursing facility days per 1, As of June , Spectrum Health's skilled nursing facility days per 1, decreased to To view a recording of the webinar, click here. To download a copy of the webinar, click here.

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