Consistent differences in medical unit fall rates: implications for research and practice

Consequences of Inpatient Falls in Acute Care: A Retrospective Register Study

AIMS

The objectives of this study were (1) to explore the consequences of falls; (2) to find out time and place of the fall events; and (3) to explore the impact of falls on the length of hospital stays in adults' inpatient acute care.

BACKGROUND

In hospitals, falls are the most common accidents that can occur to a patient during hospitalization. Injuries resulting from serious falls can cause lifelong harm to the patient due to loss of well-being and independence.

DESIGN

A retrospective, cross-sectional, register study based on the data from electronic patient records was conducted.

METHODS

The data included 114,951 inpatients, of which 743 had fallen. Data was collected between January 2014 and December 2016.

RESULTS

One-third of falls caused injury. Most injuries were to the head area, and the most common injuries were pain or confusion. The falls usually occurred at the beginning of the treatment in the patient's room or on the way to the toilet. Falls in the hospital increased the length of stay.

CONCLUSIONS

A large proportion of falls occur at the beginning of treatment, so it is important to start fall prevention measures as soon as the patient arrives at the hospital.

The investigation of effect on foot plantar massage on functional recovery in older adults with general surgery, randomized clinical trial

OBJECTIVE

Foot massage is known to be effective on the emotional state (anxiety, depression, etc.) in the postoperative period. However, studies on its effect on functional level are insufficient.

AIM

The study aimed to investigate the impact of foot plantar massage on functional recovery in older adults undergoing general surgery, employing a randomized clinical trial design.

METHODS

A total of 70 older adults aged 65 years and above who underwent abdominal surgery were included. Various assessments were conducted, including pain levels (Visual Analogue Scale), fear of mobility (Tampa Scale for Kinesiophobia), functional independence (Functional Independence Measure), balance (Berg Balance Scale), basic mobility (Rivermead Mobility Index), mental function status (Standardized Mini-Mental State Examination), and delirium (Nu-DESC).

RESULTS

Statistically significant differences were observed in some assessment parameters within the groups during the 2nd and 3rd measurement times, with the intervention group demonstrating significant mean differences.

DISCUSSIONS

The literature underscores the increase in kinesiophobia scores post-general/abdominal surgery in older adults, emphasizing the importance of evaluating functional level and kinesiophobia to expedite discharge processes and potentially plan early post-discharge rehabilitation to mitigate readmissions for functional reasons.

CONCLUSIONS

Ultimately, foot massage was found to be effective in reducing kinesiophobia, improving balance, mobility, daily living skills, and mental status in older adults post-abdominal surgery, thereby advocating for the facilitation of post-discharge rehabilitation programs or the reduction of readmission rates.

THE CLINICAL TRIALS NUMBER

NCT05534490.

Identifying and prioritizing the key performance indicators for hospital management dashboard at a national level: Viewpoint of hospital managers

Participation of main users in identifying key performance indicators (KPIs) for management dashboards contributes to their success. The aim of this study was to identify and prioritize the KPIs of hospital management dashboards from the viewpoint of hospital managers. This study was conducted on managers of public hospitals at a national level in Iran in 2020. Data were collected using a self-administrated questionnaire. The KPIs were classified into five categories, namely financial, operational, human resources, safety and quality of care, services provided to patients. A total of 234 hospital managers participated in this study. Totally, 25 KPIs were determined for the hospital management dashboard, including the patient falls rate, waiting time for patients in the emergency department, patient satisfaction, total hospital revenue, financial balance, bed occupancy rate, patients' discharge with own agreement, average length of stay, and personnel satisfaction. For designing hospital management dashboards, the domains of services provided to patients, safety and quality of care, financial resources, human resources, and operational are important from the hospital managers' viewpoint, respectively. The results of this study can be helpful for developers of business intelligence tools, such as hospital management dashboards, to visualize the most important indicators for managers.

Frequency, Characteristics, and Risk Factors for Falls at an Inpatient Cancer Rehabilitation Unit

PURPOSE

Falls in the hospital can lead to adverse events, including injuries. Studies have shown that patients with cancer and those undergoing inpatient rehabilitation (IPR) are at higher risk for falls. Therefore, we measured the frequency, degree of harm, and characteristics of patients who fell in an inpatient cancer rehabilitation unit.

METHODS

A retrospective review was conducted on inpatient cancer rehabilitation patients admitted from January 2012 to February 2016. Fall frequency, degree of harm, fall circumstances, cancer type, patient's fall risk score on the basis of the MD Anderson Cancer Center Adult Inpatient Fall Risk Assessment Tool (MAIFRAT), length of stay, and risk factors were evaluated for patients.

RESULTS

There were 72 out of 1,571 unique individual falls (4.6%), with a falls incidence of 3.76 falls per 1,000 patient-days. Most fallers (86%) suffered no harm. Risk factors for falls included presence of patient-controlled analgesia pump (P = .03), pump such as insulin or wound vacuum-assisted closure (P < .01), nasogastric, gastric, or chest tube (P = .05), and higher MAIFRAT score (P < .01). The fallers were younger (62 v 66; P = .04), had a longer IPR stay (13 v 9; P = .03), and had a lower Charlson comorbidity index (6 v 8; P < .01).

CONCLUSION

The frequency and degree of harm for falls in the IPR unit were less than previous studies, which suggests that mobilization for these patients with cancer is safe. The presence of certain medical devices may contribute to fall risk, and more research is needed to better prevent falls in this higher-risk subgroup.

Fall Prevention With the Smart Socks System Reduces Hospital Fall Rates

BACKGROUND

Falls of inpatients are common in hospitals. Existing fall prevention measures do not work consistently.

PURPOSE

To determine whether Smart Socks reduce fall rates in fall risk patients at a major academic health center's neurological and neurosurgical based units.

METHODS

A prospective study was conducted that provided fall risk patients with Smart Socks and no other fall prevention system. Data collected included duration of Smart Socks wearing, number of alarms, response times, and patient-days.

RESULTS

A total of 569 fall risk patients were included for 2211.6 patient-days. There were 4999 Smart Socks alarms, but none of the patients fell. We observed a lower fall rate, of 0 per 1000 patient-days, for patients wearing Smart Socks than the historical fall rate of 4 per 1000 patient-days. The median nurse response time was 24 seconds.

CONCLUSIONS

The Smart Socks reduced fall rates of fall risk patients included in the study.

Identification of important factors in an inpatient fall risk prediction model to improve the quality of care using EHR and electronic administrative data: A machine-learning approach

BACKGROUND

Inpatient falls, many resulting in injury or death, are a serious problem in hospital settings. Existing falls risk assessment tools, such as the Morse Fall Scale, give a risk score based on a set of factors, but don't necessarily signal which factors are most important for predicting falls. Artificial intelligence (AI) methods provide an opportunity to improve predictive performance while also identifying the most important risk factors associated with hospital-acquired falls. We can glean insight into these risk factors by applying classification tree, bagging, random forest, and adaptive boosting methods applied to Electronic Health Record (EHR) data.

OBJECTIVE

The purpose of this study was to use tree-based machine learning methods to determine the most important predictors of inpatient falls, while also validating each via cross-validation.

MATERIALS AND METHODS

A case-control study was designed using EHR and electronic administrative data collected between January 1, 2013 to October 31, 2013 in 14 medical surgical units. The data contained 38 predictor variables which comprised of patient characteristics, admission information, assessment information, clinical data, and organizational characteristics. Classification tree, bagging, random forest, and adaptive boosting methods were used to identify the most important factors of inpatient fall-risk through variable importance measures. Sensitivity, specificity, and area under the ROC curve were computed via ten-fold cross validation and compared via pairwise t-tests. These methods were also compared to a univariate logistic regression of the Morse Fall Scale total score.

RESULTS

In terms of AUROC, bagging (0.89), random forest (0.90), and boosting (0.89) all outperformed the Morse Fall Scale (0.86) and the classification tree (0.85), but no differences were measured between bagging, random forest, and adaptive boosting, at a p-value of 0.05. History of Falls, Age, Morse Fall Scale total score, quality of gait, unit type, mental status, and number of high fall risk increasing drugs (FRIDs) were considered the most important features for predicting inpatient fall risk.

CONCLUSIONS

Machine learning methods have the potential to identify the most relevant and novel factors for the detection of hospitalized patients at risk of falling, which would improve the quality of patient care, and to more fully support healthcare provider and organizational leadership decision-making. Nurses would be able to enhance their judgement to caring for patients at risk for falls. Our study may also serve as a reference for the development of AI-based prediction models of other iatrogenic conditions. To our knowledge, this is the first study to report the importance of patient, clinical, and organizational features based on the use of AI approaches.

The impact of post-fall huddles on repeat fall rates and perceptions of safety culture: a quasi-experimental evaluation of a patient safety demonstration project

BACKGROUND

Conducting post-fall huddles is considered an integral component of a fall-risk-reduction program. However, there is no evidence linking post-fall huddles to patient outcomes or perceptions of teamwork and safety culture. The purpose of this study is to determine associations between conducting post-fall huddles and repeat fall rates and between post-fall huddle participation and perceptions of teamwork and safety culture.

METHODS

During a two-year demonstration project, we developed a system for 16 small rural hospitals to report, benchmark, and learn from fall events, and we trained them to conduct post-fall huddles. To calculate a hospital's repeat fall rate, we divided the total number of falls reported by the hospital by the number of unique medical record numbers associated with each fall. We used Spearman correlations with exact P values to determine the association between the proportion of falls followed by a huddle and the repeat fall rate. At study end, we used the TeamSTEPPS® Teamwork Perceptions Questionnaire (T-TPQ) to assess perceptions of teamwork support for fall-risk reduction and the Hospital Survey on Patient Safety Culture (HSOPS) to assess perceptions of safety culture. We added an item to the T-TPQ for respondents to indicate the number of post-fall huddles in which they had participated. We used a binary logistic regression with a logit link to examine the effect of participation in post-fall huddles on respondent-level percent positive T-TPQ and HSOPS scores. We accounted for clustering of respondents within hospitals with random effects using the GLIMMIX procedure in SAS/STAT.

RESULT

Repeat fall rates were negatively associated with the proportion of falls followed by a huddle. As compared to hospital staff who did not participate in huddles, those who participated in huddles had more positive perceptions of four domains of safety culture and how team structure, team leadership, and situation monitoring supported fall-risk reduction.

CONCLUSIONS

Post-fall huddles may reduce the risk of repeat falls. Staff who participate in post-fall huddles are likely to have positive perceptions of teamwork support for fall-risk reduction and safety culture because huddles are a team-based approach to reporting, adapting, and learning.

Factors influencing fall prevention for patients with spinal cord injury from the perspectives of administrators in Canadian rehabilitation hospitals

Background

Fall prevention is a priority in Canadian tertiary rehabilitation hospitals. We aimed to understand the perspectives of hospital administrators on the challenges experienced when implementing fall prevention policies/procedures for patients with spinal cord injury (SCI) in tertiary rehabilitation hospitals.

Methods

Semi-structured interviews were conducted with 10 administrators employed in six Canadian tertiary rehabilitation hospitals. Guided by an interpretive description framework, interviews were analyzed using a constant comparison approach.

Results

Challenges with fall prevention experienced by administrators fell into the three categories: 1) fall prevention policy and procedural challenges (e.g. fall prevention policy not SCI-specific, expectation of zero falls, determining contributing factors, learning from falls, and overall effectiveness of the fall prevention policy), 2) clinician-related challenges (e.g. variable staff adherence with the organizations’ fall prevention procedures, inconsistent delivery of fall prevention education, and integrating individualized fall risks to guide clinical practice), and 3) patient-related challenges (e.g. balancing risk vs independence and rehabilitation progress, responsibility for fall prevention, and non-preventable falls).

Conclusions

Fall prevention policies/procedures required by the hospitals were insufficient for clinical practice in SCI rehabilitation.

Electronic supplementary material

The online version of this article (10.1186/s12913-019-4233-8) contains supplementary material, which is available to authorized users.

Falls among Hospitalized Patients in an Acute Care Hospital: Analyses of Incident Reports

Falls cause injuries such as fractures, skin lacerations, bleeding, and head injury, and could result in more severe medical conditions in hospitalized patients. We retrospectively investigated the incidence and characteristics of falls among hospitalized patients in an acute care hospital from incident reports by hospital staff between January and June 2013. There were 154 falls in 135 patients, 2 of which resulted in fracture. The average age of patients who fell was 63.9 (range 0 to 91) years. Many falls occurred at the bedside (68.2%). Approximately half of all falls were related to elimination (46.6%). The most common time of discovery of falls was 2:00-2:59 AM (14/154;9.1%), followed by early in the morning when patients would actively move. Fall rates in our hospital were 1.39 falls per 1,000 patient days. The department of respiratory medicine and rheumatology had the highest fall rate (3.08 falls per 1,000 patient days), followed by the departments of neurosurgery and neurology (2.98 falls per 1,000 patient days). This study revealed the characteristics of falls in an acute care hospital, and suggests that their notification in the hospital might help reduce the incidence of falls in hospitalized patients. J. Med. Invest. 65:81-84, February, 2018.

Does acute care for the elderly (ACE) unit decrease the incidence of falls?

To determine whether acute care for the elderly (ACE) units decrease the incidence of patient falls compared to general medical and surgical (GMS) units, a non-concurrent prospective study included individuals aged 65 and older admitted to ACE or GMS units over a 2-year span was done. There were 7069 admissions corresponded to 28,401 patient-days. A total of 149 falls were reported for an overall incidence rate (IR) of 5.2 falls per 1000 patient-days, 95% CI, 4.4/1000-6.1/1000 patient-days. The falls IR ratio for patients in ACE unit compared to those in non-ACE units after adjusting for age, sex, prescribed psychotropics and hypnotics, and Morse Fall Score was 0.27/1000 patient-days; 95% CI, 0.13-0.54; p < 0.001. So, an estimated 73% reduction in patient falls between ACE unit and non-ACE units. Hospitals may consider investing in ACE units to decrease the risk of falls and the associated medical and financial costs.

Decreasing Falls in Acute Care Medical Patients: An Integrative Review

Falls in acute care medical patients are a complex problem impacted by the constantly changing risk factors affecting this population. This integrative literature review analyzes current evidence to determine factors that continue to make falls a top patient safety problem within the medical unit microsystem. The goal of this review is to develop an evidence-based structure to guide process improvement and effective use of organization resources.

Associations between hyponatraemia, volume depletion and the risk of falls in US hospitalised patients: a case-control study

OBJECTIVE

We aimed to determine if abnormal laboratory values which may indicate volume depletion are associated with increased odds of experiencing a hospital-acquired fall.

DESIGN

Matched case-control study.

SETTING

Four hospitals located in the Southeast USA.

PARTICIPANTS

Data from 699 adult fallers and 1189 matched controls (non-fallers) were collected via chart review from 2005 to 2010. Controls were matched to cases by nursing unit, time of fall and length of stay.

OUTCOME MEASURES

The primary exposures included serum sodium, blood urea nitrogen (BUN), creatinine, BUN/creatinine ratio and haematocrit. Conditional logistic regression with m:n matching was used to determine adjusted and unadjusted ORs.

RESULTS

Serum sodium levels were strongly associated with falls. In models controlling for demographic and other fall risk factors, patients with serum sodium levels of 125 mEq/L or less were associated with increased odds of experiencing a fall as compared with those with serum sodium levels of greater than 134 mEq/L (adjusted OR (aOR)=5.08, 95% CI 1.43 to 18.08). Conversely, elevated BUN, creatinine and elevated BUN/creatinine ratios were not associated with increased odds of experiencing a fall (aOR=0.64, 95% CI 0.49 to 0.84; aOR=0.70, 95% CI 0.54 to 0.92 and aOR=0.77, 95% CI 0.58 to 1.04, respectively.) CONCLUSIONS: Laboratory indices that may indicate volume depletion appear to be unrelated to falls. However, hyponatraemia does appear to be a risk factor for falls, and those with serum sodium levels below 126 mEq/L are at especially high risk. It may be that other deficits associated with hyponatraemia, like altered mental status, are associated with risk of experiencing a hospital-acquired fall. These results indicate that abnormal laboratory values, like low sodium, can be useful for identifying hospitalised patients at risk of falling. Therefore, further investigation into abnormal laboratory values as predictors of hospital-acquired falls is warranted.

Can Nursing Units With High Fall Rates Be Identified Using One Year of Data? Reliability of Fall Rates As a Function of the Number of Quarters on Which They Are Based

Reliability-the extent to which multiple measurements of a target yield similar results-is critical in comparing healthcare provider quality. Hospital unit fall rates are widely tracked and used for benchmarking, but their reliability is not well-studied. Our twofold purpose was to estimate fall rate reliability, both in terms of signal (between-unit variability) relative to noise (within-unit variability) and in terms of the accuracy with which units can be classified as high-fall units; and to assess reliability as a function of the number of quarters of data used to compute fall rates. Using year 2013 data from 11,765 critical care, step-down, medical, surgical, medical-surgical, and rehabilitation units in 1,552 US hospitals, we identified high-fall-rate units, computed units' signal-noise reliability, and simulated data to assess accuracy of high-fall-rate unit classification as a function of quarters of data. When critical care units were excluded, median unit type signal-noise reliabilities for annual total and injurious fall rates, respectively, ranged from .74 to .82 and from .53 to .68. In simulation, seven quarters of data were sufficient to achieve top-decile misclassification rates at or below 10% for all unit types except critical care. Top-quartile misclassification rates were higher; even 12 quarters of data did not consistently yield top-quartile misclassification rates below 10%. In the absence of long-term data, and for units with low patient volume and unit types with very low fall rates, comparison with a unit's own historical data may be more helpful for quality monitoring than attempting to rank it among its peers. © 2016 Wiley Periodicals, Inc.

Reliability assessment of a hospital quality measure based on rates of adverse outcomes on nursing units

The purpose of this study was to develop methods for assessing the reliability of scores on a widely disseminated hospital quality measure based on nursing unit fall rates. Poisson regression interactive multilevel modeling was adapted to account for clustering of units within hospitals. Three signal-noise reliability measures were computed. Squared correlations between the hospital score and true hospital fall rate averaged 0.52 ± 0.18 for total falls (0.68 ± 0.18 for injurious falls). Reliabilities on the other two measures averaged at least 0.70 but varied widely across hospitals. Parametric bootstrap data reflecting within-unit noise in falls were generated to evaluate percentile-ranked hospital scores as estimators of true hospital fall rate ranks. Spearman correlations between bootstrap hospital scores and true fall rates averaged 0.81 ± 0.01 (0.79 ± 0.01). Bias was negligible, but ranked hospital scores were imprecise, varying across bootstrap samples with average SD 11.8 (14.9) percentiles. Across bootstrap samples, hospital-measure scores fell in the same decile as the true fall rate in about 30% of cases. Findings underscore the importance of thoroughly assessing reliability of quality measurements before deciding how they will be used. Both the hospital measure and the reliability methods described can be adapted to other contexts involving clustered rates of adverse patient outcomes.