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Implementation of a multifactorial fall intervention model to guide hospital nurses: A quasi- experimental before-and-after study Changju Liao1,4, Linghong Guo2,3,4, Pengjie Li2 & Yin Liu2

Falls are serious public health problems associated with irreversible health consequences and substantial economic burden, which are currently difficult to prevent and manage. Nurses have always been the main force in the prevention and control of falls. To develop and evaluate a comprehensive fall intervention model for hospital nurses to manage falls. This was a quasi-experimental study to evaluate the effect of a newly designed hospital fall intervention model on fall management by nurses. The control group consisted of 153,601 hospitalized patients who received standard fall care from 2015 to 2016, while the Multifactorial Fall Intervention Model (MFIM) group included 171,776 inpatients managed with the new intervention model from 2017 to 2018. Patients’ information and data were extracted from the medical records of our hospital. We recorded a total of 396 falls in the MFIM group with a remarkably declined fall rate (MFIM group: 0.22% vs. control group: 0.31%, p = 0.000) and fall rate per 1000 patient-days (0.22‰ vs. 0.29‰, p = 0.000) as compared with a total of 491 falls in the control group. The adjusted incidence rate ratio of falls was 0.721. Furthermore, the occurrence and the severity of fall injuries in the MFIM group were significantly lower than that in the control group. The MFIM model demonstrated a favorable effect in reducing the occurrence of falls and fall-related injuries among hospitalized patients. These findings suggest that the implementation of MFIM can significantly enhance patient safety and should be considered for broader adoption in hospital settings to mitigate fall risks.

Keywords Fall, Nursing guidance, Fall injuries, Fall prevention, Model

Falls are reported to be the most common adverse events in hospitals, accounting for 20–30% of all incident reports1–3. Due to unfamiliar environment, diseases and treatments, hospitalization increases fall risks4,5. In terms of hospital departments, geriatric and rehabilitation wards have higher rates of falls than surgical or acute care wards6,7. According to the Centers for Disease Control and Prevention (CDC) of the United States, approximately 700,000 to 1,000,000 older adults fall in hospitals each year, resulting in around 250,000 injuries and up to 11,000 deaths8,9. The fall rate ranges from 3.3 to 11.5 falls per 1,000 patient days10. In an Australian context, there were around 47,551 falls in hospitals during 2020–2021 that resulted in patient harm11. Falling can lead to different degrees of injury, prolong hospital stay, increase hospitalization costs, and even lead to legal disputes between the hospital and patients12–14. Approximately 30% of in-hospital falls were associated with various degrees of physical injuries15. Even non-injurious falls may result in psychological stress, functional decline and increasing the morbidity of second falls16.

In the past 20 years, considerable attention has been paid to reduce fall incidence and fall-related injuries in hospital settings. In particular, the exploration of fall prevention measures has attracted a large number of investigators17,18. Related research has also yielded many meaningful results, such as the progress of a series of fall assessment tools and the improvement of fall prevention measures for use in hospitals19. Accordingly, the importance of fall (when falls occur) and post-fall interventions is often overlooked. However, interventions for

1Department of Nursing, Zigong First People’s Hospital, Zigong, Sichuan, China. 2Department of Pharmacology and Nursing, Sichuan Vocational College of Health and Rehabilitation, Zigong First People’s Hospital, Zigong, Sichuan, China. 3Derpartment of Dermatology, West China Hospital, Sichuan University, Chengdu, Sichuan, China. 4These authors contributed equally to this work: Changju Liao and Linghong Guo. email: [email protected]

OPEN

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falls should be multiprocessing interventions involving fall prevention, fall-onset management and improvement measures after falls. In addition, many studies are only devoted to exploring the role of single factor changes in the prevention and treatment of falls20. Falls are multifactorial accidents, and interventions for falls should also include management of multiple risk factors21. Multifactorial fall prevention measures often fall short due to gaps such as lack of individualized risk assessments, inadequate interdisciplinary coordination, challenges in maintaining long-term adherence, and insufficient patient and family engagement2,20. These issues hinder the effectiveness of fall prevention strategies in clinical settings, underscoring the need for more integrated, and sustained approaches.

Nurses and nursing management play key roles in patient care and the level of nursing staff is highly associated with patient outcomes22,23. A pooled analysis showed that higher levels of nurse staffing reduced the risk of inpatient mortality by 14%22. Nurses are an important part of the hospital’s interdisciplinary team, which has been gradually implementing multiple interventions in recent years to prevent falls more effectively24,25. However, available data showed that in 2017 alone, 490 hospitals in China recorded 18,024 falls, an increase of 57.5% from 201426. The persistent incidence of falls has also captured the attention of our nursing team, highlighting deficiencies in the current fall intervention system.

In order to effectively guide nurses’ work for the management of falls, reduce the occurrence of falls and mitigate the injury caused by falls, we designed and examined a multifactorial fall intervention model (MFIM) based on a total of 325,377 inpatients. To our knowledge, this is the first systematic multifactorial fall intervention model that includes measures for pre-fall prevention, fall-onset management, and continuous improvement after falls.

Method Study design, setting and participants Our study was performed in accordance with the Declaration of Helsinki and reported in accordance with the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guidelines27. This study has been approved by the medical ethics committee of Zigong First People’s Hospital (Reference Code: ZG2014917001). All enrolled patients signed the informed consent.

This is a quasi-experimental study consisted of three phases of investigation which developed and examined the performance of a newly designed hospital fall intervention model as an aid to falls management by nurses. In the first stage, nursing teams from 26 departments of Zigong First People’ s Hospital retrospectively identified clinical characteristics of fallers from 2015 to 2016. In the second phase, nurses from the nursing department led and designed the MFIM and in the third phase, nursing teams prospectively evaluated the model from 2017 to 2018. Our study was conducted in the Zigong First People’s Hospital. We sampled a total of 325,377 inpatients from 26 wards between 2015 and 2018. All patients were divided into two groups, with the control group of 153,601 hospitalized patients receiving usual fall care (phase 1) and the MFIM group of 171,776 hospitalized patients being managed with the new fall intervention model (phase 3). The two groups were matched in baseline characteristics (control group: mean age: 48.06 ± 0.067, Male: Female: 74435:79166; MFIM group: mean age: 49.85 ± 0.062, Male: Female: 83123:88653, P > 0.05).

The 26 clinical departments served as the sites where the study was implemented, responsible for daily clinical care. The nursing department consists of nurses from all clinical departments within the hospital. They played a pivotal role in the study as the designers and leaders of the fall intervention strategy (MFIM). All patients who provided informed consent were included, with the only exclusion criterion being patients who declined to participate in the study. Nurses maintained ongoing communication with participants throughout the study, emphasizing the importance of their continued involvement.

Phase 1: identification of clinical characteristics of in-patient falls During this phase, we included patients from 26 departments for fall risk assessment after admission. We recorded detailed information on gender, age, medication, clinical features, disease, the time, location, and environment of the fall, activities before the fall, and injuries due to the fall.

Phase 2: analysis of clinical characteristics and reflection on existing defects to derive the MFIM According to the analysis of the baseline characteristics of patients with falls (phase 1, control group), the occurrence and injury of falls were related to various factors, including hospital management, medical staff, patients themselves, family members, hospital environment and hospital equipment. Based on the results of phase 1, clinical experiences, and literature reviews, we developed the MFIM, which includes targeted improvement measures. Following this, in January 2017, we organized a concentrated two-week training session for all nurses in the hospital. The MFIM was established with a standardized fall management team and a detailed and complete fall intervention process.

MFIM The usual fall care of the control group was only composed of fall risk assessment using the original tool referring to the Thomas Fall Risk Assessment Scale28 and the Johns Hopkins Fall Risk Assessment Tool29 (Fig. 1), and general fall intervention strategies shown in supplementary file 1.

The new fall intervention model used in the MFIM group included measures for fall prevention, fall management and continuous improvement, shown in detail as follows (Fig. 2):

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Part I: fall prevention In the fall prevention phase, nurses assessed the risk of falls for patients admitted to the hospital using a revised fall risk assessment tool (Fig. 1). From our phase 1 analysis of 153,601 hospitalized patients’ fall characteristics, we identified that patient age, the use of certain medications, and clinical symptoms each contributed to an increased risk of falls. Therefore, we revised the original fall risk assessment form, with modifications marked in red.

For patients with a risk score of less than 4, flexible and ongoing assessments were implemented primarily by nurses and caretaking staff, based on changes in the patient’s physical condition, mental status, medication use, and other relevant factors, to ensure a safe treatment environment (Supplementary file 1). For patients at moderate to high risk (≥ 4 points), periodic assessments were conducted by nurses, and intensive interventions

Fig. 1. The modified fall risk assessment tool. The figure illustrated the revised fall risk assessment tool, which incorporated findings from the Phase 1 analysis of 153,601 hospitalized patients. The analysis identified that patient age, the use of certain medications, and clinical symptoms each contributed to an increased risk of falls. The modifications were highlighted in red.

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were coordinated by nurses, physicians, patients, their families, and caregivers. Nurses carried out fall prevention interventions according to nursing guidelines and educated patients and their families about fall prevention (Supplementary file 2). Caretaking staff improved the patient’s environment to reduce falls caused by external factors (Supplementary file 3).

Part II: fall management When a patient fell, the nurse in charge immediately took the patient’s vital signs, checked for injuries, and notified the physician. Treatments were administered according to the severity of the patient’s injury. After providing first aid, the nurse closely observed the patient’s condition. The nurse in charge then filled out the Fall Cause Analysis Scale to analyze the causes and formulated measures to prevent recurrence (Supplementary file 4). Finally, the nurse reported the fall according to the fall reporting procedure (Supplementary file 1).

Part III: continuous improvement measures Continuous improvement measures include training for the fall care workforce and inspection and reflection on previous fall care work. Regular safety education and training is designed to improve medical staff ’s skills in fall prevention and management. The nursing department holds special fall inspection activities every two months, focusing on the following four areas: (1) timely assessment of high-risk fall; (2) timely and correct fall prevention measures; (3) active, correct and effective treatment measures after a fall; (4) timely summary and analysis of the causes of falls.

Phase 3: evaluation of the efficacy of the MFIM in reducing fall rate and fall injuries The new model was examined on patients of the MFIM group. After a patient fell, the nurse in charge or on duty filled out the Fall Cause Analysis Scale and recorded detailed data. The incidence of falls, fall injury rates and other clinical characteristics of falls in the MFIM group were calculated and compared with the data of the control group in phase 1.

Data collection All information and data were collected by reviewing electronic health records including medical records, nursing records and reports of adverse events as well as interviewing patients, family members of patients, doctors in charge of patients, and medical staff on duty. The fall risk assessment and collection of fall-related information were all completed by nursing teams in each department. To address potential drop-out rates, we maintained ongoing communication with participants throughout the study, emphasizing the importance of their continued involvement.

Variables A fall was defined as a sudden, involuntary, unintentional change in position, falling on the ground or on a lower plane. Patients’ characteristics including sex, age, drugs, clinical features, diseases, wards, time of fall, places of fall and activities when falling were collected. Among them, patients’ medication refers to the drugs they

Fig. 2. Multifactorial fall intervention workflow: fall prevention, fall management and continuous improvement measures.

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were introduced during hospital admission. Clinical features represent the complaints and clinical conditions of patients during hospitalization. Diseases are the diagnoses of patients on admission. The classification of fall injuries was defined as: (1) none. No harm; (2) severity grade 1 (mild). The degree of injury that requires only minor treatment and observation, such as bruises, contusions, small lacerations of the skin that do not require stitches, etc.; (3) severity grade 2 (moderate). The degree of injury that requires medical or nursing treatment or observation including icing, dressing, stitching or splint, such as sprains, large or deep lacerations, skin tears or minor contusions; (4) severity grade 3 (severe). The degree of injury that requires medical treatment and consultation, such as fracture, loss of consciousness, mental or physical changes; (5) death. The patient died from sustained injuries caused by the fall.

The fall rate was calculated by dividing the number of inpatients who fell by the total number of inpatients over a period (percentage), which was an assessment of overall falls.

F all rate = Number of patients with falls

T otal number of patients × 100%.

The fall rate per 1000 patient-days was defined as the ratio of the number of inpatient falls (with or without injury) to the number of inpatient bed occupancy days over a period (permillage), which was associated with fall injuries.

F all rate per 1000 patient − days = Number of falls

1000 × patient − days × 1000�.

The primary outcome was the proportion of patients who fell in the hospital and the incidence rate ratio of falls, and the secondary outcome was the proportion and degrees of injuries sustained by patients who fell. In addition, we also collected and compared the number of complaints from fallers, the number of compensations for fallers and compensation amount for fallers between the two groups. The effect of the new fall assessment tool was statically analyzed using the fall scores and corresponding fall outcomes.

Statistical methods Categorical variables were compared using Fisher’s exact test for groups with small expected cell counts (< 5) and Pearson’s chi-square test when assumptions were met (Patient characteristics, fall rate and fall rate per 1000 patient-days). The Mann-Whitney non-parametric test was used for analyzing one-way ordered data, specifically for the secondary outcome of fall-related injuries. ROC curves for the new fall assessment tool were plotted and the area under the curve was calculated. Based on the logistic regression analysis, we assessed the role of each factor in increasing the risk of falling and adjusted results. Data analyses were performed on SPSS 26.0. P value < 0.05 was considered statistically significant and all tests were two-sided.

Results Basic characteristics of fallers Four hundred and ninety-one falls (0.31%) were recorded in 153,601 hospitalized patients in the control group. More than half of the falls (57.84%) occurred among patients aged 65 and over. Noticeably, in addition to the older adults, 47 falls of children aged 0–6 were also recorded, which accounted for a relatively high percentage (9.57%). According to medication records, 480 patients with falls were found to take drugs during hospitalization prior to the fall. Antihypertive drugs (39.58%), analgesics (15.63%) and hypoglycemic drugs (15.42%) were the most common used drugs in decreasing order of frequency. In terms of clinical features, the most common symptoms of fallers were hypodynamia (40.69%), dizziness (23.33%) and anemia (10.31%). As for diseases of fallers, circulatory system diseases (20.32%) were the most common, followed by metabolic system diseases (17.98%) and nervous system diseases (15.20%). As recorded, fallers were mainly from the department of neurology (13.24%), respiratory medicine (12.83%) and pediatrics (10.18%). In addition, in-patient falls were also characterized by time, place, and activities. In-patient falls peaked between 2 am and 8 am (36.86%) and between 4 pm and 6 pm. About places of falling, most falls were found at the bedside or in the washroom (73.52%). Correspondingly, most people were toileting or moving around the bedside before they fell down (78.82%).

For the MFIM group, a total of 396 falls occurred in 171,776 inpatients (0.22%). The percentage of falls in older adults over 65 years of age reached 56.06%, and the rate of falls in children between 0 and 6 years of age was 8.59%. The distribution of clinical features of fallers in the MFIM group was significantly different from that in the control group (p = 0.02), with fewer patients being anemic (6.28% vs. 10.31%). The distribution of diseases and the time of falls in the MFIM group was similar to that of the control group (p > 0.05). However, it is noteworthy that the distribution of the location of fall occurrence in the MFIM group were statistically different from that in the control group (p = 0.042).

Baseline characteristics of fallers were shown in Table 1; Fig. 3.

Proportion of falls A total of 396 falls occurred in 171,776 inpatients in the MFIM group as compared with 491 falls detected in 153,601 hospitalized patients in the control group (fall rate: MFIM group: 0.22% vs. control group: 0.31%, p = 0.000). The result of logistic regression analysis showed that patient’s age was significantly associated with fall. Before adjusting the data for age, patients who received intervention of our model had a lower odd of falls than those who did not receive intervention (incidence rate ratio: 0.721, 95%CI: 0.631–0.823, p = 0.000, MFIM group vs. control group). After adjusting the data for age, the incidence rate ratio was 0.693 (95%CI: 0.607–0.792,

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p = 0.000), as MFIM group versus control group. Furthermore, the fall rate per 1000 patient-days in the MFIM group was significantly lower than that in the control group (fall rate per 1000 patient-days: MFIM group: 0.22‰ vs. control group: 0.29‰, p = 0.000).

Fall injuries The result of Mann-Whitney U test revealed that the implement of our multi-factorial fall intervention model remarkably decreased the occurrence (injured falls rate: MFIM group: 57.58% vs. control group: 67.01%) and the severity of fall injuries in the MFIM group as compared with the control group (Z=−4.426, p = 0.000). Specifically, the proportion of mild fall injuries in the MFIM group was higher than that in the control group (32.58% vs. 27.49%), and the rate of severe fall injuries decreased in the MFIM group comparing with that in the control group (3.79% vs. 9.98%). In addition, the number of complaints (16 vs. 5) and compensation cases (12 vs. 3) and the amount of compensation for fallers in the MFIM group (339,579 vs. 10,000) have reduced sharply compared with those in the control group.

Effect of modified fall risk assessment tool The sensitivity and specificity of the new fall risk assessment tool for the identification of high-risk patients were 64.3% and 86.8% (the area under the curve: 0.821 (95%CI: 0.757–0.885)), respectively. There was no significant difference in fall rates between high risk and low risk groups in the control group using the original fall risk assessment tool (fall rate: high risk: 0.12% vs. low risk: 0.07%, p = 0.104). However, in the MFIM group with the modified assessment tool, patients in high-risk group had higher odds of falls compared with patients in low-risk group (fall rate: high risk: 0.05% vs. low risk: 0.01%, p = 0.015), which also suggested that the modified fall assessment tool could effectively identify people at high risk of falling. In addition, patients assessed as high risk received intensive intervention in the MFIM group. The number of falls decreased in patients treated with the intensive intervention in the MFIM group than those without intensive intervention in the control group (reduction in falls by 0.07%, p = 0.029).

The comparison of inpatient falls and fall injuries between the MFIM group and the control group was shown in Table 2.

Discussion Falls are a significant public health issue, leading to irreversible health consequences and substantial economic burdens, and are currently challenging to prevent and manage30. The aim of our study was to develop and assess a comprehensive fall intervention model designed to assist healthcare staff in effectively managing falls. Overall, the implementation of the MFIM reduced the incidence of falls in our hospital from 0.31% (control group) to 0.22% (MFIM group) and decreased the incidence of falls per 1000 patient-days from 0.29‰ (control group) to 0.22‰ (MFIM group). In addition, the occurrence (injured falls rate: MFIM group: 57.58% vs. control group: 67.01%) and the severity of fall injuries in the MFIM group were significantly lower than that in the control group. A meta-analysis by Cameron et al. (2018) tested multifactorial interventions in 13 trials2. The results showed uncertainty regarding the effect on the rate of falls and these interventions may make little or no difference to the risk of falling. However, a more recent meta-analysis revealed that data from twenty trials indicated multifactorial interventions may reduce the rate of falls compared to the comparator20. This was consistent with our findings, which demonstrated an even greater effectiveness in fall prevention. A significant improvement of our model is to prevent falls in high-risk patients from multiple aspects, which are mainly carried out by nurses, while doctors, patients and family members, and care-taking staff also participate. Published studies showed that up to 85% of falls occur when patients were alone31. A randomized controlled trial demonstrated that providing education to patients enabled them to conduct safe behaviors in hospital and effectively alert staff that they need assistance, especially when alone32. Family members’ perception of patients’ risk of fall is an important factor affecting patients’ fall, especially for children, older adults, and patients with cognitive impairment33. Therefore, greater awareness of risk factors in family members and medical staffs is required to predict and prevent falls. With regard to environmental factors, many high-quality research suggested marked reduction in fall risk after physical environmental intervention offered to high-risk patients34. Our analysis of the characteristics of fallers in the control group showed that 34.42% of falls occurred in the washroom, as the wet floor greatly increased the risk of falling. To specifically reduce the risk factors for falls in the environment, care-taking staffs were required to regularly evaluate and improve the patient’s physical environment.

Given that repeated falls can cause more severe physical and psychological harm to patients, implementing continuous improvement measures after falls is crucial. According to the literature report, approximately 10% of older adult patients experienced recurrent falls within a year that posed a subsequent health risk35. In our study, 17 out of 474 fallers had repeated falls at our hospital between 2015 and 2016. Given that falling again will cause more serious physical and psychological harm to the patient, it is crucial to have continuous improvement measures after falls, which organize medical staff to analyze and improve the deficiencies of previous interventions, minimizing the likelihood of a patient falling again. In addition to regular review of fall cases, our continuous improvement measures also include supervision and inspection of fall care by the leading nurse team, aiming at promoting the improvement of fall prevention in hospitals in the long term.

Our study included a diverse range of ward types-medical, surgical, and pediatric-to capture a broad spectrum of fall events. However, this diversity may introduce potential biases and limit the generalizability of our findings. Different ward environments and patient populations could lead to varying fall rates that are not solely due to the intervention. To address this, we performed a subgroup analysis examining fall rates within specific ward types. According to the subgroup analysis results, the number of falls in the MFIM group was significantly reduced compared to the control group in most clinical departments, with the most notable reductions observed in the respiratory medicine, pediatrics, and gastroenterology departments. These findings had several practical

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implications for hospital settings. The significant reduction in fall rates in various departments suggested that the MFIM could be effectively implemented across different clinical settings. This highlighted its adaptability and potential to improve patient safety in diverse hospital environments. Hospitals could tailor the MFIM to suit the specific needs and characteristics of different departments, ensuring that the intervention addressed the unique risks associated with each setting. In addition, our results revealed that the rate of falls was increased in

Control group MFIM group

Rate Difference (%) P value

Incidence rate ratio (MFIM group vs. control group)

Adjusted incidence rate ratio (MFIM group vs. control group)

Total N. of falls 491 396

Gender (M/F) 267/224 214/182 0.946

N. of repeated falls 17 13 0.18 0.833

Fall rate (No. of patients with falls/ total No. of patients) 474/153,601 383/171,776 0.09 0.0000 0.721 (95%CI: 0.631–

0.823),p = 0.000 0.693 (95%CI: 0.607– 0.792,p = 0.000

Fall rate per 1000 patient-days (No. of falls/1000 patient-days) 491/1,693,485 396/1,831,665 0.07 0.0000

Fall injuries 0.0000

Severity grade 0 162 168 −9.43

Severity grade 1 135 129 −5.08

Severity grade 2 145 84 8.32

Severity grade 3 49 15 6.19

Death 0 0

Gender (M/F) 194/140 128/100 0.665

No. of complaints from fallers 16 5 2.00 0.052

No. of compensations for fallers 12 3 1.69 0.053

Compensation amount for fallers (¥) 339,579 10,000

High risk (Fall rate, %) 0.12 0.05 0.029

Low risk (Fall rate, %) 0.07 0.01 0.002

P value 0.104 0.015

Table 2. Comparison of inpatient falls and fall injuries between the control group and the MFIM group. Abbreviations: MFIM: multifactorial fall intervention model; N: number; M: male; F: female; CI: confidence interval. The Pearson Chi-squared test was used to compare categorical variables between the two groups (gender, fall rate, fall rate per 1000 patient-days, No. of complaints from fallers, No. of compensations for fallers, risk rate). The Mann-Whitney non-parametric test was used for analyzing fall-related injuries.

Fig. 3. Baseline characteristics of fallers in the control group and the MFIM group.

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several departments including D. of TCM, D. of oncology, D. of endocrinology and D. of rehabilitation, which was consistent with published research36–38. Noticeably, patients in these departments were accompanied by relatively serious diseases, and the burden of medical staff was relatively arduous. To a certain extent, it had increased the difficulty of medical staff in preventing falls and managing falls, so it might cause an increase in the incidence of falls. In addition, most patients in these departments were older adult patients, who were at higher risk of falls than patients in other departments as proved findings4,39. Moreover, several patients in the department of endocrinology were involved in hypoglycemia with a higher risk of falls. In the rehabilitation department, there were more patients with physical dyskinesia in urgent need of sports rehabilitation, and their risk of fall was relatively high.

This study has several limitations, primarily related to the lack of randomization and potential biases. Originally designed as a quasi-experimental study, it did not employ randomization, which limits our ability to control for confounding variables and introduces potential selection bias. The decision not to use the original fall intervention system as a control group was based on its limited effectiveness and ethical concerns regarding patient safety. Specifically, the original system lacked timely risk reassessment and individualized interventions, which may have increased the risk of harm to vulnerable patients. Therefore, its continued use as a control was considered inappropriate and potentially unethical. Additionally, potential biases could arise from differences between the intervention and control groups, impacting the validity of our results. To address these limitations and enhance the model’s validity, we plan to compare it with currently accepted fall prevention systems, such as the STRATIFY or Morse Fall Scale, in future clinical trials. Moreover, our revised system is a multifactorial fall intervention model without specifying the effectiveness of a single intervention. Since falls are associated with many factors, we believe that only by intervening simultaneously from multiple perspectives can we minimize falls. As for its applicability, hospitals can learn from our model and improve upon their existing fall intervention systems. Finally, the inclusion of diverse ward types could introduce potential biases and limitations in the generalizability of our findings. Therefore, future research should focus on more homogenous ward settings or specific ward types to refine our understanding of fall prevention effectiveness.

Our fall intervention model has several advantages. Firstly, our model provided a well-structured and detailed guidance for nurses to manage falls, which might be instructive for nursing management and clinical work. Secondly, compared with the existing fall management systems, our model forms a complete fall intervention work-flow including measures for pre-fall prevention, fall-onset management, and continuous improvement after falls to minimize the incidence of fall. Specifically, few studies pay attention to the continuous improvement after falls. However, this is important for learning lessons to improve measures and prevent second fall. Thirdly, the approach we used in developing and applying the intervention were easy to implement. For nursing staff and care-taking staff, measures for fall risk factor assessment and management are all familiar medical operations. Our model was to form a workflow to fully ensure that their work is orderly and correct. In addition, involving patients and their families in falling interventions in the form of written and oral education is also a common method in medicine to protect patients’ health together with medical staff. Lastly, our fall intervention model was constructed based on a large sample of more than 320,000 inpatients, which reflects the reliability of its effectiveness to some extent.

Conclusions In conclusion, our MFIM effectively reduced both the incidence of falls and fall-related injuries among hospitalized patients. This model provided a well-structured and detailed guidance for nurses to manage falls, which was instructive in clinical works for nursing facility managers and nurses. Future research should focus on several key areas to further validate and refine the model. Conducting randomized controlled trials (RCTs) will help compare this model with existing fall prevention strategies, establishing its relative effectiveness. Longitudinal studies are needed to assess the model’s long-term impact on fall rates and injury severity. Evaluating the model in diverse healthcare settings, including various types of hospitals and long-term care facilities, will determine its adaptability.

Implications for nursing management, practice and education In this research, we designed a new multifactorial fall intervention model which included measures for pre-fall prevention, fall-onset management, and continuous improvement after falls occur, and the results showed that the model was significantly effective in decreasing the occurrence of falls and the severity of fall injuries. This model emphasizes the importance of nurses’ work for the prevention of falls and related injuries, and further optimizes the process and content of nurses’ work for the management of falls, which might be instructive for nursing management and clinical work. Hospital nurses and its managing party could potentially benefit from our model to systematically manage, prevent and continuously improve hospital falls. This model can be used as a hands-on guide for nurses to manage hospital falls and a fall prevention system for the nursing management. Taken together, we developed and tested a comprehensive multifactorial fall intervention model to help nurses and nursing management parties to better prevent and manage hospital falls.

Data availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Received: 15 April 2024; Accepted: 18 June 2025

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Author contributions Changju Liao, Linghong Guo and Pengjie Li prepared all figures and wrote the main manuscript. Linghong Guo revised the manuscript. Yin Liu designed the study. All authors reviewed the manuscript.

Funding This work is supported by Sichuan Provincial Science and Technology Program 2024 (24KPZP0258).

Declarations

Ethics approval and consent to participate This study has been approved by the medical ethics committee of Zigong First People’s Hospital (Reference Code: ZG2014917001). All enrolled patients signed the informed consent.

Competing interests The authors declare no competing interests.

Additional information Supplementary Information The online version contains supplementary material available at h t t p s : / / d o i . o r g / 1 0 . 1 0 3 8 / s 4 1 5 9 8 - 0 2 5 - 0 8 0 9 6 - x .

Correspondence and requests for materials should be addressed to L.G.

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