New predictive models for falls among inpatients using public ADL scale in Japan: A retrospective observational study of 7,858 patients in acute care setting

Risk of falls associated with non-GABAergic hypnotics and benzodiazepines in hospitalized patients

OBJECTIVE

Non-GABAergic hypnotics introduced into clinical practice within the last two decades are pharmacologically presumed to have a lower risk of falls, but clinical investigations are scarce. We aimed to evaluate the risk of falls associated with different classes of hypnotics, namely benzodiazepines, ramelteon, suvorexant, and trazodone, in hospitalized individuals at a general hospital.

METHOD

In this retrospective cohort study, data on the incidence of falls, hypnotic use, age, sex, body mass index (BMI), activities of daily living (ADL) score, presence of surgery, emergency admission, and ambulance use were collected for hospitalized patients aged 20 years or older who had been discharged from a tertiary general hospital in Japan between April 1, 2014, and March 31, 2019. The Cox proportional hazards model was used to examine factors associated with falls, adjusting for other demographics as covariates.

RESULTS

Among 28,029 patients, 383 falls occurred in 322 patients. Ramelteon or suvorexant was not associated with an elevated incidence of falls (adjusted hazard ratio [aHR], 0.78; 95 % CI, 0.34 to 1.81 and 0.44; 95 % CI, 0.13-1.46, respectively), in contrast to benzodiazepines (aHR, 2.17; 95 % CI 1.67-2.83) or trazodone (aHR, 1.96; 95 % CI 1.25-3.07). Advanced age, lower BMI, wheelchair dependency, non-surgical status, absence of emergency admissions, and ambulance use were also associated with the elevated incidence of falls.

CONCLUSIONS

In hospitalized patients at general hospitals, ramelteon and suvorexant may not increase the risk of falls, while the use of benzodiazepines and trazodone requires careful attention to minimize this risk.

Predictive Factors and the Predictive Scoring System for Falls in Acute Care Inpatients: Retrospective Cohort Study

Background

Falls in hospitalized patients are a serious problem, resulting in physical injury, secondary complications, impaired activities of daily living, prolonged hospital stays, and increased medical costs. Establishing a fall prediction scoring system to identify patients most likely to fall can help prevent falls among hospitalized patients.

objectives

This study aimed to identify predictive factors of falls in acute care hospital patients, develop a scoring system, and evaluate its validity.

Methods

This single-center, retrospective cohort study involved patients aged 20 years or older admitted to Shizuoka General Hospital between April 2019 and September 2020. Demographic data, candidate predictors at admission, and fall occurrence reports were collected from medical records. The outcome was the time from admission to a fall requiring medical resources. Two-thirds of cases were randomly selected as the training set for analysis, and univariable and multivariable Cox regression analyses were used to identify factors affecting fall risk. We scored the fall risk based on the estimated hazard ratios (HRs) and constructed a fall prediction scoring system. The remaining one-third of cases was used as the test set to evaluate the predictive performance of the new scoring system.

Results

A total of 13,725 individuals were included. During the study period, 2.4% (326/13,725) of patients experienced a fall. In the training dataset (n=9150), Cox regression analysis identified sex (male: HR 1.60, 95% CI 1.21-2.13), age (65 to <80 years: HR 2.26, 95% CI 1.48-3.44; ≥80 years: HR 2.50, 95% CI 1.60-3.92 vs 20-<65 years), BMI (18.5 to <25 kg/m²: HR 1.36, 95% CI 0.94-1.97; <18.5 kg/m²: HR 1.57, 95% CI 1.01-2.44 vs ≥25 kg/m²), independence degree of daily living for older adults with disabilities (bedriddenness rank A: HR 1.81, 95% CI 1.26-2.60; rank B: HR 2.03, 95% CI 1.31-3.14; rank C: HR 1.23, 95% CI 0.83-1.83 vs rank J), department (internal medicine: HR 1.23, 95% CI 0.92-1.64; emergency department: HR 1.81, 95% CI 1.26-2.60 vs department of surgery), and history of falls within 1 year (yes: HR 1.66, 95% CI 1.21-2.27) as predictors of falls. Using these factors, we developed a fall prediction scoring system categorizing patients into 3 risk groups: low risk (0-4 points), intermediate risk (5-9 points), and high risk (10-15 points). The c-index indicating predictive performance in the test set (n=4575) was 0.733 (95% CI 0.684-0.782).

Conclusions

We developed a new fall prediction scoring system for patients admitted to acute care hospitals by identifying predictors of falls in Japan. This system may be useful for preventive interventions in patient populations with a high likelihood of falling in acute care settings.

Correction: New predictive models for falls among inpatients using public ADL scale in Japan: A retrospective observational study of 7,858 patients in acute care setting

[This corrects the article DOI: 10.1371/journal.pone.0236130.].

Validation of the Saga Fall Injury Risk Model

Background: Predicting fall injuries can mitigate the sequelae of falls and potentially utilize medical resources effectively. This study aimed to externally validate the accuracy of the Saga Fall Injury Risk Model (SFIRM), consisting of six factors including age, sex, emergency transport, medical referral letter, Bedriddenness Rank, and history of falls, assessed upon admission. Methods: This was a two-center, prospective, observational study. We included inpatients aged 20 years or older in two hospitals, an acute and a chronic care hospital, from October 2018 to September 2019. The predictive performance of the model was evaluated by calculating the area under the curve (AUC), 95% confidence interval (CI), and shrinkage coefficient of the entire study population. The minimum sample size of this study was 2,235 cases. Results: A total of 3,549 patients, with a median age of 78 years, were included in the analysis, and men accounted for 47.9% of all the patients. Among these, 35 (0.99%) had fall injuries. The performance of the SFIRM, as measured by the AUC, was 0.721 (95% CI: 0.662-0.781). The observed fall incidence closely aligned with the predicted incidence calculated using the SFIRM, with a shrinkage coefficient of 0.867. Conclusions: The external validation of the SFIRM in this two-center, prospective study showed good discrimination and calibration. This model can be easily applied upon admission and is valuable for fall injury prediction.

The Administration of Lemborexant at Admission is Not Associated with Inpatient Falls: A Multicenter Retrospective Observational Study

Purpose

There has been no large-scale investigation into the association between the use of lemborexant, suvorexant, and ramelteon and falls in a large population. This study, serving as a pilot investigation, was aimed at examining the relationship between inpatient falls and various prescribed hypnotic medications at admission.

Patients and Methods

This study was a sub-analysis of a multicenter retrospective observational study conducted over a period of 3 years. The target population comprised patients aged 20 years or above admitted to eight hospitals, including chronic care, acute care, and tertiary hospitals. We extracted data on the types of hypnotic medications prescribed at admission, including lemborexant, suvorexant, ramelteon, benzodiazepines, Z-drugs, and other hypnotics; the occurrence of inpatient falls during the hospital stay; and patients' background information. To determine the outcome of inpatient falls, items with low collinearity were selected and included as covariates in a forced-entry binary logistic regression analysis.

Results

Overall, 150,278 patients were included in the analysis, among whom 3,458 experienced falls. The median age of the entire cohort was 70 years, with men constituting 53.1%. Binary logistic regression analysis revealed that the prescription of lemborexant, suvorexant, and ramelteon at admission was not significantly associated with inpatient falls.

Conclusion

The administration of lemborexant, suvorexant, and ramelteon at admission may not be associated with inpatient falls.

Validation and Improvement of the Saga Fall Risk Model: A Multicenter Retrospective Observational Study

Purpose

We conducted a pilot study in an acute care hospital and developed the Saga Fall Risk Model 2 (SFRM2), a fall prediction model comprising eight items: Bedriddenness rank, age, sex, emergency admission, admission to the neurosurgery department, history of falls, independence of eating, and use of hypnotics. The external validation results from the two hospitals showed that the area under the curve (AUC) of SFRM2 may be lower in other facilities. This study aimed to validate the accuracy of SFRM2 using data from eight hospitals, including chronic care hospitals, and adjust the coefficients to improve the accuracy of SFRM2 and validate it.

Patients and Methods

This study included all patients aged ≥20 years admitted to eight hospitals, including chronic care, acute care, and tertiary hospitals, from April 1, 2018, to March 31, 2021. In-hospital falls were used as the outcome, and the AUC and shrinkage coefficient of SFRM2 were calculated. Additionally, SFRM2.1, which was modified from the coefficients of SFRM2 using logistic regression with the eight items comprising SFRM2, was developed using two-thirds of the data randomly selected from the entire population, and its accuracy was validated using the remaining one-third portion of the data.

Results

Of the 124,521 inpatients analyzed, 2,986 (2.4%) experienced falls during hospitalization. The median age of all inpatients was 71 years, and 53.2% were men. The AUC of SFRM2 was 0.687 (95% confidence interval [CI]:0.678-0.697), and the shrinkage coefficient was 0.996. SFRM2.1 was created using 81,790 patients, and its accuracy was validated using the remaining 42,731 patients. The AUC of SFRM2.1 was 0.745 (95% CI: 0.731-0.758).

Conclusion

SFRM2 showed good accuracy in predicting falls even on validating in diverse populations with significantly different backgrounds. Furthermore, the accuracy can be improved by adjusting the coefficients while keeping the model's parameters fixed.

In-Hospital Fall Risk Prediction by Objective Measurement of Lower Extremity Function in a High-Risk Population

OBJECTIVES

Limited data exist regarding association between physical performance and in-hospital falls. This study was performed to investigate the association between physical performance and in-hospital falls in a high-risk population.

DESIGN

Retrospective cohort study.

SETTING AND PARTICIPANTS

The study population consisted of 1200 consecutive patients with a median age of 74 years (50.8% men) admitted to a ward with high incidence rates of falls, primarily in the departments of geriatrics and neurology, in a university hospital between January 2019 and December 2021.

METHODS

Short Physical Performance Battery (SPPB) was measured after treatment in the acute phase. As the primary end point of the study, the incidence of in-hospital falls was examined prospectively based on data from mandatory standardized incident report forms and electronic patient records.

RESULTS

SPPB assessment was performed at a median of 3 days after admission, and the study population had a median SPPB score of 3 points. Falls occurred in 101 patients (8.4%) over a median hospital stay of 15 days. SPPB score showed a significant inverse association with the incidence of in-hospital falls after adjusting for possible confounders (adjusted odds ratio for each 1-point decrease in SPPB: 1.19, 95% CI 1.10-1.28; P < .001), and an SPPB score ≤6 was significantly associated with increased risk of in-hospital falls. Inclusion of SPPB with previously identified risk factors significantly increased the area under the curve for in-hospital falls (0.683 vs. 0.740, P = .003).

CONCLUSION AND IMPLICATIONS

This study demonstrated an inverse association of SPPB score with risk of in-hospital falls in a high-risk population and showed that SPPB assessment is useful for accurate risk stratification in a hospital setting.

A Simple and Accurate Model for Predicting Fall Injuries in Hospitalized Patients: Insights from a Retrospective Observational Study in Japan

BACKGROUND While several predictive models for falls have been reported such as we reported in 2020, those for fall "injury" have been unreported. This study was designed to develop a model to predict fall injuries in adult inpatients using simple predictors available immediately after hospitalization. MATERIAL AND METHODS This was a single-center, retrospective cohort study. We enrolled inpatients aged ≥20 years admitted to an acute care hospital from April 2012 to March 2018. The variables routinely obtained in clinical practice were compared between the patients with fall injury and the patients without fall itself or fall injury. Multivariable analysis was performed using covariables available on admission. A predictive model was constructed using only variables showing significant association in prior multivariable analysis. RESULTS During hospitalization of 17 062 patients, 646 (3.8%) had falls and 113 (0.7%) had fall injuries. Multivariable analysis showed 6 variables that were significantly associated with fall injuries during hospitalization: age (P=0.001), sex (P=0.001), emergency transport (P<0.001), medical referral letter (P=0.041), history of falls (P=0.012), and abnormal bedriddenness ranks (all P≤0.001). The area under the curve of this predictive model was 0.794 and the shrinkage coefficient was 0.955 using the same data set given above. CONCLUSIONS We developed a predictive model for fall injuries during hospitalization using 6 predictors, including bedriddenness ranks from official Activities of Daily Living indicators in Japan, which were all easily available on admission. The model showed good discrimination by internal validation and promises to be a useful tool to assess the risk of fall injuries.

Association of motor index scores with fall incidence among community-dwelling older people

BACKGROUND

Several kinds of motor dysfunction have been studied for predicting future fall risk in community-dwelling older individuals. However, no study has tested the ability of the fine motor index (FINEA) and gross motor index (GROSSA) to predict the risk of falling, as well as the specific fall type.

OBJECTIVE

We investigated the associations of FINEA/GROSSA scores with fall risk, explained falls, and unexplained falls.

METHODS

A total of 6267 community-dwelling adults aged ≥ 50 years from the Irish Longitudinal Study on Aging (TILDA) cohort were included. First, the associations of FINEA and GROSSA scores with the history of total falls, explained falls and unexplained falls were assessed in a cross-sectional study and further verified in a prospective cohort after 2 years of follow-up by Poisson regression analysis.

RESULTS

We found that high FINEA and GROSSA scores were positively associated with almost all fall histories (FINEA scores: total falls: adjusted prevalence ratio [aPR] = 1.28, P = 0.009; explained falls: aPR = 1.15, P = 0.231; unexplained falls: aPR = 1.88, P < 0.001; GROSSA scores: total falls: aPR = 1.39, P < 0.001; explained falls: aPR = 1.28, P = 0.012; unexplained falls: aPR = 2.18, P < 0.001) in a cross-sectional study. After 2 years of follow-up, high FINEA scores were associated with an increased incidence of total falls (adjusted rate ratio [aRR] = 1.42, P = 0.016) and explained falls (aRR = 1.51, P = 0.020) but not with unexplained falls (aRR = 1.41, P = 0.209). High GROSSA scores were associated with an increased incidence of unexplained falls (aRR = 1.57, P = 0.041) and were not associated with either total falls (aRR = 1.21, P = 0.129) or explained falls (aRR = 1.07, P = 0.656). Compared with individuals without limitations in either the FINEA or GROSSA, individuals with limitations in both indices had a higher risk of falls, including total falls (aRR = 1.35, P = 0.002), explained falls (aRR = 1.31, P = 0.033) and unexplained falls (aRR = 1.62, P = 0.004).

CONCLUSION

FINEA scores were positively associated with accidental falls, while GROSSA scores were positively associated with unexplained falls. The group for whom both measures were impaired showed a significantly higher risk of both explained and unexplained falls. FINEA or GROSSA scores should be investigated further as possible tools to screen for and identify community-dwelling adults at high risk of falling.

Falls as the result of interplay between nurses, patient and the environment: Using text-mining to uncover how and why falls happen

Objectives

This study aimed to explore, from the perspectives of nurses, how patients, the environment, and the practice of nurses interact with each other to contribute to patient falls.

Methods

A retrospective review of incident reports on patient falls, registered by nurses between 2016 and 2020, was conducted. The incident reports were retrieved from the database set up for the project of the Japan Council for Quality Health Care. The text descriptions of the "background of falls" were extracted verbatim, and analyzed by using a text-mining approach.

Results

A total of 4,176 incident reports on patient falls were analyzed. Of these falls, 79.0% were unwitnessed by nurses, and 8.7% occurred during direct nursing care. Document clustering identified 16 clusters. Four clusters were related to patients, such as the decline in their physiological/cognitive function, a loss of balance, and their use of hypnotic and psychotropic agents. Three clusters were related to nurses, and these included a lack of situation awareness, reliance on patient families, and insufficient implementation of the nursing process. Six clusters were concerned with patients and nurses, including the unproductive use of a bed alarm and call bells, the use of inappropriate footwear, the problematic use of walking aids and bedrails, and insufficient understanding of patients' activities of daily living. One cluster, chair-related falls, involved both patient and environmental factors. Finally, two clusters involved patient, nurse, and environmental factors, and these falls occurred when patients were bathing/showering or using a bedside commode.

Conclusions

Falls were caused by a dynamic interplay between patients, nurses, and the environment. Since many of the patient factors are difficult to modify in a short time, the focus has to be placed on nursing and environmental factors to reduce falls. In particular, improving nurses' situation awareness is of foremost importance, as it influences their decisions and actions to prevent falls.

Criterion-related validity of Bedriddenness Rank with other established objective scales of ADLs, and Cognitive Function Score with those of cognitive impairment, both are easy-to-use official Japanese scales: A prospective observational study

Aim

Bedriddenness Rank (BR) and Cognitive Function Score (CFS), issued by the Ministry of Health, Labour and Welfare, Japan, are easy-to-use and widely used in the medical and long-term care insurance systems in Japan. This study aims to clarify the criterion-related validity of the CFS with the Mini-Mental State Examination (MMSE) and ABC Dementia Scale (ABC-DS), and to re-evaluate the criterion-related validity of BR with the Barthel Index (BI) or Katz Index (KI) in more appropriate settings and a larger population compared with the previous study.

Methods

A single-center prospective observational study was conducted in an acute care hospital in a suburban city in Japan. All inpatients aged 20 years or older admitted from October 1, 2018 to September 30, 2019. The relationship between BR and the BI and KI, and the relationship between CFS and the MMSE and ABC-DS were analyzed using Spearman’s correlation coefficients.

Results

We enrolled 3,003 patients. Of these, 1,664 (56%) patients exhibited normal BR. The median (interquartile range) values of the BI and KI were 100 (65–100) and 6 (2–6), respectively. Spearman’s rank correlation coefficients between BR and the BI and KI were −0.891 (p < 0.001) and −0.877 (p < 0.001), respectively.

Of the patients, 1,967 (65.5%) showed normal CFS. The median (interquartile range) MMSE of 951 patients with abnormal CFS and ABC-DS of all patients were 15 (2–21) and 117 (102–117), respectively. Spearman’s rank correlation coefficients between CFS and MMSE and ABC-DS were −0.546 (p < 0.001) and −0.862 (p < 0.001), respectively.

Conclusions

BR and CFS showed significant criterion-related validity with well-established but complicated objective scales for assessing activities of daily living and cognitive functions, respectively. These two scales, which are easy to assess, are reliable and useful in busy clinical practice or large-scale screening settings.

History of Falls and Bedriddenness Ranks are Useful Predictive Factors for in-Hospital Falls: A Single-Center Retrospective Observational Study Using the Saga Fall Risk Model

INTRODUCTION

In our former study, we had validated the previously developed predictive model for in-hospital falls (Saga fall risk model) using eight simple factors (age, sex, emergency admission, department of admission, use of hypnotic medications, history of falls, independence of eating, and Bedriddenness ranks [BRs]), proving its high reliability. We found that only admission to the neurosurgery department, history of falls, and BRs had significant relationships with falls. In the present study, we aimed to clarify whether each of these three items had a significant relationship with falls in a different group of patients.

METHODS

This was a single-center based, retrospective study in an acute care hospital in a rural city of Japan. We enrolled all inpatients aged 20 years or older admitted from April 2015 to March 2018. We randomly selected patients to fulfill the required sample size. We performed multivariable logistic regression analysis using forced entry on the association between falls and each of the eight items in the Saga fall risk model 2.

RESULTS

A total of 2932 patients were randomly selected, of whom 95 (3.2%) fell. The median age was 79 years, and 49.9% were men. Multivariable analysis showed that female sex (odds ratio [OR] 0.6, 95% confidence interval [CI] 0.39-0.93, p = 0.022), having a history of falls (OR 1.9, 95% CI 1.16-2.99, p = 0.010), requiring help with eating (OR 1.9, 95% CI 1.12-3.35, p = 0.019), BR of A (OR 6.6, 95% CI 2.82-15.30, p < 0.001), BR of B (OR 7.5, 95% CI 2.95-19.06, p < 0.001), and BR of C (OR 4.1, 95% CI 1.53-11.04, p = 0.005) were significantly associated with falls.

CONCLUSION

History of falls and BRs were independently associated with in-hospital falls.

Inpatient Fall Prediction Models: A Scoping Review

INTRODUCTION

The digitization of hospital systems, including integrated electronic medical records, has provided opportunities to improve the prediction performance of inpatient fall risk models and their application to computerized clinical decision support systems. This review describes the data sources and scope of methods reported in studies that developed inpatient fall prediction models, including machine learning and more traditional approaches to inpatient fall risk prediction.

METHODS

This scoping review used methods recommended by the Arksey and O'Malley framework and its recent advances. PubMed, CINAHL, IEEE Xplore, and EMBASE databases were systematically searched. Studies reporting the development of inpatient fall risk prediction approaches were included. There was no restriction on language or recency. Reference lists and manual searches were also completed. Reporting quality was assessed using adherence to Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis statement (TRIPOD), where appropriate.

RESULTS

Database searches identified 1,396 studies, 63 were included for scoping assessment and 45 for reporting quality assessment. There was considerable overlap in data sources and methods used for model development. Fall prediction models typically relied on features from patient assessments, including indicators of physical function or impairment, or cognitive function or impairment. All but two studies used patient information at or soon after admission and predicted fall risk over the entire admission, without consideration of post-admission interventions, acuity changes or length of stay. Overall, reporting quality was poor, but improved in the past decade.

CONCLUSION

There was substantial homogeneity in data sources and prediction model development methods. Use of artificial intelligence, including machine learning with high-dimensional data, remains underexplored in the context of hospital falls. Future research should consider approaches with the potential to utilize high-dimensional data from digital hospital systems, which may contribute to greater performance and clinical usefulness.

External validation of a new predictive model for falls among inpatients using the official Japanese ADL scale, Bedriddenness ranks: a double-centered prospective cohort study

Background

Several reliable predictive models for falls have been reported, but are too complicated and time-consuming to evaluate. We recently developed a new predictive model using just eight easily-available parameters including the official Japanese activities of daily living scale, Bedriddenness ranks, from the Ministry of Health, Labour and Welfare. This model has not yet been prospectively validated. This study aims to prospectively validate our new predictive model for falls among inpatients admitted to two different hospitals.

Methods

A double-centered prospective cohort study was performed from October 1, 2018, to September 30, 2019 in an acute care hospital and a chronic care hospital. We analyzed data from all adult inpatients, for whom all data required by the predictive model were evaluated and recorded. The eight items required by the predictive model were age, gender, emergency admission, department of admission, use of hypnotic medications, previous falls, independence of eating, and Bedriddenness ranks. The main outcome is in-hospital falls among adult inpatients, and the model was assessed by area under the curve.

Results

A total of 3,551 adult participants were available, who experienced 125 falls (3.5%). The median age (interquartile range) was 78 (66–87) years, 1,701 (47.9%) were men, and the incidence of falls was 2.25 per 1,000 patient-days and 2.06 per 1,000 occupied bed days. The area under the curve of the model was 0.793 (95% confidence interval: 0.761–0.825). The cutoff value was set as − 2.18, making the specificity 90% with the positive predictive value and negative predictive value at 11.4% and 97%.

Conclusions

This double-centered prospective cohort external validation study showed that the new predictive model had excellent validity for falls among inpatients. This reliable and easy-to-use model is therefore recommended for prediction of falls among inpatients, to improve preventive interventions.

Trial registration

UMIN000040103 (2020/04/08)

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-02871-5.

The Relationship Between Knowledge, Attitude, Practice, and Fall Prevention for Childhood in Shanghai, China

BACKGROUND

Early childhood fall is a pressing global public health problem and one of the leading causes of child injury. China has a high proportion of children and a high burden of illness from falls. Therefore, educational interventions to prevent childhood fall would be beneficial.

METHODS

We used the outcome of knowledge, attitude and practice questionnaire, which was conducted by Pudong New District of Shanghai Municipal Government, to summarize demographic and baseline characteristics grouped by intervention or not, and analyzed descriptive statistics of continuous and categorical variables. A logistic stepwise function model was established to study the influence of different covariables on the degree of injury, and AIC/BIC/AICC was used to select the optimal model. Finally, we carried out single-factor analysis and established a multifactor model by the stepwise function method.

RESULTS

Attitude and actual behavior scores had significant differences. The intervention and control groups had 20.79 ± 3.20 and 20.39 ± 2.89 attitude scores, respectively. Compared to the control group (5.97 ± 1.32), the intervention group had higher actual behavior scores (5.75 ± 1.50). In the univariate analysis results, fathers' education level, mothers' education level, actual behavior and what cares for children had a significant influence on whether children got injured. In multivariate analysis, attitude had a positive influence on whether injured [odds ratio: 1.13 (1.05-1.21), P < 0.001].

CONCLUSION

Educational intervention for children and their guardians can effectively reduce the risk of childhood falls, and changes in behavior and attitude are the result of educational influence. Education of childhood fall prevention can be used as a public health intervention to improve children's health.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

BACKGROUND

The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.

OBJECTIVES

This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature.

METHODS

We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture.

RESULTS

Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing.

CONCLUSION

This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.