Query: prediction inpatient risk of falls

title: Effectiveness of a fall risk reduction programme for inpatient rehabilitation after stroke
abstract: To evaluate effectiveness of fall risk assessment based fall prevention for stroke rehabilitation inpatients.  A consecutive series of 232 patients admitted for the first time to a subacute stroke rehabilitation ward during 2010 2011 was studied in detail. The Assessment Sheet for Fall Prediction in Stroke Inpatients (ASFPSI by Nakagawa et al. was used to assess fall risk upon admission. Association of ASFPSI score and patient characteristics with actual falls was statistically tested. Yearly incidence of falls per 1000 hospital days (HD) was retrospectively audited for the 2006 2014 period to evaluate effectiveness of fall risk reduction measures.  The observed incidence of falls over the detailed study period was 3.0/1000 HD; 39% of the fallers fell during the first week after admission. ASFPSI score was not significantly associated with falls. Longer hospital stay, left body side affected and non extreme FIM score (55 101) were associated with higher odds of fall. Introduction of fall risk reduction measures followed by compulsory fall risk assessment lead to incidence of falls dropping from 7.1/1000 HD in 2006 to 2.8/1000 HD in 2011 and remaining at that level until 2014.  The fall risk assessment based measures appear to have led to decreasing falls risk among post stroke rehabilitation inpatients classified as being at high risk of falls. The fall prevention programme as a whole was successful. Patients with non extreme level of functional independence should receive enhanced fall prevention. Implications for Rehabilitation Recognising the fall risk upon the patient's admission is essential for preventing falls in rehabilitation wards. Assessing the fall risk is a team tasks and combines information from various sources. Assessing fall risk in stroke patients using the assessment sheet by Nakagawa et al. immediately upon admission systematically draws attention to the risk of falls in each individual patient.

title: Predicting falls: considerations for screening tool selection vs. screening tool development.
abstract: This paper discusses considerations for falls risk screening tool selection vs. the need to develop new tools.  Inpatient falls are a complex patient safety issue that represent a significant burden for the healthcare system. In the inpatient context, falls risk screening tools are most often used for predicting falls, but in some populations assessment tools are more suited, however in others, a clinician's clinical judgment may be just as effective. Limited external validity is a central issue with falls risk screening tools when used in different populations than the original study. There is clinical need for guidance regarding screening tool selection vs. the need to development new tools and how to effect change in relation to the prediction of falls.   This discussion paper is based on our own experiences and research and is supported by literature.  This paper provides clinicians with a better understanding of considerations for falls risk screening tool selection vs. the need to develop new tools. In doing so, it provides clinicians guidance on how to critique the efficacy and utility of their falls risk screening tool. This paper equips clinicians for effecting change in relation to the prediction of falls.  Falls risk prediction is a particularly complex patient safety issue. Clinicians need to be aware of the limitations of their tool used to predict falls.

title: Computer Vision and Abnormal Patient Gait: A Comparison of Methods
abstract: Abnormal gait, falls and its associated complications have high morbidity and mortality. Computer vision detects, predicts gait abnormalities, assesses fall risk, and serves as a clinical decision support tool for physicians. This paper performs a systematic review of computer vision, machine learning techniques to analyse abnormal gait. This literature outlines the use of different machine learning and poses estimation algorithms in gait analysis that includes partial affinity fields, pictorial structures model, hierarchical models, sequential prediction frameworkbased approaches, convolutional pose machines, gait energy image, 2 Directional 2 dimensional principles component analysis (2D) 2PCA) and 2G (2D) 2PCA) Enhanced Gait Energy Image (EGEI) SVM, ANN, K Star, Random Forest, KNN, to perform the image classification of the features extracted inpatient gait abnormalities.

title: Falls Risk Prediction for Older Inpatients in Acute Care Medical Wards: Is There an Interest to Combine an Early Nurse Assessment and the Artificial Neural Network Analysis?
abstract: BackgroundIdentification of the risk of falls is important among older inpatients. This study aims to examine performance criteria (i.e. sensitivity, specificity, positive predictive value, negative predictive value and accuracy) for fall prediction resulting from a nurse assessment and an artificial neural networks (ANNs) analysis in older inpatients hospitalized in acute care medical wards. total of 848 older inpatients (mean age, 83.0+ 7.2 years; 41.8% female) admitted to acute care medical wards in Angers University hospital (France) were included in this study using an observational prospective cohort design. Within 24 hours after admission of older inpatients, nurses performed a bedside clinical assessment. Participants were separated into non fallers and fallers (i.e. =1 fall during hospitalization stay) The analysis was conducted using three feed forward ANNs (multilayer perceptron [MLP] averaged neural network, and neuroevolution of augmenting topologies [NEAT].ResultsSeventy three (8.6% participants fell at least once during their hospital stay. ANNs showed a high specificity, regardless of which ANN was used, and the highest value reported was with MLP (99.8% In contrast, sensitivity was lower, with values ranging between 98.4 to 14.8% MLP had the highest accuracy (99.7).ConclusionsPerformance criteria for fall prediction resulting from a bedside nursing assessment and an ANNs analysis was associated with a high specificity but a low sensitivity, suggesting that this combined approach should be used more as a diagnostic test than a screening test when considering older inpatients in acute care medical ward.

title: Predicting Inpatient Falls Using Natural Language Processing of Nursing Records Obtained From Japanese Electronic Medical Records: Case Control Study
abstract: Falls in hospitals are the most common risk factor that affects the safety of inpatients and can result in severe harm. Therefore, preventing falls is one of the most important areas of risk management for health care organizations. However, existing methods for predicting falls are laborious and costly.  The objective of this study is to verify whether hospital inpatient falls can be predicted through the analysis of a single input unstructured nursing records obtained from Japanese electronic medical records (EMRs) using a natural language processing (NLP) algorithm and machine learning.  The nursing records of 335 fallers and 408 nonfallers for a 12 month period were extracted from the EMRs of an acute care hospital and randomly divided into a learning data set and test data set. The former data set was subjected to NLP and machine learning to extract morphemes that contributed to separating fallers from nonfallers to construct a model for predicting falls. Then, the latter data set was used to determine the predictive value of the model using receiver operating characteristic (ROC) analysis.  The prediction of falls using the test data set showed high accuracy, with an area under the ROC curve, sensitivity, specificity, and odds ratio of mean 0.834 (SD 0.005) mean 0.769 (SD 0.013) mean 0.785 (SD 0.020) and mean 12.27 (SD 1.11) for five independent experiments, respectively. The morphemes incorporated into the final model included many words closely related to known risk factors for falls, such as the use of psychotropic drugs, state of consciousness, and mobility, thereby demonstrating that an NLP algorithm combined with machine learning can effectively extract risk factors for falls from nursing records.  We successfully established that falls among hospital inpatients can be predicted by analyzing nursing records using an NLP algorithm and machine learning. Therefore, it may be possible to develop a fall risk monitoring system that analyzes nursing records daily and alerts health care professionals when the fall risk of an inpatient is increased.

title: Medical Informatics 2018 INPATIENT FALL PREDICTION AND PREVENTION
abstract: Inpatient falls are recognized to be amongst the maximum difficult adverse activities suggested in hospitals, frequently prolonging and complicating medical institution stays. In the US, among 700,000 and a million patients fall in hospitals each year, main to an average of 6 extra hospitalization days, wherein the value of treatment for fall accidents amounts to $13000.  It shows that 60 70% of all falls inside the health center occur from the mattress or bedside chair. The modern nation of art mentions sitters, mattress rails, and fall danger assessment questionnaires, alongside some of technological answers. In spite of giant research, modern day solutions are not cost green or scalable, with fall hazard evaluation protocols implemented inconsistently. Philips addresses the state of art obstacles with a generation offering faraway supervision of more than one patient simultaneously. The system receives input simultaneously from a digital camera and/or a biosensor, and assesses the fall threat of sufferers by detecting in actual time a number of dangers factors recognized to precede and make contributions to a mattress fall incident: affected person restlessness, risky posture and position within the mattress space. Based on the assessed fall threat, the device issues a notification to a remote sitter approximately sufferers at threat, allowing timely preventative interventions. Results of a laboratory take a look at on 112 tests imply that the machine triggers a notification of average 23 seconds in advance of affected person bed go out/fall events. The PPV values of the risk factors detected are 0.975 (restlessness) 0.924 (risky posture) 0.826 (hazardous position) The gadget additionally detects bed falls and exit events (PPV 1.0)  We have supplied a generation for remote supervision of a couple of patients in parallel, enabling powerful, fee green, scalable offerings for inpatient fall prediction and prevention. This work is presented at 3rd International Conference on e Health and Alternative Healthcare Innovations (E HEALTH 2020 Webinar) on October 12 13, 2020  An increased awareness is being positioned on inpatient falls. The motives for this are a couple of including related morbidity and mortality, increased price of care, and shortage of compensation from the payors. The World Health Organization defines a fall as "inadvertently coming to relaxation on the ground, floor, or other decrease level, aside from intentional exchange in position."1 Significantly, even as having been mentioned in among 2% and 12% of admissions, those events are found to purpose damage in as much as 40% of the patients. In addition to the actual personal price, the financial impact associated with these falls are of consequence, associated with an growth in medical institution prices and longer duration of stay. Given the preventable nature of lots of those events, scrutiny by payors has followed. The Centers for Medicare and Medicaid Services have indexed falls as a "never" occasion and therefore do no longer reimburse hospitals for prices associated with the take care of an inpatient fall. The nature of falls and their occurrence in sufferers with neurologic admissions will inevitably have an effect on the neuro hospitalist, offering a fantastic opportunity for involvement in hazard reduction. Patients with neurological weakness with or without a clouded sensorium are clearly at risk of falls. In addition, assessing sufferers with potentially big head injuries after a fall may additionally properly contain the neuro hospitalist by means of nature in their presence and strong point interest. Beyond the scientific care of the individual affected person, however, falls may be addressed systematically. The result is the capacity to enhance the care of the whole medical institution populace and to broaden or improve the infrastructure necessary to do so for other medical situations or activities. In this 2 part series, we are able to first check the risk stratification tools which can be available, and then outline the scope of the problem and capacity solutions via a review of the literature. The 2d article will gift a guide to implementing a excellent development undertaking around sanatorium falls. Although particular to falls in the hospitalized affected person, the aim is to provide a stepwise technique which is extensively applicable. Another key point to keep in mind is that fall prevention alone cannot be the goal of a fall prevention software. A theoretical example can illustrate this point. In theory, we could save you all falls by means of restraining all sufferers, thereby stopping them from leaving the. But restraining sufferers might be unethical and represent negative care. It could battle with the standards of patient autonomy and cause all the complications of mattress rest, consisting of deconditioning, pressure ulcers, aspiration, and deep vein thrombosis, thereby maintaining the affected person in the health facility longer and making it harder for the affected person to recover.  A literature searches the use of PubMed database changed into accomplished for articles published prior to January 21, 2012 regarding in hospital fall prevention programs. No time restrict was set. Combinations of the words unintended falls (MESH) patient falls, in hospital falls, hospitals, hospitalization (MESH) acute care, prevention, intervention, economics, threat management, and adult had been used. The Cochrane library was searched using the phrase "health facility fall interventions." References from blanketed articles and reviews had been used to finish the search. The search was used to perceive articles approximately predicting falls and interventions to lessen falls. A evaluation of all abstracts eliminated articles regarding falls in outpatient settings, children, and letters. For studies regarding interventions, research that have been not randomized had been excluded.  We described the effect of falls on hospitalized sufferers in addition to potentially powerful interventions. One can be tempted to transport immediately to intervene as soon as a problem is suspected. In order to effect real change, however, a true great development initiative should be undertaken. This type of paintings is principal to neurohospitalist practice, and part of what may additionally distinguish a neurohospitalist from a neurologist who sees inpatients. Traditional neurology residency applications may touch on these processes, but nice improvement education more normally occurs afterward, if at all.

title: Using Electronic Health Records to Enhance Predictions of Fall Risk in Inpatient Settings.
abstract: Falls are the most common adverse events of hospitalized adults. Traditional validated assessment tools have limited ability to accurately detect patients at high risk for falls. The researchers aim to develop an automated comprehensive risk score to enhance the identification of patients at high risk for falls and examine its effectiveness.  The enhanced fall algorithm (EFA) was developed from 171,515 hospitalizations and 2,659 falls, in an academic medical center, using hierarchical logistic regression. Routine nursing assessments, labs, medications, demographics, and patients' location during their hospitalization were gathered from the electronic health record (EHR) RESULTS The fall rate was 2.8 per 1,000 patient days. Morse fall score was the strongest predictor of falls (odds ratio 7.16, 95% confidence interval 6.48 7.91) with a model discrimination c statistic of 0.687. By adding patient demographics, chronic conditions, lab values, and medications, and controlling for patient clustering within units, predication was enhanced and model discrimination increased to 0.805. By applying the enhanced model, we observed redistribution of patient by risk: low risk group increased from 52.8% to 66.5% and the high risk group decreased from 28.0% to 16.2% with an increase of fall detection from 3.1% to 5.1%  The EFA redistributes and identifies patients at high risk more accurately than the Morse score alone, decreasing the population of high risk patients without increasing the rate of falls over time. The EFA requires no addition data collection and automatically updates the patient's fall risk based on new inputs in the EHR.

title: Risk factors of falls among inpatients with cancer
abstract: To investigate the risk factors and predictors of falls according to the general characteristics, conscious state, physical condition and treatment of hospitalized patients with cancer.  Inpatients with cancer experience falls more frequently than those without cancer, and the degree of injuries is more severe among inpatients with cancer. A specific fall prevention strategy is needed for each patient. Prevention of falls in patients with cancer is very important for improving the quality of nursing care.  This retrospective study included matched case control patients. We evaluated patients between January 1, 2013, and December 31, 2014. A total of 356 patients (fall group, 178; non fall group, 178) were included. For fall prediction, logistic regression was performed on the variables that were statistically significant in the univariate analysis.  The variables that were significant predictors of falls were the use of an assistive device, history of falls and fatigue.  The predictors of falls in patients with cancer include physical conditions and general characteristics. Fall prevention strategies in patients with cancer should be planned individually with multifaceted aspects, including physical symptom management.  The study was conducted at a single cancer center in Korea; thus, our results cannot be generalized. Additionally, in Korea, it is common to have family members or private caregivers for patient care, and this might have influenced the results.  The predictive factors for falls reflect the nature of the patient's environment, culture and disease. Falls have a negative effect on patient safety and can significantly influence quality of life. Policies for patient safety need more specialized and customized approaches.

title: External validation of approaches to prediction of falls during hospital rehabilitation stays and development of a new simpler tool.
abstract: To test the external validity of 4 approaches to fall prediction in a rehabilitation setting (Predict_FIRST, Ontario Modified STRATIFY (OMS) physiotherapists' judgement of fall risk (PT_Risk) and falls in the past year (Past_Falls) and to develop and test the validity of a simpler tool for fall prediction in rehabilitation (Predict_CM2) PARTICIPANTS A total of 300 consecutively admitted rehabilitation inpatients.  Prospective inception cohort study. Falls during the rehabilitation stay were monitored. Potential predictors were extracted from medical records.  Forty one patients (14% fell during their rehabilitation stay. The external validity, area under the receiver operating characteristic curve (AUC) for predicting future fallers was: 0.71 (95% confidence interval (95% CI) 0.61 0.81) for OMS (Total_Score) 0.66 (95% CI: 0.57 0.74) for Predict_FIRST; 0.65 (95% CI 0.57 0.73) for PT_Risk; and 0.52 for Past_Falls (95% CI: 0.46 0.60) A simple 3 item tool (Predict_CM2) was developed from the most predictive individual items (impaired mobility/transfer ability, impaired cognition, and male sex) The accuracy of Predict_CM2 was 0.73 (95% CI: 0.66 0.81) comparable to OMS (Total_Score) (p 0.52) significantly better than Predict_FIRST (p 0.04) and Past_Falls (p 0.001) and approaching significantly better than PT_Risk (p 0.09) CONCLUSION Predict_CM2 is a simpler screening tool with similar accuracy for predicting fallers in rehabilitation to OMS (Total_Score) and better accuracy than Predict_FIRST or Past_Falls. External validation of Predict_CM2 is required.

title: FALLS RISK PREDICTION FOR OLDER INPATIENTS: NEW APPROACH