Evidence-Based Selection of a Fall Risk Assessment Tool: A Program Evaluation Review

Fall prevention strategies are a consistent topic of discussion for healthcare regarding patient safety, as patient falls are costly to the patient and the organization. This project uses the CDC Framework for Program Evaluation to assess the fall prevention policy of a local hospital system, with particular emphasis on the fall risk assessment tool, Hester Davis. This project also explores the risks and benefits of adopting an alternative fall risk assessment tool, predictive analytics. Predictive analytics uses electronic health record (EHR) data analysis to provide a highly individualized patient fall risk score based on a large variety of patient and environmental factors. Comparative analysis of the two tools was performed in 104 chart reviews, which provided evidence for the use of predictive analytics. Recommendations are provided for a development of a new fall prevention policy that includes predictive analytics as the primary fall risk assessment tool. Based on these recommendations, this project also includes a competency-based orientation toolkit, which can be put into place should the organization choose to transition the policy to utilize predictive analytics as the primary fall risk assessment

Hospital Fall Prevention Using Interactive Patient Care Technology

The impact of patient engagement in hospital fall prevention using interactive patient care technology is not known. The purpose of this investigation was to examine the engagement of hospitalized patients in a computer-based, interactive patient care fall prevention pathway, comprised of a self-assessment of fall risk questionnaire and a fall prevention video, and hospital fall outcomes. The aims were to 1) formulate an interactive patient care technology conceptual framework to guide the study, 2) provide reliability and validity evidence for a patient self-assessment of fall risk questionnaire, and 3) explore the relationship between the fall prevention pathway engagement characteristics and a fall outcome. A conceptual framework for interactive patient care technology was developed and applied to the research investigation. The methodology included a retrospective, cross-sectional design using a convenience sample of 120 subjects to establish preliminary reliability and validity evidence for the patient self- assessment of fall risk questionnaire, and a matched 1:4 case-control design using 73 cases and 292 controls to examine the relationship between the fall prevention pathway engagement characteristics and a fall outcome. Findings indicated the patient self-assessment of fall risk questionnaire is reliable, with a Cronbach\u27s alpha of .73, and valid, with a statistically significant correlation to the nurses fall risk assessment tool, r (118) = .45, p \u3c .001. Using conditional logistic regression, length of stay, number of automatic video prompts, and fall prevention video completion status were significantly associated with a hospital fall. As length of stay increased by one day, the odds of a fall were 11% higher. With each additional automatic video prompt, the odds of a fall increased by a factor of 1.58. Cases were .38 times less likely to complete the fall prevention video than to complete it. Conclusions included an interactive fall prevention pathway promoted engagement and engagement at the empowerment level (video completion) prevented a fall. Limitations of this investigation included the use of secondary data, subject related assumptions, and the inability to generalize due to site, technology, and sample. This investigation contributes new knowledge regarding patient engagement in hospital fall prevention using interactive patient care technology

Population Health Management Risk Assessment Tool Validation: Directing Resource Utilization

The Affordable Care Act (ACA) of 2010 is transforming health care across the nation into a value-based system that emphasizes quality and continuity with reimbursement tied to patient outcomes. The shift in emphasis is best realized through the strategy of Population Health Management, a change from traditional episodic treatment of illness to management of the health needs of populations throughout the continuum of health. The goal of care is to ensure that patients, especially the chronically ill, receive effective attention to their health needs in order to improve outcomes, decrease costs, and provide a positive patient experience. An important component of coordinating care in Population Health Management is identifying those at risk for adverse outcomes or unplanned healthcare utilization, particularly at transitions of care. The purpose of this quality improvement project was to apply the LACE risk assessment tool in the emergency department (ED). Seventeen months of retrospective data was examined and Poisson regression used to examine and validate variables for use in the ED. The variables Length of Stay was modified to Length of Time between ED admissions, named Length of Stay Out of the ED (LOSO), and the Emergency Severity Index (ESI) scale of acuity was used. An ED-LACE score was calculated and validated using logistic regression. The model was found to have robust predictive ability with a with a c-statistic of 0.948

A Case for Delirium Risk Prediction Models to Aid in Triaging Resources to those Most at Risk an Integrative Literature Review

Abstract Delirium is a complex syndrome resulting from compounding effects of acute illness, comorbidities, and the environment. It results in adverse outcomes: elevated mortality rates, length of stay, readmissions, institutionalization, long-term cognitive changes, and diminished quality of life. The rate of iatrogenic delirium is astounding, ranging from 10%-89%. There are no curative treatments; thus, primary prevention is the key. The purpose of this literature review is to identify and critique the research for the accuracy of risk stratification and feasibility in practice. Support for interventions that prevent delirium is mounting; however, interventions are resource-intensive and often not implemented. Researchers have responded to this problem by developing risk stratification tools to triage interventions toward those of the highest risk. There is evidence that some of the models\u27 implementation is successful; however, they are not yet widely operationalized. A compilation of seven published models of risk prediction was critiqued and compared using the Stetler Model of Evidence-Based Practice as a guiding model. The Newcastle-Ottawa Scale and the Critical Appraisal and the Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS checklist) are employed to aid in the critical appraisal, evaluation of the study\u27s quality, and aid in data abstraction. The models show the ability to stratify risk. Still, their effectiveness in practice cannot be studied without directed interventions because they risk prediction models are created to aid healthcare staff in making clinical decisions. Therefore, a complete clinical pathway with evidence-based interventions should be employed with a delirium risk prediction model to triage the interventions to patients at the highest risk. Recommendations are to implement an automated electronic model (automatic calculation using the EMR or a machine learning model) into clinical practice along with a delirium prevention care pathway. Electronic versions of risk scores allow for an opportunity to achieve clinical efficiency and show statistical superiority to the other models. Published evidence on the impact of the models is diminutive. Their ability to triage patients and aid in clinical decision-making should be published in an impact study. Keywords: Delirium, risk assessment, risk prediction, risk model, risk score, patient safety, patient-centered outcomes researc

HealthCare Partners: Building on a Foundation of Global Risk Management to Achieve Accountable Care

Describes the progress of a medical group and independent practice association in forming an accountable care organization by working with insurers as part of the Brookings-Dartmouth ACO Pilot Program. Lists lessons learned and elements of success

Predicting the Risk of Falling with Artificial Intelligence

Predicting the Risk of Falling with Artificial Intelligence Abstract Background: Fall prevention is a huge patient safety concern among all healthcare organizations. The high prevalence of patient falls has grave consequences, including the cost of care, longer hospital stays, unintentional injuries, and decreased patient and staff satisfaction. Preventing a patient from falling is critical in maintaining a patient’s quality of life and averting the high cost of healthcare expenses. Local Problem: Two hospitals\u27 healthcare system saw a significant increase in inpatient falls. The fall rate is one of the nursing quality indicators, and fall reduction is a key performance indicator of high-quality patient care. Methods: This quality improvement evidence-based observational project compared the rate of fall (ROF) between the experimental and control unit. Pearson’s chi-square and Fisher’s exact test were used to analyze and compare results. Qualtrics surveys evaluated the nurses’ perception of AI, and results were analyzed using the Mann-Whitney Rank Sum test. Intervention. Implementing an artificial intelligence-assisted fall predictive analytics model that can timely and accurately predict fall risk can mitigate the increase in inpatient falls. Results: The pilot unit (Pearson’s chi-square = p pp\u3c0.001). Conclusions: AI-assisted automatic fall predictive risk assessment produced a significant reduction if the number of falls, the ROF, and the use of fall countermeasures. Further, nurses’ perception of AI improved after the introduction of FPAT and presentation

ANMCO/ELAS/SIBioC Consensus Document: Biomarkers in heart failure

Biomarkers have dramatically impacted the way heart failure (HF) patients are evaluated and managed. A biomarker is a characteristic that is objectively measured and evaluated as an indicator of normal biological or pathogenic processes, or pharmacological responses to a therapeutic intervention. Natriuretic peptides [B-type natriuretic peptide (BNP) and N-terminal proBNP] are the gold standard biomarkers in determining the diagnosis and prognosis of HF, and a natriuretic peptide-guided HF management looks promising. In the last few years, an array of additional biomarkers has emerged, each reflecting different pathophysiological processes in the development and progression of HF: myocardial insult, inflammation, fibrosis, and remodelling, but their role in the clinical care of the patient is still partially defined and more studies are needed before to be well validated. Moreover, several new biomarkers have the potential to identify patients with early renal dysfunction and appear to have promise to help the management cardio-renal syndrome. With different biomarkers reflecting HF presence, the various pathways involved in its progression, as well as identifying unique treatment options for HF management, a closer cardiologist-laboratory link, with a multi-biomarker approach to the HF patient, is not far ahead, allowing the unique opportunity for specifically tailoring care to the individual pathological phenotype