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

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

Clinicians’ perspectives and clinical efficacy of a health information technology tool in hospital falls risk assessment and prevention among older persons

Background The expanding ageing population has resulted in a focus on older persons within many healthcare systems. Falls present a growing problem with a significant impact on the community and healthcare system. Identifying falls risk factors and preventing falls have become priorities for many hospital and government policies, yet the evidence for the acceptability and efficacy of such interventions remains limited. Health technology has the potential to influence the field of falls prevention. Within research and clinical use, single and multi-component health technology strategies have been trialled to identify falls risk and prevent falls incidents. These have included sensor systems, video surveillance, and electronic health records. This thesis sought to evaluate the role of health technology in falls risk assessment and prevention, its perceptions by clinicians as end-users, and its effectiveness in reducing falls in hospital. More specifically, the thesis examined clinicians’ perspectives and use of a health information technology tool. This tool incorporated an iPad™ device and automatically generated visual cues to highlight individual patients’ falls risk. Its accuracy and efficacy in identifying and addressing falls risk scenarios, was evaluated compared to a standard screening tool. The aim of this study was to ultimately develop an acceptable and usable tool, in collaboration with clinicians, to deliver effective falls prevention in hospital. Methods Two methodologies and separate analyses were undertaken to complete this thesis: 1) An integrative review collated evidence for the effectiveness and clinicians’ perspectives of health technology use in falls prevention; and 2) an action research study evaluated clinicians’ perspectives on the health information technology tool, and informs its clinical use and efficacy in reducing hospital fall rates. Data was derived from focus group and survey research, with implementation of the health information technology tool occurring over consecutive 12-week periods on two medical wards at a single hospital setting. Both qualitative and quantitative analyses were applied to the data. Results Integrative review evidence, presented for the first time in this thesis, highlighted the lack of robust, consistent evidence for the acceptability and efficacy of health technology measures in falls prevention. The research conducted in this thesis addressed this gap in knowledge by evaluating staff’s attitudes towards the health information technology tool. It evaluated its positive and negative aspects, barriers to use, and recommendations for improvement; alongside its accuracy and effectiveness in reducing fall rates. Overall, clinicians were supportive for incorporating the tool into clinical practice. They perceived it as a useful, timely means of alerting staff and patients to falls risk scenarios, and resulting in better quality of care and understanding of falls risk for patients. Clinicians identified issues with usability and lack of time for tool use, and highlighted potential improvements to tool design. As befitting action research methodology, the health information technology tool has undergone refinement based on clinicians’ feedback. This has resulted in improved technology, clearer functioning of selection keys, colour coding of patients’ falls risk, having an automated trigger for patient education on falls risk, and provision of more iPad™ devices for more efficient use. The falls risk scores for the health information technology tool and standard falls risk in older person screening tool were similar, and did not differentiate between falls-risk and non-risk situations. Both tools had high sensitivity and low specificity for identifying falls-risk scenarios. They had similar rates of completion by clinicians on the wards. Implementation of the intervention tool had mixed outcomes on hospital fall rates. Conclusion This thesis contributed new information to address the knowledge gap on health technology uptake and efficacy in addressing hospital falls risk. Clinicians were willing to use the health information technology tool, and identified benefits to using the tool for themselves and their patients. The intervention tool demonstrated similar acceptability and accuracy to the standard falls risk screening tool. Staff’s concerns about usability are addressed in tool refinement, with active participation of end-users were considered key to improving intervention acceptance and usage, along with maximising useful feedback to further inform tool development. The effect of implementing the intervention tool on fall rates was mixed, highlighting the challenges of identifying and managing falls risk scenarios in hospital settings. The work arising from this thesis informed the development of a hand held android device used in the Ambience Intelligence Geriatric Management (AmbiGEM) system, incorporating printed visual cues with movement sensor alarms that alert clinicians to high-risk patient manoeuvres. Future research directions will involve evaluation of the acceptability and efficacy of the AmbiGEM system, which is currently undergoing clinical trial in two hospitals in South and Western Australia.Thesis (MPhil) -- University of Adelaide, Adelaide Medical School, 201

Improving the Quality of Electronic Documentation in Critical Care Nursing

Electronic nursing documentation systems can facilitate complete, accurate, timely documentation practices, but without effective policies and procedures in place, a gap in practice exists and quality of care may be impacted. This systematic review of literature examined current evidence regarding electronic nursing documentation quality. General systems theory and the Donabedian model of health care quality provided the framework for the project. Electronic databases PubMed and the Cumulative Index of Nursing and Allied Health were searched for articles addressing electronic nursing documentation practices. The Cochrane systematic review methodology was used to analyze the articles. Articles were excluded if published before 2001 or not in the English language. The search revealed 860 articles of which 35 were included in the final review. Most studies were quasi-experimental involving multiple interventions such as clinical decision support (CDSS), education, and audit and feedback specific documentation foci. The most reported outcomes were an improvement in documentation completeness and correctness. A multifaceted intervention strategy consisting of CDSS, education, and audit and feedback can be used to improve electronic documentation completeness and correctness. Policies and procedures regarding documentation practice should support the intended outcomes. Electronic documentation systems can improve completeness, but care should be taken not to depend on the quantity of documentation alone. Further research may shed light on the importance of concordance or plausibility, and the truth of documentation and ultimately how that can impact social determinates of health and social change

Electronic clinical decision support algorithms incorporating point-of-care diagnostic tests in low-resource settings: a target product profile

Health workers in low-resource settings often lack the support and tools to follow evidence-based clinical recommendations for diagnosing, treating and managing sick patients. Digital technologies, by combining patient health information and point-of-care diagnostics with evidence-based clinical protocols, can help improve the quality of care and the rational use of resources, and save patient lives. A growing number of electronic clinical decision support algorithms (CDSAs) on mobile devices are being developed and piloted without evidence of safety or impact. Here, we present a target product profile (TPP) for CDSAs aimed at guiding preventive or curative consultations in low-resource settings. This document will help align developer and implementer processes and product specifications with the needs of end users, in terms of quality, safety, performance and operational functionality. To identify the characteristics of CDSAs, a multidisciplinary group of experts (academia, industry and policy makers) with expertise in diagnostic and CDSA development and implementation in low-income and middle-income countries were convened to discuss a draft TPP. The TPP was finalised through a Delphi process to facilitate consensus building. An agreement greater than 75% was reached for all 40 TPP characteristics. In general, experts were in overwhelming agreement that, given that CDSAs provide patient management recommendations, the underlying clinical algorithms should be human-interpretable and evidence-based. Whenever possible, the algorithm's patient management output should take into account pretest disease probabilities and likelihood ratios of clinical and diagnostic predictors. In addition, validation processes should at a minimum show that CDSAs are implementing faithfully the evidence they are based on, and ideally the impact on patient health outcomes. In terms of operational needs, CDSAs should be designed to fit within clinic workflows and function in connectivity-challenged and high-volume settings. Data collected through the tool should conform to local patient privacy regulations and international data standards

Use of a Telerehabilitation Delivery System for Fall Risk Screening

Problem: The Centers for Disease Control and Prevention indicates that falls are the “leading cause of injury death and the most common cause of nonfatal injuries and hospital admission for trauma among people ages 65 and older.”1 Falls can have significant economic consequences to the individual and payer sources. To address these consequences, telerehabilitation was hypothesized to be a suitable supplement for fall screening efforts. Several sources concluded that support for synchronous telerehab was underdeveloped in the literature. Purpose: The purpose of this study was to explore the acceptability, feasibility, reliability, and validity of telehealth-delivered fall screening among community-dwelling older adults. Procedures: This investigation implemented an experimental, quantitative, cross-sectional design employing both pretest-posttest control group and quasi-experimental static group comparisons using non-probability sampling. This study assembled a panel of experts to provide content validation for a survey tool developed to quantify an older adult’s behavioral intension to use and attitudes towards a telerehabilitation delivery system. Seven fall screening tools were investigated for agreement among remote and face-to-face raters, and for comparison with the face-to-face reference standard (Mini-BEST). Results: All three null hypotheses were rejected. Results indicate that a telerehabilitation delivery system is a reliable and valid method of screening and determining fall risk in community-dwelling older adults. This study produced a content validated, internally consistent survey instrument designed to determine attitudes and beliefs about telerehabilitation. An experimental design was able to demonstrate a positive significant change in 4 of 7 survey constructs among the intervention group after exposure to telerehabilitation as compared to post-test controls. Overall, no significant difference was calculated between face-to-face or telerehab raters, and both environments produced equivalency with scoring, fall risk classification, and ability to discern fallers from non-fallers. Results from the telerehab STEADI fall risk conclusions were calculated to be concurrently valid with the face-to-face reference standard screening tool, the Mini-BEST. Conclusions: This investigation expanded the array of remote healthcare delivery options for clinicians and clients. Further investigation in residential and community settings are recommended

Construction safety and digital design: a review

As digital technologies become widely used in designing buildings and infrastructure, questions arise about their impacts on construction safety. This review explores relationships between construction safety and digital design practices with the aim of fostering and directing further research. It surveys state-of-the-art research on databases, virtual reality, geographic information systems, 4D CAD, building information modeling and sensing technologies, finding various digital tools for addressing safety issues in the construction phase, but few tools to support design for construction safety. It also considers a literature on safety critical, digital and design practices that raises a general concern about ‘mindlessness’ in the use of technologies, and has implications for the emerging research agenda around construction safety and digital design. Bringing these strands of literature together suggests new kinds of interventions, such as the development of tools and processes for using digital models to promote mindfulness through multi-party collaboration on safet

The Use of Tailored Interventions to Prevent Falls: A Quality Improvement Project in the Telemetry Unit

Background: Every year in the United States, hundreds of thousands of patients fall in hospitals with 30 to 50 percent resulting in injury. In Texas, the fall rate in adult patients is 33.9 percent, and in one teaching hospital in South Texas, patient fall rates have been above the national benchmark for two years (2017-2019), despite increased use of sitters for patient safety and multiple fall prevention strategies. The annual direct care cost of all fall events in the United States for individuals more than 65 years old is about $34 billion. Practice problem: The objectives of the fall initiative program were increasing adherence to documentation of data from the Morse Fall Assessment and tailored interventions in the electronic health record. The goal of the project was to promote patient safety by decreasing the fall rate per 1000 patient days to below the national benchmark of 3.44/1000 patient days. Intervention: The project was piloted in two telemetry units over 12 weeks using the Iowa Model of Evidence-based Practice. Telemetry staff received one-on-one education from the educator in the unit using a tailored intervention poster. The Nurse Champion observed 58 rooms and conducted chart documentation to ensure universal fall precautions were carried out during every shift. Incidence of falls was tracked daily, and post fall huddles were conducted after any incidents. Outcome: The average monthly fall rate after implementation was 2.47/1000 patient days, which was below the national benchmark. Conclusion: The fall assessment documentation in two telemetry units at DHR Health can be adapted or implemented hospital-wide. The results showed a statistically significant correlation between the Morse fall score assessment on EHR and monthly fall events (p=0. 0078). Champions were able to identify interventions and areas that needed to be improved such as education, patient engagement and stakeholder buy-in