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

Advancements in Medical Imaging and Diagnostics with Deep Learning Technologies

Medical imaging has long been a cornerstone in diagnostic medicine, providing clinicians with a non-invasive method to visualize internal structures and processes. However, traditional imaging techniques have faced challenges in resolution, safety concerns related to radiation exposure, and the need for invasive procedures for clearer visualization. With the advent of deep learning technologies, significant advancements have been made in the field of medical imaging, addressing many of these challenges and introducing new capabilities. This research seeks into the integration of deep learning in enhancing image resolution, leading to clearer and more detailed visualizations. Furthermore, the ability to reconstruct three-dimensional images from traditional two-dimensional scans offers a more comprehensive view of the area under examination. Automated analysis powered by deep learning algorithms not only speeds up the diagnostic process but also detects anomalies that might be overlooked by the human eye. Predictive analysis, based on these enhanced images, can forecast the likelihood of diseases, and real-time analysis during surgeries ensures immediate feedback, enhancing the precision of medical procedures. Safety in medical imaging has also seen improvements. Techniques powered by deep learning require reduced radiation, minimizing risks to patients. Additionally, the enhanced clarity and detail in images reduce the need for invasive procedures, further ensuring patient safety. The integration of imaging data with Electronic Health Records (EHR) has paved the way for personalized care recommendations, tailoring treatments based on individual patient history and current diagnostics. Lastly, the role of deep learning extends to medical education, where it aids in creating realistic simulations and models, equipping medical professionals with better training tools

Digital early warning scores in cardiac care settings: Mixed-methods research

The broad adoption of the National Early Warning Score (NEWS2) was formally endorsed for prediction of early deterioration across all settings. With current digitalisation of the Early Warning Score (EWS) through electronic health records (EHR) and automated patient monitoring, there is an excellent opportunity for facilitating and evaluating NEWS2 implementation. However, no evidence yet shows the success of such standardisation or digitalisation of EWS in cardiac care settings. Individuals with cardiovascular disease (CVD) have a significant risk of developing critical events, and CVD-related morbidity is a critical burden for health and social care. However, there is a gap in research evaluating the performance and implementation of EWS in cardiac settings and the role of digital solutions in the implementation and performance of EWS and clinicians' practice. This PhD aims to provide high-quality evidence on the effectiveness of NEWS2 in predicting worsening events in patients with CVD, the implementation of the digital NEWS2 in two healthcare settings, the experience of escalation of care during the COVID-19 pandemic, and the evaluation of EHR-integrated dashboard for auditing NEWS2 and clinicians' performance

Technical Viewpoint of Challenges, Opportunities, and Future Directions of Policy Change and Information-Flow in Digital Healthcare Systems

Source: https://www.thinkmind.org/.Digital healthcare systems often run on heterogeneous devices in a distributed multi-cluster environment, and maintain their healthcare policies for managing data, securing information flow, and controlling interactions among systems components. As healthcare systems become more digitally distributed, lack of integration and safe interpretation between heterogeneous systems clusters become problematic and might lead to healthcare policy violations. Communication overhead and high computation consumption might impact the system at different levels and affect the flow of information among system clusters. This paper provides a technical viewpoint of the challenges, opportunities, and future work in digital healthcare systems, focusing on the mechanisms of monitoring, detecting, and recovering healthcare policy change/update and its imprint on information flow

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

The Impact of Individual Learning on Electronic Health Record Routinization: An Empirical Study

Since the passage of the HITECH Act, adoption of electronic health records (EHR) has increased significantly EHR refers to an electronic version of a patient’s medical history. The adoption of EHR has potential to reduce medical errors, duplication of testing, and delays in treatment. However, current literature indicates that implementation of EHR is not resulting in the automatic routinization of EHR. Routinization refers to the notion that truly successful technological innovations are no longer perceived as being new or out-of-the-ordinary. The complexity of EHRs allow individual users to use these systems at different levels of sophistication. Research shows that healthcare professionals are using non-standard ways to use or circumvent the EHR to complete their work and are limited in EHR systems use. Further, although workarounds may seem necessary to physicians and are not perceived to be problematic, they can pose a threat to patient safety and hinder the potential benefits. Hence, we argue the EHR implementations are limited in their potential due to the lack of routinization. Any new technological innovation requires the physician support and willingness to learn about the system to move to the routinization phase of implementation. Hence, we draw from the literature on organization learning, individual learning, and routines to understand factors that influence EHR routinization

The Impact of Individual Learning on Electronic Health Record Routinization: An Empirical Study

Since the passage of the HITECH Act, adoption of electronic health records (EHR) has increased significantly EHR refers to an electronic version of a patient’s medical history. The adoption of EHR has potential to reduce medical errors, duplication of testing, and delays in treatment. However, current literature indicates that implementation of EHR is not resulting in the automatic routinization of EHR. Routinization refers to the notion that truly successful technological innovations are no longer perceived as being new or out-of-the-ordinary. The complexity of EHRs allow individual users to use these systems at different levels of sophistication. Research shows that healthcare professionals are using non-standard ways to use or circumvent the EHR to complete their work and are limited in EHR systems use. Further, although workarounds may seem necessary to physicians and are not perceived to be problematic, they can pose a threat to patient safety and hinder the potential benefits. Hence, we argue the EHR implementations are limited in their potential due to the lack of routinization. Any new technological innovation requires the physician support and willingness to learn about the system to move to the routinization phase of implementation. Hence, we draw from the literature on organization learning, individual learning, and routines to understand factors that influence EHR routinization

(Re)defining Healthcare Quality: Metrics, Protocols, and the Restructuring of Care Delivery

Healthcare organizations in the United States are increasingly evaluated by systems that link quality measurement with regulatory and payment approaches. Operationalized through quality measurement, quality is affirmed as the basis for improving healthcare processes, outcomes, and health systems broadly. At the same time, electronic health record (EHR) and other information technology (IT) systems aimed to make care safer and more efficient, have become standard tools in healthcare settings. Galvanized by these technical advancements, quality metrics are considered crucial components of ensuring accountability for improved health outcomes and care equity.This dissertation aims to understand healthcare quality measurement by investigating how systems of quality measurement are implemented in clinical spaces, particularly how they structure care delivery and define quality. This dissertation offers a qualitative study of the organizational and structural elements of quality and quality measurement. I conducted ethnographic observation (15 months) and interviews (n=31) at a 600-bed, acute-care hospital in New York City, which I call Borough Hospital. My analysis utilizes the accounts of healthcare clinicians and administrators, and their experiences navigating care delivery and quality in their hospital. Through this analysis, I investigate the variable meanings of quality, processes of measuring quality, and the conditions under which care is delivered at Borough Hospital. Using the qualitative analytic methods of grounded theory and situational analysis, I deconstruct the ways in which quality and quality measurement are constructed as neutral and inevitable, how care delivery is increasingly protocolized to ensure quality, and the ensuing distancing of quality care away from the bedside. Meeting and complying with quality metrics require specific clinical care protocols and extensive documentation for reporting. These new requirements have changed the roles and responsibilities of frontline clinicians, shifting the organization of labor in the clinic. I argue that measurement-based, clinical protocols that rely on surveillance and abstracted documentation data increasingly standardize processes of quality care and distance care—that is, clinician labor— away from the bedside. The findings of this dissertation suggest a tendency toward protocolization and narrowing definitions of quality, which can be extended into other hospital systems particularly in light of widespread consolidation. I argue that administrative prioritization of quality measurement, and in particular quality metrics, necessitates the protocolization of complex healthcare processes and increasingly relies on data-driven decision-making. Ultimately, I suggest quality care has been (re)defined by measurement-based, clinical protocols, which I call abstracted surveillance protocols, that increasingly standardize and constrain care delivery