Investing in Skilled Specialists to Grow Hospital Infrastructure for Quality Improvement.

OBJECTIVES: Hospitals can reduce labor costs by hiring lowest skill possible for the job, stretching clinical hours, and reducing staff not at bedside. However, these labor constraints designed to reduce costs may paradoxically increase costs. Specialty staff, such as board-certified clinicians, can redesign health systems to evaluate the needs of complex patients and prevent complications. The aim of the study was to evaluate whether investing in skilled specialists for supporting hospital quality infrastructure improves value and performance. METHODS: We evaluated pressure injury rates as an indicator of performance in a retrospective observational cohort of 55 U.S. academic hospitals from the Vizient clinical database between 2007 and 2012. Pressure injuries were defined by U.S. Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicator 3 (PSI-03) for stage 3, 4, and unstageable pressure injuries not present on admission in hospitalized adults. We compared ratios of board-certified wound care nurses per 1000 hospital beds to hospital-acquired pressure injury rates in these hospitals using mixed-effects regression of hospital quarters. RESULTS: High-performing hospitals invested in prevention infrastructure with skilled specialists and observed performance improvements. Regression indicated that by adding one board-certified wound care nurse per 1000 hospital beds, hospitals had associated decreases in pressure injury rates by -17.7% relative to previous quarters, controlling for other interruptions. Highest performers supplied fewer skilled specialists and achieve improved outcomes. CONCLUSIONS: Skilled specialists bring important value to health systems as a representation of investment in infrastructure, and the proportion of these specialists could be scaled relative to the hospital's patient capacity. Policy should support hospitals to make investments in infrastructure to drive down patient costs and improve quality

41250 Machine Learning to Identify Predictors of Iatrogenic Injury Using Empirical Bayes Estimates: A Cohort Study of Pressure Injury Prevention

ABSTRACT IMPACT: A machine learning approach using electronic health records can combine descriptive, population-level factors of pressure injury outcomes. OBJECTIVES/GOALS: Pressure injuries cause 60,000 deaths and cost $26 billion annually in the US, but prevention is laborious. We used clinical data to develop a machine learning algorithm for predicting pressure injury risk and prescribe the timing of intervention to help clinicians balance competing priorities. METHODS/STUDY POPULATION: We obtained 94,745 electronic health records with 7,000 predictors to calibrate a predictive algorithm of pressure injury risk. Machine learning was used to mine features predicting changes in pressure injury risk; random forests outperformed neural networks, boosting and bagging in feature selection. These features were fit to multilevel ordered logistic regression to create an algorithm that generated empirical Bayes estimates informing a decision-rule for follow-up based on individual risk trajectories over time. We used cross-validation to verify predictive validity, and constrained optimization to select a best-fit algorithm that reduced the time required to trigger patient follow-up. RESULTS/ANTICIPATED RESULTS: The algorithm significantly improved prediction of pressure injury risk (p<0.001) with an area under the ROC curve of 0.60 compared to the Braden Scale, a traditional clinician instrument of pressure injury risk. At a specificity of 0.50, the model achieved a sensitivity of 0.63 within 2.5 patient-days. Machine learning identified categorical increases in risk when patients were prescribed vasopressors (OR=16.4, p<0.001), beta-blockers (OR=4.8, p<0.001), erythropoietin stimulating agents (OR=3.0, p<0.001), or were ordered a urinalysis screen (OR=9.1, p<0.001), lipid panel (OR=5.7, p<0.001) or pre-albumin panel (OR=2.0, p<0.001). DISCUSSION/SIGNIFICANCE OF FINDINGS: This algorithm could help hospitals conserve resources within a critical period of patient vulnerability for pressure injury not reimbursed by Medicare. Savings generated by this approach could justify investment in machine learning to develop electronic warning systems for many iatrogenic injuries.</jats:p

The impact of a mixed reality technology-driven health enhancing physical activity program among community-dwelling older adults: a study protocol.

BACKGROUND: Physical inactivity and a sedentary lifestyle among community-dwelling older adults poses a greater risk for progressive physical and cognitive decline. Mixed reality technology-driven health enhancing physical activities such as the use of virtual coaches provide an emerging and promising solution to support healthy lifestyle, but the impact has not been clearly understood. METHODS AND ANALYSIS: An observational explanatory sequential mixed-method research design was conceptualized to examine the potential impact of a user-preferred mixed reality technology-driven health enhancing physical activity program directed toward purposively selected community-dwelling older adults in two senior centers in the Philippines. Quantitative components of the study will be done through a discreet choice experiment and a quasi-experimental study. A total of 128, or 64 older adults in each center, will be recruited via posters at community senior centers who will undergo additional screening or health records review by a certified gerontologist to ensure safety and proper fit. Treatments (live coaching with video-based exercise and mixed reality technology-driven exercise) will be assigned to each of the two senior center sites for the quasi-experiment. The participants from the experimental group shall be involved in the discreet choice experiment, modeling, and usability evaluations. Finally, a qualitative sample of participants (n = 6) as key informants shall be obtained from the experimental group using purposive selection. DISCUSSION: This study protocol will examine the health impact of a promising mixed reality program in health promotion among older adults. The study utilizes a human-centered mixed method research design in technology development and evaluation in the context of developing nations.Clinical trial registration: ClinicalTrials.gov, identifier NCT06136468

Transparency in health economic modeling : options, issues and potential solutions

Economic models are increasingly being used by health economists to assess the value of health technologies and inform healthcare decision making. However, most published economic models represent a kind of black box, with known inputs and outputs but undisclosed internal calculations and assumptions. This lack of transparency makes the evaluation of the model results challenging, complicates comparisons between models, and limits the reproducibility of the models. Here, we aim to provide an overview of the possible steps that could be undertaken to make economic models more transparent and encourage model developers to share more detailed calculations and assumptions with their peers. Scenarios with different levels of transparency (i.e., how much information is disclosed) and reach of transparency (i.e., who has access to the disclosed information) are discussed, and five key concerns (copyrights, model misuse, confidential data, software, and time/resources) pertaining to model transparency are presented, along with possible solutions. While a shift toward open-source models is underway in health economics, as has happened before in other research fields, the challenges ahead should not be underestimated. Importantly, there is a pressing need to find an acceptable trade-off between the added value of model transparency and the time and resources needed to achieve such transparency. To this end, it will be crucial to set incentives at different stakeholder levels. Despite the many challenges, the many benefits of publicly sharing economic models make increased transparency a goal worth pursuing

Pressure RElieving Support SUrfaces: a Randomised Evaluation 2 (PRESSURE 2): study protocol for a randomised controlled trial

Background Pressure ulcers represent a major burden to patients, carers and the healthcare system, affecting approximately 1 in 17 hospital and 1 in 20 community patients. They impact greatly on an individual’s functional status and health-related quality of life. The mainstay of pressure ulcer prevention practice is the provision of pressure redistribution support surfaces and patient repositioning. The aim of the PRESSURE 2 study is to compare the two main mattress types utilised within the NHS: high-specification foam and alternating pressure mattresses, in the prevention of pressure ulcers. Methods/Design PRESSURE 2 is a multicentre, open-label, randomised, double triangular, group sequential, parallel group trial. A maximum of 2954 ‘high-risk’ patients with evidence of acute illness will be randomised on a 1:1 basis to receive either a high-specification foam mattress or alternating-pressure mattress in conjunction with an electric profiling bed frame. The primary objective of the trial is to compare mattresses in terms of the time to developing a new Category 2 or above pressure ulcer by 30 days post end of treatment phase. Secondary endpoints include time to developing new Category 1 and 3 or above pressure ulcers, time to healing of pre-existing Category 2 pressure ulcers, health-related quality of life, cost-effectiveness, incidence of mattress change and safety. Validation objectives are to determine the responsiveness of the Pressure Ulcer Quality of Life-Prevention instrument and the feasibility of having a blinded endpoint assessment using photography. The trial will have a maximum of three planned analyses with unequally spaced reviews at event-driven coherent cut-points. The futility boundaries are constructed as non-binding to allow a decision for stopping early to be overruled by the Data Monitoring and Ethics Committee. Discussion The double triangular, group sequential design of the PRESSURE 2 trial will provide an efficient design through the possibility of early stopping for demonstrating either superiority, inferiority of mattresses or futility of the trial. The trial optimises the potential for producing robust clinical evidence on the effectiveness of two commonly used mattresses in clinical practice earlier than in a conventional design

What drives older adults’ acceptance of virtual humans? A conjoint and latent class analysis on virtual exercise coach attributes for a community-based exercise program

As an example of health-enhancing physical activities (HEPA), structured physical exercise is valuable in promoting healthy lifestyles among community-dwelling older adults. Technology-driven virtual coaches have the potential to enhance and improve exercise programs, but the preferences of the aging population were not previously explored. This study examined and analyzed the attributes and levels related to the acceptance of virtual coaches among the aging cohort via experience-based conjoint and latent class analysis. Purposively selected respondents (n = 232) from two senior centers in the Philippines completed a conjoint activity followed by a computer-based survey focusing on attributes related to platform, appearance, gender, language, and music. Results revealed the subjects' inclination to a humanlike, feminine, local language-speaking virtual coach projected through a mixed reality platform with a contemporary music background. Additionally, latent class analysis outcomes produced three subgroups based on the pattern of preferences among the technology users. Study outcomes reinforce the importance of human-centered design and multidisciplinary approaches to health technology development

Vertical Heterophoria and Postural Control in Nonspecific Chronic Low Back Pain

The purpose of this study was to test postural control during quiet standing in nonspecific chronic low back pain (LBP) subjects with vertical heterophoria (VH) before and after cancellation of VH; also to compare with healthy subjects with, and without VH. Fourteen subjects with LBP took part in this study. The postural performance was measured through the center of pressure displacements with a force platform while the subjects fixated on a target placed at either 40 or 200 cm, before and after VH cancellation with an appropriate prism. Their postural performance was compared to that of 14 healthy subjects with VH and 12 without VH (i.e. vertical orthophoria) studied previously in similar conditions. For LBP subjects, cancellation of VH with a prism improved postural performance. With respect to control subjects (with or without VH), the variance of speed of the center of pressure was higher, suggesting more energy was needed to stabilize their posture in quiet upright stance. Similarly to controls, LBP subjects showed higher postural sway when they were looking at a target at a far distance than at a close distance. The most important finding is that LBP subjects with VH can improve their performance after prism-cancellation of their VH. We suggest that VH reflects mild conflict between sensory and motor inputs involved in postural control i.e. a non optimal integration of the various signals. This could affect the performance of postural control and perhaps lead to pain. Nonspecific chronic back pain may results from such prolonged conflict

A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution

We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton-proton collisions at an energy of s = 13 TeV at the CERN LHC. The algorithm is trained on a large sample of simulated b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 fb - 1 . A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet. The results of the algorithm are used to improve the sensitivity of analyses that make use of b jets in the final state, such as the observation of Higgs boson decay to b b ¯

Search for dark matter produced in association with a Higgs boson decaying to a pair of bottom quarks in proton-proton collisions at root s=13TeV

A search for dark matter produced in association with a Higgs boson decaying to a pair of bottom quarks is performed in proton-proton collisions at a center-of-mass energy of 13 TeV collected with the CMS detector at the LHC. The analyzed data sample corresponds to an integrated luminosity of 35.9 fb(-1). The signal is characterized by a large missing transverse momentum recoiling against a bottom quark-antiquark system that has a large Lorentz boost. The number of events observed in the data is consistent with the standard model background prediction. Results are interpreted in terms of limits both on parameters of the type-2 two-Higgs doublet model extended by an additional light pseudoscalar boson a (2HDM+a) and on parameters of a baryonic Z simplified model. The 2HDM+a model is tested experimentally for the first time. For the baryonic Z model, the presented results constitute the most stringent constraints to date.Peer reviewe

CMS pythia 8 colour reconnection tunes based on underlying-event data

A preprint version of the article is available at arXiv (https://arxiv.org/abs/2205.02905).Copyright © CERN for the benefit of the CMS collaboration 2023. New sets of parameter tunes for two of the colour reconnection models, quantum chromodynamics-inspired and gluon-move, implemented in the PYTHIA 8 event generator, are obtained based on the default CMS PYTHIA 8 underlying-event tune, CP5. Measurements sensitive to the underlying event performed by the CMS experiment at centre-of-mass energies s√=7 and 13TeV , and by the CDF experiment at 1.96TeV are used to constrain the parameters of colour reconnection models and multiple-parton interactions simultaneously. The new colour reconnection tunes are compared with various measurements at 1.96, 7, 8, and 13TeV including measurements of the underlying-event, strange-particle multiplicities, jet substructure observables, jet shapes, and colour flow in top quark pair (tt¯) events. The new tunes are also used to estimate the uncertainty related to colour reconnection modelling in the top quark mass measurement using the decay products of tt¯ events in the semileptonic channel at 13TeV.SCOAP3