Augmented Reality Demonstration Survey Results From a Veteran Affairs Medical Center.

Background: Augmented reality (AR) has a wide range of potential applications to enhance health care. Understanding how the introduction of a new technology may impact employees is essential for overall health care system success. Methods: Survey responses were obtained before and after a health care-focused interactive AR demonstration at a US Department of Veterans Affairs (VA) medical center. Data were assessed with descriptive statistics, Wilcoxon signed rank matched pairs test, pooled t test, and analysis of variance. Results: A total of 166 individuals participated in the demonstration and survey. Statistically significant improvements were seen after the use of the new AR technology in each of the categories assessed using a 5-point Likert scale. Scores for perceptions of institutional innovativeness increased from 3.4 to 4.5 (a 22% increase; P < .001); employee excitement about the VA increased from 3.7 to 4.3 (a 12% increase; P < .001); and employee likelihood to continue working at VA increased from 4.2 to 4.5 (a 6% increase; P < .001). Subgroup analysis demonstrated statistically significant differences by employee veteran status, VA tenure, and sex. Respondents felt strongly that this type of work will positively impact health care and that the VA should continue these efforts. Conclusions: An AR demonstration significantly increased employee excitement and intention to continue employment at the VA and provided valuable insights about the most impactful uses of AR in health care.

Data acquisition process for VA and non-VA emergency department and hospital utilization by veterans with spinal cord injury and disorders in California using VA and state data.

Context: To identify VA and non-VA Emergency Department (ED) and hospital utilization by veterans with spinal cord injury and disorders (SCI/D) in California.Design: Retrospective cohort study.Setting: VA and Office of Statewide Health Planning and Development (OSHPD) in California.Participants: Total 300 veterans admitted to the study VA SCI/D Center for initial rehabilitations from 01/01/1999 through 08/17/2014.Interventions: N/A.Outcome Measures: Individual-level ED visits and hospitalizations during the first-year post-rehabilitation.Results: Among 145 veterans for whom ED visit data available, 168 ED visits were identified: 94 (55.2%) at non-VA EDs and 74 (44.8%) at the VA ED, with a mean of 1.16 (±2.21) ED visit/person. Seventy-seven (53.1%) veterans did not visit any ED. Of 68 (46.9%) veterans with ≥ one ED visit, 20 (29.4%) visited the VA ED only, 34 (50.0%) visited non-VA EDs only, and 14 (20.6%) visited both VA and non-VA EDs. Among 212 Veterans for whom hospitalization data were available, 247 hospitalizations were identified: 82 (33.2%) non-VA hospitalizations and 165 (66.8%) VA hospitalization with a mean of 1.17 (±1.62) hospitalizations/person. One hundred-seven (50.5%) veterans had no hospitalizations. Of 105 veterans with ≥ one hospitalization, 58 (55.2%) were hospitalized at the study VA hospital, 15 (14.3%) at a non-VA hospital, and 32 (30.5%) at both VA and non-VA hospitals.Conclusion: Non-VA ED and hospital usage among veterans with SCI/D occurred frequently. The acquisition of non-VA healthcare data managed by state agencies is vital to accurately and comprehensively evaluate needs and utilization rates among veteran populations.

Risk Factors for Readmissions After Total Joint Replacement: A Meta-Analysis.

¼: We performed a systematic review and meta-analysis of predictive modeling studies examining the risk of readmission after total hip arthroplasty (THA) and total knee arthroplasty (TKA) in order to synthesize key risk factors and evaluate their pooled effects. Our analysis entailed 15 compliant studies for qualitative review and 17 compliant studies for quantitative meta-analysis. ¼: A qualitative review of 15 predictive modeling studies highlighted 5 key risk factors for risk of readmission after THA and/or TKA: age, length of stay, readmission reduction policy, use of peripheral nerve block, and type of joint replacement procedure. ¼: A meta-analysis of 17 studies unveiled 3 significant risk factors: discharge to a skilled nursing facility rather than to home (approximately 61% higher risk), surgery at a low- or medium-procedure-volume hospital (approximately 26% higher risk), and the presence of patient obesity (approximately 34% higher risk). We demonstrated clinically meaningful relationships between these factors and moderator variables of procedure type, source of data used for model-building, and the proportion of male patients in the cohort. ¼: We found that many studies did not adhere to gold-standard criteria for reporting and study construction based on the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) and NOS (Newcastle-Ottawa Scale) methodologies. ¼: We recommend that these risk factors be considered in clinical practice and future work alike as they relate to surgical, discharge, and care decision-making. Future work should also prioritize greater observance of gold-standard reporting criteria for predictive models.

Association of mortality and aspirin prescription for COVID-19 patients at the Veterans Health Administration.

There is growing evidence that thrombotic and inflammatory pathways contribute to the severity of COVID-19. Common medications such as aspirin, that mitigate these pathways, may decrease COVID-19 mortality. This retrospective assessment was designed to quantify the correlation between pre-diagnosis aspirin and mortality for COVID-19 positive patients in our care. Data from the Veterans Health Administration national electronic health record database was utilized for the evaluation. Veterans from across the country with a first positive COVID-19 polymerase chain reaction lab result were included in the evaluation which comprised 35,370 patients from March 2, 2020 to September 13, 2020 for the 14-day mortality cohort and 32,836 patients from March 2, 2020 to August 28, 2020 for the 30-day mortality cohort. Patients were matched via propensity scores and the odds of mortality were then compared. Among COVID-19 positive Veterans, preexisting aspirin prescription was associated with a statistically and clinically significant decrease in overall mortality at 14-days (OR 0.38, 95% CI 0.32-0.46) and at 30-days (OR 0.38, 95% CI 0.33-0.45), cutting the odds of mortality by more than half. Findings demonstrated that pre-diagnosis aspirin prescription was strongly associated with decreased mortality rates for Veterans diagnosed with COVID-19. Prospective evaluation is required to more completely assess this correlation and its implications for patient care.

Risk Factors for Readmission After Knee Arthroplasty Based on Predictive Models: A Systematic Review.

BACKGROUND: An increase in the aging yet active US population will continue to make total knee arthroplasty (TKA) procedures routine in the coming decades. For such joint procedures, the Centers for Medicare and Medicaid Services introduced programs such as the Comprehensive Care for Joint Replacement to emphasize accountable and efficient transitions of care. Accordingly, many studies have proposed models using risk factors for predicting readmissions after the procedure. We performed a systematic review of TKA literature to identify such models and risk factors therein using a reliable appraisal tool for their quality assessment. METHODS: Five databases were searched to identify studies that examined correlations between post-TKA readmission and risk factors using multivariate models. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis methodology and Transparent Reporting of a multivariate prediction model for Individual Prognosis Or Diagnosis criteria established for quality assessment of prognostic studies. RESULTS: Of 29 models in the final selection, 6 models reported performance using a C-statistic, ranging from 0.51 to 0.76, and 2 studies used a validation cohort for assessment. The average 30-day and 90-day readmission rates across the studies were 5.33% and 7.12%, respectively. Three new significant risk factors were discovered. CONCLUSIONS: Current models for TKA readmissions lack in performance measurement and reporting when assessed with established criteria. In addition to using new techniques for better performance, work is needed to build models that follow the systematic process of calibration, external validation, and reporting for pursuing their deployment in clinical settings.

Predictive models for identifying risk of readmission after index hospitalization for hip arthroplasty: A systematic review.

BACKGROUND: An aging United States population profoundly impacts healthcare from both a medical and financial standpoint, especially with an increase in related procedures such as Total Hip Arthroplasty (THA). The Hospital Readmission Reduction Program and Comprehensive Care for Joint Replacement Program incentivize hospitals to decrease post-operative readmissions by correlating reimbursements with smoother care transitions, thereby decreasing hospital burden and improving quantifiable patient outcomes. Many studies have proposed predictive models built upon risk factors for predicting 30-day THA readmissions. QUESTIONS: (1) Are there validated statistical models that predict 30-day readmissions for THA patients when appraised with a standards-based, reliable assessment tool?. (2) Which evidence-based factors are significant and have support across models for predicting risk of 30-day readmissions post-THA? METHODS: Five major electronic databases were searched to identify studies that examined correlations between post-THA readmission and risk factors using multivariate models. We rigorously applied the PRISMA methodology and TRIPOD criteria for assessment of the prognostic studies. RESULTS: We found 26 studies that offered predictive models, of which two presented models tested with validation cohorts. In addition to the many factors grouped into demographic, administrative, and clinical categories, bleeding disorder, higher ASA status, discharge disposition, and functional status appeared to have broad and significant support across the studies. CONCLUSIONS: Reporting of recent predictive models establishing risk factors for 30-day THA readmissions against the current standard could be improved. Aside from building better performing models, more work is needed to follow the thorough process of undergoing calibration, external validation, and integration with existing EHR systems for pursuing their use in clinical settings. There are several risk factors that are significant in multiple models; these factors should be closely examined clinically and leveraged in future risk modeling efforts.

Combining Structured and Unstructured Data for Predicting Risk of Readmission for Heart Failure Patients.

Researchers have studied many models for predicting the risk of readmission for heart failure over the last decade. Most models have used a parametric statistical approach while a few have ventured into using machine learning methods such as statistical natural language processing. We created three predictive models by combining these two techniques for the cohort of 1,629 patients from six hosptials using structured data along with their 136,963 clinical notes till their index admission, stored in the EMR system over five years. The AUCs for structured and combined models were very close (0.6494 and 0.6447) and that for the unstructured model was 0.5219. The clinical impact of the models using decision curve analysis showed that, at a threshold predicted probability of 0.20, the combined model offered 15%, 30%, and 70% net benefit over its individual counterparts, treat-all, and treat-none strategy respectively.

Using Ensemble Machine Learning Methods for Predicting Risk of Readmission for Heart Failure.

Recently, researchers have been applying many new machine learning techniques for predicting the risk of readmission for heart failure. Combining such techniques through ensemble schemes holds a promise to further harness predictive performance of the resulting models. To that end, we examined two ensemble schemes and applied them to a real world dataset obtained from the EMR systems for 36,245 patients from 117 hospitals across the United States over five years. Both the ensemble schemes provided similar discriminative ability (AUC: 0.70, F1-score: 0.58) that was at least equal to or better than the base models that used a single machine learning method. The clinical impact of the models using decision curve analysis showed that at a threshold predicted probability of 0.40, the ensemble models offered 20% net benefit over the treat-all and treat-none strategies.

Quantitative Analyses of the Effectiveness of a Newly Designed Preceptor Workshop.

Educational preparation for nurse preceptors helps reduce anxiety and stress in precepting. A quality improvement project was initiated in a large U.S. West Coast federal healthcare system to evaluate an existing training program for preceptors. A new 2-day preceptor workshop was subsequently created to address identified gaps. Results demonstrated statistically significant gains in preceptors' essential knowledge and skills required in precepting, and room for improvement in faculty teaching techniques and course materials.

Can We Do More With Less While Building Predictive Models? A Study in Parsimony of Risk Models for Predicting Heart Failure Readmissions.

Hospital readmission due to heart failure is a topic of concern for patients and hospitals alike: it is both the most frequent and expensive diagnosis for hospitalization. Therefore, accurate prediction of readmission risk while patients are still in the hospital helps to guide appropriate postdischarge interventions. As our understanding of the disease and the volume of electronic health record data both increase, the number of predictors and model-building time for predicting risk grow rapidly. This suggests a need to use methods for reducing the number of predictors without losing predictive performance. We explored and described three such methods and demonstrated their use by applying them to a real-world dataset consisting of 57 variables from health data of 1210 patients from one hospital system. We compared all models generated from predictor reduction methods against the full, 57-predictor model for predicting risk of 30-day readmissions for patients with heart failure. Our predictive performance, measured by the C-statistic, ranged from 0.630 to 0.840, while model-building time ranged from 10 minutes to 10 hours. Our final model achieved a C-statistic (0.832) comparable to the full model (0.840) in the validation cohort while using only 16 predictors and providing a 66-fold improvement in model-building time.

Predictive models for identifying risk of readmission after index hospitalization for heart failure: A systematic review.

AIMS: Readmission rates for patients with heart failure have consistently remained high over the past two decades. As more electronic data, computing power, and newer statistical techniques become available, data-driven care could be achieved by creating predictive models for adverse outcomes such as readmissions. We therefore aimed to review models for predicting risk of readmission for patients admitted for heart failure. We also aimed to analyze and possibly group the predictors used across the models. METHODS: Major electronic databases were searched to identify studies that examined correlation between readmission for heart failure and risk factors using multivariate models. We rigorously followed the review process using PRISMA methodology and other established criteria for quality assessment of the studies. RESULTS: We did a detailed review of 334 papers and found 25 multivariate predictive models built using data from either health system or trials. A majority of models was built using multiple logistic regression followed by Cox proportional hazards regression. Some newer studies ventured into non-parametric and machine learning methods. Overall predictive accuracy with C-statistics ranged from 0.59 to 0.84. We examined significant predictors across the studies using clinical, administrative, and psychosocial groups. CONCLUSIONS: Complex disease management and correspondingly increasing costs for heart failure are driving innovations in building risk prediction models for readmission. Large volumes of diverse electronic data and new statistical methods have improved the predictive power of the models over the past two decades. More work is needed for calibration, external validation, and deployment of such models for clinical use.

Regional Differences in Predicting Risk of 30-Day Readmissions for Heart Failure.

Many researchers are working toward the goal of data-driven care by predicting the risk of 30-day readmissions for patients with heart failure. Most published predictive models have used only patient level data from either single-center studies or secondary data analysis of randomized control trials. This study describes a hierarchical model that captures regional differences in addition to patient-level data from 1778 unique patients across 31 geographically distributed hospitals from one health system. The model was developed using Bayesian techniques operating on a large set of predictors. It provided Area Under Curve (AUC) of 0.64 for the validation cohort. We confirmed that the regional differences indeed exist in the observed data and verified that our model was able to capture the regional variances in predicting the risk of 30-day readmission for patients in our cohort.

Predicting Risk of 30-Day Readmissions Using Two Emerging Machine Learning Methods.

Decades-long research efforts have shown that Heart Failure (HF) is the most expensive diagnosis for hospitalizations and the most frequent diagnosis for 30-day readmissions. If risk stratification for readmission of HF patients could be carried out at the time of discharge from the index hospitalization, corresponding appropriate post-discharge interventions could be arranged to avoid potential readmission. We, therefore, sought to explore and compare two newer machine learning methods of risk prediction using 56 predictors from electronic health records data of 1778 unique HF patients from 31 hospitals across the United States. We used two approaches boosted trees and spike-and-slab regression for analysis and found that boosted trees provided better predictive results (AUC: 0.719) as compared to spike-and-slab regression (AUC: 0.621) in our dataset.

A Validated Risk Model for 30-Day Readmission for Heart Failure.

One of the goals of the Precision Medicine Initiative launched in the United States in 2016 is to use innovative tools and sources in data science. We realized this goal by implementing a use case that identified patients with heart failure at Veterans Health Administration using data from the Electronic Health Records from multiple health domains between 2005 and 2013. We applied a regularized logistic regression model and predicted 30-day readmission risk for 1210 unique patients. Our validation cohort resulted in a C-statistic of 0.84. Our top predictors of readmission were prior diagnosis of heart failure, vascular and renal diseases, and malnutrition as comorbidities, compliance with outpatient follow-up, and low socioeconomic status. This validated risk prediction scheme delivered better performance than the published models so far (C-Statistics: 0.69). It can be used to stratify patients for readmission and to aid clinicians in delivering precise health interventions.

A curved edge diffraction-utilized displacement sensor for spindle metrology.

This paper presents a new dimensional metrological sensing principle for a curved surface based on curved edge diffraction. Spindle error measurement technology utilizes a cylindrical or spherical target artifact attached to the spindle with non-contact sensors, typically a capacitive sensor (CS) or an eddy current sensor, pointed at the artifact. However, these sensors are designed for flat surface measurement. Therefore, measuring a target with a curved surface causes error. This is due to electric fields behaving differently between a flat and curved surface than between two flat surfaces. In this study, a laser is positioned incident to the cylindrical surface of the spindle, and a photodetector collects the total field produced by the diffraction around the target surface. The proposed sensor was compared with a CS within a range of 500 µm. The discrepancy between the proposed sensor and CS was 0.017% of the full range. Its sensing performance showed a resolution of 14 nm and a drift of less than 10 nm for 7 min of operation. This sensor was also used to measure dynamic characteristics of the spindle system (natural frequency 181.8 Hz, damping ratio 0.042) and spindle runout (22.0 µm at 2000 rpm). The combined standard uncertainty was estimated as 85.9 nm under current experiment conditions. It is anticipated that this measurement technique allows for in situ health monitoring of a precision spindle system in an accurate, convenient, and low cost manner.

Growth of Bi2Te3 quantum dots/rods in glass: a unique highly stable nanosystem with novel functionality for high performance magneto optical devices.

Magneto optical materials are currently of great interest, primarily for modern applications in optical isolation, modulation and switching in telecommunication. However, single crystals are the benchmark materials still used in these devices which are rather expensive and very difficult to fabricate. In this context, we are reporting herewith a stable and novel Bi(2)Te(3) quantum dot-glass nanosystem obtained using a controlled thermo-chemical method. The Q-dots of hexagonal Bi(2)Te(3) of size 4 to 14 nm were grown along the <1 1 3> direction. Surprisingly, we obtained quantum rods of Bi(2)Te(3) of size 6 × 10 nm for the first time. The strong quantum confinement in the nanosystem is clearly shown by the optical study. The band gap of the host glass was drastically reduced (from 4.00 to 1.88 eV) due to the growth of Bi(2)Te(3) quantum dots whereas photoluminescence showed a Stokes shift ~175 meV. Faraday Rotation (FR) investigations of the Bi(2)Te(3) quantum dot-glass nanosystem show a nonlinear response in Verdet constant with a decrease in the Bi(2)Te(3) dot sizes. The Bi(2)Te(3) Q-dot-glass nanosystem with ~4 nm dots shows significant enhancement (70 times) in Verdet constant compared to the host glass and more radically better than conventional single crystal (TGG). This is the first time that such a type of unique nanosystem has been architectured and has given extremely good magneto-optical performance. We strongly feel that this novel nanosystem has tremendous applications in magneto-optical devices. It is noteworthy that expensive single crystals can be replaced with this cost effective novel glass nanosystem. Interestingly, the present quantum dot-glass nanosystem can be transformed into optical fibers very easily, which will have an exceptionally high impact on the fabrication of high performance magneto optical devices.

Poisson's effects in electrical field flow fractionation.

Recent and earlier models of electrical field flow fractionation (ELFFF) have assumed that the electric field within the fluid domain is governed by Laplace's equation. This assumption results in a linear potential and a spatially constant field across the channel and is generally true for very dilute systems and relatively high effective potentials. Experimental studies show, however, that the effective potential within the channel may be less than 1% of the applied potential; this is apparently due to double layer formation and charge buildup at the poles. In such cases, local analyte concentrations can, nonetheless, be orders of magnitude higher than the bulk mean and the local potential small, both of which can lead to a nonlinear spatial distribution of the field strength. In such cases Poisson's equation must be used rather than Laplace's equation. Steady-state ELFFF simulations were performed using a Poisson's equation-based model. The domain in which Laplace's equation is valid was identified and the effects of concentration and effective field strength on device performance were explored.

Electric circuit model for electrical field flow fractionation.

In electrical field flow fractionation (EFFF or ElFFF), an electric potential is applied across a narrow gap filled with a weak electrolyte fluid. Charge buildup at the two poles (electrodes) and the formation of an electric double layer shields the channel, making the effective field in the bulk fluid very weak. Recent computational research suggests that pulsed field protocols, however, should improve retention and may enhance separation in EFFF through systematic disruptions of the double layer resulting in a stronger effective field in the bulk fluid. Improved retention has already been demonstrated experimentally. Accurate modeling and subsequent device optimization and design, however, depends, in part, on formulating a suitable model for the capacitative response of the channel and double layer at the electrode surfaces. Early models do not correctly describe experimentally observed current-time response and are not physically meaningful even when accurate mathematical fits of the data are realized. A new model and conceptual framework based on electrical resistance and capacitance variations of the double layer is suggested here. Physical interpretations of the electrical response have been developed and compared to published experimental data sets.