Automated Electronic Health Record Score to Predict Mortality Risk at the US Department of Veterans Affairs.

BACKGROUND: Terminally ill patients benefit from earlier engagement in palliative care. However, this does not always occur. This project assessed if an already available risk score, the Care Assessment Needs (CAN) score, would be able to identify patients at greatest risk for mortality within 30 days of hospital admission within the Veterans Health Administration (VHA). METHODS: The cohort of this retrospective analysis included all VA acute are patients over 18 years of age with a recent CAN score. The CAN score is an automatically calculated VA risk score that was repurposed to determine if it could predict risk of mortality after acute care admission. Univariate logistic regression was performed to obtain the probability of mortality within 30 days of admission, based on their CAN score. RESULTS: 298,467 patient records were assessed from January 1, 2019, to December 31, 2019. There was 6% mortality after 30 days of admissions, and 17% mortality within 1-year post-admission. Mean CAN score was 65 (SD: 29). On average, each incremental increase in the CAN score increased the probability of mortality by 7%. Patients with a CAN score of 90 had a 10% probability of 30-day post-admission mortality. CONCLUSION: A readily available risk score, automatically calculated from EHR data, was able to identify patients at high risk for 30-day mortality in the acute care setting. Next steps will be to assess how the CAN score can be utilized to in improve end of life care for high-risk hospitalized Veterans.

Mixed Reality Surgical Navigation System; Positional Accuracy Based on Food and Drug Administration Standard.

BACKGROUND: Computer assisted surgical navigation systems are designed to improve outcomes by providing clinicians with procedural guidance information. The use of new technologies, such as mixed reality, offers the potential for more intuitive, efficient, and accurate procedural guidance. The goal of this study is to assess the positional accuracy and consistency of a clinical mixed reality system that utilizes commercially available wireless head-mounted displays (HMDs), custom software, and localization instruments. METHODS: Independent teams using the second-generation Microsoft HoloLens© hardware, Medivis SurgicalAR© software, and localization instruments, tested the accuracy of the combined system at different institutions, times, and locations. The ASTM F2554-18 consensus standard for computer-assisted surgical systems, as recognized by the U.S. FDA, was utilized to measure the performance. 288 tests were performed. RESULTS: The system demonstrated consistent results, with an average accuracy performance that was better than one millimeter (.75 ± SD .37 mm). CONCLUSION: Independently acquired positional tracking accuracies exceed conventional in-market surgical navigation tracking systems and FDA standards. Importantly, the performance was achieved at two different institutions, using an international testing standard, and with a system that included a commercially available off-the-shelf wireless head mounted display and software.

Assessment of a wearable fall prevention system at a veterans health administration hospital.

Objective: In-hospital falls are a significant cause of morbidity and mortality. The Veterans Health Administration (VHA) has designated fall prevention as a major focus area. The objective of this report is to assess the performance of a new sensor-enabled wearable system to prevent patient falls. Methods: An integrated sensor-enabled wearable SmartSock system was utilized to prevent falls at the acute care wards of a large VA hospital. Individual patients were only provided the SmartSocks when they were determined to be at high risk of falling. All fall count rates, with and without using the SmartSock, were evaluated and compared for individual patients. SmartSock sensor and electronic health record data were combined to assess the system's performance from February 10, 2021, through October 31, 2021. Results: There were 20.7 falls per 1000 ward days of care (WDOC) for those not using the SmartSocks compared to 9.2 falls per 1000 WDOC for patients using the SmartSocks. This represents a reduction of falls by more than half. These findings are further confirmed with a negative binomial regression model, which showed the use of the SmartSock had a statistically significant effect on the rate of falls (p = 0.03) when length of stay was held constant and demonstrated the odds of fall incident rate of 0.48 (95% CI, 0.24-0.92), that is less than half compared to when patients were not wearing the SmartSock. Conclusion: The use of a sensor-enabled wearable SmartSock fall prevention system resulted in a clinically meaningful and statistically significant decrease in falls in the acute care setting.

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.

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.

Automated EHR score to predict COVID-19 outcomes at US Department of Veterans Affairs.

The sudden emergence of COVID-19 has brought significant challenges to the care of Veterans. An improved ability to predict a patient's clinical course would facilitate optimal care decisions, resource allocation, family counseling, and strategies for safely easing distancing restrictions. The Care Assessment Need (CAN) score is an existing risk assessment tool within the Veterans Health Administration (VA), and produces a score from 0 to 99, with a higher score correlating to a greater risk. The model was originally designed for the nonacute outpatient setting and is automatically calculated from structured data variables in the electronic health record. This multisite retrospective study of 6591 Veterans diagnosed with COVID-19 from March 2, 2020 to May 26, 2020 was designed to assess the utility of repurposing the CAN score as objective and automated risk assessment tool to promptly enhance clinical decision making for Veterans diagnosed with COVID-19. We performed bivariate analyses on the dichotomized CAN 1-year mortality score (high vs. low risk) and each patient outcome using Chi-square tests of independence. Logistic regression models using the continuous CAN score were fit to assess its predictive power for outcomes of interest. Results demonstrated that a CAN score greater than 50 was significantly associated with the following outcomes after positive COVID-19 test: hospital admission (OR 4.6), prolonged hospital stay (OR 4.5), ICU admission (3.1), prolonged ICU stay (OR 2.9), mechanical ventilation (OR 2.6), and mortality (OR 7.2). Repurposing the CAN score offers an efficient way to risk-stratify COVID-19 Veterans. As a result of the compelling statistical results, and automation, this tool is well positioned for broad use across the VA to enhance clinical decision-making.