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

ABSTRACT: 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.

SUBMITTER: Osborne TF

PROVIDER: S-EPMC7384633 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Osborne Thomas F TF Veigulis Zachary P ZP Arreola David M DM Röösli Eliane E Curtin Catherine M CM

PloS one 20200727 7

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. Th ...[more]

PMID: 32716922

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Project description:ObjectiveStudies describe COVID-19 patient characteristics and outcomes across populations, but reports of variation across healthcare facilities are lacking. The objectives were to examine differences in COVID-19 patient volume and mortality across facilities, and understand whether facility variation in mortality was due primarily to differences in patient versus facility characteristics.DesignObservational cohort study with multilevel mixed effects logistic regression modelling.SettingThe Veterans Health Administration (VA) is the largest healthcare system in the USA.ParticipantsPatients with COVID-19.Main outcomeAll-cause mortality within 45 days after COVID-19 testing (March-May, follow-up through 16 July 2020).ResultsAmong 13 510 patients with COVID-19, 3942 (29.2%) were admitted (2266/3942 (57.5%) ward; 1676/3942 (42.5%) intensive care unit (ICU)) and 679/3942 (17.2%) received mechanical ventilation. Marked heterogeneity was observed across facilities in median age (range: 34.3-83.9 years; facility mean: 64.7, SD 7.2 years); patient volume (range: 1-737 at 160 facilities; facility median: 48.5, IQR 14-105.5); hospital admissions (range: 1-286 at 133 facilities; facility median: 11, IQR 1-26.5); ICU caseload (range: 1-85 at 115 facilities; facility median: 4, IQR 0-12); and mechanical ventilation (range: 1-53 at 90 facilities; facility median: 1, IQR 0-5). Heterogeneity was also observed in facility mortality for all patients with COVID-19 (range: 0%-29.7%; facility median: 8.9%, IQR 2.4%-13.7%); inpatients (range: 0%-100%; facility median: 18.0%, IQR 5.6%-28.6%); ICU patients (range: 0%-100%; facility median: 28.6%, IQR 14.3%-50.0%); and mechanical ventilator patients (range: 0%-100%; facility median: 52.7%, IQR 33.3%-80.6%). The majority of variation in facility mortality was attributable to differences in patient characteristics (eg, age).ConclusionsMarked heterogeneity in COVID-19 patient volume, characteristics and mortality were observed across VA facilities nationwide. Differences in patient characteristics accounted for the majority of explained variation in mortality across sites. Variation in unadjusted COVID-19 mortality across facilities or nations should be considered with caution.

Project description:BackgroundAcceptability of mobile phone text messaging as a means of asynchronous communication between health care systems and patients is growing. The US Department of Veterans Affairs (VA) has adopted an automated texting system (aTS) for national rollout. The aTS allows providers to develop clinical texting protocols to promote patient self-management and allows clinical teams to monitor patient progress between in-person visits. Texting-supported hepatitis C virus (HCV) treatment has not been previously tested.ObjectiveGuided by the Practical, Robust Implementation and Sustainability Model (PRISM), we developed an aTS HCV protocol and conducted a mixed methods, hybrid type 2 effectiveness implementation study comparing two programs supporting implementation of the aTS HCV protocol for medication adherence in patients with HCV.MethodsSeven VA HCV specialty clinics were randomized to usual aTS implementation versus an augmented implementation facilitation program. Implementation process measures included facilitation metrics, usability, and usefulness. Implementation outcomes included provider and patient use of the aTS HCV protocol, and effectiveness outcomes included medication adherence, health perceptions and behaviors, and sustained virologic response (SVR).ResultsAcross the seven randomized clinics, there were 293 facilitation events using a core set of nine implementation strategies (157 events in augmented implementation facilitation, 136 events in usual implementation). Providers found the aTS appropriate with high potential for scale-up but not without difficulties in startup, patient selection and recruitment, and clinic workflow integration. Patients largely found the aTS easy to use and helpful; however, low perceived need for self-management support contributed to high declination. Reach and use was modest with 197 patients approached, 71 (36%) enrolled, 50 (25%) authenticated, and 32 (16%) using the aTS. In augmented implementation facilitation clinics, more patients actively used the aTS HCV protocol compared with usual clinic patients (20% vs 12%). Patients who texted reported lower distress about failing HCV treatment (13/15, 87%, vs 8/15, 53%; P=.05) and better adherence to HCV medication (11/15, 73%, reporting excellent adherence vs 6/15, 40%; P=.06), although SVR did not differ by group.ConclusionsThe aTS is a promising intervention for improving patient self-management; however, augmented approaches to implementation may be needed to support clinician buy-in and patient engagement. Considering the behavioral, social, organizational, and technical scale-up challenges that we documented, successful and sustained implementation of the aTS may require implementation strategies that operate at the clinic, provider, and patient levels.Trial registrationRetrospectively registered at ClinicalTrials.gov NCT03898349; https://clinicaltrials.gov/ct2/show/NCT03898349.

Dataset Information

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

Publications

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

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