Dataset Information

Measuring Use of Evidence-Based Psychotherapy for PTSD in VA Residential Treatment Settings with Clinician Survey and Electronic Medical Record Templates.

ABSTRACT:

Objective

Available studies on implementation of evidence-based psychotherapy (EBP) for patients attending Department of Veterans Affairs (VA) residential post-traumatic stress disorder (PTSD) programs rely on therapist self-report of EBP delivery. Patient-level data on receipt of EBP are needed both to corroborate therapist self-report and to understand patient factors that predict receipt of EBPs for PTSD.

Materials and methods

We identified 159 therapists from 38 VA residential PTSD programs who responded to a survey about EBP implementation during the 2015 fiscal year (FY15). Therapists self-reported their use of two EBPs, including prolonged exposure delivered in an individual format (PE-I) and cognitive processing therapy delivered in individual and group formats (CPT-I and CPT-G). Using electronic medical record (EMR) templates mandated for EBP documentation in FY15, we measured contemporaneous patient-level receipt of EBPs for PTSD. We assessed the degree of correlation between therapist self-reported EBP delivery and patient receipt of EBT as measured by EMR templates using polychoric correlation coefficients. We determined patient and therapist factors that predicted the receipt of EBPs with multivariable logistic regression, using random effects and robust standard error estimation, and controlling for site. The Veterans IRB of Northern New England provided a waiver of informed consent; as this was a retrospective review, no patients or therapists were contacted, and all data were stored, transmitted, and analyzed on secure VA servers. The VA Connecticut Health Care System Human Research Protection Program approved secondary use of therapist survey data for this project.

Results

When EMR template use became mandated in FY15, the proportion of patients in residential PTSD programs who received at least one EBP session that was recorded with an EMR template increased dramatically from 8.8% to 33.9%. There was adequate correlation and between survey-based and EMR-based measures of EBP receipt, with polychoric correlation values of 0.77 for PE-I, 0.69 for CPT-I, and 0.82 for CPT-G. Multiple patient factors were positive (e.g., female gender) and negative (e.g., depressive disorders) predictors of receipt of EBPs, even after controlling for site. Among therapist factors, only EBP consultant or trainer status was a positive predictor of EBP provision and only therapist race was a negative predictor of EBT provision after controlling for site.

Conclusion

Following a FY15 mandate, EMR templates documenting EBP delivery were widely used by therapists working in VA residential PTSD programs. EBP receipt measured using EMR templates was consistent with therapist self-report of EBT delivery. There were several patient-level predictors of EBP receipt and therapist-level predictors of EBP delivery. However, therapists most likely to deliver EBPs were clustered at a limited number of sites.

SUBMITTER: Shiner B

PROVIDER: S-EPMC6115864 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCollaboration is vital within health care institutions, and it allows for the effective use of collective health care worker (HCW) expertise. Human-computer interactions involving electronic health records (EHRs) have become pervasive and act as an avenue for quantifying these collaborations using statistical and network analysis methods.ObjectiveWe aimed to measure HCW collaboration and its characteristics by analyzing concurrent EHR usage.MethodsBy extracting concurrent EHR usage events from audit log data, we defined concurrent sessions. For each HCW, we established a metric called concurrent intensity, which was the proportion of EHR activities in concurrent sessions over all EHR activities. Statistical models were used to test the differences in the concurrent intensity between HCWs. For each patient visit, starting from admission to discharge, we measured concurrent EHR usage across all HCWs, which we called temporal patterns. Again, we applied statistical models to test the differences in temporal patterns of the admission, discharge, and intermediate days of hospital stay between weekdays and weekends. Network analysis was leveraged to measure collaborative relationships among HCWs. We surveyed experts to determine if they could distinguish collaborative relationships between high and low likelihood categories derived from concurrent EHR usage. Clustering was used to aggregate concurrent activities to describe concurrent sessions. We gathered 4 months of EHR audit log data from a large academic medical center's neonatal intensive care unit (NICU) to validate the effectiveness of our framework.ResultsThere was a significant difference (P<.001) in the concurrent intensity (proportion of concurrent activities: ranging from mean 0.07, 95% CI 0.06-0.08, to mean 0.36, 95% CI 0.18-0.54; proportion of time spent on concurrent activities: ranging from mean 0.32, 95% CI 0.20-0.44, to mean 0.76, 95% CI 0.51-1.00) between the top 13 HCW specialties who had the largest amount of time spent in EHRs. Temporal patterns between weekday and weekend periods were significantly different on admission (number of concurrent intervals per hour: 11.60 vs 0.54; P<.001) and discharge days (4.72 vs 1.54; P<.001), but not during intermediate days of hospital stay. Neonatal nurses, fellows, frontline providers, neonatologists, consultants, respiratory therapists, and ancillary and support staff had collaborative relationships. NICU professionals could distinguish high likelihood collaborative relationships from low ones at significant rates (3.54, 95% CI 3.31-4.37 vs 2.64, 95% CI 2.46-3.29; P<.001). We identified 50 clusters of concurrent activities. Over 87% of concurrent sessions could be described by a single cluster, with the remaining 13% of sessions comprising multiple clusters.ConclusionsLeveraging concurrent EHR usage workflow through audit logs to analyze HCW collaboration may improve our understanding of collaborative patient care. HCW collaboration using EHRs could potentially influence the quality of patient care, discharge timeliness, and clinician workload, stress, or burnout.