Quantifying the spatiotemporal variability of nitrate in irrigation water across the Wisconsin Central Sands.

The Wisconsin Central Sands is home to large scale vegetable production on sandy soils and managed with frequent irrigation, fertigation, and widespread nitrogen fertilizer application, all of which make the region highly susceptible to nitrate loss to groundwater. While the groundwater is used as the primary source of drinking water for many communities and rural residences across the region, it is also used for irrigation. Considering the high levels of nitrate found in the groundwater, it has been proposed that growers more accurately account for the nitrate in their irrigation water as part of nitrogen management plans. Our objectives were to 1) determine the magnitude of nitrate in irrigation water, 2) quantify the spatiotemporal variability of nitrate, and 3) determine key predictors of nitrate concentration in the region. We sampled irrigation water from 38 fields across six farms from 2018 to 2020. Across the 3 years of our study, nitrate concentration varied more across space than time. On average, our samples were tested at 19.0 mg L-1 nitrate-nitrogen, or nearly two times the U.S. Environmental Protection Agency (EPA) threshold for safe drinking water, equivalent to 48.1 kg ha-1 of applied nitrate-nitrogen with 25.4 cm (or 10 in.) of irrigation. To better understand the spatiotemporal variability in nitrate levels, week of sampling, year, well depth, well casing, and nitrogen application rate were analyzed for their role as predictor variables. Based on our linear mixed effects model, nitrogen application rate was the greatest predictor of the nitrate concentration of irrigation water (p < 0.05).

Disseminating child abuse clinical decision support among commercial electronic health records: Effects on clinical practice.

Objectives: The use of electronic health record (EHR)-embedded child abuse clinical decision support (CA-CDS) may help decrease morbidity from child maltreatment. We previously reported on the development of CA-CDS in Epic and Allscripts. The objective of this study was to implement CA-CDS into Epic and Allscripts and determine its effects on identification, evaluation, and reporting of suspected child maltreatment. Materials and Methods: After a preimplementation period, CA-CDS was implemented at University of Wisconsin (Epic) and Northwell Health (Allscripts). Providers were surveyed before the go-live and 4 months later. Outcomes included the proportion of children who triggered the CA-CDS system, had a positive Child Abuse Screen (CAS) and/or were reported to Child Protective Services (CPS). Results: At University of Wisconsin (UW), 3.5% of children in the implementation period triggered the system. The CAS was positive in 1.8% of children. The proportion of children reported to CPS increased from 0.6% to 0.9%. There was rapid uptake of the abuse order set.At Northwell Health (NW), 1.9% of children in the implementation period triggered the system. The CAS was positive in 1% of children. The child abuse order set was rarely used. Preimplementation, providers at both sites were similar in desire to have CA-CDS system and perception of CDS in general. After implementation, UW providers had a positive perception of the CA-CDS system, while NW providers had a negative perception. Discussion: CA-CDS was able to be implemented in 2 different EHRs with differing effects on clinical care and provider feedback. At UW, the site with higher uptake of the CA-CDS system, the proportion of children who triggered the system and the rate of positive CAS was similar to previous studies and there was an increase in the proportion of cases of suspected abuse identified as measured by reports to CPS. Our data demonstrate how local environment, end-users' opinions, and limitations in the EHR platform can impact the success of implementation. Conclusions: When disseminating CA-CDS into different hospital systems and different EHRs, it is critical to recognize how limitations in the functionality of the EHR can impact the success of implementation. The importance of collecting, interpreting, and responding to provider feedback is of critical importance particularly with CDS related to child maltreatment.

Improved Detection of Child Maltreatment with Routine Screening in a Tertiary Care Pediatric Hospital.

OBJECTIVES: To evaluate the impact of layering routine child abuse screening on top of a preexisting electronic health record-embedded Child Abuse Clinical Decision Support System (CA-CDSS) in a pediatric emergency department. STUDY DESIGN: The Pittsburgh Child Abuse Screening Tool (P-CAST) was performed in all children aged <13 years and in nonverbal children aged ≥13 years who presented to a pediatric tertiary care center over a 6-month period. The P-CAST was layered on top of a preexisting CA-CDSS that included passive triggers, alerts, and abuse-specific order sets. RESULTS: Of the 28 797 screens performed, 1.8% were positive in children aged <13 years and 1.6% were positive in nonverbal children aged ≥13 years. One-half of the children with a positive P-CAST also triggered the CA-CDSS; the other one-half triggered only because of the P-CAST. Nineteen percent of the patients with a positive P-CAST were reported to Child Protective Services (CPS). There was no relationship between race and the odds of a positive P-CAST or between race and the likelihood of a report being made to CPS. CONCLUSIONS: Active routine child abuse screening improves identification of suspected child maltreatment in a children's hospital above and beyond what is identified with a CA-CDSS, which depends on passive triggers. The lack of a relationship between race and a positive P-CAST or a report to CPS suggest that systematic child abuse screening may mitigate well-recognized racial disparities in identifying and reporting suspected child maltreatment.

Dissemination of child abuse clinical decision support: Moving beyond a single electronic health record.

BACKGROUND: Child maltreatment is a leading cause of pediatric morbidity and mortality. We previously reported on development and implementation of a child abuse clinical decision support system (CA-CDSS) in the Cerner electronic health record (EHR). Our objective was to develop a CA-CDSS in two different EHRs. METHODS: Using the CA-CDSS in Cerner as a template, CA-CDSSs were developed for use in four hospitals in the Northwell Health system who use Allscripts and two hospitals in the University of Wisconsin health system who use Epic. Each system had a combination of triggers, alerts and child abuse-specific order sets. Usability evaluation was done prior to launch of the CA-CDSS. RESULTS: Over an 18-month period, a CA-CDSS was embedded into Epic and Allscripts at two hospital systems. The CA-CDSSs vary significantly from each other in terms of the type of triggers which were able to be used, the type of alert, the ability of the alert to link directly to child abuse-specific order sets and the order sets themselves. CONCLUSIONS: Dissemination of CA-CDSS from one EHR into the EHR in other health care systems is possible but time-consuming and needs to be adapted to the strengths and limitations of the specific EHR. Site-specific usability evaluation, buy-in of multiple stakeholder groups and significant information technology support are needed. These barriers limit scalability and widespread dissemination of CA-CDSS.

Integration of physical abuse clinical decision support at 2 general emergency departments.

OBJECTIVE: The study sought to develop and evaluate an electronic health record-based child abuse clinical decision support system in 2 general emergency departments. MATERIALS AND METHODS: A combination of a child abuse screen, natural language processing, physician orders, and discharge diagnoses were used to identify children <2 years of age with injuries suspicious for physical abuse. Providers received an alert and were referred to a physical abuse order set whenever a child triggered the system. Physician compliance with clinical guidelines was compared before and during the intervention. RESULTS: A total of 242 children triggered the system, 86 during the preintervention and 156 during the intervention. The number of children identified with suspicious injuries increased 4-fold during the intervention (P < .001). Compliance was 70% (7 of 10) in the preintervention period vs 50% (22 of 44) in the intervention, a change that was not statistically different (P = .55). Fifty-two percent of providers said that receiving the alert changed their clinical decision making. There was no relationship between compliance and provider or patient demographics. CONCLUSIONS: A multifaceted child abuse clinical decision support system resulted in a marked increase in the number of young children identified as having injuries suspicious for physical abuse in 2 general emergency departments. Compliance with published guidelines did not change; we hypothesize that this is related to the increased number of children identified with suspicious, but less serious injuries. These injuries were likely missed preintervention. Tracking compliance with guidelines over time will be important to assess whether compliance increases as physician comfort with evaluation of suspected physical abuse in young children improves.

Integration of physical abuse clinical decision support into the electronic health record at a Tertiary Care Children's Hospital.

Objective: To evaluate the effect of a previously validated electronic health record-based child abuse trigger system on physician compliance with clinical guidelines for evaluation of physical abuse. Methods: A randomized controlled trial (RCT) with comparison to a preintervention group was performed. RCT-experimental subjects' providers received alerts with a direct link to a physical abuse-specific order set. RCT-control subjects' providers had no alerts, but could manually search for the order set. Preintervention subjects' providers had neither alerts nor access to the order set. Compliance with clinical guidelines was calculated. Results: Ninety-nine preintervention subjects and 130 RCT subjects (73 RCT-experimental and 57 RCT-control) met criteria to undergo a physical abuse evaluation. Full compliance with clinical guidelines was 84% pre-intervention, 86% in RCT-control group, and 89% in RCT-experimental group. The physical abuse order set was used 43 times during the 7-month RCT. When the abuse order set was used, full compliance was 100%. The proportion of cases in which there was partial compliance decreased from 10% to 3% once the order set became available (P = .04). Male gender, having >10 years of experience and completion of a pediatric emergency medicine fellowship were associated with increased compliance. Discussion/Conclusion: A child abuse clinical decision support system comprised of a trigger system, alerts and a physical abuse order set was quickly accepted into clinical practice. Use of the physical abuse order set always resulted in full compliance with clinical guidelines. Given the high baseline compliance at our site, evaluation of this alert system in hospitals with lower baseline compliance rates will be more valuable in assessing the efficacy in adherence to clinical guidelines for the evaluation of suspected child abuse.

Development of an electronic medical record-based child physical abuse alert system.

Objective: Physical abuse is a leading cause of pediatric morbidity and mortality. Physicians do not consistently screen for abuse, even in high-risk situations. Alerts in the electronic medical record may help improve screening rates, resulting in early identification and improved outcomes. Methods: Triggers to identify children < 2 years old at risk for physical abuse were coded into the electronic medical record at a freestanding pediatric hospital with a level 1 trauma center. The system was run in "silent mode"; physicians were unaware of the system, but study personnel received data on children who triggered the alert system. Sensitivity, specificity, and negative and positive predictive values of the child abuse alert system for identifying physical abuse were calculated. Results: Thirty age-specific triggers were embedded into the electronic medical record. From October 21, 2014, through April 6, 2015, the system was in silent mode. All 226 children who triggered the alert system were considered subjects. Mean (SD) age was 9.1 (6.5) months. All triggers were activated at least once. Sensitivity was 96.8% (95% CI, 92.4-100.0%), specificity was 98.5% (95% CI, 98.3.5-98.7), and positive and negative predictive values were 26.5% (95% CI, 21.2-32.8%) and 99.9% (95% CI, 99.9-100.0%), respectively, for identifying children < 2 years old with possible, probable, or definite physical abuse. Discussion/Conclusion: Triggers embedded into the electronic medical record can identify young children with who need to be evaluated for physical abuse with high sensitivity and specificity.