Development and Evaluation of High-Value Pediatrics: A High-Value Care Pediatric Resident Curriculum.

2018

785-792

2018 12

2154-1663

<p>Low-value health care is pervasive in the United States, and clinicians need to be trained to be stewards of health care resources. Despite a mandate by the Accreditation Council for Graduate Medical Education to educate trainee physicians on cost awareness, only 10% of pediatric residency programs have a high-value care (HVC) curriculum. To meet this need, we set out to develop and evaluate the impact of High-Value Pediatrics, an open-access HVC curriculum. High-Value Pediatrics is a 3-part curriculum that includes 4 standardized didactics, monthly interactive morning reports, and an embedded HVC improvement project. Curriculum evaluation through an anonymous, voluntary survey revealed an improvement in the self-reported knowledge of health care costs, charges, reimbursement, and value ( &lt; .05). Qualitative results revealed self-reported behavior changes, and HVC improvement projects resulted in higher-value patient care. The implementation of High-Value Pediatrics is feasible and reveals improved knowledge and attitudes about HVC. HVC improvement projects augmented curricular knowledge gains and revealed behavior changes. It is imperative that formal high-value education be taught to every pediatric trainee to lead the culture change that is necessary to turn the tide against low-value health care. In addition, simultaneous work on faculty education and attention to the hidden curriculum of low-value care is needed for sustained and long-term improvements.</p>

10.1542/hpeds.2018-0115

Hosp Pediatr

30425056

Design and Implementation of a Pediatric ICU Acuity Scoring Tool as Clinical Decision Support.

2018

576-587

2018 07

1869-0327

<p><strong>BACKGROUND AND OBJECTIVE: </strong>Pediatric in-hospital cardiac arrest most commonly occurs in the pediatric intensive care unit (PICU) and is frequently preceded by early warning signs of clinical deterioration. In this study, we describe the implementation and evaluation of criteria to identify high-risk patients from a paper-based checklist into a clinical decision support (CDS) tool in the electronic health record (EHR).</p>

<p><strong>MATERIALS AND METHODS: </strong>The validated paper-based tool was first adapted by PICU clinicians and clinical informaticians and then integrated into clinical workflow following best practices for CDS design. A vendor-based rule engine was utilized. Littenberg's assessment framework helped guide the overall evaluation. Preliminary testing took place in EHR development environments with more rigorous evaluation, testing, and feedback completed in the live production environment. To verify data quality of the CDS rule engine, a retrospective Structured Query Language (SQL) data query was also created. As a process metric, preparedness was measured in pre- and postimplementation surveys.</p>

<p><strong>RESULTS: </strong>The system was deployed, evaluating approximately 340 unique patients monthly across 4 clinical teams. The verification against retrospective SQL of 15-minute intervals over a 30-day period revealed no missing triggered intervals and demonstrated 99.3% concordance of positive triggers. Preparedness showed improvements across multiple domains to our a priori goal of 90%.</p>

<p><strong>CONCLUSION: </strong>We describe the successful adaptation and implementation of a real-time CDS tool to identify PICU patients at risk of deterioration. Prospective multicenter evaluation of the tool's effectiveness on clinical outcomes is necessary before broader implementation can be recommended.</p>

10.1055/s-0038-1667122

Appl Clin Inform

30068013

Performance of a Clinical Decision Support Tool to Identify PICU Patients at High Risk for Clinical Deterioration.

2019

2019 Oct 02

1529-7535

<p><strong>OBJECTIVES: </strong>To evaluate the translation of a paper high-risk checklist for PICU patients at risk of clinical deterioration to an automated clinical decision support tool.</p>

<p><strong>DESIGN: </strong>Retrospective, observational cohort study of an automated clinical decision support tool, the PICU Warning Tool, adapted from a paper checklist to predict clinical deterioration events in PICU patients within 24 hours.</p>

<p><strong>SETTING: </strong>Two quaternary care medical-surgical PICUs-The Children's Hospital of Philadelphia and Cincinnati Children's Hospital Medical Center.</p>

<p><strong>PATIENTS: </strong>The study included all patients admitted from July 1, 2014, to June 30, 2015, the year prior to the initiation of any focused situational awareness work at either institution.</p>

<p><strong>INTERVENTIONS: </strong>We replicated the predictions of the real-time PICU Warning Tool by retrospectively querying the institutional data warehouse to identify all patients that would have flagged as high-risk by the PICU Warning Tool for their index deterioration.</p>

<p><strong>MEASUREMENTS AND MAIN RESULTS: </strong>The primary exposure of interest was determination of high-risk status during PICU admission via the PICU Warning Tool. The primary outcome of interest was clinical deterioration event within 24 hours of a positive screen. The date and time of the deterioration event was used as the index time point. We evaluated the sensitivity, specificity, positive predictive value, and negative predictive value of the performance of the PICU Warning Tool. There were 6,233 patients evaluated with 233 clinical deterioration events experienced by 154 individual patients. The positive predictive value of the PICU Warning Tool was 7.1% with a number needed to screen of 14 patients for each index clinical deterioration event. The most predictive of the individual criteria were elevated lactic acidosis, high mean airway pressure, and profound acidosis.</p>

<p><strong>CONCLUSIONS: </strong>Performance of a clinical decision support translation of a paper-based tool showed inferior test characteristics. Improved feasibility of identification of high-risk patients using automated tools must be balanced with performance.</p>

10.1097/PCC.0000000000002106

Pediatr Crit Care Med

31577691

Safety Huddle Intervention for Reducing Physiologic Monitor Alarms: A Hybrid Effectiveness-Implementation Cluster Randomized Trial.

2018

2018 Feb 27

1553-5606

<p><strong>BACKGROUND: </strong>Monitor alarms occur frequently but rarely warrant intervention.</p>

<p><strong>OBJECTIVE: </strong>This study aimed to determine if a safety huddle-based intervention reduces unit-level alarm rates or alarm rates of individual patients whose alarms are discussed, as well as evaluate implementation outcomes.</p>

<p><strong>DESIGN: </strong>Unit-level, cluster randomized, hybrid effectiveness-implementation trial with a secondary patient-level analysis.</p>

<p><strong>SETTING: </strong>Children's hospital.</p>

<p><strong>PATIENTS: </strong>Unit-level: all patients hospitalized on 4 control (n = 4177) and 4 intervention (n = 7131) units between June 15, 2015 and May 8, 2016. Patient-level: 425 patients on randomly selected dates postimplementation.</p>

<p><strong>INTERVENTION: </strong>Structured safety huddle review of alarm data from the patients on each unit with the most alarms, with a discussion of ways to reduce alarms.</p>

<p><strong>MEASUREMENTS: </strong>Unit-level: change in unit-level alarm rates between baseline and postimplementation periods in intervention versus control units. Patient-level: change in individual patients' alarm rates between the 24 hours leading up to huddles and the 24 hours after huddles in patients who were discussed versus not discussed in huddles.</p>

<p><strong>RESULTS: </strong>Alarm data informed 580 huddle discussions. In unit-level analysis, intervention units had 2 fewer alarms/patient-day (95% CI: 7 fewer to 6 more, P = .50) compared with control units. In patient-level analysis, patients discussed in huddles had 97 fewer alarms/patientday (95% CI: 52-138 fewer, P &lt; .001) in the posthuddle period compared with patients not discussed in huddles. Implementation outcome analysis revealed a low intervention dose of 0.85 patients/unit/day.</p>

<p><strong>CONCLUSIONS: </strong>Safety huddle-based alarm discussions did not influence unit-level alarm rates due to low intervention dose but were effective in reducing alarms for individual children.</p>

10.12788/jhm.2956

J Hosp Med

29489921

A pragmatic checklist to identify pediatric ICU patients at risk for cardiac arrest or code bell activation.

2016

33-7

2016 Feb

1873-1570

<p><strong>BACKGROUND: </strong>In-hospital cardiac arrest is a rare event associated with significant morbidity and mortality. The ability to identify the ICU patients at risk for cardiac arrest could allow the clinical team to prepare staff and equipment in anticipation.</p>

<p><strong>METHODS: </strong>This pilot study was completed at a large tertiary care pediatric intensive care unit to determine the feasibility of a simple checklist of clinical variables to predict deterioration. The daily checklist assessed patient risk for critical deterioration defined as cardiac arrest or code bell activation within 24h of the checklist screen. The Phase I checklist was developed by expert consensus and evaluated to determine standard diagnostic test performance. A modified Phase II checklist was developed to prospectively test the feasibility and bedside provider "number needed to train".</p>

<p><strong>RESULTS: </strong>For identifying patients requiring code bell activation, both checklists demonstrated a sensitivity of 100% with specificity of 76.0% during Phase I and 97.7% during Phase II. The positive likelihood ratio improved from 4.2 to 43.7. For identifying patients that had a cardiac arrest within 24h, the Phase I and II checklists demonstrated a sensitivity of 100% with specificity again improving from 75.7% to 97.6%. There was an improved positive likelihood ratio from 4.1 in Phase I to 41.9 in Phase II, with improvement of "number needed to train" from 149 to 7.4 providers.</p>

<p><strong>CONCLUSIONS: </strong>A novel high-risk clinical indicators checklist is feasible and provides timely and accurate identification of the ICU patients at risk for cardiac arrest or code bell activation.</p>

10.1016/j.resuscitation.2015.11.017

Resuscitation

26703460