Info

The purpose of this section is to:

Provide input to initial SHIELD implementation Plan
Define the WHAT needs to be done to achieve laboratory data interoperability
Set stage for HOW to deliver the “what’s”
Set stage for milestones, goals, timeline

Table of Contents

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Clinical Scenario 2 Use Case

A third year house staff in emergency medicine is preparing a quality of care project to meet certification requirements of the ACEM. In collaboration with a dozen of their colleagues at sister institutions they will compare opioid testing results on patients presenting to the ED with recurring abdominal pain. They wish to be inclusive of any urinary or blood results for any opioid-receptor agonist agent. They plan to identify whether any testing was done within 24 hours of the ED visit and to classify the results as COMPLETE, NOT DONE, OPIOD PRESENT or OPIOD NOT DETECTED. The project will evaluate the effects of the screening data on the expense and outcomes of the ED evaluation.

Research Scenario Use Case

A research faculty at U of Anywhere is designing a study to compare the sensitivity, specificity and positive predictive value of two novel screening tests for COVID-19 respiratory infection in patients with diabetes mellitus. The test strategies are nasal swab for SARS-CoV-2 N gene versus salivary SARS-CoV-2 N gene. Diabetes mellitus is defined by a history of random non-stimulated blood glucose >= 200mg/dL or Hemoglobin A1C/Hemoglobin ratio >6.5%. The research team plans to conduct this as an observational trial across the N3C network and they are struggling to define the value sets of coded test results which they will incorporate into their SAS query for cohort identification and outcomes assessment.

Quality Use Case

Goal: Earlier and more sensitive detection of laboratory test component deficiencies. Some testing device defects are not readily detectable by existing, standard manufacturing and process controls. Surveillance of aggregated laboratory testing data could speed the detection of such defects, thus decreasing the potential harm. In addition, this higher resolution labeling of test results would facilitate better cleaning of data for additional secondary uses.

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Analysis of data grouped by UDIs (and other contextual factors) for changes in aggregate results

Implementation Plan

Implementation

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Plan Power Point

Powerpoint

name	ImplementationCommitteePlan_20210707_final.pptx