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
Use Case Scenarios
Use cases below are meant to provide tangible scenarios where laboratory data is created, stored, exchanged and/or analyzed for purposes of contextualization of laboratory data interoperability
Clinical and Public Health Scenario Use Cases
Patient A, a 65 y/o male, originally seen at a critical access hospital is transferred to the regional health center and admitted for a suspected case of infectious pneumonia and low oxygen levels. The patient also has cirrhosis of the liver. A respiratory pathogen panel is ordered by the admitting physician to confirm the pneumonia is due to a bacterial infection. The lab test is ordered in the EHR. A n/p swab specimen is collected and sent to the micro lab for testing.
The test performed in the lab is a PCR-based panel. Within 4 hours, the laboratory reports the positive identification of a Klebsiella pneumoniae infection. A reflex susceptibility test is ordered by the laboratory. In the meantime, the physician orders the preferred antibiotic in line with empirical evidence provided by the institutional antibiogram and in accordance with the antimicrobial stewardship protocols of the hospital.
Susceptibility results indicate that the Klebsiella pneumoniae is an extended spectrum beta lactamase (ESBL) producing organism that is resistant to the initially prescribed therapy. It is only susceptible to antibiotics in the carbapenem family, but many of the readily available carbapenems impact the liver and will be difficult for the patient to tolerate. The physician must identify a suitable alternative. In previous years, this would have been a very challenging scenario and involved consultations with the infectious disease attending physician and the pharmacist on duty. However, the hospital has adopted the integrated terminology standards that link lab testing, organisms and medicinal products. As a result, the physician is able to query the EHR/CDSS for alternative carbapenem drugs on the hospital formulary. The physician is able to identify a suitable cabapenem in the hospital formulary that will control the infection and should be tolerated well by the patient. The medications are ordered. The physician requests that past hepatic profile test results from the critical access hospital be imported into the EHR, and the physician orders a series of hepatic function tests to monitor the patient’s liver function compared to baseline function during carbapenem therapy. The patient recovers after 5 days of therapy and is subsequently discharged.
During the course of laboratory testing, the organism identified along with its susceptibility profile is recorded. The data are immediately incorporated into the hospital’s antibiogram calculations and will be incorporated into the next publication by the antimicrobial stewardship (ASM) team. The organism and susceptibility data are further reported to the state department of epidemiology via HL7 message including S/I/R and MIC data along with testing methods for organism identification and susceptibility test results as ESBL-producing Klebsiella pneumoniae are a reportable condition due to the rise in antimicrobial resistance in this species. Furthermore, isolates of the organism are sequenced and profiled for known or suspected genes associated with antimicrobial resistance as part of a broader AMS discovery and intervention program.
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 (reagent, calibrator, container) 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.
Examples:
Class 2 Device Recall of specific collection transport media for SARS-CoV-2 Antigen testing (Recall Number: Z-1266-2021). Specific viral transport media led to false-positive results.
Class 2 Device Recall of specific Unconjugated estriol reagent lots (Recall Number: Z-3006-2020; 08/26/2020). Specific formulation of reagent lot led to falsely elevated results in a subset of patients, potentially leading to false-positive prenatal screening results for Down Syndrome.
Class 2 Device Recall of specific Parathyroid hormone reagent lots (Recall Number: Z-1342-2021; 11/23/2020). These specific reagent lots had potential to ‘produce falsely elevated…results’, potentially leading to false-positive diagnoses for hyperparathyroidism.
Class 2 Device Recall of specific Rheumatoid Factor calibrator lots (Recall Number: Z-0283-2020; 08/09/2019). These specific calibrator lots caused falsely low results, potentially leading to missed clinical diagnoses.
Explanation:
Laboratory tests are performed using several components, including specimen collection devices and reagents, test reagents, and test calibrators. Components are labeled with device identifiers and lot numbers, which indicate the batch in which they were prepared. Together these identifiers comprise the Universal Device Identifier (UDI). Not all potential combinations of devices can be evaluate pre-market. Performing laboratories should locally verify specific collection devices and reagents, but these practices are variable and limited. Specific component lots are not evaluated pre-market. For some assays, batch-to-batch variability can be substantial and clinically impactful. Manufacturers verify each batch, but typically using few or no patient samples. Clinical laboratories verify batches, but practices are variable and use few to no patient samples. These processes are not designed to detect errors that affect some but not all patients.
Barriers:
Not all relevant devices and components are assigned UDIs
The transmission of UDIs is not required
Data is not aggregated
Requirements:
Device, reagent, calibrator, collection container are all assigned a UDI
Testing device transmits the set of UDIs (OBX-18 field in HL7 v2.5.1) (to LIS to EHR)
o Instrument -> (Middleware / LIS)
o Middleware -> LIS
o LIS -> EHR
o EHR -> EHR
Analysis of data grouped by UDIs (and other contextual factors) for changes in aggregate results
Implementation Plan
Presentation slides and extended draft below
Implementation Plan Power Point
Implementation Plan Word Document
Insert comments, gaps and edits in line in each subsection below
Add Comment