PCORI funded the development of PCORnet®, the National Patient-Centered Clinical Research Network, to make research faster, easier, and less costly to conduct. PCORnet is made up of Partner Networks of healthcare systems, patients and communities, and health plans that harness the power of large amounts of health data.
PCORI supports projects, designed in partnership with federal funding organizations, to improve the quality of data used in clinical research. This project is one of two projects designed to link data from PCORnet and the U.S. Food and Drug Administration’s Sentinel medical product monitoring system.
To examine the capacity to track congenital Zika syndrome using data contained in clinical data research networks within PCORnet and to create a novel query strategy to track congenital Zika syndrome in the future
|Retrospective descriptive analysis project
|Data Sources and Data Sets
|Records from 182,000 births between 2012 and 2017 from OneFlorida Clinical Research Consortium
|EHR data review
|Aim 1. Data elements predicting Zika syndrome present in PCORnet CDM and EHRs
Aim 2. Data entry error frequency
Aim 3. Validation of microcephaly diagnoses
Aim 4. Completeness of data for key birth characteristics needed to track birth defects from Zika syndrome
Aim 5. EHR query strategy to identify evidence of Zika syndrome in birth records
The project included electronic health record (EHR) data from 182,000 births linked using the PCORnet common data model (CDM). The CDM organizes data into a standard structure for use by researchers. Birth records were from the years 2012 through 2017. All records were from the OneFlorida Clinical Research Consortium, a PCORnet clinical data research network containing data for 15 million patients across Florida. The team also examined claims data from Sentinel.
The project had five aims:
Aim 1. Determine what data elements predictive of Zika syndrome exist in the CDM and the difficulty of extracting additional data elements from EHRs themselves.
Aim 2. Evaluate the frequency of data error entries in the CDM.
Aim 3. In a subset of birth records, evaluate the validity of data extracted from EHRs for Zika tracking by having a doctor review records and determine if births were accurately diagnosed with microcephaly.
Aim 4. In a subset of birth records, determine the completeness of data elements needed for tracking Zika syndrome.
Aim 5. Develop a novel query to search EHR and Sentinel claims data for evidence of congenital Zika syndrome.
Aim 1. The project team found that many data elements necessary for detecting Zika syndrome existed in the CDM, such as diagnoses, demographics, and laboratory test results. The team categorized other data elements not currently in the CDM in terms of how difficult they would be to extract from EHRs:
- Relatively easy to extract, such as head circumference and estimated gestational age
- Moderately difficult to extract, such as hearing screening
- Very difficult to extract, such as results of vision screening
Aim 2. The project team found that 0.005% of observations within the CDM had a date that preceded patient date of birth, and 1.3% had out-of-plausible-range head circumference measurements.
Aim 3. Based on a doctor’s review of 36 records from one hospital, 69% of infants flagged as possible microcephaly cases had the condition.
Aim 4. The project team found that data elements needed for Zika tracking were highly complete based on a review of 2,800 birth records from one hospital in 2016. For example, 99.8% of records had head circumference data, and 99.8% had estimated gestational age at birth.
Aim 5. The team developed a query strategy to detect congenital Zika syndrome using SAS software. The strategy queries EHR and hospital claims data from both mothers and infants.
The CDM data do not contain key data elements that may assist in detecting Zika syndrome, such as head circumference. The project team had to review underlying patient records to retrieve these data.
Conclusions and Relevance
The project team examined CDM data to identify important data elements that can be used to track Zika syndrome. The team examined the completeness and validity of data and created a search strategy to track cases of congenital Zika syndrome using EHR and claims data. Future projects can consider using CDM data to track other diseases and health conditions.
Future projects could investigate the accuracy and completeness of hospital claims data for use in tracking congenital Zika syndrome. Future projects could also extend the tracking method to other healthcare topics.