Integrated Health Services to Reduce Opioid Use While Managing Chronic Pain (INSPIRE Trial), North Carolina and Tennessee, 2019-2023 (ICPSR 39271)
INtegrated Services for Pain: Interventions to Reduce Pain Effectively (INSPIRE) was a pragmatic randomized trial conducted from 2019 to 2023 with adults receiving chronic opioid therapy (COT) of at least 20 morphine milligram equivalents (MME) daily for chronic noncancer pain (CNCP). Participants were recruited from primary care and specialty pain clinics at three academic health centers in North Carolina and Tennessee. The study compared the effectiveness of the two behavioral interventions, 1) shared decision making (SDM) versus 2) motivational interviewing plus cognitive behavioral therapy for chronic pain (MI+CBT), on change in opioid dose, physical function, and pain interference. INSPIRE combined data from electronic health records (EHR) on opioid dose from baseline to 18 months and comorbidities with participant survey data at baseline, 6, and 12 months on the following topics:
- physical function,
- pain interference,
- pain intensity,
- anxiety,
- depression,
- pain severity,
- discontinuation of opioids,
- intent to reduce opioids,
- opioid use relative to baseline,
- adverse events,
- demographics,
- health insurance coverage,
- health literacy,
- patient-centered communication, and
- types of pain treatment used.
The collection includes three analysis datasets:
- Adverse Events Dataset - one record per subject per adverse event
- Opioid Prescriptions Dataset (post-processed opioid prescriptions used to derive the study's primary outcome) - one record per subject per opioid prescription
- Outcomes Dataset (contains all of the study's demographics, primary, secondary, exploratory, and subgroup analysis variables) - one record per subject per timepoint
Statistical Methods and Designs for Addressing Correlated Errors in Outcomes and Covariates in Studies Using Electronic Health Records Data [Methods Study], Tennessee, 2016-2021 (ICPSR 39726)
Electronic health records, or EHRs, have data on patient traits, health problems, and treatments. Researchers can use EHR data to study how treatments work or which patient traits affect health outcomes. But EHR data can have errors.
The best way to get accurate EHR data is to closely review patients' original records. But reviewing all patient records isn't possible when many patients are in a study. In such cases, researchers can review and correct records for a few patients and use the revised records to adjust data for all patients. But existing methods for using revised records don't address some kinds of errors, such as errors that are related. For example, errors in a treatment starting date can lead to mistakes in the data on length of treatment.
In this project, the research team created and tested new methods to improve the accuracy of EHR data. The new methods corrected records from some patients. Then the team used the corrections to address related errors for all patients.
To access the methods and software, please visit the MeasurementErrorMethods GitHub repository.