How do contraindications to non-opioid analgesics and opioids affect the likelihood that patients with back pain diagnoses in the primary care setting receive an opioid prescription? An observational cross-sectional study.

BACKGROUND: Given the risks of opioids, clinicians are under growing pressure to treat pain with non-opioid medications. Yet non-opioid analgesics such as non-steroidal anti-inflammatory drugs (NSAIDs) have their own risks: patients with kidney disease or gastrointestinal diseases can experience serious adverse events. We examined the likelihood that patients with back pain diagnoses and contraindications to NSAIDs and opioids received an opioid prescription in primary care. METHODS: We identified office visits for back pain from 2012 to 2017 and sampled the first office visit per patient per year (N = 24,543 visits). We created indicators reflecting contraindications for NSAIDs (kidney, liver, cardiovascular/cerebrovascular, and gastrointestinal diseases; concurrent or chronic use of anticoagulants/antiplatelets, chronic corticosteroid use) and opioids (depression, anxiety, substance use (SUD) and bipolar disorders, and concurrent benzodiazepines) and estimated four logistic regression models, with the one model including all patient visits and models 2-4 stratifying for previous opioid use. We estimated the population attributable risk for each contraindication. RESULTS: In our model with all patients-visits, patients received an opioid prescription at 4% of visits. The predicted probability (PP) of receiving an opioid was 4% without kidney disease vs. 7% with kidney disease; marginal effect (ME): 3%; 95%CI: 1-4%). For chronic or concurrent anticoagulant/antiplatelet prescriptions, the PPs were 4% vs. 6% (ME: 2%; 95%CI: 1-3%). For concurrent benzodiazepines, the PPs were 4% vs. 11% (ME: 7%, 95%CI: 5-9%) and for SUD, the PPs were 4% vs. 5% (ME: 1%, 95%CI: 0-3%). For the model including patients with previous long-term opioid use, the PPs for concurrent benzodiazepines were 25% vs. 24% (ME: -1%; 95%CI: - 18-16%). The population attributable risk (PAR) for NSAID and opioid contraindications was small. For kidney disease, the PAR was 0.16% (95%CI: 0.08-0.23%), 0.44% (95%CI: 0.30-0.58%) for anticoagulants and antiplatelets, 0.13% for substance use (95%CI: 0.03-0.22%) and 0.20% for concurrent benzodiazepine use (95%CI: 0.13-0.26%). CONCLUSIONS: Patients with diagnoses of kidney disease and concurrent use of anticoagulants/antiplatelet medications had a higher probability of receiving an opioid prescription at a primary care visit for low back pain, but these conditions do not explain a large proportion of the opioid prescriptions.

Accessing Patient Electronic Health Record Portals Safely Using Social Credentials: Demonstration Pilot Study.

BACKGROUND: Patient portals allow communication with clinicians, access to test results, appointments, etc, and generally requires another set of log-ins and passwords, which can become cumbersome, as patients often have records at multiple institutions. Social credentials (eg, Google and Facebook) are increasingly used as a federated identity to allow access and reduce the password burden. Single Federated Identity Log-in for Electronic health records (Single-FILE) is a real-world test of the feasibility and acceptability of federated social credentials for patients to access their electronic health records (EHRs) at multiple organizations with a single sign-on (SSO). OBJECTIVE: This study aims to deploy a federated identity system for health care in a real-world environment so patients can safely use a social identity to access their EHR data at multiple organizations. This will help identify barriers and inform guidance for the deployment of such systems. METHODS: Single-FILE allowed patients to pick a social identity (such as Google or Facebook) as a federated identity for multisite EHR patient portal access with an SSO. Binding the identity to the patient's EHR records was performed by confirming that the patient had a valid portal log-in and sending a one-time passcode to a telephone (SMS text message or voice) number retrieved from the EHR. This reduced the risk of stolen EHR portal credentials. For a real-world test, we recruited 8 patients and (or) their caregivers who had EHR data at 2 independent health care facilities, enrolled them into Single-FILE, and allowed them to use their social identity credentials to access their patient records. We used a short qualitative interview to assess their interest and use of a federated identity for SSO. Single-FILE was implemented as a web-based patient portal, although the concept can be readily implemented on a variety of mobile platforms. RESULTS: We interviewed the patients and their caregivers to assess their comfort levels with using a social identity for access. Patients noted that they appreciated only having to remember 1 log-in as part of Single-FILE and being able to sign up through Facebook. CONCLUSIONS: Our results indicate that from a technical perspective, a social identity can be used as a federated identity that is bound to a patient's EHR data. The one-time passcode sent to the patient's EHR phone number provided assurance that the binding is valid. The patients indicated that they were comfortable with using their social credentials instead of having to remember the log-in credentials for their EHR portal. Our experience will help inform the implementation of federated identity systems in health care in the United States.

Calculating maximum morphine equivalent daily dose from prescription directions for use in the electronic health record: a case report.

To demonstrate a process of calculating the maximum potential morphine milligram equivalent daily dose (MEDD) based on the prescription Sig for use in quality improvement initiatives. To calculate an opioid prescription's maximum potential Sig-MEDD, we developed SQL code to determine a prescription's maximum units/day using discrete field data and text-parsing in the prescription instructions. We validated the derived units/day calculation using 3000 Sigs, then compared the Sig-MEDD calculation against the Epic-MEDD calculator. Of the 101 782 outpatient opioid prescriptions ordered over 1 year, 80% used discrete-field Sigs, 7% used free-text Sigs, and 3% used both types. We determined units/day and calculated a Sig-MEDD for 98.3% of all the prescriptions, 99.99% of discrete-Sig prescriptions, and 81.5% of free-text-Sig prescriptions. Analyzing opioid prescription Sigs to determine a maximum potential Sig-MEDD provides greater insight into a patient's risk for opioid exposure.