A Multicenter Retrospective Cohort Study to Characterize Patients Hospitalized With Multisystem Inflammatory Syndrome in Adults and Coronavirus Disease 2019 in the United States, 2020-2021.

BACKGROUND: The diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-associated multisystem inflammatory syndrome in adults (MIS-A) requires distinguishing it from acute coronavirus disease 2019 (COVID-19) and may affect clinical management. METHODS: In this retrospective cohort study, we applied the US Centers for Disease Control and Prevention case definition to identify adults hospitalized with MIS-A at 6 academic medical centers from 1 March 2020 to 31 December 2021. Patients MIS-A were matched by age group, sex, site, and admission date at a 1:2 ratio to patients hospitalized with acute symptomatic COVID-19. Conditional logistic regression was used to compare demographic characteristics, presenting symptoms, laboratory and imaging results, treatments administered, and outcomes between cohorts. RESULTS: Through medical record review of 10 223 patients hospitalized with SARS-CoV-2-associated illness, we identified 53 MIS-A cases. Compared with 106 matched patients with COVID-19, those with MIS-A were more likely to be non-Hispanic black and less likely to be non-Hispanic white. They more likely had laboratory-confirmed COVID-19 ≥14 days before hospitalization, more likely had positive in-hospital SARS-CoV-2 serologic testing, and more often presented with gastrointestinal symptoms and chest pain. They were less likely to have underlying medical conditions and to present with cough and dyspnea. On admission, patients with MIS-A had higher neutrophil-to-lymphocyte ratio and higher levels of C-reactive protein, ferritin, procalcitonin, and D-dimer than patients with COVID-19. They also had longer hospitalization and more likely required intensive care admission, invasive mechanical ventilation, and vasopressors. The mortality rate was 6% in both cohorts. CONCLUSIONS: Compared with patients with acute symptomatic COVID-19, adults with MIS-A more often manifest certain symptoms and laboratory findings early during hospitalization. These features may facilitate diagnosis and management.

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.

Evaluation of California Non-Comprehensive Death File Against National Death Index.

The National Death Index (NDI) by the Centers for Disease Control and Prevention and Death Master File (DMF) by Social Security Administration are the two most broadly utilized data files for mortality outcomes in clinical research. NDI's high costs and the elimination of protected death records from California in DMF calls for alternative death files. The recently emerged California Non-Comprehensive Death File (CNDF) serves as an alternative source for vital statistics. This study aims to evaluate the sensitivity and specificity of CNDF compared to NDI. Of 40,724 consented subjects in the Cedars-Sinai Cardiac Imaging Research Registry, 25,836 eligible subjects were queried through the NDI and the CDNF. After exclusion of death records to establish the same temporal and geographic availability of data, NDI identified 5,707 exact matches, while CNDF identified 6,051 death records. CNDF had a sensitivity of 94.3% and specificity of 96.4% compared to NDI exact matches. NDI also produced 581 close matches: all were verified as deaths by CNDF through matching death date and patient identifiers. Combining all NDI death records, CNDF had a sensitivity of 94.8% and specificity of 99.5%. CNDF is a reliable source for obtaining mortality outcomes and providing additional mortality validation. The use of CNDF can aid and replace the use of NDI in the state of California.

Privacy-protecting, reliable response data discovery using COVID-19 patient observations.

OBJECTIVE: To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online. MATERIALS AND METHODS: We developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data. RESULTS: Our public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems. DISCUSSION AND CONCLUSIONS: We present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems.

Accuracy of an Electronic Health Record Patient Linkage Module Evaluated between Neighboring Academic Health Care Centers.

BACKGROUND: Patients often seek medical treatment among different health care organizations, which can lead to redundant tests and treatments. One electronic health record (EHR) platform, Epic Systems, uses a patient linkage tool called Care Everywhere (CE), to match patients across institutions. To the extent that such linkages accurately identify shared patients across organizations, they would hold potential for improving care. OBJECTIVE: This study aimed to understand how accurate the CE tool with default settings is to identify identical patients between two neighboring academic health care systems in Southern California, The University of California Los Angeles (UCLA) and Cedars-Sinai Medical Center. METHODS: We studied CE patient linkage queries received at UCLA from Cedars-Sinai between November 1, 2016, and April 30, 2017. We constructed datasets comprised of linkages ("successful" queries), as well as nonlinkages ("unsuccessful" queries) during this time period. To identify false positive linkages, we screened the "successful" linkages for potential errors and then manually reviewed all that screened positive. To identify false-negative linkages, we applied our own patient matching algorithm to the "unsuccessful" queries and then manually reviewed a sample to identify missed patient linkages. RESULTS: During the 6-month study period, Cedars-Sinai attempted to link 181,567 unique patient identities to records at UCLA. CE made 22,923 "successful" linkages and returned 158,644 "unsuccessful" queries among these patients. Manual review of the screened "successful" linkages between the two institutions determined there were no false positives. Manual review of a sample of the "unsuccessful" queries (n = 623), demonstrated an extrapolated false-negative rate of 2.97% (95% confidence interval [CI]: 1.6-4.4%). CONCLUSION: We found that CE provided very reliable patient matching across institutions. The system missed a few linkages, but the false-negative rate was low and there were no false-positive matches over 6 months of use between two nearby institutions.

Privacy-Protecting, Reliable Response Data Discovery Using COVID-19 Patient Observations.

There is an urgent need to answer questions related to COVID-19's clinical course and associations with underlying conditions and health outcomes. Multi-center data are necessary to generate reliable answers, but centralizing data in a single repository is not always possible. Using a privacy-protecting strategy, we launched a public Questions & Answers web portal (https://covid19questions.org) with analyses of comorbidities, medications and laboratory tests using data from 202 hospitals (59,074 COVID-19 patients) in the USA and Germany. We find, for example, that 8.6% of hospitalizations in which the patient was not admitted to the ICU resulted in the patient returning to the hospital within seven days from discharge and that, when adjusted for age, mortality for hospitalized patients was not significantly different by gender or ethnicity.

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.