Peer Review in a General Medical Research Journal Before and During the COVID-19 Pandemic.

Importance: Although peer review is an important component of publication for new research, the viability of this process has been questioned, particularly with the added stressors of the COVID-19 pandemic. Objective: To characterize rates of peer reviewer acceptance of invitations to review manuscripts, reviewer turnaround times, and editor-assessed quality of reviews before and after the start of the COVID-19 pandemic at a large, open-access general medical journal. Design, Setting, and Participants: This retrospective, pre-post cohort study examined all research manuscripts submitted to JAMA Network Open between January 1, 2019, and June 29, 2021, either directly or via transfer from other JAMA Network journals, for which at least 1 peer review of manuscript content was solicited. Measures were compared between the period before the World Health Organization declaration of a COVID-19 pandemic on March 11, 2020 (14.3 months), and the period during the pandemic (15.6 months) among all reviewed manuscripts and between pandemic-period manuscripts that did or did not address COVID-19. Main Outcomes and Measures: For each reviewed manuscript, the number of invitations sent to reviewers, proportions of reviewers accepting invitations, time in days to return reviews, and editor-assessed quality ratings of reviews were determined. Results: In total, the journal sought review for 5013 manuscripts, including 4295 Original Investigations (85.7%) and 718 Research Letters (14.3%); 1860 manuscripts were submitted during the prepandemic period and 3153 during the pandemic period. Comparing the prepandemic with the pandemic period, the mean (SD) number of reviews rated as high quality (very good or excellent) per manuscript increased slightly from 1.3 (0.7) to 1.5 (0.7) (P < .001), and the mean (SD) time for reviewers to return reviews was modestly shorter (from 15.8 [7.6] days to 14.4 [7.0] days; P < .001), a difference that persisted in linear regression models accounting for manuscript type, study design, and whether the manuscript addressed COVID-19. Conclusions and Relevance: In this cohort study, the speed and editor-reported quality of peer reviews in an open-access general medical journal improved modestly during the initial year of the pandemic. Additional study will be necessary to understand how the pandemic has affected reviewer burden and fatigue.

Clinical Outcome and Utilization Profiles Among Latent Groups of High-Risk Patients: Moving from Segmentation Towards Intervention.

BACKGROUND: The ability of latent class models to identify clinically distinct groups among high-risk patients has been demonstrated, but it is unclear how healthcare data can inform group-specific intervention design. OBJECTIVE: Examine how utilization patterns across latent groups of high-risk patients provide actionable information to guide group-specific intervention design. DESIGN: Cohort study using data from 2012 to 2015. PATIENTS: Participants were 934,787 patients receiving primary care in the Veterans Health Administration, with predicted probability of 12-month hospitalization in the top 10th percentile during 2014. MAIN MEASURES: Patients were assigned to latent groups via mixture-item response theory models based on 28 chronic conditions. We modeled odds of all-cause mortality, hospitalizations, and 30-day re-hospitalizations by group membership. Detailed outpatient and inpatient utilization patterns were compared between groups. KEY RESULTS: A total of 764,257 (81.8%) of patients were matched with a comorbidity group. Groups were characterized by substance use disorders (14.0% of patients assigned), cardiometabolic conditions (25.7%), mental health conditions (17.6%), pain/arthritis (19.1%), cancer (15.3%), and liver disease (8.3%). One-year mortality ranged from 2.7% in the Mental Health group to 14.9% in the Cancer group, compared to 8.5% overall. In adjusted models, group assignment predicted significantly different odds of each outcome. Groups differed in their utilization of multiple types of care. For example, patients in the Pain group had the highest utilization of in-person primary care, with a mean (SD) of 5.3 (5.0) visits in the year of follow-up, while the Substance Use Disorder group had the lowest, with 3.9 (4.1) visits. The Substance Use Disorder group also had the highest rates of using services for housing instability (25.1%), followed by the Liver group (10.1%). CONCLUSIONS: Latent groups of high-risk patients had distinct hospitalization and utilization profiles, despite having comparable levels of predicted baseline risk. Utilization profiles pointed towards system-specific care needs that could inform tailored interventions.

The Primary Care Analytics Team: Integrating research and clinical care within the Veterans Health Administration Office of Primary Care.

By designing and evaluating health system improvements and providing evidence to clinical decision-makers, embedded researchers are a critical part of a Learning Health System (LHS). In this article, we describe the evolution and mission of the Primary Care Analytics Team (PCAT), an integrated research team within the Veterans Health Administration Office of Primary Care. We discuss challenges and strategies for success in working with clinical operations partners and provide recommendations for other Learning Health Systems units embedded in large integrated health care organizations.

Use of Chronic Care Management Among Primary Care Clinicians.

The Centers for Medicare and Medicade Services (CMS) initiated chronic care management (CCM) codes to reimburse clinicians for coordination activities, but little is known about uptake over time. We find that primary care clinicians drove increasing use over 4 years-a trend that may reflect either new coordination activities or new reimbursements for existing activities. That 5% of chronic care management was denied by Medicare underscores the need for future work evaluating facilitators and barriers to use. Such insight is especially vital given the large number of eligible beneficiaries that have not received chronic care management to date, as well as the limited number of clinicians who currently deliver these services.

The impact of survey nonresponse on estimates of healthcare employee burnout.

Accurate estimates of burnout prevalence are critical for workforce planning. We assessed survey nonresponse bias and its impact on burnout estimates by linking 27,226 primary care employees to administrative data, categorized by whether they responded to a 2016 workforce survey (19.2% response). We adjusted burnout prevalence by response propensity using mixed-effects logistic regression. Thirty-six percent of respondents screened positive for burnout. There were significant differences between respondents and non-respondents (e.g.,gender, tenure), but no difference between unadjusted (i.e., respondents only) and propensity-adjusted estimates of burnout among the workforce. This provides support that workforce surveys may yield valid burnout estimates despite low response.