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Objectives Primary care attachment improves access, chronic disease prevention and management. Growing proportions of Canadians are unattached and registering with provincial primary care waitlists for family doctors or nurse practitioners. We compare emergency department utilization and hospitalization by waitlist registration status both before and during the first two waves of COVID-19. Approach This study is the first to link a provincial primary care waitlist with routinely collected administrative billing data. Access and linking required collaboration and processes to establish permissions and rigour. A descriptive cohort design estimates quarterly population-based rates of emergency department utilization and ambulatory care sensitive condition (ACSC) hospitalizations among persons on and off the waitlist between Jan/01/2017-Dec/24/2020. Emergency department utilization and ACSC hospitalization rates by current waitlist status were quantified from physician claims and hospitalization data. Relative differences during COVID-19 first (Q2 2020; April-June 2020) and second waves (Q4 2020; October-December 2020) were compared with the previous year. Results Centralized waitlist and administrative billing data were successfully linked. During the study period, 100,867 primary care-eligible Nova Scotians (10.1% of the population) were on the waitlist. Those on the waitlist had higher emergency department utilization each quarter, and more ACSC hospitalizations for most quarters, than those not on the waitlist. Emergency department utilization was higher for individuals ≥65 years and females; lowest during the first two COVID-19 waves; and differed more by waitlist status for those

Study Design: Retrospective analysis of administrative billing data.Objective: To evaluate the extent to which a metric of serious complications determined from administrative data can reliably profile hospital performance in spine fusion surgery.Summary Of Background Data: While payers are increasingly focused on implementing pay-for-performance measures, quality metrics must reliably reflect true differences in performance among the hospitals profiled.Methods: We used State Inpatient Databases from nine states to characterize serious complications after elective cervical and thoracolumbar fusion. Hierarchical logistic regression was used to risk-adjust differences in case mix, along with variability from low case volumes. The reliability of this risk-stratified complication rate (RSCR) was assessed as the variation between hospitals that was not due to chance alone, calculated separately by fusion type and year. Finally, we estimated the proportion of hospitals that had sufficient case volumes to obtain reliable (>0.7) complication estimates.Results: From 2010 to 2017 we identified 154,078 cervical and 213,133 thoracolumbar fusion surgeries. 4.2% of cervical fusion patients had a serious complication, and the median RSCR increased from 4.2% in 2010 to 5.5% in 2017. The reliability of the RSCR for cervical fusion was poor and varied substantially by year (range 0.04-0.28). Overall, 7.7% of thoracolumbar fusion patients experienced a serious complication, and the RSCR varied from 6.8% to 8.0% during the study period. Although still modest, the RSCR reliability was higher for thoracolumbar fusion (range 0.16-0.43). Depending on the study year, 0% to 4.5% of hospitals had sufficient cervical fusion case volume to report reliable (>0.7) estimates, whereas 15% to 36% of hospitals reached this threshold for thoracolumbar fusion.Conclusion: A metric of serious complications was unreliable for benchmarking cervical fusion outcomes and only modestly reliable for thoracolumbar fusion. When assessed using administrative datasets, these measures appear inappropriate for high-stakes applications, such as public reporting or pay-for-performance.Level of Evidence: 3. [ABSTRACT FROM AUTHOR]

Objective: To stratify children using available software, Clinical Risk Groups (CRGs), in a tertiary children's hospital, Seattle Children's Hospital (SCH), and a state's Medicaid claims data, Washington State (WSM), into 3 condition groups: complex chronic disease (C-CD); noncomplex chronic disease (NC-CD), and nonchronic disease (NC)., Methods: A panel of pediatricians developed consensus definitions for children with C-CD, NC-CD, and NC. Using electronic medical record review and expert consensus, a gold standard population of 700 children was identified and placed into 1 the 3 groups: 350 C-CD, 100 NC-CD, and 250 NC. CRGs v1.9 stratified the 700 children into the condition groups using 3 years of WSM and SCH encounter data (2008-2010). WSM data included encounters/claims for all sites of care. SCH data included only inpatient, emergency department, and day surgery claims., Results: A total of 678 of 700 children identified in SCH data were matched in WSM data. CRGs demonstrated good to excellent specificity in correctly classifying all 3 groups in SCH and WSM data; C-CD in SCH (94.3%) and in WSM (91.1%); NC-CD in SCH (88.2%) and in WSM (83.7%); and NC in SCH (84.9%) and in WSM (94.6%). There was good to excellent sensitivity for C-CD in SCH (75.4%) and in WSM (82.1%) and for NC in SCH (98.4%) and in WSM (81.1%). CRGs demonstrated poor sensitivity for NC-CD in SCH (31.0%) and WSM (58.0%). Reasons for poor sensitivity in NC-CD are explored., Conclusions: CRGs can be used to stratify children receiving care at a tertiary care hospital according to complexity in both hospital and Medicaid administrative data. This method will enhance reporting of health-related outcome data., (Copyright © 2015 Academic Pediatric Association. Published by Elsevier Inc. All rights reserved.)