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2.
J Gen Intern Med ; 36(5): 1310-1318, 2021 May.
Article in English | MEDLINE | ID: mdl-33564947

ABSTRACT

BACKGROUND: The evolving COVID-19 pandemic has and continues to present a threat to health system capacity. Rapidly expanding an existing acute care physician workforce is critical to pandemic response planning in large urban academic health systems. INTERVENTION: The Medical Emergency-Pandemic Operations Command (MEOC)-a multi-specialty team of physicians, operational leaders, and support staff within an academic Department of Medicine in Calgary, Canada-partnered with its provincial health system to rapidly develop a comprehensive, scalable pandemic physician workforce plan for non-ventilated inpatients with COVID-19 across multiple hospitals. The MEOC Pandemic Plan comprised seven components, each with unique structure and processes. METHODS: In this manuscript, we describe MEOC's Pandemic Plan that was designed and implemented from March to May 2020 and re-escalated in October 2020. We report on the plan's structure and process, early implementation outcomes, and unforeseen challenges. Data sources included MEOC documents, health system, public health, and physician engagement implementation data. KEY RESULTS: From March 5 to October 26, 2020, 427 patients were admitted to COVID-19 units in Calgary hospitals. In the initial implementation period (March-May 2020), MEOC communications reached over 2500 physicians, leading to 1446 physicians volunteering to provide care on COVID-19 units. Of these, 234 physicians signed up for hospital shifts, and 227 physicians received in-person personal protective equipment simulation training. Ninety-three physicians were deployed on COVID-19 units at four large acute care hospitals. The resurgence of cases in September 2020 has prompted re-escalation including re-activation of COVID-19 units. CONCLUSIONS: MEOC leveraged an academic health system partnership to rapidly design, implement, and refine a comprehensive, scalable COVID-19 acute care physician workforce plan whose components are readily applicable across jurisdictions or healthcare crises. This description may guide other institutions responding to COVID-19 and future health emergencies.


Subject(s)
COVID-19 , Physicians , Canada , Humans , Pandemics , SARS-CoV-2 , Workforce
3.
Am J Kidney Dis ; 57(1): 29-43, 2011 Jan.
Article in English | MEDLINE | ID: mdl-21184918

ABSTRACT

BACKGROUND: Information in health administrative databases increasingly guides renal care and policy. STUDY DESIGN: Systematic review of observational studies. SETTING & POPULATION: Studies describing the validity of codes for acute kidney injury (AKI) and chronic kidney disease (CKD) in administrative databases operating in any jurisdiction. SELECTION CRITERIA: After searching 13 medical databases, we included observational studies published from database inception though June 2009 that validated renal diagnostic and procedural codes for AKI or CKD against a reference standard. INDEX TESTS: Renal diagnostic or procedural administrative data codes. REFERENCE TESTS: Patient chart review, laboratory values, or data from a high-quality patient registry. RESULTS: 25 studies of 13 databases in 4 countries were included. Validation of diagnostic and procedural codes for AKI was present in 9 studies, and validation for CKD was present in 19 studies. Sensitivity varied across studies and generally was poor (AKI median, 29%; range, 15%-81%; CKD median, 41%; range, 3%-88%). Positive predictive values often were reasonable, but results also were variable (AKI median, 67%; range, 15%-96%; CKD median, 78%; range, 29%-100%). Defining AKI and CKD by only the use of dialysis generally resulted in better code validity. The study characteristic associated with sensitivity in multivariable meta-regression was whether the reference standard used laboratory values (P < 0.001); sensitivity was 39% lower when laboratory values were used (95% CI, 23%-56%). LIMITATIONS: Missing data in primary studies limited some of the analyses that could be done. CONCLUSIONS: Administrative database analyses have utility, but must be conducted and interpreted judiciously to avoid bias arising from poor code validity.


Subject(s)
Clinical Coding , Databases, Factual , Kidney Diseases/classification , Australia , Canada , Health Services Research , Humans , Sensitivity and Specificity , Spain , United States
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