A regionally tailored epidemiological forecast and monitoring program to guide a healthcare system in the COVID-19 pandemic.

BACKGROUND: During the COVID-19 pandemic, analytics and predictive models built on regional data provided timely, accurate monitoring of epidemiological behavior, informing critical planning and decision-making for health system leaders. At Atrium Health, a large, integrated healthcare system in the southeastern United States, a team of statisticians and physicians created a comprehensive forecast and monitoring program that leveraged an array of statistical methods. METHODS: The program utilized the following methodological approaches: (i) exploratory graphics, including time plots of epidemiological metrics with smoothers; (ii) infection prevalence forecasting using a Bayesian epidemiological model with time-varying infection rate; (iii) doubling and halving times computed using changepoints in local linear trend; (iv) death monitoring using combination forecasting with an ensemble of models; (v) effective reproduction number estimation with a Bayesian approach; (vi) COVID-19 patients hospital census monitored via time series models; and (vii) quantified forecast performance. RESULTS: A consolidated forecast and monitoring report was produced weekly and proved to be an effective, vital source of information and guidance as the healthcare system navigated the inherent uncertainty of the pandemic. Forecasts provided accurate and precise information that informed critical decisions on resource planning, bed capacity and staffing management, and infection prevention strategies. CONCLUSIONS: In this paper, we have presented the framework used in our epidemiological forecast and monitoring program at Atrium Health, as well as provided recommendations for implementation by other healthcare systems and institutions to facilitate use in future pandemics.

A predictive internet-based model for COVID-19 hospitalization census.

The COVID-19 pandemic has strained hospital resources and necessitated the need for predictive models to forecast patient care demands in order to allow for adequate staffing and resource allocation. Recently, other studies have looked at associations between Google Trends data and the number of COVID-19 cases. Expanding on this approach, we propose a vector error correction model (VECM) for the number of COVID-19 patients in a healthcare system (Census) that incorporates Google search term activity and healthcare chatbot scores. The VECM provided a good fit to Census and very good forecasting performance as assessed by hypothesis tests and mean absolute percentage prediction error. Although our study and model have limitations, we have conducted a broad and insightful search for candidate Internet variables and employed rigorous statistical methods. We have demonstrated the VECM can potentially be a valuable component to a COVID-19 surveillance program in a healthcare system.

Improvement in the assessment of diastolic function in a clinical echocardiography laboratory following implementation of a quality improvement initiative.

BACKGROUND: The aim of this study was to demonstrate improvement in the characterization of diastolic function in the routine practice of a clinical echocardiography laboratory after the implementation of a quality improvement initiative. The echocardiographic analysis of left ventricular (LV) diastolic dysfunction is an inherently complex process involving the integration of multiple indices for accurate assessment. METHODS: A baseline survey of 50 randomly chosen echocardiographic studies was reviewed for the accuracy of diastolic function assessment. A four-step quality improvement protocol was then initiated: (1) sonographer and physician education; (2) the implementation of data acquisition protocol changes using LV inflow, tissue Doppler velocity of the mitral annulus in early diastole (e'), flow propagation velocity of LV inflow (Vp), and left atrial volume index (LAVI), along with the establishment of uniform criteria for diagnostic interpretation; (3) peer review of performance; and (4) focused interactive case review sessions. RESULTS: At baseline, measurements of LV inflow were most often correct (100% accurate), while measurements of e' (82% accurate), Vp (12% accurate), and LAVI (12% accurate) and the proper classification of diastolic function (44% accurate) were significantly limited. After the quality improvement initiative, there were significant increases in the accuracy of all recorded measurements, with e' 92% accurate (a 10% improvement; P < .10), Vp 67% accurate (a 55% improvement; P < .001), LAVI 80% accurate (a 68% improvement, P < .001), and proper characterization of diastolic function 76% accurate (a 32% improvement, P < .001). CONCLUSIONS: A multifaceted quality improvement protocol including staff education, systematic support with enhanced infrastructure, and peer review with feedback can be effective for improving the clinical performance of a nonacademic echocardiography laboratory in the characterization of diastolic function.

Sal y tensión arterial en Rio Grande do Sul, Brasil / Salt and blood pressure in Rio Grande do Sul, Brazil

Salt and blood pressure in Rio Grande do Sul, Brazil

Se publica en ingles en: Bull. Pan Am.Health Organ 24(2)