ABSTRACT
Considering the ongoing pandemic of Coronavirus disease which has engulfed almost the entire world, it has become vitally important to predict the outbreak of the virus in the coming days using appropriate algorithms. Forecasting the cases will aid healthcare in prompting policies to control the disease. This chapter aims to analyze the trend pattern of COVID-19 with the help of different prediction models. In this chapter, we used time series models to analyze the real-world time series data of COVID-19 cases for India. The analysis shows that the ARIMA model has proved to be more effective for forecasting COVID-19 prevalence. © 2022 Scrivener Publishing LLC.
ABSTRACT
STATEMENT OF PROBLEM/QUESTION: Racial bias impacts health outcomes however, little is known about patient perspectives about this at San Francisco VA's Downtown Clinic (DTC), an urban clinic serving a higher proportion of Black and Latinx Veterans. DESCRIPTION OF PROGRAM/INTERVENTION: Patient satisfaction survey responses for DTC have historically been low with 34 patients (4%) completing surveys in 2019-20. To develop a novel survey assessing patient perspectives on satisfaction and the impact of bias and racism on care delivery in DTC, we modified two well-studied instruments, the Interpersonal Processes of Care (IPC-18) and Microaggressions in Health Care Scale (MHCS), and gave opportunities for free text feedback. We surveyed Veterans in 2021. Using root cause analysis, we identified barriers to survey response including those related to: process (surveys only sent via mail but many patients unhoused), patients (low motivation), providers/staff (no unified messaging about surveys, no interprofessional teamwork), and the COVID-19 pandemic (few in-person visits). Our interventions targeted: 1) ease of access: having electronic and paper surveys, and electronic tablets to use;2) maximizing patient engagement: surveys at COVID vaccine clinics and gifts for participation;and 3) communication/teamwork: posters advertising surveys, frequent staff check-ins. MEASURES OF SUCCESS: We assessed survey response rates and analyzed responses. FINDINGS TO DATE: N=236 Veterans completed surveys (25% of all clinic;93% male, 39% White). Overall, participants did not report microaggressions. On a 3-point scale (1=never happened, 2=happened but it didn't bother me, 3=happened & I was bothered by it) mean score on the MHCS=1.05 for the statement: “staff/providers were insensitive about your cultural group when trying to understand/treat your issues” and 1.02 for the statement: “staff/providers seemed to have stereotypes about your cultural group even if they did not express them directly”. Scores varied minimally by race. The Interpersonal Style domain of the IPC was scored the worst by African-American and Mixed Race Veterans, including that they felt the most discriminated against due to race/ethnicity (1.76 on a 1-5 scale, higher=worse). Patient-Centered Decision Making was scored worst by White Veterans, and overall Communication was scored the worst by Asian Veterans. N=18 (12% of respondents) preferred/strongly preferred a racially concordant PCP. N=10 (9%) disagreed/strongly disagreed that police officers at clinic treat all patients fairly with no significant differences by race using Fisher's exact test. Feedback mentioned: Veterans want providers to ask instead of assume about identities related to race, gender, sex, sexuality or those related to disabilities or chronic illnesses. Several suggested more minority representation in staff and trauma-informed care. KEY LESSONS FOR DISSEMINATION: Through accounting for multifactorial barriers to survey participation using Lean principles, we dramatically increased responses. Our survey elicited valuable perspectives to inform leadership.