Your neighborhood matters: A machine‐learning approach to the geospatial and social determinants of health in 9‐1‐1 activated chest pain

2021 ◽  
Author(s):  
Ziad Faramand ◽  
Mohammad Alrawashdeh ◽  
Stephanie Helman ◽  
Zeineb Bouzid ◽  
Christian Martin‐Gill ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chest Pain ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Determinants Of Health ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Influence of social determinants of health and county vaccination rates on machine learning models to predict COVID-19 case growth in Tennessee

2021 ◽  
Vol 28 (1) ◽  
pp. e100439
Author(s):  
Lukasz S Wylezinski ◽  
Coleman R Harris ◽  
Cody N Heiser ◽  
Jamieson D Gray ◽  
Charles F Spurlock
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Determinants Of Health ◽  
Learning Models ◽  
Vaccination Rates ◽  
Data Framework ◽  
The Impact ◽  
Machine Learning Models

IntroductionThe SARS-CoV-2 (COVID-19) pandemic has exposed health disparities throughout the USA, particularly among racial and ethnic minorities. As a result, there is a need for data-driven approaches to pinpoint the unique constellation of clinical and social determinants of health (SDOH) risk factors that give rise to poor patient outcomes following infection in US communities.MethodsWe combined county-level COVID-19 testing data, COVID-19 vaccination rates and SDOH information in Tennessee. Between February and May 2021, we trained machine learning models on a semimonthly basis using these datasets to predict COVID-19 incidence in Tennessee counties. We then analyzed SDOH data features at each time point to rank the impact of each feature on model performance.ResultsOur results indicate that COVID-19 vaccination rates play a crucial role in determining future COVID-19 disease risk. Beginning in mid-March 2021, higher vaccination rates significantly correlated with lower COVID-19 case growth predictions. Further, as the relative importance of COVID-19 vaccination data features grew, demographic SDOH features such as age, race and ethnicity decreased while the impact of socioeconomic and environmental factors, including access to healthcare and transportation, increased.ConclusionIncorporating a data framework to track the evolving patterns of community-level SDOH risk factors could provide policy-makers with additional data resources to improve health equity and resilience to future public health emergencies.

scholarly journals Influence of social determinants of health and county vaccination rates on machine learning models to predict COVID-19 case growth in Tennessee

2021 ◽  
Author(s):  
Lukasz S Wylezinski ◽  
Coleman R Harris ◽  
Cody N Heiser ◽  
Jamieson D Gray ◽  
Charles F Spurlock
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
The United States ◽  
Determinants Of Health ◽  
Learning Models ◽  
Vaccination Rates ◽  
The Impact ◽  
Machine Learning Models

The SARS-CoV-2 (COVID-19) pandemic has exposed health disparities throughout the United States, particularly among racial and ethnic minorities. As a result, there is a need for data-driven approaches to pinpoint the unique constellation of clinical and social determinants of health (SDOH) risk factors that give rise to poor patient outcomes following infection in US communities. We combined county-level COVID-19 testing data, COVID-19 vaccination rates, and SDOH information in Tennessee. Between February-May 2021, we trained machine learning models on a semi-monthly basis using these datasets to predict COVID-19 incidence in Tennessee counties. We then analyzed SDOH data features at each time point to rank the impact of each feature on model performance. Our results indicate that COVID-19 vaccination rates play a crucial role in determining future COVID-19 disease risk. Beginning in mid-March 2021, higher vaccination rates significantly correlated with lower COVID-19 case growth predictions. Further, as the relative importance of COVID-19 vaccination data features grew, demographic SDOH features such as age, race, and ethnicity decreased while the impact of socioeconomic and environmental factors, including access to healthcare and transportation, increased. Incorporating a data framework to track the evolving patterns of community-level SDOH risk factors could provide policymakers with additional data resources to improve health equity and resilience to future public health emergencies.

Social Determinants of Health Screening by Pre-clinical Medical Students during the COVID-19 Pandemic: A Service-Based Learning Case Study (Preprint)

2021 ◽  
Author(s):  
Tara Herrera ◽  
Kevin P Fiori ◽  
Heather Archer-Dyer ◽  
David W. Lounsbury ◽  
Judith Wylie-Rosett
Keyword(s):  
Medical Student ◽  
Community Health ◽  
Social Determinants Of Health ◽  
Health Worker ◽  
Community Health Worker ◽  
Social Determinants ◽  
Social Needs ◽  
Determinants Of Health ◽  
Learning Approach ◽  
Health Record

BACKGROUND Background: Inclusion of social determinants of health is mandated for undergraduate medical education. However, little is known about how prepare pre-clinical students for “real world” screening and referrals to address social determinants of health. OBJECTIVE The pilot project objective was to evaluate the feasibility of using “real world” service-based learning approach in training pre-clinical students to assess social needs and make relevant referrals via the electronic medical record during in COVID-19 pandemic (May-June 2020). METHODS This project was designed to address an acute community service need and to teach pre-clinical second-year medical student (n=11) volunteers how to assess social needs and make referrals using the 10-item Social Determinants of Screening Questionnaire in the electronic health record (epic platform). Third-year medical student volunteers (n= 3), who had completed six clinical rotations, led the one-day skills development orientation and were available for on-going mentoring and peer support. All student-patient communication was by telephone, and bi-lingual (English/Spanish) students called the patients, who preferred to communicate in Spanish. We analyzed EHR data extracted from epic to evaluate screening and data extracted from REDCap to evaluate community health worker notes. We elicited feedback from the participating pre-clinical students to evaluate the future use of this community-based service-learning approach in our pre-clinical curriculum. RESULTS The pre-clinical students completed 45 screening interviews; 20 patients (44%) screened positive for at least one social need. Almost all (19/20) were referred to the community health worker. Half (8/16) patients, who had community health worker consultation, were connected with a relevant social service resource. The pre-clinical students indicated that project participation increased their ability to assess social needs and to make needed electronic health record referrals. Food insecurity was the most common social needs. CONCLUSIONS Practical exposure to social needs assessment has the potential to develop pre-clinical medical students’ ability to address social concerns prior to entering clinical clerkships in their third year of medical school. The students can also become familiar with the EHR prior to entering third year clerkships. Physicians, who are aware of social needs and have EMR tools and staff resources to act, can create workflows to make social needs assessment and services integral components of health care. Research studies and quality improvement initiatives need to investigate how to integrate screening for social needs and connecting patients to the appropriate social services into routine primary care procedures. CLINICALTRIAL not a clinical trial

Sign in / Sign up

Export Citation Format

Share Document