scholarly journals Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities (Preprint)

2020 ◽  
Author(s):  
Marissa Tan ◽  
Elham Hatef ◽  
Delaram Taghipour ◽  
Kinjel Vyas ◽  
Hadi Kharrazi ◽  
...  
Keyword(s):  
Health Care ◽  
Predictive Models ◽  
Health Care Disparities ◽  
Prediction Models ◽  
Predictive Analytics ◽  
The United States ◽  
Digital Data ◽  
Behavioral Determinants ◽  
Wide Range ◽  
Improve Health

UNSTRUCTURED In an era of accelerated health information technology capability, health care organizations increasingly use digital data to predict outcomes such as emergency department use, hospitalizations, and health care costs. This trend occurs alongside a growing recognition that social and behavioral determinants of health (SBDH) influence health and medical care use. Consequently, health providers and insurers are starting to incorporate new SBDH data sources into a wide range of health care prediction models, although existing models that use SBDH variables have not been shown to improve health care predictions more than models that use exclusively clinical variables. In this viewpoint, we review the rationale behind the push to integrate SBDH data into health care predictive models and explore the technical, strategic, and ethical challenges faced as this process unfolds across the United States. We also offer several recommendations to overcome these challenges to reach the promise of SBDH predictive analytics to improve health and reduce health care disparities.

scholarly journals Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities

10.2196/18084 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e18084
Author(s):  
Marissa Tan ◽  
Elham Hatef ◽  
Delaram Taghipour ◽  
Kinjel Vyas ◽  
Hadi Kharrazi ◽  
...  
Keyword(s):  
Health Care ◽  
Predictive Models ◽  
Health Care Disparities ◽  
Prediction Models ◽  
Predictive Analytics ◽  
The United States ◽  
Digital Data ◽  
Behavioral Determinants ◽  
Wide Range ◽  
Improve Health

In an era of accelerated health information technology capability, health care organizations increasingly use digital data to predict outcomes such as emergency department use, hospitalizations, and health care costs. This trend occurs alongside a growing recognition that social and behavioral determinants of health (SBDH) influence health and medical care use. Consequently, health providers and insurers are starting to incorporate new SBDH data sources into a wide range of health care prediction models, although existing models that use SBDH variables have not been shown to improve health care predictions more than models that use exclusively clinical variables. In this viewpoint, we review the rationale behind the push to integrate SBDH data into health care predictive models and explore the technical, strategic, and ethical challenges faced as this process unfolds across the United States. We also offer several recommendations to overcome these challenges to reach the promise of SBDH predictive analytics to improve health and reduce health care disparities.

scholarly journals Risk and the Republican National Convention: Application of the Novel COVID-19 Operational Risk Assessment

2021 ◽  
pp. 1-6
Author(s):  
David Callaway ◽  
Jeff Runge ◽  
Lucia Mullen ◽  
Lisa Rentz ◽  
Kevin Staley ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Health Care ◽  
Care Delivery ◽  
The United States ◽  
Lessons Learned ◽  
World Health ◽  
Local Health ◽  
Mass Gathering ◽  
Wide Range ◽  
National Convention

Abstract The United States Centers for Disease Control and Prevention and the World Health Organization broadly categorize mass gathering events as high risk for amplification of coronavirus disease 2019 (COVID-19) spread in a community due to the nature of respiratory diseases and the transmission dynamics. However, various measures and modifications can be put in place to limit or reduce the risk of further spread of COVID-19 for the mass gathering. During this pandemic, the Johns Hopkins University Center for Health Security produced a risk assessment and mitigation tool for decision-makers to assess SARS-CoV-2 transmission risks that may arise as organizations and businesses hold mass gatherings or increase business operations: The JHU Operational Toolkit for Businesses Considering Reopening or Expanding Operations in COVID-19 (Toolkit). This article describes the deployment of a data-informed, risk-reduction strategy that protects local communities, preserves local health-care capacity, and supports democratic processes through the safe execution of the Republican National Convention in Charlotte, North Carolina. The successful use of the Toolkit and the lessons learned from this experience are applicable in a wide range of public health settings, including school reopening, expansion of public services, and even resumption of health-care delivery.

scholarly journals Cultural Awareness and Sensitivity of Students in a Physical Therapy Program – A Pilot Survey

2018 ◽  
Author(s):  
Olaide Oluwole-Sangoseni ◽  
Michelle Jenkins-Unterberg
Keyword(s):  
Health Care ◽  
Physical Therapy ◽  
Cultural Sensitivity ◽  
Health Care Professionals ◽  
Cultural Awareness ◽  
Health Care Disparities ◽  
Care Delivery ◽  
The United States ◽  
Cross Sectional Survey ◽  
Significant Difference

Background: Attempts to address health and health care disparities in the United States have led to a renewed focus on the training of healthcare professionals including physical therapists. Current health care policies emphasize culturally competent care as a means of promoting equity in care delivery by health care professionals. Experts agree that cultural insensitivity has a negative association with health professionals’ ability to provide quality care. Objective: To evaluate the cultural awareness and sensitivity of physical therapy (PT) students in a didactic curriculum aimed to increase cultural awareness. Methods: Using the Multicultural Sensitivity Scale (MSS), a cross-sectional survey was conducted to assess cultural sensitivity among three groups of students, (N = 139) from a doctor of physical therapy (DPT) program at a liberal arts university in Saint Louis, MO. Results: Response rate was 76.3%. Participants (n=100) were students in first (DPT1, n=36), third (DPT3, n=36), and sixth (DPT6, n=28) year of the program. Mean ranked MSS score was DPT1 = 45.53, DPT3 = 46.60 DPT6 = 61.91. Kruskal-Wallis analysis of the mean ranked scores showed a significant difference among three groups, H = 6.05 (2, N=100), p ≤ .05. Discussion: Students who have completed the cultural awareness curriculum, and undergone clinical experiences rated themselves higher on the cultural sensitivity/awareness. Results provide initial evidence that experiential learning opportunities may help PT students to more effectively integrate knowledge from classroom activities designed to facilitate cultural competence.

scholarly journals Eliminating disparities and implicit bias in health care delivery by utilizing a hub-and-spoke model

2018 ◽  
Vol 4 ◽  
pp. e26370
Author(s):  
Pradeep Joseph
Keyword(s):  
Health Care ◽  
Health Care Disparities ◽  
Clinical Decision Making ◽  
Care Delivery ◽  
Implicit Bias ◽  
Complex Form ◽  
Community Outreach ◽  
The United States ◽  
Great Success ◽  
Nurse Navigator

The state of health disparities in the United States has remained relatively stable over a number of years. Although overall outcomes for all patients have improved, a difference persists in how different racial, ethnic, and gender groups have fared in our health care system. Many programs that have sought to combat this problem have been predicated on the belief that only a small number of providers in the medical community are aware of their own biases. Accordingly, it was believed that bias awareness is the direct conduit for this particular change in the health system. However, the results of such programs have been unsatisfactory. The reason for such ineffectiveness is that many programs have not taken into account the presence of implicit bias within the patient-provider relationship. This complex form of bias operates in specific ways, and must be dealt with appropriately. The use of digital checklists to aid in clinical decision making has proved to be both a way that patients can receive equitable care, and a way to improve overall patient outcomes. Secondly, in order to reach the most at-risk populations, health care must expand beyond the hospital walls, and out into the community. Nurse navigator programs have been shown to accomplish this with great success. Together, checklists and nurse navigators are the necessary next-step in the battle against health care disparities. What’s more, this two-pronged approach is relatively simple to implement. By making use of current electronic medical records, digital checklists can be quickly installed. Likewise, nurse navigator programs, a comparatively inexpensive option, can be rolled out quickly because of their simple design. A focus on the patient-provider relationship and community outreach is critical for progress in eliminating health care disparities.

scholarly journals Using Data Analytics to Predict Hospital Mortality in Sepsis Patients

2019 ◽  
Vol 14 (3) ◽  
pp. 40-57
Author(s):  
Yazan Alnsour ◽  
Rassule Hadidi ◽  
Neetu Singh
Keyword(s):  
Hospital Mortality ◽  
Predictive Model ◽  
Health Care Providers ◽  
Prediction Models ◽  
Predictive Analytics ◽  
Area Under The Curve ◽  
The United States ◽  
Hospital Death ◽  
Care Providers ◽  
Chi Square

Predictive analytics can be used to anticipate the risks associated with some patients, and prediction models can be employed to alert physicians and allow timely proactive interventions. Recently, health care providers have been using different types of tools with prediction capabilities. Sepsis is one of the leading causes of in-hospital death in the United States and worldwide. In this study, the authors used a large medical dataset to develop and present a model that predicts in-hospital mortality among Sepsis patients. The predictive model was developed using a dataset of more than one million records of hospitalized patients. The independent predictors of in-hospital mortality were identified using the chi-square automatic interaction detector. The authors found that adding hospital attributes to the predictive model increased the accuracy from 82.08% to 85.3% and the area under the curve from 0.69 to 0.84, which is favorable compared to using only patients' attributes. The authors discuss the practical and research contributions of using a predictive model that incorporates both patient and hospital attributes in identifying high-risk patients.

scholarly journals MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care

2020 ◽  
Vol 27 (12) ◽  
pp. 2011-2015 ◽  
Author(s):  
Tina Hernandez-Boussard ◽  
Selen Bozkurt ◽  
John P A Ioannidis ◽  
Nigam H Shah
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Clinical Decision ◽  
Digital Data ◽  
Reporting Standards ◽  
Minimum Information ◽  
Support Tools ◽  
Hidden Biases

Abstract The rise of digital data and computing power have contributed to significant advancements in artificial intelligence (AI), leading to the use of classification and prediction models in health care to enhance clinical decision-making for diagnosis, treatment and prognosis. However, such advances are limited by the lack of reporting standards for the data used to develop those models, the model architecture, and the model evaluation and validation processes. Here, we present MINIMAR (MINimum Information for Medical AI Reporting), a proposal describing the minimum information necessary to understand intended predictions, target populations, and hidden biases, and the ability to generalize these emerging technologies. We call for a standard to accurately and responsibly report on AI in health care. This will facilitate the design and implementation of these models and promote the development and use of associated clinical decision support tools, as well as manage concerns regarding accuracy and bias.

Faculty Role in Retaining Hispanic Nursing Students

2010 ◽  
Vol 16 (2) ◽  
pp. 80-83 ◽  
Author(s):  
Margarita Vélez-McEvoy
Keyword(s):  
Health Care ◽  
Nursing Students ◽  
Nursing Faculty ◽  
Health Care Disparities ◽  
Hispanic Students ◽  
The United States ◽  
Nursing Workforce ◽  
Minority Population ◽  
New Paradigm ◽  
Faculty Role

Hispanics, the fastest-growing minority population in the United States, make up only 5% of the nursing workforce. To help eliminate health care disparities, recruiting and retaining Hispanic nursing students is a necessary step. This article discusses barriers that Hispanic students encounter and responsibilities of nursing faculty in retaining Hispanic students, and proposes the use of frameworks that enhance a new paradigm to encourage more inclusive teaching in a positive environment.

Mexican Traditional Medicine: Application of a Traditional and Complementary Medicine System to Improve Opioid Use Treatment in Latinos

2020 ◽  
Vol 38 (4) ◽  
pp. 384-399 ◽  
Author(s):  
Tanya R. Sorrell
Keyword(s):  
Health Care ◽  
Traditional Medicine ◽  
Complementary Medicine ◽  
The United States ◽  
Medical System ◽  
Theory And Practice ◽  
Medical Systems ◽  
Opioid Use ◽  
Integrative Health ◽  
Improve Health

Background: Initially considered a primarily rural, White issue, opioid use and overdose rates have risen faster for Latinos (52.5%) than for White, non-Hispanics (45.8%) from 2014 to 2016. With an estimated 45% to 65% of Latino immigrant families using Mexican traditional medicine (MTM) practices before seeking Western medical services, these practices could be used as a method to increase access to care and improve outcomes. Practice Model: Although not well known, MTM is founded on a defined set of theoretical tenets that comprise a whole medical system as defined by the National Center for Complementary and Integrative Health. Whole medical systems are characterized as complete systems of theory and practice that develop independently and parallel allopathic medicine. Classifying MTM as a whole medical system to encourage further research and utilization of traditional and complementary medicine (T&CM) practices could help improve health outcomes for Latino patients. Specific T&CM practices that could be used in opioid treatment integration to decrease stigma and increase treatment utilization are then discussed. Conclusion: Incorporating T&CM practices will allow more effective, culturally competent and culturally sensitive health care provision for Latino immigrants in the United States to decrease stigma, improve health care outcomes, and address disparities in opioid use treatment.

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