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Sheikhtaheri A, Tabatabaee Jabali SM, Bitaraf E, TehraniYazdi A, Kabir A. A near real-time electronic health record-based COVID-19 surveillance system: An experience from a developing country. HEALTH INF MANAG J 2024; 53:145-154. [PMID: 35838165 PMCID: PMC9289498 DOI: 10.1177/18333583221104213] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/15/2022] [Indexed: 11/24/2022]
Abstract
CONTEXT Access to real-time data that provide accurate and timely information about the status and extent of disease spread could assist management of the COVID-19 pandemic and inform decision-making. AIM To demonstrate our experience with regard to implementation of technical and architectural infrastructure for a near real-time electronic health record-based surveillance system for COVID-19 in Iran. METHOD This COVID-19 surveillance system was developed from hospital information and electronic health record (EHR) systems available in the study hospitals in conjunction with a set of open-source solutions; and designed to integrate data from multiple resources to provide near real-time access to COVID-19 patients' data, as well as a pool of health data for analytical and decision-making purposes. OUTCOMES Using this surveillance system, we were able to monitor confirmed and suspected cases of COVID-19 in our population and to automatically notify stakeholders. Based on aggregated data collected, this surveillance system was able to facilitate many activities, such as resource allocation for hospitals, including managing bed allocations, providing and distributing equipment and funding, and setting up isolation centres. CONCLUSION Electronic health record systems and an integrated data analytics infrastructure are effective tools to enable policymakers to make better decisions, and for epidemiologists to conduct improved analyses regarding COVID-19. IMPLICATIONS Improved quality of clinical coding for better case finding, improved quality of health information in data sources, data-sharing agreements, and increased EHR coverage in the population can empower EHR-based COVID-19 surveillance systems.
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Affiliation(s)
- Abbas Sheikhtaheri
- Department of Health Information
Management, School of Health Management and Information Sciences, Iran University of Medical
Sciences, Tehran, Iran
| | | | - Ehsan Bitaraf
- Center for Statistics and
Information Technology, Iran University of Medical
Sciences, Tehran, Iran
| | - Alireza TehraniYazdi
- Center for Statistics and
Information Technology, Iran University of Medical
Sciences, Tehran, Iran
| | - Ali Kabir
- Minimally Invasive Surgery Research
Center, Iran University of Medical
Sciences, Tehran, Iran
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Hirani R, Noruzi K, Khuram H, Hussaini AS, Aifuwa EI, Ely KE, Lewis JM, Gabr AE, Smiley A, Tiwari RK, Etienne M. Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities. Life (Basel) 2024; 14:557. [PMID: 38792579 PMCID: PMC11122160 DOI: 10.3390/life14050557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 04/22/2024] [Accepted: 04/24/2024] [Indexed: 05/26/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a powerful tool in healthcare significantly impacting practices from diagnostics to treatment delivery and patient management. This article examines the progress of AI in healthcare, starting from the field's inception in the 1960s to present-day innovative applications in areas such as precision medicine, robotic surgery, and drug development. In addition, the impact of the COVID-19 pandemic on the acceleration of the use of AI in technologies such as telemedicine and chatbots to enhance accessibility and improve medical education is also explored. Looking forward, the paper speculates on the promising future of AI in healthcare while critically addressing the ethical and societal considerations that accompany the integration of AI technologies. Furthermore, the potential to mitigate health disparities and the ethical implications surrounding data usage and patient privacy are discussed, emphasizing the need for evolving guidelines to govern AI's application in healthcare.
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Affiliation(s)
- Rahim Hirani
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
- Graduate School of Biomedical Sciences, New York Medical College, Valhalla, NY 10595, USA
| | - Kaleb Noruzi
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
| | - Hassan Khuram
- College of Medicine, Drexel University, Philadelphia, PA 19129, USA
| | - Anum S. Hussaini
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Esewi Iyobosa Aifuwa
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
| | - Kencie E. Ely
- Kirk Kerkorian School of Medicine, University of Nevada Las Vegas, Las Vegas, NV 89106, USA
| | - Joshua M. Lewis
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
| | - Ahmed E. Gabr
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
| | - Abbas Smiley
- School of Medicine and Dentistry, University of Rochester, Rochester, NY 14642, USA
| | - Raj K. Tiwari
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
- Graduate School of Biomedical Sciences, New York Medical College, Valhalla, NY 10595, USA
| | - Mill Etienne
- School of Medicine, New York Medical College, 40 Sunshine Cottage Road, Valhalla, NY 10595, USA; (R.H.)
- Department of Neurology, New York Medical College, Valhalla, NY 10595, USA
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Rayo MF, Faulkner D, Kline D, Thornhill Iv T, Malloy S, Della Vella D, Morey DA, Zhang N, Gonsalves G. Using Bandit Algorithms to Maximize SARS-CoV-2 Case-Finding: Evaluation and Feasibility Study. JMIR Public Health Surveill 2023; 9:e39754. [PMID: 37581924 PMCID: PMC10430782 DOI: 10.2196/39754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 04/13/2023] [Accepted: 06/15/2023] [Indexed: 08/16/2023] Open
Abstract
BACKGROUND The Flexible Adaptive Algorithmic Surveillance Testing (FAAST) program represents an innovative approach for improving the detection of new cases of infectious disease; it is deployed here to screen and diagnose SARS-CoV-2. With the advent of treatment for COVID-19, finding individuals infected with SARS-CoV-2 is an urgent clinical and public health priority. While these kinds of Bayesian search algorithms are used widely in other settings (eg, to find downed aircraft, in submarine recovery, and to aid in oil exploration), this is the first time that Bayesian adaptive approaches have been used for active disease surveillance in the field. OBJECTIVE This study's objective was to evaluate a Bayesian search algorithm to target hotspots of SARS-CoV-2 transmission in the community with the goal of detecting the most cases over time across multiple locations in Columbus, Ohio, from August to October 2021. METHODS The algorithm used to direct pop-up SARS-CoV-2 testing for this project is based on Thompson sampling, in which the aim is to maximize the average number of new cases of SARS-CoV-2 diagnosed among a set of testing locations based on sampling from prior probability distributions for each testing site. An academic-governmental partnership between Yale University, The Ohio State University, Wake Forest University, the Ohio Department of Health, the Ohio National Guard, and the Columbus Metropolitan Libraries conducted a study of bandit algorithms to maximize the detection of new cases of SARS-CoV-2 in this Ohio city in 2021. The initiative established pop-up COVID-19 testing sites at 13 Columbus locations, including library branches, recreational and community centers, movie theaters, homeless shelters, family services centers, and community event sites. Our team conducted between 0 and 56 tests at the 16 testing events, with an overall average of 25.3 tests conducted per event and a moving average that increased over time. Small incentives-including gift cards and take-home rapid antigen tests-were offered to those who approached the pop-up sites to encourage their participation. RESULTS Over time, as expected, the Bayesian search algorithm directed testing efforts to locations with higher yields of new diagnoses. Surprisingly, the use of the algorithm also maximized the identification of cases among minority residents of underserved communities, particularly African Americans, with the pool of participants overrepresenting these people relative to the demographic profile of the local zip code in which testing sites were located. CONCLUSIONS This study demonstrated that a pop-up testing strategy using a bandit algorithm can be feasibly deployed in an urban setting during a pandemic. It is the first real-world use of these kinds of algorithms for disease surveillance and represents a key step in evaluating the effectiveness of their use in maximizing the detection of undiagnosed cases of SARS-CoV-2 and other infections, such as HIV.
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Affiliation(s)
- Michael F Rayo
- Department of Integrated Systems Engineering, College of Engineering, The Ohio State University, Columbus, OH, United States
| | - Daria Faulkner
- College of Public Health, The Ohio State University, Columbus, OH, United States
| | - David Kline
- Department of Biostatistics and Data Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Thomas Thornhill Iv
- Public Health Modeling Unit, Department of the Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, United States
| | - Samuel Malloy
- Battelle Center for Science, Engineering, and Public Policy, John Glenn College of Public Affairs, The Ohio State University, Columbus, OH, United States
| | - Dante Della Vella
- Department of Integrated Systems Engineering, College of Engineering, The Ohio State University, Columbus, OH, United States
| | - Dane A Morey
- Department of Integrated Systems Engineering, College of Engineering, The Ohio State University, Columbus, OH, United States
| | - Net Zhang
- Battelle Center for Science, Engineering, and Public Policy, John Glenn College of Public Affairs, The Ohio State University, Columbus, OH, United States
| | - Gregg Gonsalves
- Public Health Modeling Unit, Department of the Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, United States
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Öcek ZA, Geise M, Volkmann AM, Basili A, Klünder V, Coenen M. Strengthening the social resilience of people living at the intersection of precariousness and migration during pandemics: action recommendations developed in Munich, Germany. Front Public Health 2023; 11:1201215. [PMID: 37601211 PMCID: PMC10433162 DOI: 10.3389/fpubh.2023.1201215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 07/19/2023] [Indexed: 08/22/2023] Open
Abstract
Introduction An EU-funded project in five countries examined vulnerability mechanisms during the COVID-19 pandemic. The research team in Germany concentrated on people living at the intersection of migration and precariousness. The study aimed first to provide an understanding of how migrants living in precarious conditions in Munich had been affected by the pandemic, both from their own and from experts' perspectives. The second aim was to develop action recommendations to reduce structural vulnerabilities and increase resilience with a view towards improved pandemic preparedness. Methods The study followed a two-phase process. The first was a qualitative study based on interviews with 25 migrants and 13 experts. In the second, researchers developed action recommendations based on the vulnerability/ resilience factors that had been generated in the first phase. Three consecutive meetings with stakeholders (expert panel, focus group discussion with two migrant organization, meeting with the Munich Migration Council) were then held to further strengthen the draft recommendations. Results Content analysis revealed twelve vulnerability and eight resilience factors in three domains (COVID-19 prevention; human rights, living and housing environment; social support). Migrants had limited access to COVID-19 prevention measures; living conditions made outbreaks inevitable; uncertainty about legal status, employment, and housing, as well as stigma and discrimination, exacerbated their precariousness; social support had decreased; and resilience mechanisms had failed. The initial draft of recommendations contained 24 proposed actions. The meetings added recommendations such as enhancing psychosocial support, preventing ghettoization, improving social housing, preventing the interruption of language education in times of crisis, severe penalties for media stigmatisation and proactive truth-telling. The final list included 30 actions. Conclusion In Munich, the COVID-19 pandemic exacerbated vulnerability mechanisms commonly associated with being a migrant. The recommendations developed here speak to those vulnerabilities but need to be refined further to be more actionable and comprehensive. Nonetheless, the recommendations and the processes that led to them highlight the importance of migrant-inclusive approaches and empowerment in increasing migrants' resilience to future crises.
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Affiliation(s)
- Zeliha Asli Öcek
- Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Chair for Public Health and Health Services Research, Medical Faculty, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
- Rachel Carson Centre for Environment and Society, LMU Munich, Munich, Germany
| | - Mandy Geise
- Netherlands Institute for Health Services Research, Utrecht, Netherlands
| | | | - Acelya Basili
- Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Chair for Public Health and Health Services Research, Medical Faculty, LMU Munich, Munich, Germany
| | - Vera Klünder
- Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Chair for Public Health and Health Services Research, Medical Faculty, LMU Munich, Munich, Germany
| | - Michaela Coenen
- Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Chair for Public Health and Health Services Research, Medical Faculty, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
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Vo A, Tao Y, Li Y, Albarrak A. The Association Between Social Determinants of Health and Population Health Outcomes: Ecological Analysis. JMIR Public Health Surveill 2023; 9:e44070. [PMID: 36989028 PMCID: PMC10131773 DOI: 10.2196/44070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/21/2022] [Accepted: 02/23/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND With the increased availability of data, a growing number of studies have been conducted to address the impact of social determinants of health (SDOH) factors on population health outcomes. However, such an impact is either examined at the county level or the state level in the United States. The results of analysis at lower administrative levels would be useful for local policy makers to make informed health policy decisions. OBJECTIVE This study aimed to investigate the ecological association between SDOH factors and population health outcomes at the census tract level and the city level. The findings of this study can be applied to support local policy makers in efforts to improve population health, enhance the quality of care, and reduce health inequity. METHODS This ecological analysis was conducted based on 29,126 census tracts in 499 cities across all 50 states in the United States. These cities were grouped into 5 categories based on their population density and political affiliation. Feature selection was applied to reduce the number of SDOH variables from 148 to 9. A linear mixed-effects model was then applied to account for the fixed effect and random effects of SDOH variables at both the census tract level and the city level. RESULTS The finding reveals that all 9 selected SDOH variables had a statistically significant impact on population health outcomes for ≥2 city groups classified by population density and political affiliation; however, the magnitude of the impact varied among the different groups. The results also show that 4 SDOH risk factors, namely, asthma, kidney disease, smoking, and food stamps, significantly affect population health outcomes in all groups (P<.01 or P<.001). The group differences in health outcomes for the 4 factors were further assessed using a predictive margin analysis. CONCLUSIONS The analysis reveals that population density and political affiliation are effective delineations for separating how the SDOH affects health outcomes. In addition, different SDOH risk factors have varied effects on health outcomes among different city groups but similar effects within city groups. Our study has 2 policy implications. First, cities in different groups should prioritize different resources for SDOH risk mitigation to maximize health outcomes. Second, cities in the same group can share knowledge and enable more effective SDOH-enabled policy transfers for population health.
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Affiliation(s)
- Ace Vo
- Information Systems and Business Analytics Department, Loyola Marymount University, Los Angeles, CA, United States
| | - Youyou Tao
- Information Systems and Business Analytics Department, Loyola Marymount University, Los Angeles, CA, United States
| | - Yan Li
- Center for Information Systems and Technology, Claremont Graduate University, Claremont, CA, United States
| | - Abdulaziz Albarrak
- Information Systems Department, King Faisal University, Al-Ahsa, Saudi Arabia
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6
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Lee JM, Jansen R, Sanderson KE, Guerra F, Keller-Olaman S, Murti M, O'Sullivan TL, Law MP, Schwartz B, Bourns LE, Khan Y. Public health emergency preparedness for infectious disease emergencies: a scoping review of recent evidence. BMC Public Health 2023; 23:420. [PMID: 36864415 PMCID: PMC9979131 DOI: 10.1186/s12889-023-15313-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 02/23/2023] [Indexed: 03/04/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic continues to demonstrate the risks and profound health impacts that result from infectious disease emergencies. Emergency preparedness has been defined as the knowledge, capacity and organizational systems that governments, response and recovery organizations, communities and individuals develop to anticipate, respond to, or recover from emergencies. This scoping review explored recent literature on priority areas and indicators for public health emergency preparedness (PHEP) with a focus on infectious disease emergencies. METHODS Using scoping review methodology, a comprehensive search was conducted for indexed and grey literature with a focus on records published from 2017 to 2020 onward, respectively. Records were included if they: (a) described PHEP, (b) focused on an infectious emergency, and (c) were published in an Organization for Economic Co-operation and Development country. An evidence-based all-hazards Resilience Framework for PHEP consisting of 11 elements was used as a reference point to identify additional areas of preparedness that have emerged in recent publications. The findings were analyzed deductively and summarized thematically. RESULTS The included publications largely aligned with the 11 elements of the all-hazards Resilience Framework for PHEP. In particular, the elements related to collaborative networks, community engagement, risk analysis and communication were frequently observed across the publications included in this review. Ten emergent themes were identified that expand on the Resilience Framework for PHEP specific to infectious diseases. Planning to mitigate inequities was a key finding of this review, it was the most frequently identified emergent theme. Additional emergent themes were: research and evidence-informed decision making, building vaccination capacity, building laboratory and diagnostic system capacity, building infection prevention and control capacity, financial investment in infrastructure, health system capacity, climate and environmental health, public health legislation and phases of preparedness. CONCLUSION The themes from this review contribute to the evolving understanding of critical public health emergency preparedness actions. The themes expand on the 11 elements outlined in the Resilience Framework for PHEP, specifically relevant to pandemics and infectious disease emergencies. Further research will be important to validate these findings, and expand understanding of how refinements to PHEP frameworks and indicators can support public health practice.
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Affiliation(s)
- Jessica M Lee
- Public Health Ontario, 480 University Avenue, Suite 300, M5G 1V2, Toronto, ON, Canada
| | - Rachel Jansen
- Public Health Ontario, 480 University Avenue, Suite 300, M5G 1V2, Toronto, ON, Canada
| | - Kate E Sanderson
- Public Health Ontario, 480 University Avenue, Suite 300, M5G 1V2, Toronto, ON, Canada
| | - Fiona Guerra
- Public Health Ontario, 661 University Avenue, Suite 1701, M5G 1M1, Toronto, ON, Canada
| | - Sue Keller-Olaman
- Public Health Ontario, 480 University Avenue, Suite 300, M5G 1V2, Toronto, ON, Canada
| | - Michelle Murti
- Office of the Chief Medical Officer of Health, Government of Ontario, 393 University Avenue, Suite 2100, M5G 2M2, Toronto, ON, Canada
| | | | - Madelyn P Law
- Brock University, 1812 Sir Isaac Brock Way, L2S 3A1, St. Catharines, ON, Canada
| | - Brian Schwartz
- Public Health Ontario, 661 University Avenue, Suite 1701, M5G 1M1, Toronto, ON, Canada
| | - Laura E Bourns
- Public Health Ontario, 661 University Avenue, Suite 1701, M5G 1M1, Toronto, ON, Canada
| | - Yasmin Khan
- Public Health Ontario, 480 University Avenue, Suite 300, M5G 1V2, Toronto, ON, Canada.
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Schwartz DL, Stewart A, Harris L, Ozdenerol E, Thomas F, Johnson KC, Davis R, Shaban-Nejad A. The Memphis Pandemic Health Informatics System (MEMPHI-SYS)-Creating a Metropolitan COVID-19 Data Registry Linked Directly to Community Testing to Enhance Population Health Surveillance. Disaster Med Public Health Prep 2022; 17:e326. [PMID: 36503600 PMCID: PMC9947040 DOI: 10.1017/dmp.2022.284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The current coronavirus disease (COVID-19) pandemic has placed unprecedented strain on underfunded public health resources in the Southeastern United States. The Memphis, TN, metropolitan region has lacked infrastructure for health data exchange.This manuscript describes a multidisciplinary initiative to create a community-focused COVID-19 data registry, the Memphis Pandemic Health Informatics System (MEMPHI-SYS). MEMPHI-SYS leverages test result data updated directly from community-based testing sites, as well as a full complement of public health data sets and knowledge-based informatics. It has been guided by relationships with community stakeholders and is managed alongside the largest publicly funded community-based COVID-19 testing response in the Mid-South. MEMPHI-SYS has supported interactive Web-based analytic resources and informs federally funded COVID-19 outreach directed toward neighborhoods most in need of pandemic support.MEMPHI-SYS provides an instructive case study of how to collaboratively establish the technical scaffolding and human relationships necessary for data-driven, health equity-focused pandemic surveillance, and policy interventions.
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Affiliation(s)
- David L. Schwartz
- Department of Radiation Oncology, University of Tennessee Health Science Center College of Medicine, Memphis, TN, USA
- Department of Preventive Medicine, University of Tennessee Health Science Center College of Medicine, Memphis, TN, USA
- Corresponding authors: David L. Schwartz, ; Arash Shaban-Nejad,
| | - Altha Stewart
- Department of Psychiatry, University of Tennessee Health Sciences Center College of Medicine, Memphis, TN, USA
- Office of Community Health Engagement, University of Tennessee Health Science Center College of Medicine, Memphis, TN, USA
| | - Laura Harris
- Department of Psychiatry, University of Tennessee Health Sciences Center College of Medicine, Memphis, TN, USA
- Office of Community Health Engagement, University of Tennessee Health Science Center College of Medicine, Memphis, TN, USA
| | - Esra Ozdenerol
- Department of Earth Sciences, Spatial Analysis and Geographic Education Laboratory, University of Memphis, Memphis, TN, USA
| | - Fridtjof Thomas
- Department of Preventive Medicine, University of Tennessee Health Science Center College of Medicine, Memphis, TN, USA
| | - Karen C. Johnson
- Department of Preventive Medicine, University of Tennessee Health Science Center College of Medicine, Memphis, TN, USA
| | - Robert Davis
- University of Tennessee Health Science Center—Oak Ridge National Laboratory Center for Biomedical Informatics, Oak Ridge, TN, USA
- Department of Pediatrics, University of Tennessee Health Science Center College of Medicine, Memphis, TN, USA
| | - Arash Shaban-Nejad
- University of Tennessee Health Science Center—Oak Ridge National Laboratory Center for Biomedical Informatics, Oak Ridge, TN, USA
- Department of Pediatrics, University of Tennessee Health Science Center College of Medicine, Memphis, TN, USA
- Corresponding authors: David L. Schwartz, ; Arash Shaban-Nejad,
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Shaban-Nejad A, Michalowski M, Bianco S, Brownstein JS, Buckeridge DL, Davis RL. Applied artificial intelligence in healthcare: Listening to the winds of change in a post-COVID-19 world. Exp Biol Med (Maywood) 2022; 247:1969-1971. [PMID: 36426683 PMCID: PMC9703021 DOI: 10.1177/15353702221140406] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
This editorial article aims to highlight advances in artificial intelligence (AI) technologies in five areas: Collaborative AI, Multimodal AI, Human-Centered AI, Equitable AI, and Ethical and Value-based AI in order to cope with future complex socioeconomic and public health issues.
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Affiliation(s)
- Arash Shaban-Nejad
- UTHSC-ORNL Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN 38103, USA,Arash Shaban-Nejad.
| | - Martin Michalowski
- School of Nursing, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA
| | - Simone Bianco
- Altos Labs – Bay Area Institute of Science, Redwood City, CA 94065, USA
| | | | - David L Buckeridge
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC H3A 1G1, Canada
| | - Robert L Davis
- UTHSC-ORNL Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN 38103, USA
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9
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Brakefield WS, Olusanya OA, Shaban-Nejad A. Association Between Neighborhood Factors and Adult Obesity in Shelby County, Tennessee: Geospatial Machine Learning Approach. JMIR Public Health Surveill 2022; 8:e37039. [PMID: 359437 PMCID: PMC9399828 DOI: 10.2196/37039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 06/21/2022] [Accepted: 07/06/2022] [Indexed: 11/20/2022] Open
Abstract
Background Obesity is a global epidemic causing at least 2.8 million deaths per year. This complex disease is associated with significant socioeconomic burden, reduced work productivity, unemployment, and other social determinants of health (SDOH) disparities. Objective The objective of this study was to investigate the effects of SDOH on obesity prevalence among adults in Shelby County, Tennessee, the United States, using a geospatial machine learning approach. Methods Obesity prevalence was obtained from the publicly available 500 Cities database of Centers for Disease Control and Prevention, and SDOH indicators were extracted from the US census and the US Department of Agriculture. We examined the geographic distributions of obesity prevalence patterns, using Getis-Ord Gi* statistics and calibrated multiple models to study the association between SDOH and adult obesity. Unsupervised machine learning was used to conduct grouping analysis to investigate the distribution of obesity prevalence and associated SDOH indicators. Results Results depicted a high percentage of neighborhoods experiencing high adult obesity prevalence within Shelby County. In the census tract, the median household income, as well as the percentage of individuals who were Black, home renters, living below the poverty level, 55 years or older, unmarried, and uninsured, had a significant association with adult obesity prevalence. The grouping analysis revealed disparities in obesity prevalence among disadvantaged neighborhoods. Conclusions More research is needed to examine links between geographical location, SDOH, and chronic diseases. The findings of this study, which depict a significantly higher prevalence of obesity within disadvantaged neighborhoods, and other geospatial information can be leveraged to offer valuable insights, informing health decision-making and interventions that mitigate risk factors of increasing obesity prevalence.
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Affiliation(s)
- Whitney S Brakefield
- Bredesen Center for Data Science and Engineering, University of Tennessee, Knoxville, TN, United States.,Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Olufunto A Olusanya
- Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Arash Shaban-Nejad
- Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States
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10
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Brakefield WS, Ammar N, Shaban-Nejad A. An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model. JMIR Form Res 2022; 6:e36055. [PMID: 35857363 PMCID: PMC9350817 DOI: 10.2196/36055] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 05/03/2022] [Accepted: 06/07/2022] [Indexed: 01/16/2023] Open
Abstract
Background Many researchers have aimed to develop chronic health surveillance systems to assist in public health decision-making. Several digital health solutions created lack the ability to explain their decisions and actions to human users. Objective This study sought to (1) expand our existing Urban Population Health Observatory (UPHO) system by incorporating a semantics layer; (2) cohesively employ machine learning and semantic/logical inference to provide measurable evidence and detect pathways leading to undesirable health outcomes; (3) provide clinical use case scenarios and design case studies to identify socioenvironmental determinants of health associated with the prevalence of obesity, and (4) design a dashboard that demonstrates the use of UPHO in the context of obesity surveillance using the provided scenarios. Methods The system design includes a knowledge graph generation component that provides contextual knowledge from relevant domains of interest. This system leverages semantics using concepts, properties, and axioms from existing ontologies. In addition, we used the publicly available US Centers for Disease Control and Prevention 500 Cities data set to perform multivariate analysis. A cohesive approach that employs machine learning and semantic/logical inference reveals pathways leading to diseases. Results In this study, we present 2 clinical case scenarios and a proof-of-concept prototype design of a dashboard that provides warnings, recommendations, and explanations and demonstrates the use of UPHO in the context of obesity surveillance, treatment, and prevention. While exploring the case scenarios using a support vector regression machine learning model, we found that poverty, lack of physical activity, education, and unemployment were the most important predictive variables that contribute to obesity in Memphis, TN. Conclusions The application of UPHO could help reduce health disparities and improve urban population health. The expanded UPHO feature incorporates an additional level of interpretable knowledge to enhance physicians, researchers, and health officials' informed decision-making at both patient and community levels. International Registered Report Identifier (IRRID) RR2-10.2196/28269
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Affiliation(s)
- Whitney S Brakefield
- Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States.,Bredesen Center for Data Science, University of Tennessee, Knoxville, TN, United States
| | - Nariman Ammar
- Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States.,Ochsner Xavier Institute for Health Equity and Research, Ochsner Clinic Foundation, New Orleans, LA, United States
| | - Arash Shaban-Nejad
- Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States
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11
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Hubler A, Wakefield DV, Makepeace L, Carnell M, Sharma AM, Jiang B, Dove AP, Garner WB, Edmonston D, Little JG, Ozdenerol E, Hanson RB, Martin MY, Shaban-Nejad A, Pisu M, Schwartz DL. Independent Predictors for Hospitalization-Associated Radiotherapy Interruptions. Adv Radiat Oncol 2022; 7:101041. [PMID: 36158745 PMCID: PMC9489733 DOI: 10.1016/j.adro.2022.101041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 07/24/2022] [Indexed: 12/01/2022] Open
Abstract
Purpose Radiation treatment interruption associated with unplanned hospitalization remains understudied. The intent of this study was to benchmark the frequency of hospitalization-associated radiation therapy interruptions (HARTI), characterize disease processes causing hospitalization during radiation, identify factors predictive for HARTI, and localize neighborhood environments associated with HARTI at our academic referral center. Methods and Materials This retrospective review of electronic health records provided descriptive statistics of HARTI event rates at our institutional practice. Uni- and multivariable logistic regression models were developed to identify significant factors predictive for HARTI. Causes of hospitalization were established from primary discharge diagnoses. HARTI rates were mapped according to patient residence addresses. Results Between January 1, 2015, and December 31, 2017, 197 HARTI events (5.3%) were captured across 3729 patients with 727 total missed treatments. The 3 most common causes of hospitalization were malnutrition/dehydration (n = 28; 17.7%), respiratory distress/infection (n = 24; 13.7%), and fever/sepsis (n = 17; 9.7%). Factors predictive for HARTI included African-American race (odds ratio [OR]: 1.48; 95% confidence interval [CI], 1.07-2.06; P = .018), Medicaid/uninsured status (OR: 2.05; 95% CI, 1.32-3.15; P = .0013), Medicare coverage (OR: 1.7; 95% CI, 1.21-2.39; P = .0022), lung (OR: 5.97; 95% CI, 3.22-11.44; P < .0001), and head and neck (OR: 5.6; 95% CI, 2.96-10.93; P < .0001) malignancies, and prescriptions >20 fractions (OR: 2.23; 95% CI, 1.51-3.34; P < .0001). HARTI events clustered among Medicaid/uninsured patients living in urban, low-income, majority African-American neighborhoods, and patients from middle-income suburban communities, independent of race and insurance status. Only the wealthiest residential areas demonstrated low HARTI rates. Conclusions HARTI disproportionately affected socioeconomically disadvantaged urban patients facing a high treatment burden in our catchment population. A complementary geospatial analysis also captured the risk experienced by middle-income suburban patients independent of race or insurance status. Confirmatory studies are warranted to provide scale and context to guide intervention strategies to equitably reduce HARTI events.
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Affiliation(s)
- Adam Hubler
- Department of Radiation Oncology, University of Tennessee Health Science Center, Memphis, Tennessee
| | - Daniel V. Wakefield
- Department of Radiation Oncology, University of Tennessee Health Science Center, Memphis, Tennessee
- Tennessee Oncology, Nashville, Tennessee
| | - Lydia Makepeace
- Department of Radiation Oncology, University of Tennessee Health Science Center, Memphis, Tennessee
| | - Matt Carnell
- University of Tennessee Health Science Center College of Medicine, Memphis, Tennessee
| | - Ankur M. Sharma
- Department of Radiation Oncology, University of Tennessee Health Science Center, Memphis, Tennessee
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Bo Jiang
- Department of Radiation Oncology, University of Tennessee Health Science Center, Memphis, Tennessee
| | - Austin P. Dove
- Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, Tennesse
| | - Wesley B. Garner
- Department of Radiation Oncology, University of Tennessee Health Science Center, Memphis, Tennessee
| | - Drucilla Edmonston
- Department of Radiation Oncology, University of Tennessee Health Science Center, Memphis, Tennessee
| | - John G. Little
- University of Tennessee Health Science Center College of Medicine, Memphis, Tennessee
| | - Esra Ozdenerol
- Department of Earth Sciences, University of Memphis, Memphis, Tennessee
| | - Ryan B. Hanson
- Department of Earth Sciences, University of Memphis, Memphis, Tennessee
| | - Michelle Y. Martin
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee
| | - Arash Shaban-Nejad
- UTHSC-ORNL Center for Biomedical Informatics, University of Tennessee Health Science Center, Memphis, Tennesse
| | - Maria Pisu
- Division of Preventive Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - David L. Schwartz
- Department of Radiation Oncology, University of Tennessee Health Science Center, Memphis, Tennessee
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee
- Corresponding author: David L. Schwartz, MD, FACR
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12
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Olusanya OA, White B, Melton CA, Shaban-Nejad A. Examining the Implementation of Digital Health to Strengthen the COVID-19 Pandemic Response and Recovery and Scale up Equitable Vaccine Access in African Countries. ARXIV 2022:arXiv:2206.03286v1. [PMID: 35677423 PMCID: PMC9176651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
The COVID-19 pandemic has profoundly impacted the world, having taken the lives of over 6 million individuals. Accordingly, this pandemic has caused a shift in conversations surrounding the burden of diseases worldwide, welcoming insights from multidisciplinary fields including digital health and artificial intelligence. Africa faces a heavy disease burden that exacerbates the current COVID-19 pandemic and limits the scope of public health preparedness, response, containment, and case management. Herein, we examined the potential impact of transformative digital health technologies in mitigating the global health crisis with reference to African countries. Furthermore, we proposed recommendations for scaling up digital health technologies and artificial intelligence-based platforms to tackle the transmission of the SARS-CoV-2 and enable equitable vaccine access. Challenges related to the pandemic are numerous. Rapid response and management strategies-that is, contract tracing, case surveillance, diagnostic testing intensity, and most recently vaccine distribution mapping-can overwhelm the health care delivery system that is fragile. Although challenges are vast, digital health technologies can play an essential role in achieving sustainable resilient recovery and building back better. It is plausible that African nations are better equipped to rapidly identify, diagnose, and manage infected individuals for COVID-19, other diseases, future outbreaks, and pandemics.
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Affiliation(s)
- Olufunto A Olusanya
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN, United States
| | - Brianna White
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN, United States
| | - Chad A Melton
- Bredesen Center for Interdisciplinary Research and Graduate Education, The University of Tennessee, Knoxville, TN, United States
| | - Arash Shaban-Nejad
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN, United States
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Olusanya OA, White B, Melton CA, Shaban-Nejad A. Examining the Implementation of Digital Health to Strengthen the COVID-19 Pandemic Response and Recovery and Scale up Equitable Vaccine Access in African Countries. JMIR Form Res 2022; 6:e34363. [PMID: 35512271 PMCID: PMC9116456 DOI: 10.2196/34363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 03/08/2022] [Accepted: 04/21/2022] [Indexed: 12/01/2022] Open
Abstract
The COVID-19 pandemic has profoundly impacted the world, having taken the lives of over 6 million individuals. Accordingly, this pandemic has caused a shift in conversations surrounding the burden of diseases worldwide, welcoming insights from multidisciplinary fields including digital health and artificial intelligence. Africa faces a heavy disease burden that exacerbates the current COVID-19 pandemic and limits the scope of public health preparedness, response, containment, and case management. Herein, we examined the potential impact of transformative digital health technologies in mitigating the global health crisis with reference to African countries. Furthermore, we proposed recommendations for scaling up digital health technologies and artificial intelligence–based platforms to tackle the transmission of the SARS-CoV-2 and enable equitable vaccine access. Challenges related to the pandemic are numerous. Rapid response and management strategies—that is, contract tracing, case surveillance, diagnostic testing intensity, and most recently vaccine distribution mapping—can overwhelm the health care delivery system that is fragile. Although challenges are vast, digital health technologies can play an essential role in achieving sustainable resilient recovery and building back better. It is plausible that African nations are better equipped to rapidly identify, diagnose, and manage infected individuals for COVID-19, other diseases, future outbreaks, and pandemics.
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Affiliation(s)
- Olufunto A Olusanya
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN, United States
| | - Brianna White
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN, United States
| | - Chad A Melton
- Bredesen Center for Interdisciplinary Research and Graduate Education, The University of Tennessee, Knoxville, TN, United States
| | - Arash Shaban-Nejad
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN, United States
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Social Determinants and Indicators of COVID-19 Among Marginalized Communities: A Scientific Review and Call to Action for Pandemic Response and Recovery. Disaster Med Public Health Prep 2022; 17:e193. [PMID: 35492024 PMCID: PMC9237492 DOI: 10.1017/dmp.2022.104] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Coronavirus disease 2019 (COVID-19) has placed massive socio-psychological, health, and economic burdens including deaths on countless lives; however, it has disproportionally impacted certain populations. Co-occurring Social Determinants of Health (SDoH) disparities and other underlying determinants have exacerbated the COVID-19 pandemic. This literature review sought to (1) examine literature focused on SDoH and COVID-19 outcomes ie, infectivity, hospitalization, and death rates among marginalized communities; and (2) identify SDoH disparities associated with COVID-19 outcomes. We searched electronic databases for studies published from October 2019 to October 2021. Studies that were selected were those intersecting SDoH indicators and COVID-19 outcomes and were conducted in the United States. Our review underscored the disproportionate vulnerabilities and adverse outcomes from COVID-19 that have impacted racial/ethnic minority communities and other disadvantaged groups (ie, senior citizens, and displaced/homeless individuals). COVID-19 outcomes were associated with SDoH indicators, ie, race/ethnicity, poverty, median income level, housing density, housing insecurity, health-care access, occupation, transportation/commuting patterns, education, air quality, food insecurity, old age, etc. Our review concluded with recommendations and a call to action to integrate SDoH indicators along with relevant health data when implementing intelligent solutions and intervention strategies to pandemic response/recovery among vulnerable populations.
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Exploring temporal varying demographic and economic disparities in COVID-19 infections in four U.S. areas: based on OLS, GWR, and random forest models. COMPUTATIONAL URBAN SCIENCE 2021; 1:27. [PMID: 34901952 PMCID: PMC8642183 DOI: 10.1007/s43762-021-00028-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 11/22/2021] [Indexed: 10/27/2022]
Abstract
Although studies have previously investigated the spatial factors of COVID-19, most of them were conducted at a low resolution and chose to limit their study areas to high-density urbanized regions. Hence, this study aims to investigate the economic-demographic disparities in COVID-19 infections and their spatial-temporal patterns in areas with different population densities in the United States. In particular, we examined the relationships between demographic and economic factors and COVID-19 density using ordinary least squares, geographically weighted regression analyses, and random forest based on zip code-level data of four regions in the United States. Our results indicated that the demographic and economic disparities are significant. Moreover, several areas with disadvantaged groups were found to be at high risk of COVID19 infection, and their infection risk changed at different pandemic periods. The findings of this study can contribute to the planning of public health services, such as the adoption of smarter and comprehensive policies for allocating economic recovery resources and vaccines during a public health crisis.
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