1
|
Peng Y, Duan J, Hou J, Xu N, Wu J, Bao X, Yao Q, Li Y. Knowledge, attitudes and practices among medical workers toward outpatient diabetes information platform. BMC Health Serv Res 2024; 24:328. [PMID: 38475848 DOI: 10.1186/s12913-024-10711-y] [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: 12/01/2023] [Accepted: 02/13/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND This study aimed to assess the knowledge, attitudes and practices among medical workers toward outpatient diabetes information platform. METHODS This web-based cross-sectional study was conducted between May 2023 and June 2023 at the First Hospital of Zhangjiakou, China. A self-designed questionnaire was developed to collect demographic information of medical workers, and assess their knowledge, attitudes and practices toward outpatient diabetes information platform. RESULTS A total of 685 questionnaires were collected. Among the participants, 603 (88.03%) were female, 432 (63.07%) work in a tertiary hospital, 548 (80.00%) have a bachelor degree, 270 (39.42%) of them work in the department of internal medicine and 315 (45.99%) of them received previous training on outpatient diabetes information platform. The mean knowledge, attitudes and practices scores were 4.32 ± 1.27 (possible range: 0-6), 56.76 ± 5.72 (possible range: 14-70), and 32.22 ± 8.42 (possible range: 9-45), respectively. 350 (51.09%) of them have sufficient knowledge, 168 (24.53%) have positive attitudes and 395 (57.66%) have active practices. Pearson correlation analysis showed that knowledge was positively correlated with attitudes (r = 0.397, P < 0.001), and attitudes were positively correlated with practices (r = 0.306, P < 0.001). Multivariate analysis showed that primary hospital (OR = 0.32, 95% CI: 0.14-0.71, P = 0.005), secondary hospital (OR = 0.48, 95% CI: 0.32-0.72, P < 0.001), doctor (OR = 2.44, 95% CI: 1.39-4.28, P = 0.002) were independently associated with sufficient knowledge. Knowledge (OR = 1.49, 95% CI: 1.29-1.73, P < 0.001), community hospital staff (OR = 0.21, 95% CI: 0.05-0.88, P = 0.032) were independently associated with positive attitudes. Attitudes (OR = 1.13, 95% CI: 1.09-1.17, P < 0.001), junior college (OR = 1.72, 95% CI: 1.07-2.77, P = 0.026) were independently associated with active practices. The structural equation model demonstrated that knowledge had a direct effect on attitudes (path coefficient = 0.521, P < 0.001), and attitudes had a direct effect on practices (path coefficient = 0.542, P < 0.001). Moreover, the type of hospital had a direct effect on knowledge (path coefficient = 0.085, P < 0.001). Additionally, previous training on the outpatient diabetes platform had direct effects on attitudes (path coefficient = 0.191, P < 0.001) and practices (path coefficient = 0.184, P < 0.001). CONCLUSION These findings revealed that medical workers have insufficient knowledge, positive attitudes and inactive practices toward the outpatient diabetes information platform. Comprehensive training programs are needed to improve medical staff's practices in this area.
Collapse
Affiliation(s)
- Yi Peng
- Department of Endocrinology, Zhangjiakou First Hospital, Zhangjiakou, 075000, China.
| | - Jianying Duan
- Department of Endocrinology, Zhangjiakou First Hospital, Zhangjiakou, 075000, China
| | - Jian Hou
- Department of Pharmacology, Zhangjiakou First Hospital, Zhangjiakou, 075000, China
| | - Nan Xu
- Department of Endocrinology, Zhangjiakou First Hospital, Zhangjiakou, 075000, China
| | - Jiaming Wu
- Department of Endocrinology, Zhangjiakou First Hospital, Zhangjiakou, 075000, China
| | - Xijing Bao
- Department of Endocrinology, Zhangjiakou First Hospital, Zhangjiakou, 075000, China
| | - Qian Yao
- Department of Endocrinology, Zhangjiakou First Hospital, Zhangjiakou, 075000, China
| | - Yang Li
- Department of Endocrinology, Zhangjiakou First Hospital, Zhangjiakou, 075000, China
| |
Collapse
|
2
|
Carbonaro A, Marfoglia A, Nardini F, Mellone S. CONNECTED: leveraging digital twins and personal knowledge graphs in healthcare digitalization. Front Digit Health 2023; 5:1322428. [PMID: 38130576 PMCID: PMC10733505 DOI: 10.3389/fdgth.2023.1322428] [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: 10/16/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023] Open
Abstract
Healthcare has always been a strategic domain in which innovative technologies can be applied to increase the effectiveness of services and patient care quality. Recent advancements have been made in the adoption of Digital Twins (DTs) and Personal Knowledge Graphs (PKGs) in this field. Despite this, their introduction has been hindered by the complex nature of the context itself which leads to many challenges both technical and organizational. In this article, we reviewed the literature about these technologies and their integrations, identifying the most critical requirements for clinical platforms. These latter have been used to design CONNECTED (COmpreheNsive and staNdardized hEalth-Care plaTforms to collEct and harmonize clinical Data), a conceptual framework aimed at defining guidelines to overcome the crucial issues related to the development of healthcare applications. It is structured in a multi-layer shape, in which heterogeneous data sources are first integrated, then standardized, and finally used to realize general-purpose DTs of patients backed by PKGs and accessible through dedicated APIs. These DTs will be the foundation on which smart applications can be built.
Collapse
Affiliation(s)
- Antonella Carbonaro
- Department of Computer Science and Engineering, Università di Bologna, Cesena, Italy
| | - Alberto Marfoglia
- Department of Computer Science and Engineering, Università di Bologna, Cesena, Italy
| | - Filippo Nardini
- Department of Industrial Engineering, Università di Bologna, Bologna, Italy
| | - Sabato Mellone
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, Università di Bologna, Cesena, Italy
| |
Collapse
|
3
|
Ammar N, Olusanya OA, Melton C, Chinthala L, Huang X, White BM, Shaban-Nejad A. Digital Personal Health Coaching Platform for Promoting Human Papillomavirus Infection Vaccinations and Cancer Prevention: Knowledge Graph-Based Recommendation System. JMIR Form Res 2023; 7:e50210. [PMID: 37966885 PMCID: PMC10687687 DOI: 10.2196/50210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 10/09/2023] [Accepted: 10/24/2023] [Indexed: 11/16/2023] Open
Abstract
BACKGROUND Health promotion can empower populations to gain more control over their well-being by using digital interventions that focus on preventing the root causes of diseases. Digital platforms for personalized health coaching can improve health literacy and information-seeking behavior, leading to better health outcomes. Personal health records have been designed to enhance patients' self-management of a disease or condition. Existing personal health records have been mostly designed and deployed as a supplementary service that acts as views into electronic health records. OBJECTIVE We aim to overcome some of the limitations of electronic health records. This study aims to design and develop a personal health library (PHL) that generates personalized recommendations for human papillomavirus (HPV) vaccine promotion and cancer prevention. METHODS We have designed a proof-of-concept prototype of the Digital Personal Health Librarian, which leverages machine learning; natural language processing; and several innovative technological infrastructures, including the Semantic Web, social linked data, web application programming interfaces, and hypermedia-based discovery, to generate a personal health knowledge graph. RESULTS We have designed and implemented a proof-of-the-concept prototype to showcase and demonstrate how the PHL can be used to store an individual's health data, for example, a personal health knowledge graph. This is integrated with web-scale knowledge to support HPV vaccine promotion and prevent HPV-associated cancers among adolescents and their caregivers. We also demonstrated how the Digital Personal Health Librarian uses the PHL to provide evidence-based insights and knowledge-driven explanations that are personalized and inform health decision-making. CONCLUSIONS Digital platforms such as the PHL can be instrumental in improving precision health promotion and education strategies that address population-specific needs (ie, health literacy, digital competency, and language barriers) and empower individuals by facilitating knowledge acquisition to make healthy choices.
Collapse
Affiliation(s)
- Nariman Ammar
- Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States
- School of Information Technology, Illinois State University, Normal, IL, United States
- Ochsner Xavier Institute for Health Equity and Research, Ochsner Clinic Foundation, New Orleans, LA, 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
| | - Chad Melton
- Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN, United States
| | - Lokesh Chinthala
- Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Xiaolei Huang
- Department of Computer Science, University of Memphis, Memphis, TN, United States
| | - Brianna M White
- 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
| |
Collapse
|
4
|
Rivera-Romero O, Gabarron E, Ropero J, Denecke K. Designing personalised mHealth solutions: An overview. J Biomed Inform 2023; 146:104500. [PMID: 37722446 DOI: 10.1016/j.jbi.2023.104500] [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: 02/28/2023] [Revised: 09/14/2023] [Accepted: 09/16/2023] [Indexed: 09/20/2023]
Abstract
INTRODUCTION Mobile health, or mHealth, is based on mobile information and communication technologies and provides solutions for empowering individuals to participate in healthcare. Personalisation techniques have been used to increase user engagement and adherence to interventions delivered as mHealth solutions. This study aims to explore the current state of personalisation in mHealth, including its current trends and implementation. MATERIALS AND METHODS We conducted a review following PRISMA guidelines. Four databases (PubMed, ACM Digital Library, IEEE Xplore, and APA PsycInfo) were searched for studies on mHealth solutions that integrate personalisation. The retrieved papers were assessed for eligibility and useful information regarding integrated personalisation techniques. RESULTS Out of the 1,139 retrieved studies, 62 were included in the narrative synthesis. Research interest in the personalisation of mHealth solutions has increased since 2020. mHealth solutions were mainly applied to endocrine, nutritional, and metabolic diseases; mental, behavioural, or neurodevelopmental diseases; or the promotion of healthy lifestyle behaviours. Its main purposes are to support disease self-management and promote healthy lifestyle behaviours. Mobile applications are the most prevalent technological solution. Although several design models, such as user-centred and patient-centred designs, were used, no specific frameworks or models for personalisation were followed. These solutions rely on behaviour change theories, use gamification or motivational messages, and personalise the content rather than functionality. A broad range of data is used for personalisation purposes. There is a lack of studies assessing the efficacy of these solutions; therefore, further evidence is needed. DISCUSSION Personalisation in mHealth has not been well researched. Although several techniques have been integrated, the effects of using a combination of personalisation techniques remain unclear. Although personalisation is considered a persuasive strategy, many mHealth solutions do not employ it. CONCLUSIONS Open research questions concern guidelines for successful personalisation techniques in mHealth, design frameworks, and comprehensive studies on the effects and interactions among multiple personalisation techniques.
Collapse
Affiliation(s)
- Octavio Rivera-Romero
- Electronic Technology Department, Universidad de Sevilla, Spain; Instituto de Investigación en Informática de la Universidad de Sevilla, Spain.
| | - Elia Gabarron
- Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway; Department of Education, ICT and Learning, Østfold University College, Halden, Norway
| | - Jorge Ropero
- Electronic Technology Department, Universidad de Sevilla, Spain
| | | |
Collapse
|
5
|
Kumsa FA, Prasad R, Shaban-Nejad A. Medication abortion via digital health in the United States: a systematic scoping review. NPJ Digit Med 2023; 6:128. [PMID: 37438435 PMCID: PMC10338479 DOI: 10.1038/s41746-023-00871-2] [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: 01/27/2023] [Accepted: 06/26/2023] [Indexed: 07/14/2023] Open
Abstract
Digital health, including telemedicine, has increased access to abortion care. The convenience, flexibility of appointment times, and ensured privacy to abortion users may make abortion services via telemedicine preferable. This scoping review systematically mapped studies conducted on abortion services via telemedicine, including their effectiveness and acceptability for abortion users and providers. All published papers included abortion services via telemedicine in the United States were considered. Articles were searched in PubMed, CINAHL, and Google Scholar databases in September 2022. The findings were synthesized narratively, and the PRISMA-ScR guidelines were used to report this study. Out of 757 retrieved articles, 33 articles were selected based on the inclusion criteria. These studies were published between 2011 and 2022, with 24 published in the last 3 years. The study found that telemedicine increased access to abortion care in the United States, especially for people in remote areas or those worried about stigma from in-person visits. The effectiveness of abortion services via telemedicine was comparable to in-clinic visits, with 6% or fewer abortions requiring surgical intervention. Both care providers and abortion seekers expressed positive perceptions of telemedicine-based abortion services. However, abortion users reported mixed emotions, with some preferring in-person visits. The most common reasons for choosing telemedicine included the distance to the abortion clinic, convenience, privacy, cost, flexibility of appointment times, and state laws imposing waiting periods or restrictive policies. Telemedicine offered a preferable option for abortion seekers and providers. The feasibility of accessing abortion services via telemedicine in low-resource settings needs further investigation.
Collapse
Affiliation(s)
- Fekede Asefa Kumsa
- The University of Tennessee Health Science Center (UTHSC) - Oak Ridge National Laboratory (ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, Memphis, TN, 38103, USA.
| | - Rameshwari Prasad
- The University of Tennessee Health Science Center (UTHSC) - Oak Ridge National Laboratory (ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, Memphis, TN, 38103, USA
| | - Arash Shaban-Nejad
- The University of Tennessee Health Science Center (UTHSC) - Oak Ridge National Laboratory (ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, Memphis, TN, 38103, USA.
| |
Collapse
|
6
|
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: 2] [Impact Index Per Article: 1.0] [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.
Collapse
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,
| |
Collapse
|
7
|
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: 6] [Impact Index Per Article: 3.0] [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.
Collapse
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
| |
Collapse
|
8
|
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
Collapse
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
| |
Collapse
|
9
|
Zhang H, Lyu T, Yin P, Bost S, He X, Guo Y, Prosperi M, Hogan WR, Bian J. A scoping review of semantic integration of health data and information. Int J Med Inform 2022; 165:104834. [PMID: 35863206 DOI: 10.1016/j.ijmedinf.2022.104834] [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: 03/21/2022] [Revised: 07/06/2022] [Accepted: 07/13/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVE We summarized a decade of new research focusing on semantic data integration (SDI) since 2009, and we aim to: (1) summarize the state-of-art approaches on integrating health data and information; and (2) identify the main gaps and challenges of integrating health data and information from multiple levels and domains. MATERIALS AND METHODS We used PubMed as our focus is applications of SDI in biomedical domains and followed the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) to search and report for relevant studies published between January 1, 2009 and December 31, 2021. We used Covidence-a systematic review management system-to carry out this scoping review. RESULTS The initial search from PubMed resulted in 5,326 articles using the two sets of keywords. We then removed 44 duplicates and 5,282 articles were retained for abstract screening. After abstract screening, we included 246 articles for full-text screening, among which 87 articles were deemed eligible for full-text extraction. We summarized the 87 articles from four aspects: (1) methods for the global schema; (2) data integration strategies (i.e., federated system vs. data warehousing); (3) the sources of the data; and (4) downstream applications. CONCLUSION SDI approach can effectively resolve the semantic heterogeneities across different data sources. We identified two key gaps and challenges in existing SDI studies that (1) many of the existing SDI studies used data from only single-level data sources (e.g., integrating individual-level patient records from different hospital systems), and (2) documentation of the data integration processes is sparse, threatening the reproducibility of SDI studies.
Collapse
Affiliation(s)
- Hansi Zhang
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Tianchen Lyu
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Pengfei Yin
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Sarah Bost
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Xing He
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Yi Guo
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Mattia Prosperi
- Department of Epidemiology, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Willian R Hogan
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Jiang Bian
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States.
| |
Collapse
|
10
|
Applying Collective Intelligence in Health Recommender Systems for Smoking Cessation: A Comparison Trial. ELECTRONICS 2022. [DOI: 10.3390/electronics11081219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Background: Health recommender systems (HRSs) are intelligent systems that can be used to tailor digital health interventions. We compared two HRSs to assess their impact providing smoking cessation support messages. Methods: Smokers who downloaded a mobile app to support smoking abstinence were randomly assigned to two interventions. They received personalized, ratable motivational messages on the app. The first intervention had a knowledge-based HRS (n = 181): it selected random messages from a subset matching the users’ demographics and smoking habits. The second intervention had a hybrid HRS using collective intelligence (n = 190): it selected messages applying the knowledge-based filter first, and then chose the ones with higher ratings provided by other similar users in the system. Both interventions were compared on: (a) message appreciation, (b) engagement with the system, and (c) one’s own self-reported smoking cessation status, as indicated by the last seven-day point prevalence report in different time intervals during a period of six months. Results: Both interventions had similar message appreciation, number of rated messages, and abstinence results. The knowledge-based HRS achieved a significantly higher number of active days, number of abstinence reports, and better abstinence results. The hybrid algorithm led to more quitting attempts in participants who completed their user profiles.
Collapse
|
11
|
Olusanya OA, Ammar N, Davis RL, Bednarczyk RA, Shaban-Nejad A. A Digital Personal Health Library for Enabling Precision Health Promotion to Prevent Human Papilloma Virus-Associated Cancers. Front Digit Health 2021; 3:683161. [PMID: 34713154 PMCID: PMC8521976 DOI: 10.3389/fdgth.2021.683161] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 06/25/2021] [Indexed: 11/13/2022] Open
Abstract
Human papillomavirus (HPV) causes the most prevalent sexually transmitted infection (STI) in the United States. Sexually active young adults are susceptible to HPV, accounting for approximately 50% of new STIs. Oncogenic HPV subtypes 16 and 18 are associated with squamous intraepithelial lesions and cancers and are mostly preventable through prophylactic HPV vaccination. Accordingly, this study's objectives are to (1) summarize SDoH barriers and implication for low HPV vaccination rates among young adults (18-26 years), (2) propose a digital health solution that utilizes the PHL to collect, integrate, and manage personalized sexual and health information, and (3) describe the features of the PHL-based app. Through the application of novel techniques from artificial intelligence, specifically knowledge representation, semantic web, and natural language processing, this proposed PHL-based application will compile clinical, biomedical, and SDoH data from multi-dimensional sources. Therefore, this application will provide digital health interventions that are customized to individuals' specific needs and capacities. The PHL-based application could promote management and usage of personalized digital health information to facilitate precision health promotion thereby, informing health decision-making regarding HPV vaccinations, routine HPV/STI testing, cancer screenings, vaccine safety/efficacy/side effects, and safe sexual practices. In addition to detecting vaccine hesitancy, disparities and perceived barriers, this application could address participants' specific needs/challenges with navigating health literacy, technical skills, peer influence, education, language, cultural and spiritual beliefs. Precision health promotion focused on improving knowledge acquisition and information-seeking behaviors, promoting safe sexual practices, increasing HPV vaccinations, and facilitating cancer screenings could be effective in preventing HPV-associated cancers.
Collapse
Affiliation(s)
- Olufunto A. Olusanya
- UTHSC-Oak Ridge National Laboratory (ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, The University of Tennessee Health Science Center, Memphis, TN, United States
| | - Nariman Ammar
- UTHSC-Oak Ridge National Laboratory (ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, The University of Tennessee Health Science Center, Memphis, TN, United States
| | - Robert L. Davis
- UTHSC-Oak Ridge National Laboratory (ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, The University of Tennessee Health Science Center, Memphis, TN, United States
| | - Robert A. Bednarczyk
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States
| | - Arash Shaban-Nejad
- UTHSC-Oak Ridge National Laboratory (ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, The University of Tennessee Health Science Center, Memphis, TN, United States
| |
Collapse
|