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Vasquez HM, Pianarosa E, Sirbu R, Diemert LM, Cunningham H, Harish V, Donmez B, Rosella LC. Human factors methods in the design of digital decision support systems for population health: a scoping review. BMC Public Health 2024; 24:2458. [PMID: 39256672 PMCID: PMC11385511 DOI: 10.1186/s12889-024-19968-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Accepted: 09/02/2024] [Indexed: 09/12/2024] Open
Abstract
BACKGROUND While Human Factors (HF) methods have been applied to the design of decision support systems (DSS) to aid clinical decision-making, the role of HF to improve decision-support for population health outcomes is less understood. We sought to comprehensively understand how HF methods have been used in designing digital population health DSS. MATERIALS AND METHODS We searched English documents published in health sciences and engineering databases (Medline, Embase, PsychINFO, Scopus, Comendex, Inspec, IEEE Xplore) between January 1990 and September 2023 describing the development, validation or application of HF principles to decision support tools in population health. RESULTS We identified 21,581 unique records and included 153 studies for data extraction and synthesis. We included research articles that had a target end-user in population health and that used HF. HF methods were applied throughout the design lifecycle. Users were engaged early in the design lifecycle in the needs assessment and requirements gathering phase and design and prototyping phase with qualitative methods such as interviews. In later stages in the lifecycle, during user testing and evaluation, and post deployment evaluation, quantitative methods were more frequently used. However, only three studies used an experimental framework or conducted A/B testing. CONCLUSIONS While HF have been applied in a variety of contexts in the design of data-driven DSSs for population health, few have used Human Factors to its full potential. We offer recommendations for how HF can be leveraged throughout the design lifecycle. Most crucially, system designers should engage with users early on and throughout the design process. Our findings can support stakeholders to further empower public health systems.
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Affiliation(s)
- Holland M Vasquez
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Emilie Pianarosa
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Renee Sirbu
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Lori M Diemert
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Heather Cunningham
- Gerstein Science Information Centre, University of Toronto, Toronto, Ontario, Canada
| | - Vinyas Harish
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Birsen Donmez
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Laura C Rosella
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
- Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
- Institute for Better Health, Trillium Health Partners, Mississauga, Ontario, Canada.
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Chen J, Elsaid MI, Handley D, Plascak JJ, Andersen BL, Carson WE, Pawlik TM, Fareed N, Obeng-Gyasi S. Association Between Neighborhood Opportunity, Allostatic Load, and All-Cause Mortality in Patients With Breast Cancer. J Clin Oncol 2024; 42:1788-1798. [PMID: 38364197 PMCID: PMC11095867 DOI: 10.1200/jco.23.00907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 12/06/2023] [Accepted: 12/18/2023] [Indexed: 02/18/2024] Open
Abstract
PURPOSE Adverse neighborhood contextual factors may affect breast cancer outcomes through environmental, psychosocial, and biological pathways. The objective of this study is to examine the relationship between allostatic load (AL), neighborhood opportunity, and all-cause mortality among patients with breast cancer. METHODS Women age 18 years and older with newly diagnosed stage I-III breast cancer who received surgical treatment between January 1, 2012, and December 31, 2020, at a National Cancer Institute Comprehensive Cancer Center were identified. Neighborhood opportunity was operationalized using the 2014-2018 Ohio Opportunity Index (OOI), a composite measure derived from neighborhood level transportation, education, employment, health, housing, crime, and environment. Logistic and Cox regression models tested associations between the OOI, AL, and all-cause mortality. RESULTS The study cohort included 4,089 patients. Residence in neighborhoods with low OOI was associated with high AL (adjusted odds ratio, 1.21 [95% CI, 1.05 to 1.40]). On adjusted analysis, low OOI was associated with greater risk of all-cause mortality (adjusted hazard ratio [aHR], 1.45 [95% CI, 1.11 to 1.89]). Relative to the highest (99th percentile) level of opportunity, risk of all-cause mortality steeply increased up to the 70th percentile, at which point the rate of increase plateaued. There was no interaction between the composite OOI and AL on all-cause mortality (P = .12). However, there was a higher mortality risk among patients with high AL residing in lower-opportunity environments (aHR, 1.96), but not in higher-opportunity environments (aHR, 1.02; P interaction = .02). CONCLUSION Lower neighborhood opportunity was associated with higher AL and greater risk of all-cause mortality among patients with breast cancer. Additionally, environmental factors and AL interacted to influence all-cause mortality. Future studies should focus on interventions at the neighborhood and individual level to address socioeconomically based disparities in breast cancer.
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Affiliation(s)
- J.C. Chen
- Division of Surgical Oncology, Department of Surgery, The Ohio State University, Columbus, OH
| | - Mohamed I. Elsaid
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH
- Secondary Data Core, Center for Biostatistics, College of Medicine, The Ohio State University, Columbus, OH
| | - Demond Handley
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH
- Secondary Data Core, Center for Biostatistics, College of Medicine, The Ohio State University, Columbus, OH
| | - Jesse J. Plascak
- Division of Cancer Prevention and Control, Department of Internal Medicine, The Ohio State University, Columbus, OH
| | | | - William E. Carson
- Division of Surgical Oncology, Department of Surgery, The Ohio State University, Columbus, OH
| | - Timothy M. Pawlik
- Division of Surgical Oncology, Department of Surgery, The Ohio State University, Columbus, OH
| | - Naleef Fareed
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH
| | - Samilia Obeng-Gyasi
- Division of Surgical Oncology, Department of Surgery, The Ohio State University, Columbus, OH
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Rabiei R, Bastani P, Ahmadi H, Dehghan S, Almasi S. Developing public health surveillance dashboards: a scoping review on the design principles. BMC Public Health 2024; 24:392. [PMID: 38321469 PMCID: PMC10848508 DOI: 10.1186/s12889-024-17841-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 01/22/2024] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND Public Health Dashboards (PHDs) facilitate the monitoring and prediction of disease outbreaks by continuously monitoring the health status of the community. This study aimed to identify design principles and determinants for developing public health surveillance dashboards. METHODOLOGY This scoping review is based on Arksey and O'Malley's framework as included in JBI guidance. Four databases were used to review and present the proposed principles of designing PHDs: IEEE, PubMed, Web of Science, and Scopus. We considered articles published between January 1, 2010 and November 30, 2022. The final search of articles was done on November 30, 2022. Only articles in the English language were included. Qualitative synthesis and trend analysis were conducted. RESULTS Findings from sixty-seven articles out of 543 retrieved articles, which were eligible for analysis, indicate that most of the dashboards designed from 2020 onwards were at the national level for managing and monitoring COVID-19. Design principles for the public health dashboard were presented in five groups, i.e., considering aim and target users, appropriate content, interface, data analysis and presentation types, and infrastructure. CONCLUSION Effective and efficient use of dashboards in public health surveillance requires implementing design principles to improve the functionality of these systems in monitoring and decision-making. Considering user requirements, developing a robust infrastructure for improving data accessibility, developing, and applying Key Performance Indicators (KPIs) for data processing and reporting purposes, and designing interactive and intuitive interfaces are key for successful design and development.
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Affiliation(s)
- Reza Rabiei
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Peivand Bastani
- College of Business, Government and Law, Flinders University, Adelaide, SA, 5042, Australia
| | - Hossein Ahmadi
- Centre for Health Technology, Faculty of Health, University of Plymouth, Plymouth, PL4 8AA, UK
| | - Shirin Dehghan
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sohrab Almasi
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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4
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Field C, Lynch CD, Fareed N, Joseph JJ, Wu J, Thung SF, Gabbe SG, Landon MB, Grobman WA, Venkatesh KK. Association of community walkability and glycemic control among pregnant individuals with pregestational diabetes mellitus. Am J Obstet Gynecol MFM 2023; 5:100898. [PMID: 36787839 DOI: 10.1016/j.ajogmf.2023.100898] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 02/13/2023]
Abstract
BACKGROUND Neighborhood walkability is a community-level social determinant of health that measures whether people who live in a neighborhood walk as a mode of transportation. Whether neighborhood walkability is associated with glycemic control among pregnant individuals with pregestational diabetes remains to be defined. OBJECTIVE This study aimed to evaluate the association between community-level neighborhood walkability and glycemic control as measured by hemoglobin A1c (A1C) among pregnant individuals with pregestational diabetes. STUDY DESIGN This was a retrospective analysis of pregnant individuals with pregestational diabetes enrolled in an integrated prenatal and diabetes care program from 2012 to 2016. Participant addresses were geocoded and linked at the census-tract level. The exposure was community walkability, defined by the US Environmental Protection Agency National Walkability Index (score range 1-20), which incorporates intersection density (design), proximity to transit stops (distance), and a mix of employment and household types (diversity). Individuals from neighborhoods that were the most walkable (score, 15.26-20.0) were compared with those from neighborhoods that were less walkable (score <15.26), as defined per national Environmental Protection Agency recommendations. The outcomes were glycemic control, including A1C <6.0% and <6.5%, measured both in early and late pregnancy, and mean change in A1C across pregnancy. Modified Poisson regression and linear regression were used, respectively, and adjusted for maternal age, body mass index at delivery, parity, race and ethnicity as a social determinant of health, insurance status, baseline A1C, gestational age at A1C measurement in early and late pregnancy, and diabetes type. RESULTS Among 417 pregnant individuals (33% type 1, 67% type 2 diabetes mellitus), 10% were living in the most walkable communities. All 417 individuals underwent A1C assessment in early pregnancy (median gestational age, 9.7 weeks; interquartile range, 7.4-14.1), and 376 underwent another A1C assessment in late pregnancy (median gestational age, 30.4 weeks; interquartile range, 27.8-33.6). Pregnant individuals living in the most walkable communities were more likely to have an A1C <6.0% in early pregnancy (15% vs 8%; adjusted relative risk, 1.46; 95% confidence interval, 1.00-2.16), and an A1C <6.5% in late pregnancy compared with those living in less walkable communities (13% vs 9%; adjusted relative risk, 1.33; 95% confidence interval, 1.08-1.63). For individuals living in the most walkable communities, the median A1C was 7.5 (interquartile range, 6.0-9.4) in early pregnancy and 5.9 (interquartile range, 5.4-6.4) in late pregnancy. For those living in less walkable communities, the median A1C was 7.3 (interquartile range, 6.2-9.2) in early pregnancy and 6.2 (interquartile range, 5.6-7.1) in late pregnancy. Change in A1C across pregnancy was not associated with walkability. CONCLUSION Pregnant individuals with pregestational diabetes mellitus living in more walkable communities had better glycemic control in both early and late pregnancy. Whether community-level interventions to enhance neighborhood walkability can improve glycemic control in pregnancy requires further study.
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Affiliation(s)
- Christine Field
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Ohio State University, Columbus, OH (Drs Field and Lynch, Mr Wu, and Drs Thung, Gabbe, Landon, Grobman, and Venkatesh).
| | - Courtney D Lynch
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Ohio State University, Columbus, OH (Drs Field and Lynch, Mr Wu, and Drs Thung, Gabbe, Landon, Grobman, and Venkatesh)
| | - Naleef Fareed
- Department of Biomedical Informatics, Ohio State University College of Medicine, Columbus, OH (Dr Fareed)
| | - Joshua J Joseph
- Division of Endocrinology, Diabetes and Metabolism, Department of Internal Medicine, Ohio State University College of Medicine, Columbus, OH (Dr Joseph)
| | - Jiqiang Wu
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Ohio State University, Columbus, OH (Drs Field and Lynch, Mr Wu, and Drs Thung, Gabbe, Landon, Grobman, and Venkatesh)
| | - Stephen F Thung
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Ohio State University, Columbus, OH (Drs Field and Lynch, Mr Wu, and Drs Thung, Gabbe, Landon, Grobman, and Venkatesh)
| | - Steven G Gabbe
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Ohio State University, Columbus, OH (Drs Field and Lynch, Mr Wu, and Drs Thung, Gabbe, Landon, Grobman, and Venkatesh)
| | - Mark B Landon
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Ohio State University, Columbus, OH (Drs Field and Lynch, Mr Wu, and Drs Thung, Gabbe, Landon, Grobman, and Venkatesh)
| | - William A Grobman
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Ohio State University, Columbus, OH (Drs Field and Lynch, Mr Wu, and Drs Thung, Gabbe, Landon, Grobman, and Venkatesh)
| | - Kartik K Venkatesh
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Ohio State University, Columbus, OH (Drs Field and Lynch, Mr Wu, and Drs Thung, Gabbe, Landon, Grobman, and Venkatesh)
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Canfell OJ, Kodiyattu Z, Eakin E, Burton-Jones A, Wong I, Macaulay C, Sullivan C. Real-world data for precision public health of noncommunicable diseases: a scoping review. BMC Public Health 2022; 22:2166. [PMID: 36434553 PMCID: PMC9694563 DOI: 10.1186/s12889-022-14452-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 10/25/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Global public health action to address noncommunicable diseases (NCDs) requires new approaches. NCDs are primarily prevented and managed in the community where there is little investment in digital health systems and analytics; this has created a data chasm and relatively silent burden of disease. The nascent but rapidly emerging area of precision public health offers exciting new opportunities to transform our approach to NCD prevention. Precision public health uses routinely collected real-world data on determinants of health (social, environmental, behavioural, biomedical and commercial) to inform precision decision-making, interventions and policy based on social position, equity and disease risk, and continuously monitors outcomes - the right intervention for the right population at the right time. This scoping review aims to identify global exemplars of precision public health and the data sources and methods of their aggregation/application to NCD prevention. METHODS The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) was followed. Six databases were systematically searched for articles published until February 2021. Articles were included if they described digital aggregation of real-world data and 'traditional' data for applied community, population or public health management of NCDs. Real-world data was defined as routinely collected (1) Clinical, Medication and Family History (2) Claims/Billing (3) Mobile Health (4) Environmental (5) Social media (6) Molecular profiling (7) Patient-centred (e.g., personal health record). Results were analysed descriptively and mapped according to the three horizons framework for digital health transformation. RESULTS Six studies were included. Studies developed population health surveillance methods and tools using diverse real-world data (e.g., electronic health records and health insurance providers) and traditional data (e.g., Census and administrative databases) for precision surveillance of 28 NCDs. Population health analytics were applied consistently with descriptive, geospatial and temporal functions. Evidence of using surveillance tools to create precision public health models of care or improve policy and practice decisions was unclear. CONCLUSIONS Applications of real-world data and designed data to address NCDs are emerging with greater precision. Digital transformation of the public health sector must be accelerated to create an efficient and sustainable predict-prevent healthcare system.
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Affiliation(s)
- Oliver J. Canfell
- grid.1003.20000 0000 9320 7537Centre for Health Services Research, Faculty of Medicine, The University of Queensland, St Lucia, QLD Australia ,grid.1003.20000 0000 9320 7537UQ Business School, Faculty of Business, Economics and Law, The University of Queensland, St Lucia, QLD Australia ,grid.450426.10000 0001 0124 2253Digital Health Cooperative Research Centre, Australian Government, Sydney, NSW Australia ,grid.453171.50000 0004 0380 0628Health and Wellbeing Queensland, Queensland Government, The State of Queensland, Milton, QLD Australia ,grid.1003.20000 0000 9320 7537Queensland Digital Health Centre, Faculty of Medicine, The University of Queensland, Herston, QLD Australia
| | - Zack Kodiyattu
- grid.1003.20000 0000 9320 7537School of Clinical Medicine, Faculty of Medicine, The University of Queensland, St Lucia, QLD Australia
| | - Elizabeth Eakin
- grid.1003.20000 0000 9320 7537School of Public Health, Faculty of Medicine, The University of Queensland, St Lucia, QLD Australia
| | - Andrew Burton-Jones
- grid.1003.20000 0000 9320 7537UQ Business School, Faculty of Business, Economics and Law, The University of Queensland, St Lucia, QLD Australia
| | - Ides Wong
- grid.453171.50000 0004 0380 0628Department of Health, Office of the Chief Clinical Information Officer, Clinical Excellence Queensland, Queensland Government, Brisbane, QLD Australia
| | - Caroline Macaulay
- grid.453171.50000 0004 0380 0628Health and Wellbeing Queensland, Queensland Government, The State of Queensland, Milton, QLD Australia
| | - Clair Sullivan
- grid.1003.20000 0000 9320 7537Centre for Health Services Research, Faculty of Medicine, The University of Queensland, St Lucia, QLD Australia ,grid.453171.50000 0004 0380 0628Health and Wellbeing Queensland, Queensland Government, The State of Queensland, Milton, QLD Australia ,grid.1003.20000 0000 9320 7537Queensland Digital Health Centre, Faculty of Medicine, The University of Queensland, Herston, QLD Australia ,grid.453171.50000 0004 0380 0628Department of Health, Metro North Hospital and Health Service, Queensland Government, Herston, QLD Australia
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Bergmann KR. Neighborhood Opportunity and Life Expectancy at Birth. JAMA Netw Open 2022; 5:e2235923. [PMID: 36239945 DOI: 10.1001/jamanetworkopen.2022.35923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Kelly R Bergmann
- Department of Pediatric Emergency Medicine, Children's Minnesota, Minneapolis
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Fareed N, Singh P, Jonnalagadda P, Swoboda C, Odden C, Doogan N. Construction of the Ohio Children's Opportunity Index. Front Public Health 2022; 10:734105. [PMID: 35942261 PMCID: PMC9356199 DOI: 10.3389/fpubh.2022.734105] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 06/17/2022] [Indexed: 11/13/2022] Open
Abstract
Objective To describe the development of an area-level measure of children's opportunity, the Ohio Children's Opportunity Index (OCOI). Data Sources/Study Setting Secondary data were collected from US census based-American Community Survey (ACS), US Environmental Protection Agency, US Housing and Urban Development, Ohio Vital Statistics, US Department of Agriculture-Economic Research Service, Ohio State University Center for Urban and Regional Analysis, Ohio Incident Based Reporting System, IPUMS National Historical Geographic Information System, and Ohio Department of Medicaid. Data were aggregated to census tracts across two time periods. Study Design OCOI domains were selected based on existing literature, which included family stability, infant health, children's health, access, education, housing, environment, and criminal justice domains. The composite index was developed using an equal weighting approach. Validation analyses were conducted between OCOI and health and race-related outcomes, and a national index. Principal Findings Composite OCOI scores ranged from 0–100 with an average value of 74.82 (SD, 17.00). Census tracts in the major metropolitan cities across Ohio represented 76% of the total census tracts in the least advantaged OCOI septile. OCOI served as a significant predictor of health and race-related outcomes. Specifically, the average life expectancy at birth of children born in the most advantaged septile was approximately 9 years more than those born in least advantaged septile. Increases in OCOI were associated with decreases in proportion of Black (48 points lower in the most advantaged vs. least advantaged septile), p < 0.001) and Minority populations (54 points lower in most advantaged vs. least advantaged septile, p < 0.001). We found R-squared values > 0.50 between the OCOI and the national Child Opportunity Index scores. Temporally, OCOI decreased by 1% between the two study periods, explained mainly by decreases in the children health, accessibility and environmental domains. Conclusion As the first opportunity index developed for children in Ohio, the OCOI is a valuable resource for policy reform, especially related to health disparities and health equity. Health care providers will be able to use it to obtain holistic views on their patients and implement interventions that can tackle barriers to childhood development using a more tailored approach.
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Affiliation(s)
- Naleef Fareed
- CATALYST—The Center for the Advancement of Team Science, Analytics, and Systems Thinking, College of Medicine, The Ohio State University Institute for Behavioral Medicine Research, Columbus, OH, United States
- Department of Biomedical Informatics, College of Medicine, The Ohio State University Institute for Behavioral Medicine Research, Columbus, OH, United States
- *Correspondence: Naleef Fareed
| | - Priti Singh
- CATALYST—The Center for the Advancement of Team Science, Analytics, and Systems Thinking, College of Medicine, The Ohio State University Institute for Behavioral Medicine Research, Columbus, OH, United States
| | - Pallavi Jonnalagadda
- CATALYST—The Center for the Advancement of Team Science, Analytics, and Systems Thinking, College of Medicine, The Ohio State University Institute for Behavioral Medicine Research, Columbus, OH, United States
| | - Christine Swoboda
- CATALYST—The Center for the Advancement of Team Science, Analytics, and Systems Thinking, College of Medicine, The Ohio State University Institute for Behavioral Medicine Research, Columbus, OH, United States
- Department of Family Medicine, College of Medicine, The Ohio State University Institute for Behavioral Medicine Research, Columbus, OH, United States
| | - Colin Odden
- Department of Research Information Technology, College of Medicine, The Ohio State University, Columbus, OH, United States
| | - Nathan Doogan
- Ohio Colleges of Medicine Government Resource Center, The Ohio State University, Columbus, OH, United States
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Jonnalagadda P, Swoboda C, Singh P, Gureddygari H, Scarborough S, Dunn I, Doogan NJ, Fareed N. Developing Dashboards to Address Children's Health Disparities in Ohio. Appl Clin Inform 2022; 13:100-112. [PMID: 35081656 PMCID: PMC8791762 DOI: 10.1055/s-0041-1741482] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 11/27/2021] [Indexed: 01/28/2023] Open
Abstract
OBJECTIVES Social determinants of health (SDoH) can be measured at the geographic level to convey information about neighborhood deprivation. The Ohio Children's Opportunity Index (OCOI) is a composite area-level opportunity index comprised of eight health domains. Our research team has documented the design, development, and use cases of a dashboard solution to visualize OCOI. METHODS The OCOI is a multidomain index spanning the following eight domains: (1) family stability, (2) infant health, (3) children's health, (4) access, (5) education, (6) housing, (7) environment, and (8) criminal justice. Information on these eight domains is derived from the American Community Survey and other administrative datasets. Our team used the Tableau Desktop visualization software and applied a user-centered design approach to developing the two OCOI dashboards-main OCOI dashboard and OCOI-race dashboard. We also performed convergence analysis to visualize the census tracts where different health indicators simultaneously exist at their worst levels. RESULTS The OCOI dashboard has multiple, interactive components as follows: a choropleth map of Ohio displaying OCOI scores for a specific census tract, graphs presenting OCOI or domain scores to compare relative positions for tracts, and a sortable table to visualize scores for specific county and census tracts. A case study using the two dashboards for convergence analysis revealed census tracts in neighborhoods with low infant health scores and a high proportion of minority population. CONCLUSION The OCOI dashboards could assist health care leaders in making decisions that enhance health care delivery and policy decision-making regarding children's health particularly in areas where multiple health indicators exist at their worst levels.
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Affiliation(s)
- Pallavi Jonnalagadda
- CATALYST, Center for the Advancement of Team Science, Analytics, and Systems Thinking in Health Services and Implementation Science Research, College of Medicine, The Ohio State University, Columbus, Ohio, United States
| | - Christine Swoboda
- CATALYST, Center for the Advancement of Team Science, Analytics, and Systems Thinking in Health Services and Implementation Science Research, College of Medicine, The Ohio State University, Columbus, Ohio, United States
| | - Priti Singh
- CATALYST, Center for the Advancement of Team Science, Analytics, and Systems Thinking in Health Services and Implementation Science Research, College of Medicine, The Ohio State University, Columbus, Ohio, United States
| | - Harish Gureddygari
- CATALYST, Center for the Advancement of Team Science, Analytics, and Systems Thinking in Health Services and Implementation Science Research, College of Medicine, The Ohio State University, Columbus, Ohio, United States
| | - Seth Scarborough
- CATALYST, Center for the Advancement of Team Science, Analytics, and Systems Thinking in Health Services and Implementation Science Research, College of Medicine, The Ohio State University, Columbus, Ohio, United States
| | - Ian Dunn
- The Ohio Colleges of Medicine Government Resource Center, Columbus, Ohio, United States
| | - Nathan J. Doogan
- The Ohio Colleges of Medicine Government Resource Center, Columbus, Ohio, United States
| | - Naleef Fareed
- CATALYST, Center for the Advancement of Team Science, Analytics, and Systems Thinking in Health Services and Implementation Science Research, College of Medicine, The Ohio State University, Columbus, Ohio, United States
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, United States
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Abstract
OBJECTIVE Human factors and ergonomics (HF/E) frameworks and methods are becoming embedded in the health informatics community. There is now broad recognition that health informatics tools must account for the diverse needs, characteristics, and abilities of end users, as well as their context of use. The objective of this review is to synthesize the current nature and scope of HF/E integration into the health informatics community. METHODS Because the focus of this synthesis is on understanding the current integration of the HF/E and health informatics research communities, we manually reviewed all manuscripts published in primary HF/E and health informatics journals during 2020. RESULTS HF/E-focused health informatics studies included in this synthesis focused heavily on EHR customizations, specifically clinical decision support customizations and customized data displays, and on mobile health innovations. While HF/E methods aimed to jointly improve end user safety, performance, and satisfaction, most HF/E-focused health informatics studies measured only end user satisfaction. CONCLUSION HF/E-focused health informatics researchers need to identify and communicate methodological standards specific to health informatics, to better synthesize findings across resource intensive HF/E-focused health informatics studies. Important gaps in the HF/E design and evaluation process should be addressed in future work, including support for technology development platforms and training programs so that health informatics designers are as diverse as end users.
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Wu E, Villani J, Davis A, Fareed N, Harris DR, Huerta TR, LaRochelle MR, Miller CC, Oga EA. Community dashboards to support data-informed decision-making in the HEALing communities study. Drug Alcohol Depend 2020; 217:108331. [PMID: 33070058 PMCID: PMC7528750 DOI: 10.1016/j.drugalcdep.2020.108331] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 09/18/2020] [Accepted: 09/24/2020] [Indexed: 11/26/2022]
Abstract
BACKGROUND With opioid misuse, opioid use disorder (OUD), and opioid overdose deaths persisting at epidemic levels in the U.S., the largest implementation study in addiction research-the HEALing Communities Study (HCS)-is evaluating the impact of the Communities That Heal (CTH) intervention on reducing opioid overdose deaths in 67 disproportionately affected communities from four states (i.e., "sites"). Community-tailored dashboards are central to the CTH intervention's mandate to implement a community-engaged and data-driven process. These dashboards support a participating community's decision-making for selection and monitoring of evidence-based practices to reduce opioid overdose deaths. METHODS/DESIGN A community-tailored dashboard is a web-based set of interactive data visualizations of community-specific metrics. Metrics include opioid overdose deaths and other OUD-related measures, as well as drivers of change of these outcomes in a community. Each community-tailored dashboard is a product of a co-creation process between HCS researchers and stakeholders from each community. The four research sites used a varied set of technical approaches and solutions to support the scientific design and CTH intervention implementation. Ongoing evaluation of the dashboards involves quantitative and qualitative data on key aspects posited to shape dashboard use combined with website analytics. DISCUSSION The HCS presents an opportunity to advance how community-tailored dashboards can foster community-driven solutions to address the opioid epidemic. Lessons learned can be applied to inform interventions for public health concerns and issues that have disproportionate impact across communities and populations (e.g., racial/ethnic and sexual/gender minorities and other marginalized individuals). TRIAL REGISTRATION ClinicalTrials.gov (NCT04111939).
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Affiliation(s)
- Elwin Wu
- Social Intervention Group, Columbia University School of Social Work, 1255 Amsterdam Avenue, New York, NY, 10027, USA.
| | - Jennifer Villani
- National Institute on Drug Abuse, 3WFN RM 08A45 MSC 6025, 301 North Stonestreet Avenue, Rockville, MD, 20892, USA
| | - Alissa Davis
- Social Intervention Group, Columbia University School of Social Work, 1255 Amsterdam Avenue, New York, NY, 10027, USA
| | - Naleef Fareed
- CATALYST - The Center for the Advancement of Team Science, Analytics, and Systems Thinking, College of Medicine, The Ohio State University, 460 Medical Center Drive, Columbus, OH, 43210, USA; Department of Biomedical Informatics, College of Medicine, The Ohio State University, 1585 Neil Avenue, Columbus, OH, 43210, USA
| | - Daniel R Harris
- Institute for Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Kentucky, Lexington, KY, 40506, USA; Center for Clinical and Translational Sciences, University of Kentucky, Lexington, KY, 40506, USA
| | - Timothy R Huerta
- CATALYST - The Center for the Advancement of Team Science, Analytics, and Systems Thinking, College of Medicine, The Ohio State University, 460 Medical Center Drive, Columbus, OH, 43210, USA; Department of Biomedical Informatics, College of Medicine, The Ohio State University, 1585 Neil Avenue, Columbus, OH, 43210, USA
| | - Marc R LaRochelle
- Clinical Addiction Research and Education Unit, Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine and Boston Medical Center, 801 Massachusetts Avenue, 2nd Floor, Boston, MA, 02218, USA
| | - Cortney C Miller
- Biostatistics and Epidemiology Data Analytics Center, Boston University School of Public Health, Boston, MA, USA
| | - Emmanuel A Oga
- RTI International, 6110 Executive Boulevard, Rockville, MD, 20852, USA
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