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Yearbook of Medical Informatics Aug 2021To identify gaps and challenges in health informatics and health information management during the COVID-19 pandemic. To describe solutions and offer recommendations... (Review)
Review
OBJECTIVES
To identify gaps and challenges in health informatics and health information management during the COVID-19 pandemic. To describe solutions and offer recommendations that can address the identified gaps and challenges.
METHODS
A literature review of relevant peer-reviewed and grey literature published from January 2020 to December 2020 was conducted to inform the paper.
RESULTS
The literature revealed several themes regarding health information management and health informatics challenges and gaps: information systems and information technology infrastructure; data collection, quality, and standardization; and information governance and use. These challenges and gaps were often driven by public policy and funding constraints.
CONCLUSIONS
COVID-19 exposed complexities related to responding to a world-wide, fast moving, quickly spreading novel virus. Longstanding gaps and ongoing challenges in the local, national, and global health and public health information systems and data infrastructure must be addressed before we are faced with another global pandemic.
Topics: COVID-19; Data Accuracy; Data Collection; Humans; Information Management; Medical Informatics; Public Health Administration; Public Health Practice; United States
PubMed: 34479380
DOI: 10.1055/s-0041-1726499 -
Journal of Injury & Violence Research Jul 2021Sufficient data should be gathered and analyzed to increase awareness and attention of the community and policymakers in the field of road traffic injury (RTI)...
BACKGROUND
Sufficient data should be gathered and analyzed to increase awareness and attention of the community and policymakers in the field of road traffic injury (RTI) prevention. While various organizations and stakeholders are involved in road traffic crashes, there is no clear lead agency for data collection system in RTIs. Exploring stakeholders' perspective is one of the key sources for understanding this system. The purpose of this study is to identify the process of RTI data collection system based on stakeholders' experience.
METHODS
This qualitative study was conducted employing grounded theory approach since September 2017 to December 2018 in Iran. Participants in this study were the authorities of the Emergency organizations, police, Ministry of Health and Medical Education, faculty members, as well as executive staff and road users who were involved in collecting and recording data (n=15). Data collection was carried out through face-to-face interviews using purposeful and theoretical sampling. Data analysis was performed based on Strauss and Corbin 2008.
RESULTS
The core category was identified as "separated registration" explaining the process of collecting and recording road traffic injury data. Other variables obtained using the Strauss and Corbin Paradigm model were categorized as context, casual, intervening, strategies, and outcomes factors. The findings were classified into five groups including lack of trust in road safety promotion, process factors, management and organizational factors, failure of quality assurance, and administrative and organizational culture.
CONCLUSIONS
The most important theory is "separated registration" and non-systematic registry system of road traffic injury data which is shown in a conceptual model. The findings of this study will help policymakers for better understanding the collecting and recording of RTI information.
Topics: Accidents, Traffic; Data Collection; Grounded Theory; Humans; Iran; Registries; Wounds and Injuries
PubMed: 33875628
DOI: 10.5249/jivr.v13i2.1406 -
Australian Critical Care : Official... Mar 2023Successful implementation of rapid response teams (RRTs) requires robust data collection and reporting processes. However, there is variation in data collection practice... (Review)
Review
BACKGROUND
Successful implementation of rapid response teams (RRTs) requires robust data collection and reporting processes. However, there is variation in data collection practice in RRT activity between hospitals, leading to difficulties in quality review, collaboration and research. Although a standardised RRT data collection model would be a key step in addressing this, there is uncertainty regarding existing RRT data collection practice across Victoria.
OBJECTIVES
This study was endorsed by Safer Care Victoria (SCV) to evaluate existing RRT data collection practice across Victoria.
METHODOLOGY
Between 2016 and 2017, hospitals in Victoria were surveyed on data collection practice for RRT activity. Data collected included the fields populated and the mode of data collection. Qualitative content analysis, utilising a blend of pre-existing frameworks and ground-up data-driven approaches for derivation of a coding frame, was used to identify common categories. Validation of the analysis and results was performed by consultation with stakeholder groups.
RESULTS
Twenty five hospitals across 18 health networks contributed data, with a mix of tertiary (9/25), metropolitan (11/25) and rural (5/25) hospitals. Seven hospitals collected data electronically, the remainder using paper with abstraction to electronic spreadsheets. None of the hospitals linked with existing hospital data systems to reduce manual data entry requirements. Dataset size varied from 16 to 97 variables but demonstrated content consistency and could be mapped onto seven key categories (comprising antecedent, afferent, event, post-event, audit, context and patient data). Within each category, there was substantial variation in terminology and variable values, but consistency in the collection of a certain subset of variables.
CONCLUSION
Despite broad variation in data collection practice, existing datasets can be readily mapped into seven key categories, with the consistent collection of a subset of variables within each category. These variables could inform the development of a minimum dataset within a standardised RRT reporting framework and accommodate data submission from hospitals of differing resource bases.
Topics: Humans; Victoria; Hospital Rapid Response Team; Surveys and Questionnaires; Hospitals
PubMed: 35058119
DOI: 10.1016/j.aucc.2021.12.001 -
International Journal of Medical... Oct 2022Golestan Population-based Cancer Registry (GPCR) with more than 15-years experiences developed an in-house online software called Cancer Data Collection and Processing...
Development of an online cancer data collection and processing tool for population-based cancer registries in a low-resource setting: The CanDCap experience from Golestan, Iran.
BACKGROUND
Golestan Population-based Cancer Registry (GPCR) with more than 15-years experiences developed an in-house online software called Cancer Data Collection and Processing (CanDCap) to improve its data collection operations from the conventional offline method to new online method. We aimed to report the methods and framework that GPCR applied to design and implementation of the CanDCap.
METHODS
CanDCap was designed based on International Agency for Research on Cancer (IARC) protocols and standards and according to the GPCR workflow. CanDCap has two parts including a web-based online part for data collection and a windows-based part for data processing consisting of quality control and deduplication of repeated records. Questionnaire for User Interface Satisfaction (QUIS) was used in order to assess user interaction satisfaction.
RESULTS
CanDCap was implemented in 2018 and could improve the quality of the GPCR data during its first three years of activity (2018-2020), during which about 9,000 records were registered. The coverage for optional items including national ID, father name, address and telephone number were improved from 23 %, 32 %, 83 % and 82 % in conventional offline method (2015-2017) to 83 %, 81 %, 87 %, and 90 % after using the CanDCap (2018-2020), respectively. The timeliness was also improved from 4 years to 2 years. Overall, user interaction satisfaction was acceptable (7.8 out of 9).
CONCLUSION
CanDCap could resulted in improvement in data quality and timeliness of the GPCR as a cancer registry unit with limited resources. It has the potential to be considered as a model for population-based cancer registries in lower-resource settings.
Topics: Data Accuracy; Data Collection; Humans; Iran; Neoplasms; Registries; Surveys and Questionnaires
PubMed: 35981480
DOI: 10.1016/j.ijmedinf.2022.104846 -
The Lancet. Diabetes & Endocrinology Jul 2023
Topics: Humans; Health Equity; Data Collection
PubMed: 37230102
DOI: 10.1016/S2213-8587(23)00148-1 -
BMC Research Notes Aug 2019Electronic data collection (EDC) has become a suitable alternative to paper based data collection (PBDC) in biomedical research even in resource poor settings. During a...
OBJECTIVE
Electronic data collection (EDC) has become a suitable alternative to paper based data collection (PBDC) in biomedical research even in resource poor settings. During a survey in Nepal, data were collected using both systems and data entry errors compared between both methods. Collected data were checked for completeness, values outside of realistic ranges, internal logic and date variables for reasonable time frames. Variables were grouped into 5 categories and the number of discordant entries were compared between both systems, overall and per variable category.
RESULTS
Data from 52 variables collected from 358 participants were available. Discrepancies between both data sets were found in 12.6% of all entries (2352/18,616). Differences between data points were identified in 18.0% (643/3580) of continuous variables, 15.8% of time variables (113/716), 13.0% of date variables (140/1074), 12.0% of text variables (86/716), and 10.9% of categorical variables (1370/12,530). Overall 64% (1499/2352) of all discrepancies were due to data omissions, 76.6% (1148/1499) of missing entries were among categorical data. Omissions in PBDC (n = 1002) were twice as frequent as in EDC (n = 497, p < 0.001). Data omissions, specifically among categorical variables were identified as the greatest source of error. If designed accordingly, EDC can address this short fall effectively.
Topics: Biomedical Research; Data Collection; Electronic Health Records; Humans; Nepal; Publications; Reproducibility of Results; Surveys and Questionnaires; Text Messaging
PubMed: 31439025
DOI: 10.1186/s13104-019-4574-8 -
Disaster Medicine and Public Health... Oct 2020The worst rates of preventable mortality and morbidity among women and children occur in humanitarian settings. Reliable, easy-to-use, standardized, and efficient tools...
The worst rates of preventable mortality and morbidity among women and children occur in humanitarian settings. Reliable, easy-to-use, standardized, and efficient tools for data collection are needed to enable different organizations to plan and act in the most effective way. In 2015, the World Health Organization (WHO) commissioned a review of tools for data collection on the health of women and children in humanitarian emergencies. An update of this review was conducted to investigate whether the recommendations made were taken forward and to identify newly developed tools. Fifty-three studies and 5 new tools were identified. Only 1 study used 1 of the tools identified in our search. Little has been done in terms of the previous recommendations. Authors may not be aware of the availability of such tools and of the importance of documenting their data using the same methods as other researchers. Currently used tools may not be suitable for use in humanitarian settings or may not include the domains of the authors' interests. The development of standardized instruments should be done with all key workers in the area and could be coordinated by the WHO.
Topics: Data Collection; Humans; Maternal-Child Health Services; Relief Work
PubMed: 31818343
DOI: 10.1017/dmp.2019.103 -
Journal of Medical Internet Research Jan 2022Potential is seen in web data collection for population health surveys due to its combined cost-effectiveness, implementation ease, and increased internet penetration....
BACKGROUND
Potential is seen in web data collection for population health surveys due to its combined cost-effectiveness, implementation ease, and increased internet penetration. Nonetheless, web modes may lead to lower and more selective unit response than traditional modes, and this may increase bias in the measured indicators.
OBJECTIVE
This research assesses the unit response and costs of a web study versus face-to-face (F2F) study.
METHODS
Alongside the Belgian Health Interview Survey by F2F edition 2018 (BHISF2F; net sample used: 3316), a web survey (Belgian Health Interview Survey by Web [BHISWEB]; net sample used: 1010) was organized. Sociodemographic data on invited individuals was obtained from the national register and census linkages. Unit response rates considering the different sampling probabilities of both surveys were calculated. Logistic regression analyses examined the association between mode system and sociodemographic characteristics for unit nonresponse. The costs per completed web questionnaire were compared with the costs for a completed F2F questionnaire.
RESULTS
The unit response rate is lower in BHISWEB (18.0%) versus BHISF2F (43.1%). A lower response rate was observed for the web survey among all sociodemographic groups, but the difference was higher among people aged 65 years and older (15.4% vs 45.1%), lower educated people (10.9% vs 38.0%), people with a non-Belgian European nationality (11.4% vs 40.7%), people with a non-European nationality (7.2% vs 38.0%), people living alone (12.6% vs 40.5%), and people living in the Brussels-Capital (12.2% vs 41.8%) region. The sociodemographic characteristics associated with nonresponse are not the same in the 2 studies. Having another European (OR 1.60, 95% CI 1.20-2.13) or non-European nationality (OR 2.57, 95% CI 1.79-3.70) compared to a Belgian nationality and living in the Brussels-Capital (OR 1.72, 95% CI 1.41-2.10) or Walloon (OR 1.47, 95% CI 1.15-1.87) regions compared to the Flemish region are associated with a higher nonresponse only in the BHISWEB study. In BHISF2F, younger people (OR 1.31, 95% CI 1.11-1.54) are more likely to be nonrespondents than older people, and this was not the case in BHISWEB. In both studies, lower educated people have a higher probability of being nonrespondent, but this effect is more pronounced in BHISWEB (low vs high education level: Web, OR 2.71, 95% CI 2.21-3.39 and F2F OR 1.70, 95% CI 1.48-1.95). The BHISWEB study had a considerable advantage; the cost per completed questionnaire was almost 3 times lower (€41 [US $48]) compared with F2F data collection (€111 [US $131]).
CONCLUSIONS
The F2F unit response rate was generally higher, yet for certain groups the difference between web and F2F was more limited. Web data collection has a considerable cost advantage. It is therefore worth experimenting with adaptive mixed-mode designs to optimize financial resources without increasing selection bias (eg, only inviting sociodemographic groups who are keener to participate online for web surveys while continuing to focus on increasing F2F response rates for other groups).
Topics: Aged; Cross-Sectional Studies; Data Collection; Health Surveys; Home Environment; Humans; Internet; Surveys and Questionnaires
PubMed: 34994701
DOI: 10.2196/26299 -
Using real world data to advance the provision of supportive cancer care: mucositis as a case study.Current Opinion in Supportive and... Sep 2022For decades, clinical decision making and practice has been largely informed by data generated through randomized clinical trials (RCTs). By design, RCTs are highly... (Review)
Review
PURPOSE OF REVIEW
For decades, clinical decision making and practice has been largely informed by data generated through randomized clinical trials (RCTs). By design, RCTs are highly restricted in both scope and scale, resulting in narrow indications and iterative advances in clinical practice. With the transition to electronic health records, there are now endless opportunities to utilize these 'real world' data (RWD) to make more substantive advances in our understanding that are, by nature, more applicable to reality. This review discusses the current paradigm of using big data to advance and inform the provision of supportive cancer care, using mucositis as a case study.
RECENT FINDINGS
Global efforts to synthesize RWD in cancer have almost exclusively focused on tumor classification and treatment efficacy, leveraging on routine tumor pathology and binary response outcomes. In contrast, clinical notes and billing codes are not as applicable to treatment side effects which require integration of both clinical and biological data, as well as patient-reported outcomes.
SUMMARY
Cancer treatment-induced toxicities are heterogeneous and complex, and as such, the use of RWD to better understand their etiology and interaction is challenging. Multidisciplinary cooperation and leadership are needed to improve data collection and governance to ensure the right data is accessible and reliable.
Topics: Clinical Decision-Making; Data Collection; Humans; Mucositis; Neoplasms
PubMed: 35929562
DOI: 10.1097/SPC.0000000000000600 -
Journal of Medical Internet Research Mar 2021The collection of data from a personal digital device to characterize current health conditions and behaviors that determine how an individual's health will evolve has...
The collection of data from a personal digital device to characterize current health conditions and behaviors that determine how an individual's health will evolve has been called digital phenotyping. In this paper, we describe the development of and early experiences with a comprehensive digital phenotyping platform: Health Outcomes through Positive Engagement and Self-Empowerment (HOPES). HOPES is based on the open-source Beiwe platform but adds a wider range of data collection, including the integration of wearable devices and further sensor collection from smartphones. Requirements were partly derived from a concurrent clinical trial for schizophrenia that required the development of significant capabilities in HOPES for security, privacy, ease of use, and scalability, based on a careful combination of public cloud and on-premises operation. We describe new data pipelines to clean, process, present, and analyze data. This includes a set of dashboards customized to the needs of research study operations and clinical care. A test use case for HOPES was described by analyzing the digital behavior of 22 participants during the SARS-CoV-2 pandemic.
Topics: Computers, Handheld; Data Collection; Humans; Machine Learning; Mobile Applications; Phenotype; Research Design; Schizophrenia; Smartphone; Wearable Electronic Devices
PubMed: 33720028
DOI: 10.2196/23984