AI and Big Data approaches to addressing the opioid crisis: a scoping review protocol
Ontology highlight
ABSTRACT:
SUBMITTER: Amjad M
PROVIDER: S-EPMC11367284 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
Similar Datasets
Project description: Not available
| S-EPMC10852231 | biostudies-literature
Project description:IntroductionNew Mexico is currently ranked 17th in the United States for drug overdose death rates. Our project seeks to decrease opioid overdose deaths in a community by increasing the number of patients with naloxone in a local family medicine residency clinic.MethodsWe developed a protocol wherein providers asked patients at risk of opioid overdose about naloxone access. Free naloxone was distributed in partner with the county health department, accompanied by teaching of use. We reviewed patient encounters during a 45-day control and study period to measure naloxone possession among patients at risk.ResultsNearly two-thirds of patients at risk of opioid overdose had no naloxone. A standardized protocol implemented to distribute an opioid reversal agent doubled naloxone prescribed by providers at visits (10.3%) compared to a control period (4.3%), but lacked statistical significance.ConclusionPatients in a family medicine residency clinic who were at risk of opioid overdose overwhelmingly did not have naloxone, and a standardized protocol with a community-based partnership increased access to naloxone. Further project data will have implications for ongoing naloxone distribution programs in primary care.
| S-EPMC7205132 | biostudies-literature
Project description:BackgroundThe rapid advancement of digital technologies, particularly in big data analytics (BDA), artificial intelligence (AI), machine learning (ML), and deep learning (DL), is reshaping the global health care system, including in Bangladesh. The increased adoption of these technologies in health care delivery within Bangladesh has sparked their integration into health care and public health research, resulting in a noticeable surge in related studies. However, a critical gap exists, as there is a lack of comprehensive evidence regarding the research landscape; regulatory challenges; use cases; and the application and adoption of BDA, AI, ML, and DL in the health care system of Bangladesh. This gap impedes the attainment of optimal results. As Bangladesh is a leading implementer of digital technologies, bridging this gap is urgent for the effective use of these advancing technologies.ObjectiveThis scoping review aims to collate (1) the existing research in Bangladesh's health care system, using the aforementioned technologies and synthesizing their findings, and (2) the limitations faced by researchers in integrating the aforementioned technologies into health care research.MethodsMEDLINE (via PubMed), IEEE Xplore, Scopus, and Embase databases were searched to identify published research articles between January 1, 2000, and September 10, 2023, meeting the following inclusion criteria: (1) any study using any of the BDA, AI, ML, and DL technologies and health care and public health datasets for predicting health issues and forecasting any kind of outbreak; (2) studies primarily focusing on health care and public health issues in Bangladesh; and (3) original research articles published in peer-reviewed journals and conference proceedings written in English.ResultsWith the initial search, we identified 1653 studies. Following the inclusion and exclusion criteria and full-text review, 4.66% (77/1653) of the articles were finally included in this review. There was a substantial increase in studies over the last 5 years (2017-2023). Among the 77 studies, the majority (n=65, 84%) used ML models. A smaller proportion of studies incorporated AI (4/77, 5%), DL (7/77, 9%), and BDA (1/77, 1%) technologies. Among the reviewed articles, 52% (40/77) relied on primary data, while the remaining 48% (37/77) used secondary data. The primary research areas of focus were infectious diseases (15/77, 19%), noncommunicable diseases (23/77, 30%), child health (11/77, 14%), and mental health (9/77, 12%).ConclusionsThis scoping review highlights remarkable progress in leveraging BDA, AI, ML, and DL within Bangladesh's health care system. The observed surge in studies over the last 5 years underscores the increasing significance of AI and related technologies in health care research. Notably, most (65/77, 84%) studies focused on ML models, unveiling opportunities for advancements in predictive modeling. This review encapsulates the current state of technological integration and propels us into a promising era for the future of digital Bangladesh.
| S-EPMC11555453 | biostudies-literature
Project description:Overdoses of prescription or illicit opioids claimed the lives of 116 Americans each day in 2016, and the crisis continues to escalate. As healthcare systems evolve to address the crisis, the potential of pharmacists to make a positive difference is significant. In addition to utilizing available prescription drug monitoring programs to help prevent diversion of opioids, practicing pharmacists can be alert for signs of opioid misuse by patients (e.g., multiple prescriptions from different physicians) as well as inappropriate prescribing or hazardous drug combinations that physicians may not be aware of (e.g., opioid analgesics combined with benzodiazepines). They can also supply patients with information on risks of opioids, proper storage and disposal of medications, and the harms (and illegality) of sharing medications with other people. Increasingly, pharmacies are sites of distribution of the opioid antagonist naloxone, which has been shown to save lives when made available to opioid users and their families or other potential bystanders to an overdose; and pharmacists can provide guidance about its use and even legal protections for bystanders to an overdose that customers may not be aware of. Pharmacists can also recommend addiction treatment to patients and be a resource for information on addiction treatment options in the community. As addiction treatment becomes more integrated with general healthcare, pharmacies are also increasingly dispensing medications like buprenorphine and, in the future, possibly methadone. Pharmacists in private research labs and at universities are helping to develop the next generation of addiction treatments and safer, non-addictive pain medications; they can also play a role in implementation research to enhance the delivery of addiction interventions and medications in pharmacy settings. Meanwhile, pharmacists in educational settings can promote improved education about the neurobiology and management of pain and its links to opioid misuse and addiction.
| S-EPMC7373345 | biostudies-literature
Project description:During the COVID-19 pandemic, economists have struggled to obtain reliable economic predictions, with standard models becoming outdated and their forecasting performance deteriorating rapidly. This paper presents two novelties that could be adopted by forecasting institutions in unconventional times. The first innovation is the construction of an extensive data set for macroeconomic forecasting in Europe. We collect more than a thousand time series from conventional and unconventional sources, complementing traditional macroeconomic variables with timely big data indicators and assessing their added value at nowcasting. The second novelty consists of a methodology to merge an enormous amount of non-encompassing data with a large battery of classical and more sophisticated forecasting methods in a seamlessly dynamic Bayesian framework. Specifically, we introduce an innovative "selection prior" that is used not as a way to influence model outcomes, but as a selecting device among competing models. By applying this methodology to the COVID-19 crisis, we show which variables are good predictors for nowcasting Gross Domestic Product and draw lessons for dealing with possible future crises.
| S-EPMC9633630 | biostudies-literature
Project description:BackgroundThe success of big data initiatives depends on public support. Public involvement and engagement could be a way of establishing public support for big data research.ObjectiveThis review aims to synthesize the evidence on public involvement and engagement in big data research.MethodsThis scoping review mapped the current evidence on public involvement and engagement activities in big data research. We searched 5 electronic databases, followed by additional manual searches of Google Scholar and gray literature. In total, 2 public contributors were involved at all stages of the review.ResultsA total of 53 papers were included in the scoping review. The review showed the ways in which the public could be involved and engaged in big data research. The papers discussed a broad range of involvement activities, who could be involved or engaged, and the importance of the context in which public involvement and engagement occur. The findings show how public involvement, engagement, and consultation could be delivered in big data research. Furthermore, the review provides examples of potential outcomes that were produced by involving and engaging the public in big data research.ConclusionsThis review provides an overview of the current evidence on public involvement and engagement in big data research. While the evidence is mostly derived from discussion papers, it is still valuable in illustrating how public involvement and engagement in big data research can be implemented and what outcomes they may yield. Further research and evaluation of public involvement and engagement in big data research are needed to better understand how to effectively involve and engage the public in big data research.International registered report identifier (irrid)RR2-https://doi.org/10.1136/bmjopen-2021-050167.
| S-EPMC11364952 | biostudies-literature
Project description:Big data trends in biomedical and health research enable large-scale and multi-dimensional aggregation and analysis of heterogeneous data sources, which could ultimately result in preventive, diagnostic and therapeutic benefit. The methodological novelty and computational complexity of big data health research raises novel challenges for ethics review. In this study, we conducted a scoping review of the literature using five databases to identify and map the major challenges of health-related big data for Ethics Review Committees (ERCs) or analogous institutional review boards. A total of 1093 publications were initially identified, 263 of which were included in the final synthesis after abstract and full-text screening performed independently by two researchers. Both a descriptive numerical summary and a thematic analysis were performed on the full-texts of all articles included in the synthesis. Our findings suggest that while big data trends in biomedicine hold the potential for advancing clinical research, improving prevention and optimizing healthcare delivery, yet several epistemic, scientific and normative challenges need careful consideration. These challenges have relevance for both the composition of ERCs and the evaluation criteria that should be employed by ERC members when assessing the methodological and ethical viability of health-related big data studies. Based on this analysis, we provide some preliminary recommendations on how ERCs could adaptively respond to those challenges. This exploration is designed to synthesize useful information for researchers, ERCs and relevant institutional bodies involved in the conduction and/or assessment of health-related big data research.
| S-EPMC6181558 | biostudies-literature
Project description:BackgroundIn the last few years, there has been an increasing interest in the development of artificial intelligence (AI)-based clinical decision support systems (CDSS). However, there are barriers to the successful implementation of such systems in practice, including the lack of acceptance of these systems. Participatory approaches aim to involve future users in designing applications such as CDSS to be more acceptable, feasible, and fundamentally more relevant for practice. The development of technologies based on AI, however, challenges the process of user involvement and related methods.ObjectiveThe aim of this review is to summarize and present the main approaches, methods, practices, and specific challenges for participatory research and development of AI-based decision support systems involving clinicians.MethodsThis scoping review will follow the Joanna Briggs Institute approach to scoping reviews. The search for eligible studies was conducted in the databases MEDLINE via PubMed; ACM Digital Library; Cumulative Index to Nursing and Allied Health; and PsycInfo. The following search filters, adapted to each database, were used: Period January 01, 2012, to October 31, 2023, English and German studies only, abstract available. The scoping review will include studies that involve the development, piloting, implementation, and evaluation of AI-based CDSS (hybrid and data-driven AI approaches). Clinical staff must be involved in a participatory manner. Data retrieval will be accompanied by a manual gray literature search. Potential publications will then be exported into reference management software, and duplicates will be removed. Afterward, the obtained set of papers will be transferred into a systematic review management tool. All publications will be screened, extracted, and analyzed: title and abstract screening will be carried out by 2 independent reviewers. Disagreements will be resolved by involving a third reviewer. Data will be extracted using a data extraction tool prepared for the study.ResultsThis scoping review protocol was registered on March 11, 2023, at the Open Science Framework. The full-text screening had already started at that time. Of the 3,118 studies screened by title and abstract, 31 were included in the full-text screening. Data collection and analysis as well as manuscript preparation are planned for the second and third quarter of 2024. The manuscript should be submitted towards the end of 2024.ConclusionsThis review will describe the current state of knowledge on participatory development of AI-based decision support systems. The aim is to identify knowledge gaps and provide research impetus. It also aims to provide relevant information for policy makers and practitioners.International registered report identifier (irrid)DERR1-10.2196/58185.
| S-EPMC11413541 | biostudies-literature
Project description:BackgroundArtificial intelligence (AI) has become a pivotal element in health care, leading to significant advancements across various medical domains, including palliative care and hospice services. These services focus on improving the quality of life for patients with life-limiting illnesses, and AI's ability to process complex datasets can enhance decision-making and personalize care in these sensitive settings. However, incorporating AI into palliative and hospice care requires careful examination to ensure it reflects the multifaceted nature of these settings.ObjectiveThis scoping review aims to systematically map the landscape of AI in palliative care and hospice settings, focusing on the data diversity and model robustness. The goal is to understand AI's role, its clinical integration, and the transparency of its development, ultimately providing a foundation for developing AI applications that adhere to established ethical guidelines and principles.MethodsOur scoping review involves six stages: (1) identifying the research question; (2) identifying relevant studies; (3) study selection; (4) charting the data; (5) collating, summarizing, and reporting the results; and (6) consulting with stakeholders. Searches were conducted across databases including MEDLINE through PubMed, Embase.com, IEEE Xplore, ClinicalTrials.gov, and Web of Science Core Collection, covering studies from the inception of each database up to November 1, 2023. We used a comprehensive set of search terms to capture relevant studies, and non-English records were excluded if their abstracts were not in English. Data extraction will follow a systematic approach, and stakeholder consultations will refine the findings.ResultsThe electronic database searches conducted in November 2023 resulted in 4614 studies. After removing duplicates, 330 studies were selected for full-text review to determine their eligibility based on predefined criteria. The extracted data will be organized into a table to aid in crafting a narrative summary. The review is expected to be completed by May 2025.ConclusionsThis scoping review will advance the understanding of AI in palliative care and hospice, focusing on data diversity and model robustness. It will identify gaps and guide future research, contributing to the development of ethically responsible and effective AI applications in these settings.International registered report identifier (irrid)DERR1-10.2196/56353.
| S-EPMC11496913 | biostudies-literature
Project description:IntroductionSuicide is one of the leading causes of death among children and young people globally and a major public health issue. Government policies determine how much recognised health issues are prioritised and set the context for investment, development and delivery of services. A review of policies concerning children and young people who are suicidal could shed light on the extent that this public health issue is prioritised and highlight examples of best practice in this area. There has never been a review to explore how policy worldwide addresses the specific needs of this vulnerable population. This review will map the key policy documents and identify their relevance to the review question: how does policy address the treatment and care of children and young people who experience suicidality? (international, national (UK) and local (Scotland)).MethodologyEmploying scoping review methodological guidance a systematic and transparent approach will be taken. Preliminary searches will facilitate the identification of MeSh terms, subject headings, individual database and platform nuances. A full search strategy will be created to search five databases: CINAHL, PsychInfo, Medline, Web of Science and Cochrane. Government and other key agency websites (eg, WHO, Unicef) will be searched to identify policy documents. The reference lists of identified documents will be checked. A second reviewer will independently screen and cross validate eligible studies for final inclusion. A data extraction template will then be used to extract key information. We will report our findings using narrative synthesis and tabulate findings, by agreed key components.Ethics and disseminationEthical approval is not required to conduct a scoping review. We will disseminate the findings through a peer-reviewed publication and conference presentation.
| S-EPMC6144395 | biostudies-literature