Liar! Liar! Identifying eligibility fraud by applicants in digital health research.
Ontology highlight
ABSTRACT:
SUBMITTER: Glazer JV
PROVIDER: S-EPMC8164029 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Digital fraud has immensely affected ordinary consumers and the finance industry. Our dependence on internet banking has made digital fraud a substantial problem. Financial institutions across the globe are trying to improve their digital fraud detection and deterrence capabilities. Fraud detection is a reactive process, and it usually incurs a cost to save the system from an ongoing malicious activity. Fraud deterrence is the capability of a system to withstand any fraudulent attempts. Fraud deterrence is a challenging task and researchers across the globe are proposing new solutions to improve deterrence capabilities. In this work, we focus on the very important problem of fraud deterrence. Our proposed work uses an Intimation Rule Based (IRB) alert generation algorithm. These IRB alerts are classified based on severity levels. Our proposed solution uses a richer domain knowledge base and rule-based reasoning. In this work, we propose an ontology-based financial fraud detection and deterrence model.
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Project description:BackgroundDigital innovations in medicine are disruptive technologies that can change the way diagnostic procedures and treatments are delivered. Such innovations are typically designed in teams with different disciplinary backgrounds. This paper concentrates on 2 interdisciplinary research teams with 20 members from the medicine and engineering sciences working jointly on digital health solutions.ObjectiveThe aim of this paper was to identify factors on the individual, team, and organizational levels that influence the implementation of interdisciplinary research projects elaborating on digital applications for medicine and, based on the results, to draw conclusions for the proactive design of the interdisciplinary research process to make these projects successful.MethodsTo achieve this aim, 2 interdisciplinary research teams were observed, and a small case study (response rate: 15/20, 75%) was conducted using a web-based questionnaire containing both closed and open self-report questions. The Spearman rank correlation coefficient was calculated to analyze the quantitative data. The answers to the open-ended questions were subjected to qualitative content analysis.ResultsWith regard to the interdisciplinary research projects investigated, the influencing factors of the three levels presented (individual, team, and organization) have proven to be relevant for interdisciplinary research cooperation.ConclusionsWith regard to recommendations for the future design of interdisciplinary cooperation, management aspects are addressed, that is, the installation of a coordinator, systematic definition of goals, required resources, and necessary efforts on the part of the involved interdisciplinary research partners. As only small groups were investigated, further research in this field is necessary to derive more general recommendations for interdisciplinary research teams.Trial registrationGerman Clinical Trials Register, DRKS00023909, https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00023909 ; German Clinical Trials Register, DRKS00025077, https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00025077.
| S-EPMC9118027 | biostudies-literature
Project description:BackgroundWith the increased popularity of mobile menstrual tracking apps and boosted Facebook posts, there is a unique opportunity to recruit research study participants from across the globe via these modalities to evaluate women's health. However, no studies to date have assessed the feasibility of using these recruitment sources for epidemiological research on ovulation and menstruation.ObjectiveThe objective of this study was to assess the feasibility of recruiting a diverse sample of women to an epidemiological study of ovulation and menstruation (OM) health (OM Global Health Study) using digital recruitment sources. The feasibility and diversity were assessed via click and participation rates, geographic location, BMI, smoking status, and other demographic information.MethodsParticipants were actively recruited via in-app messages using the menstrual tracking app Clue (BioWink GmbH) and a boosted Facebook post by DivaCup (Diva International Inc.). Other passive recruitment methods also took place throughout the recruitment period (eg, email communications, blogs, other social media). The proportion of participants who visited the study website after viewing and clicking the hypertext link (click rates) in the in-app messages and boosted Facebook post and the proportion of participants who completed the surveys per the number of completed consent and eligibility screeners (participation rates) were used to quantify the success of recruiting participants to the study website and study survey completion, respectively. Survey completion was defined as finishing the pregnancy and birth history section of the OM Global Health Study questionnaire.ResultsThe recruitment period was from February 27, 2018, through January 24, 2020. In-app messages and the boosted Facebook post were seen by 104,000 and 21,400 people, respectively. Overall, 215 participants started the OM Global Health Study survey, of which 140 (65.1%), 39 (18.1%), and 36 (16.8%) participants were recruited via the app, the boosted Facebook post, and other passive recruitment methods, respectively. The click rate via the app was 18.9% (19,700 clicks/104,000 ad views) and 1.6% via the boosted Facebook post (340 clicks/21,400 ad views.) The overall participation rate was 44.6% (198/444), and the average participant age was 21.8 (SD 6.1) years. In terms of geographic and racial/ethnic diversity, 91 (44.2%) of the participants resided outside the United States and 147 (70.7%) identified as non-Hispanic White. In-app recruitment produced the most geographically diverse stream, with 44 (32.8%) of the 134 participants in Europe, 77 (57.5%) in North America, and 13 (9.8%) in other parts of the world. Both human error and nonhuman procedural breakdowns occurred during the recruitment process, including a computer programming error related to age eligibility and a hacking attempt by an internet bot.ConclusionsIn-app messages using the menstrual tracking app Clue were the most successful method for recruiting participants from many geographic regions and producing the greatest numbers of started and completed surveys. This study demonstrates the utility of digital recruitment to enroll participants from diverse geographic locations and provides some lessons to avoid technical recruitment errors in future digital recruitment strategies for epidemiological research.
| S-EPMC9520381 | biostudies-literature
Project description:When implemented in practice, digital technologies have shown improvements in morbidity and mortality outcomes in patients with cardiovascular disease (CVD). For scholars, research into digital technologies in cardiovascular care has been relatively recent, thus it is important to understand the history of digital health technology in cardiovascular research-its emergence, rate of growth, hot topics, and its temporal evolution. The aim of this study was to analyse more than 16,000 articles in this domain based on their scientometric indicators. Web of Science (WoS) Core Collection was accessed and searched at several levels, including titles, abstracts, keywords, authors, sources and individual articles. Analysis examined the temporal shifts in research and scholarly focus based on keywords, networks of collaboration, topical divisions in relation to digital technologies, and influential publications. Findings showed this research area is growing exponentially. Co-citation analysis revealed twenty prominent research streams and identified variation in the magnitude of activities in each stream. A recent emergence of research activities in digital technology in cardiovascular rehabilitation (CR), out-of-hospital cardiac arrest (OHCA), and arrythmia research was also demonstrated. Conversely, wearable technologies, activity tracking and electronic medical records research are now past their peak of reported research activity. With increasing amounts of novel technologies becoming available and more patients taking part in remote health care monitoring, further evaluation and research into digital technologies, including their long-term effectiveness, is needed. Furthermore, emerging technologies, which are evaluated and/or validated should be considered for implementation into clinical practice as treatment and prevention modalities for CVD.
| S-EPMC9808768 | biostudies-literature
Project description:BackgroundPsychiatry has long needed a better and more scalable way to capture the dynamics of behavior and its disturbances, quantitatively across multiple data channels, at high temporal resolution in real time. By combining 24/7 data-on location, movement, email and text communications, and social media-with brain scans, genetics, genomics, neuropsychological batteries, and clinical interviews, researchers will have an unprecedented amount of objective, individual-level data. Analyzing these data with ever-evolving artificial intelligence could one day include bringing interventions to patients where they are in the real world in a convenient, efficient, effective, and timely way. Yet, the road to this innovative future is fraught with ethical dilemmas as well as ethical, legal, and social implications (ELSI).ObjectiveThe goal of the Ethics Checklist is to promote careful design and execution of research. It is not meant to mandate particular research designs; indeed, at this early stage and without consensus guidance, there are a range of reasonable choices researchers may make. However, the checklist is meant to make those ethical choices explicit, and to require researchers to give reasons for their decisions related to ELSI issues. The Ethics Checklist is primarily focused on procedural safeguards, such as consulting with experts outside the research group and documenting standard operating procedures for clearly actionable data (eg, expressed suicidality) within written research protocols.MethodsWe explored the ELSI of digital health research in psychiatry, with a particular focus on what we label "deep phenotyping" psychiatric research, which combines the potential for virtually boundless data collection and increasingly sophisticated techniques to analyze those data. We convened an interdisciplinary expert stakeholder workshop in May 2020, and this checklist emerges out of that dialogue.ResultsConsistent with recent ELSI analyses, we find that existing ethical guidance and legal regulations are not sufficient for deep phenotyping research in psychiatry. At present, there are regulatory gaps, inconsistencies across research teams in ethics protocols, and a lack of consensus among institutional review boards on when and how deep phenotyping research should proceed. We thus developed a new instrument, an Ethics Checklist for Digital Health Research in Psychiatry ("the Ethics Checklist"). The Ethics Checklist is composed of 20 key questions, subdivided into 6 interrelated domains: (1) informed consent; (2) equity, diversity, and access; (3) privacy and partnerships; (4) regulation and law; (5) return of results; and (6) duty to warn and duty to report.ConclusionsDeep phenotyping research offers a vision for vastly more effective care for people with, or at risk for, psychiatric disease. The potential perils en route to realizing this vision are significant; however, and researchers must be willing to address the questions in the Ethics Checklist before embarking on each leg of the journey.
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Project description:BackgroundEvidence synthesis is key in promoting evidence-based health care, but it is resource-intense. Methods are needed to identify and prioritize evidence synthesis needs within health care systems. We describe a collaboration between an agency charged with facilitating the implementation of evidence-based research and practices across the Military Health System and a research center specializing in evidence synthesis.MethodsScoping searches targeted 15 sources, including the Veterans Affairs/Department of Defense Guidelines and National Defense Authorization Acts. We screened for evidence gaps in psychological health management approaches relevant to the target population. We translated gaps into potential topics for evidence maps and/or systematic reviews. Gaps amenable to evidence synthesis format provided the basis for stakeholder input. Stakeholders rated topics for their potential to inform psychological health care in the military health system. Feasibility scans determined whether topics were ready to be pursued, that is, sufficient literature exists, and duplicative efforts are avoided.ResultsWe identified 58 intervention, 9 diagnostics, 12 outcome, 19 population, and 24 health services evidence synthesis gaps. Areas included: posttraumatic stress disorder (PTSD) (19), suicide prevention (14), depression (9), bipolar disorder (9), substance use (24), traumatic brain injury (20), anxiety (1), and cross-cutting (14) synthesis topics. Stakeholder input helped prioritize 19 potential PTSD topics and 22 other psychological health topics. To date, 46 topics have undergone feasibility scans. We document lessons learned across clinical topics and research methods.ConclusionWe describe a transparent and structured approach to evidence synthesis topic selection for a health care system using scoping searches, translation into evidence synthesis format, stakeholder input, and feasibility scans.
| S-EPMC6750194 | biostudies-literature
Project description:Digital storytelling is an arts-based research method with potential to elucidate complex narratives in a compelling manner, increase participant engagement, and enhance the meaning of research findings. This method involves the creation of a 3- to 5-min video that integrates multimedia materials including photos, participant voices, drawings, and music. Given the significant potential of digital storytelling to meaningfully capture and share participants' lived experiences, a systematic review of its use in healthcare research is crucial to develop an in-depth understanding of how researchers have used this method, with an aim to refine and further inform future iterations of its use.We aim to identify and synthesize evidence on the use, impact, and ethical considerations of using digital storytelling in health research. The review questions are as follows: (1) What is known about the purpose, definition, use (processes), and contexts of digital storytelling as part of the research process in health research? (2) What impact does digital storytelling have upon the research process, knowledge development, and healthcare practice? (3) What are the key ethical considerations when using digital storytelling within qualitative, quantitative, and mixed method research studies? Key databases and the grey literature will be searched from 1990 to the present for qualitative, quantitative, and mixed methods studies that utilized digital storytelling as part of the research process. Two independent reviewers will screen and critically appraise relevant articles with established quality appraisal tools. We will extract narrative data from all studies with a standardized data extraction form and conduct a thematic analysis of the data. To facilitate innovative dissemination through social media, we will develop a visual infographic and three digital stories to illustrate the review findings, as well as methodological and ethical implications.In collaboration with national and international experts in digital storytelling, we will synthesize key evidence about digital storytelling that is critical to the development of methodological and ethical expertise about arts-based research methods. We will also develop recommendations for incorporating digital storytelling in a meaningful and ethical manner into the research process.PROSPERO registry number CRD42017068002 .
| S-EPMC5838876 | biostudies-other
Project description:BACKGROUND:Healthcare services are being increasingly digitalised in European countries. However, in studies evaluating digital health technology, some people are less likely to participate than others, e.g. those who are older, those with a lower level of education and those with poorer digital skills. Such non-participation in research - deriving from the processes of non-recruitment of targeted individuals and self-selection - can be a driver of old-age exclusion from new digital health technologies. We aim to introduce, discuss and test an instrument to measure non-participation in digital health studies, in particular, the process of self-selection. METHODS:Based on a review of the relevant literature, we designed an instrument - the NPART survey questionnaire - for the analysis of self-selection, covering five thematic areas: socioeconomic factors, self-rated health and subjective overall quality of life, social participation, time resources, and digital skills and use of technology. The instrument was piloted on 70 older study persons in Sweden, approached during the recruitment process for a trial study. RESULTS:Results indicated that participants, as compared to decliners, were on average slightly younger and more educated, and reported better memory, higher social participation, and higher familiarity with and greater use of digital technologies. Overall, the survey questionnaire was able to discriminate between participants and decliners on the key aspects investigated, along the lines of the relevant literature. CONCLUSIONS:The NPART survey questionnaire can be applied to characterise non-participation in digital health research, in particular, the process of self-selection. It helps to identify underrepresented groups and their needs. Data generated from such an investigation, combined with hospital registry data on non-recruitment, allows for the implementation of improved sampling strategies, e.g. focused recruitment of underrepresented groups, and for the post hoc adjustment of results generated from biased samples, e.g. weighting procedures.
| S-EPMC6842243 | biostudies-literature