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Health-related social needs (HRSNs) encompass various non-medical risks from a patient's life circumstances. The emergency department (ED) is a crucial yet challenging setting for addressing patient HRSNs, a clinical decision support (CDS) intervention could assist in identifying patients at high risk of having HRSNs. This project aims to implement and evaluate a CDS intervention that offers ED clinicians risk prediction scores to determine which patients will likely screen positive for one or more HRSNs. The FHIR-based CDS intervention, implemented in the ED setting of a health system in Indianapolis, Indiana, will use health information exchange data to generate logit-derived probability scores that estimate an adult patient's likelihood of screening positive for each of the following HRSNs: housing instability, food insecurity, transportation barriers, financial strain, and history of legal involvement. For each HRSN, ED clinicians will have access to the patient's likelihood of screening positive categorized as \"high,\" \"medium,\" or \"low\" based on tertiles in the distribution of each likelihood score. Clinician participation in the CDS will be voluntary. The intervention's effects will be assessed using a difference-in-difference approach with a pre-post design and a propensity-matched comparison group of ED patients from the same metropolitan area. Outcomes of interest include whether a formal HRSN screening was conducted, whether a referral was made to an HRSN service provider (e.g., social worker), and whether a repeat ED revisit (at 3, 7, and 30 days) or primary care follow-up (within 7 days) occurred. Efficiently and accurately identifying patients with HRSNs could help link them to needed services, improving outcomes and reducing healthcare costs. This protocol will contribute to a growing body of research on the role of CDS interventions in facilitating improved screenings and referrals for HRSNs. Clincialtrials.gov NCT06655974.

Background Motivated by the Triple Aim, US health care policy is expanding its focus from individual patient care to include population health management. Health Information Exchanges are positioned to play an important role in that expansion. Objective The objective is to describe the evolution of the Indiana Network for Patient Care (INPC) and discuss examples of its innovations that support both population health and clinical applications. Methods A descriptive analytical approach was used to gather information on the INPC. This included a literature review of recent systematic and scoping reviews, collection of research that used INPC data as a resource, and data abstracted by Regenstrief Data Services to understand the breadth of uses for the INPC as a data resource. Results Although INPC data are primarily gathered from and used in healthcare settings, their use for population health management and research has increased. By December 2023, the INPC contained nearly 25 million patients, a significant growth from 3.5 million in 2004. This growth was a result of the use of INPC data for population health surveillance, clinical applications for data, disease registries, Patient-Centered Data Homes, non-clinical population health advancements, and accountable care organization connections with Health Information Exchanges. Conclusion By structuring services on the fundamental building blocks, expanding the focus to population health, and ensuring value in the services provided to the stakeholders, Health Information Exchanges are uniquely positioned to support both population health and clinical applications.

PurposeTo aid prescribers in assessing a patient’s risk for statin-induced myopathy (SIM), we performed a comprehensive review of currently known risk factors and calculated aggregated odds ratios for each risk factor through a meta-analysis.MethodsThis meta-analysis was done through four phases: (1) Identification of the relevant primary literature; (2) abstract screening using inclusion and exclusion criteria; (3) detailed review and data extraction; and (4) synthesis and statistical analysis.ResultsOut of 44 papers analyzed from 836 papers searched from MEDLINE, 18 different potential risk factors were collected, divided into three categories: three demographics (11 papers), ten clinical factors (31 papers), and five pharmacogenetics/biomarkers (12 papers). Risk factors significant for myopathy and/or rhabdomyolysis included age, gender, diabetes, renal impairment, cardiovascular disease, certain interacting drugs, and mutations of the SLCO1B1 gene, which encodes a transporter protein in the liver. Several factors, such as gender, race, cardiovascular disease, and the GATM gene, which encodes a protein for creatine synthesis, appeared to be protective in terms of the outcomes of interest.ConclusionsThis comprehensive assessment of risk factors can help support clinicians in reducing the incidence of SIM in their patient population on statins.

The National Comprehensive Cancer Care Network (NCCN) provides recommendations for patients with colorectal cancer. Concordance with evidence-based guidelines improves outcomes. Our objectives were to 1) examine rates of guideline non-concordance in a large vertically integrated health system; 2) examine factors associated with non-concordant care, and 3) identify geographical patterns of non-concordant care. Colorectal cancer patients were identified from a single-state 16 hospital health-system cancer registry diagnosed between 2011 and 2021. We defined major (MAJ) and minor (MIN) quality indicators of guideline-concordance based on NCCN guidelines. Regression methods were used to identify predictors of major quality criteria non-concordance. County-level mapping was used to identify geographical locations of the highest rates of non-concordance. Overall, 2324 patients with colon and rectal cancer were analyzed. There was a complete guideline concordance (an absence of guideline non-concordance) rate of 24.7​% (n ​= ​573), MIN only guideline non-concordance rate of 63.3​% (n ​= ​1471), and MAJ non-concordance rate of 12.4​% (n ​= ​280). Predictors of MAJ non-concordance for colon cancer were stage, >1 Charleson-Deyo Score, >60 days from diagnosis to treatment, and >1 hospital network used for care. Predictors of MAJ non-concordance for rectal cancer were >60 days from diagnosis to treatment, and >1 facility used for care. Marion county had the highest rates of non-concordance for colon and rectal cancer. The majority of colon and rectal cancer patients in a large health system received guideline concordant major quality indicators, however 12​% of patients do not. An identifiable geographical location with the highest rates of non-concordance and its associated factors serves as a target for future quality improvement. •Concordance with National Comprehensive Cancer Network guidelines improves outcomes.•Major (MAJ) and minor (MIN) quality indicators of guideline-concordance were defined.•Among patients with colorectal cancers there was a MAJ non-concordance rate of 12.4 ​%.•Patients with >60 days from diagnosis to treatment had increased MAJ non-concordance.•The largest county in the state had the highest rates of MAJ non-concordance.

Although clopidogrel is a frequently used antiplatelet medication to treat and prevent atherothrombotic disease, clinicians must balance its clinical effectiveness with the potential side effect of bleeding. However, many previous studies have evaluated beneficial and adverse factors separately. The objective of our study was to perform a comprehensive meta‐analysis of studies of clopidogrel’s clinical effectiveness and/or risk of bleeding in order to identify and assess all reported risk factors, thus helping clinicians to balance patient safety with drug efficacy. We analyzed randomized controlled trials (RCTs) of maintenance use in four stages: search for relevant primary articles; and full article screening; quality assessment and data extraction; and synthesis and data analysis. Screening of 7,109 articles yielded 52 RCTs that met the inclusion criteria. Twenty‐seven risk factors were identified. “Definite risk factors” were defined as those with aggregated odds ratios (ORs) > 1 and confidence intervals (CIs) > 1 if analyzed in more than one study. Definite risk factors for major bleeding were concomitant aspirin use (OR 2.83, 95% CI 2.04–3.94) and long duration of clopidogrel therapy (> 6 months) (OR 1.74, 95% CI 1.21–2.50). Dual antiplatelet therapy, extended clopidogrel therapy, and high maintenance dose (150 mg/day) of clopidogrel were definite risk factors for any bleeding. Reduced renal function, both mild and severe, was the only definite risk factor for clinical ineffectiveness. These findings can help clinicians predict the risks and effectiveness of clopidogrel use for their patients and be used in clinical decision support tools.

Use of mobile health (mHealth) apps is growing at an exponential rate in the United States and around the world. Mild cognitive impairment (MCI), Alzheimer disease, and related dementias are a global health problem. Numerous mHealth interventions exist for this population, yet the effect of these interventions on health has not been systematically described. The aim of this study is to catalog the types of health outcomes used to measure effectiveness of mHealth interventions and assess which mHealth interventions have been shown to improve the health of persons with MCI, Alzheimer disease, and dementia. We searched 13 databases, including Ovid MEDLINE, PubMed, EMBASE, the full Cochrane Library, CINAHL, PsycINFO, Ei Compendex, IEEE Xplore, Applied Science & Technology Source, Scopus, Web of Science, ClinicalTrials.gov, and Google Scholar from inception through May 2017 for mHealth studies involving persons with cognitive impairment that were evaluated using at least one quantitative health outcome. Proceedings of the Annual ACM Conferences on Human Factors in Computing Systems, the ACM User Interface Software and Technology Symposium, and the IEEE International Symposium on Wearable Computers were searched in the ACM Digital Library from 2012 to 2016. A hand search of JMIR Publications journals was also completed in July 2017. After removal of duplicates, our initial search returned 3955 records. Of these articles, 24 met final inclusion criteria as studies involving mHealth interventions that measured at least one quantitative health outcome for persons with MCI, Alzheimer disease, and dementia. Common quantitative health outcomes included cognition, function, mood, and quality of life. We found that 21.2% (101/476) of the fully reviewed articles were excluded because of a lack of health outcomes. The health outcomes selected were observed to be inconsistent between studies. For those studies with quantitative health outcomes, more than half (58%) reported postintervention improvements in outcomes. Results showed that many mHealth app interventions targeting those with cognitive impairment lack quantitative health outcomes as a part of their evaluation process and that there is a lack of consensus as to which outcomes to use. The majority of mHealth app interventions that incorporated health outcomes into their evaluation noted improvements in the health of persons with MCI, Alzheimer disease, and dementia. However, these studies were of low quality, leading to a grade C level of evidence. Clarification of the benefits of mHealth interventions for people with cognitive impairment requires more randomized controlled trials, larger numbers of participants, and trial designs that minimize bias. PROSPERO Registration: PROSPERO 2016:CRD42016033846; http://www.crd.york.ac.uk/PROSPERO/ display_record.asp?ID=CRD42016033846 (Archived by WebCite at http://www.webcitation.org/6sjjwnv1M).

Research networking systems hold great promise for helping biomedical scientists identify collaborators with the expertise needed to build interdisciplinary teams. Although efforts to date have focused primarily on collecting and aggregating information, less attention has been paid to the design of end-user tools for using these collections to identify collaborators. To be effective, collaborator search tools must provide researchers with easy access to information relevant to their collaboration needs. The aim was to study user requirements and preferences for research networking system collaborator search tools and to design and evaluate a functional prototype. Paper prototypes exploring possible interface designs were presented to 18 participants in semistructured interviews aimed at eliciting collaborator search needs. Interview data were coded and analyzed to identify recurrent themes and related software requirements. Analysis results and elements from paper prototypes were used to design a Web-based prototype using the D3 JavaScript library and VIVO data. Preliminary usability studies asked 20 participants to use the tool and to provide feedback through semistructured interviews and completion of the System Usability Scale (SUS). Initial interviews identified consensus regarding several novel requirements for collaborator search tools, including chronological display of publication and research funding information, the need for conjunctive keyword searches, and tools for tracking candidate collaborators. Participant responses were positive (SUS score: mean 76.4%, SD 13.9). Opportunities for improving the interface design were identified. Interactive, timeline-based displays that support comparison of researcher productivity in funding and publication have the potential to effectively support searching for collaborators. Further refinement and longitudinal studies may be needed to better understand the implications of collaborator search tools for researcher workflows.

The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) is an important source for detecting adverse drug event (ADE) signals. In this article, we propose a three‐component mixture model (3CMM) for FAERS signal detection. In 3CMM, a drug‐ADE pair is assumed to have either a zero relative risk (RR), or a background RR (mean RR = 1), or an increased RR (mean RR >1). By clearly defining the second component (mean RR = 1) as the null distribution, 3CMM estimates local false discovery rates (FDRs) for ADE signals under the empirical Bayes framework. Compared with existing approaches, the local FDR's top signals have noninferior or better sensitivities to detect true signals in both FAERS analysis and simulation studies. Additionally, we identify that the top signals of different approaches have different patterns, and they are complementary to each other.

Science has developed from a solitary pursuit into a team-based collaborative activity and, more recently, into a multidisciplinary research enterprise. The increasingly collaborative character of science, mandated by complex research questions and problems that require many competencies, requires that researchers lower the barriers to the creation of collaborative networks of experts, such as communities of practice (CoPs). The aim was to assess the information needs of prospective members of a CoP in an emerging field, dental informatics, and to evaluate their expectations of an e-community in order to design a suitable electronic infrastructure. A Web-based survey instrument was designed and administered to 2768 members of the target audience. Benefit expectations were analyzed for their relationship to (1) the respondents' willingness to participate in the CoP and (2) their involvement in funded research. Two raters coded the respondents' answers regarding expected benefits using a 14-category coding scheme (Kappa = 0.834). The 256 respondents (11.1% response rate) preferred electronic resources over traditional print material to satisfy their information needs. The most frequently expected benefits from participation in the CoP were general information (85% of respondents), peer networking (31.1%), and identification of potential collaborators and/or research opportunities (23.2%). The competitive social-information environment in which CoPs are embedded presents both threats to sustainability and opportunities for greater integration and impact. CoP planners seeking to support the development of emerging biomedical science disciplines should blend information resources, social search and filtering, and visibility mechanisms to provide a portfolio of social and information benefits. Assessing benefit expectations and alternatives provides useful information for CoP planners seeking to prioritize community infrastructure development and encourage participation.

Working together efficiently and effectively presents a significant challenge in large-scale, complex, interdisciplinary research projects. Collaboratories are a nascent method to help meet this challenge. However, formal collaboratories in biomedical research centers are the exception rather than the rule. The main purpose of this paper is to compare and describe two collaboratories that used off-the-shelf tools and relatively modest resources to support the scientific activity of two biomedical research centers. The two centers were the Great Lakes Regional Center for AIDS Research (HIV/AIDS Center) and the New York University Oral Cancer Research for Adolescent and Adult Health Promotion Center (Oral Cancer Center). In each collaboratory, we used semistructured interviews, surveys, and contextual inquiry to assess user needs and define the technology requirements. We evaluated and selected commercial software applications by comparing their feature sets with requirements and then pilot-testing the applications. Local and remote support staff cooperated in the implementation and end user training for the collaborative tools. Collaboratory staff evaluated each implementation by analyzing utilization data, administering user surveys, and functioning as participant observers. The HIV/AIDS Center primarily required real-time interaction for developing projects and attracting new participants to the center; the Oral Cancer Center, on the other hand, mainly needed tools to support distributed and asynchronous work in small research groups. The HIV/AIDS Center's collaboratory included a center-wide website that also served as the launch point for collaboratory applications, such as NetMeeting, Timbuktu Conference, PlaceWare Auditorium, and iVisit. The collaboratory of the Oral Cancer Center used Groove and Genesys Web conferencing. The HIV/AIDS Center was successful in attracting new scientists to HIV/AIDS research, and members used the collaboratory for developing and implementing new research studies. The Oral Cancer Center successfully supported highly distributed and asynchronous research, and the collaboratory facilitated real-time interaction for analyzing data and preparing publications. The two collaboratory implementations demonstrated the feasibility of supporting biomedical research centers using off-the-shelf commercial tools, but they also identified several barriers to successful collaboration. These barriers included computing platform incompatibilities, network infrastructure complexity, variable availability of local versus remote IT support, low computer and collaborative software literacy, and insufficient maturity of available collaborative software. Factors enabling collaboratory use included collaboration incentives through funding mechanism, a collaborative versus competitive relationship of researchers, leadership by example, and tools well matched to tasks and technical progress. Integrating electronic collaborative tools into routine scientific practice can be successful but requires further research on the technical, social, and behavioral factors influencing the adoption and use of collaboratories.