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The Partnership-Driven Clinical Federated (PCF) Data sharing Model illustrates four quadrants of themes used to define each iteration cycle of development.
Source publication
Objective:
Building federated data sharing architectures requires supporting a range of data owners, effective and validated semantic alignment between data resources, and consistent focus on end-users. Establishing these resources requires development methodologies that support internal validation of data extraction and translation processes, sus...
Contexts in source publication
Context 1
... PCF Model Description-A generic spiral model for partnership-driven clinical federated (PCF) data sharing, based on Boehm's spiral model for software development, [31- 32] emerged from our iterative and qualitative based methods (Figure 1). This model identified four themes to anchor the iterative process of development: 1) developing partnerships, 2) defining system requirements, 3) determining technical architecture, and 4) conducting effective promotion and evaluation. ...
Context 2
... PCF Model Description-A generic spiral model for partnership-driven clinical federated (PCF) data sharing, based on Boehm's spiral model for software development, [31- 32] emerged from our iterative and qualitative based methods (Figure 1). This model identified four themes to anchor the iterative process of development: 1) developing partnerships, 2) defining system requirements, 3) determining technical architecture, and 4) conducting effective promotion and evaluation. ...
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Citations
... Ref [85] To structure iterations of development and facilitate knowledge sharing between two health network development teams coordination to support and manage shared phases, a spiral model for implementation and assessment was utilized. ...
With the advent of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML)/DeepLearning (DL) algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and quality of service (QoS) standards. Recent developments in Federated Learning (FL) have made it possible to train complex machine-learned models in a distributed manner and has become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this paper, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unravelling the complexities of designing reliable and scalable FL models. Our paper outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of FL, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. Recent literature has shown that FL models are robust and generalize well to new data, which is essential for medical applications. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state-of-the-art and identifying open problems and future research directions.
... A significant body of healthcare literature addressed EHR barriers such as the security of the health records (Al-Sharhan et al., 2019), patients' perceptions (Asan et al., 2016), trust (Stephens et al., 2016), and channel richness (Mirzaei and Esmaeilzadeh, 2021), but there are limited studies mitigating EHR barriers using new technologies, e.g. blockchain. ...
A primary objective of blockchain technology is to address information security and efficiency issues related to existing information sharing systems. For the sharing of health records, little is known about the application of blockchain in management information systems. There are strict regulations for sharing health information due to insecure systems and privacy concerns. To significantly and effectively improve medical diagnosis, it is beneficial to have efficient, reliable, and accurate accessibility to a patient's full medical history. Due to concerns with the security of health systems and privacy concerns, medical histories are not always accessible to healthcare providers. To help increase accessibility options, this research proposes a blockchain-based model that facilitates sharing medical records in a manner that is beneficial to both healthcare provider and patients. Social exchange theory provides the theoretical support for the conceptual model presented. Experimental findings based on 151 participants revealed that the blockchain technology can provide a secure information system and increase patient motivation to share medical records. To show the applicability of the proposed use of blockchain from a practical managerial perspective, we show feasibility by developing an Android software application.
... These systems will combine patient data, clinician-specific data and competencies, and healthcare system data including staffing, patient census, and supply availability to optimize healthcare delivery and improve equity. This can occur only when large-scale data across different institutions and healthcare systems can be securely combined into a single identity-protected mega-dataset for widespread use (16,17). Such large-scale federation of patient, clinician, and healthcare system data will refine practices in different environments and help detect and mitigate disparities in healthcare delivery. ...
While technological innovations are the invariable crux of speculation about the future of critical care, they cannot replace the clinician at the bedside. This article summarizes the work of the Society of Critical Care Medicine–appointed multiprofessional task for the Future of Critical Care. The Task Force notes that critical care practice will be transformed by novel technologies, integration of artificial intelligence decision support algorithms, and advances in seamless data operationalization across diverse healthcare systems and geographic regions and within federated datasets. Yet, new technologies will be relevant and meaningful only if they improve the very human endeavor of caring for someone who is critically ill.
... 20,[22][23][24][25] The Partnership-Driven Clinically Federated (PCF) Data Sharing Model provides a 4-quadrant framework that can enable data sharing for populations health communities studying disaster response through an iterative spiral model of development across (1) building collaborative partnerships, (2) defining system requirements, (3) developing technical architecture, and (4) conducting promotion and evaluation through measurable system utility. 26 The PCF model highlights the need for clear use case definitions to drive the iterative cycles of development, and the need to identify key facilitators and barriers to ensure that the technical system architecture and evaluation iterations lead to success. ...
... We asked about 4 main constructs informed by prior disaster research and the PCF model. [9][10][11]26 These constructs include (1) researcher role and expertise, (2) readiness for future hurricanes and floods, (3) barriers and facilitators to collaborative research, and (4) barriers and facilitators to data and technology adoption. Specifically, construct 1 is drawn from known challenges to prioritizing research gaps amid a disaster, 11 and constructs 2-4 are drawn from PCF's promotion and evaluation quadrant, collaborative partnerships quadrant, and both the system requirements and technical architecture quadrants, respectively. ...
... Barriers and facilitators to hurricane and flood disaster preparedness We used template analysis 32 in Dedoose to analyze interviews according to predefined themes aligning with the interview guide's 4 constructs. Informed by PCF and disaster research challenges, 11,26 the first author (J.P.) created the following codebook: For researcher roles and expertise (construct 1), we coded for research training, subject matter expertise, analytical methods, current occupational setting, and context in research (ie, place of focus, temporal scale, geospatial scale). For readiness for future hurricanes and floods (construct 2), we coded for indications of planning and preparations, barriers and facilitators they envisioned for future disaster research, and prior disaster management experience. ...
Objective
Information gaps that accompany hurricanes and floods limit researchers’ ability to determine the impact of disasters on population health. Defining key use cases for sharing complex disaster data with research communities and facilitators, and barriers to doing so are key to promoting population health research for disaster recovery.
Materials and Methods
We conducted a mixed-methods needs assessment with 15 population health researchers using interviews and card sorting. Interviews examined researchers’ information needs by soliciting barriers and facilitators in the context of their expertise and research practices. Card sorting ranked priority use cases for disaster preparedness.
Results
Seven barriers and 6 facilitators emerged from interviews. Barriers to collaborative research included process limitations, collaboration dynamics, and perception of research importance. Barriers to data and technology adoption included data gaps, limitations in information quality, transparency issues, and difficulty to learn. Facilitators to collaborative research included collaborative engagement and human resource processes. Facilitators to data and technology adoption included situation awareness, data quality considerations, adopting community standards, and attractive to learn. Card sorting prioritized 15 use cases and identified 30 additional information needs for population health research in disaster preparedness.
Conclusions
Population health researchers experience barriers to collaboration and adoption of data and technology that contribute to information gaps and limit disaster preparedness. The priority use cases we identified can help address information gaps by informing the design of supportive research tools and practices for disaster preparedness. Supportive tools should include information on data collection practices, quality assurance, and education resources usable during failures in electric or telecommunications systems.
... Data QUEST includes patient-level data stored securely within local practices' firewalls in aligned data repositories and uses a federated data-sharing infrastructure to support regulation-compliant governance of data between primary care partners and researchers. 17,18 Data QUEST's infrastructure and governance maintain compliance with Health Insurance Portability and Accountability Act (HIPAA) and Institutional Review Board (IRB) regulations. Data QUEST targets extraction from main data domains in the EHRs, including demographics, vital signs, diagnosis codes, diagnostic test results, social and family history, prescriptions and physical examination findings. ...
We use prescription of statin medications and prescription of warfarin to explore the capacity of electronic health record data to (1) describe cohorts of patients prescribed these medications and (2) identify cohorts of patients with evidence of adverse events related to prescription of these medications. This study was conducted in the WWAMI region Practice and Research Network (WPRN)., a network of primary care practices across Washington, Wyoming, Alaska, Montana and Idaho DataQUEST, an electronic data-sharing infrastructure. We used electronic health record data to describe cohorts of patients prescribed statin or warfarin medications and reported the proportions of patients with adverse events. Among the 35,445 active patients, 1745 received at least one statin prescription and 301 received at least one warfarin prescription. Only 3 percent of statin patients had evidence of myopathy; 51 patients (17% of those prescribed warfarin) had a bleeding complication. Primary-care electronic health record data can effectively be used to identify patients prescribed specific medications and patients potentially experiencing medication adverse events.
... The WPRN is partnered with the Pacific Northwest Node of the NIDA National Drug Abuse Treatment Clinical Trials Network (CTN). Four independent small and medium-sized community-based primary care organizations in the WPRN with 21 clinics compose Data QUEST, a technical infrastructure that supports harmonization of EHR data across disparate primary care practices for the purpose of translational research (Cole, Stephens, Keppel, Estiri, & Baldwin, 2016;Stephens et al., 2012;Stephens, Anderson, Lin, & Estiri, 2016). The 21 primary care clinics within these organizations include community health clinics that provide primary care to patients with limited income, insurance, or other resources, as well as rural critical access hospital-associated clinics that mostly serve patients in small towns and rural areas. ...
Background
Patients with a substance use disorder (SUD) often present with co-occurring chronic conditions in primary care. Despite the high co-occurrence of chronic medical conditions and SUD, little is known about whether chronic condition outcomes or related service utilization in primary care varies between patients with versus without documented SUDs. This study examined whether having a SUD influenced the use of primary care services and common chronic condition outcomes for patients with diabetes, hypertension, and obesity.
Methods
A longitudinal cohort observational study examined electronic health record data from 21 primary care clinics in Washington and Idaho to examine differences in service utilization and clinical outcomes for diabetes, hypertension, and obesity in patients with and without a documented SUD diagnosis. Differences between patients with and without documented SUD diagnoses were compared over a three-year window for clinical outcome measures, including hemoglobin A1c, systolic and diastolic blood pressure, and body mass index, as well as service outcome measures, including number of encounters with primary care and co-located behavioral health providers, and orders for prescription opioids. Adult patients (N = 10,175) diagnosed with diabetes, hypertension, or obesity before the end of 2014, and who had ≥2 visits across a three-year window including at least one visit in 2014 (baseline) and at least one visit occurring 12 months or longer after the 2014 visit (follow-up) were examined.
Results
Patients with SUD diagnoses and co-occurring chronic conditions were seen by providers more frequently than patients without SUD diagnoses (p's < 0.05), and patients with SUD diagnoses were more likely to be prescribed opioid medications. Chronic condition outcomes were no different for patients with versus without SUD diagnoses.
Discussion
Despite the higher visit rates to providers in primary care, a majority of patients with SUD diagnoses and chronic medical conditions in primary care did not get seen by co-located behavioral health providers, who can potentially provide and support evidence informed care for both SUD and chronic conditions. Patients with chronic medical conditions also were more likely to get prescribed opioids if they had an SUD diagnosis. Care pathway innovations for SUDs that include greater utilization of evidence-informed co-treatment of SUDs and chronic conditions within primary care settings may be necessary for improving care overall for patients with comorbid SUDs and chronic conditions.
... For instance, the Data Query, Extraction, Standardization, Translation (Data QUEST) and Cross-Institutional Clinical Translational Research (CICTR) projects utilize a federated model in which local partners store their own data and approve each data extraction. These health data platforms provide a socio-technical approach to providing data sharing infrastructure for demographic and medical visit data [55]. Accountability Act (HIPAA), institutional requirements and sectorbased ethical concerns. ...
Data too sensitive to be "open" for analysis and re-purposing typically remains "closed" as proprietary information. This dichotomy undermines efforts to make algorithmic systems more fair, transparent, and accountable. Access to proprietary data in particular is needed by government agencies to enforce policy, researchers to evaluate methods, and the public to hold agencies accountable; all of these needs must be met while preserving individual privacy and firm competitiveness. In this paper, we describe an integrated legal-technical approach provided by a third-party public-private data trust designed to balance these competing interests. Basic membership allows firms and agencies to enable low-risk access to data for compliance reporting and core methods research, while modular data sharing agreements support a wide array of projects and use cases. Unless specifically stated otherwise in an agreement, all data access is initially provided to end users through customized synthetic datasets that offer a) strong privacy guarantees, b) removal of signals that could expose competitive advantage, and c) removal of biases that could reinforce discriminatory policies, all while maintaining fidelity to the original data. We find that using synthetic data in conjunction with strong legal protections over raw data strikes a balance between transparency, proprietorship, privacy, and research objectives. This legal-technical framework can form the basis for data trusts in a variety of contexts.
... Conducting and advancing biomedical research has long been and remains an essential part of the mission of academic health centers (AHCs). Recent and ongoing initiatives focused on accelerating clinical, translational, and foundational biomedical research have led AHCs to invest in infrastructure and capabilities that enable and support research activities which are increasingly information and data management-intensive [1][2][3]. Research informatics and information technology (research IT) encompasses technological, human and organizational resources, systems and methods that manage and analyze data, information and knowledge to improve biomedical and health research [4]. The success and growth of institutions' research enterprise relies upon advanced research IT solutions and capabilities, with investments in such infrastructure steadily rising [5,6]. ...
This paper proposes the creation and application of maturity models to guide institutional strategic investment in research informatics and information technology (research IT) and to provide the ability to measure readiness for clinical and research infrastructure as well as sustainability of expertise. Conducting effective and efficient research in health science increasingly relies upon robust research IT systems and capabilities. Academic health centers are increasing investments in health IT systems to address operational pressures, including rapidly growing data, technological advances, and increasing security and regulatory challenges associated with data access requirements. Current approaches for planning and investment in research IT infrastructure vary across institutions and lack comparable guidance for evaluating investments, resulting in inconsistent approaches to research IT implementation across peer academic health centers as well as uncertainty in linking research IT investments to institutional goals. Maturity models address these issues through coupling the assessment of current organizational state with readiness for deployment of potential research IT investment, which can inform leadership strategy. Pilot work in maturity model development has ranged from using them as a catalyst for engaging medical school IT leaders in planning at a single institution to developing initial maturity indices that have been applied and refined across peer medical schools.
... We describe below the tool architecture that encompasses the workflow, data model, and execution. To demonstrate DQ e -v's functionality, we use data from the WWAMI region Practice and Research Network's Data QUEST EHR data warehouse 33 to present examples of the tool's outputs. ...
... The outputs we use here are for illustration purposes and were generated for different conditions and time units using data from Data QUEST. 33 Our interpretations of the output may not be exactly extended to the outputs and interpretations of other tabs because they are case specific to different queries from the Data QUEST data. ...
Data variability is a commonly observed phenomenon in Electronic Health Records (EHR) data networks. A common question asked in scientific investigations of EHR data is whether the cross-site and -time variability reflects an underlying data quality error at one or more contributing sites versus actual differences driven by various idiosyncrasies in the healthcare settings. Although research analysts and data scientists have commonly used various statistical methods to detect and account for variability in analytic datasets, self service tools to facilitate exploring cross-organizational variability in EHR data warehouses are lacking and could benefit from meaningful data visualizations. DQe-v, an interactive, database-agnostic tool for visually exploring variability in EHR data provides such a solution. DQe-v is built on an open source platform, R statistical software, with annotated scripts and a readme document that makes it fully reproducible. To illustrate and describe functionality of DQe-v, we describe the DQe-v’s readme document which includes a complete guide to installation, running the program, and interpretation of the outputs. We also provide annotated R scripts and an example dataset as supplemental materials. DQe-v offers a self service tool to visually explore data variability within EHR datasets irrespective of the data model. GitHub and CIELO offer hosting and distribution of the tool and can facilitate collaboration across any interested community of users as we target improving usability, efficiency, and interoperability.
Background:
Most people with alcohol or opioid use disorders (AUD or OUD) are not diagnosed or treated for these conditions in primary care. This study takes a critical step toward quantifying service gaps and directing improvement efforts for AUD and OUD by using electronic health record (EHR) data from diverse primary care organizations to quantify the extent to which AUD and OUD are underdiagnosed and undertreated in primary care practices.
Methods:
We extracted and integrated diagnosis, medication, and behavioral health visit data from the EHRs of 21 primary care clinics within four independent healthcare organizations representing community health centers and rural hospital-associated clinics in the Pacific Northwest United States. Rates of documented AUD and OUD diagnoses, pharmacological treatments, and behavioral health visits were evaluated over a two-year period (2015-2016).
Results:
Out of 47,502 adult primary care patients, 1476 (3.1%) had documented AUD; of these, 115 (7.8%) had orders for AUD medications and 271 (18.4%) had at least one documented visit with a non-physician behavioral health specialist. Only 402 (0.8%) patients had documented OUD, and of these, 107 (26.6%) received OUD medications and 119 (29.6%) had at least one documented visit with a non-physician behavioral health specialist. Rates of AUD diagnosis and AUD and OUD medications were higher in clinics that had co-located non-physician behavioral health specialists.
Conclusions:
AUD and OUD are underdiagnosed and undertreated within a sample of independent primary care organizations serving mostly rural patients. Primary care organizations likely need service models, technologies, and workforces, including non-physician behavioral health specialists, to improve capacities to diagnose and treat AUD and OUD.
