Health Sciences Libraries Advancing Collaborative Clinical Research Data Management in Universities

Artificial intelligence of imaging and clinical neurological data for predictive, preventive and personalized (P3) medicine for Parkinson Disease: The NeuroArtP3 protocol for a multi-center research study

BACKGROUND

The burden of Parkinson Disease (PD) represents a key public health issue and it is essential to develop innovative and cost-effective approaches to promote sustainable diagnostic and therapeutic interventions. In this perspective the adoption of a P3 (predictive, preventive and personalized) medicine approach seems to be pivotal. The NeuroArtP3 (NET-2018-12366666) is a four-year multi-site project co-funded by the Italian Ministry of Health, bringing together clinical and computational centers operating in the field of neurology, including PD.

OBJECTIVE

The core objectives of the project are: i) to harmonize the collection of data across the participating centers, ii) to structure standardized disease-specific datasets and iii) to advance knowledge on disease's trajectories through machine learning analysis.

METHODS

The 4-years study combines two consecutive research components: i) a multi-center retrospective observational phase; ii) a multi-center prospective observational phase. The retrospective phase aims at collecting data of the patients admitted at the participating clinical centers. Whereas the prospective phase aims at collecting the same variables of the retrospective study in newly diagnosed patients who will be enrolled at the same centers.

RESULTS

The participating clinical centers are the Provincial Health Services (APSS) of Trento (Italy) as the center responsible for the PD study and the IRCCS San Martino Hospital of Genoa (Italy) as the promoter center of the NeuroartP3 project. The computational centers responsible for data analysis are the Bruno Kessler Foundation of Trento (Italy) with TrentinoSalute4.0 -Competence Center for Digital Health of the Province of Trento (Italy) and the LISCOMPlab University of Genoa (Italy).

CONCLUSIONS

The work behind this observational study protocol shows how it is possible and viable to systematize data collection procedures in order to feed research and to advance the implementation of a P3 approach into the clinical practice through the use of AI models.

A Road Map to Creating a High-Quality Clinical Database in Plastic Surgery

BACKGROUND

Detailed in-house databases are a staple of surgical research and a crucial source of data for many studies from which clinical guidelines are built. Despite the importance of generating a clear and thorough developmental design, the literature on database creation and management is limited. In this article, the authors present their stepwise single-institution process of developing a clinical facial fracture database.

METHODS

The authors outline the process of development of a large single-institution clinical pediatric facial fracture database. The authors highlight critical steps from conception, regulatory approval, data safety/integrity, human resource allocation, data collection, quality assurance, and error remediation. The authors recorded patient characteristics, comorbidities, details of the sustained fracture, associated injuries, hospitalization information, treatments, outcomes, and follow-up information on Research Electronic Data Capture. Protocols were created to ensure data quality assurance and control. Error identification analysis was subsequently performed on the database to evaluate the completeness and accuracy of the data.

RESULTS

A total of 4451 records from 3334 patients between 2006 and 2021 were identified and evaluated to generate a clinical database. Overall, there were 259 incorrect entries of 120,177 total entries, yielding a 99.8% completion rate and a 0.216% error rate.

CONCLUSIONS

The quality of clinical research is intrinsically linked to the quality and accuracy of the data collection. Close attention must be paid to quality control at every stage of a database setup. More studies outlining the process of database design are needed to promote transparent, accurate, and replicable research practices.

Prognostic significance of early platelet dynamics in Staphylococcus aureus bacteremia

BACKGROUND

Platelets are recognized as key immune effectors, but they are targets of bacterial virulence factors. In the present study, we aimed to examine the relationship between early platelet dynamics and the outcome of Staphylococcus aureus bacteremia (SAB).

METHOD

Electronic medical records of adult patients hospitalized for SAB between July 2012 and November 2020 were retrospectively reviewed for relevant demographic, laboratory, and clinical data. The outcome endpoints were mortality and microbial persistence.

RESULTS

Among the 811 patients evaluated, 29% experienced thrombocytopenia on Day 1. Platelet count nadir occurred on Days 2-3 following SAB onset, and Day 4 was a determining point of platelet count trajectory and mortality. Mortality risk was 6% or less for those with normal platelet count by Day 4 regardless of whether they experienced thrombocytopenia on Day 1, but the risk increased to 16-21% for those who experienced thrombocytopenia on Day 4 regardless of whether they had normal platelet count on Day 1 or sustained thrombocytopenia. The duration of bacteremia was prolonged by one day (median 3 d vs. 2 d) for those with sustained thrombocytopenia compared to those without.

CONCLUSION

Early platelet dynamics during SAB have prognostic significance and represent an early window for potential platelet-directed therapeutic interventions to improve outcome.

Research data management systems and the organization of universities and research institutes: A systematic literature review

New technological developments, the availability of big data, and the creation of research platforms open a variety of opportunities to generate, store, and analyze research data. To ensure the sustainable handling of research data, the European Commission as well as scientific commissions have recently highlighted the importance of implementing a research data management system (RDMS) in higher education institutes (HEI) which combines technical as well as organizational solutions. A deep understanding of the requirements of research data management (RDM), as well as an overview of the different stakeholders, is a key prerequisite for the implementation of an RDMS. Based on a scientific literature review, the aim of this study is to answer the following research questions: “What organizational factors need to be considered when implementing an RDMS? How do these organizational factors interact with each other and how do they constrain or facilitate the implementation of an RDMS?” The structure of the analysis is built on the four components of Leavitt’s classical model of organizational change: task, structure, technology, and people. The findings reveal that the implementation of RDMS is strongly impacted by the organizational structure, infrastructure, labor culture as well as strategic considerations. Overall, this literature review summarizes different approaches for the implementation of an RDMS. It also identifies areas for future research.

Translational Personas and Hospital Library Services

Academic health centers, CTSA hubs, and hospital libraries experience similar funding challenges and charges to do more with less. In recent years academic health center and hospital librarians have risen to these challenges by examining their service models, and beyond that, examining their patron base and users' needs. To meet the needs of employees, patients, and those who assist patients, hospital librarians can employ the CTS Personas, a project of the Clinical and Translational Science Awards (CTSA) Program National Center for Data to Health. The Persona profiles, which outline the motivations, goals, pain points, wants, and needs of twelve employees and two patients in translational science, provide vital information and insights that can inform everything from designing software tools and educational services, to advertising these services, to designing impactful and collaborative library spaces.

Value-based syncope evaluation and management: Perspectives of health care professionals on readiness, barriers and enablers

BACKGROUND

Syncope is a common condition seen in the emergency department. Given the multitude of etiologies, research exists on the evaluation and management of syncope. Yet, physicians' approach to patients with syncope is variable and often not value based. The 2017 ACC/AHA/HRS Guideline for the Evaluation and Management of Patients with Syncope includes a focus on unnecessary medical testing. However, little research assesses implementation of the guidelines.

METHODS

Mixed methods approach was applied. The targeted provider specialties include emergency medicine, hospital medicine and cardiology. The Evidence-based Practice Attitude Scale-36 and the Organizational Readiness to Change Assessment surveys were distributed to four different hospital sites. We then conducted focus groups and key informant interviews to obtain more information about clinicians' perceptions to guideline-based practice and barriers/facilitators to implementation. Descriptive statistics and bivariate analyses were used for survey analysis. Two-stage coding was used to identify themes with NVivo.

RESULTS

Analysis of surveys revealed that overall attitude toward evidence-based practices was moderate and implementation of new guidelines were seen as a burden, potentially decreasing compliance. There were differences across hospital settings. Five common themes emerged from interviews: uncertainty of a syncope diagnosis, rise of consumerism in health care, communication challenge with patient, provider differences in standardized care, and organizational processes to change.

CONCLUSIONS

Despite recommendations for the use of syncope guidelines, adherence is suboptimal. Overcoming barriers to use will require a paradigm shift. A multifaceted approach and collaborative relationships are needed to adhere to the Guidelines to improve patient care and operational efficiency.

Adapting data management education to support clinical research projects in an academic medical center

Background

Librarians and researchers alike have long identified research data management (RDM) training as a need in biomedical research. Despite the wealth of libraries offering RDM education to their communities, clinical research is an area that has not been targeted. Clinical RDM (CRDM) is seen by its community as an essential part of the research process where established guidelines exist, yet educational initiatives in this area are unknown.

Case Presentation

Leveraging my academic library's experience supporting CRDM through informationist grants and REDCap training in our medical center, I developed a 1.5 hour CRDM workshop. This workshop was designed to use established CRDM guidelines in clinical research and address common questions asked by our community through the library's existing data support program. The workshop was offered to the entire medical center 4 times between November 2017 and July 2018. This case study describes the development, implementation, and evaluation of this workshop.

Conclusions

The 4 workshops were well attended and well received by the medical center community, with 99% stating that they would recommend the class to others and 98% stating that they would use what they learned in their work. Attendees also articulated how they would implement the main competencies they learned from the workshop into their work. For the library, the effort to support CRDM has led to the coordination of a larger institutional collaborative training series to educate researchers on best practices with data, as well as the formation of institution-wide policy groups to address researcher challenges with CRDM, data transfer, and data sharing.