To Assess Pathway Activities in Clinical Data: Lessons Learned from Src

2013 ◽  
Vol 1 (2) ◽  
pp. 98-106
Author(s):  
Xiang H.-F. Zhang ◽  
Bin Zhang ◽  
Shiyu Du
Keyword(s):  
Clinical Data ◽  
Lessons Learned

Novel Data Interface for Evaluating Cardiovascular Outcomes in Women

2011 ◽  
pp. 2094-2113
Author(s):  
Amparo C. Villablanca ◽  
Hassan Baxi ◽  
Kent Anderson
Keyword(s):  
Clinical Data ◽  
Success Factors ◽  
Data Transfer ◽  
Cardiovascular Outcomes ◽  
Lessons Learned ◽  
High Risk Women ◽  
Critical Success ◽  
Visual Basic For Applications ◽  
Extensible Markup ◽  
User Friendly

This chapter discusses critical success factors in the design, implementation, and utility of a new construct and interface for data transfer with broad applicability to clinical data set management. In the context of a data coordinating center for evaluating cardiovascular outcomes in high-risk women, we detail and provide a framework for bridging the gap between extensible markup language (XML) and XML schema definition file (XSD) in order to provide greater accessibility using visual basic for applications (VBA) and Excel. Applications and lessons learned are discussed in light of current challenges to healthcare information technology management and clinical data administration. The authors hope that this approach, as well as the logic utilized and implementation examples, will provide a user-friendly model for data management and relational database design that is replicable, flexible, understandable, and has broad utility to research professionals in healthcare.

scholarly journals Adapting electronic health records-derived phenotypes to claims data: Lessons learned in using limited clinical data for phenotyping

2020 ◽  
Vol 102 ◽  
pp. 103363 ◽  
Author(s):  
Anna Ostropolets ◽  
Christian Reich ◽  
Patrick Ryan ◽  
Ning Shang ◽  
George Hripcsak ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Clinical Data ◽  
Claims Data ◽  
Lessons Learned ◽  
Health Records ◽  
Electronic Health

scholarly journals Regulatory considerations for prospective patient care registries: lessons learned from the National Neurosurgery Quality and Outcomes Database

2013 ◽  
Vol 34 (1) ◽  
pp. E5 ◽  
Author(s):  
Anthony L. Asher ◽  
Matthew J. McGirt ◽  
Steven D. Glassman ◽  
Rachel Groman ◽  
Dan K. Resnick ◽  
...  
Keyword(s):  
Health Care ◽  
Clinical Data ◽  
Human Subjects ◽  
Modern Medicine ◽  
Lessons Learned ◽  
Quality Data ◽  
Clinical Registries ◽  
Restricted Environment ◽  
Current Resource ◽  
Prospective Longitudinal

Clinical registries have emerged in the current resource-restricted environment of modern medicine as useful and logical mechanisms for providing health care stakeholders with high-quality data related to the safety, effectiveness, and value of specific interventions. Temporal and qualitative requirements for data acquisition in the context of clinical registries have rapidly expanded as clinicians and other stakeholders increasingly recognize the central importance of this information to the intelligent transformation of health care processes. Despite the potential of more robust clinical data collection efforts to advance the science of care, certain aspects of these newer systems, particularly the prospective, longitudinal acquisition of clinical data and direct patient contact, represent areas of structural overlap between emerging quality improvement efforts and traditional models of human subjects research. This overlap has profound implications for the design and implementation of modern clinical registries. In this paper, the authors describe the evolution of clinical registries as important tools for advancing the science of practice, and review the existing federal regulations that apply to these systems.

Novel Data Interface for Evaluating Cardiovascular Outcomes in Women

2011 ◽  
pp. 34-53 ◽  
Author(s):  
Amparo C. Villablanca ◽  
Hassan Baxi ◽  
Kent Anderson
Keyword(s):  
Clinical Data ◽  
Success Factors ◽  
Data Transfer ◽  
Cardiovascular Outcomes ◽  
Lessons Learned ◽  
High Risk Women ◽  
Critical Success ◽  
Visual Basic For Applications ◽  
Extensible Markup ◽  
User Friendly

This chapter discusses critical success factors in the design, implementation, and utility of a new construct and interface for data transfer with broad applicability to clinical data set management. In the context of a data coordinating center for evaluating cardiovascular outcomes in high-risk women, we detail and provide a framework for bridging the gap between extensible markup language (XML) and XML schema definition file (XSD) in order to provide greater accessibility using visual basic for applications (VBA) and Excel. Applications and lessons learned are discussed in light of current challenges to healthcare information technology management and clinical data administration. The authors hope that this approach, as well as the logic utilized and implementation examples, will provide a user-friendly model for data management and relational database design that is replicable, flexible, understandable, and has broad utility to research professionals in healthcare.

Towards a definition of daptomycin optimal dose: Lessons learned from experimental and clinical data

2016 ◽  
Vol 47 (1) ◽  
pp. 12-19 ◽  
Author(s):  
Eric Senneville ◽  
Jocelyne Caillon ◽  
Brigitte Calvet ◽  
François Jehl
Keyword(s):  
Clinical Data ◽  
Optimal Dose ◽  
Lessons Learned ◽  
Definition Of

scholarly journals Piloting Electronic Case Reporting for Improved Surveillance of Sexually Transmitted Diseases in Utah

2019 ◽  
Vol 11 (2) ◽  
Author(s):  
Amanda Whipple ◽  
Joseph Jackson ◽  
Joshua Ridderhoff ◽  
Allyn K. Nakashima
Keyword(s):  
Public Health ◽  
Clinical Data ◽  
Surveillance System ◽  
Public Health Surveillance ◽  
Transmitted Disease ◽  
Health Surveillance ◽  
Lessons Learned ◽  
Public Health Informatics ◽  
Sexually Transmitted ◽  
Public Health Surveillance System

Objectives: The Utah Department of Health (UDOH) developed an electronic case reporting (eCR) process to automatically transfer clinical data from a provider to the state health department, with aims of improving sexually transmitted disease (STD) surveillance data quality, decreasing the time spent on STD case investigations, and expanding the process to other diseases and larger healthcare systems.Methods: Reportable Conditions Trigger Codes (RCTC) were placed into the electronic health record (EHR) system at Planned Parenthood Association of Utah (PPAU) to trigger the automatic transfer of clinical data to Utah’s public health surveillance system. Received data were deduplicated, processed, and assigned directly to the public health surveillance system, with minimal manual intervention.Results: Eighteen new data elements, important for STD case investigations, were transferred to cases with eCR. Additionally, the clinical time spent transmitting data was vastly reduced. With the new eCR process more complete and timely data is received by public health. Providers, as well as public health, now spend less time manually transmitting clinical data by fax and/or phone.Discussion: Automated processes are challenging but can be achieved with a robust disease surveillance system, flexible rules engine, skillful programming, on-going analysis, and successful partnerships. The eCR process created for this project can potentially be useful for other conditions outside of STDs.Conclusion: Results of this demonstration project offer an opportunity for readers to learn about eCR and apply lessons learned to improve their existing eCR systems, or future public health informatics initiatives, at any state-level jurisdiction.

scholarly journals Preprocessing structured clinical data for predictive modeling and decision support

2016 ◽  
Vol 07 (04) ◽  
pp. 1135-1153 ◽  
Author(s):  
Mónica Oliveira ◽  
Filipe Janela ◽  
Henrique Martins ◽  
José Ferrão
Keyword(s):  
Decision Support ◽  
Predictive Modeling ◽  
Clinical Data ◽  
Healthcare Delivery ◽  
Data Retrieval ◽  
Lessons Learned ◽  
Research Experience ◽  
Multiple Feature ◽  
Technical Challenges ◽  
Feature Values

SummaryBackground EHR systems have high potential to improve healthcare delivery and management. Although structured EHR data generates information in machine-readable formats, their use for decision support still poses technical challenges for researchers due to the need to preprocess and convert data into a matrix format. During our research, we observed that clinical informatics literature does not provide guidance for researchers on how to build this matrix while avoiding potential pitfalls.Objectives This article aims to provide researchers a roadmap of the main technical challenges of preprocessing structured EHR data and possible strategies to overcome them.Methods Along standard data processing stages – extracting database entries, defining features, processing data, assessing feature values and integrating data elements, within an EDPAI framework –, we identified the main challenges faced by researchers and reflect on how to address those challenges based on lessons learned from our research experience and on best practices from related literature. We highlight the main potential sources of error, present strategies to approach those challenges and discuss implications of these strategies.Results Following the EDPAI framework, researchers face five key challenges: (1) gathering and integrating data, (2) identifying and handling different feature types, (3) combining features to handle redundancy and granularity, (4) addressing data missingness, and (5) handling multiple feature values. Strategies to address these challenges include: crosschecking identifiers for robust data retrieval and integration; applying clinical knowledge in identifying feature types, in addressing redundancy and granularity, and in accommodating multiple feature values; and investigating missing patterns adequately.Conclusions This article contributes to literature by providing a roadmap to inform structured EHR data preprocessing. It may advise researchers on potential pitfalls and implications of methodological decisions in handling structured data, so as to avoid biases and help realize the benefits of the secondary use of EHR data.Citation: Ferrão JC, Oliveira MD, Janela F, Martins HMG. Preprocessing structured clinical data for predictive modeling and decision support – a roadmap to tackle the challenges.

scholarly journals Precision Infection Prevention (PIP) as a New Standard of Practice Within Longitudinal Infection Prevention and Surveillance

2020 ◽  
Vol 41 (S1) ◽  
pp. s449-s450
Author(s):  
Donald Chen ◽  
Moira Quinn ◽  
Rita M. Sussner ◽  
Teresa Rowland ◽  
Georgeta Rinck ◽  
...  
Keyword(s):  
Clinical Data ◽  
Infection Prevention ◽  
Tertiary Care ◽  
Lessons Learned ◽  
Infection Prevention And Control ◽  
Care Hospital ◽  
Sample Collection ◽  
Philips Healthcare ◽  
Standard Of Practice

Background: Infection prevention and control (IPC) workflows are often retrospective and manual. New tools, however, have entered the field to facilitate rapid prospective monitoring of infections in hospitals. Although artificial intelligence (AI)–enabled platforms facilitate timely, on-demand integration of clinical data feeds with pathogen whole-genome sequencing (WGS), a standardized workflow to fully harness the power of such tools is lacking. We report a novel, evidence-based workflow that promotes quicker infection surveillance via AI-assisted clinical and WGS data analysis. The algorithm suggests clusters based on a combination of similar minimum inhibitory concentration (MIC) data, timing of sample collection, and shared location stays between patients. It helps to proactively guide IPC professionals during investigation of infectious outbreaks and surveillance of multidrug-resistant organisms and healthcare-acquired infections. Methods: Our team established a 1-year workgroup comprised of IPC practitioners, clinical experts, and scientists in the field. We held weekly roundtables to study lessons learned in an ongoing surveillance effort at a tertiary care hospital—utilizing Philips IntelliSpace Epidemiology (ISEpi), an AI-powered system—to understand how such a tool can enhance practice. Based on real-time case discussions and evidence from the literature, a workflow guidance tool and checklist were codified. Results: In our workflow, data-informed clusters posed by ISEpi underwent triage and expert follow-up analysis to assess: (1) likelihood of transmission(s); (2) potential vector(s) identity; (3) need to request WGS; and (4) intervention(s) to be pursued, if warranted. In a representative sample (spanning October 17, 2019, to November 7, 2019) of 67 total isolates suggested for inclusion in 19 unique cluster investigations, we determined that 9 investigations merited follow-up. Collectively, these 9 investigations involved 21 patients and required 115 minutes to review in ISEpi and an additional 70 minutes of review outside of ISEpi. After review, 6 investigations were deemed unlikely to represent a transmission; the other 3 had potential to represent transmission for which interventions would be performed. Conclusions: This study offers an important framework for adaptation of existing infection control workflow strategies to leverage the utility of rapidly integrated clinical and WGS data. This workflow can also facilitate time-sensitive decisions regarding sequencing of specific pathogens given the preponderance of available clinical data supporting investigations. In this regard, our work sets a new standard of practice: precision infection prevention (PIP). Ongoing effort is aimed at development of AI-powered capabilities for enterprise-level quality and safety improvement initiatives.Funding: Philips Healthcare provided support for this study.Disclosures: Alan Doty and Juan Jose Carmona report salary from Philips Healthcare.

scholarly journals Engaging patients, clinicians, and the community in a Clinical Data Research Network: Lessons learned from the CAPriCORN CDRN

2019 ◽  
Vol 3 (2) ◽  
pp. e10079 ◽  
Author(s):  
Ellen Tambor ◽  
Madeleine Shalowitz ◽  
Joseph M. Harrington ◽  
Kevin Hull ◽  
Natalie Watson ◽  
...  
Keyword(s):  
Clinical Data ◽  
Lessons Learned ◽  
Research Network
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