6 research outputs found
Recommended from our members
CHCi - A Dynamic Data Platform for Clinical Data Capture and Use.
All academic medical centers have a strong desire to maximize the value of their clinical data for secondary use purposes such as quality improvement (QI) and research. However, this need has not been adequately fulfilled due in part to the fact that the data capture functions in current electronic health record systems predominantly focus on clinical documentation and billing, lacking the flexibility to allow the collection of additional data elements critical to QI or research. To address this gap, we designed and developed a dynamic data platform to support clinicians' varied needs for recording additional data about their patients outside of direct patient care (e.g. classifying patient conditions based on the inclusion criteria of a research-oriented patient registry). In this paper, we describe the design considerations of this platform such as data models, query functions, coding and controlled vocabulary, user interface design, access control, and data interoperability. In developing the platform, we partnered with the frontline clinicians in an academic congenital heart canter, and adopted the agile software development approach with numerous rounds of evaluation and iterative refinement. Since 2013, this platform has been successfully used to meet the dynamic QI and research data needs of clinicians in the congenital heart center. Future work includes improving the efficiency and effectiveness of the platform and incorporating cutting-edge data interoperability standards
Recommended from our members
CHCi - A Dynamic Data Platform for Clinical Data Capture and Use.
All academic medical centers have a strong desire to maximize the value of their clinical data for secondary use purposes such as quality improvement (QI) and research. However, this need has not been adequately fulfilled due in part to the fact that the data capture functions in current electronic health record systems predominantly focus on clinical documentation and billing, lacking the flexibility to allow the collection of additional data elements critical to QI or research. To address this gap, we designed and developed a dynamic data platform to support clinicians' varied needs for recording additional data about their patients outside of direct patient care (e.g. classifying patient conditions based on the inclusion criteria of a research-oriented patient registry). In this paper, we describe the design considerations of this platform such as data models, query functions, coding and controlled vocabulary, user interface design, access control, and data interoperability. In developing the platform, we partnered with the frontline clinicians in an academic congenital heart canter, and adopted the agile software development approach with numerous rounds of evaluation and iterative refinement. Since 2013, this platform has been successfully used to meet the dynamic QI and research data needs of clinicians in the congenital heart center. Future work includes improving the efficiency and effectiveness of the platform and incorporating cutting-edge data interoperability standards
Recommended from our members
Using EHR audit trail logs to analyze clinical workflow: A case study from community-based ambulatory clinics.
To develop a workflow-supported clinical documentation system, it is a critical first step to understand clinical workflow. While Time and Motion studies has been regarded as the gold standard of workflow analysis, this method can be resource consuming and its data may be biased due to the cognitive limitation of human observers. In this study, we aimed to evaluate the feasibility and validity of using EHR audit trail logs to analyze clinical workflow. Specifically, we compared three known workflow changes from our previous study with the corresponding EHR audit trail logs of the study participants. The results showed that EHR audit trail logs can be a valid source for clinical workflow analysis, and can provide an objective view of clinicians' behaviors, multi-dimensional comparisons, and a highly extensible analysis framework
Assessing the readability of ClinicalTrials.gov
Objective ClinicalTrials.gov serves critical functions of disseminating trial information to the public and helping the trials recruit participants. This study assessed the readability of trial descriptions at ClinicalTrials.gov using multiple quantitative measures. Materials and Methods The analysis included all 165 988 trials registered at ClinicalTrials.gov as of April 30, 2014. To obtain benchmarks, the authors also analyzed 2 other medical corpora: (1) all 955 Health Topics articles from MedlinePlus and (2) a random sample of 100 000 clinician notes retrieved from an electronic health records system intended for conveying internal communication among medical professionals. The authors characterized each of the corpora using 4 surface metrics, and then applied 5 different scoring algorithms to assess their readability. The authors hypothesized that clinician notes would be most difficult to read, followed by trial descriptions and MedlinePlus Health Topics articles. Results Trial descriptions have the longest average sentence length (26.1 words) across all corpora; 65% of their words used are not covered by a basic medical English dictionary. In comparison, average sentence length of MedlinePlus Health Topics articles is 61% shorter, vocabulary size is 95% smaller, and dictionary coverage is 46% higher. All 5 scoring algorithms consistently rated CliniclTrials.gov trial descriptions the most difficult corpus to read, even harder than clinician notes. On average, it requires 18 years of education to properly understand these trial descriptions according to the results generated by the readability assessment algorithms. Discussion and Conclusion Trial descriptions at CliniclTrials.gov are extremely difficult to read. Significant work is warranted to improve their readability in order to achieve CliniclTrials.gov’s goal of facilitating information dissemination and subject recruitment