scholarly journals "Smart Forms" in an Electronic Medical Record: Documentation-based Clinical Decision Support to Improve Disease Management

2008 ◽  
Vol 15 (4) ◽  
pp. 513-523 ◽  
Author(s):  
J. L. Schnipper ◽  
J. A. Linder ◽  
M. B. Palchuk ◽  
J. S. Einbinder ◽  
Q. Li ◽  
...  
Keyword(s):  
Decision Support ◽  
Disease Management ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Medical Record Documentation

scholarly journals Impact of an Electronic Medical Record-Based Clinical Decision Support Tool for Dysphagia Screening on Care Quality

Stroke ◽  
2012 ◽  
Vol 43 (12) ◽  
pp. 3399-3401 ◽  
Author(s):  
Kamakshi Lakshminarayan ◽  
Nassir Rostambeigi ◽  
Candace C. Fuller ◽  
James M. Peacock ◽  
Albert W. Tsai
Keyword(s):  
Decision Support ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Care Quality ◽  
Support Tool ◽  
Clinical Decision Support Tool

scholarly journals Guiding Glucose Management Discussions Among Adults With Type 2 Diabetes in General Practice: Development and Pretesting of a Clinical Decision Support Tool Prototype Embedded in an Electronic Medical Record

10.2196/17785 ◽  
2020 ◽  
Vol 4 (9) ◽  
pp. e17785
Author(s):  
Breanne E Kunstler ◽  
John Furler ◽  
Elizabeth Holmes-Truscott ◽  
Hamish McLachlan ◽  
Douglas Boyle ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Decision Support ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Clinical Decision Support ◽  
Pharmacological Treatment ◽  
Design Method ◽  
Clinical Decision ◽  
Treatment Intensification

Background Managing type 2 diabetes (T2D) requires progressive lifestyle changes and, sometimes, pharmacological treatment intensification. General practitioners (GPs) are integral to this process but can find pharmacological treatment intensification challenging because of the complexity of continually emerging treatment options. Objective This study aimed to use a co-design method to develop and pretest a clinical decision support (CDS) tool prototype (GlycASSIST) embedded within an electronic medical record, which uses evidence-based guidelines to provide GPs and people with T2D with recommendations for setting glycated hemoglobin (HbA1c) targets and intensifying treatment together in real time in consultations. Methods The literature on T2D-related CDS tools informed the initial GlycASSIST design. A two-part co-design method was then used. Initial feedback was sought via interviews and focus groups with clinicians (4 GPs, 5 endocrinologists, and 3 diabetes educators) and 6 people with T2D. Following refinements, 8 GPs participated in mock consultations in which they had access to GlycASSIST. Six people with T2D viewed a similar mock consultation. Participants provided feedback on the functionality of GlycASSIST and its role in supporting shared decision making (SDM) and treatment intensification. Results Clinicians and people with T2D believed that GlycASSIST could support SDM (although this was not always observed in the mock consultations) and individualized treatment intensification. They recommended that GlycASSIST includes less information while maintaining relevance and credibility and using graphs and colors to enhance visual appeal. Maintaining clinical autonomy was important to GPs, as they wanted the capacity to override GlycASSIST’s recommendations when appropriate. Clinicians requested easier screen navigation and greater prescribing guidance and capabilities. Conclusions GlycASSIST was perceived to achieve its purpose of facilitating treatment intensification and was acceptable to people with T2D and GPs. The GlycASSIST prototype is being refined based on these findings to prepare for quantitative evaluation.

scholarly journals Electronic medical record phenotyping using the anchor and learn framework

2016 ◽  
Vol 23 (4) ◽  
pp. 731-740 ◽  
Author(s):  
Yoni Halpern ◽  
Steven Horng ◽  
Youngduck Choi ◽  
David Sontag
Keyword(s):  
Emergency Department ◽  
Septic Shock ◽  
Nursing Home ◽  
Decision Support ◽  
Real Time ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

ABSTRACT Background Electronic medical records (EMRs) hold a tremendous amount of information about patients that is relevant to determining the optimal approach to patient care. As medicine becomes increasingly precise, a patient’s electronic medical record phenotype will play an important role in triggering clinical decision support systems that can deliver personalized recommendations in real time. Learning with anchors presents a method of efficiently learning statistically driven phenotypes with minimal manual intervention. Materials and Methods We developed a phenotype library that uses both structured and unstructured data from the EMR to represent patients for real-time clinical decision support. Eight of the phenotypes were evaluated using retrospective EMR data on emergency department patients using a set of prospectively gathered gold standard labels. Results We built a phenotype library with 42 publicly available phenotype definitions. Using information from triage time, the phenotype classifiers have an area under the ROC curve (AUC) of infection 0.89, cancer 0.88, immunosuppressed 0.85, septic shock 0.93, nursing home 0.87, anticoagulated 0.83, cardiac etiology 0.89, and pneumonia 0.90. Using information available at the time of disposition from the emergency department, the AUC values are infection 0.91, cancer 0.95, immunosuppressed 0.90, septic shock 0.97, nursing home 0.91, anticoagulated 0.94, cardiac etiology 0.92, and pneumonia 0.97. Discussion The resulting phenotypes are interpretable and fast to build, and perform comparably to statistically learned phenotypes developed with 5000 manually labeled patients. Conclusion Learning with anchors is an attractive option for building a large public repository of phenotype definitions that can be used for a range of health IT applications, including real-time decision support.

Effect of Clinical-Decision Support on Documentation Compliance in an Electronic Medical Record

2009 ◽  
Vol 114 (2, Part 1) ◽  
pp. 311-317 ◽  
Author(s):  
Shoshana Haberman ◽  
Joseph Feldman ◽  
Zaher O. Merhi ◽  
Glenn Markenson ◽  
Wayne Cohen ◽  
...  
Keyword(s):  
Decision Support ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Clinical Decision Support ◽  
Clinical Decision

scholarly journals Temporal Change in Alert Override Rate with a Minimally Interruptive Clinical Decision Support on a Next-Generation Electronic Medical Record

Medicina ◽  
2020 ◽  
Vol 56 (12) ◽  
pp. 662
Author(s):  
Won Chul Cha ◽  
Weon Jung ◽  
Jaeyong Yu ◽  
Junsang Yoo ◽  
Jinwook Choi
Keyword(s):  
Regression Analysis ◽  
Decision Support ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Clinical Decision Support ◽  
Temporal Change ◽  
Linear Regression Analysis ◽  
Clinical Decision ◽  
Next Generation ◽  
Factors Associated

Background and objectives: The aim of this study is to describe the temporal change in alert override with a minimally interruptive clinical decision support (CDS) on a Next-Generation electronic medical record (EMR) and analyze factors associated with the change. Materials and Methods: The minimally interruptive CDS used in this study was implemented in the hospital in 2016, which was a part of the new next-generation EMR, Data Analytics and Research Window for Integrated kNowledge (DARWIN), which does not generate modals, ‘pop-ups’ but show messages as in-line information. The prescription (medication order) and alerts data from July 2016 to December 2017 were extracted. Piece-wise regression analysis and linear regression analysis was performed to determine the temporal change and factors associated with it. Results: Overall, 2,706,395 alerts and 993 doctors were included in the study. Among doctors, 37.2% were faculty (professors), 17.2% were fellows, and 45.6% trainees (interns and residents). The overall override rate was 61.9%. There was a significant change in an increasing trend at month 12 (p < 0.001). We found doctors’ positions and specialties, along with the number of alerts and medication variability, were significantly associated with the change. Conclusions: In this study, we found a significant temporal change of alert override. We also found factors associated with the change, which had statistical significance.

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