Analyzing pathways data to reduce unwarranted variation, understand practice patterns, and update pathways.

2018 ◽  
Vol 36 (30_suppl) ◽  
pp. 300-300
Author(s):  
Julia Cooper Hall ◽  
Joanna M. Hamilton ◽  
David Michael Jackman ◽  
Carole Kathleen Tremonti ◽  
Teresa L. Greenberg ◽  
...  
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Pathway Analysis ◽  
Practice Patterns ◽  
Clinical Pathways ◽  
Treatment Decisions ◽  
Provider Education ◽  
Early Adoption ◽  
Total Treatment ◽  
Unwarranted Variation

300 Background: A clinical pathways program requires regular updates to pathways content, real-time decision support, and data collection and analysis of decisions made. A goal of Dana-Farber Pathways (DFP) is to analyze data to reduce unwarranted variation and inform granular, warranted variation. Methods: Each of our 31 medical oncology pathways is created and updated by DF experts (clinicians and scientists). DFP is implemented through a web-based portal that provides real-time decision support. DFP captures on- and off- pathway treatment decisions and reasons for off-pathway decisions; this is shared with each disease group monthly. For each interval review meeting we assess on-pathway rates and decisions for each node in the pathway. Low on-pathway rates prompt discussion about possible action, including provider education or pathway modification. Results: In 2017, 7,460 total treatment decisions were collected through DFP; 78% were on-pathway. We have clinical reasoning for 90% of the off-pathway decisions. Off-pathway analysis has been used in several important ways: 1. Catalyzing change: we detected early adoption of immunotherapy in small cell lung cancer and discussed the relevant data. The pathway was updated to adopt this class of treatment. 2. Understanding unexpected events: we identified a recent etoposide shortage and discussed alternate recommendations. They were added to the pathway in case of future shortages. 3. Provider education: we detected a consistently low on-pathway rate in one location, mostly driven by a specific provider. This provided a mechanism to discuss practice patterns and provide targeted education. Conclusions: Off-pathway analysis provides insight into user variation, fosters and supports peer coaching, and supports the creation of dynamic, up-to-date pathways.[Table: see text]

Abstract 13: Real-time Decision Support and Physician Feedback Optimizes Use of Vascular Closure Devices in High Bleed Risk Patients Undergoing Percutaneous Coronary Intervention

2016 ◽  
Vol 9 (suppl_2) ◽  
Author(s):  
Christopher R Han ◽  
Craig E Strauss ◽  
Ross F Garberich ◽  
Ivan J Chavez ◽  
Timothy D Henry ◽  
...  
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Practice Patterns ◽  
Bleeding Complications ◽  
Physician Practice ◽  
Physician Practice Patterns ◽  
Vascular Closure Devices ◽  
Percutaneous Coronary ◽  
Risk Patients ◽  
Individual Provider

Background: Bleeding complications following Percutaneous Coronary Interventions (PCI) occur in 2-6% of cases. Designed to provide rapid hemostasis, vascular closure devices (VCDs) have been found to have the largest benefit in high bleed risk cases but are utilized less often in these patients. Decision support tools and real-time feedback may impact physician practice patterns and increase utilization of VCDs in cases with high bleed risk. Methods: In May 2012, a real-time decision support tool was introduced to prospectively evaluate PCI bleed risk and stratify patients into high, intermediate, and low bleeding risk based on a validated model using data from the National Cardiovascular Data Registry. In January 2014, a group goal of 50% utilization of VCDs in high-bleed risk patients was set and weekly reports to individual interventional cardiologists performing PCI were provided. Group and individual physician practice patterns before and after the goal and real-time feedback were assessed. Results: From May 2012 to August 2015, 5,285 patients received PCI, including 1,399 (26.5%) who were classified as high bleed risk. Prior to the group utilization goal and real-time feedback being implemented, VCD use in high bleed risk patients was 40.3% (292 of 725) and utilization by individual provider ranged from 7.4% (5 of 68) to 83.1% (103 of 124) of cases. After implementation of the group utilization goal and real-time feedback, 74.2% (500 of 674) high bleed risk received VCDs and utilization by individual provider ranged from 61.4% (43 of 70) to 87.9% (87 of 99) of cases. Therefore, the physician group saw a relative increase of 84.1% in VCD utilization (40.3% vs. 74.2%; p<0.001). Additionally, among individual providers, VCD use significantly increased for 7 of 8 physicians (see figure) and the group practice variation in use of VCDs decreased (standard deviation of VCD utilization across physicians decreased from 22.9% to 8.7%, p=0.021). Conclusions: Implementation of real-time feedback and clearly defined group goals for the use of VCDs in high bleed risk cases significantly increased VCD use in patients known to have the greatest benefit. These interventions also reduced the variation in care for high bleed risk patients.

778-P: The Impact of Self-Management Blood Glucose (SMBG) Real-Time Decision Support on Glycemic Control in Adult with T1D in China

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 778-P
Author(s):  
ZIYU LIU ◽  
CHAOFAN WANG ◽  
XUEYING ZHENG ◽  
SIHUI LUO ◽  
DAIZHI YANG ◽  
...  
Keyword(s):  
Decision Support ◽  
Blood Glucose ◽  
Glycemic Control ◽  
Real Time ◽  
Self Management ◽  
The Impact

scholarly journals A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 294
Author(s):  
Nicholas F. McCarthy ◽  
Ali Tohidi ◽  
Yawar Aziz ◽  
Matt Dennie ◽  
Mario Miguel Valero ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Support ◽  
Real Time ◽  
Forest Fires ◽  
Wildland Fire ◽  
Fire Spread ◽  
Low Earth Orbit ◽  
Practical Implementation ◽  
Spatiotemporal Resolution ◽  
Active Fire

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.

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