A Case Study Perspective for Balanced Perioperative Workflow Achievement through Data-Driven Process Improvement

2020 ◽  
pp. 45-70
Author(s):  
Jim Ryan ◽  
Barbara Doster ◽  
Sandra Daily ◽  
Carmen Lewis
Keyword(s):  
Process Improvement ◽  
Operations Management ◽  
Management Practices ◽  
Medical Center ◽  
Academic Medical Center ◽  
Data Driven ◽  
Process Reengineering ◽  
Academic Medical ◽  
Complex Relationships

Based on a 143-month longitudinal study of an academic medical center, this paper examines operations management practices of continuous improvement, workflow balancing, benchmarking, and process reengineering within a hospital's perioperative operations. Specifically, this paper highlights data-driven efforts within perioperative sub-processes to balance overall patient workflow by eliminating bottlenecks, delays, and inefficiencies. This paper illustrates how dynamic technological activities of analysis, evaluation, and synthesis applied to internal and external organizational data can highlight complex relationships within integrated processes to identify process limitations and potential process capabilities, ultimately yielding balanced patient workflow through data-driven perioperative process improvement. Study implications and/or limitations are also included.

A Case Study Perspective for Balanced Perioperative Workflow Achievement through Data-Driven Process Improvement

2016 ◽  
Vol 11 (3) ◽  
pp. 19-41 ◽  
Author(s):  
Jim Ryan ◽  
Barbara Doster ◽  
Sandra Daily ◽  
Carmen Lewis
Keyword(s):  
Process Improvement ◽  
Operations Management ◽  
Management Practices ◽  
Medical Center ◽  
Academic Medical Center ◽  
Data Driven ◽  
Process Reengineering ◽  
Academic Medical ◽  
Complex Relationships

Based on a 143-month longitudinal study of an academic medical center, this paper examines operations management practices of continuous improvement, workflow balancing, benchmarking, and process reengineering within a hospital's perioperative operations. Specifically, this paper highlights data-driven efforts within perioperative sub-processes to balance overall patient workflow by eliminating bottlenecks, delays, and inefficiencies. This paper illustrates how dynamic technological activities of analysis, evaluation, and synthesis applied to internal and external organizational data can highlight complex relationships within integrated processes to identify process limitations and potential process capabilities, ultimately yielding balanced patient workflow through data-driven perioperative process improvement. Study implications and/or limitations are also included.

Aligning the Perioperative Process to Hospital Strategy

2018 ◽  
pp. 33-57
Author(s):  
Jim Ryan ◽  
Barbara Doster ◽  
Sandra Daily ◽  
Carmen C. Lewis
Keyword(s):  
Process Management ◽  
Management Practices ◽  
Medical Center ◽  
Academic Medical Center ◽  
Business Analytics ◽  
Balanced Scorecards ◽  
Workflow Efficiency ◽  
Complex Relationships ◽  
Hospital Strategy

This chapter identifies how dynamic technological activities of analysis, evaluation, and synthesis, applied to internal and external organizational data, can highlight complex relationships within integrated hospital processes to target opportunity for improvement and ultimately yield improved capabilities aligned to hospital strategy. This case study examines process management practices of balanced scorecards and dashboards to monitor, improve, and align the perioperative process to overall hospital goals at strategic, tactical, and operational levels. Based on a 168-month longitudinal study of a 1,157 registered-bed academic medical center, this case study investigates the impact of integrated hospital information systems and business analytics to improve perioperative workflow efficiency, patient care perspective, stakeholder satisfaction, clinical operations, and financial cost effectiveness. The conclusion includes discussion of study implications and limitations.

scholarly journals Conversion of Provider EMR Training from Instructor-Led Training to eLearning at an Academic Medical Center

2017 ◽  
Vol 08 (03) ◽  
pp. 754-762
Author(s):  
Karen Sharp ◽  
Michele Williams ◽  
Adrienne Bogacz ◽  
Sighle Denier ◽  
Ann McAlearney ◽  
...  
Keyword(s):  
Medical Record ◽  
Medical Center ◽  
Academic Medical Center ◽  
End User ◽  
Provider Training ◽  
Step Process ◽  
Training Tool ◽  
Academic Medical ◽  
Training Approach

SummaryThis case study overviews the conversion of provider training of the electronic medical record (EMR) from an instructor-led training (ILT) program to eLearning at an Academic Medical Center (AMC). This conversion provided us with both a useful training tool and the opportunity to maximize efficiency within both our training and optimization team and organization. eLearning Development Principles were created and served as a guide to assist us with designing an eLearning curriculum using a five step process. The result was a new training approach that allowed learners to complete training at their own pace, and even test out of sections based on demonstrated competency. The information we have leads us to believe that a substantial return on our investment can be obtained from the conversion with positive impacts that have served as the foundation for the future of end user EMR training at our AMC.Citation: Sharp K, Williams M, Aldrich A, Bogacz A, Denier S, McAlearney AS. Conversion of Provider EMR Training from Instructor Led Training to eLearning at an Academic Medical Center. Appl Clin Inform 2017; 8: 754–762 https://doi.org/10.4338/ACI-2017-03-CR-0040

scholarly journals Two Data-Driven Approaches to Identifying the Spectrum of Problematic Opioid Use: A Pilot Study within a Chronic Pain Cohort

2021 ◽  
Author(s):  
Lori Schirle ◽  
Alvin D Jeffery ◽  
Ali Yaqoob ◽  
Sandra Sanchez-Roige ◽  
David C. Samuels
Keyword(s):  
Language Processing ◽  
Medical Center ◽  
Academic Medical Center ◽  
Correlation Coefficients ◽  
Pilot Project ◽  
Data Driven ◽  
Risk Scores ◽  
Opioid Use ◽  
Academic Medical ◽  
Manual Review

Background: Although electronic health records (EHR) have significant potential for the study of opioid use disorders (OUD), detecting OUD in clinical data is challenging. Models using EHR data to predict OUD often rely on case/control classifications focused on extreme opioid use. There is a need to expand this work to characterize the spectrum of problematic opioid use. Methods: Using a large academic medical center database, we developed 2 data-driven methods of OUD detection: (1) a Comorbidity Score developed from a Phenome-Wide Association Study of phenotypes associated with OUD and (2) a Text-based Score using natural language processing to identify OUD-related concepts in clinical notes. We evaluated the performance of both scores against a manual review with correlation coefficients, Wilcoxon rank sum tests, and area-under the receiver operating characteristic curves. Records with the highest Comorbidity and Text-based scores were re-evaluated by manual review to explore discrepancies. Results: Both the Comorbidity and Text-based OUD risk scores were significantly elevated in the patients judged as High Evidence for OUD in the manual review compared to those with No Evidence (p = 1.3E-5 and 1.3E-6, respectively). The risk scores were positively correlated with each other (rho = 0.52, p < 0.001). AUCs for the Comorbidity and Text-based scores were high (0.79 and 0.76, respectively). Follow-up manual review of discrepant findings revealed strengths of data-driven methods over manual review, and opportunities for improvement in risk assessment. Conclusion: Risk scores comprising comorbidities and text offer differing but synergistic insights into characterizing problematic opioid use. This pilot project establishes a foundation for more robust work in the future.

scholarly journals The “Data Visualization Clinic”: a library-led critique workshop for data visualization

2018 ◽  
Vol 106 (4) ◽  
Author(s):  
Fred Willie Zametkin LaPolla ◽  
Denis Rubin
Keyword(s):  
Data Visualization ◽  
Medical Center ◽  
Academic Medical Center ◽  
Service Area ◽  
Constructive Feedback ◽  
Strong Interest ◽  
Academic Medical ◽  
Library Users ◽  
Library Programming

Background: The authors’ main university library and affiliated academic medical center library sought to increase library programming around data visualization, a new service area for both libraries. Additionally, our institution is home to many researchers with a strong interest in data visualization but who are generally working in isolation of one another.Case Presentation: This case study describes an innovative workshop, the “Data Visualization Clinic,” where members of our library’s community bring in data visualization projects such as figures in papers, projects hosted online, and handouts and receive constructive feedback from a group of peers. The authors detail the process of hosting a clinic and the feedback that we received from participants.Conclusions: The “Data Visualization Clinic” offers a viable workshop to leverage expertise of library users and build the library’s reputation as a hub of data visualization services without heavy investment in infrastructure like special monitors or coding skills. That said, it faces the challenge of relying on the participation of the broader community, which is often pressed for time. The event can also serve as an opportunity for researchers who have an interest in data visualization to meet and network.

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