Use of a Surgical Debriefing Checklist to Achieve Higher Value Health Care

Efforts to improve surgical care by using checklists have been inconsistent in results and not reproducible at scale. The ideal manner for using checklists, along with the time horizon for achieving meaningful and measurable benefits, has been unclear. This article describes a novel process for utilizing debriefing checklists to improve value in surgical care. Debriefings of 54 003 consecutive surgical cases and subsequent analysis of 4523 defects in care by multidisciplinary teams led to rapid-cycle iterative changes in care design and processes. Four dimensions of health care value were achieved: debrief-driven improvements reduced the proportion of surgical cases with reported defects, was associated with a significant reduction in the 30-day unadjusted surgical mortality, lowered costs by substantial gains in efficiency and productivity, and led to a better workforce safety climate. Meaningful and sustained improvements required consistent broad-based teamwork over multiple years, an evidence-based data-driven approach, and senior leader and governance engagement.

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Multi-dimensional register classification using bigrams

International Journal of Corpus Linguistics ◽

10.1075/ijcl.12.4.02cro ◽

2007 ◽

Vol 12 (4) ◽

pp. 453-478 ◽

Cited By ~ 13

Author(s):

Scott Crossley ◽

Max M. Louwerse

Keyword(s):

Factor Analysis ◽

Linguistic Analysis ◽

Data Driven ◽

Four Dimensions ◽

Corpus Linguistic ◽

Data Driven Approach ◽

Spatial Discourse

A corpus linguistic analysis investigated register classification using frequency of bigrams in nine spoken and two written corpora. Four dimensions emerged from a factor analysis using bigram frequencies shared across corpora: (1) Scripted vs. Unscripted Discourse, (2) Deliberate vs. Unplanned Discourse, (3) Spatial vs. Non-Spatial Discourse, and (4) Directional vs. Non-Directional Discourse. These findings were replicated in a second analysis. Both analyses demonstrate the strength of bigrams for classifying spoken and written registers, especially in locating distinct collocations among spoken corpora, as well as revealing syntactic and discourse features through a data-driven approach.

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Observational Study Using the Tools of Lean Six Sigma to Improve the Efficiency of the Resident Rounding Process

Journal of Graduate Medical Education ◽

10.4300/jgme-d-10-00116.1 ◽

2011 ◽

Vol 3 (2) ◽

pp. 144-150 ◽

Cited By ~ 23

Author(s):

David V. Chand

Keyword(s):

Health Care ◽

Observational Study ◽

Survey Data ◽

Six Sigma ◽

Care Delivery ◽

Quality Care ◽

Lean Six Sigma ◽

Data Driven ◽

Data Driven Approach ◽

Family Centered

Abstract Background Recent focus on resident work hours has challenged residency programs to modify their curricula to meet established duty hour restrictions and fulfill their mission to develop the next generation of clinicians. Simultaneously, health care systems strive to deliver efficient, high-quality care to patients and families. The primary goal of this observational study was to use a data-driven approach to eliminate examples of waste and variation identified in resident rounding using Lean Six Sigma methodology. A secondary goal was to improve the efficiency of the rounding process, as measured by the reduction in nonvalue-added time. Methods We used the “DMAIC” methodology: define, measure, analyze, improve, and control. Pediatric and family medicine residents rotating on the pediatric hospitalist team participated in the observation phase. Residents, nurses, hospitalists, and parents of patients completed surveys to gauge their attitudes toward rounds. The Mann-Whitney test was used to test for differences in the median times measured during the preimprovement and postimprovement phases, and the Student t test was used for comparison of survey data. Results and Discussion Collaborative, family-centered rounding with elimination of the “prerounding” process, as well as standard work instructions and pacing the process to meet customer demand (takt time), were implemented. Nonvalue-added time per patient was reduced by 64% (P = .005). Survey data suggested that team members preferred the collaborative, family-centered approach to the traditional model of rounding. Conclusions Lean Six Sigma provides tools, a philosophy, and a structured, data-driven approach to address a problem. In our case this facilitated an effort to adhere to duty hour restrictions while promoting education and quality care. Such approaches will become increasingly useful as health care delivery and education continue to transform.

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Digital Twin Technology: Revolutionary to improve personalized healthcare

Science Progress and Research ◽

10.52152/spr.2020.01.104 ◽

2020 ◽

Vol 1 (1) ◽

Author(s):

Harita Garg

Keyword(s):

Health Care ◽

Human Enhancement ◽

Data Driven ◽

Societal Implications ◽

Preventative Care ◽

Ethical Implications ◽

Health Care Practices ◽

Digital Twins ◽

The Status ◽

Data Driven Approach

Personalized medicine uses fine grained information on individual persons, to pinpoint deviations from the normal. ‘Digital Twins’ in engineering provide a conceptual framework to analyze these emerging data-driven health care practices, as well as their conceptual and ethical implications for therapy, preventative care and human enhancement. Digital Twins stand for a specific engineering paradigm, where individual physical artifacts are paired with digital models that dynamically reflects the status of those artifacts. Moral distinctions namely may be based on patterns found in these data and the meanings that are grafted on these patterns. Ethical and societal implications of Digital Twins are explored. Digital Twins imply a data-driven approach to health care. This approach has the potential to deliver significant societal benefits, and can function as a social equalizer, by allowing for effective equalizing enhancement interventions

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