A Process Improvement Project Decreases Blood Culture Contamination Rates in the Emergency Room

2012 ◽  
Vol 40 (5) ◽  
pp. e132
Author(s):  
Maria Montero
Keyword(s):  
Blood Culture ◽  
Emergency Room ◽  
Process Improvement ◽  
Improvement Project

Ergonomic Process Improvement for Mapping Risk and Identifying Key Interventions for Eliminating Injuries from Needles and Sharps in the OR

2020 ◽  
Vol 64 (1) ◽  
pp. 716-720
Author(s):  
Pramila Kalaga ◽  
Barbara Wolford ◽  
Matthew Mormino ◽  
Timothy Kingston ◽  
Julie Fedderson ◽  
...  
Keyword(s):  
Risk Factors ◽  
Process Improvement ◽  
Injury Risk ◽  
Best Practice ◽  
Physical Workload ◽  
Physical Factors ◽  
Improvement Project ◽  
Targeted Interventions ◽  
Effective Interventions ◽  
Sharps Injury

The risk of a needle stick or sharps injury in the operating room (OR) is high due to conditions such as minimal physical protective measures, frequent transfer of sharps, and reliance on human attention and skill for injury avoidance. An ergonomic process improvement project was initiated at a large metro teaching hospital to identify ergonomic risk factors for these OR injuries. To maximize the engagement of the front- end users, an ergonomic process improvement (EPI) team was developed, consisting of representatives from participating OR teams, an employee health nurse and two ergonomists. Surveys, observations, and interviews were conducted to quantify injury risk for the OR teams, evaluate barriers to best practice adherence, and identify opportunities for targeted interventions. Risk mapping was completed for the surgeons, surgical techs and OR nurses identifying double gloving and safe passing zone as areas in need of improvement. Through observation and interviews, researchers identified physical factors relating to musculoskeletal pain and cognitive factors leading to distractions as safety risk concerns. The overall success of the EPI was the engagement of the OR teams and surgeons in the process of identifying risk factors and potential opportunities for ergonomic solutions related to cognitive workload, physical workload, teamwork, and work design for injury prevention. The risk factors identified will provide the basis for developing targeted, effective interventions for eliminating injuries from needles and sharps within the OR.

Estimating performance from capabilities in business process improvement

2016 ◽  
Vol 22 (6) ◽  
pp. 1099-1117 ◽  
Author(s):  
Boyd A. Nicholds ◽  
John P.T. Mo
Keyword(s):  
Process Improvement ◽  
Business Process ◽  
Expected Improvement ◽  
Management Decision ◽  
Published Data ◽  
Performance Gain ◽  
Data Set ◽  
Business Process Improvement ◽  
Improvement Project ◽  
Content Type

Purpose The research indicates there is a positive link between the improvement capability of an organisation and the intensity of effort applied to a business process improvement (BPI) project or initiative. While a degree of stochastic variation in applied effort to any particular improvement project may be expected there is a clear need to quantify the causal relationship, to assist management decision, and to enhance the chance of achieving and sustaining the expected improvement targets. The paper aims to discuss these issues. Design/methodology/approach The paper presents a method to obtain the function that estimates the range of applicable effort an organisation can expect to be able to apply based on their current improvement capability. The method used analysed published data as well as regression analysis of new data points obtained from completed process improvement projects. Findings The level of effort available to be applied to a process improvement project can be expressed as a regression function expressing the possible range of achievable BPI performance within 90 per cent confidence limits. Research limitations/implications The data set applied by this research is limited due to constraints during the research project. A more accurate function can be obtained with more industry data. Practical implications When the described function is combined with a separate non-linear function of performance gain vs effort a model of performance gain for a process improvement project as a function of organisational improvement capability is obtained. The probability of success in achieving performance targets may be estimated for a process improvement project. Originality/value The method developed in this research is novel and unique and has the potential to be applied to assessing an organisation’s capability to manage change.

Process improvement project failure: a systematic literature review and future research agenda

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiju Antony ◽  
Bart A. Lameijer ◽  
Hans P. Borgman ◽  
Kevin Linderman
Keyword(s):  
Literature Review ◽  
Process Improvement ◽  
Systematic Literature Review ◽  
Future Research ◽  
Research Directions ◽  
Improvement Project ◽  
Project Failure ◽  
Content Type ◽  
Future Research Directions ◽  
Research Propositions

Purpose Although scholars have considered the success factors of process improvement (PI) projects, limited research has considered the factors that influence failure. The purpose of this paper is to extend the understanding of PI project failure by systematically reviewing the research on generic project failure, and developing research propositions and future research directions specifically for PI projects. Design/methodology/approach A systematic literature review protocol resulted in a total of 97 research papers that are reviewed for contributions on project failure. Findings An inductive category formation process resulted in three categories of findings. The first category are the causes for project failure, the second category is about relatedness between failure factors and the third category is on failure mitigation strategies. For each category, propositions for future research on PI projects specifically are developed. Additional future research directions proposed lay in better understanding PI project failure as it unfolds (i.e. process studies vs cross-sectional), understanding PI project failure from a theoretical perspective and better understanding of PI project failure antecedents. Originality/value This paper takes a multi-disciplinary and project type approach, synthesizes the existing knowledge and reflects upon the developments in the field of research. Propositions and a framework for future research on PI project failure are presented.

scholarly journals Blood culture: improvement project through training and feedback of results and quality criteria

2010 ◽  
Vol 25 (1) ◽  
Author(s):  
Annamaria Calvo ◽  
Enrica Martini ◽  
Claudia Cutrini ◽  
Sandra Savini ◽  
Andrei Zhdan ◽  
...  
Keyword(s):  
Blood Culture ◽  
Quality Criteria ◽  
Improvement Project

scholarly journals Machine Learning, Ethics, and Change Management: A Data-Driven Approach to Improving Hospital Observation Unit Operations

2021 ◽  
Author(s):  
Dessislava Pachamanova ◽  
Vera Tilson ◽  
Keely Dwyer-Matzky
Keyword(s):  
Machine Learning ◽  
Change Management ◽  
Process Improvement ◽  
Operations Management ◽  
Model Performance ◽  
Machine Learning Algorithms ◽  
Business Analytics ◽  
Observation Unit ◽  
Improvement Project ◽  
Data Driven Approach

This case discusses a process improvement project aimed at maximizing the use of hospital capacity as the flu season looms. Dr. Erin Kelly heads the observation unit and turns to predictive models to improve the assignment of patients to her unit. The case covers three major themes: (1) data analytics life cycle and interface of predictive and prescriptive analytics in the context of process improvement, (2) design and ethical application of machine learning models, and (3) effecting organizational change to operationalize the findings of the analysis. Realistic data, R code, and Excel models are provided. The rich context of the case allows for discussing change management in a healthcare organization, analytics problem framing and model mapping, service process capacity analysis and Little’s law, data summaries and visualizations, interpretable machine learning algorithms, evaluations of predictive model performance, algorithmic bias, and dealing with dirty data. The case is appropriate for use in courses in machine learning, business analytics, operations management, and operations research, both at the advanced undergraduate level and at the master’s/MBA level.

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