34 Analyzing the Impact of Electronic Charting on Physician Productivity and Charge Capture Using Statistical Process Control: A Pilot Study

2012 ◽  
Vol 60 (4) ◽  
pp. S14 ◽  
Author(s):  
F. Barrueto ◽  
L. Pimentel ◽  
D.J. Hornyak
Keyword(s):  
Pilot Study ◽  
Process Control ◽  
Statistical Process Control ◽  
Statistical Process ◽  
Physician Productivity ◽  
The Impact

scholarly journals The Impact of Statistics in Industry and the Role of Statisticians

2016 ◽  
Vol 30 (1) ◽  
pp. 7-20
Author(s):  
Ronald J.M.M. Does ◽  
Albert Trip
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Six Sigma ◽  
Practical Experience ◽  
Successful Implementation ◽  
Statistical Process ◽  
Quality Program ◽  
Quality Programs ◽  
The Impact

The use of statistics in quality management has a long history. Pioneers in this field, such as Walter A. Shewhart and W. Edwards Deming, refer to themselves as industrial statisticians. Statistical thinking in industry means that all work is regarded as a series of interconnected processes, that all processes show variation, and that a reduction in variation is the key for continuous improvement. In literature we find several quantitative quality programs to achieve this. We may mention Statistical Process Control (SPC)and the Six Sigma quality program, among others. We have implemented Statistical Process Control and Six Sigma in several industries. In this paper we briefly describe the philosophies of both programs and the steps needed for a successful implementation. Based on practical experience with both programs we describe the role that a statistician can play in industry. We shall also give an overview of research initiated by the projects we have carried out.

scholarly journals 1232. Potential Health and Cost Outcomes of Optimized Statistical Process Control Use for Surgical Site Infection Surveillance

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S443-S444
Author(s):  
Nicole Nehls ◽  
Iulian Ilieş ◽  
James C Benneyan ◽  
Arthur W Baker ◽  
Deverick J Anderson
Keyword(s):  
United States ◽  
Pilot Study ◽  
Process Control ◽  
Statistical Process Control ◽  
Healthcare Costs ◽  
Cost Savings ◽  
The United States ◽  
Outbreak Detection ◽  
Statistical Process ◽  
Traditional Surveillance

Abstract Background Surgical site infections (SSIs) are common (160,000–300,000 per year in the United States) and costly ($6,000–$25,500 per event) healthcare-associated infections with potentially lethal outcomes (2.1%–6.7% mortality rate). A prior analysis by our group suggested that statistical process control (SPC) can detect SSI outbreaks earlier than traditional epidemiological surveillance methods. This study aimed to quantify the potential impact of SPC surveillance on patient outcomes (prevented SSIs and deaths) and healthcare costs. Methods We retrospectively analyzed 30 SSI outbreaks occurring over a period of 8 years in a network of 50 community hospitals from the Southeastern United States. We applied 24 control chart variations, including 2 optimized for SSI surveillance, 6 with expert-defined pre-outbreak baselines (used in our pilot study), 4 with lagged rolling baselines (idem), and 12 common practice ones (using rolling baselines with no lag or fixed baselines). The charts used procedure-specific data from either the outbreak hospital or the entire network to compute baseline SSI rates. We calculated the average SSI rates during, before and after the outbreaks, and the months elapsed between SPC and traditional detection. We then used these values to estimate the number of SSIs that could have been prevented by SPC, and corresponding deaths avoided and cost savings (Figure 1). Results Optimized charts detected 96% of the outbreaks earlier than traditional surveillance, while pilot study and common practice charts did so only 65% (58%) of the time (Figure 2). Optimized charts could potentially prevent 15.2 SSIs, 0.64 deaths, and save $226,000 in excess care costs per outbreak. Overall, charts using network baselines performed better than those relying on local hospital data. Commonly used variations were the least effective, but were still able to improve on traditional surveillance (Figure 3). Conclusion SPC methods provide a great opportunity to prevent infections and deaths and generate cost savings, ultimately improving patient safety and care quality. While common practice SPC charts can also speed up outbreak detection, optimized SPC methods have a significantly higher potential to prevent SSIs and reduce healthcare costs. Disclosures All authors: No reported disclosures.

scholarly journals National Health Service (NHS) trust boards adopt statistical process control reporting: the impact of the Making Data Count Training Programme

BMJ Leader ◽  
2021 ◽  
pp. leader-2020-000357
Author(s):  
Samantha Riley ◽  
Anna Burhouse ◽  
Thomas Nicholas
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Training Session ◽  
Board Members ◽  
Statistical Process ◽  
Behavioural Changes ◽  
Performance Reports ◽  
Board Training ◽  
The Impact ◽  
Over Time

BackgroundRed, amber, green (RAG) reports persist as the tool most commonly used by NHS trust boards to understand performance and gain assurance, despite statistical process control (SPC) being a more reliable way of presenting data over time. The aim of this study is to report board members’ feedback on an educational intervention focusing on the use of SPC in NHS trust performance reports, review the presence of SPC charts in performance reports and explore board members’ experience of behavioural changes in their board or fellow board members following the intervention.MethodsA 90-minute board training session in the use of SPC—Making Data Count—was delivered to 61 NHS trust boards between November 2017 and July 2019. This paper describes the approach taken with boards to enable them to understand the limitations of RAG reports and the benefits of using SPC and analyses the extent to which the Making Data Count training has led to boards adopting SPC. The paper provides quantitative analysis of the increase in SPC use across the 61 participating boards, summaries from the board evaluation forms and qualitative reflections of seven senior leaders from four boards who consented to participate in post-training interviews with an independent evaluator.ResultsDuring the period covered by this study, 583 participants of board training provided feedback. 99% of respondents agreed that the training session was a good use of their time. 97% of respondents agreed that participating in the event would enhance their ability to make good decisions. A review of the presence of SPC charts in the board papers of the 61 trusts prior to the board training revealed that 72% contained 0–5 SPC charts. A review of the same trusts’ papers 6–12 months after the training revealed a significant increase in the presence of SPC with 85% of reports containing a minimum of six charts.ConclusionThe Making Data Count education intervention has increased the use of SPC in board reports and has had some self-reported impact on individual and collective behavioural changes by board members, including reducing the amount of time wasted by boards discussing insignificant changes in data and providing a clearer focus on those issues requiring board attention. Further research is required to see if this immediate impact is sustained over time and to identify the key enablers and barriers to organisational adoption of SPC by boards in the NHS.

scholarly journals Performance of statistical process control methods for regional surgical site infection surveillance: a 10-year multicentre pilot study

2017 ◽  
Vol 27 (8) ◽  
pp. 600-610 ◽  
Author(s):  
Arthur W Baker ◽  
Salah Haridy ◽  
Joseph Salem ◽  
Iulian Ilieş ◽  
Awatef O Ergai ◽  
...  
Keyword(s):  
Pilot Study ◽  
Process Control ◽  
Statistical Process Control ◽  
Improve Patient Safety ◽  
Moving Average ◽  
Surgical Site Infections ◽  
Outbreak Detection ◽  
Large Network ◽  
Statistical Process ◽  
Ewma Chart

BackgroundTraditional strategies for surveillance of surgical site infections (SSI) have multiple limitations, including delayed and incomplete outbreak detection. Statistical process control (SPC) methods address these deficiencies by combining longitudinal analysis with graphical presentation of data.MethodsWe performed a pilot study within a large network of community hospitals to evaluate performance of SPC methods for detecting SSI outbreaks. We applied conventional Shewhart and exponentially weighted moving average (EWMA) SPC charts to 10 previously investigated SSI outbreaks that occurred from 2003 to 2013. We compared the results of SPC surveillance to the results of traditional SSI surveillance methods. Then, we analysed the performance of modified SPC charts constructed with different outbreak detection rules, EWMA smoothing factors and baseline SSI rate calculations.ResultsConventional Shewhart and EWMA SPC charts both detected 8 of the 10 SSI outbreaks analysed, in each case prior to the date of traditional detection. Among detected outbreaks, conventional Shewhart chart detection occurred a median of 12 months prior to outbreak onset and 22 months prior to traditional detection. Conventional EWMA chart detection occurred a median of 7months prior to outbreak onset and 14 months prior to traditional detection. Modified Shewhart and EWMA charts additionally detected several outbreaks earlier than conventional SPC charts. Shewhart and SPC charts had low false-positive rates when used to analyse separate control hospital SSI data.ConclusionsOur findings illustrate the potential usefulness and feasibility of real-time SPC surveillance of SSI to rapidly identify outbreaks and improve patient safety. Further study is needed to optimise SPC chart selection and calculation, statistical outbreak detection rules and the process for reacting to signals of potential outbreaks.

scholarly journals Statistical process control part 2: using statistical process control to measure process improvement in patient flow

2009 ◽  
Vol 33 (3) ◽  
pp. 412 ◽  
Author(s):  
Tamara G Chetter
Keyword(s):  
Health Care ◽  
Quality Improvement ◽  
Process Control ◽  
Statistical Process Control ◽  
Process Improvement ◽  
Patient Flow ◽  
Care Process ◽  
Statistical Process ◽  
The Impact ◽  
Control Part

THE FIRST ARTICLE in this series, ?Statistical process control part 1: a primer for using statistical process control in health care process improvement? 1 (in this issue of the Journal), introduced the basic concepts of statistical process control (SPC) and its main tool, the control chart. While this set of techniques was originally developed in the manufacturing sector, there is growing realisation in recent years that SPC (and also other quality improvement techniques, such as Six Sigma and lean thinking) can be successfully applied to health care quality improvement.2 The reason for this is that SPC is a potent and powerful, yet simple tool for tracking, and detecting any variation in, process performance over time; which creates the opportunity for health professionals to promptly respond to any improvement or deterioration in the process. Perhaps the most valuable feature of SPC techniques however, is the ability to place a change in the outcome of a process in close temporal proximity to the redesign and improvement of the process. This means SPC can reliably evaluate the effectiveness of quality improvement initiatives implemented at the front line of health service delivery, despite the complexities of the hospital system and the challenges this often poses for health services research (for example, the inability to use robust research designs). The purpose of this companion article is to therefore demonstrate the practical application of SPC in a health care organisation. Specifically, the technique of control charting was used to track the impact of patient flow process improvement interventions in a public hospital, in the hope that this will exemplify to health care professionals the value and simplicity in applying SPC as part of their daily work.

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