Use of Graphical Statistical Process Control Tools to Monitor and Improve Outcomes in Cardiac Surgery

Smith, Ian; Garlick, Bruce; Gardner, Martin; Brighouse, Russell; Foster, Kelley A.; Rivers, John

doi:10.1016/j.hlc.2012.08.060

Cited by 10 publications

(8 citation statements)

References 35 publications

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“…In their work, they highlighted need of frequent and continuous data collection on different indicators via patient experience surveys. In other health settings, SPC has been used for monitoring mortality [3,4], the frequency of health care acquired infection [5] and [6,7], and a number of process measures including readmission rates, lengths of stay, or bed days [8,9].…”

Section: Introductionmentioning

confidence: 99%

Shewhart-Type Charts for Masked Data: A Strategy for Handling the Privacy Issue

Shah

Hussain²,

Riaz

et al. 2020

Mathematical Problems in Engineering

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Data privacy is a serious issue and therefore needs our attention. In this study, we propose masking through randomized response techniques (RRTs) to ensure the privacy and thus to avoid falsification. We assume that the process characteristic is of sensitive nature, and due to privacy issue, the actual measurements cannot be shared with the monitoring team. In such situations, the producer is very likely to falsify the measurements. Consequently, the usual control charting techniques will mislead about the process status. We discuss different data masking strategies to be used with Shewhart-type control charts. The usual Shewhart-type control chart appears to be a subchart of the proposed charts. Average run length (ARL) is used as a performance measure of the study proposals. We have evaluated the performance of the proposed charts for different shift sizes and under different intensities of masking. We have also carried out a comparative analysis for various models under varying sensitivity parameters. We have also compared the performance of the proposals with the traditional Shewhart chart. It is observed that the B-L control chart under the RRT model performs better for smaller shifts and for larger shift sizes, the G-B chart under an unrelated question model tperforms better. A real-life application of the study proposal is also considered where monitoring of thickness of paint on refrigerators is of interest.

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Section: Introductionmentioning

confidence: 99%

Shewhart-Type Charts for Masked Data: A Strategy for Handling the Privacy Issue

Shah

Hussain²,

Riaz

et al. 2020

Mathematical Problems in Engineering

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“…Statistical Process Monitoring (SPM) as a means to monitor performance has become a popular method in the health care sector, for example in the monitoring of surgical outcomes or clinical performance [1][2][3][4][5]. Using SPM and in particular the cumulative sum (CUSUM) method applied here, it is for example possible to decide whether a recent change in personnel or hospital organisation has lead to a decline in quality or whether, on the contrary, the new surgical team or hospital reorganisation has improved the quality of care in the hospital.…”

Section: Introductionmentioning

confidence: 99%

Statistical process monitoring to improve quality assurance of inpatient care

Hubig

Lack

Mansmann

2020

BMC Health Serv Res

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Background: Statistical Process Monitoring (SPM) is not typically used in traditional quality assurance of inpatient care. While SPM allows a rapid detection of performance deficits, SPM results strongly depend on characteristics of the evaluated process. When using SPM to monitor inpatient care, in particular the hospital risk profile, hospital volume and properties of each monitored performance indicator (e.g. baseline failure probability) influence the results and must be taken into account to ensure a fair process evaluation. Here we study the use of CUSUM charts constructed for a predefined false alarm probability within a single process, i.e. a given hospital and performance indicator. We furthermore assess different monitoring schemes based on the resulting CUSUM chart and their dependence on the process characteristics. Methods: We conduct simulation studies in order to investigate alarm characteristics of the Bernoulli log-likelihood CUSUM chart for crude and risk-adjusted performance indicators, and illustrate CUSUM charts on performance data from the external quality assurance of hospitals in Bavaria, Germany. Results: Simulating CUSUM control limits for a false alarm probability allows to control the number of false alarms across different conditions and monitoring schemes. We gained better understanding of the effect of different factors on the alarm rates of CUSUM charts. We propose using simulations to assess the performance of implemented CUSUM charts. Conclusions: The presented results and example demonstrate the application of CUSUM charts for fair performance evaluation of inpatient care. We propose the simulation of CUSUM control limits while taking into account hospital and process characteristics.

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“…The importance of SPC in the healthcare perspective has been addressed by different authors; for example, [1] proposed control chart for the infection monitoring, and [3,5,6] discussed the applications of the various control charts in healthcare improvement. Noyez [7] proposed the cumulative sum control (CUSUM) charts for the improved monitoring of health-related issues; Woodall [3] used the control chart for the monitoring of public health issues; control charts for the monitoring of the cardiac results were applied by [7,8]; reference [9] used the control chart tools for healthcare regulation; and Steiner et al [1] evaluated the surgical performance using risk-adjusted cumulative sum control charts. SPC applications in healthcare are focused in several books, for example, references [10][11][12][13][14][15]; and methodological interpretation for attribute data and comparison of control charts for monitoring clinical performance using binary data are given by [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…The exponentially weighted moving average (EWMA) control charts are more "sensitive" to detect a small shift in the process, (see [2]). The EWMA control charts have had many applications in healthcare: in cardiac surgery [8]; reference [20] used an EWMA chart for the early detection of abnormal patient arrivals at hospital emergency department; reference [21] developed the repetitive sampling plan; worked on the modification of the EWMA chart [22]; reference [23] worked on X-bar chart using the repetitive sampling; reference [24] designed t-chart using the repetitive sampling; reference [25] worked on sign chart using the repetitive sampling. The details about repetitive group sampling plans can be seen in [20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

EWMA Control Chart Using Repetitive Sampling for Monitoring Blood Glucose Levels in Type-II Diabetes Patients

2019

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In this paper, we discuss the application of the exponentially weighted moving average (EWMA) control chart for the monitoring blood glucose in type-II diabetes patients. We present tables for the practical use in healthcare. From the simulation results and a real example, the efficiency of the proposed chart in detecting a shift in diabetic level is compared with the existing chart. It is found that the proposed chart provides a strict method to monitor the diabetic levels in diabetes patients. From the simulation results and a real example, it is concluded that the use of the proposed chart in health care issues may reduce the risk of heart disease by monitoring diabetic levels in an effective way.

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Use of Graphical Statistical Process Control Tools to Monitor and Improve Outcomes in Cardiac Surgery

Cited by 10 publications

References 35 publications

Shewhart-Type Charts for Masked Data: A Strategy for Handling the Privacy Issue

Shewhart-Type Charts for Masked Data: A Strategy for Handling the Privacy Issue

Statistical process monitoring to improve quality assurance of inpatient care

EWMA Control Chart Using Repetitive Sampling for Monitoring Blood Glucose Levels in Type-II Diabetes Patients

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