Patient Safety/Quality Improvement Primer, Part IV: How to Measure and Track Improvements

Patient safety and quality improvement (PS/QI) has become an integral part of the health care system, and the ability to effectively use data to track, understand, and communicate performance is essential to designing and implementing quality initiatives, as well as assessing their impact. Though many otolaryngologists are proficient in the methodologies of traditional research pursuits, educational gaps remain in the foundational principles of PS/QI measurement strategies. Part IV of this PS/QI primer discusses the fundamentals of measurement design and data analysis methods specific to PS/QI. Consideration is given to the selection of appropriate measures when designing a PS/QI project, as well as the method and frequency for collecting these measures. In addition, this primer reviews key aspects of tracking and analyzing data, providing an overview of statistical process control methods while highlighting the construction and utility of run and control charts. Lastly, this article discusses strategies to successfully develop and execute PS/QI initiatives in a way that facilitates the ability to appropriately measure their effectiveness and sustainability.

Multivariate control charts for monitoring a bivariate correlated count process with application to meningococcal disease

In recent years, with the increasing number and complexity of infectious diseases, the idea of using control charts to monitor public health and disease has been proposed. In this paper, we study multivariate control charts for monitoring a bivariate integer-valued autocorrelation process with bivariate Poisson distribution and select the optimal control scheme by comparing the performance of control charts. Furthermore, the meningococcal patient event in two states in Australia serves as an example to illustrate the application of these methods. The results show that the D exponentially weighted moving average control scheme can detect the changes in the mean value faster, which is a significant advantage.

Evaluation of the application of sequence data to the identification of outbreaks of disease using anomaly detection methods

Anomaly detection methods have a great potential to assist the detection of diseases in animal production systems. We used sequence data of Porcine Reproductive and Respiratory Syndrome (PRRS) to define the emergence of new strains at the farm level. We evaluated the performance of 24 anomaly detection methods based on machine learning, regression, time series techniques and control charts to identify outbreaks in time series of new strains and compared the best methods using different time series: PCR positives, PCR requests and laboratory requests. We introduced synthetic outbreaks of different size and calculated the probability of detection of outbreaks (POD), sensitivity (Se), probability of detection of outbreaks in the first week of appearance (POD1w) and background alarm rate (BAR). The use of time series of new strains from sequence data outperformed the other types of data but POD, Se, POD1w were only high when outbreaks were large. The methods based on Long Short-Term Memory (LSTM) and Bayesian approaches presented the best performance. Using anomaly detection methods with sequence data may help to identify the emergency of cases in multiple farms, but more work is required to improve the detection with time series of high variability. Our results suggest a promising application of sequence data for early detection of diseases at a production system level. This may provide a simple way to extract additional value from routine laboratory analysis. Next steps should include validation of this approach in different settings and with different diseases.

Multivariate Curve Resolution Methodology Applied to the ATR-FTIR Data for Adulteration Assessment of Virgin Coconut Oil

Virgin coconut oil (VCO) is a functional food with important health benefits. Its economic interest encourages fraudsters to deliberately adulterate VCO with cheap and low-quality vegetable oils for financial gain, causing health and safety problems for consumers. In this context, there is an urgent need for rapid, accurate, and precise analytical techniques to detect VCO adulteration. In this study, the use of Fourier transform infrared (FTIR) spectroscopy combined with multivariate curve resolution-alternating least squares (MCR-ALS) methodology was evaluated to verify the purity or adulteration of VCO with reference to low-cost commercial oils such as sunflower (SO), maize (MO) and peanut (PO) oils. A two-step analytical procedure was developed, where an initial control chart approach was designed to assess the purity of oil samples using the MCR-ALS score values calculated on a data set of pure and adulterated oils. The pre-treatment of the spectral data by derivatization with the Savitzky-Golay algorithm allowed to obtain the classification limits able to distinguish the pure samples with 100% of correct classifications in the external validation. In the next step, three calibration models were developed using MCR-ALS with correlation constraints for analysis of adulterated coconut oil samples in order to assess the blend composition. Different data pre-treatment strategies were tested to best extract the information contained in the sample fingerprints. The best results were achieved by derivative and standard normal variate procedures obtaining RMSEP and RE% values in the ranges of 1.79-2.66 and 6.48-8.35%, respectively. The models were optimized using a genetic algorithm (GA) to select the most important variables and the final models in the external validations gave satisfactory results in quantifying adulterants, with absolute errors and RMSEP of less than 4.6% and 1.470, respectively.

Monitoring the Weibull shape parameter under progressive censoring in presence of independent competing risks

In this paper, monitoring the Weibull shape parameter arising from progressively censored competing risks data is investigated. The competing risks are assumed to be independent and not identically distributed from the Weibull distributions with different shape and scale parameters. Both the shape parameters can be monitored separately by the proposed control charts using censored and predicted observations. We also introduced a control chart for monitoring both shape parameters simultaneously to detect possible shifts in both opposite and the same directions. In addition, the problem of mask data is discussed and an efficient prediction method is proposed. The behavior of the average run length with and without mask data is investigated through extensive simulations. Furthermore, the effects of sample size, number of failures due to each risk, and censoring scheme on the charts' performance are also studied. Finally, an illustrative example is presented to demonstrate the application of the proposed control charts by investigating a real data set of the failure times of two-component ARC-1 VHF communication transmitter receivers of a single commercial airline. Although this data set has been widely investigated in reliability analysis studies, this is the first time it has been analyzed in a statistical process monitoring setting.

A double generally weighted moving average control chart for monitoring the process variability

In the present article, a double generally weighted moving average (DGWMA) control chart based on a three-parameter logarithmic transformation is proposed for monitoring the process variability, namely the S 2 -DGWMA chart. Monte-Carlo simulations are utilized in order to evaluate the run-length performance of the S 2 -DGWMA chart. In addition, a detailed comparative study is conducted to compare the performance of the S 2 -DGWMA chart with several well-known memory-type control charts in the literature. The comparisons indicate that the proposed one is more efficient in detecting small shifts, while it is more sensitive in identifying upward shifts in the process variability. A real data example is given to present the implementation of the new S 2 -DGWMA chart.

Healthcare Quality Improvement Analytics: An Example Using Computerized Provider Order Entry

Evaluation of sustainability after quality improvement (QI) projects in healthcare settings is an essential part of monitoring and future QI planning. With limitations in adopting quasi-experimental study design in real-world practice, healthcare professionals find it challenging to present the sustained effect of QI changes effectively. To provide quantitative methodological approaches for demonstrating the sustainability of QI projects for healthcare professionals, we conducted data analyses based on a QI project to improve the computerized provider order entry system to reduce patients' dosing frequencies in Korea. Data were collected for 5 years: 24-month pre-intervention, 12-month intervention, and 24-month post-intervention. Then, analytic approaches including control chart, Analysis of Variance (ANOVA), and segmented regression were performed. The control chart intuitively displayed how the outcomes changed over the entire period, and ANOVA was used to test whether the outcomes differed between groups. Last, segmented regression analysis was conducted to evaluate longitudinal effects of interventions over time. We found that the impact of QI projects in healthcare settings should be initiated following the Plan-Do-Study-Act cycle and evaluated long-term effects while widening the scope of QI evaluation with sustainability. This study can serve as a guide for healthcare professionals to use a number of statistical methodologies in their QI evaluations.

Chemometric Discrimination of the Varietal Origin of Extra Virgin Olive Oils: Usefulness of 13C Distortionless Enhancement by Polarization Transfer Pulse Sequence and 1H Nuclear Magnetic Resonance Data and Effectiveness of Fusion with Mid-Infrared Spectroscopy Data

The label authentication of monovarietal extra virgin olives is of great relevance from a socio-economical point of view. This work aims to gain insights into the prediction of the varietal origin of extra virgin olive oil (EVOO) samples obtained from single olive cultivars, French cultivars Olivière, Salonenque, and Tanche and Portuguese cultivars Blanqueta, Carrasquenha, and Galega Vulgar, collected in 2016-2017 and 2017-2018 harvest seasons. To pursue this study, spectroscopic approaches based on one-dimensional nuclear magnetic resonance (1D NMR) spectroscopy, namely, ¹H and ¹³C NMR distortionless enhancement by polarization transfer (DEPT) 45 pulse sequence, and Fourier transform mid-infrared spectroscopy (FT-MIR) are used in combination with partial least square discriminant analysis (PLS1-DA). The results obtained by PLS1-DA models using ¹H and ¹³C NMR DEPT 45 data are compared to those of PLS1-DA models using MIR data. The application of a control chart method allows for the optimization of the interpretation of the PLS1-DA results, and an efficient two-step strategy is proposed to improve the discrimination of the six studied cultivars. Then, NMR and MIR data are combined by either a mid- or high-level data fusion approach to further improve the discrimination. The models are also tested on samples from other cultivars to check their ability to reject varieties that were not considered in the calibration process.

Treatment plan quality control using multivariate control charts

PURPOSE

Statistical process control tools such as control charts were recommended by the American Association of Physicists in Medicine (AAPM) Task Group 218 for radiotherapy quality assurance. However, the tools needed to analyze multivariate, correlated data that are often encountered in treatment plan quality measures, are lacking. In this study, we develop quality control tools that can model multivariate plan quality measures with correlations and account for patient-specific risk factors, without adding a significant burden to clinical workflow.

METHODS AND MATERIALS

A multivariate, quality control chart is developed that includes a risk-adjustment model, Hotelling's T² statistic, and principal component analysis (PCA). Principal component analysis accounts for correlations among a set of organ-at-risk (OAR) dose-volume histogram (DVH) points that serves as proxies for plan quality. Risk-adjustment models estimate the principal components from PCA using a set of patient- and treatment-specific risk factors. The resulting residuals from the risk-adjustment models are used to compute the Hotelling's T² statistic; the corresponding multivariate control chart is then plotted based on the beta distribution followed by the statistic. Further, the box-cox transformation is used to account for non-normality in DVH points. We investigate the application of the proposed methodology via three multivariate control charts - a conventional chart that ignores risk-adjustment and PCA, a risk-adjusted chart ignoring PCA, and a PCA-based, risk-adjusted chart. These control charts are evaluated on 69 head-and-neck cases.

RESULTS

The conventional multivariate control chart fails to account for important patient-specific risk factors, including volumes and cross-sectional areas of the tumor and OARs and distances in-between. This failure leads to a larger number of false alarms. While the multivariate risk-adjusted control chart is able to reduce false alarms, it fails to account for correlations in DVH points. The multivariate PCA-based, risk-adjusted control chart can detect unusual plans after accounting for the correlations. By replanning, improvements are shown on an unusual plan identified by both risk-adjusted methods.

CONCLUSIONS

The multivariate risk-adjusted control chart developed here enables quality control of plans prior to delivery. This methodology is generic and can be readily applied for other radiotherapy quality assurance protocols, such as gamma analysis pass rates.

Control Charting Genomic Data

BACKGROUND

Control charting is routine in the quality assurance of traditional clinical laboratory testing. Genomic tests are not typically managed by control charting. We examined control charting to monitor the performance of a clinical next-generation sequencing (NGS) assay.

METHODS

We retrospectively examined 3 years of control material (NA12878) data from clinical genomic epilepsy testing. Levey-Jennings plots were used to visualize changes in control material depth of sequencing coverage in genomic regions of an epilepsy genomic panel. Changes in depth of coverage were correlated with changes in the manufactured lot of capture probe reagent. Depth of coverage was also correlated between quality control material and clinical samples.

RESULTS

Fifty-seven sequencing runs of NA12878 were analyzed for 1811 genomic regions targeting 108 genes. Manufactured probe lot changes were associated with significant changes in the average coverage of 537 genomic regions and the lowest coverage of 173 regions (using a critical cut-off of P < 5.52 x 10-6). Genomic regions with the highest sensitivity to lot-to-lot variation by average sequencing depth of coverage were not the same regions with the highest sensitivity by lowest sequencing depth of coverage. Levey-Jennings plots displayed differences in genomic depth of coverage across capture probe reagent lot changes. There was moderate correlation between the changes in depth of sequencing across lot changes for control material and clinical cases (r2 = 0.45).

CONCLUSIONS

Genomic control charting can be used routinely by clinical laboratories to monitor assay performance and ensure the quality of testing.

Application of statistical process control for monitoring bulk tank milk somatic cell count of smallholder dairy farms

Background and Aim:

Consistency in producing raw milk with less variation in bulk tank milk somatic cell count (BMSCC) is important for dairy farmers as their profit is highly affected by it in the long run. Statistical process control (SPC) is widely used for monitoring and detecting variations in an industrial process. Published reports on the application of the SPC method to smallholder farm data are very limited. Thus, the purpose of this study was to assess the capability of the SPC method for monitoring the variation of BMSCC levels in milk samples collected from smallholder dairy farms.

Materials and Methods:

Bulk tank milk samples (n=1302) from 31 farms were collected 3 times/month for 14 consecutive months. The samples were analyzed to determine the BMSCC levels. The SPC charts, including the individual chart (I-chart) and the moving range chart (MR-chart), were created to determine the BMSCC variations, out of control points, and process signals for each farm every month. The interpretation of the SPC charts was reported to dairy cooperative authorities and veterinarians.

Results:

Based on a set of BMSCC values as well as their variance from SPC charts, a series of BMSCC data could be classified into different scenarios, including farms with high BMSCC values but with low variations or farms with low BMSCC values and variations. Out of control points and signals or alarms corresponding to the SPC rules, such as trend and shift signals, were observed in some of the selected farms. The information from SPC charts was used by authorities and veterinarians to communicate with dairy farmers to monitor and control BMSCC for each farm.

Conclusion:

This study showed that the SPC method can be used to monitor the variation of BMSCC in milk sampled from smallholder farms. Moreover, information obtained from the SPC charts can serve as a guideline for dairy farmers, dairy cooperative boards, and veterinarians to manage somatic cell counts in bulk tanks from smallholder dairy farms.

A Bayesian control chart based on the beta distribution for monitoring the two-dimensional gamma index pass rate in the context of patient-specific quality assurance

PURPOSE

In the context of quality assurance in intensity modulated radiation therapy (IMRT), the aim of this work was two-fold: (a) to show that the beta distribution characterizes the two-dimensional gamma index pass rate (GIPR), and that the quantiles of the distribution should be used in order to compute the control limit (CL) for the detection of abnormally low GIPR, and (b) to introduce a Bayesian control chart that allows calculation of CLs from the first measurement.

METHODS

In order to enable monitoring of the GIPR from the first measurement, we developed a Bayesian control chart based on the beta distribution, elaborated according to the following two steps: (a) an iterative bayesian inference approach without any prior information on the GIPR distribution was used at the start of monitoring and the CL was progressively updated; and (b) when sufficient in-control arcs had been recorded and the estimators of the parameters of the beta distribution were sufficiently accurate, the CL of the chart was fixed to a constant value corresponding to the quantile of the beta distribution. The clinical utility of this approach is illustrated through a real data case study: monitoring the GIPR of patients treated with a moving gantry IMRT technique RapidArc^TM on a Novalis TrueBeam STx (Varian Medical Systems) linear accelerator equipped with an aS1200 electronic portal imager device.

RESULTS

We showed that some commonly used distributions for monitoring GIPR in the literature, such as normal or logarithm transformation, are not appropriate. We compared the CLs of those solutions with the CL of our chart based on the BD (CL = 95.14%). The comparison revealed that the CL for the normal law (CL = 97.62%) generated too many false positives, and that the CL of the Logarithm transformation (CL = 83.74%) could fail to efficiently detect (i.e., sufficiently early on or faster) changes in the process.

CONCLUSIONS

Successful GIPR monitoring requires careful and rigorous application of well-established statistical concepts in the field of statistical process control. In this paper, we stress the importance of carefully analyzing the distribution of the monitored characteristic that is plotted on the control chart. We propose a Bayesian control chart that can be viewed as a practical solution for early implementation of GIPR monitoring, starting from the first arc. We demonstrate that beta distribution is a better method for characterizing the GIPR, and thus, the use of this approach is expected to improve patient-specific quality assurance plans in radiotherapy.

Patient safety culture in Oman: A national study

RATIONAL, AIM, AND OBJECTIVES

A positive patient safety culture in maternity units is linked to higher quality of care and better outcomes for mothers. However, safety culture varies across maternity units. Analyses of variation in safety culture using statistical process control (SPC) methods may help provider units to learn from each other's performance. This study aims to measure patient safety culture across maternity units in Oman using SPC methods.

METHODS

The 36-item Safety Attitude Questionnaire (SAQ) was distributed to all doctors, nurses, and midwifes working in ten maternity care units in Oman's hospitals and analysed using SPC methods. The SAQ considers six domains: job satisfaction, perception of management, safety climate, stress recognition, teamwork, and work condition.

RESULTS

Of the 892 targeted participants, 735 (82%) questionnaires were returned. The overall percentage of positive safety responses in all hospitals ranged from 53% to 66%, but no hospital had the targeted response of above 75%. Job satisfaction had the highest safety score (4.10) while stress recognition was the lowest (3.17). SPC charts showed that the overall percentage of positive responses in three maternity units (H1, H7, and H10) was above and one (H4) was below the control limits that represent special cause variation that merits further investigation.

CONCLUSION

Generally, the safety culture in maternity units in Oman is below target and suggests that considerable work is required to enhance safety culture. Several maternity units showed evidence of high/low special cause variation that may offer a useful starting point for understanding and enhancing safety culture.

Implementation of an experiential learning strategy to reduce the risk of ventilator-associated pneumonia in critically ill adult patients

Objective

We evaluated the impact of an experiential learning strategy on both the adherence to the use of bundles and the incidence of ventilator-associated pneumonia in critically ill adult patients.

Methods

Longitudinal, quasi-experimental interrupted time-series study in a tertiary teaching hospital in Buenos Aires, Argentina. Successive measurements were made before and after the intervention was implemented between January 2016 and December 2018. Our main exposure was experiential learning, which was based on a combination of play activities, simulation models, knowledge and attitude competencies, role-playing and feedback. The adherence to the bundle for the care of mechanically ventilated critically-ill adult patients and the occurrence of ventilator-associated pneumonia were the main outcomes of interest. We used generalized linear models including time as a linear spline to estimate the effect of the experiential learning strategy both on the adherence to the bundle of care and the occurrence of ventilator-associated pneumonia during long-term follow-up.

Results

The overall proportion of adequate bundle use before and after the implementation of the intervention was 60.8% (95% CI: 56.9-64.7) and 85.6% (95% CI: 81.2-90.1), respectively. The incidence rate of ventilator-associated pneumonia before and after the intervention was 6.11 (95% CI: 5.82-6.40) and 3.55 (95% CI: 2.96-4.14) every 1000 days of mechanical ventilation, respectively. The estimated baseline monthly change in the adherence to the mechanical ventilation bundle was 0.4% (95%CI: -0.3-1.2%, p = 0.31) and 1.1% (95% CI: 0.2-2.2%, p < 0.01) before and after the implementation of the intervention, respectively. These results were consistent across our statistical quality control analysis.

Conclusions

The implementation of experiential learning strategies improves the adherence to bundles in the care of mechanically ventilated critically ill adult patients. Such strategies also decrease the incidence rate of ventilator-associated pneumonia. Both effects appear to remain constant during long-term follow-up.

New Risk Methodology Based on Control Charts to Assess Occupational Risks in Manufacturing Processes

The accident rate in the EU-28 region of the European Union showed a value of 2 fatal accidents per 100,000 people in 2019 that mainly affect construction (24%), manufacturing (19%) and logistics (19 %). To manage situations that affect occupational risk at work, a review of existing tools is first carried out taking into account three prevention, simultaneity and immediacy characteristics. As a result, a new dynamic methodology called Statistical Risk Control (SRC) based on Bayesian inference, control charts and analysis of the hidden Markov chain is presented. The objective is to detect a situation outside the limits early enough to allow corrective actions to reduce the risk before an accident occurs. A case is developed in a medium-density fiberboard (MDF) manufacturing plant, in which five inference models based on Poisson, exponential and Weibull distributions and risk parameters following gamma and normal distributions have been tested. The results show that the methodology offers all three characteristics, together with a better understanding of the evolution of the operators in the plant and the safety barriers in the scenario under study.

The STRAND Chart: A survival time control chart

The STRAND Chart (Survival Time, Risk‐Adjusted, N‐Division Chart) is a new tool for online risk‐adjusted (RA) monitoring of survival outcomes. The chart is drawn in continuous time, making it responsive to change in the process of interest, for example, performance over time of a surgical unit and the procedures that they employ. Though it is difficult to achieve with charts designed for the purpose described, we show that our suggested chart keeps patient ordering intact. We discuss the difficulties maintaining patient ordering poses, making reference to other charts in the literature. Our conclusion is that the best approach to preserving patient ordering on any chart of this nature involves compromising on the fullness of presentation of the recorded data. The chart is divided into N strands, each strand relating to a benchmark patient's survival information at t_n days following treatment, n = 1,2,…,N. We present a simple version of the chart where the strands consist of Bernoulli RA exponentially weighted moving averages, recording RA failure rates at t_n days. It can detect recent process change and historical change. We illustrate the STRAND Chart using a well‐known UK post cardiac surgery survival dataset, where the nature of a certain cluster in the data can be seen.

Match 2017: Blindsided or Fumbled?

The field of otolaryngology has historically enjoyed extreme interest among residency applicants. However, in the past few years, the number of applicants has precipitously dropped, so that there is no longer a significant excess of applications. It remains important for academic programs to promote student interest in otolaryngology, to break down barriers that may dissuade excellent candidates, and to widen the welcome.

Application of capability indices and control charts in the analytical method control strategy

In this study, we assessed the usefulness of control charts in combination with the process capability indices, C_pm and C_pk , in the control strategy of an analytical method. The traditional X-chart and moving range chart were used to monitor the analytical method over a 2-year period. The results confirmed that the analytical method is in-control and stable. Different criteria were used to establish the specifications limits (i.e. analyst requirements) for fixed method performance (i.e. method requirements). If the specification limits and control limits are equal in breadth, the method can be considered "capable" (C_pm  = 1), but it does not satisfy the minimum method capability requirements proposed by Pearn and Shu (2003). Similar results were obtained using the C_pk index. The method capability was also assessed as a function of method performance for fixed analyst requirements. The results indicate that the method does not meet the requirements of the analytical target approach. A real-example data of a SEC with light-scattering detection method was used as a model whereas previously published data were used to illustrate the applicability of the proposed approach.

Statistical process control: separating signal from noise in emergency department operations

BACKGROUND

Statistical process control (SPC) is a visually appealing and statistically rigorous methodology very suitable to the analysis of emergency department (ED) operations.

OBJECTIVE

We demonstrate that the control chart is the primary tool of SPC; it is constructed by plotting data measuring the key quality indicators of operational processes in rationally ordered subgroups such as units of time. Control limits are calculated using formulas reflecting the variation in the data points from one another and from the mean. SPC allows managers to determine whether operational processes are controlled and predictable. We review why the moving range chart is most appropriate for use in the complex ED milieu, how to apply SPC to ED operations, and how to determine when performance improvement is needed.

DISCUSSION

SPC is an excellent tool for operational analysis and quality improvement for these reasons: 1) control charts make large data sets intuitively coherent by integrating statistical and visual descriptions; 2) SPC provides analysis of process stability and capability rather than simple comparison with a benchmark; 3) SPC allows distinction between special cause variation (signal), indicating an unstable process requiring action, and common cause variation (noise), reflecting a stable process; and 4) SPC keeps the focus of quality improvement on process rather than individual performance.

CONCLUSION

Because data have no meaning apart from their context, and every process generates information that can be used to improve it, we contend that SPC should be seriously considered for driving quality improvement in emergency medicine.

Control charts for accident frequency: a motivation for real-time occupational safety monitoring

This paper quantitatively motivates the need for active monitoring of occupational safety incident data through the use of cumulative sum (CUSUM) control charts. The frequency of incidents within a subset of historical accident data is analysed. The performance of Poisson CUSUM and exponential CUSUM (time-between-events) charts is compared in an illustrative example to show that shorter periods of aggregation and time-between-events monitoring lead to more timely indications of increased accident frequency. An extension showing the anticipated performance of these charts with real-time data is given. Various adjustments to the monitoring system are also simulated to show that quick implementation of hazard controls can significantly impact safety performance. Decreases in the frequency of safety incidents as a result of implemented hazard controls can also be monitored.

Statistical process control analysis for patient-specific IMRT and VMAT QA

This work applied statistical process control to establish the control limits of the % gamma pass of patient-specific intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) quality assurance (QA), and to evaluate the efficiency of the QA process by using the process capability index (Cpml). A total of 278 IMRT QA plans in nasopharyngeal carcinoma were measured with MapCHECK, while 159 VMAT QA plans were undertaken with ArcCHECK. Six megavolts with nine fields were used for the IMRT plan and 2.5 arcs were used to generate the VMAT plans. The gamma (3%/3 mm) criteria were used to evaluate the QA plans. The % gamma passes were plotted on a control chart. The first 50 data points were employed to calculate the control limits. The Cpml was calculated to evaluate the capability of the IMRT/VMAT QA process. The results showed higher systematic errors in IMRT QA than VMAT QA due to the more complicated setup used in IMRT QA. The variation of random errors was also larger in IMRT QA than VMAT QA because the VMAT plan has more continuity of dose distribution. The average % gamma pass was 93.7% ± 3.7% for IMRT and 96.7% ± 2.2% for VMAT. The Cpml value of IMRT QA was 1.60 and VMAT QA was 1.99, which implied that the VMAT QA process was more accurate than the IMRT QA process. Our lower control limit for % gamma pass of IMRT is 85.0%, while the limit for VMAT is 90%. Both the IMRT and VMAT QA processes are good quality because Cpml values are higher than 1.0.

Attempted early detection of influenza A (H1N1) pandemic with surveillance data of influenza-like illness and unexplained pneumonia

BACKGROUND

To collect disease information and provide data for early detection of epidemics, two surveillance systems were established for influenza-like illness (ILI) and unexplained pneumonia (UP) in Wuxi, People's Republic of China.

OBJECTIVES

The current study aims to describe the performance of these surveillance systems during 2004-2009 and to evaluate the value of surveillance data in detection of influenza epidemics.

METHODS

Two national ILI sentinel hospitals and three UP sentinel hospitals provided data to the surveillance systems. The surveillance data from hospital-based outpatient clinics and emergency rooms were compared by year. The ILI data of 2009 were further modeled based on previous data using both a control chart method and a moving average regression method. Alarms of potential epidemics would be raised when the input surveillance data surpassed a threshold.

RESULTS

In 2009, the proportions of ILI and respiratory illness with fever (one surveillance syndrome of the UP system) to total patient visits (3·40% and 11·76%, respectively) were higher than the previous years. The surveillance data of both systems also showed developing trends similar to the influenza A (H1N1) pandemic in 2009. When the surveillance data of 2009 were fitted in the two detection models, alarms were produced on the occurrence of the first local case of influenza A (H1N1), outbreaks in schools and in general populations.

CONCLUSIONS

The results indicated the potential for using ILI and UP surveillance data as syndromic indicators to detect and provide an early warning for influenza epidemics.

Creation of a hyponatremia registry supported by an industry-derived quality control methodology

BACKGROUND

A clinical registry encompasses a selective set of rigorously collected and stored clinical data focused on a specific condition. Hyponatremia has multiple, complex underlying causes and is one of the most frequent laboratory abnormalities. No systematic registries of hyponatremic patients have been reported in the medical literature. The purpose of this project was to create a registry for hyponatremia in order to obtain epidemiological data that will help to better understand this condition.

OBJECTIVE

This paper describes the creation of a registry for hyponatremia within a single institution that employs industry-based approaches for quality management to optimize data accuracy and completeness.

METHODS

A prospective registry of incident hyponatremia cases was created for this study. A formalized statistically based quality control methodology was developed and implemented to analyze and monitor all the process indicators that were developed to ensure data quality.

RESULTS

Between December 2006 and April 2009, 2443 episodes of hyponatremia were included. Six process indicators that reflect the integrity of the system were evaluated monthly, looking for variation that would suggest systematic problems. The graphical representation of the process measures through control charts allowed us to identify and subsequently address problems with maintaining the registry.

CONCLUSION

In this project we have created a novel hyponatremia registry. To ensure the quality of the data in this registry we have implemented a quality control methodology based on industrial principles that allows us to monitor the performance of the registry over time through process indicators in order to detect systematic problems. We postulate that this approach could be reproduced for other registries.