Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals

Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.

Development and use of risk adjusted statistical process control tools for the monitoring and improvement of clinical outcomes in interventional cardiology

This thesis explored the development of statistical methods to support the monitoring and improvement in quality of treatment delivered to patients undergoing coronary angioplasty procedures. To achieve this goal, a suite of outcome measures was identified to characterise performance of the service, statistical tools were developed to monitor the various indicators and measures to strengthen governance processes were implemented and validated. Although this work focused on pursuit of these aims in the context of a an angioplasty service located at a single clinical site, development of the tools and techniques was undertaken mindful of the potential application to other clinical specialties and a wider, potentially national, scope.

Performance-Based Quality Assurance/Quality Control (QA/QC) Acceptance Procedures for In-Place Soil Testing Phase 3

One of the objectives of this study was to evaluate soil testing equipment based on its capability of measuring in-place stiffness or modulus values. As design criteria transition from empirical to mechanistic-empirical, soil test methods and equipment that measure properties such as stiffness and modulus and how they relate to Florida materials are needed. Requirements for the selected equipment are that they be portable, cost effective, reliable, a ccurate, and repeatable. A second objective is that the selected equipment measures soil properties without the use of nuclear materials.The current device used to measure soil compaction is the nuclear density gauge (NDG). Equipment evaluated in this research included lightweight deflectometers (LWD) from different manufacturers, a dynamic cone penetrometer (DCP), a GeoGauge, a Clegg impact soil tester (CIST), a Briaud compaction device (BCD), and a seismic pavement analyzer (SPA). Evaluations were conducted over ranges of measured densities and moistures.Testing (Phases I and II) was conducted in a test box and test pits. Phase III testing was conducted on materials found on five construction projects located in the Jacksonville, Florida, area. Phase I analyses determined that the GeoGauge had the lowest overall coefficient of variance (COV). In ascending order of COV were the accelerometer-type LWD, the geophone-type LWD, the DCP, the BCD, and the SPA which had the highest overall COV. As a result, the BCD and the SPA were excluded from Phase II testing.In Phase II, measurements obtained from the selected equipment were compared to the modulus values obtained by the static plate load test (PLT), the resilient modulus (MR) from laboratory testing, and the NDG measurements. To minimize soil and moisture content variability, the single spot testing sequence was developed. At each location, test results obtained from the portable equipment under evaluation were compared to the values from adjacent NDG, PLT, and laboratory MR measurements. Correlations were developed through statistical analysis. Target values were developed for various soils for verification on similar soils that were field tested in Phase III. The single spot testing sequence also was employed in Phase III, field testing performed on A-3 and A-2-4 embankments, limerock-stabilized subgrade, limerock base, and graded aggregate base found on Florida Department of Transportation construction projects. The Phase II and Phase III results provided potential trend information for future research—specifically, data collection for in-depth statistical analysis for correlations with the laboratory MR for specific soil types under specific moisture conditions. With the collection of enough data, stronger relationships could be expected between measurements from the portable equipment and the MR values. Based on the statistical analyses and the experience gained from extensive use of the equipment, the combination of the DCP and the LWD was selected for in-place soil testing for compaction control acceptance. Test methods and developmental specifications were written for the DCP and the LWD. The developmental specifications include target values for the compaction control of embankment, subgrade, and base materials.

A simple and fast two-locus quality control test to detect false positives due to batch effects in genome-wide association studies

The impact of erroneous genotypes having passed standard quality control (QC) can be severe in genome-wide association studies, genotype imputation, and estimation of heritability and prediction of genetic risk based on single nucleotide polymorphisms (SNP). To detect such genotyping errors, a simple two-locus QC method, based on the difference in test statistic of association between single SNPs and pairs of SNPs, was developed and applied. The proposed approach could detect many problematic SNPs with statistical significance even when standard single SNP QC analyses fail to detect them in real data. Depending on the data set used, the number of erroneous SNPs that were not filtered out by standard single SNP QC but detected by the proposed approach varied from a few hundred to thousands. Using simulated data, it was shown that the proposed method was powerful and performed better than other tested existing methods. The power of the proposed approach to detect erroneous genotypes was approximately 80% for a 3% error rate per SNP. This novel QC approach is easy to implement and computationally efficient, and can lead to a better quality of genotypes for subsequent genotype-phenotype investigations.

A modified quality control method for manufacturing process in mask industry

Masks are widely used in different industries, for example, traditional metal industry, hospitals or semiconductor industry. Quality is a critical issue in mask industry as it is related to public health and safety. Traditional quality practices for manufacturing process have some limitations in implementing them in mask industries. This paper aims to investigate the suitability of Six Sigma quality control method for the manufacturing process in the mask industry to provide high quality products, enhancing the process capacity, reducing the defects and the returned goods arising in a selected mask manufacturing company. This paper suggests that modifications necessary in Six Sigma method for effective implementation in mask industry.

Typical accuracy and quality control of a process for creating CT-Based virtual bone models

A pragmatic method for assessing the accuracy and precision of a given processing pipeline required for converting computed tomography (CT) image data of bones into representative three dimensional (3D) models of bone shapes is proposed. The method is based on coprocessing a control object with known geometry which enables the assessment of the quality of resulting 3D models. At three stages of the conversion process, distance measurements were obtained and statistically evaluated. For this study, 31 CT datasets were processed. The final 3D model of the control object contained an average deviation from reference values of −1.07±0.52 mm standard deviation (SD) for edge distances and −0.647±0.43 mm SD for parallel side distances of the control object. Coprocessing a reference object enables the assessment of the accuracy and precision of a given processing pipeline for creating CTbased 3D bone models and is suitable for detecting most systematic or human errors when processing a CT-scan. Typical errors have about the same size as the scan resolution.

Identification of evidence-based biospecimen quality-control tools

Control of biospecimen quality that is linked to processing is one of the goals of biospecimen science. Consensus is lacking, however, regarding optimal sample quality-control (QC) tools (ie, markers and assays). The aim of this review was to identify QC tools, both for fluid and solid-tissue samples, based on a comprehensive and critical literature review. The most readily applicable tools are those with a known threshold for the preanalytical variation and a known reference range for the QC analyte. Only a few meaningful markers were identified that meet these criteria, such as CD40L for assessing serum exposure at high temperatures and VEGF for assessing serum freeze-thawing. To fully assess biospecimen quality, multiple QC markers are needed. Here we present the most promising biospecimen QC tools that were identified.

Quality demand in vaccination and how to improve service quality under Total Quality Control [论全面质量管理模式下预防接种工作的质量要求与改进对策]

Under the concept of Total Quality Control, based on their experience, the authors discussed potential demand for quality of immunization services and possible solutions to these demands. Abstract in Chinese 全面质量管理(total quality control,TQC)是在20世纪60年代由美国人V,Feigonbaum和J.unan先后提出的新的质量管理观念,众所周知的ISO9000族标准即建立在TQC理念下的质量管理标准,该标准已成为当今世界全球一致、最具权威的质量管理和质量保证的国际规则[1-2].21世纪是质量世纪,推行TQC,不断改进产品和服务质量,目前已成为我国各行各业在不断激烈的市场竞争下完善自我、保证生存和发展的重要手段.实施预防接种是预防和控制传染病,保护人群健康的重要措施,预防接种工作中,产品即预防接种服务,需方(顾客)为接受预防接种服务的广大人群,是产品的消费者.随社会的迅速发展,人们对健康需求的不断提高,对预防接种工作也提出了更高的质量要求.本文对TQC模式下顾客对预防接种服务的质量要求进行了综合分析,并对如何改进服务质量进行了初步探讨.

Interventions to control nosocomial infections : study designs and statistical issues

There is a wide range of potential study designs for intervention studies to decrease nosocomial infections in hospitals. The analysis is complex due to competing events, clustering, multiple timescales and time-dependent period and intervention variables. This review considers the popular pre-post quasi-experimental design and compares it with randomized designs. Randomization can be done in several ways: randomization of the cluster [intensive care unit (ICU) or hospital] in a parallel design; randomization of the sequence in a cross-over design; and randomization of the time of intervention in a stepped-wedge design. We introduce each design in the context of nosocomial infections and discuss the designs with respect to the following key points: bias, control for nonintervention factors, and generalizability. Statistical issues are discussed. A pre-post-intervention design is often the only choice that will be informative for a retrospective analysis of an outbreak setting. It can be seen as a pilot study with further, more rigorous designs needed to establish causality. To yield internally valid results, randomization is needed. Generally, the first choice in terms of the internal validity should be a parallel cluster randomized trial. However, generalizability might be stronger in a stepped-wedge design because a wider range of ICU clinicians may be convinced to participate, especially if there are pilot studies with promising results. For analysis, the use of extended competing risk models is recommended.

Quality-based benefit design in health insurance : the impact of a product benefit design change on the utilisation of oral health services by members of a private health insurance fund in regional and rural New South Wales, Australia

Objective: To examine the impact on dental utilisation following the introduction of a participating provider scheme (Regional and Rural Oral Health Program {RROHP)). In this model dentists receive higher third party payments from a private health insurance fund for delivering an agreed range of preventive and diagnostic benefits at no out-ofpocket cost to insured patients. Data source/Study setting: Hospitals Contribution Fund of Australia (HCF) dental claims for all members resident in New South Wales over the six financial years from l99811999 to 200312004. Study design: This cohort study involves before and after analyses of dental claims experience over a six year period for approximately 81,000 individuals in the intervention group (HCF members resident in regional and rural New South Wales, Australia) and 267,000 in the control group (HCF members resident in the Sydney area). Only claims for individuals who were members of HCF at 31 December 1997 were included. The analysis groups claims into the three years prior to the establishment of the RROHP and the three years subsequent to implementation. Data collection/Extraction methods: The analysis is based on all claims submitted by users of services for visits between 1 July 1988 and 30 June 2004. In these data approximately 1,000,000 services were provided to the intervention group and approximately 4,900,000 in the control group. Principal findings: Using Statistical Process Control (SPC) charts, special cause variation was identified in total utilisation rate of private dental services in the intervention group post implementation. No such variation was present in the control group. On average in the three years after implementation of the program the utilisation rate of dental services by regional and rural residents of New South Wales who where members of HCF grew by 12.6%, over eight times the growth rate of 1.5% observed in the control group (HCF members who were Sydney residents). The differences were even more pronounced in the areas of service that were the focus of the program: diagnostic and preventive services. Conclusion: The implementation of a benefit design change, a participating provider scheme, that involved the removal of CO-payments on a defined range of preventive and diagnostic dental services combined with the establishment and promotion of a network of dentists, appears to have had a marked impact on HCF members' utilisation of dental services in regional and rural New South Wales, Australia.