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.

Drugs for cardiac arrhythmmias, antithrombotic drugs and lipid modulating drugs

13.1 Drugs for cardiac arrhythmias 13.1.1 Introduction to cardiac arrhythmias 13.1.2 Cardiac action potentials 13.1.3 Mechanisms of cardiac arrhythmias 13.1.3 Class I 13.1.4 Class II 13.1.5 Class III 12.1.6 Class IV 13.1.7 Amiodarone 13.1.8 Adenosine 13.2 Antithrombotic drugs 13.2.1 Thrombus formation 13.2.2 Platelet aggregation and anti-platelet drugs 13.2.3 Coagulation 13.2.4 Anticoagulants 13.2.5 Fibrinolysis and fibrinolytics 13.3. Lipid modulating drugs 13.3.1 Cholesterol 13.3.2 Statins 13.3.3 Fibric acid derivatives 13.3.4 Ezetimibe

Molecular dynamics studies of the effects of branching characteristics on the crystalline structure of polyethylene

Molecular dynamics simulations were carried out on single chain models of linear low-density polyethylene in vacuum to study the effects of branch length, branch content, and branch distribution on the polymer’s crystalline structure at 300 K. The trans/gauche (t/g) ratios of the backbones of the modeled molecules were calculated and utilized to characterize their degree of crystallinity. The results show that the t/g ratio decreases with increasing branch content regardless of branch length and branch distribution, indicating that branch content is the key molecular parameter that controls the degree of crystallinity. Although t/g ratios of the models with the same branch content vary, they are of secondary importance. However, our data suggests that branch distribution (regular or random) has a significant effect on the degree of crystallinity for models containing 10 hexyl branches/1,000 backbone carbons. The fractions of branches that resided in the equilibrium crystalline structures of the models were also calculated. On average, 9.8% and 2.5% of the branches were found in the crystallites of the molecules with ethyl and hexyl branches while C13 NMR experiments showed that the respective probabilities of branch inclusion for ethyl and hexyl branches are 10% and 6% [Hosoda et al., Polymer 1990, 31, 1999–2005]. However, the degree of branch inclusion seems to be insensitive to the branch content and branch distribution.

Average structure of an An (sub 48) plagioclase from the Hogarth Ranges

The average structure (CI) of a volcanic plagioclase megacryst with composition Ano, from the Hogarth Ranges, Australia, has been determined using three-dimensional, singlecrystal neutron and X-ray diffraction data. Least squaresr efinements, incorporating anisotropic thermal motion of all atoms and an extinction correction, resulted in weighted R factors (based on intensities) of 0.076 and 0.056, respectively, for the neutron and X-ray data. Very weak e reflections could be detected in long-exposure X-ray and electron diffraction photographs of this crystal, but the refined average structure is believed to be unaffected by the presence of such a weak superstructure. The ratio of the scattering power of Na to that of Ca is different for X ray and neutron radiation, and this radiation-dependence of scattering power has been used to determine the distribution of Na and Ca over a split-atom M site (two sites designated M' and M") in this Ano, plagioclase. Relative peak-height ratios M'/M", revealed in difference Fourier sections calculated from neutron and X-ray data, formed the basis for the cation-distribution analysis. As neutron and X-ray data sets were directly compared in this analysis, it was important that systematic bias between refined neutron and X-ray positional parameters could be demonstrated to be absent. In summary, with an M-site model constrained only by the electron-microprobedetermined bulk composition of the crystal, the following values were obtained for the M-site occupanciesN: ar, : 0.29(7),N ar. : 0.23(7),C ar, : 0.15(4),a nd Car" : 0.33(4). These results indicate that restrictive assumptions about M sites, on which previous plagioclase refinements have been based, are not applicable to this Ano, and possibly not to the entire compositional range. T-site ordering determined by (T-O) bond-length variation-t,o : 0.51(l), trm = t2o = t2m = 0.32(l)-is weak, as might be expectedf rom the volcanic origin of this megacryst.

Access to cardiac rehabilitation does not equate to attendance

Background/Aims Timely access to appropriate cardiac care is critical for optimizing positive outcomes after a cardiac event. Attendance at cardiac rehabilitation (CR) remains less than optimal (10%–30%). Our aim was to derive an objective, comparable, geographic measure reflecting access to cardiac services after a cardiac event in Australia. Methods An expert panel defined a single patient care pathway and a hierarchy of the minimum health services for CR and secondary prevention. Using geographic information systems a numeric/alpha index was modelled to describe access before and after a cardiac event. The aftercare phase was modelled into five alphabetical categories: from category A (access to medical service, pharmacy, CR, pathology within 1 h) to category E (no services available within 1 h). Results Approximately 96% or 19 million people lived within 1 h of the four basic services to support CR and secondary prevention, including 96% of older Australians and 75% of the indigenous population. Conversely, 14% (64,000) indigenous people resided in population locations that had poor access to health services that support CR after a cardiac event. Conclusion Results demonstrated that the majority of Australians had excellent ‘geographic’ access to services to support CR and secondary prevention. Therefore, it appears that it is not the distance to services that affects attendance. Our ‘geographic’ lens has identified that more research on socioeconomic, sociological or psychological aspects to attendance is needed.

Access to Cardiac Rehabilitation does not Equate to Attendance. (On Behalf of the Cardiac ARIA Project. Winner Nursing/Allied Health Professional Investigator Award)

Background/Aims Timely access to appropriate cardiac care is critical for optimizing positive outcomes after a cardiac event. Attendance at cardiac rehabilitation (CR) remains less than optimal (10%–30%). Our aim was to derive an objective, comparable, geographic measure reflecting access to cardiac services after a cardiac event in Australia. Methods An expert panel defined a single patient care pathway and a hierarchy of the minimum health services for CR and secondary prevention. Using geographic information systems a numeric/alpha index was modelled to describe access before and after a cardiac event. The aftercare phase was modelled into five alphabetical categories: from category A (access to medical service, pharmacy, CR, pathology within 1 h) to category E (no services available within 1 h). Results Approximately 96% or 19 million people lived within 1 h of the four basic services to support CR and secondary prevention, including 96% of older Australians and 75% of the indigenous population. Conversely, 14% (64,000) indigenous people resided in population locations that had poor access to health services that support CR after a cardiac event. Conclusion Results demonstrated that the majority of Australians had excellent ‘geographic’ access to services to support CR and secondary prevention. Therefore, it appears that it is not the distance to services that affects attendance. Our ‘geographic’ lens has identified that more research on socioeconomic, sociological or psychological aspects to attendance is needed.