Multiresponse optimisation of powder metals via probabilistic loss functions

Quadratic loss functions have been used extensively within the context of quality engineering and experimental design for process and product optimisation and robust design. In general, this approach determines optimal parameter settings based on minimising the sum of individual or mean loss of the associated response(s) of interest in a defined response surface. While the method is neat and handy, it totally neglects the effect of deviations on the desirable value of loss function. This paper utilises variance and probability distribution of loss functions for developing an in depth optimisation scheme that balances mean and variance of loss in a Pareto optimal manner. Since losses are usually defined in financial terms, this model then further improved to handle the user determined risk levels so that financial losses are being restricted within a certain region of interest. Application of the model is illustrated on a multiresponse optimisation problem from powder metallurgy industry.Publisher's VersionAuthor Post Prin

Using Prediction to Improve Patient Flow in a Health Care Delivery Chain

Often, in a health care delivery chain, lack of coordination has been detrimental to timely, high quality care. This paper focuses on the two steps of the hospital health care delivery chain, an emergency department and a hospital’s inpatient units. Past research into this chain has suggested that early prediction of patient need for admission can be used to better align flow between the two departments. This chain and the nature of prediction in health care delivery are discussed as well as a how prediction may be useful in this context. Finally tools for making admission predictions are tested and their possible implications are explored. The results of this exploration show that both expert opinion and a Naïve Bayesian statistical approach have predictive value in this context

Implementation, evaluation, and recommendations for extension of AHRQ Common Formats to capture patient- and carepartner-generated safety data

Abstract Objectives The Common Formats, published by the Agency for Healthcare Research and Quality, represent a standard for safety event reporting used by Patient Safety Organizations (PSOs). We evaluated its ability to capture patient-reported safety events. Materials and methods We formally evaluated gaps between the Common Formats and a safety concern reporting system for use by patients and their carepartners (ie friends/families) at Brigham and Women’s Hospital. Results Overall, we found large gaps between Common Formats (versions 1.2, 2.0) and our patient/carepartner reporting system, with only 22–30% of the data elements matching. Discussion We recommend extensions to the Common Formats, including concepts that capture greater detail about the submitter and safety categories relevant to unsafe conditions and near misses that patients and carepartners routinely observe. Conclusion Extensions to the Common Formats could enable more complete safety data sets and greater understanding of safety from key stakeholder perspectives, especially patients, and carepartners. </jats:sec

Mixed-Methods Evaluation of Real-Time Safety Reporting by Hospitalized Patients and Their Care Partners:The MySafeCare Application

OBJECTIVE: To evaluate the amount and content of data patients and carepartners reported using a real-time electronic safety tool compared to other reporting mechanisms, and understand their perspectives on safety concerns and reporting in the hospital. METHODS: Mixed-methods study including 20 month pre- and post-implementation trial evaluating MySafeCare, a web-based application which allows hospitalized patients/carepartners to report safety concerns in real-time. Comparison of MySafeCare submission rates for three hospital units (oncology acute care; vascular intermediate care; medical intensive care) to submissions rates of Patient Family Relations (PFR) Department, a hospital service to address patient/family concerns. Triangulation of quantitative data with thematic analysis of safety concern submissions and patient/carepartner interviews to understand submission content and perspectives on safety reporting. RESULTS: Thirty-two MySafeCare submissions were received with an average rate of 1.7 submissions per 1,000 patient-days and a range of 0.3 to 4.8 submissions per 1,000 patient-days across all units, indicating notable variation between units. MySafeCare submission rates were significantly higher than PFR submission rates during the post-intervention period on the vascular unit (4.3 [95% CI 2.8 – 6.5] versus 1.5 [95% CI 0.7 – 3.1], Poisson) (P=0.01). Overall trends indicated a decrease in PFR submissions after MySafeCare implementation. Triangulated data indicated patients preferred to report anonymously and did not want concerns submitted directly to their care team. CONCLUSIONS: MySafeCare evaluation confirmed the potential value of providing an electronic, anonymous reporting tool in the hospital to capture safety concerns in real-time. Such applications should be tested further as part of patient safety programs

Use of Binary Cumulative Sums and Moving Averages in Nosocomial Infection Cluster Detection1

Clusters of nosocomial infection often occur undetected, at substantial cost to the medical system and individual patients. We evaluated binary cumulative sum (CUSUM) and moving average (MA) control charts for automated detection of nosocomial clusters. We selected two outbreaks with genotyped strains and used resistance as inputs to the control charts. We identified design parameters for the CUSUM and MA (window size, k, α, β, p0, p1) that detected both outbreaks, then calculated an associated positive predictive value (PPV) and time until detection (TUD) for sensitive charts. For CUSUM, optimal performance (high PPV, low TUD, fully sensitive) was for 0.1 <α ≤0.25 and 0.2 <β <0.25, with p0 = 0.05, with a mean TUD of 20 (range 8–43) isolates. Mean PPV was 96.5% (relaxed criteria) to 82.6% (strict criteria). MAs had a mean PPV of 88.5% (relaxed criteria) to 46.1% (strict criteria). CUSUM and MA may be useful techniques for automated surveillance of resistant infections

Modeling Tools for Environmental and Economic Uncertainties in Nanomanufacturing

With the increasing trend for use of nanomaterials in various products such as biosensors, batteries, etc., there will be a demand for nanomanufacturing production scale-up. Given the uncertainties in production scale-up, modeling is likely to become a useful planning tool. The model will predict the production capacity periodically by taking various uncertainties into account. This research aims to develop both deterministic and stochastic models with specific variables. This first step will contribute to final complex models by including various uncertainties

Some approaches to quality in the presence of inspection error: With application to optimal laboratory cancer screening policies

This research addresses several quality control problems which arise in a variety of manufacturing, healthcare, service, finance, and other industries given the existence of human and automated attribute detection error. Several mathematical and economic models are developed for various types of single and multiple inspection screening policies in order first to examine inherent tradeoffs between type I errors, type II errors, and all associated inspection, false-rejection, and false-acceptance costs and then, ultimately, to help identify the minimum expected total cost policy and the optimal amount of inspection for any particular scenario. While originally motivated by industrial problems, these models also have been adapted to various non-manufacturing concerns, including service processes and laboratory cancer screening policies. In particular, similar methods are developed and used to analyze the policy for screening Pap smears for early indications of cervical cancer currently required by the Congressional Laboratory Improvements Amendments Act of 1988 (CLIA\u2788), to compare this policy with possible alternatives, and to develop an algorithm that identifies the optimal policy in any given scenario. Results show that the mandated CLIA policy never is optimal and always increases total costs, that overall sensitivity of CLIA never can be improved beyond a certain mathematical bound, that CLIA\u27s 10% minimum requirement nor any other amount of partial resampling ever is optimal, that multiple readings in some realistic cases can result in very significant benefits, and that the proposed use of automated rescreening technology recently approved by the FDA may not result in improvements over CLIA nor the optimal policy derived here. Sensitivity analyses indicate that the improvement possible by switching to this optimal policy ranges from 90,000 to 165,000 fewer false-negatives and $250 million to \$1 billion savings annually nationwide. Similar results and savings are demonstrated in several industrial and service applications, with Deming\u27s$k\sb1/k\sb2$ minimum cost criterion in some cases now resulting in the maximum expected cost policy and with multiple inspections often being economically optimal. Several directions for further work are suggested, including extending results to mammography and other cancer screening tests, and an extensive bibliography is provided

Exact and approximate probability distributions of evidence-based bundle composite compliance measures

Evidence-based medicine, Patient safety, Bundle reliability, Core measure sets,