José L. Zayas-Castro

Evaluation of an error-reduction training program for surgical residents.

Purpose To reduce errors in surgery using a resident training program based on a taxonomy that highlights three kinds of errors: judgment, inattention to detail, and problem understanding. Method The training program module at the University of South Florida incorporated a three-item situational judgment test, video training (which included a lecture and behavior modeling), and role-plays (in which residents participated and received feedback from faculty). Two kinds of outcome data were collected from 33 residents during 2006–2007: (1) behaviors during the training and (2) on-the-job surgical complication records 12 months before and 6 months after training. For the data collected during training, participants were assigned to a condition (19 video condition, 13 control condition); for the data collected on the job, an interrupted time series design was used. Results Data from 32 residents were analyzed (one residents data were excluded). One of the situational judgment items improved significantly over time (d = 0.45); the other two did not (d = 0.36, 0.25). Surgical complications and errors decreased over the course of the study (the correlation between complications and time in months was r = −0.47, for errors and time, r = −0.55). Effects of video behavior modeling on specific errors measured during role-plays were not significant (effect sizes for binary outcomes were phi = −0.05 and phi = 0.01, and for continuous outcomes, d ranged from −0.02 to 0.34). Conclusions The training seemed to reduce errors in surgery, but the training had little effect on the specific kinds of errors targeted during training.

Simulating less‐than‐truckload terminal operations

Purpose – Recent surveys indicate that transportation companies are not utilizing technology in decision making despite growing complexity of transportation systems. This paper aims to present a discrete simulation approach to benchmarking performance measures of terminal operations of less‐than‐truckload (LTL) freight carriers.Design/methodology/approach – The methodology involves design of heuristics for the dock assignment problem and uses discrete event simulation to evaluate performance of the various heuristics.Findings – Intelligent dock assignment for incoming trailers can greatly improve performance of LTL terminals and simulation is an effective tool to determine the effects of various assignments on terminal performance.Originality/value – The paper introduces a dock assignment heuristic and integrates the tactical level decision‐making process and operational aspects in LTL terminals to evaluate the performance of the system. A case study is used to demonstrate the use of the heuristic and per...

Clustering-based methodology for analyzing near-miss reports and identifying risks in healthcare delivery

Near-miss reports are qualitative descriptions of events that could have harmed patients but did not due to a timely intervention or a convenient evolution of the circumstances. Near-miss reporting has increasingly become a relevant tool to support patient safety efforts since they provide some evidence of risk in the system before patients suffer adverse consequences. Near-misses are usually classified into pre-specified categories that correspond to sources of risk in the system or its processes. Their analysis often consists of tallying classified near-misses to determine risk priorities based on frequency within each pre-specified risk category. Our research aims to use different combinations of near-miss reports to find potential sources of risk. We propose an unsupervised bisecting k-prototypes algorithm for clustering coded near-miss reports to identify relationships between events that would not otherwise have been easily identified. Subsequent study of resulting clusters will lead to the identification of potentially dangerous, but unsuspected system interactions. We illustrate or methodology with preliminary results of its implementation at the University of South Florida Health clinics.

Preventable Readmission Risk Factors for Patients With Chronic Conditions.

Abstract:Evidence indicates that the largest volume of hospital readmissions occurs among patients with preexisting chronic conditions. Identifying these patients can improve the way hospital care is delivered and prioritize the allocation of interventions. In this retrospective study, we identify factors associated with readmission within 30 days based on claims and administrative data of nine hospitals from 2005 to 2012. We present a data inclusion and exclusion criteria to identify potentially preventable readmissions. Multivariate logistic regression models and a Cox proportional hazards extension are used to estimate the readmission risk for 4 chronic conditions (congestive heart failure [CHF], chronic obstructive pulmonary disease [COPD], acute myocardial infarction, and type 2 diabetes) and pneumonia, known to be related to high readmission rates. Accumulated number of admissions and discharge disposition were identified to be significant factors across most disease groups. Larger odds of readmission were associated with higher severity index for CHF and COPD patients. Different chronic conditions are associated with different patient and case severity factors, suggesting that further studies in readmission should consider studying conditions separately.

Learning high-dimensional networks with nonlinear interactions by a novel tree-embedded graphical model

We propose a novel network learning method that can detect network structure with both linear and nonlinear interactions.Most existing network learning methods focus on linear interactions.Integration of generalized linear model, sparse learning, and decision tree learning.Interesting clinical associations are discovered from a real-world application using the proposed method. Network models have been widely used in many domains to characterize relationships between physical entities. Although extensive research efforts have been conducted for learning networks from data, many of them were developed for learning networks with linear relationships. As both linear and nonlinear relationships may appear in many applications, in this paper, we developed a novel graphical model, the sparse tree-embedded graphical model (STGM), which is able to uncover both linear and nonlinear relationships from a large number of variables. We further proposed an efficient regression-based algorithm for learning the STGM from data. We conducted simulation studies that demonstrated the superiority of the STGM over other network learning methods and applied the STGM on a real-world application that demonstrated its efficacy on discovering interesting nonlinear relationships in practice.

Challenges and opportunities in the analysis of risk in healthcare

Since 1999, the estimates of annual preventable deaths in U.S. hospitals suggest that healthcare services add some risk to the patients clinical condition. Such risks are often associated with harm resulting from errors in medication, diagnosis, and clinical procedures, among others. Preventing harm to patients demands the timely identification of risks to support the selection and implementation of effective strategies. While identifying and assessing risks in healthcare are particularly challenging due to the ambiguity and uncertainty that characterize such systems, most of the risk analysis methods currently used or proposed in healthcare have been developed for systems where unsafe conditions and risk metrics are well-defined. Therefore, their actual use in healthcare systems is inconsistent. The objective of this paper is to provide systems engineers, and researchers from related fields, with an overview of the current state of healthcare risk analysis and to highlight research contributions needed to support the proactive identification and assessment of risks in healthcare systems.

A strategic gaming model for health information exchange markets

Current market conditions create incentives for some providers to exercise control over patient data in ways that unreasonably limit its availability and use. Here we develop a game theoretic model for estimating the willingness of healthcare organizations to join a health information exchange (HIE) network and demonstrate its use in HIE policy design. We formulated the model as a bi-level integer program. A quasi-Newton method is proposed to obtain a strategy Nash equilibrium. We applied our modeling and solution technique to 1,093,177 encounters for exchanging information over a 7.5-year period in 9 hospitals located within a three-county region in Florida. Under a set of assumptions, we found that a proposed federal penalty of up to

A literature review of preventable hospital readmissions: Preceding the Readmissions Reduction Act

2,000,000 has a higher impact on increasing HIE adoption than current federal monetary incentives. Medium-sized hospitals were more reticent to adopt HIE than large-sized hospitals. In the presence of collusion among multiple hospitals to not adopt HIE, neither federal incentives nor proposed penalties increase hospitals’ willingness to adopt. Hospitals’ apathy toward HIE adoption may threaten the value of inter-connectivity even with federal incentives in place. Competition among hospitals, coupled with volume-based payment systems, creates no incentives for smaller hospitals to exchange data with competitors. Medium-sized hospitals need targeted actions (e.g., outside technological assistance, group purchasing arrangements) to mitigate market incentives to not adopt HIE. Strategic game theoretic models help to clarify HIE adoption decisions under market conditions at play in an extremely complex technology environment.

Osteonecrosis in Individuals With Systemic Lupus Erythematosus: A Predictive Model

ABSTRACT Preventable readmissions are a large and growing concern throughout healthcare in the United States, representing as many as 20% of all hospitalizations (30-day post-discharge) and an estimated

Human error, not communication and systems, underlies surgical complications.

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