Integrating Research and Quality Improvement Using TeamSTEPPS: A Health Team Communication Project to Improve Hospital Discharge

Purpose/Objectives: The purpose of this article is to describe an innovative approach to the integration of quality improvement and research processes. A project with the objective of improving health team communication about hospital discharge provides an exemplar case. Description of the Project/Program: The TeamSTEPPS 10-step action planning guide provided the structure for planning, developing, and evaluating a redesign of interprofessional health team communication to improve hospital discharge led by 2 clinical nurse specialists. The redesign involved development of processes for team bedside rounding, registered nurse bedside shift reports, and briefing tools to support the rounding processes. Outcome: Using the TeamSTEPPS process, a 4-phase combined quality improvement and research project was designed and implemented. Implementation is ongoing, supported by process evaluation for continuing process improvement. Longitudinal analysis of research outcomes will follow in the future. Conclusions: Led by unit-based clinical nurse specialists, use of an integrated process of quality improvement and research creates evidence-based innovation to solve interprofessional practice problems. Incorporating research within the project design allows for data-based decisions to inform the clinical process improvement, as well as documentation of both the processes and outcomes of the local improvements that can inform replications in other sites

Effects of Implementing a Health Team Communication Redesign on Hospital Readmissions Within 30 Days

Background and Rationale Poor communication between health team members can interfere with timely, coordinated preparation for hospital discharge. Research on daily bedside interprofessional health team rounds and nursing bedside shift handoff reports provides evidence that these strategies can improve communication. Aims To improve health team communication and collaboration about hospital discharge; improve patient experience of discharge measured by patient‐reported quality of discharge teaching, readiness for discharge, and postdischarge coping difficulty; and reduce readmissions and emergency department (ED) visits postdischarge. Methods A two‐sample pre‐ and postintervention design provided baseline data for redesign of health team communication processes and comparison data for evaluation of the new process’ impact. Health team members (n = 105 [pre], n = 95 [post]) from two surgical units of an academic medical center in the midwestern United States provided data on discharge‐related communication and collaboration. Patients (n = 413 [pre], n = 191 [post]) provided data on their discharge experience (quality of discharge teaching, readiness for discharge, postdischarge coping difficulty) and outcomes (readmissions, ED visits). Chi‐square and t tests were used for unadjusted pre‐ and postintervention comparisons. Logistic regression of readmissions with a matched pre‐ and postintervention sample included adjustments for patient characteristics and hospitalization factors. Results Readmissions decreased from 18% to 12% (p \u3c .001); ED visits decreased from 4.4% to 1.5% (p \u3c .001). Changes in health team communication and collaboration and patients’ experience of discharge were minimal. Discussion The targeted outcomes of readmission and ED visits improved after the health team communication process redesign. The process indicators did not improve; potential explanations include unmeasured hospital and unit discharge, and other care process changes during the study timeframe. Linking Evidence to Practice Evidence from daily interprofessional team bedside rounding and bedside shift report studies was translated into a redesign of health team communication for discharge. These strategies support readmission reduction efforts

Statistical Mechanics of Learning in the Presence of Outliers

Using methods of statistical mechanics, we analyse the effect of outliers on the supervised learning of a classification problem. The learning strategy aims at selecting informative examples and discarding outliers. We compare two algorithms which perform the selection either in a soft or a hard way. When the fraction of outliers grows large, the estimation errors undergo a first order phase transition.Comment: 24 pages, 7 figures (minor extensions added

Interprofessional Health Team Communication About Hospital Discharge: An Implementation Science Evaluation Study

The Consolidated Framework for Implementation Research guided formative evaluation of the implementation of a redesigned interprofessional team rounding process. The purpose of the redesigned process was to improve health team communication about hospital discharge. Themes emerging from interviews of patients, nurses, and providers revealed the inherent value and positive characteristics of the new process, but also workflow, team hierarchy, and process challenges to successful implementation. The evaluation identified actionable recommendations for modifying the implementation process

Gradient descent learning in and out of equilibrium

Relations between the off thermal equilibrium dynamical process of on-line learning and the thermally equilibrated off-line learning are studied for potential gradient descent learning. The approach of Opper to study on-line Bayesian algorithms is extended to potential based or maximum likelihood learning. We look at the on-line learning algorithm that best approximates the off-line algorithm in the sense of least Kullback-Leibler information loss. It works by updating the weights along the gradient of an effective potential different from the parent off-line potential. The interpretation of this off equilibrium dynamics holds some similarities to the cavity approach of Griniasty. We are able to analyze networks with non-smooth transfer functions and transfer the smoothness requirement to the potential.Comment: 08 pages, submitted to the Journal of Physics

Statistical mechanics of the multi-constraint continuous knapsack problem

We apply the replica analysis established by Gardner to the multi-constraint continuous knapsack problem,which is one of the linear programming problems and a most fundamental problem in the field of operations research (OR). For a large problem size, we analyse the space of solution and its volume, and estimate the optimal number of items to go into the knapsack as a function of the number of constraints. We study the stability of the replica symmetric (RS) solution and find that the RS calculation cannot estimate the optimal number of items in knapsack correctly if many constraints are required.In order to obtain a consistent solution in the RS region,we try the zero entropy approximation for this continuous solution space and get a stable solution within the RS ansatz.On the other hand, in replica symmetry breaking (RSB) region, the one step RSB solution is found by Parisi's scheme. It turns out that this problem is closely related to the problem of optimal storage capacity and of generalization by maximum stability rule of a spherical perceptron.Comment: Latex 13 pages using IOP style file, 5 figure

Storage of correlated patterns in a perceptron

We calculate the storage capacity of a perceptron for correlated gaussian patterns. We find that the storage capacity $\alpha_c$ can be less than 2 if similar patterns are mapped onto different outputs and vice versa. As long as the patterns are in general position we obtain, in contrast to previous works, that $\alpha_c \geq 1$ in agreement with Cover's theorem. Numerical simulations confirm the results.Comment: 9 pages LaTeX ioplppt style, figures included using eps

Secure exchange of information by synchronization of neural networks

A connection between the theory of neural networks and cryptography is presented. A new phenomenon, namely synchronization of neural networks is leading to a new method of exchange of secret messages. Numerical simulations show that two artificial networks being trained by Hebbian learning rule on their mutual outputs develop an antiparallel state of their synaptic weights. The synchronized weights are used to construct an ephemeral key exchange protocol for a secure transmission of secret data. It is shown that an opponent who knows the protocol and all details of any transmission of the data has no chance to decrypt the secret message, since tracking the weights is a hard problem compared to synchronization. The complexity of the generation of the secure channel is linear with the size of the network.Comment: 11 pages, 5 figure

Learning from Minimum Entropy Queries in a Large Committee Machine

In supervised learning, the redundancy contained in random examples can be avoided by learning from queries. Using statistical mechanics, we study learning from minimum entropy queries in a large tree-committee machine. The generalization error decreases exponentially with the number of training examples, providing a significant improvement over the algebraic decay for random examples. The connection between entropy and generalization error in multi-layer networks is discussed, and a computationally cheap algorithm for constructing queries is suggested and analysed.Comment: 4 pages, REVTeX, multicol, epsf, two postscript figures. To appear in Physical Review E (Rapid Communications

Inferring hidden states in Langevin dynamics on large networks: Average case performance

We present average performance results for dynamical inference problems in large networks, where a set of nodes is hidden while the time trajectories of the others are observed. Examples of this scenario can occur in signal transduction and gene regulation networks. We focus on the linear stochastic dynamics of continuous variables interacting via random Gaussian couplings of generic symmetry. We analyze the inference error, given by the variance of the posterior distribution over hidden paths, in the thermodynamic limit and as a function of the system parameters and the ratio {\alpha} between the number of hidden and observed nodes. By applying Kalman filter recursions we find that the posterior dynamics is governed by an "effective" drift that incorporates the effect of the observations. We present two approaches for characterizing the posterior variance that allow us to tackle, respectively, equilibrium and nonequilibrium dynamics. The first appeals to Random Matrix Theory and reveals average spectral properties of the inference error and typical posterior relaxation times, the second is based on dynamical functionals and yields the inference error as the solution of an algebraic equation.Comment: 20 pages, 5 figure