Alberto Maria Segre

Using Sensor Networks to Study the Effect of Peripatetic Healthcare Workers on the Spread of Hospital-Associated Infections

BACKGROUND Super-spreading events, in which an individual with measurably high connectivity is responsible for infecting a large number of people, have been observed. Our goal is to determine the impact of hand hygiene noncompliance among peripatetic (eg, highly mobile or highly connected) healthcare workers compared with less-connected workers. METHODS We used a mote-based sensor network to record contacts among healthcare workers and patients in a 20-bed intensive care unit. The data collected from this network form the basis for an agent-based simulation to model the spread of nosocomial pathogens with various transmission probabilities. We identified the most- and least-connected healthcare workers. We then compared the effects of hand hygiene noncompliance as a function of connectedness. RESULTS The data confirm the presence of peripatetic healthcare workers. Also, agent-based simulations using our real contact network data confirm that the average number of infected patients was significantly higher when the most connected healthcare worker did not practice hand hygiene and significantly lower when the least connected healthcare workers were noncompliant. CONCLUSIONS Heterogeneity in healthcare worker contact patterns dramatically affects disease diffusion. Our findings should inform future infection control interventions and encourage the application of social network analysis to study disease transmission in healthcare settings.

Center competition and outcomes following liver transplantation

In the United States, livers for transplantation are distributed within donation service areas (DSAs). In DSAs with multiple transplant centers, competition among centers for organs and recipients may affect recipient selection and outcomes in comparison with DSAs with only 1 center. The objective of this study was to determine whether competition within a DSA is associated with posttransplant outcomes and variations in patients wait‐listed within the DSA. United Network for Organ Sharing data for 38,385 adult cadaveric liver transplant recipients undergoing transplantation between January 1, 2003 and December 31, 2009 were analyzed to assess differences in liver recipients and donors and in posttransplant survival by competition among centers. The main outcome measures that were studied were patient characteristics, actual and risk‐adjusted graft and patient survival rates after transplantation, organ quality as quantified by the donor risk index (DRI), wait‐listed patients per million population by DSA, and competition as quantified by the Hirschman‐Herfindahl index (HHI). Centers were stratified by HHI levels as no competition or as low, medium (or mid), or high competition. In comparison with DSAs without competition, the low‐, mid‐, and high‐competition DSAs (1) performed transplantation for patients with a higher risk of graft failure [hazard ratio (HR) = 1.24, HR = 1.26, and HR = 1.34 (P < 0.001 for each)] and a higher risk of death [HR = 1.21, HR = 1.23, and HR = 1.34 (P < 0.001 for each)] and for a higher proportion of sicker patients as quantified by the Model for End‐Stage Liver Disease (MELD) score [10.0% versus 14.8%, 20.1%, and 28.2% with a match MELD score of 31‐40 (P < 0.001 for each comparison)], (2) were more likely to use organs in the highest risk quartile as quantified by the DRI [18.3% versus 27.6%, 20.4%, and 31.7% (P ≤ 0.001 for each)], and (3) listed more patients per million population [18 (median) versus 34 (P = not significant), 37 (P = 0.005), and 45 (P = 0.0075)]. Significant variability in patient selection for transplantation is associated with market variables characterizing competition among centers. These findings suggest both positive and negative effects of competition among health care providers. Liver Transpl 19:96–104, 2013.

Method for automated monitoring of hand hygiene adherence without radio-frequency identification.

Many efforts to automatically measure hand hygiene activity depend on radio-frequency identification equipment or similar technology that can be expensive to install. We have developed a method for automatically tracking the use of hand hygiene dispensers before healthcare workers enter (or after they exit) patient rooms that is easily and quickly deployed without permanent hardware.

Explanation-based manipulator learning: Acquisition of planning ability through observation

This paper describes a robot manipulator system currently under development which learns from observation. The system improves its problem-solving capabilities through the acquisition of task-related concepts. The system observes manipulator command sequences that solve problems currently beyond its own panning abilities. General problem-solving schemata are automatically constructed via a knowledge-based analysis of how the observed command sequence achieved the goal. This learning technique is based on explanatory schema acquisition. It is a knowledge-based approach, requiring sufficient background knowledge to understand the observed sequence. The acquired schemata serve two purposes: they allow the system to solve problems that were previously unsolvable, and they aid in the understanding of later observations.

A Critical Look at Experimental Evaluations of EBL

A number of experimental evaluations of explanation-based learning(EBL) have been reported in the literature on machine learning. A close examination of the design of these experiments reveals certain methodological problems that could affect the conclusions drawn from the experiments. This article analyzes some of the more common methodological difficulties, and illustrates them using selected previous studies.

Mutations in Extracellular Matrix Genes NID1 and LAMC1 Cause Autosomal Dominant Dandy–Walker Malformation and Occipital Cephaloceles

We performed whole‐exome sequencing of a family with autosomal dominant Dandy–Walker malformation and occipital cephaloceles and detected a mutation in the extracellular matrix (ECM) protein‐encoding gene NID1. In a second family, protein interaction network analysis identified a mutation in LAMC1, which encodes a NID1‐binding partner. Structural modeling of the NID1–LAMC1 complex demonstrated that each mutation disrupts the interaction. These findings implicate the ECM in the pathogenesis of Dandy–Walker spectrum disorders.

Do Peer Effects Improve Hand Hygiene Adherence among Healthcare Workers

OBJECTIVE To determine whether hand hygiene adherence is influenced by peer effects and, specifically, whether the presence and proximity of other healthcare workers has a positive effect on hand hygiene adherence. DESIGN An observational study using a sensor network. SETTING A 20-bed medical intensive care unit at a large university hospital. PARTICIPANTS Hospital staff assigned to the medical intensive care unit. METHODS We deployed a custom-built, automated, hand hygiene monitoring system that can (1) detect whether a healthcare worker has practiced hand hygiene on entering and exiting a patients room and (2) estimate the location of other healthcare workers with respect to each healthcare worker exiting or entering a room. RESULTS We identified a total of 47,694 in-room and out-of-room hand hygiene opportunities during the 10-day study period. When a worker was alone (no recent healthcare worker contacts), the observed adherence rate was 20.85% (95% confidence interval [CI], 19.78%-21.92%). In contrast, when other healthcare workers were present, observed adherence was 27.90% (95% CI, 27.48%-28.33%). This absolute increase was statistically significant (P < .01). We also found that adherence increased with the number of nearby healthcare workers but at a decreasing rate. These results were consistent at different times of day, for different measures of social context, and after controlling for possible confounding factors. CONCLUSIONS The presence and proximity of other healthcare workers is associated with higher hand hygiene rates. Furthermore, our results also indicate that rates increase as the social environment becomes more crowded, but with diminishing marginal returns.

A high-performance explanation-based learning algorithm

Abstract The main contribution of this paper is a new domain-independent explanation-based learning (EBL) algorithm. The new EBL∗DI algorithm significantly outperforms traditional EBL algorithms both by learning in situations where traditional algorithms cannot learn as well as by providing greater problem-solving performance improvement in general. The superiority of the EBL∗DI algorithm is demonstrated with experiments in three different application domains. The EBL∗DI algorithm is developed using a novel formal framework in which traditional EBL techniques are reconstructed as the structured application of three explanation-transformation operators. We extend this basic framework by introducing two additional operators that, when combined with the first three operators, allow us to prove a completeness result: in the formal framework, every EBL algorithm is equivalent to the application of the five transformation operators according to some control strategy. The EBL∗DI algorithm employs all five proof-transformation operators guided by five domain-independent control heuristics.

Nagging: a scalable fault-tolerant paradigm for distributed search

This paper describes nagging, a technique for parallelizing search in a heterogeneous distributed computing environment. Nagging exploits the speedup anomaly often observed when parallelizing problems by playing multiple reformulations of the problem or portions of the problem against each other. Nagging is both fault tolerant and robust to long message latencies. In this paper, we show how nagging can be used to parallelize several different algorithms drawn from the artificial intelligence literature, and describe how nagging can be combined with partitioning, the more traditional search parallelization strategy. We present a theoretical analysis of the advantage of nagging with respect to partitioning, and give empirical results obtained on a cluster of 64 processors that demonstrate naggings effectiveness and scalability as applied to A* search, αβ minimax game tree search, and the Davis-Putnam algorithm.

Healthcare worker contact networks and the prevention of hospital-acquired infections.

We present a comprehensive approach to using electronic medical records (EMR) for constructing contact networks of healthcare workers in a hospital. This approach is applied at the University of Iowa Hospitals and Clinics (UIHC) – a 3.2 million square foot facility with 700 beds and about 8,000 healthcare workers – by obtaining 19.8 million EMR data points, spread over more than 21 months. We use these data to construct 9,000 different healthcare worker contact networks, which serve as proxies for patterns of actual healthcare worker contacts. Unlike earlier approaches, our methods are based on large-scale data and do not make any a priori assumptions about edges (contacts) between healthcare workers, degree distributions of healthcare workers, their assignment to wards, etc. Preliminary validation using data gathered from a 10-day long deployment of a wireless sensor network in the Medical Intensive Care Unit suggests that EMR logins can serve as realistic proxies for hospital-wide healthcare worker movement and contact patterns. Despite spatial and job-related constraints on healthcare worker movement and interactions, analysis reveals a strong structural similarity between the healthcare worker contact networks we generate and social networks that arise in other (e.g., online) settings. Furthermore, our analysis shows that disease can spread much more rapidly within the constructed contact networks as compared to random networks of similar size and density. Using the generated contact networks, we evaluate several alternate vaccination policies and conclude that a simple policy that vaccinates the most mobile healthcare workers first, is robust and quite effective relative to a random vaccination policy.