More than S.K.I.N. Deep: Decreasing Pressure Ulcer Development in the Pediatric Intensive Care Unit

Pressure ulcers are defined as localized areas of tissue destruction that develop when soft tissue is compressed between a bony prominence and an external surface for a prolonged period of time. Although any hospitalized child is at risk for the development of a pressure ulcer, the critically ill child is at increased risk. The critical care environment poses special challenges to preventing the development of pressure ulcers secondary to the high acuity of patients and the highly invasive nature of interventions and therapies those patients receive. The incidence of pediatric pressure ulcer development in the critical care population has been reported to be as high as 10.2 to 27%. This prospective, quasi-experimental study was conducted in order to determine whether a specific pressure ulcer prevention bundle was associated with a significant reduction in pressure ulcer development in infants 0 to 3 months old in the pediatric intensive care unit. The four main components of the pressure ulcer prevention bundle were (S) support surfaces, (K) keep turning every 2 hours, (I) incontinence management, and (N) nutrition consultation. The second element of the study was a survey of the nursing staff of the pediatric intensive care unit to gain a better understanding of the barriers and facilitators to implementing the S.K.I.N. care pressure ulcer prevention bundle. The implementation of the S.K.I.N. care bundle is associated with a significant drop in pressure ulcer incidence from 18.8% to 6.8%. The infants who developed pressure ulcers in the experimental group received significantly more mechanical support and had significantly longer lengths of stay than the infants who did not develop a pressure ulcer. The survey demonstrated that competing demands on nurses\u27 time as the biggest barrier to implementation of the pressure ulcer prevention bundle. Having appropriate supplies and easy access to the support surfaces were the biggest facilitators of implementing the bundle

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest

Generalizability of machine learning models in predicting patient deterioration

Predicting patient deterioration in an Intensive Care Unit (ICU) effectively is a critical health care task serving patient health and resource allocation. At times, the task may be highly complex for a physician, yet high-stakes and time-critical decisions need to be made based on it. In this work, we investigate the ability of a set of machine learning models to algorithimically predict future occurrence of in hospital death based on Electronic Health Record (EHR) data of ICU-patients. For one, we will assess the generalizability of the models. We do this by evaluating the models on hospitals the data of which has not been considered when training the models. For another, we consider the case in which we have access to some EHR data for the patients treated at a hospital of interest. In this setting, we assess how EHR data from other hospitals can be used in the optimal way to improve the prediction accuracy. This study is important for the deployment and integration of such predictive models in practice, e.g., for real-time algorithmic deterioration prediction for clinical decision support. In order to address these questions, we use the eICU collaborative research database, which is a database containing EHRs of patients treated at a heterogeneous collection of hospitals in the United States. In this work, we use the patient demographics, vital signs and Glasgow coma score as the predictors. We devise and describe three computational experiments to test the generalization in different ways. The used models are the random forest, gradient boosted trees and long short-term memory network. In our first experiment concerning the generalization, we show that, with the chosen limited set of predictors, the models generalize reasonably across hospitals but that only a small data mismatch is observed. Moreover, with this setting, our second experiment shows that the model performance does not significantly improve when increasing the heterogeneity of the training set. Given these observations, our third experiment shows tha

Implementation of the Provider Bull\u27s-eye: A Tool to Guide Clinical Reasoning and Communication for Nurse Practitioners

Problem In a large urban pediatric hospital in the southeastern region of the United States, Pediatric Intensive Care Unit nurse practitioners (NPs) had difficulty transitioning to practice. Identified NP transition to practice barriers were arduous clinical reasoning and ineffective communication. Transition to practice barriers impact patient outcomes and healthcare cost due to patient care errors, delays in care, and NP turnover from poor practice perceptions. The goal of the Doctor of Nursing Practice project was to examine whether the Provider Bull’s-Eye Tool (PBT), a tool to guide clinical reasoning and communication for NPs, would decrease time to diagnosis and intervention selection, while simultaneously improving communication and perceptions of practice confidence in new NPs. Methods The PBT was evaluated using a two-group comparison. All pilot participants were volunteers, actively enrolled in a NP program or had less than or equal to two years NP experience. Project participants, N=17, were randomly divided into those who completed simulation in medicine education (SIM) evaluation prior to PBT education, and those who completed SIM evaluation post PBT education. During SIM, project participant time to diagnosis and interventions were documented using a validated checklist. After SIM, each participant verbalized a recorded handoff report to a transferring facility. Recorded handoff reports of both groups were analyzed for communication enhancements from PBT training. Once both groups concluded all project components, a Likert survey evaluating perception of practice confidence after PBT training was completed. Results The PBT trained group was observed to be marginally slower during SIMS due to increased cognitive processing; however, they were more likely than the non-PBT group to diagnose and intervene appropriately in several areas. The PBT group also had more effective communication patterns during handoff reports than the non-PBT group. Further, PBT training increased perception of practice confidence in both groups. Conclusion Based on findings, the PBT is a promising tool that has the capacity to enhance NP clinical reasoning while simultaneously promoting effective handoff communication. Improving these skills increased perceptions of practice confidence. Combined, these improvements could result in decreased healthcare cost by reducing patient errors, delays, and NP turnover

Comparison of the effectiveness of traditional nursing medication administration with the Color Coding Kids system in a sample of undergraduate nursing students

The problem of medication errors in hospitals and the vulnerability of pediatric patients to adverse drug events (ADE) was investigated and well substantiated. The estimated additional cost of inpatient care for ADE’s in the hospital setting alone was conservatively estimated at an annual rate per incident of 400,000 preventable events each incurring an extra cost of approximately $5,857. The purpose of the researcher was to compare the effectiveness of traditional nursing medication administration with the Color Coding Kids (CCK) system (developed by Broselow and Luten for standardizing dosages) to reduce pediatric medication errors. A simulated pediatric rapid response scenario was used in a randomized clinical study to measure the effects of the CCK system to the traditional method of treatment using last semester nursing students. Safe medication administration, workflow turnaround time and hand-off communication were variables studied. A multivariate analysis of variance was used to reveal a significant difference between the groups on safe medication administration. No significant difference between the groups on time and communication was found. The researcher provides substantial evidence that the CCK system of medication administration is a promising technological breakthrough in the prevention of pediatric medication errors

CLINICAL DIAGNOSTIC WHOLE EXOME SEQUENCING FOR INFANTS IN INTENSIVE CARE SETTINGS: OUTCOMES ANALYSIS AND ECONOMIC EVALUATION

Whole exome sequencing (ES) is an extensive form of genetic testing and increasingly used as a diagnostic tool. Clinical uptake of genome-scale sequencing occurred without clear guidelines for application or robust information regarding potential impact on patient health outcomes or cost of care. For infants in intensive care with suspected genetic conditions, ES can be especially powerful to identify a specific diagnosis and inform crucial decisions about medical care. However, little is known about the cost-effectiveness of ES compared to other diagnostic strategies. This project first assessed the literature on pediatric clinical ES. Then, using electronic medical record, diagnostic laboratory, and hospital cost data, we analyzed and compared outcomes and costs of care for patients with suspected genetic etiologies admitted to intensive care within the first year of life in two patient cohorts: those who had ES (ES, n=368) and did not have ES (No-ES, n=368) as part of a diagnostic workup at a large children’s hospital. Molecular diagnostic yield (25.8% No-ES, 27.7% ES; p=0.56) and 1-year survival (84.8% No-ES, 80.2% ES; p=0.10) were similar between cohorts, while ES patients had higher total cost, diagnostic investigation cost, and genetic test cost during the index admission and for the year after the date of first inpatient genetics consultation (all p\u3c0.01). ES demonstrated important diagnostic utility for patients with monogenic disease, yet other genetic tests, especially chromosomal microarray, remain important given the burden of chromosomal abnormalities in this population. As clinically applied over the first 5 years, ES does not appear to be a cost-effective diagnostic tool for the broad population of newborns and infants with suspected genetic disease compared to standard diagnostic tests such as chromosomal microarray analysis and panel/single gene testing. Further work is needed to develop outcome measures to capture utility of ES results – both diagnostic and non-diagnostic – for clinicians, patients, and patients’ families, and to specify clinical guidelines for appropriate ES application

BETTER MODELS FOR HIGH-STAKES TASKS

The intersection of machine learning and healthcare has the potential to transform medical diagnosis, treatment, and research. Machine learning models can analyze vast amounts of medical data and identify patterns that may be too complex for human analysis. However, one of the major challenges in this field is building trust between users and the model. Due to things like high false alarm rate and the black box nature of machine learning models, patients and medical professionals need to understand how the model arrives at its recommendations. In this work, we present several methods that aim to improve machine learning models in high-stakes environments like healthcare. Our work unifies two sub-fields of machine learning, explainable AI, and uncertainty quantification. First we develop a model-agnostic approach to deliver instance-level explanations using influence functions. Next, we show that these influence functions function are fairly robust across domains. Then, we develop an efficient method that reduces model uncertainty while modeling data uncertainty via Bayesian Neural Networks. Finally, we show that when combined our methods deliver significant utility beyond traditional methods while retaining a high level of performance via a real world deployment. Overall, the integration of uncertainty quantification and explainable AI can help overcome some of the major challenges of machine learning in healthcare. Together, they can provide healthcare professionals with powerful tools for improving patient outcomes and advancing medical research

How family-centered care and being a good parent impacts parent experiences in the pediatric intensive care unit

Being a parent to a critically ill child requiring care in the pediatric intensive care unit (PICU) can be a stressful experience for parents. Family-centered care (FCC) has been shown to improve outcomes for pediatric patients and families, however there has been little research examining FCC in the PICU from the parent perspective. This dissertation consists of three distinct studies that examined the delivery of family-centered care and the parenting of a critically ill child in the PICU. The first study synthesized the research literature regarding FCC in the PICU from the parent perspective based on the Institute for Patient and Family Centered Care (IPFCC) identified core concepts (e.g., respect and dignity, information sharing, participation, and collaboration). This literature synthesis revealed that parents described both met and unmet needs regarding the implementation of FCC and led to development of a conceptual model of FCC in the PICU that included respect and dignity, information sharing, and participation as interacting with one another within the physical and cultural environment of the PICU. Based on the findings of the first study, the second study aimed to further develop the PICU FCC conceptual model and examined parental perspectives of the impact of the physical and cultural environment of the PICU in the delivery of FCC. The physical and cultural environment was found to exert both positive and negative contextual influence in the delivery of FCC per parent report. The third study examined and expanded on parental perception of the good parent construct as applied to parenting a child in the PICU over the first year of life. Previously identified good parent themes including being an advocate, focusing on my child’s quality of life, and being there for my child were present in parent interviews. Newly identified themes included knowing my child, developing relationships with other PICU infants and families, and developing a trusting relationship with members of the health care team. The findings of this dissertation add information to the PICU FCC body of literature by examining the delivery of FCC in the PICU from the parental perspective, acknowledging how the physical and cultural environments of the PICU impact parents of critically ill children, and informing how the good parent construct in the PICU evolves over time. Future studies are needed to explore facilitators and barriers to implementation of FCC in the PICU as conceptualized by the IPFCC and other professional organizationsDoctor of Philosoph