Nephi Walton

Modeling the variations in pediatric respiratory syncytial virus seasonal epidemics

BackgroundSeasonal respiratory syncytial virus (RSV) epidemics occur annually in temperate climates and result in significant pediatric morbidity and increased health care costs. Although RSV epidemics generally occur between October and April, the size and timing vary across epidemic seasons and are difficult to predict accurately. Prediction of epidemic characteristics would support management of resources and treatment.MethodsThe goals of this research were to examine the empirical relationships among early exponential growth rate, total epidemic size, and timing, and the utility of specific parameters in compartmental models of transmission in accounting for variation among seasonal RSV epidemic curves. RSV testing data from Primary Childrens Medical Center were collected on children under two years of age (July 2001-June 2008). Simple linear regression was used explore the relationship between three epidemic characteristics (final epidemic size, days to peak, and epidemic length) and exponential growth calculated from four weeks of daily case data. A compartmental model of transmission was fit to the data and parameter estimated used to help describe the variation among seasonal RSV epidemic curves.ResultsThe regression results indicated that exponential growth was correlated to epidemic characteristics. The transmission modeling results indicated that start time for the epidemic and the transmission parameter co-varied with the epidemic season.ConclusionsThe conclusions were that exponential growth was somewhat empirically related to seasonal epidemic characteristics and that variation in epidemic start date as well as the transmission parameter over epidemic years could explain variation in seasonal epidemic size. These relationships are useful for public health, health care providers, and infectious disease researchers.

Predicting the start week of respiratory syncytial virus outbreaks using real time weather variables

BackgroundRespiratory Syncytial Virus (RSV), a major cause of bronchiolitis, has a large impact on the census of pediatric hospitals during outbreak seasons. Reliable prediction of the week these outbreaks will start, based on readily available data, could help pediatric hospitals better prepare for large outbreaks.MethodsNaïve Bayes (NB) classifier models were constructed using weather data from 1985-2008 considering only variables that are available in real time and that could be used to forecast the week in which an RSV outbreak will occur in Salt Lake County, Utah. Outbreak start dates were determined by a panel of experts using 32,509 records with ICD-9 coded RSV and bronchiolitis diagnoses from Intermountain Healthcare hospitals and clinics for the RSV seasons from 1985 to 2008.ResultsNB models predicted RSV outbreaks up to 3 weeks in advance with an estimated sensitivity of up to 67% and estimated specificities as high as 94% to 100%. Temperature and wind speed were the best overall predictors, but other weather variables also showed relevance depending on how far in advance the predictions were made. The weather conditions predictive of an RSV outbreak in our study were similar to those that lead to temperature inversions in the Salt Lake Valley.ConclusionsWe demonstrate that Naïve Bayes (NB) classifier models based on weather data available in real time have the potential to be used as effective predictive models. These models may be able to predict the week that an RSV outbreak will occur with clinical relevance. Their clinical usefulness will be field tested during the next five years.

IBM Watson for Clinical Decision Support

One approach to the design of a medical decisioning system is to avoid making specific decisions, but rather scan through the hugely voluminous structured (databases) and unstructured (text) data sources, and present a list of evidence-based alternatives. These alternatives can be submitted to the most sophisticated non-linear analytical processing system in the universe: the minds of the physicians charged with providing the right diagnosis and treatment for the right patient at the right time. This chapter describes an early attempt to use the IBM Watson computer (winner of the TV contest show, Jeopardy!) to create such a tool, which in the hands of the physician is a very sophisticated extension of his or her own brain.

Chapter 3 – Biomedical Informatics

This chapter is an introduction to proactive decisioning in medicine and health care, facilitated by the construction of analytical models to predict future states, rather than react to existing healthcare conditions.

Platform for Data Integration and Analysis, and Publishing Medical Knowledge as Done in a Large Hospital

The Military Institute of Medicine in Poland had a problem. They wanted to build a predictive analytics decisioning system, but realized that it had to be designed as a whole system – they could not just cobble together some existing parts and resources. They designed the entire system from scratch, including standardized data access from a variety of sources, data preparation, and data analysis to guide medical diagnosis and treatment. This chapter describes how they did it.

The Nature of Insight from Data and Implications for Automated Decisioning: Predictive and Prescriptive Models, Decisions, and Actions

In this chapter we move into the next phase of the predictive analytics operation – combining the various hardware and software elements into an automated decision-making system. These “decisioning” systems are the goal; the “Holy Grail” of predictive analytics. Individual algorithms and methodologies should not be viewed as ends in themselves, but as means to an end – the predictive analytics systems that receive input data, conduct necessary data preparation operations, train the appropriate modeling algorithms, and output the decisions themselves – not just some information that can be used by subjective humans to make decisions

Root Cause Analysis, Six Sigma, and Overall Quality Control and Lean Concepts: The First Process to Bring Quality and Cost-Effectiveness to Medical Care Delivery

In 1996, the Institute of Medicine (IOM) launched a program to improve the quality of health in the nation. This focus led to the incorporation of the Six Sigma predictive analytical process developed in other industries. The combination of Six Sigma processing and Deming’s emphasis on continuous quality improvement led to the development of the fishbone process model. The core element of the fishbone model is root cause analysis. This chapter presents a rich landscape of quality control issues in health care, with a focus on root cause analysis in hospitals.

Predictive Analytics in Nursing Informatics

From the beginning of medical treatment in our civilization, the prevailing view was that medicine was a “noble” art practiced by the physician, rather than a science pursued by the learned. That view began to change in the mid-1800s, instigated not by a physician but by a nurse – Florence Nightingale. The intention of this chapter is to inspire the nurse informatician with the possibilities of predictive analytics in nursing informatics, and examples of current research projects. There is endless potential in this field for effectively using predictive analytics and data mining. This chapter is not meant to be a complete assessment of the possibilities, or to describe all the areas in which PA can be used, but rather to be a guide with examples to stimulate the mind. Modern nursing informatics, as presented in this chapter, follows in Florence Nightingale’s footsteps.

The Predictive Potential of Connected Digital Health

With the development of a healthcare-centered democracy, we have seen an explosion in the volume and velocity of patient-generated data. This development has become a driving force in the connection of digital health records to each other and to diagnosis and treatment practitioners. The volume of patient-generated data and their digital format provide hints that point to the promise and potential of what predictive analytics will be able to do if we can harness this torrent of bits and bytes. Mobile connected health devices and applications are proving to be one of the most disruptive forces within health care – but this disruption is moving health care in new, exciting directions. These “bring your own device” technologies are generally smaller, faster, better, and cheaper than many traditional products.

The Joint Commission: Formerly the Joint Commission on Accreditation of Healthcare Organizations (JCAHO)

This chapter raises the level of medical operations to the level of the hospital, as the center of medical treatment, rather than the physician’s office. The central issue in hospital-centered care is the quality of the services provided by the hospital; high quality goods and services can become submerged in the “broader” issues of administrative operations and the need to “keep the lights on” in the facility. Certification of hospitals is the best way to keep quality central, and this is the task of the Joint Commission.