Informatics

With the increase in the amount of public health data along with the growth of public health informatics, it is important for epidemiologists to understand the current trends in technology and the impact they may have in the field. Because it is unfeasible for public health professionals to be an expert in every emerging technology, this presentation seeks to provide them with a better understanding of how emerging technologies may impact the field and the level of expertise required to realize benefits from the new technologies. Furthermore, understanding the capabilities provided by emerging technologies may guide future training and continuing education for public health professionals.

Objective: The objective of this presentation is to explore emerging technologies and how they will impact the public health field. New technologies such as blockchain, artificial intelligence (AI), and the Internet of Things (IoT) will likely be incorporated into epidemiological methods and processes. This presentation will provide an overview of these technologies and focus on how they may impact public health surveillance in the future.

The emerging threat of antimicrobial resistant organisms is a pressing public health concern. Surveillance for antimicrobial resistance can prevent infections, protect patients in the healthcare setting and improve antimicrobial use. In 2018, the Utah Department of Health mandated the reporting of antimicrobial susceptibility panels performed on selected organisms. Utah utilizes the Electronic Message Staging Area (EMSA), a home-grown application to translate, process, and enter electronic laboratory results into UT-NEDSS, Utah'™s integrated disease surveillance system. Processing these results electronically is challenging due to the need to interpret results based on the antimicrobial agent combined with the organism it was performed on. The receipt of antimicrobial susceptibility panels has required enhancements to EMSA for these results to be automatically processed.

Objective: Illustrate how the Utah Department of Health automatically processes antimicrobial susceptibility results that are received electronically

Most outbreaks are small and localized in nature, although it is larger outbreaks that result in the most public attention. So a solution to manage an outbreak has to be able to accommodate a response to small outbreaks in a single jurisdiction scalable up to outbreaks that involve thousands of cases across multiple jurisdictions and to handle different types of situations with different questions and response required. To make this happen, information and resources need to be shared more consistently and efficiently to help facilitate the communication that occurs at all levels and to support day-to-day operations in order to ensure consistent use.

Objective

1.To provide a flexible solution to perform an outbreak investigation by improving communications during an incident.

2.To provide all users with a common set of data for decision support.

3.To provide standard forms for a consistent approach and to improve data quality.

There is national recognition of the need for cross-programmatic data and system coordination and integration for surveillance, prevention, response, and control implementation. To accomplish this public health must develop an informatics competency and create an achievable roadmap, supported by performance measures, for the future. Within the New York State Department of Health, Office of Public Health (OPH), a cross-organizational and cross-functional Public Health Information Management Workgroup (PHIM-WG) was formed to align public health information and technology goals, objectives, strategies, and resources across OPH. In June 2011, the OPH Performance Management Initiative, funded by the Centers for Disease Control and Prevention, offered strategic planning workshops to PHIM-WG.

Objective 

To develop strategic objectives necessary to optimize the collection, integration, and use of information across public health programs and internal and external partners for improving the overall health and safety of people and their communities.

The critical need for population-level interventions to support the health needs of the growing population of older adults is widely recognized1. In addition, there is a need for novel indicators to monitor wellness as a resource for living and a means for prediction and prevention of changes in community health status2. Smart homes, defined as residential infrastructure equipped with technology features that enable passive monitoring of residents to proactively support wellness, have the potential to support older adults for independence at the residence of their choice. However, a characterization of the current state of smart homes research as a population health intervention is lacking. In addition, there is a knowledge translation gap between the smart homes research and public health practice communities. The EBPH movement identifies three types of evidence along a continuum to inform population health interventions: Type 1 (something should be done), Type 2 (this should be done) and Type 3 (how it should be done)3. Type 2 evidence consists of a classification scheme for interventions (emerging, promising, effective and evidence-based)3. To illustrate typology use with an example: the need for population health interventions for aging populations is well known (Type 1 evidence), many studies show that smart home technologies can support aging in place (Type 2 evidence) but there are few, if any, examples of smart homes as population health interventions to support aging in place (Type 3 evidence). Our research questions for this systematic review are: 1) What categories of Type 2 evidence from the scientific literature uphold smart homes as an EBPH intervention? 2) What are the novel health indicators identified from smart home studies to inform design of a community health registry that supports prediction and prevention of negative changes in health status? 3) What stakeholders are reported in studies that contribute Type 2 evidence for smart homes as an EBPH intervention? 4) What gaps exist between Type 2 and Type 3 evidence for smart homes as an EBPH intervention?

Objective

This study aims to 1) characterize the state of smart homes research as a population health intervention to support aging in place through systematic review and classification of scientific literature using an evidence-based public health (EBPH) typology and 2) identify novel indicators of health captured by monitoring technologies to inform design of a community health registry.

Although the advent of the ONCs "meaningful use" criteria has added significant new incentives for healthcare organizations to provide the necessary data for implementing syndromic surveillance, incentives alone are not sufficient to sustain a robust community of practice that engages public health and healthcare practitioners working together to fully achieve meaningful use objectives. The process for building a successful community of practice around syndromic surveillance is primarily application-agnostic. The business process has many of the same characteristics regardless of application features, and can be incrementally customized for each community based on the unique needs and opportunities and the functional characteristics of the application. This presentation will explore lessons-learned in the north central Texas region with BioSense 1 and ESSENCE over the past six years, and will describe the multi-phase process currently underway for BioSense 2.0. Key program process steps and success criteria for public health and healthcare practitioners will be described. This road map will enable other local health department jurisdictions to replicate proven methodologies in their own communities. The presentation will also highlight what it takes for an existing community of practice with a home grown system to move processes and protocols to the cloud.

Objective

To explore the lessons learned from the Advanced Practice Center methodology regarding the implementation of syndromic surveillance while considering what it takes to create, enhance, and sustain relationships between hospitals, public health practitioners, and the community.

The Extended Syndromic Surveillance Ontology (ESSO) is an open source terminological ontology designed to facilitate the text mining of clinical reports in English [1,2]. At the core of ESSO are 279 clinical concepts (for example, fever, confusion, headache, hallucination, fatigue) grouped into eight syndrome categories (rash, hemorrhagic, botulism, neurological, constitutional, influenza-like-illness, respiratory, and gastrointestinal). In addition to syndrome groupings, each concept is linked to synonyms, variant spellings and UMLS Concept Unique Identifiers. ESSO builds on the Syndromic Surveillance Ontology [3], a resource developed by a working group of eighteen researchers representing ten syndromic surveillance systems in North America. ESSO encodes almost three times as many clinical concepts as the Syndromic Surveillance Ontology, and incorporates eight syndrome categories, in contrast to the Syndromic Surveillance Ontology's four (influenza-like-illness, constitutional, respiratory and gastrointestinal). The new clinical concepts and syndrome groupings in ESSO were developed by a board-certified infectious disease physician (author JD) in conjunction with an informaticist (author MC).

Objective

In order to evaluate and audit these new syndrome definitions, we initiated a survey of syndromic surveillance practitioners. We present the results of an online survey designed to evaluate syndrome definitions encoded in the Extended Syndromic Surveillance Ontology.