, and Noam Arzt r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r I n February of 2004, the New York City Department of Health and Mental Hygiene completed the integration of its childhood immunization and blood lead test registry databases, each containing over 2 million children. A modular approach was used to build a separate integrated system, called Master Child Index, to include all children in both the immunization and lead test registries. The principal challenge of this integration was to properly align records so that a child represented in one database is matched with the same child in the other database. To accomplish this task as well as to identify internal duplicate records within each database, an artificial intelligence record linkage system was created. The preliminary results show high rates of accurate merging of records both within and between the two databases. The 4,610,585 records contained in both databases before Master Child Index implementation consolidated into 2,977,290 records in the integrated system. The matching system eliminated 523,720 duplicate records within the two databases and matched and merged 1,109,575 records between the two databases. The Department of Health and Mental Hygiene plans to further develop the Master Child Index and use it as the department-wide, record-matching system.
This article focuses on the requirements and current developments in clinical decision support technologies for immunizations (CDSi) in both the public health and clinical communities, with an emphasis on shareable solutions. The requirements of the Electronic Health Record Incentive Programs have raised some unique challenges for the clinical community, including vocabulary mapping, update of changing guidelines, single immunization schedule, and scalability. This article discusses new, collaborative approaches whose long-term goal is to make CDSi more sustainable for both the public and private sectors.
Public health agencies established immunization registries - now called Immunization Information Systems (IIS) - to consolidate records across provider locations to support more effective immunization of patients and public health surveillance. While initially collecting data through interactive client-server and then web-based interfaces, IIS now collect the vast majority of their data through automated interfaces to electronic health record (EHR) systems using standard application programming interfaces (API). IIS have sophisticated processing rules for the incoming data to ensure data accuracy and completeness. This paper will review the existing workflow, standards, and processes used by IIS to accept, process, and make immunization data available. This will include a review of emerging standards - Fast Healthcare Interoperability Resources (FHIR) - which will likely become dominant over the next few years.
Introduction:The immunization calculation engine (ICE) is a free, open-source immunization forecasting evaluation and software system whose default immunization schedule supports all routine childhood, adolescent, and adult immunizations based on the recommendations of the Advisory Committee on Immunization Practices (ACIP). ICE utilizes its immunization rules and patient data to evaluate and return the validity of each immunization in the patient's history along with one or more evaluation reasons. It also returns a recommendation for each vaccine group along with one or more recommendation reasons.Methods: In January 2020, ICE was first released as a Docker image along with the traditional zip archive file which had been used up to that point. Docker enables software providers to easily distribute their software so that it can be run "out of the box" in the user's local environment. Software running in Docker containers drastically reduces the complexity of software distribution and set up.Results: Clinical systems of many types use ICE. The project began within the public health arena as a feature of Immunization Information Systems (IIS), but electronic health records (EHR) and personal health records (PHR) have also deployed ICE. While it is not possible to identify the specific impact of ICE on clinical care without additional research, it should be pointed out that once deployed within an IIS, EHR, or PHR the display of ICE results is performed for every patient viewed by a user and often for every patient appearing on a report. In a typical month, thousands if not millions of evaluations and forecasts are performed by ICE and displayed to the users. Conclusions:The ICE Project believes in minimizing the barriers to installing and using ICE anywhere. To that end, there is no registration required to download the source code or runtime code for the ICE service and its default rule. Similarly, the Project created a Docker image of ICE to facilitate easy and seamless implementation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.