8,155 research outputs found
Inter-organizational collaboration among health and social care: TRT©, a transactional approach
Inter-organizational collaboration (IOC) supported by information and communication technologies (ICTs) faces challenges on many fronts in 21st century England as well as globally. Between the somewhat desirable ideal of 'joined up' systems providing efficient services to customers and clients on one side of the continuum, and the costs and risk factors associated with integrating data or constructing large databases on the other side, a fundamental tension exists.
This paper addresses this issue in two parts. Firstly, it argues that there is a way forward for information sharing among heterogeneous organizations which does not involve the integration of systems, interoperability, joined up recordkeeping, database linkage, or construction of yet another large database. Transactions in Real Time© (TRT©), the transaction by transaction information sharing approach, satisfies all the requirements of each collaborating organization for information sharing.
Secondly, this paper briefly considers the future of IOC among health and social care and possible pathways forward through this uncertain area. The health and social care information sharing transaction is often unique among the particular transaction situation, and the micro and macro environments
Leveraging text data for causal inference using electronic health records
Text is a ubiquitous component of medical data, containing valuable
information about patient characteristics and care that are often missing from
structured chart data. Despite this richness, it is rarely used in clinical
research, owing partly to its complexity. Using a large database of patient
records and treatment histories accompanied by extensive notes by attendant
physicians and nurses, we show how text data can be used to support causal
inference with electronic health data in all stages, from conception and design
to analysis and interpretation, with minimal additional effort. We focus on
studies using matching for causal inference. We augment a classic matching
analysis by incorporating text in three ways: by using text to supplement a
multiple imputation procedure, we improve the fidelity of imputed values to
handle missing data; by incorporating text in the matching stage, we strengthen
the plausibility of the matching procedure; and by conditioning on text, we can
estimate easily interpretable text-based heterogeneous treatment effects that
may be stronger than those found across categories of structured covariates.
Using these techniques, we hope to expand the scope of secondary analysis of
clinical data to domains where quantitative data is of poor quality or
nonexistent, but where text is available, such as in developing countries
Consolidated List of Requirements
This document is a consolidated catalogue of requirements for the Electronic
Health Care Record (EHCR) and Electronic Health Care Record Architecture
(EHCRA), gleaned largely from work done in the EU Framework III and IV
programmes and CEN, but also including input from other sources including world-wide
standardisation initiatives. The document brings together the relevant work done into a
classified inventory of requirements to inform the on-going standardisation process as
well as act as a guide to future implementation of EHCRA-based systems. It is meant as
a contribution both to understanding of the standard and to the work that is being
considered to improve the standard. Major features include the classification into issues
affecting the Health Care Record, the EHCR, EHCR processing, EHCR interchange and
the sharing of health care information and EHCR systems. The principal information
sources are described briefly. It is offered as documentation that is complementary to the
four documents of the ENV 13606 Parts I-IV produced by CEN Pts 26,27,28,29. The
requirements identified and classified in this deliverable are referenced in other
deliverables
A Coherent Healthcare System with RDBMS, NoSQL and GIS Databases
With new database system development and new data types emerging, many applications are no longer using a monolithic, simple client/server structure, but using more than one types of database systems to store heterogeneous data. In this project, we exploit the benefits of combing Relational Database Management System (RDBMS) and NoSQL systems in the development of better Electronic Health Records (EHRs) and Clinical Decision Support Systems (CDSS). Specifically, MySQL, MongoDB, and GIS databases are integrated to improve EHR systems and to provide better clinical decision supports. The ACID (atomicity, consistency, isolation, durability) properties of the RDBMS ensure data integrity, database security, efficient SQL queries, easy data access, and effective transaction processing. MongoDB provides the system with clear internal data structure, easy scaling-out, fine-Tuning, and convenient mapping of application objects to the database objects. The GIS database allows vivid visualization of the geographic locations of patients, physician offices, and medical facilities. The integrations of these database systems in healthcare help application systems to comply with the EHR HIPAA requirements without compromising on scalability and performance
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