14,475 research outputs found
Operational Plan for HMIS Rollout to be Read in Conjunction with the MoH&SW Document of October 2007
The MoH&SW, with a consortium of partners, in October 2007, developed a Proposal to Strengthen the HMIS in Tanzania. This document builds on that proposal to develop a budgeted 6‐month plan to kick‐start implementation of the Revised MTUHA in one region and at national level, to develop a replicable model that can be scaled up to other regions as additional funds become available. The overall HMIS revision process will ensure that, within a period of five years the HMIS will be functional in all 21 regions of the country, in a phased manner Six months intensive systems and database development in Mtwara region Eighteen months implementation in one region in each of the six zones Within 5 years, National rollout to every region The initial six months implementation process, described in depth in this document, will use action research and participatory development methodology that will integrate the six work packages in the HMIS document, in line with the HSSP III proposals for strengthening M&E. A number of dedicated teams will roll out the HMIS, develop a toolkit for implementation in other regions and produce a modern web based data warehouse. The project logframe aims to provide quality routine data for monitoring MDGs and the NHSSPIII by producing five outputs – HMIS revision, HMIS implementation, Capacity development, the DHIS software and action research. Terms of reference are developed for each of the HMIS teams, based on the activities in the logframe – Indicator and dataset revision, HMIS design, Database development and training team. An action‐based budget of US1,25 million for the first year, including the rollout activities, the development of training material, adaptation of software etc. The other six regions will cost 1,05million for first year; all regions will reduce to 300,000 in the third year. National level costs will reduce from 1,2 million for the first year, including the rollout activities, the development of training material, adaptation of software et
Semantic processing of EHR data for clinical research
There is a growing need to semantically process and integrate clinical data
from different sources for clinical research. This paper presents an approach
to integrate EHRs from heterogeneous resources and generate integrated data in
different data formats or semantics to support various clinical research
applications. The proposed approach builds semantic data virtualization layers
on top of data sources, which generate data in the requested semantics or
formats on demand. This approach avoids upfront dumping to and synchronizing of
the data with various representations. Data from different EHR systems are
first mapped to RDF data with source semantics, and then converted to
representations with harmonized domain semantics where domain ontologies and
terminologies are used to improve reusability. It is also possible to further
convert data to application semantics and store the converted results in
clinical research databases, e.g. i2b2, OMOP, to support different clinical
research settings. Semantic conversions between different representations are
explicitly expressed using N3 rules and executed by an N3 Reasoner (EYE), which
can also generate proofs of the conversion processes. The solution presented in
this paper has been applied to real-world applications that process large scale
EHR data.Comment: Accepted for publication in Journal of Biomedical Informatics, 2015,
preprint versio
Digital curation and the cloud
Digital curation involves a wide range of activities, many of which could benefit from cloud
deployment to a greater or lesser extent. These range from infrequent, resource-intensive tasks
which benefit from the ability to rapidly provision resources to day-to-day collaborative activities
which can be facilitated by networked cloud services. Associated benefits are offset by risks
such as loss of data or service level, legal and governance incompatibilities and transfer
bottlenecks. There is considerable variability across both risks and benefits according to the
service and deployment models being adopted and the context in which activities are
performed. Some risks, such as legal liabilities, are mitigated by the use of alternative, e.g.,
private cloud models, but this is typically at the expense of benefits such as resource elasticity
and economies of scale. Infrastructure as a Service model may provide a basis on which more
specialised software services may be provided.
There is considerable work to be done in helping institutions understand the cloud and its
associated costs, risks and benefits, and how these compare to their current working methods,
in order that the most beneficial uses of cloud technologies may be identified. Specific
proposals, echoing recent work coordinated by EPSRC and JISC are the development of
advisory, costing and brokering services to facilitate appropriate cloud deployments, the
exploration of opportunities for certifying or accrediting cloud preservation providers, and
the targeted publicity of outputs from pilot studies to the full range of stakeholders within the
curation lifecycle, including data creators and owners, repositories, institutional IT support
professionals and senior manager
Proposal to Strenghern Health Information System [HIS]
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The HMIS Program described in this document aims at improving and strengthening the current Health Management Information System (HMIS) in Tanzania, known as MTUHA. The consortium behind the HMIS Program is headed by the Ministry of Health & Social Welfare (MOHSW) and consists of the following additional partners; Ifakara Health Research and Development Centre, University of Dar es Salaam and the University of Oslo, representing national and international capacity in HMIS. The HMIS Program is linked to the Payment for performance (P4P) funding scheme which is initiated by the Norway Tanzania Partnership Initiative. The P4P has a focus on maternal and child health and relies upon quality indicators on performance in these areas from health facilities and districts. The provision of quality data and indicators on MDG 4 & 5 is therefore a key target for the HMIS Program. The chosen approach is, however, to derive these data from the HMIS and not to establish a separate data collection structure, hence the HMIS Program. Quality information by way of essential indicators, such as for monitoring the Millennium Development Goals 4 & 5, are crucial for health services delivery and program management as well as for M&E. Currently, however, the HMIS is not providing such needed data of sufficient completeness, timeliness and quality, leading health programs and funding agencies to establish their own structures for data collection, and thus creating fragmentation and adding to the problem. The HMIS Program aims at changing this negative trend and turning the HMIS into the key source of shared essential quality information in Tanzania by; focusing on action oriented use of information for management at each level of the health services and by providing timely quality information to all stakeholders, including all health programs and funding agencies in the HMIS strengthening process – making it an all-inclusive national process, focusing on capacity development; on-site support and facilitation, short courses and continuous education, building capacity in the MOHSW and establishing a national network of HMIS support, and by building on experience, methods and tools from Africa’s “best practices” HMIS, such as South Africa – and Zanzibar Within this proposal the aim is to carry out the HMIS strengthening process in 1/3 of the districts in the country, 7 regions, during the first 3 years. The objective, however, is to cover the entire country during the 5 years duration of the NTPI. By aiming at quick and tangible results, the expectation is that other funding agencies will join forces and thereby ensuring national coverage.\ud
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Enabling quantitative data analysis through e-infrastructures
This paper discusses how quantitative data analysis in the social sciences can engage with and exploit an e-Infrastructure. We highlight how a number of activities which are central to quantitative data analysis, referred to as ‘data management’, can benefit from e-infrastructure support. We conclude by discussing how these issues are relevant to the DAMES (Data Management through e-Social Science) research Node, an ongoing project that aims to develop e-Infrastructural resources for quantitative data analysis in the social sciences
Addendum to Informatics for Health 2017: Advancing both science and practice
This article presents presentation and poster abstracts that were mistakenly omitted from the original publication
From access and integration to mining of secure genomic data sets across the grid
The UK Department of Trade and Industry (DTI) funded BRIDGES project (Biomedical Research Informatics Delivered by Grid Enabled Services) has developed a Grid infrastructure to support cardiovascular research. This includes the provision of a compute Grid and a data Grid infrastructure with security at its heart. In this paper we focus on the BRIDGES data Grid. A primary aim of the BRIDGES data Grid is to help control the complexity in access to and integration of a myriad of genomic data sets through simple Grid based tools. We outline these tools, how they are delivered to the end user scientists. We also describe how these tools are to be extended in the BBSRC funded Grid Enabled Microarray Expression Profile Search (GEMEPS) to support a richer vocabulary of search capabilities to support mining of microarray data sets. As with BRIDGES, fine grain Grid security underpins GEMEPS
All-Payer Claims Database Development Manual: Establishing a Foundation for Health Care Transparency and Informed Decision Making
With support from the Gary and Mary West Health Policy Center, the APCD Council has developed a manual for states to develop all-payer claims databases. Titled All-Payer Claims Database Development Manual: Establishing a Foundation for Health Care Transparency and Informed Decision Making, the manual is a first-of its-kind resource that provides states with detailed guidance on common data standards, collection, aggregation and analysis involved with establishing these databases
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