The Resident Assessment Instrument/Minimum Data Set (RAI/MDS) is a comprehensive assessment and care planning process used by the nursing home industry since 1990 as a requirement for nursing home participation in the Medicare and Medicaid programs. The RAI/MDS provides data for monitoring changes in resident status that are consistent and reliable over time. The VA commitment to quality propelled the implementation of the RAI/MDS in its nursing homes now known as VA Community Living Centers (CLC). In addition to providing consistent clinical information, the RAI/MDS can be used as a measure of both quality and resource utilization, thereby serving as a benchmark for quality and cost data within the VA as well as with community based nursing facilities. Workload based on RAI/MDS can be calculated electronically by the interactions of the elements of the MDS data and grouped into 53 categories referred to as Resource Utilization Groups (RUG-IV). Residents are assessed quarterly. The data is grouped for analysis at the Austin Information Technology Center (AITC). Conversion to electronic data entry and transmission to the AITC was completed system-wide by year-end 2000. In 2010, the Centeres for Medicare and Medicaide Services released a significantly upgraded version, MDS 3.0, to begin to be implemented on October 1, 2011 in VHA CLCs. Training is underway currently. The MDS 3.0 will generate a new set of Quality Indicators and Quality Monitors as well the RUGs will increase to 64 RUGs from the current 53 RUG groups.

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The Long-Term Care Minimum Data Set (MDS) is a standardized, primary screening and assessment tool of health status that forms the foundation of the comprehensive assessment for all residents in a Medicare and or Medicaid-certified long-term care facility. The MDS contains items that measure physical, psychological and psychosocial functioning. The items in the MDS give a multidimensional view of the patients functional capacities and helps staff to identify health problems.

The Long-Term Care Minimum Data Set (MDS) is a standardized, primary screening and assessment tool of health status that forms the foundation of the comprehensive assessment for all residents in a Medicare and or Medicaid-certified long-term care facility. The MDS contains items that measure physical, psychological and psychosocial functioning. The items in the MDS give a multidimensional view of the patients functional capacities and helps staff to identify health problems.

The Patient Assessment File (PAF) database compiles the results of the Patient Assessment Instrument (PAI) questionnaire filled out for intermediate care Veterans Health Administration (VHA) patients. The PAI is filled out within two weeks of admission. It is also completed semi-annually on April 1st and October 1st for each patient by a registered nurse familiar with the patient. The PAI questions cover medical treatments, conditions, selected diagnoses, activities of daily living, behaviors, some rehabilitation therapies, and chronic respiratory support. The database is managed by the Geriatrics & Extended Care Strategic Health Care Group in the Office of Patient Care Services. It is currently running at the Austin Information Technology Center (AITC) and is stored in flat files. PAF's primary customer is the Allocation Resource Center (ARC) in Braintree MA. The ARC receives the data from AITC and combines it with data from the Patient Treatment File (PTF) which contains more detailed demographic and treatment information. The ARC builds ORACLE tables, assigning RUG II (Resource Utilization Group II) scores and weighted work units reflecting the level and type of care needed. The 16 different weighted work units, ranging from 479 to 1800, are a factor in the resource allocation and budget decisions on long-term care, and are used to measure efficiency. The data is also used in other reports to Central Office, the Veterans Integrated Service Networks, and the facilities. Several other units also use PAF information including the Decision Support System (DSS). Currently, PAF is in the process of being replaced by the Resident Assessment Instrument/Minimum Data Set (RAI/MDS). RAI/MDS uses a much more extensive questionnaire as its source of information. The RAI/MDS provides clinical data and care protocols in addition to the newer RUG III scores, and is required by the Centers for Medicare and Medicaid Service funded hospitals.

The Veterans Equitable Resource Allocation (VERA) database, is operated by the Allocation Resource Center (ARC) in Braintree, MA. The ARC is part of the Resource Allocation & Execution Office of the Office of Finance. The database is developed from the Patient Treatment File, National Patient Care Database, Fee Basis Medical and Pharmacy System, Decision Support System (DSS) National extracts, DSS Derived Monthly Program Cost Report (MPCR), Resident Assessment Instrument (RAI) Minimum Data Set (MDS), Clinical Case Registry (CCR), and Home Dialysis Data Collection System, the Pharmacy Benefits Management database and the Consolidated Enrollment File. Most of the clinical data is Veterans Health Information Systems and Technology Architecture data which is transmitted to the Austin Information Technology Center (AITC) where it is retrieved by the ARC each month. The ARC also retrieves DSS cost data from the AITC as well. Some additional information is received from the Hines Pharmacy Benefits Management and the CCR databases. The data from these sources is combined to develop patient-specific care and cost data for each hospitalization or visit at the location or treatment level. Aggregate tables summarize this data for reporting and analysis purposes. The VERA databases are the basis for resource allocation in the Veterans Health Administration.

To describe the application of the Behaviour Change Wheel (BCW) to the development of a family-centred intervention for families of children with hearing loss transitioning into early intervention. The BCW was used in a mixed methods design to understand the gaps in family-centred service provision and to identify appropriate intervention functions and implementation options to address these gaps. Families and health professionals participated in different steps of the BCW. The qualitative interviews revealed that families required individualised information and support. The quantitative and interview data suggested inconsistencies in the provision of information and support to some families. It was determined that administration of a needs assessment by the support professionals was the most appropriate target behaviour to address this issue. In the analysis of the professionals’ capabilities, opportunities, and motivations for administration of a needs assessment, a resource limitation was identified, and therefore, a Minimum Data Set for a Needs Assessment Tool was developed to facilitate the assessment of families’ needs during the transition period. This study provided an example of how the BCW could be successfully applied to the design of a family-centred intervention for families of children with hearing loss.

IntroductionThere is a need to develop harmonized procedures and a Minimum Data Set (MDS) for cross-border Multi Casualty Incidents (MCI) in medical emergency scenarios to ensure appropriate management of such incidents, regardless of place, language and internal processes of the institutions involved. That information should be capable of real-time communication to the command-and-control chain. It is crucial that the models adopted are interoperable between countries so that the rights of patients to cross-border healthcare are fully respected.ObjectiveTo optimize management of cross-border Multi Casualty Incidents through a Minimum Data Set collected and communicated in real time to the chain of command and control for each incident. To determine the degree of agreement among experts.MethodWe used the modified Delphi method supplemented with the Utstein technique to reach consensus among experts. In the first phase, the minimum requirements of the project, the profile of the experts who were to participate, the basic requirements of each variable chosen and the way of collecting the data were defined by providing bibliography on the subject. In the second phase, the preliminary variables were grouped into 6 clusters, the objectives, the characteristics of the variables and the logistics of the work were approved. Several meetings were held to reach a consensus to choose the MDS variables using a Modified Delphi technique. Each expert had to score each variable from 1 to 10. Non-voting variables were eliminated, and the round of voting ended. In the third phase, the Utstein Style was applied to discuss each group of variables and choose the ones with the highest consensus. After several rounds of discussion, it was agreed to eliminate the variables with a score of less than 5 points. In phase four, the researchers submitted the variables to the external experts for final assessment and validation before their use in the simulations. Data were analysed with SPSS Statistics (IBM, version 2) software.ResultsSix data entities with 31 sub-entities were defined, generating 127 items representing the final MDS regarded as essential for incident management. The level of consensus for the choice of items was very high and was highest for the category ‘Incident’ with an overall kappa of 0.7401 (95% CI 0.1265–0.5812, p 0.000), a good level of consensus in the Landis and Koch model. The items with the greatest degree of consensus at ten were those relating to location, type of incident, date, time and identification of the incident. All items met the criteria set, such as digital collection and real-time transmission to the chain of command and control.ConclusionsThis study documents the development of a MDS through consensus with a high degree of agreement among a group of experts of different nationalities working in different fields. All items in the MDS were digitally collected and forwarded in real time to the chain of command and control. This tool has demonstrated its validity in four large cross-border simulations involving more than eight countries and their emergency services.

Participant characteristics, by context and data collection type.

This is an export of the data archived from the 2022 National Incident Feature Service.Sensitive fields and features have been removed.Each edit to a feature is captured in the Archive. The GDB_FROM and GDB_TO fields show the date range that the feature existed in the National Incident Feature Service.The National Incident Feature Service is based on the National Wildfire Coordinating Group (NWCG) data standard for Wildland Fire Event. The Wildland Fire Event data standard defines the minimum attributes necessary for collection, storage and dissemination of incident based data on wildland fires (wildfires and prescribed fires). The standard is not intended for long term data storage, rather a standard to assist in the creation of incident based data management tools, minimum standards for data exchange, and to assist users in meeting the NWCG Standards for Geospatial Operations (PMS 936).