The Minimum Data Set (MDS) Frequency data summarizes health status indicators for active residents currently in nursing homes. The MDS is part of the Federally-mandated process for clinical assessment of all residents in Medicare and Medicaid certified nursing homes. This process provides a comprehensive assessment of each resident's functional capabilities and helps nursing home staff identify health problems. Care Area Assessments (CAAs) are part of this process, and provide the foundation upon which a resident's individual care plan is formulated. MDS assessments are completed for all residents in certified nursing homes, regardless of source of payment for the individual resident. MDS assessments are required for residents on admission to the nursing facility, periodically, and on discharge. All assessments are completed within specific guidelines and time frames. In most cases, participants in the assessment process are licensed health care professionals employed by the nursing home. MDS information is transmitted electronically by nursing homes to the national MDS database at CMS. When reviewing the MDS 3.0 Frequency files, some common software programs e.g., ‘Microsoft Excel’ might inaccurately strip leading zeros from designated code values (i.e., "01" becomes "1") or misinterpret code ranges as dates (i.e., O0600 ranges such as 02-04 are misread as 04-Feb). As each piece of software is unique, if you encounter an issue when reading the CSV file of Frequency data, please open the file in a plain text editor such as ‘Notepad’ or ‘TextPad’ to review the underlying data, before reaching out to CMS for assistance.

The Facility-Level Minimum Data Set (MDS) Frequency dataset provides information for active nursing home residents on topics, such as race/ethnicity, age, or marital status; discharge dispositions; hearing, speech, and vision; cognitive patterns; mood; functional abilities and goals; bladder and bowel; active diagnoses; health conditions; swallowing/nutritional status; oral/dental status; skin conditions; medications; special treatments, procedures, and programs; restraints and alarms; and participation in assessment and goal setting. Note: The MDS dataset contains more records than most spreadsheet programs can handle. The use of a database or statistical software is generally required. The dataset can be filtered to a more manageable size for use in a spreadsheet program by clicking on the “View Data” button. Additional filter information can be found in the methodology, if needed.

The MHMDS is a regular return of data generated by providers of adult secondary mental health services in England, in the course of delivering services to patients. From Q1 2011/12 onwards, the MHMDS also includes data from Independent Sector Organisations and is processed using the new system. Full details of the methods used in processing can be found in the MHMDS Version 4 User Guidance and Appendices (see related links). The MHMDS dataset is received by the HSCIC as record level anonymised data from patient administration systems, Care Programme Approach systems and Mental Health Act administration systems. Changes to this publication From April 2013 the submission of MHMDS data will be made every month, rather than every quarter, to support the implementation of PbR for mental health. From April 2013 NHS wide changes also took place as a result of the Health and Social Care Act 2012. As a result, the frequency and content of this publication will be changing from this point onwards and this publication is the last time the data will be reported on in its current format.

The MZG is a registration with which all non-psychiatric hospitals in Belgium must make their (anonymised) administrative, medical and nursing data available to the Federal Public Service (FPS) Public Health. The aim of the MZG is to support the government's health policy by

• Determining the needs for hospital facilities;
• Describing the qualitative and quantitative accreditation standards of hospitals and their services;
• Organising the financing of hospitals;
• Determining policy for the practice of medicine;
• To outline epidemiological policy.

The MZG aims also to support the health policy of hospitals by providing national and individual feedback so that a hospital can compare itself with other hospitals and adapt its internal policy.

All reports can be found here (in French/Dutch).

This statistical release makes available the most recent Monthly Mental Health Minimum Data Set (MHMDS): July 2014 (final data) and August 2014 (provisional data). Further analysis to support currencies and payment in adult and older people's mental health services was added to the publication of April 2014 (final data). These changes are described in the Methodological Change paper. This information will be of particular interest to organisations involved in delivering secondary mental health care to adults and older people, as it presents timely information to support discussions between providers and commissioners of services. The MHMDS Monthly Report now includes the 10 nationally recommended quality and outcome indicators to support the implementation of currencies and payment in mental health. For patients, researchers, agencies, and the wider public it aims to provide up to date information about the numbers of people using services, spending time in psychiatric hospitals and subject to the Mental Health Act (MHA). Some of these measures are currently experimental analysis. The Currency and Payment (CaP) measures can be found in a separate machine-readable data file and may also be accessed via an on-line interactive visualisation tool that supports benchmarking. This can be accessed through the related links at the bottom of the page. This release also includes paper which announces changes to the MHMDS monthly report, following changes made to the dataset to include learning disability services. Please note - The Currency and Payment file is designed to be analysed by Care Cluster. Each measure is shown by the 21 Care Clusters and by an 'All' Care Cluster category, which gives the total for all Care Clusters. Failure to analyse information by the Care Cluster field will result in duplication. Following publication of this release, on 28th October 2014 an issue was identified with the July Currency and Pricing file. Due to an error in our automated process, organisation names with commas were split across columns, resulting in figures being out of line of the appropriate column header for these organisations. A corrected version of the July 2014 Currency and Pricing .csv was issued on 29th October and we apologise for any inconvenience caused.

description:

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.

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.

The purpose of the project is to detect unreported Supplemental Security Income (SSI) recipient admissions to Title XIX institutions. A file containing SSN's of SSI recipients (all eligible individuals and members of eligible couples in current pay) will be matched against the Health Care Financing Administration's (HCFA) Minimum Data Set (MDS) database which contains admission, discharge, re-entry and assessment information about persons in Title XIX facilities for all 50 States and Washington, D.C. This database is updated monthly. The match will produce an output file containing MDS data pertinent to SSI eligibility on matched records. This data will be compared back to the SSR data to generate alerts to the Field Offices for their actions.

Find more Pacific data on PDH.stat.

Abstract

A standardized screening and assessment tool of health status with comprehensive assessments of all residents in CMS-certified NHs regardless of insurance type. Completed on admission, quarterly and with change in status.

Usage

ResDAC Data Use Agreement

Cost to reuse the data

Collaboration Notes

I am open to new collaborations AND I am open to supporting a doctoral student

Start and End Dates of Data

2011-2022

For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease.

The Minimum Data Set (MDS) is part of the federally mandated process for clinical assessment of all residents in Medicare or Medicaid certified nursing homes. This process provides a comprehensive assessment of each residents functional capabilities and helps nursing home staff identify health problems. These public use reports are meant to begin the process of sharing information from the national MDS database.

The Older Persons and Informal Caregivers Survey - Minimum DataSet (TOPICS-MDS) is a public data repository which contains information on the physical and mental health and well-being of older persons and informal caregivers and their care use across the Netherlands. The database was developed at the start of The National Care for the Elderly Programme (‘Nationaal Programma Ouderenzorg’ - NPO) on behalf of the Organisation of Health Research and Development (ZonMw - The Netherlands), in part to ensure uniform collection of outcome measures, thus promoting comparability between studies.Between 2008 en 2016, 53 different research projects have contributed data to this initiative, resulting in a pooled dataset with cross-sectional and (partly) longitudinal data of >43,000 older persons and >9,000 informal caregivers. Out of these numbers, a number of 7,600 concerns care receiver - caregiver dyads of whom information on both the care receiver and caregiver is available.The 'TOPICS-MDS NPO caregiver’ dataset contains no care receiver (older person) data, only informal caregiver data.

Nationally defined dataset containing administrative details for stays within an adult, pead or neonatal critical care unit. Items are coded using the national definitions.

As ChatGPT emerges as a potential ally in healthcare decision-making, it is imperative to investigate how users leverage and perceive it. The repurposing of technology is innovative but brings risks, especially since AI’s effectiveness depends on the data it’s fed. In healthcare, ChatGPT might provide sound advice based on current medical knowledge, which could turn into misinformation if its data sources later include erroneous information. Our study assesses user perceptions of ChatGPT, particularly of those who used ChatGPT for healthcare-related queries. By examining factors such as competence, reliability, transparency, trustworthiness, security, and persuasiveness of ChatGPT, the research aimed to understand how users rely on ChatGPT for health-related decision-making. A web-based survey was distributed to U.S. adults using ChatGPT at least once a month. Bayesian Linear Regression was used to understand how much ChatGPT aids in informed decision-making. This analysis was conducted on subsets of respondents, both those who used ChatGPT for healthcare decisions and those who did not. Qualitative data from open-ended questions were analyzed using content analysis, with thematic coding to extract public opinions on urban environmental policies. Six hundred and seven individuals responded to the survey. Respondents were distributed across 306 US cities of which 20 participants were from rural cities. Of all the respondents, 44 used ChatGPT for health-related queries and decision-making. In the healthcare context, the most effective model highlights ’Competent + Trustworthy + ChatGPT for healthcare queries’, underscoring the critical importance of perceived competence and trustworthiness specifically in the realm of healthcare applications of ChatGPT. On the other hand, the non-healthcare context reveals a broader spectrum of influential factors in its best model, which includes ’Trustworthy + Secure + Benefits outweigh risks + Satisfaction + Willing to take decisions + Intent to use + Persuasive’. In conclusion our study findings suggest a clear demarcation in user expectations and requirements from AI systems based on the context of their use. We advocate for a balanced approach where technological advancement and user readiness are harmonized.

The Older Persons and Informal Caregivers Survey - Minimum DataSet (TOPICS-MDS) is a public data repository which contains information on the physical and mental health and well-being of older persons and informal caregivers and their care use across the Netherlands. The database was developed at the start of The National Care for the Elderly Programme (‘Nationaal Programma Ouderenzorg’ - NPO) on behalf of the Organisation of Health Research and Development (ZonMw - The Netherlands), in part to ensure uniform collection of outcome measures, thus promoting comparability between studies.Since September 2014, TOPICS-MDS data are also collected within the ZonMw funded ‘Memorabel’ programme, that is specifically aimed at improving the quality of life for people with dementia and the care and support provided to them. In Memorabel round 1 through 4, 11 different research projects have collected TOPICS-MDS data, which has resulted in a pooled database with cross-sectional and (partly) longitudinal data of 1,400 older persons with early onset or advanced dementia and about 950 informal caregivers. Out of these numbers, a number of 919 concerns care receiver - caregiver dyads of whom information on both the care receiver and caregiver is available.More background information on both NPO and Memorabel 1-4 can be found in the overall information on TOPICS-MDS under the tab ‘Data files’ in DANS EASY (doi.org/10.17026/dans-xvh-dbbf).At the moment, 3 different research projects have collected data for TOPICS-MDS Memorabel 5.The 'TOPICS-MDS Memorabel 5 caregiver dataset, as part of the Memorabel 5 database, contains only data about the caregiver, such as data on physical and emotional health, time spent on care receiver and quality of life. Date Submitted: 2023-10-05

On 24 June 2014 this page was edited and the National Statistics logo was removed. The HSCIC apologises for any confusion this may have caused. This statistical release makes available the most recent Mental Health Minimum Data Set (MHMDS) final monthly data (November 2013). This publication series replaces the Routine Quarterly MHMDS Reports, last published for the period Q4 2012-13, reflecting the change in the frequency of submissions. Further information about these changes and format of the monthly release can be found through the Resource links. This information will be of particular interest to organisations involved in delivering secondary mental health care for adults, as it presents timely information to support discussions between providers and commissioners of services. For patients, researchers, agencies and the wider public it aims to provide up to date information about the numbers of people using services, spending time in psychiatric hospitals and subject to the Mental Health Act (MHA). Some of these measures are currently experimental analysis. For this month's report we have added two new measures in the machine readable dataset - 16 year old bed days and 17 year old days. We've also added national, year to date figures on the number of people who had contact with secondary mental health services and the number of people who have spent at least one night as an inpatient in psychiatric hospital to the executive summary. In addition to the standard monthly outputs, this month's report includes a special feature focusing on our experimental analysis of uses of the Mental Health Act in adult mental health services from MHMDS

Minimum Data Set (MDS) enables integration in data collection, uniform data reporting, and data exchange across clinical and research information systems. The current study was conducted to determine a comprehensive national MDS for the Epidermolysis Bullosa (EB) information management system in Iran. This cross-sectional descriptive study consists of three steps: systematic review, focus group discussion, and the Delphi technique. A systematic review was conducted using relevant databases. Then, a focus group discussion was held to determine the extracted data elements with the help of contributing multidisciplinary experts. Finally, MDSs were selected through the Delphi technique in two rounds. The collected data were analyzed using Microsoft Excel 2019. In total, 103 data elements were included in the Delphi survey. The data elements, based on the experts’ opinions, were classified into two main categories: administrative data and clinical data. The final categories of data elements consisted of 11 administrative items and 92 clinical items. The national MDS, as the core of the EB surveillance program, is essential for enabling appropriate and informed decisions by healthcare policymakers, physicians, and healthcare providers. In this study, a MDS was developed and internally validated for EB. This research generated new knowledge to enable healthcare professionals to collect relevant and meaningful data for use. The use of this standardized approach can help benchmark clinical practice and target improvements worldwide.

BackgroundWorldwide, health systems have been challenged by the overwhelming demands of the COVID-19 pandemic. In Ethiopia, maintaining essential health services during the COVID-19 pandemic is critical to preventing severe outcomes and protecting the gains made over the past years in the health sector. This project aims to explore the health system’s response to maintaining essential healthcare services in Addis Ababa, Ethiopia.MethodsA total of 60 key informant interviews were conducted by purposively selecting key stakeholders from Federal Ministry of Health, Addis Ababa Regional Health Bureau, Sub-city Health Offices, and frontline healthcare providers. Interviews were transcribed verbatim and coded using Open Code. Thematic analysis was employed to analyze the data.ResultCOVID-19 affected the delivery of essential health services in several ways, namely: decline in health service utilization, fear of infection among healthcare providers, stigma towards healthcare providers, and perceived decrease in quality-of-service provision. However, the health system actors made efforts to sustain services while responding to the pandemic by enacting changes in the service delivery modality. The most significant service delivery changes included repurposing health centers and prolonged prescriptions (multi-month medication dispensing). The primary challenges encountered were burnout of the health workforce and a shortage of personal protective equipment.ConclusionCOVID-19 has affected the delivery of essential health services in multifaceted ways. System actors have accordingly made efforts to sustain services while responding to the pandemic.

This statistical release makes available the most recent Mental Health Minimum Data Set (MHMDS) final monthly data (March 2014). This publication series replaces the Routine Quarterly MHMDS Reports, last published for the period Q4 2012-13, reflecting the change in the frequency of submissions. Further information about these changes and format of the monthly release can be found through the Resource links. This information will be of particular interest to organisations involved in delivering secondary mental health care for adults, as it presents timely information to support discussions between providers and commissioners of services. For patients, researchers , agencies and the wider public it aims to provide up to date information about the numbers of people using services, spending time in psychiatric hospitals and subject to the Mental Health Act (MHA). Some of these measures are currently experimental analysis. Further analysis to support currency and payment in adult mental health services will be added to the release for April 2014 and changes to the content of the current monthly release are described in the Methodological Change paper referenced below. IMPORTANT - PLEASE NOTE: Following publication of this release, on 23rd June 2014 an issue was identified with the data consistency lines DCM1-DCM6 in the March 2014 Final Data Quality Measures spreadsheet. Whilst these measures should be based on final data for each provider (using the refresh submission or the primary if there was none), it was actually based on both types of submission. A corrected version of the March 2014 Final Data Quality Measures spreadsheet has been issued and we apologise for any inconvenience caused.