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 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.

SPSS Dataset with the ranking data of the health domains of the TOPICS-MDS as assessed by the participants of the first round of the Delphi study.

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.

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 Workforce & Quality Incentive Program Quarterly Performance Reports (WQIP) contain the following WQIP metrics: acuity-adjusted staffing, staffing completeness, staffing turnover, MDS clinical metrics, MDS data completeness, claims-based clinical metrics, Medi-Cal disproportionate share metric, and MDS racial and ethnic data completeness. The data set will include the distribution of facility-level scores for the currently available performance period data, as well as a comparison to the benchmark values for the MDS clinical and acuity-adjusted staffing hour metrics. If you need to reference the source for the HCAI IDs used in the Quarterly Performance Report, you can find it at ELMS-OSHPD - Licensed and Certified Healthcare.

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.

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.

The Treatment Episode Data Set -- Admissions (TEDS-A) is a national census data system of annual admissions to substance abuse treatment facilities. TEDS-A provides annual data on the number and characteristics of persons admitted to public and private substance abuse treatment programs that receive public funding. The unit of analysis is a treatment admission. TEDS consists of data reported to state substance abuse agencies by the treatment programs, which in turn report it to SAMHSA. A sister data system, called the Treatment Episode Data Set -- Discharges (TEDS-D), collects data on discharges from substance abuse treatment facilities. The first year of TEDS-A data is 1992, while the first year of TEDS-D is 2006. TEDS variables that are required to be reported are called the "Minimum Data Set (MDS)", while those that are optional are called the "Supplemental Data Set (SuDS)". Variables in the MDS include: information on service setting, number of prior treatments, primary source of referral, gender, race, ethnicity, education, employment status, substance(s) abused, route of administration, frequency of use, age at first use, and whether methadone was prescribed in treatment. Supplemental variables include: diagnosis codes, presence of psychiatric problems, living arrangements, source of income, health insurance, expected source of payment, pregnancy and veteran status, marital status, detailed not in labor force codes, detailed criminal justice referral codes, days waiting to enter treatment, and the number of arrests in the 30 days prior to admissions (starting in 2008). Substances abused include alcohol, cocaine and crack, marijuana and hashish, heroin, nonprescription methadone, other opiates and synthetics, PCP, other hallucinogens, methamphetamine, other amphetamines, other stimulants, benzodiazepines, other non-benzodiazepine tranquilizers, barbiturates, other non-barbiturate sedatives or hypnotics, inhalants, over-the-counter medications, and other substances. Created variables include total number of substances reported, intravenous drug use (IDU), and flags for any mention of specific substances.

*Adjustment for multiple comparisons: Bonferroni.(a) Mean Difference.(b) medical doctor.

Comprehensibility of the questions (Qualitative data from the focus groups, Round 2).

Users need to be aware of intended changes to the presentation of these statistics. For further information, please read the "revisions and issues section" of this month's bulletin. Provisional monthly figures for headcount, full-time equivalent, role count and turnover of NHS Hospital and Community Health Service (HCHS) staff groups working in England (excluding primary care staff). As expected with provisional statistics, some figures may be revised from month to month as issues are uncovered and resolved. No refreshes of the provisional data will take place either as part of the regular publication process, or where minor enhancements to the methodology have an insignificant impact on the figures at a national level. However, the provisional status allows for this to occur if it is determined that a refresh of data is required subsequent to initial release. Where a refresh of data occurs, it will be clearly documented in the publications. The monthly publication is an accurate summary of the validated data extracted from the NHS's HR and Payroll system. It has a provisional status as the data may change slightly over time where trusts make updates to their live operational systems. Given the size of the NHS workforce and the changing composition, particularly during this period of transition, it is likely that we will see some additional fluctuations in the workforce numbers over the next few months, reflecting both national and local changes as a result of the NHS reforms. These statistics relate to the contracted positions within English NHS organisations and may include those where the person assigned to the position is temporarily absent, for example on maternity leave. Please note: Concerns have been raised relating to the accuracy of the Health Visitor Minimum Data Set (MDS) training figures. This note is just to highlight a potential issue that may require a future revision to these figures. This does not affect any other data items within this publication. We welcome feedback on the methodology and tables within this publication. Please email us with your comments and suggestions, clearly stating 'Monthly HCHS Workforce' as the subject heading, via enquiries@hscic.gov.uk or 0300 303 5678.

BackgroundThe Older Persons and Informal Caregivers Survey—Minimum Dataset (TOPICS-MDS) collects uniform information from research projects funded under the Dutch National Care for the Elderly Programme. To compare the effectiveness of these projects a preference-weighted outcome measure that combined multidimensional TOPICS-MDS outcomes into a composite endpoint (TOPICS-CEP) was developed based on the health state preferences of older persons and informal caregivers.ObjectivesTo derive preference weights for TOPICS-CEP’s components based on health state preferences of healthcare professionals and to investigate whether these weights differ between disciplines and differ from those of older persons and informal caregivers.Materials and MethodsVignette studies were conducted. Participants assessed the general wellbeing of older persons described in vignettes on a scale (0-10). Mixed linear analyses were used to obtain and compare the preference weights of the eight TOPICS-CEP components: morbidities, functional limitations, emotional wellbeing, pain experience, cognitive problems, social functioning, self-perceived health, and self-perceived quality of life (QOL).ResultsOverall, 330 healthcare professionals, 124 older persons and 76 informal caregivers participated. The preference weights were not significantly different between disciplines. However, the professionals’ preference weights differed significantly from those of older persons and informal caregivers. Morbidities and functional limitations were given more weight by older persons and informal caregivers than by healthcare professionals [difference between preference weights: 0.12 and 0.07] while the opposite was true for pain experience, social functioning, and self-perceived QOL [difference between preference weights: 0.13, 0.15 and 0.26].ConclusionIt is important to recognize the discrepancies between the health state preferences of various stakeholders to (1) correctly interpret results when studying the effectiveness of interventions in elderly care and (2) establish appropriate healthcare policies. Furthermore, we should strive to include older persons in our decision making process through a shared decision making approach.

Awareness of MDs among physicians.

Abstract Background The Older Persons and Informal Caregivers Survey- Minimum Dataset (TOPICS-MDS) collects uniform information from research projects funded under the Dutch National Care for the Elderly Programme. To compare the effectiveness of these projects a reference-weighted outcome measure that combined multidimensional TOPICS-MDS outcomes into a composite endpoint (TOPICS-CEP) was developed based on the health state preferences of olde persons and informal caregivers. Objectives To derive preference weights for TOPICS-CEP’s components based on health state preferences of healthcare professionals and to investigate whether these weights differ between disciplines and differ from those of older persons and informal caregivers. Materials and methods Vignette studies were conducted. Participants assessed the general wellbeing of older persons described in vignettes on a scale (0-10). Mixed linear analyses were used to obtain and compare the preference weights of the eight TOPICS-CEP components: morbidities, functional limitations, emotional wellbeing, pain experience, cognitive problems, social functioning, self-perceived health, and self-perceived quality of life (QOL). Results Overall, 330 healthcare professionals, 124 older persons and 76 informal caregivers participated. The preference weights were not significantly different between disciplines. However, the professionals’ preference weights differed significantly from those of older persons and informal caregivers. Morbidities and functional limitations were given more weight by older persons and informal caregivers than by healthcare professionals [difference between preference weights: 0.12 and 0.07] while the opposite was true for pain experience, social functioning, and self-perceived QOL [difference between preference weights: 0.13, 0.15 and 0.26]. Conclusion It is important to recognize the discrepancies between the health state preferences of various stakeholders to (1) correctly interpret results when studying the effectiveness of interventions in elderly care and (2) establish appropriate healthcare policies. Furthermore, we should strive to include older persons in our decision making process trough a shared decision making approach.

BackgroundMedicare Advantage (MA) and Medicare fee-for-service (FFS) plans have different financial incentives. Medicare pays predetermined rates per beneficiary to MA plans for providing care throughout the year, while providers serving FFS patients are reimbursed per utilization event. It is unknown how these incentives affect post-acute care in skilled nursing facilities (SNFs). The objective of this study was to examine differences in rehabilitation service use, length of stay, and outcomes for patients following hip fracture between FFS and MA enrollees.Methods and findingsThis was a retrospective cohort study to examine differences in health service utilization and outcomes between FFS and MA patients in SNFs following hip fracture hospitalization during the period January 1, 2011, to June 30, 2015, and followed up until December 31, 2015. We linked the Master Beneficiary Summary File, Medicare Provider and Analysis Review data, Healthcare Effectiveness Data and Information Set data, the Minimum Data Set, and the American Community Survey. The 6 primary outcomes of interest in this study included 2 process measures and 4 patient-centered outcomes. Process measures included length of stay in the SNF and average rehabilitation therapy minutes (physical and occupational therapy) received per day. Patient-centered outcomes included 30-day hospital readmission, changes in functional status as measured by the 28-point late loss MDS-ADL scale, likelihood of becoming a long-term resident, and successful discharge to the community. Successful discharge from a SNF was defined as being discharged to the community within 100 days of SNF admission and remaining alive in the community without being institutionalized in any acute or post-acute setting for at least 30 days. We analyzed 211,296 FFS and 75,554 MA patients with hip fracture admitted directly to a SNF following an index hospitalization who had not been in a nursing facility or hospital in the preceding year. We used inverse probability of treatment weighting (IPTW) and nursing facility fixed effects regression models to compare treatments and outcomes between MA and FFS patients. MA patients were younger and less cognitively impaired upon SNF admission than FFS patients. After applying IPTW, demographic and clinical characteristics of MA patients were comparable with those of FFS patients. After adjusting for risk factors using IPTW-weighted fixed effects regression models, MA patients spent 5.1 (95% CI -5.4 to -4.8) fewer days in the SNF and received 463 (95% CI to -483.2 to -442.4) fewer minutes of total rehabilitation therapy during the first 40 days following SNF admission, i.e., 12.1 (95% CI -12.7 to -11.4) fewer minutes of rehabilitation therapy per day compared to FFS patients. In addition, MA patients had a 1.2 percentage point (95% CI -1.5 to -1.1) lower 30-day readmission rate, 0.6 percentage point (95% CI -0.8 to -0.3) lower rate of becoming a long-stay resident, and a 3.2 percentage point (95% CI 2.7 to 3.7) higher rate of successful discharge to the community compared to FFS patients. The major limitation of this study was that we only adjusted for observed differences to address selection bias between FFS and MA patients with hip fracture. Therefore, results may not be generalizable to other conditions requiring extensive rehabilitation.ConclusionsCompared to FFS patients, MA patients had a shorter course of rehabilitation but were more likely to be discharged to the community successfully and were less likely to experience a 30-day hospital readmission. Longer lengths of stay may not translate into better outcomes in the case of hip fracture patients in SNFs.

Levels of agreement between participating agencies.

The Medical Device (MD) Regulation EU 2017/745 (MDR) has provided a completely new and more robust regulatory framework at guarantee of the safety and efficacy of therapeutic options accessing the market. At the same time, the MDR poses several challenges for stakeholders, among which, the most significant lying on MDs made of substances (MDMS) whose mechanism of action should be non-pharmacological, immunological, or metabolic.Moving from single active substances to very complex mixtures, such as the case of natural products, the demonstration of the non-targeted, non-pharmacological mechanism, is even much more challenging since it is very hard to specifically identify and characterize all the interactions each constituent can have within the body.New scientific paradigms to investigate these multiple interactions and delineate the principal mechanism of action through which the effect is achieved are necessary for the correct regulatory classification and placement in the market of MDMS.This article will discuss the difficulties in delineating the boundaries between pharmacological and non-pharmacological mechanisms, practical approaches to the study of complex mixtures and the challenges on the application of current experimental paradigms to the study of the mechanism of action of MDMS.