Development of a Minimum Data Set (MDS) for C-Section Anesthesia Information Management System (AIMS).
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SUBMITTER: Sheykhotayefeh M
PROVIDER: S-EPMC5556329 | biostudies-other | 2017 Apr
REPOSITORIES: biostudies-other
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Project description:ObjectivesTo evaluate the predictive validity of an adapted version of the Minimum Data Set (MDS) Mortality Risk Index-Revised (MMRI-R) based on MDS version 3.0 assessment items (MMRI-v3) and to compare the predictive validity of the MMRI-v3 with that of a single MDS item indicating limited life expectancy (LLE).DesignRetrospective, cross-sectional study of MDS assessments. Other data sources included the Veterans Affairs (VA) Residential History File and Vital Status File.SettingVA nursing homes (NHs).ParticipantsVeterans aged 65 and older newly admitted to VA NHs between July 1, 2012, and September 30, 2015.MeasurementsThe dependent variable was death within 6 months of admission date. Independent variables included MDS items used to calculate MMRI-v3 scores (renal failure, chronic heart failure, sex, age, dehydration, cancer, unintentional weight loss, shortness of breath, activity of daily living scale, poor appetite, acute change in mental status) and the MDS item indicating LLE.ResultsThe predictive ability of the MMRI-v3 for 6-month mortality (c-statistic 0.81) is as good as that of the original MMRI-R (c-statistic 0.76). Scores generated using the MMRI-v3 had greater predictive ability than that of the single MDS indicator for LLE (c-statistic 0.76); using the 2 together resulted in greater predictive ability (c-statistic 0.86).ConclusionThe MMRI-v3 is a useful tool in research and clinical practice that accurately predicts 6-month mortality in veterans residing in Veterans Affairs NHs. Identification of residents with LLE has great utility for studying palliative care interventions and may be helpful in guiding allocation of these services in clinical practice. J Am Geriatr Soc 66:2353-2359, 2018.
| S-EPMC7477706 | biostudies-literature
Project description:A laboratory information management system (LIMS) is central to the informatics infrastructure that underlies biobanking activities. To date, a wide range of commercial and open-source LIMSs are available and the decision to opt for one LIMS over another is often influenced by the needs of the biobank clients and researchers, as well as available financial resources. The Baobab LIMS was developed by customizing the Bika LIMS software ( www.bikalims.org ) to meet the requirements of biobanking best practices. The need to implement biobank standard operation procedures as well as stimulate the use of standards for biobank data representation motivated the implementation of Baobab LIMS, an open-source LIMS for Biobanking. Baobab LIMS comprises modules for biospecimen kit assembly, shipping of biospecimen kits, storage management, analysis requests, reporting, and invoicing. The Baobab LIMS is based on the Plone web-content management framework. All the system requirements for Plone are applicable to Baobab LIMS, including the need for a server with at least 8 GB RAM and 120 GB hard disk space. Baobab LIMS is a server-client-based system, whereby the end user is able to access the system securely through the internet on a standard web browser, thereby eliminating the need for standalone installations on all machines.
| S-EPMC5397207 | biostudies-literature
Project description:BackgroundCare homes provide long term care for older people. Countries with standardised approaches to residents' assessment, care planning and review (known as minimum data sets (MDS)) use the aggregate data to guide resource allocation, monitor quality, and for research. Less is known about how an MDS affects how staff assess, provide and review residents' everyday care. The review aimed to develop a theory-driven understanding of how care home staff can effectively implement and use MDS to plan and deliver care for residents.MethodsThe realist review was organised according to RAMESES (Realist And Meta-narrative Evidence Synthesis: and Evolving Standards) guidelines. There were three overlapping stages: 1) defining the scope of the review and theory development on the use of minimum data set 2) testing and refining candidate programme theories through iterative literature searches and stakeholders' consultations as well as discussion among the research team; and 3) data synthesis from stages 1 and 2. The following databases were used MEDLINE via OVID, Embase, CINAHL (Cumulative Index to Nursing and Allied Health Literature), ASSIA [Applied Social Sciences Citation Index and Abstracts]) and sources of grey literature.ResultsFifty-one papers informed the development of three key interlinked theoretical propositions: motivation (mandates and incentives for Minimum Data Set completion); frontline staff monitoring (when Minimum Data Set completion is built into the working practices of the care home); and embedded recording systems (Minimum Data Set recording system is integral to collecting residents' data). By valuing the contributions of staff and building on existing ways of working, the uptake and use of an MDS could enable all staff to learn with and from each other about what is important for residents' care CONCLUSIONS: Minimum Data Sets provides commissioners service providers and researchers with standardised information useful for commissioning planning and analysis. For it to be equally useful for care home staff it requires key activities that address the staff experiences of care, their work with others and the use of digital technology.RegistrationPROSPERO registration number CRD42020171323.
| S-EPMC8739629 | biostudies-literature
Project description:BackgroundTo develop a score to predict mortality using the Minimum Data Set 3.0 (MDS 3.0) that can be readily calculated from items collected during nursing home (NH) residents' admission assessments.ParticipantsWe developed a training cohort of Medicare beneficiaries newly admitted to United States NHs during 2012 (N = 1,426,815) and a testing cohort from 2013 (N = 1,160,964).MethodsData came from the MDS 3.0 assessments linked to the Medicare Beneficiary Summary File. Using the training dataset, we developed a composite MDS 3.0 Mortality Risk Score (MRS3) consisting of 17 clinical items and patients' age groups based on their relation to 30-day mortality. We assessed the calibration and discrimination of the MRS3 in predicting 30- and 60-day mortality and compared its performance to the Charlson Comorbidity Index and the clinician's assessment of 6-month prognosis measured at admission.ResultsThe 30- and 60-day mortality rates for the testing population were 2.8% and 5.6%, respectively. Results from logistic regression models suggest that the MRS3 performed well in predicting death within 30 and 60 days (C-Statistics of 0.744 [95% confidence limit (CL) = 0.741, 0.747] and 0.709 [95% CL = 0.706, 0.711], respectively). The MRS3 was a superior predictor of mortality compared to the Charlson Comorbidity Index (C-statistics of 0.611 [95% CL = 0.607, 0.615] and 0.608 [95% CL = 0.605, 0.610]) and the clinicians' assessments of patients' 6-month prognoses (C-statistics of 0.543 [95% CL = 0.542, 0.545] and 0.528 [95% CL = 0.527, 0.529]).ConclusionsThe MRS3 is a good predictor of mortality and can be useful in guiding decision-making, informing plans of care, and adjusting for patients' risk of mortality.
| S-EPMC6599280 | biostudies-literature
Project description:IntroductionCare homes provide nursing and social care for older people who can no longer live independently at home. In the UK, there is no consistent approach to how information about residents' medical history, care needs and preferences are collected and shared. This limits opportunities to understand the care home population, have a systematic approach to assessment and documentation of care, identifiy care home residents at risk of deterioration and review care. Countries with standardised approaches to residents' assessment, care planning and review (eg, minimum data sets (MDS)) use the data to understand the care home population, guide resource allocation, monitor services delivery and for research. The aim of this realist review is to develop a theory-driven understanding of how care home staff implement and use MDS to plan and deliver care of individual residents.Methods and analysisA realist review will be conducted in three research stages.Stage 1 will scope the literature and develop candidate programme theories of what ensures effective uptake and sustained implementation of an MDS.Stage2 will test and refine these theories through further iterative searches of the evidence from the literature to establish how effective uptake of an MDS can be achieved.Stage 3 will consult with relevant stakeholders to test or refine the programme theory (theories) of how an MDS works at the resident level of care for different stakeholders and in what circumstances. Data synthesis will use realist logic to align data from each eligible article with possible context-mechanism-outcome configurations or specific elements that answer the research questions.Ethics and disseminationThe University of Hertfordshire Ethics Committee has approved this study (HSK/SF/UH/04169). Findings will be disseminated through briefings with stakeholders, conference presentations, a national consultation on the use of an MDS in UK long-term care settings, publications in peer-reviewed journals and in print and social media publications accessible to residents, relatives and care home staff.Prospero registration numberCRD42020171323; this review protocol is registered on the International Prospective Register of Systematic Reviews.
| S-EPMC7668360 | biostudies-literature
Project description:BackgroundThe minimum data set (MDS) is a collection of data elements to be grouped using a standard approach to allow the use of data for clinical and research purposes. Health data are typically voluminous, complex, and sometimes too ambiguous to generate indicators that can provide knowledge and information on health. This complexity extends further to the rare disease (RD) domain. MDSs are essential for health surveillance as they help provide services and generate recommended population indicators. There is a bottleneck in international literature that reveals a global problem with data collection, recording, and structuring in RD.ObjectiveThis study aimed to identify and analyze the MDSs used for RD in health care networks worldwide and compare them with World Health Organization (WHO) guidelines.MethodsThe population, concept, and context methodology proposed by the Joanna Briggs Institute was used to define the research question of this systematic review. A total of 4 databases were reviewed, and all the processes were reported using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. The data elements were analyzed, extracted, and organized into 10 categories according to WHO digital health guidelines. The quality assessment used the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist.ResultsWe included 20 studies in our review, 70% (n=14) of which focused on a specific health domain and 30% (n=6) of which referred to RD in general. WHO recommends that health systems and networks use standard terminology to exchange data, information, knowledge, and intelligence in health. However, there was a lack of terminological standardization of the concepts in MDSs. Moreover, the selected studies did not follow the same standard structure for classifying the data from their MDSs. All studies presented MDSs with limitations or restrictions because they covered only a specific RD, or their scope of application was restricted to a specific context or geographic region. Data science methods and clinical experience were used to design, structure, and recommend a fundamental global MDS for RD patient records in health care networks.ConclusionsOur study highlights the difficulties in standardizing and categorizing findings from MDSs for RD because of the varying structures used in different studies. The fundamental RD MDS designed in this study comprehensively covers the data needs in the clinical and management sectors. These results can help public policy makers support other aspects of their policies. We highlight the potential of our results to help strategic decisions related to RD.Trial registrationPROSPERO CRD42021221593; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=221593.International registered report identifier (irrid)RR2-10.1016/j.procs.2021.12.034.
| S-EPMC10415943 | biostudies-literature
Project description:Poisoning, as a well-known medical condition, puts everyone at risk. As a data management tool, a registry plays an important role in monitoring the poisoned patients. Having a poisoning minimum data set is a major requirement for creating a poisoning registry. Therefore, the present systematic review was conducted in 2019 to identify the minimum data set for a poisoning registry. Searches were performed in four scientific databases, i.e., PubMed, Scopus, Web of Science, and Embase. The keywords used in the searches included minimum data set, "poison", and "registry". Two researchers independently evaluated the titles, abstracts, and texts of the papers. The data were collected from the related papers. Ultimately, the minimum data set was identified for the poisoning registry. Data elements extracted from the sources were classified into two general categories: administrative data and clinical data. Ninety-eight data elements in the administrative data category were subdivided into three sections: general data, admission data, and discharge data. One-hundred and thirty-one data elements in the clinical data category were subdivided into five sections: clinical observation data, clinical assessment data, past medical history data, diagnosis data, and treatment plan data. The minimum data set is a prerequisite for creating and using a poisoning registry and data system. It is suggested to evaluate and use the poisoning minimum data set in accordance with the national laws, needs, and standards based on the opinion of the local experts.
| S-EPMC8457722 | biostudies-literature
Project description:In this work, classic intensity formulae were united with an empirical spot-fading model in order to calculate the diameter of a spherical crystal that will scatter the required number of photons per spot at a desired resolution over the radiation-damage-limited lifetime. The influences of molecular weight, solvent content, Wilson B factor, X-ray wavelength and attenuation on scattering power and dose were all included. Taking the net photon count in a spot as the only source of noise, a complete data set with a signal-to-noise ratio of 2 at 2 A resolution was predicted to be attainable from a perfect lysozyme crystal sphere 1.2 microm in diameter and two different models of photoelectron escape reduced this to 0.5 or 0.34 microm. These represent 15-fold to 700-fold less scattering power than the smallest experimentally determined crystal size to date, but the gap was shown to be consistent with the background scattering level of the relevant experiment. These results suggest that reduction of background photons and diffraction spot size on the detector are the principal paths to improving crystallographic data quality beyond current limits.
| S-EPMC2852304 | biostudies-literature
Project description:Background: Ethiopia is investing in the routine Health Management Information System. Improved routine data are needed for decision-making in the health sector. Objective: To analyse the quality of the routine Health Management Information System data and triangulate with other sources, such as the Demographic and Health Surveys. Methods: We analysed national Health Management Information System data on 19 indicators of maternal health, neonatal survival, immunization, child nutrition, malaria, and tuberculosis over the 2012-2018 time period. The analyses were conducted by 38 analysts from the Ministry of Health, Ethiopia, and two government agencies who participated in the Operational Research and Coaching for Analysts (ORCA) project between June 2018 and June 2020. Using a World Health Organization Data Quality Review toolkit, we assessed indicator definitions, completeness, internal consistency over time and between related indicators, and external consistency compared with other data sources. Results: Several services reported coverage of above 100%. For many indicators, denominators were based on poor-quality population data estimates. Data on individual vaccinations had relatively good internal consistency. In contrast, there was low external consistency for data on fully vaccinated children, with the routine Health Management Information System showing 89% coverage but the Demographic and Health Survey estimate at 39%. Maternal health indicators displayed increasing coverage over time. Indicators on child nutrition, malaria, and tuberculosis were less consistent. Data on neonatal mortality were incomplete and operationalised as mortality on day 0-6. Our comparisons with survey and population projections indicated that one in eight early neonatal deaths were reported in the routine Health Management Information System. Data quality varied between regions. Conclusions: The quality of routine data gathered in the health system needs further attention. We suggest regular triangulation with data from other sources. We recommend addressing the denominator issues, reducing the complexity of indicators, and aligning indicators to international definitions.
| S-EPMC7833046 | biostudies-literature
Project description:PurposeDesigning mobile-based applications is one of the tools to raise the awareness of patients and the care team. Aim of this study is to identify the data elements of a mobile-based application to overweight and obesity management for children and adolescents from the experts' point of view.MethodsIn this descriptive-analytical article, data collection was conducted through library and Internet research. The research population comprised 30 nutritionists selected via simple sampling method. The research instrument was a questionnaire developed by the researcher in four sections: demographic data, assessment data, therapeutic recommendations and application capabilities. Validity and reliability were confirmed by Content Validity Ratio (CVR) and Delphi method respectively.ResultsThe Minimum Data Set (MDS) required for overweight and obesity management in children and adolescents was designed based on the data from the guidelines of the United States, Canada, Australia, Britain, Iran, and experts' opinions. The importance of this MDS suggested was calculated based on the percentage points given by experts for the demographic data of 100%, the assessment data of 88.33%, the therapeutic recommendations of 97.67%, and the application capabilities of 88.94%.ConclusionIdentifying prevention and control minimum data set of overweight and obesity in children and adolescents from the point of view of experts will be effective in improving the applications in this field. This MDS has two parts of data elements: the first for recognition of the framework of evaluating and applying therapeutic methods that can empower parents to manage the child's body mass and the second as a patient's personal record for storage a set of data that can be used by nutritionists in visits to healthcare centers.Supplementary informationThe online version contains supplementary material available at 10.1007/s40200-021-00807-1.
| S-EPMC8212241 | biostudies-literature