Developing a pressure ulcer risk factor minimum data set and risk assessment framework

AIM: To agree a draft pressure ulcer risk factor Minimum Data Set to underpin the development of a new evidenced-based Risk Assessment Framework.BACKGROUND: A recent systematic review identified the need for a pressure ulcer risk factor Minimum Data Set and development and validation of an evidenced-based pressure ulcer Risk Assessment Framework. This was undertaken through the Pressure UlceR Programme Of reSEarch (RP-PG-0407-10056), funded by the National Institute for Health Research and incorporates five phases. This article reports phase two, a consensus study.DESIGN: Consensus study.METHOD: A modified nominal group technique based on the Research and Development/University of California at Los Angeles appropriateness method. This incorporated an expert group, review of the evidence and the views of a Patient and Public Involvement service user group. Data were collected December 2010-December 2011.FINDINGS: The risk factors and assessment items of the Minimum Data Set (including immobility, pressure ulcer and skin status, perfusion, diabetes, skin moisture, sensory perception and nutrition) were agreed. In addition, a draft Risk Assessment Framework incorporating all Minimum Data Set items was developed, comprising a two stage assessment process (screening and detailed full assessment) and decision pathways.CONCLUSION: The draft Risk Assessment Framework will undergo further design and pre-testing with clinical nurses to assess and improve its usability. It will then be evaluated in clinical practice to assess its validity and reliability. The Minimum Data Set could be used in future for large scale risk factor studies informing refinement of the Risk Assessment Framework

DETERMINING THE MINIMUM DATA SET FOR DIABETES REGISTRY

Introduction: The number of people with diabetes's increasing. More than 220 million people have diabetes, more than 70% of whom live in middle and lower-income countries. already exist many innovations around the world on improving the managed care of diabetes  .diabetes registries are one of them. in Iran, development and evaluation of diabetes information systems is one of the most research priorities. since defining health regulations and evaluation of diabetes prevention programs depend on the powerful information system, but in Iran don't exist complete information about incidence and prevalence of diabetes. determine standard data elements (Des) and design diabetes registry is one the most important country requirements. the main purpose of this study is investigating to this subject.   Methods: This is a descriptive- analytic study. Resource related to diabetes DEs collected from selective minimum data sets. Then diabetes DEs set derived from selective minimum data sets were investigated in focus group sessions with endocrine specialists, health informatics, and health information management. Duplicate DEs were removed and similar DEs were combined. Then seven endocrine specialists evaluated diabetes DEs set. They determine the value of each DEs using the Delphi technique (scores range from 0 to 5). The DEs that received more than 75% of grade 4 and 5 remained in the study. Following the expert opinion, the final version of the diabetes DEs set was designed.   Results: According to literature review 455 DEs included studying, after Delphi sessions, 293 data element remained to study. Main categories of DEs are:1-patient demographic characterizes (12 DEs), 2-patient referral (5 DEs), 3-diabetes care follow up (15 DEs), 4-physical exam, chief complaint and assessment (40 DEs), 5-history (such as: individual, grow up, family, drug abuse) (10 DEs), 6-pregnancy management (13 DEs), 7-screening (10 DEs), 8-specialty evolutions ( such as: cardiovascular (18 DEs), neuropathy (16 DEs), nephropathy (7 DEs), teeth and mouse (3 DEs), eyes (14 DEs), psychology situation (2 DEs),  sexual ability (1 DEs)), 9-laboratory exams (33 DEs), 10-drugs (oral antidiabetics drugs (14 DEs), injectable antidiabetics (7 DEs), lipid (11 DEs), hypertension (20 DEs), anti placates (2 DEs)), cardiac (3 DEs), preparing insulin method (5 DEs)), 11-physical activity (4 DEs),12- diet (12 DEs), 13-education and self care (13 DEs). Conclusion: In the study diabetes, DEs set were determined that provide appropriate yield for data gathering and record all required information for diabetes care. Hence diabetes is a chronic disease and Patients suffer from it for years, implementation diabetes DEs can improve documentation and improve diabetes care.&nbsp

Can the US Minimum Data Set Be Used for Predicting Admissions to Acute Care Facilities?

This paper is intended to give an overview of Knowledge Discovery in Large Datasets (KDD) and data mining applications in healthcare particularly as related to the Minimum Data Set, a resident assessment tool which is used in US long-term care facilities. The US Health Care Finance Administration, which mandates the use of this tool, has accumulated massive warehouses of MDS data. The pressure in healthcare to increase efficiency and effectiveness while improving patient outcomes requires that we find new ways to harness these vast resources. The intent of this preliminary study design paper is to discuss the development of an approach which utilizes the MDS, in conjunction with KDD and classification algorithms, in an attempt to predict admission from a long-term care facility to an acute care facility. The use of acute care services by long term care residents is a negative outcome, potentially avoidable, and expensive. The value of the MDS warehouse can be realized by the use of the stored data in ways that can improve patient outcomes and avoid the use of expensive acute care services. This study, when completed, will test whether the MDS warehouse can be used to describe patient outcomes and possibly be of predictive value

Can the US Minimum Data Set Be Used for Predicting Admissions to Acute Care Facilities?

This paper is intended to give an overview of Knowledge Discovery in Large Datasets (KDD) and data mining applications in healthcare particularly as related to the Minimum Data Set, a resident assessment tool which is used in US long-term care facilities. The US Health Care Finance Administration, which mandates the use of this tool, has accumulated massive warehouses of MDS data. The pressure in healthcare to increase efficiency and effectiveness while improving patient outcomes requires that we find new ways to harness these vast resources. The intent of this preliminary study design paper is to discuss the development of an approach which utilizes the MDS, in conjunction with KDD and classification algorithms, in an attempt to predict admission from a long-term care facility to an acute care facility. The use of acute care services by long term care residents is a negative outcome, potentially avoidable, and expensive. The value of the MDS warehouse can be realized by the use of the stored data in ways that can improve patient outcomes and avoid the use of expensive acute care services. This study, when completed, will test whether the MDS warehouse can be used to describe patient outcomes and possibly be of predictive value

DESIGNING THE MINIMUM DATA SET OF PSYCHIATRIC EMERGENCY RECORD

Introduction: Psychiatric emergencies are acute mental health disturbances, behavior and social relationship that require immediate intervention. The major role of psychiatric emergency services is to provide mental health care services for patients with acute mental health problems. Design emergency psychiatry core dataset has improved the coordination and integration of services and improved the outcomes for the patient with severe and persistent mental illness with complex needs. So the aim of this study was to design data elements (DEs) in emergency psychiatry for Iran.   Methods: This is an applied study. Emergency psychiatry (DEs) collected via literature review and then psychologist and psychiatrist (16 experts) assign the score from 0 to 5 to them according to the value of each data element. (DEs)  selected as core Emergency psychiatry (DEs) that were achieved 4 or 5 scores from 75% specialist.   Results: According to the literature review, 110 (DEs) included studying. 13 experts (8 psychologists, 8 Clinical Psychologist) evaluated psychiatric emergency (DEs) set. The average work experience of psychiatrists and psychologists was 16 years and their work experience ranged from 2 to 25 years (table 1). according to the experts opinion, 54 (DEs) with at least 75% of the agreement were identified as the psychiatric emergency (DEs). Emergency psychiatric (DEs) and average agreement of each of them were: demographic characteristics (6 DEs with an agreement average of 82.5%), history of mental illness (9 DEs with an agreement average of 79%), family history of psychology (3 DEs with an average agreement of 77.08%), medical history (1 DEs with an average agreement of 81.25 %) Assessment of mental status ( 20 DEs with an average agreement of 82%), assessment of the self harm risk or harm risk for others ( 13 DEs with an average agreement of 93.6%) and diagnosis and treatment (3 DEs with an average agreement of 81.25%). Conclusion: Given the importance of psychiatric disorder and lack of the national system for gathering psychiatric information, perform the same study abut psychiatric data element is very important. The results of this study can be used for design psychiatric emergency forms and gather accurate and complete patient information

Minimum data set to measure rehabilitation needs and health outcome after major trauma : application of an international framework

BACKGROUND: Measurement of long term health outcome after trauma remains non-standardized and ambiguous which limits national and international comparison of burden of injuries. The World Health Organization (WHO) has recommended the application of the International Classification of Function, Disability and Health (ICF) to measure rehabilitation and health outcome worldwide. No previous poly-trauma studies have applied the ICF comprehensively to evaluate outcome after injury. AIM: To apply the ICF categorization in patients with traumatic injuries to identify a minimum data set of important rehabilitation and health outcomes to enable national and international comparison of outcome data. DESIGN: A mixed methods design of patient interviews and an on-line survey. SETTING: An ethnically diverse urban major trauma center in London. POPULATION: Adult patients with major traumatic injuries (poly-trauma) and international health care professionals (HCPs) working in acute and post-acute major trauma settings. METHODS: Mixed methods investigated patients and health care professionals (HCPs) perspectives of important rehabilitation and health outcomes. Qualitative patient data and quantitative HCP data were linked to ICF categories. Combined data were refined to identify a minimum data set of important rehabilitation and health outcome categories. RESULTS: Transcribed patient interview data (N.=32) were linked to 234 (64%) second level ICF categories. Two hundred and fourteen HCPs identified 121 from a possible 140 second level ICF categories (86%) as relevant and important. Patients and HCPs strongly agreed on ICF body structures and body functions categories which include temperament, energy and drive, memory, emotions, pain and repair function of the skin. Conversely, patients prioritised domestic tasks, recreation and work compared to HCP priorities of self-care and mobility. Twenty six environmental factors were identified. Patient and HCP data were refined to recommend a 109 possible ICF categories for a minimum data set. CONCLUSIONS: The comprehensive measurement of health outcomes after trauma is important for patients, health professionals and trauma systems. An internationally applied ICF minimum data set will standardize the language used and concepts measured after major trauma to enable national and international comparison of outcome data

Oral education for nursing home staff: minimum data set 3.0

This research study is based on an educational module presented to nursing home staff addressing assessment criteria of the Minimum Data Set 3.0 (MDS) dental section, a tool used by staff to evaluate residents\u27 overall health. Relationships were tested between educating nursing home staff on the dental section and accurate completion of the MDS; between educating staff on correct oral assessment and resulting subsequent referrals for dental treatment; and between dental education and staff perceptions regarding the provision of oral assessment and home care. MDS assessments for nursing home residents (N=176) were collected pre- and post-implementation of the educational module, showing an increase in oral conditions identified by nursing home staff but a decrease in total assessments completed. Referral rates were collected and statistically significant difference was found using McNemar\u27s test (p=.0018) between the pre- implementation referral rate of 16% and post-implementation referral rate of 30%. Nursing home staff were given pre-implementation and post-implementation Likert surveys. Wilcoxon Signed Rank Test found the education module made them feel more comfortable performing oral assessments (p =.0009) and referring for subsequent dental treatment (p=.0313). These results suggest educating nursing home staff on identification of oral conditions and completing the MDS 3.0 dental section increases their knowledge and perceptions in providing oral assessments. Additionally, referrals to an oral health care provider may increase. Further longitudinal studies may determine best practices for educating nursing home staff to increase their ability to assess the oral cavity and provide appropriate measures to improve oral health of nursing home residents --Document

Creating a Minimum Data Set on ageing in sub-Saharan Africa

The World Health Organisation, together with representatives of four sub-Saharan African countries (Ghana, South Africa, Tanzania, Zimbabwe) and other stakeholders, launched a project in 1999 to establish a Minimum Data Set on ageing and older persons in Africa. The project focusses on identifying what data are needed to build knowledge on the situation of older Africans and forging the centralised, in-country collation and dissemination of this information. This paper summarises the current state of the project and touches on issues of data availability and quality, while exploring methods for data collection, integration, collation and dissemination

Using the Juvenile Justice National Minimum Data Set to measure returns to sentenced youth justice supervision: stage 2

This is the second of 2 reports presenting measures of returns to sentenced youth justice supervision using data from the Juvenile Justice National Minimum Data Set. Summary In Australia, youth justice departments are responsible for providing services to young people sentenced to community-based supervision or detention. These services aim to reduce the frequency and seriousness of youth offending. When considered with other outcome indicators, the rate of return to sentenced supervision is one possible indicator of the performance of a youth justice department (although there are factors beyond the control of youth justice departments that will impact on the level of reoffending). This is the second report that examines measures of returns to sentenced youth justice supervision using data from the Juvenile Justice National Minimum Data Set (JJ NMDS). The first report (AIHW 2013) explored the feasibility of using this longitudinal person-based data set, which contains information on young people under supervision, and found that it was possible to fulfil many of the principles developed by Richards (2011). This second report further examines timeframes for measuring returns and explores the potential for using JJ NMDS data to measure the seriousness of reoffending. Timeframes for measuring returns to sentenced supervision Returns to sentenced supervision can be measured over a number of timeframes. While longer timeframes will capture more returns, the nature of youth justice supervision means that the cohort used for analysis must be age restricted. Shorter timeframes allow for more recent data to be used, but will be affected by the length of time required for administrative procedures such as court proceedings. Based on a comparison of 5 timeframes (returns within 3 months, 6 months, 1 year, 2 years and at any time during possible youth justice supervision), it is recommended that timeframes of 6 months and 1 year be used. It is also recommended that analyses be contextualised by the impact of prior supervised sentences to account for the effect of offending history on the type of sentence received. Measuring the seriousness of reoffending Measuring whether the seriousness of offending has escalated could also provide valuable information on performance. The JJ NMDS contains offence data for 3 states and preliminary analyses of the escalation of reoffending are provided in this report. However, it is unlikely that offence data for the remaining states and territories will be available in the foreseeable future. One possible alternative that uses available JJ NMDS data is the severity of supervised sentences received for reoffending. A preliminary analysis found that an increase in sentence severity was more likely to correspond with an increase in offence seriousness than with a decrease or no change in offence seriousness. This indicates that an increase in sentence severity can be used as a proxy indicator of an escalation of offending behaviour in the absence of offence data, although sentence severity is also influenced by other factors. It is recommended that this measure be used until offence data for all states and territories are available. Future work Recommendations for future work include agreeing on measures to be reported annually and exploring the feasibility of using other data sources in addition to the JJ NMDS to enable a more comprehensive analysis on youth recidivism