The medical registry
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by pro.dr. Ahmed Hassouna at eldemerdash hospital, cardiology department Ainshams university 15-1-2011 1st bimonthly meeting of YIF
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This document provides an overview of clinical data management processes. It discusses the goals of clinical data management which are to provide high-quality, accurate data through processes like case report form design, data entry, validation, and coding. It describes some commonly used clinical data management software and standards/guidelines like 21 CFR Part 11 and SCDM's Good Clinical Data Management Practices. The document is a project report submitted by students to fulfill requirements for a degree at Apollo Hospitals, New Delhi.
Data Management in Clinical Research
A crucial stage in clinical research is clinical data management CDM , which produces high quality, reliable, and statistically sound data from clinical trials. This results in a significantly shorter period of time between drug development and marketing. Team members of CDM are laboriously involved in all stages of clinical trials right from commencement to completion. They should be able to sustain the quality standards set by CDM processes by having sufficient process expertise. colorful procedures in CDM including Case Report Form CRF designing, CRF reflection, database designing, data entry, data confirmation, distinction operation, medical coding, data birth, and database locking are assessed for quality at regular intervals during a trial. In the present script, theres an increased demand to ameliorate the CDM norms to meet the nonsupervisory conditions and stay ahead of the competition by means of brisk commercialization of products. With the perpetration of nonsupervisory biddable data operation tools, the CDM platoon can meet these demands. also, its getting obligatory for companies to submit the data electronically. CDM professionals should meet applicable prospects and set norms for data quality and also have the drive to acclimatize to the fleetly changing technology. This composition highlights the processes involved and provides the anthology an overview of the tools and norms espoused as well as the places and liabilities in CDM. Syed Shahnawaz Quadri | Syeda Saniya Ifteqar | Syed Shafa Raoof "Data Management in Clinical Research" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-2 , April 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd55050.pdf Paper URL: https://www.ijtsrd.com.com/pharmacy/other/55050/data-management-in-clinical-research/syed-shahnawaz-quadri
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Defining a Central Monitoring Capability: Sharing the Experience of TransCele...
Central monitoring, on-site monitoring, and off-site monitoring provide an integrated approach to clinical trial quality management. TransCelerate distinguishes central monitoring from other types of central data review activities and puts it in the context of an overall monitoring strategy. Any organization seeking to implement central monitoring will need people with the right skills, technology options that support a holistic review of study-related information, and adaptable processes. There are different approaches actively being used to implement central monitoring. This article provides a description of how companies are deploying central monitoring, as well as samples of the workflows that illustrate how some have implemented it. The desired outcomes include earlier, more predictive detection of quality issues. This paper describes the initial implementation steps designed to learn what organizational capabilities are necessary.
Billing compliance results management-2013
The document discusses compliance and accounts receivable risk areas for skilled nursing facilities. It identifies five main risk areas for bad debt and lost revenue: bad debt, compliance issues, inefficiencies and waste, cash flow problems, and theft. It also provides tips for minimizing these risks through best practices in admissions, compliance processes, personnel management, billing and collection standards, and oversight and monitoring.
Data Management and Analysis in Clinical Trials
Data management and analysis play a critical role in the successful conduct of clinical trials. Proper collection, validation, and handling of data are essential for ensuring the reliability and integrity of study findings. Data management involves the design and implementation of data capture tools, such as electronic case report forms eCRFs, to efficiently collect and store clinical data. Additionally, data analysis is a crucial step that involves applying statistical methods to extract meaningful insights from the collected data. This paper provides an overview of the key components of data management and analysis in clinical trials, highlighting the importance of adherence to data standards, ensuring data quality, and maintaining data security. Effective data management and analysis not only lead to robust study outcomes but also contribute to the overall advancement of medical knowledge and patient care. S. Reddemma | Chetana Menda | Manoj Kumar "Data Management and Analysis in Clinical Trials" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-4, August 2023, URL: https://www.ijtsrd.com/papers/ijtsrd59667.pdf Paper Url:https://www.ijtsrd.com/pharmacy/pharmacology-/59667/data-management-and-analysis-in-clinical-trials/s-reddemma
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The document discusses healthcare analytics and data management. It begins by outlining the typical evolution of data collection, sharing, and analysis that occurs in industries. It then discusses key principles for healthcare analytics including regularly evaluating goals, measures, and how to achieve them. The remainder of the document discusses challenges around data binding, governance, and adoption models for healthcare analytics. It emphasizes the importance of analytics for return on investment and outlines strategic options and considerations for healthcare organizations evaluating their analytic capabilities.
Healthcare 2.0: The Age of Analytics
While Healthcare 1.0 was broadly defined by a focus on defensive medicine, billing, and fee-for-service, culminating in the mass adoption of EMRs, Healthcare 2.0 is a new wave focused on improving clinical efficiency, quality of care, affordability, and fee-for-value; culminating in a new age of healthcare analytics. This new age of analytics will require a new set of organizational skills and a foundational set of analytic information systems that many executives have not anticipated.
Join Dale Sanders, a 20-year healthcare CIO veteran and the industry's leading analytics expert, as he discusses his lessons learned, best practices in analytics, and what the C-level suite needs to know about this topic, now. Listen to Dale discuss 1) A step-by-step curriculum for analytic adoption and maturity in healthcare organizations, 2) the basic approach to a late-binding data warehouse, 3) pros and cons of early versus late binding, 4) the volatility in vocabulary and business rules in healthcare, 5) how to engineer your data to accommodate volatility in the future
Compliance Today
- EHR audit logs track access and changes to electronic health records and can help detect fraud and abuse, but many CMS contractors have not fully utilized audit logs for program integrity efforts.
- The OIG recommends that CMS provide guidance to contractors on reviewing EHR documentation and signatures, and to direct contractors to review providers' audit logs as part of medical reviews.
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Clinical Data Capture
This document summarizes a presentation on clinical data capture from third parties like CROs and AROs. It discusses:
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Data collection and reporting of key performance indicators
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Parna Das' paper discusses hospital information systems. It begins with an introduction explaining that hospital information systems are used to manage patient data and hospital operations. It then covers objectives like centralized patient management; functions like online appointments and payments; hardware and software requirements; and classifications by functional area and management level. The paper references sources on hospital information systems and acknowledges help from teachers.
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The medical registry
- 1. The Medical Registry Ahmed Hassouna, MD. Diploma of Medical Statistics & Clinical Trials, Paris 6 University.
- 2. The medical registry is a database of clearly defined set of health and demographic data of identifiable persons that are collected mainly to serve a specific public health purpose. www.statistics-for-clinicians.com
- 3. A- Implementation plan Development of a timeline, actual and expected registry size in the next (n) years. Plan necessary hardware and software, training of personnel and development of a quality control system. Test our plan through a pilot phase with sufficient cases and time to detect problems in all the phases of the registry. www.statistics-for-clinicians.com
- 4. B- Adequate documentation 1- Personal: The organization chart: staff, advisory board and consultants. 2- Processing and equipment: Data processing procedures, and hardware and software manuals. 3- Patients: Inclusion and/or exclusion criteria. Sources. Confidentiality guidelines. 4- Data Definitions and coding. Flow chart of data collection, editing, entry and analysis. Access procedures and reporting to relevant authorities. www.statistics-for-clinicians.com
- 5. C- Quality control procedures Completeness and validity: The aim of any registry is to be complete so as to include all cases in the target population and to be valid for all subgroups of patients. Validity has to be checked with the reliable independent sources of health authority and expressed as the percentage of agreement between registry data and those sources. Rules of quality assurance: 1- have to be built-in from the start, 2- with an assigned responsible at each point, 3- pre-defined standards and procedures for evaluation, 4- action / correction procedures and, 5- feed back loop to inform data handlers. www.statistics-for-clinicians.com
- 6. Inclusion and exclusion criteria have to be clear and unambiguous so that people involved in case-finding will make consistent decisions about inclusion across the range of potential cases that arise. As example, specifying test values or laboratory results as an inclusion criterion rather than a diagnosis. Ascertainment is the method(s) used to locate persons to be included in the registry. We should identify: a) its sources, b) whether it will be active or passive or some combination of the two and, c) method of enforcementof reporting cases:by request, legislative mandate or better by administrative rules directed by legislative mandate. www.statistics-for-clinicians.com
- 7. The clearness of the purpose of registry ensures that all but only necessary data are collected since the start, knowing that changing data collection with time creates confusion and bias in data sets. A golden advise is to keep data collection as simple as possible to reduce effort and cost, maximize compliance and later on, to reduce the time taken to incorporate these data into the system. Plan linkage with other registries and information systems by being in conformity with definition of data elements to avoid wasting effort in recoding, reformatting and merging. Remember that coding of categorical variables is a source of big bias. www.statistics-for-clinicians.com
- 8. This is the part that consumes the major portion of resources. It represents the key area to maintain data quality and hence, has to be correctly and clearly defined, implemented and verified. Data are either abstracted from a health provider's records or directly collected through forms to reduce time, cost and especially errors. a pilot phase allows re-truncation of abstracted records and permits checking the representativeness of forms to the information in question. www.statistics-for-clinicians.com
- 9. The analyzed registry findings have to be fitted in reports that fulfill the goals of the registry, and We have to have a policy and a mechanism on how those reports are disseminated to relevant audiences so as to satisfy the purpose of the registry. Accessto registry data and maintaining confidentiality are related and not necessarily conflicting issues and we have to have a clear policy and mechanism of responding to requests coming from non-authorized sectors. www.statistics-for-clinicians.com