Data Stewardship Standards and Guidelines

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Nov 24, 2024

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Data Stewardship Standards and Guidelines Trisha Dutrow GCU: HCA-360 September 29, 2023
Data Stewardship Standards and Guidelines When it comes to healthcare information technology and HMIS, ensuring that you are obtaining accurate data is extremely important for the progress of a health system. Collecting, interpreting, and modeling of data allows information healthcare administrators, technology specialists, and healthcare providers the ability to provide high quality care to their patients. The data obtained is only effective when/if users have the ability to access and understand the data. When the integrity of the data is well constructed it makes it easier to understand. In this essay, I will present a plan that enforces data stewardship standards across the both the public and clinical sectors of health based on the American Health Information Management Association (AHMA) and Public Health Data Standards Consortium (PHDSC) partnership. Goal When the alignment of the public and clinical data standards are obtain it allows the healthcare needs to be addressed more accurately. The goal is to enable and develop a new development of practice for each clinic. Clinical data that is obtained within the process must represent both the public and clinical data resources that are centered around healthcare progress. When the data captured during the product development stages and activities such as “clinical research trials and studies, or as party of the care delivery process, the data is fundamental to the time of delivery, care value to patients, and essential to building a system that is learning from care that is being delivered” (National Academics Press (US), 2010). Differences of Clinical and Public Health Data The main difference between public and clinical health data is the information that is obtained from the perspective of patients. When it comes to public health their data is based on
the community, meaning the patients they treat. “While population health focus on more specific approaches to ones care especially within specific populations, public health’s main focus is promoting a healthy lifestyle and doing research to detect and prevent certain diseases in a geographic region” (Tan, 2021). A patient is diagnosed by a provider based on the symptoms they describe, and the provider does diagnostic tests that are necessary for their symptoms. Public health providers diagnose health problems within the community while stressing about ways to prevent disease while doing research. Similarities of Clinical and Public Health Data Despite clinical and public health data having differences, there are some similarities. Contemplation reinforces the similarities between the population’s concerns of public health and the individual responsibility of clinical health. There are both historical and practical reasons why education in public health and medicine in general is proceeded much more parallel than by intersection. There have been numerous initiatives that are bringing the principles, values, experiences, and analytic perspectives of public health into the daily practice of those practicing medical education (Fiebach, 2011). The developments of technology combined with the need for electronic health records have arranged public health professionals to conduct this type of work. “This cannot be done without having the collaboration of clinical and other sectors, which allows the partnership on parallels” (Tan, 2021). Proposal A proposal for a new broader semantic strategy of data mining has become more popular and increasingly necessary for “the development of advanced healthcare decision support systems via Data Mining and Machine Learning” (Tan, 2021). Conflicting clinical data can have
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a significant impact on the spread of electronic health records and knowledge development. Establishing strategies to use to help with approaching the clinical data for health insights, warranted dedicated cooperative efforts by public health developers and apply them. Action Plan Business understanding, that allows the ability to identify specific business objectives, such as data mining goals, is the first steps when it comes to standardizing data stewardship guidelines. “Understanding the data, preparation of the data is the second important step, because no data means no mining” (Tan, 2021). To make sure that healthcare is maintained consistency, the adaptation of business intelligence techniques helps to make sure that clinics are performing and corresponding better with patients when it comes to clinical and public health sectors. All healthcare professionals will have the ability to review and share data after consenting patients properly. “Data is collected by using predictive analytics that are involving statistical methods and technology over voluminous data to help predict the outcome for each patient, to help predict fraud in healthcare, or predict the requirements for resources” (Tan, 2021). This plan will primarily focus on confidentiality and maintenance of the interpretation and distribution of data throughout the EHR system. Collaboration of Organizations Data stewardship is a technical concept that has deep roots in the science and practice of data collection, sharing and analysis (Rosenbaum, 2010). Collection, sharing, and analysis intend to raise the level of trust and responsibility towards the data. This collaboration has an effect on patients, healthcare providers, and public and private health care organizations by considering
the value of fair information practice, “data stewardship indicates the approach to the management of data that has the ability to identify individuals” (Rosenbaum, 2010). Infrastructure of Health Care Systems Having this collaboration and partnership gives the infrastructure of health care systems and technology strength, it gives the opportunity to advance health information technology data standards while also aligning data standards for both public and clinical health. “Both public and clinical health have a commitment to the advancement of their professions through health information technology and standards” (Tan, 2021). “Both public and clinical health have worked on standardizing health information technology for population health” (Tan, 2021). This plan to introduce a new broader semantic strategy of data mining analysis for the development of strategies to help establish common data standards among both public and clinical health sectors that will help ensure successful mergers and partnerships.
References Fiebach N.H., Rao D., Hamm M.E. (2011). A curriculum in health systems and public health for internal medicine residents. American Journal of Preventative Medicine. 2011; 41: S264- S269 National Academies Press (US). (2010). Summary . Clinical Data as the Basic Staple of Health Learning - NCBI Bookshelf. https://www.ncbi.nlm.nih.gov/books/NBK54290/ Rosenbaum S. (2010). Data governance and stewardship: designing data stewardship entities and advancing data access. Health services research, 45(5 Pt 2), 1442–1455. https://doi.org/10.1111/j.1475-6773.2010.01140.x Tan, J. (with Olla, P. & Tan, J.) (2021). Adaptive health management information systems: Concepts, cases, and practical applications (4th ed.). Burlington, MA: Jones & Bartlett Learning. ISBN-13: 9781284153897
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