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| BMC Medical Informatics and Decision Making | Source: https://bmcmedinformdecismak.biomedcentral.com/ |
Overview
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| ICD-10 (International Classification of Diseases, Revision 10) | Source: http://www.nsradiology.com/ |
Different Industries, Different Purposes
Patient Data - The Lifeblood of The Healthcare Industry
As patient files were compacted, the disjointed and the exceedingly structured character of the industry caused data to be dispersed. Spread out throughout doctor’s offices, hospital networks, and even pharmacies, devoid of any effective, and safe way to bring it all collectively. Not very long ago, a hospital data center comprised just a few servers locally interconnected to provide intranet, email, and perhaps a basic file method. Nowadays, the intrusion into patient data, the Federal directives administering its confidentiality, digitization, and guideline have involuntarily forced healthcare providers to architect state-of-the-art, extremely secure networks and storage administration systems to justify compliance procedures. Regrettably, these procedures have saddled organizations with huge expenses and operative challenges that every so often exceeds the tax fines incurred for their failure to comply. Healthcare providers have to virtualize assertively to realize cost-effectiveness, yet virtualization itself presents further difficulties needing management, which is exactly where it all starts.
Bettering Data Collection throughout the Health Care System
Although a range of health care entities amasses data, these data do not flow to these entities in a unified or homogeneous means. Entities in the health care systems often encounter challenges when collecting ethnicity, race, and language data from members, patients, enrollees, and respondents. Unambiguously articulating the reasoning for the data collection, training staff, organizational leadership, and the public to realize the necessity to utilize valid collection tools may well improve the current situation. Indirect assessment methods, whenever used with a comprehension of the probabilistic environment of the data, can complement direct data collection efforts. Even though federal authorities, community health centers, hospitals, local, state, physician practices, and health plans can all play important roles in integrating ethnicity. Additionally, race and language data hooked on existing data gathering and excellent reporting efforts, each faces wide opportunities and countless challenges in its efforts in achieving this objective.
A report on an initiative on “Statewide Race and Ethnicity Data Collection: Massachusetts,” notes:
"The new efforts in Massachusetts are unique in the constellation of requirements and approaches being implemented in the state today. First, all acute care hospitals are required to collect these data and a recommended data collection tool has been developed jointly by the city [Boston] and Commonwealth to standardize efforts across hospitals. Second, the tool and the required categories in which hospitals must provide patient-level discharge data to the [state] include an exceptionally detailed list of ethnicities, with 31 reporting categories that include 144 ethnicities or countries of origin. Third, the collaboration between the City of Boston, the Commonwealth of Massachusetts, and hospitals have been crucial to turning policy attention to reducing disparities in the quality of health care."
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| The Health Information Technology | Source: https://www.rasmussen.edu/ |
The Health IT
Nonetheless, several entities face health information technology (Health IT) limitations and internal opposition. Disparities in addressing health and health care entail the maximum participation of industries that possess prevailing organizational structure for obtaining quality measurement and enhancement. To pinpoint the next stage of refining data gathering, it is beneficial to recognize the value of opportunities and disputes in the framework of existing systems. Though in some occasions, the opportunities and disputes are distinctive to each type of industry, in others, they are plain communal to all industries and it includes the following;
• How to train
staff to elicit information respectfully and efficiently.
• How to ask
enrollees and patients questions concerning ethnicity, race, and language, and
communication needs.
• How to address
pushback politely for potential patient or enrollee.
• How to address
the uneasiness of registration/admission staff particularly in hospitals,
health centers, and clinics or call center staff in this case health plans
about asking for information.
• How to address
system-level questions, for instance, deviations inpatient registration screens
and data flow.
Healthcare providers include a sundry set of public and private data gathering procedures, involving administrative enrollment and billing records, medical records, and health surveys utilized by many entities, including hospitals, CHCs, physicians, and health plans. Data on race, ethnicity, and language are collected, to some extent, by all these entities, suggesting the potential of each to contribute information on patients or enrollees. At present, the disintegration of data flow occurs because of silos of data collection (NRC, 2009).
Conclusion
Health information technology (Health IT) can have the possibility to advance the gathering and exchange of self-reported ethnicity, race and language data, as these data could be integrated, for example, in a person's health record (PHR) and then applied in electronic health record (EHR) and added into other data systems.
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References:
1. Cloudian
| Health Data Management: Benefits, Challenges, and Storage
2.
Agency for Healthcare Research and Quality | Race, Ethnicity, and Language Data: Standardization for
Health Care Quality Improvement
3. North State Radiology | Countdown to ICD 10 | North State Radiology
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