Can an organization survive without information? Can an organization function with information but no structure? No, they cannot. Outside of actual brains, the data utilized in healthcare organizations or businesses are the foundation of the success, especially dealing with health information. Studies in recent years on data quality show where the state of data quality measured poorly. It found that the storage of records up to 20 percent was significantly affected (Johns, 2015). Findings also indicated that up to 80 percent of the records regarding consent of physician-patient encounter was did not add up (Johns, 2015). Data quality management in organizations is vital for data structure and for information to remain trustworthy and useful.
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Rita’s can experience an array of benefits for implementing data quality management into their healthcare organization. Data quality brings about clarity and protection from fraud and abuse, assist providers in decision-making, and identifying suitable treatments of care or which practices work best for their patients (Koh & Tan, 2011). Data quality can enhance treatment effectiveness by delivering an analysis to provide outcomes and exploring methods in decreasing costs, identifying a standardized or individualized treatment plan for patients, and arranging similar symptoms to properly diagnose and treat patients (Koh & Tan, 2011). Another benefit is detection of fraud or abuse. Systems are capable of setting standards to catch patterns or things that seem unordinary. This detection can reduce incorrect dosage and prescriptions or false insurance or medical claims. Koh & Tan (2011) found that ReliaStar Financial Corporation reported an increase of 20 percent in yearly savings just from having a fraud and abuse detection system in place (p. 68).
Two methods were mentioned by St. Rita’s Enterprise Information Management (EIM) team that could be implemented or imitated into their healthcare organization. However, the Canadian Institute for Health Information (CIHI) Data Quality Framework is the proposed method for data quality assessment for St. Rita’s (Johns, 2015). The CIHI framework focuses on prevention, early detection, and resolution of data issues (Johns, 2015, p. …show more content…
258).
This data quality framework model also encompasses three components—which includes a data assessment tool—that examines five dimensions of data. These dimensions include accuracy, timeliness, comparability, usability, and relevance (Johns, 2015). With this tool, it measures data based on a 19 relatable characteristics in those five dimensions and receive ratings such as met, unmet, unknown, or not applicable (Johns, 2015). There may be circumstances where the ratings are not utilized, and at this point, data quality is measured by a different set of ratings.
The next step in the data quality framework is implementation. Implementation of this data quality assessment involves the development of required procedures to y make the framework successful. Implementation takes planning, documentation, and finally, the actual implementation (Johns, 2015). The planning phase is for categorizing and issues that may impact data and figuring out what methods work best for addressing those problems. After that phase, the implemented of said procedures is the next step. It allows the processes mentioned to assess the issue of data quality (Johns, 2015). To fully assess the issues, the organization must create an action plan to supervise the issues and recommend strategies while also providing a time limit for the issues, noting the responsible parties, and an estimated date of completion. Documenting all of this is the
Rita’s can experience an array of benefits for implementing data quality management into their healthcare organization. Data quality brings about clarity and protection from fraud and abuse, assist providers in decision-making, and identifying suitable treatments of care or which practices work best for their patients (Koh & Tan, 2011). Data quality can enhance treatment effectiveness by delivering an analysis to provide outcomes and exploring methods in decreasing costs, identifying a standardized or individualized treatment plan for patients, and arranging similar symptoms to properly diagnose and treat patients (Koh & Tan, 2011). Another benefit is detection of fraud or abuse. Systems are capable of setting standards to catch patterns or things that seem unordinary. This detection can reduce incorrect dosage and prescriptions or false insurance or medical claims. Koh & Tan (2011) found that ReliaStar Financial Corporation reported an increase of 20 percent in yearly savings just from having a fraud and abuse detection system in place (p. 68).
Two methods were mentioned by St. Rita’s Enterprise Information Management (EIM) team that could be implemented or imitated into their healthcare organization. However, the Canadian Institute for Health Information (CIHI) Data Quality Framework is the proposed method for data quality assessment for St. Rita’s (Johns, 2015). The CIHI framework focuses on prevention, early detection, and resolution of data issues (Johns, 2015, p. …show more content…
258).
This data quality framework model also encompasses three components—which includes a data assessment tool—that examines five dimensions of data. These dimensions include accuracy, timeliness, comparability, usability, and relevance (Johns, 2015). With this tool, it measures data based on a 19 relatable characteristics in those five dimensions and receive ratings such as met, unmet, unknown, or not applicable (Johns, 2015). There may be circumstances where the ratings are not utilized, and at this point, data quality is measured by a different set of ratings.
The next step in the data quality framework is implementation. Implementation of this data quality assessment involves the development of required procedures to y make the framework successful. Implementation takes planning, documentation, and finally, the actual implementation (Johns, 2015). The planning phase is for categorizing and issues that may impact data and figuring out what methods work best for addressing those problems. After that phase, the implemented of said procedures is the next step. It allows the processes mentioned to assess the issue of data quality (Johns, 2015). To fully assess the issues, the organization must create an action plan to supervise the issues and recommend strategies while also providing a time limit for the issues, noting the responsible parties, and an estimated date of completion. Documenting all of this is the