Data Quality Management in a Health Care Setting

Introduction

• We are living in an era that is characterized by a wide rage of data. Finding accurate data serves as the basis of data quality. Data quality is also characterized by other attributes such as relevance, and accessibility.
• The innovation present in this dynamic world has resulted to various techniques of coding data such as the use of internet as well as other data recording sources. This has resulted to a challenge of adapting to these systems, in order to provide adequate data (Hernandez & Blanquer, 2006).

Effects of Poor Data Quality

• Poor data quality emerges as one of the critical problem that a healthcare setting finds challenging to address (Dyro, 2004).
• The impact of poor data quality influences the quality of health care delivery to the patients. This is because it is associated with adverse effects such as increases in costs and inefficiency of the health care system.
• In addition, it creates a risk for the health care setting as health workers are unable to give the correct medication.
• it also undermines the reliability of technology used in the data collection and analysis and other health care delivery services such as the billing services (Ramsaroop & Beaulieu, 2001).
• Besides causing an alarm in the health care institution, poor data has resulted to unnecessary inconveniencies while dealing with the insurance bills, as a high level of data accuracy is required for the health care providers and the patients’ insurance reimbursement (McLaaghlin, 2004). This can lead to spending additional time while correcting the errors.
• More over, Poor data can have serious consequences for the privacy of the patients’ information, and especially in a networked health care environment. This is because single errors results to further discussion and analysis of the problems, and thus creating chances of interfering with patients’ privacy.
• With a high innovation of information exchange networks in a healthcare setting, the issue of poor data quality needs to be addressed.
• This is so because this problem has attained an epidemic proportion. The problems have resulted to creating an alarm for the health care setting, thus calling for an intervention (Lezzoni, 2010).
• This can be achieved by developing a data quality culture within the health care institutions at the network level, and then listing the possible issues and options that are available for consideration.

Recommendations to Create an Environment That Promotes Data Quality

• The health care institutions that have the ability of incorporating data quality with financial and administrative systems have a competitive advantage over other health care organizations, which collect data without using these systems (Mooney, 1998).
• This is because they provide accurate data that is of utmost importance as it monitors performance as well providing the relevant outcomes.
• The use of this technologies also help to compare patients and the health care institution by putting more emphasis on people, since people are the managers of the system (Mooney, 1998).
• Therefore, much emphasis should be put on the planning phase and data preparation.
• A data manager must therefore ensure that a proper definition of what the data is intended to achieve must be defined and must apply to the result.
• He should then come up with ways that will help to achieve the stated objective of the data quality.
• The data collection methods should be assessed which must be user friendly as user-friendly data collection within a health care institution prevents the percentage of recording faulty data (Mooney, 1998).
• Since quality data should not only be timely but also accurate, a heath care institution should provide ways for data management and a proper analysis for the stored data.
• An analysis is not only aimed at determining how to evaluate a clinical process but also to improve the health care clinical processes.
• Incorporating the information technology system in data collection should done cautiously in order to come up with an accurate data analysis that can successfully lead to quality data for a health care institution.

References

Dyro, J ( 2004). Clinical engineering handbook. Durham, NC‎: Academic Press

Hernandez, V. and Blanquer, I.(2006). Challenges and opportunities of HealthGrids: proceedings of Health grid. 1013 BG Amsterdam: IOS Press,

Lezzoni, L. (2010). Assessing quality using administrative data. Animal of internal medicine