Definitions of Data Quality Characteristics: Strengths and Weakness Highlighted
Amongst the most common subject matters in the current health information management is the ‘large data’. The good news is that it is popular for good reason, as the data or information helps in informing the thinking of humankind and compels more efficient business decision making and successful healthcare. However, for the data to become of the supreme value, it ought to be data of quality. Consequently, quality data needs a precise definition to establish whether it is hitting an organization’s target or goals (McGee, 2004). This paper, therefore, looks at the definitions of data quality characteristics in health information management and possibly highlights the strengths and weakness of each under every great explanation.
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Accuracy
Accuracy refers to the presence of exactness and precision in the data, which in other words managers of the information described as the freedom or liberty from error. Notably, in healthcare, it has significant strength in the process of clinical actions and decision-making, which relies on the data gathered or collected concerning the patients under treatment. Nevertheless, weakness can arise in the event of any inaccuracy present in the data, and that could result in considerable harm or at times the death of the patient.
Accessibility
Accessibility demands that the users of data must access and obtain it without difficulty. It is an important characteristic of data quality because if data intends to offer information to its users, then it must be readily available to them. A significant weakness can arise in case the health record leaves its data to for hunting by the users since precious time cold get lost. As well, the hunting of data could result in frustrations and possibly make providers surrender on searching for valuable information (McGee, 2004).
Consistency
The consistency of data in the health information management relates to uniformity and reliability of actions or events that happen every time. Data pertaining healthcare ought to be consistent to thwart results misinterpretation or stop presentation of any ambiguous information in the documentation. As such, EHRs having comprehensive data fields and templates on documentation help in ensuring consistency (Lorence & Jameson, 2002).
Comprehensiveness
Data must capture every element of data for health information managers to consider it comprehensive; for this reason, the capturing of all elements makes the health records complete. Precisely, they need to contain all the necessary data to inform about the patient’s full story; otherwise, rendering them weak would be very easy (Johns, 2002).
Timeliness and Currency
Health-related data that is timely and current ensures its recording and updating happen very close to the time of observation or event. Timely and current data record of patient care makes sure that healthcare providers comprehend the patients’ needs in the present instant of time (Lorence & Jameson, 2002).
Definition
One of the most important definitions of the data quality is the ‘data definitions’ since the users of data must recognize and understand the meaning of data and what it represents at the time of its usage. For that reason, every element of data must have an actual significance or meaning (Johns, 2002).
Granularity
A collection of data needs to happen at the right stage of specificity; taking into account that the healthcare business is approaching ICD-10 CM/PCS and that data will involve several personal essentials, for example, body sites laterality, as well as need the exact extent of detail (Lorence & Jameson, 2002).
Relevancy
Any collected healthcare data should encompass a bearing on the current issue. As such, it has to be relevant and applicable to the rationale for which its collection occur; for instance, for a patient suffering from chest pain, gathering data such as EKG results and cardiac laboratory values, among other, would be significant to the patient’s care (Johns, 2002).
References
Johns, M. (2002). Information management for health professions (2nd ed.). Albany, NY: Thomson Learning. Ch. 7-9, pp. 250-373. ProQuest Links Adoption of information quality management practices in US healthcare organizations: A national assessment
Lorence, D. & Jameson, R. (2002). International Journal of Quality and Reliability Management, 19(6/7), 737-756. Retrieved October 2, 2004, from ABI/INFORM Complete database. Doctors get new data source to improve care
McGee, M. (2004. Nov.). InformationWeek, 1012, 26.
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