DNP 830 Discussion Accuracy of Data in Data-based Patient Records

March 8, 2022
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DNP 830 Discussion Accuracy of Data in Data-based Patient Records

DNP 830 Discussion Accuracy of Data in Data-based Patient Records

Describe the process of a retrospective chart review. How are these data collected? How would you access the data? What is the validity and reliability of these data? What steps would you need to take to ensure these data were accurately pulled from the database?

Data in computer-based patient records (CPRs) have many uses beyond their primary role in patient care, including research and health-system management. Although the accuracy of CPR data directly affects these applications, there has been only sporadic interest in, and no previous review of, data accuracy in CPRs. This paper reviews the published studies of data accuracy in CPRs. These studies report highly variable levels of accuracy. This variability stems from differences in study design, in types of data studied, and in the CPRs themselves. These differences confound interpretation of this literature. We conclude that our knowledge of data accuracy in CPRs is not commensurate with its importance and further studies are needed. We propose methodological guidelines for studying accuracy that address shortcomings of the current literature. As CPR data are used increasingly for research, methods used in research databases to continuously monitor and improve accuracy should be applied to CPRs.

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DNP 830 Discussion Accuracy of Data in Data-based Patient Records

DNP 830 Discussion Accuracy of Data in Data-based Patient Records

Data in computer-based patient records (CPRs) are used in patient care, clinical research, health-system management, health-services planning, total quality improvement, billing, risk management, and government reporting. The accuracy of these data is therefore of great importance. On the basis of inaccurate data, clinicians may make treatment errors,1 researchers may underestimate disease prevalence,2 health-system managers may underestimate compliance with standards of care such as vaccination guidelines,3 and alerting systems may send false alarms to physicians.4 It is therefore surprising that the amount of research devoted to measuring data accuracy in CPRs has been relatively small.

In contrast, there is extensive literature on data accuracy in paper-based records, disease registries, and clinical trial databases.5,6,7,8,9,10,11,12,13 This body of work provides a well-developed framework for the analysis of data accuracy that we can apply to CPRs. For example, Komaroff provides a comprehensive review of the complex processes by which different types of medical data are recorded into traditional paper-based medical records (and how error may be introduced at each step).5 His description of these processes can be expanded to describe how CPRs capture data (Fig. 1), thereby providing a model for the study of causes of inaccuracy. The literature on data accuracy in computer-based registries and clinical trial databases provides standard methods for the study of data accuracy,10,11,14 which are applicable to CPRs. In this literature, accuracy is calculated using two measures—one that measures the proportion of recorded observations in the system that are correct (correctness)* and a second that measures the proportion of observations that are actually recorded in the system (completeness) (Fig. 2). These measures are viewed as complementary; both measures are necessary for a complete understanding of accuracy in a system. From Figure 2, we can see that completeness decreases as the number of false negatives (cell c in Figure 2) increases, and correctness decreases as the number of false positives (cell b in Figure 2) increases. The false positives and negatives are largely independent of one another; thus, both measures provide valuable information about the accuracy of data in any system.

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Posted in nursing by Clarissa