Data can be defined as the physical representation of information in a way suitable for communication, interpretation, or processing by individuals or through automatic means. Thus, data results from the pursuit of knowledge (Sun et al., 2018). Additionally, data can be organized into structures such as tables which help in providing additional context and meaning. It can also be used as a variable in a computational process and may represent abstract ideas or concrete measurements. It is used in virtually all areas, such as economics, medicine, and even scientific research. In healthcare, various challenges are associated with normalizing, abstracting, and reconciling clinical data from various disparate sources.
Abstracting, Reconciling, and Normalizing Data
Abstracting refers to abridging complex data by removing redundant details and concentrating on the most significant features. This can be done by summarizing data, eliminating outliers, or grouping data into categories, thus helping capture key clinical data elements. Abstracting data makes it easier to comprehend and use and is often essential when working with large and complex data sets. Normalizing is confirming that data is dependable and conforms to certain standards. This can be done by removing duplicates, correcting errors, or transforming data into a standardized format (Samuel et al., 2020). On the other hand, reconciliation can be defined as comparing and matching data from diverse sources to identify and resolve inconsistencies and errors. This can be done by comparing data fields, using algorithms, or manual checking.
Abstracting Clinical Data
There are various methods of abstracting clinical data. These methods include the use of manual chart review. This most traditional method involves a trained person physically reviewing the chart of the patient and extracting the relevant information, which may include their treatment plans, diagnoses, and demographics (Green et al., 2018). However, this method is time-consuming and is at risk of many errors, but it can provide a comprehensive view of the patient’s medical history. Additionally, individuals can use electronic data extraction software to mechanically extract information from electronic health records (EHRs) and other electronic sources. This technique is faster and less prone to errors than manual chart review, but it may not capture all relevant information. Additionally, the software used for data extraction needs to be designed to extract the appropriate data fields, which requires a certain level of technical expertise.
Normalization of Data
Various steps are involved in the process of normalizing data. These steps include data cleaning, fixing misspellings, and removing errors and duplicates in the data. Data integration is also performed to integrate data from various sources (Saheb & Izadi, 2019). Moreover, data transformation is done to convert data from various measurement units, i.e., from diverse data formats. Normalization of data also involves standardization to ensure data is consistent and comparable, and data validation involves ensuring errors and inconsistencies are identified and corrected.
Reconciling Data
Data reconciliation involves process various steps, including identifying the various sources of data, extracting data from identified sources, and organizing into a structured format for comparison and comparing data to help identify discrepancies, missing information, and inconsistencies (Rostami et al., 2018). Additionally, it includes resolving discrepancies through concluding with relevant healthcare providers, thus determining the correct information, merging data into a single and comprehensive record, and data validation to ensure data is accurate, consistent, and complete.
Challenges Associated with Dealing with Data from Different Sources
Various challenges are linked to using data from different sources, including different sources using different coding systems and types, making it hard to compare them (Saheb & Izadi, 2019). Additionally, combining data from various sources increases the risk of data breaches and violation of patient privacy. Having data from different sources poses the risk of duplication, thus leading to wrong conclusions about a patient; it is hard to integrate data from various sources, and data from different sources may have different levels of quality and accuracy, thus making it hard to trust data and can lead to inaccuracies in analysis and decision-making.
In conclusion, data can be defined as the physical representation of information and is involved in virtually every part of research; some of the processes involved in data include normalizing, abstracting, and reconciling clinical data from various disparate sources, which are important in coming into a conclusion about a patient. Some of the challenges linked with data from various sources include privacy, data duplication, and complexity in data integration.
References
Green, M. L., Moeller, J. J., & Spak, J. M. (2018). Test-enhanced learning in health professions education: a systematic review: BEME Guide No. 48. Medical teacher, 40(4), 337-350.
Rostami, P., Ashcroft, D. M., & Tully, M. P. (2018). A formative evaluation of implementing a medication safety data collection tool in English healthcare settings: a qualitative interview study using normalization process theory. PLoS One, 13(2), e0192224.
Saheb, T., & Izadi, L. (2019). A paradigm of IoT big data analytics in the healthcare industry: A review of scientific literature and mapping research trends. Telematics and informatics, 41, 70-85.
Samuel, A., Konopasky, A., Schuwirth, L. W., King, S. M., & Durning, S. J. (2020). Five principles for using educational theory: strategies for advancing health professions education research. Academic Medicine, 95(4), 518-522.
Sun, W., Cai, Z., Li, Y., Liu, F., Fang, S., & Wang, G. (2018). Data processing and text mining technologies on electronic medical records: a review. Journal of healthcare engineering, 2018.
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