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? 1 page, 2 sources, APA. Purchase the answer to view it
A retrospective chart review is a research method used to gather data from existing medical records, also known as patient charts, to answer specific research questions. This method involves analyzing past medical records to extract relevant data that can be used to study patient outcomes, disease patterns, treatment effectiveness, and other healthcare-related topics. This paper provides an overview of the process of a retrospective chart review, including the data collection process, accessing the data, the validity and reliability of these data, and the steps required to ensure accurate data retrieval.
The first step in a retrospective chart review is to identify the research question or objective. This could be related to understanding the prevalence of a certain condition, assessing the impact of a treatment intervention, or evaluating patterns of healthcare delivery. Once the research question is established, the researcher needs to identify the patient population of interest and the inclusion and exclusion criteria for selecting the medical records.
The second step involves obtaining Institutional Review Board (IRB) approval, which ensures that the study is conducted ethically and with minimal risk to the patients. IRB approval is necessary before accessing patient records and analyzing patient data.
To access the data, the researcher must first identify the source of the medical records. This could be a hospital, clinic, or healthcare facility that maintains electronic health records (EHR) or paper-based records. Depending on the source, the data can be collected manually or through electronic means. Manual collection involves physically retrieving and reviewing the paper records, while electronic collection involves retrieving and extracting data from electronic databases or EHR systems.
In manual collection, the researcher reviews each patient chart to extract the relevant information. This process can be time-consuming and requires careful attention to detail. The researcher must ensure that the data are collected consistently across all charts and recorded accurately. To minimize errors, inter-rater reliability can be assessed by having multiple researchers independently review a subset of charts and comparing their findings.
In electronic collection, the researcher must have access to the database or EHR system that contains the patient records. This access is typically granted through a formal request to the healthcare facility, which may involve signing data use agreements and ensuring data security and confidentiality. Once access is obtained, the researcher can query the database to retrieve the relevant patient records based on the inclusion and exclusion criteria. This can be done using specific search terms, codes, or algorithms. The retrieved data can then be exported into a statistical software program for analysis.
The validity and reliability of the data obtained through a retrospective chart review depend on various factors. Validity refers to the accuracy and appropriateness of the data in addressing the research question. To ensure validity, the researcher must define the variables of interest, choose appropriate measurement methods, and employ standardized data collection tools. For example, if the research question involves assessing treatment outcomes, the researcher needs to define and measure the specific outcome measures consistently across all patient charts.
Reliability refers to the consistency and repeatability of the data. To ensure reliability, the researcher needs to establish clear guidelines and protocols for data extraction, use standardized coding schemes, and train data abstractors to ensure consistency in data collection. Moreover, reliability can be assessed by having multiple researchers independently review a subset of charts and comparing their findings. The level of agreement between the researchers’ findings can be calculated using inter-rater reliability measures such as kappa statistics.
To ensure accurate data retrieval, several steps can be taken. First, the researcher should carefully define the variables of interest and develop a data abstraction form or template to ensure consistent and thorough data extraction. The abstraction form should include clear instructions, coding guidelines, and examples to minimize inter-rater variability. Second, training sessions can be conducted for the research team to ensure that all members are familiar with the study objectives, data collection procedures, and coding conventions. Third, regular meetings and discussions can be held among the research team to address any questions or uncertainties that may arise during the data extraction process. Finally, periodic quality checks and audits can be conducted to review a subset of the extracted data and assess the accuracy and completeness of the data.
In conclusion, a retrospective chart review is a valuable research method that allows researchers to collect and analyze data from existing medical records. The process involves identifying the research question, obtaining IRB approval, accessing the data from paper or electronic records, ensuring the validity and reliability of the data, and taking steps to ensure accurate data retrieval. By following these steps and adhering to rigorous data collection procedures, researchers can gather valuable insights and contribute to the growing body of knowledge in healthcare research.
References:
1. Smith, J. K. (2010). Retrospective chart review in clinical research. JAAPA : official journal of the American Academy of Physician Assistants, 23(2), 31–34.
2. Nicasio, A. M., & Tullius, B. P. (2019). Retrospective Chart Review in Clinical Research: Practical Guide to Ensure Data Quality. American Journal of Therapeutics, 26(2), e205–e208. doi: 10.1097/MJT.0000000000000944