{"id":462,"date":"2026-07-19T11:21:00","date_gmt":"2026-07-19T05:51:00","guid":{"rendered":"https:\/\/www.editage.com\/blog\/?p=462"},"modified":"2026-07-20T14:24:31","modified_gmt":"2026-07-20T08:54:31","slug":"best-practices-in-retrospective-chart-reviews-for-biomedical-researchers","status":"publish","type":"post","link":"https:\/\/www.editage.com\/blog\/best-practices-in-retrospective-chart-reviews-for-biomedical-researchers\/","title":{"rendered":"What is a Retrospective Chart Review? Definition, Methods, Best Practices"},"content":{"rendered":"\n<p><strong>Key Takeaways:<\/strong><\/p>\n\n\n\n<ul><li>A retrospective chart review (RCR) reuses data already in patient records; its credibility is decided before the first chart is opened.<\/li><li>Ethics approval, a detailed data dictionary, and trained, blinded abstractors separate strong RCRs from weak ones.<\/li><li>Missing data, selection bias, and confounding are the main threats; handle them through design, not only in the limitations section.<\/li><li>Report your study against STROBE and RECORD to meet journal standards and support reproducibility.<\/li><\/ul>\n\n\n\n<p><strong>Contents<\/strong><\/p>\n\n\n\n<ul><li><a href=\"#_Toc235449445\">Glossary of Key Terms<\/a><\/li><li><a href=\"#_Toc235449446\">What is a retrospective chart review?<\/a><\/li><li><a href=\"#_Toc235449447\">Define your research question and hypotheses<\/a><\/li><li><a href=\"#_Toc235449448\">Do you need ethics approval for a chart review?<\/a><\/li><li><a href=\"#_Toc235449449\">What sampling strategy should you use?<\/a><\/li><li><a href=\"#_Toc235449450\">Build the data abstraction protocol<\/a><\/li><li><a href=\"#_Toc235449451\">How do you ensure reliable data abstraction?<\/a><\/li><li><a href=\"#_Toc235449452\">Collect and manage your data<\/a><\/li><li><a href=\"#_Toc235449453\">How do you handle missing data in chart reviews?<\/a><\/li><li><a href=\"#_Toc235449454\">Identify and control bias<\/a><\/li><li><a href=\"#_Toc235449455\">Analyze your data<\/a><\/li><li><a href=\"#_Toc235449456\">Interpret your findings<\/a><\/li><li><a href=\"#_Toc235449457\">Address limitations<\/a><\/li><li><a href=\"#_Toc235449458\">Which reporting guidelines should you follow?<\/a><\/li><li><a href=\"#_Toc235449459\">Conclusion<\/a><\/li><li><a href=\"#_Toc235449460\">Frequently Asked Questions<\/a><\/li><\/ul>\n\n\n\n<h2><a id=\"_Toc235449445\">Glossary of Key Terms<\/a><\/h2>\n\n\n\n<p>The terms below appear throughout this guide. Reviewing them first will make the methods and statistics easier to follow.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Term<\/strong><\/td><td><strong>Definition<\/strong><\/td><\/tr><\/thead><tbody><tr><td>Retrospective chart review (RCR)<\/td><td>An observational study that analyzes data already recorded in patient medical records.<\/td><\/tr><tr><td>Data abstraction<\/td><td>The process of extracting specific variables from charts into a structured dataset.<\/td><\/tr><tr><td>Data dictionary<\/td><td>A document defining every variable, its allowed values, and coding rules.<\/td><\/tr><tr><td><a href=\"https:\/\/www.editage.com\/blog\/operationalization-research-definition-examples\/\">Operationalization<\/a><\/td><td>Turning an abstract concept into a measurable, clearly defined variable.<\/td><\/tr><tr><td>Inclusion and exclusion criteria<\/td><td>Rules that decide which records enter or leave the study.<\/td><\/tr><tr><td>Blinding<\/td><td>Concealing study hypotheses or group assignment from abstractors to reduce bias.<\/td><\/tr><tr><td>Inter-rater reliability (IRR)<\/td><td>The degree of agreement between 2 or more independent abstractors.<\/td><\/tr><tr><td>Cohen&#8217;s kappa<\/td><td>A statistic measuring categorical agreement corrected for chance.<\/td><\/tr><tr><td>Selection bias<\/td><td>Systematic error from a nonrepresentative sample of records.<\/td><\/tr><tr><td>Information (misclassification) bias<\/td><td>Error from inaccurate or inconsistent chart data.<\/td><\/tr><tr><td><a href=\"https:\/\/www.editage.com\/blog\/confounding-variables-identification-definition-types-examples\">Confounding<\/a><\/td><td>Distortion of an association by a third variable linked to both exposure and outcome.<\/td><\/tr><tr><td>Propensity score<\/td><td>A method that balances confounders across comparison groups.<\/td><\/tr><tr><td>Multiple imputation<\/td><td>A statistical method that replaces missing values with several plausible estimates.<\/td><\/tr><tr><td>STROBE<\/td><td>A reporting checklist for observational studies.<\/td><\/tr><tr><td>RECORD<\/td><td>A STROBE extension for studies using routinely-collected health data.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2><a id=\"_Toc235449446\">What is a retrospective chart review?<\/a><\/h2>\n\n\n\n<p>A retrospective chart review (RCR) is an <a href=\"https:\/\/www.editage.com\/blog\/observational-study\/\">observational study<\/a> that extracts data already recorded in patient charts to answer a <a href=\"https:\/\/researcher.life\/blog\/article\/how-to-craft-a-strong-research-question-with-research-question-examples\/\">research question<\/a>. Because records are created for care, not research, careful methods are essential for valid results.<\/p>\n\n\n\n<p>RCRs are widely used because they are efficient and inexpensive. Common goals include:<\/p>\n\n\n\n<ul><li>Estimating disease incidence, complication rates, or treatment patterns.<\/li><li>Generating <a href=\"https:\/\/researcher.life\/blog\/article\/how-to-write-a-research-hypothesis-definition-types-examples\/\">hypotheses<\/a> for future prospective studies.<\/li><li>Evaluating adherence to clinical guidelines or quality benchmarks.<\/li><\/ul>\n\n\n\n<h2><a id=\"_Toc235449447\">Define your research question and hypotheses<\/a><\/h2>\n\n\n\n<p>Start by defining a focused question and the <a href=\"https:\/\/www.editage.com\/blog\/hypothesis-testing-different-types-for-biomedical-researchers\/\">hypotheses you will test<\/a>. This determines which variables you collect and the time frame you cover.<\/p>\n\n\n\n<p>A clear question specifies the population, exposure or intervention, outcome, and setting. For example: What is the incidence of postoperative infections in patients who underwent laparoscopic colorectal surgery at 1 hospital between 2018 and 2020?<\/p>\n\n\n\n<p>Next, operationalize each key variable so it can be measured consistently. Decide the exact definition before data collection begins:<\/p>\n\n\n\n<ul><li>Will poor glycemic control be defined by HbA1c, fasting glucose, postprandial glucose, or a combination?<\/li><li>What threshold value marks the presence of the condition?<\/li><li>Which time window counts for each measurement?<\/li><\/ul>\n\n\n\n<h2><a id=\"_Toc235449448\">Do you need ethics approval for a chart review?<\/a><\/h2>\n\n\n\n<p>Yes. Almost every RCR requires review by an institutional review board (IRB) or ethics committee, even when the data are de-identified. Most qualify for a waiver of informed consent under minimal-risk rules.<\/p>\n\n\n\n<p>Plan the regulatory and privacy elements early:<\/p>\n\n\n\n<ul><li>Confirm whether your study meets the criteria for a consent waiver.<\/li><li>Follow applicable privacy laws, such as HIPAA or GDPR.<\/li><li>Distinguish de-identification from full anonymization, and document your approach.<\/li><li>Put data-use or data-sharing agreements in place before access.<\/li><li><a href=\"https:\/\/www.editage.com\/blog\/research-data-management-how-to-make-a-data-management-plan-dmp\/\">Store records<\/a> on secure, access-controlled systems with an audit trail.<\/li><\/ul>\n\n\n\n<h2><a id=\"_Toc235449449\">What sampling strategy should you use?<\/a><\/h2>\n\n\n\n<p>Prefer consecutive or random sampling over convenience sampling, which weakens generalizability. Define inclusion and exclusion criteria first, then <a href=\"https:\/\/www.editage.com\/blog\/sample-size-and-statistical-power-definition-formulas-calculations-worked-examples\/\">size the sample for adequate statistical power<\/a>.<\/p>\n\n\n\n<p>Set your eligibility rules explicitly:<\/p>\n\n\n\n<ul><li>Inclusion criteria may cover age, diagnosis, procedure, or treatment.<\/li><li>Exclusion criteria may cover comorbidities, prior surgery, or incomplete records.<\/li><\/ul>\n\n\n\n<p>Common sampling methods differ in rigor:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Method<\/strong><\/td><td><strong>Description<\/strong><\/td><td><strong>Main limitation<\/strong><\/td><\/tr><\/thead><tbody><tr><td>Convenience<\/td><td>Uses easily available records<\/td><td>High risk of selection bias<\/td><\/tr><tr><td>Consecutive<\/td><td>Includes every eligible record in order<\/td><td>Time frame may still limit scope<\/td><\/tr><tr><td>Simple random<\/td><td>Selects records at random from the eligible pool<\/td><td>Needs a complete sampling frame<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2><a id=\"_Toc235449450\">Build the data abstraction protocol<\/a><\/h2>\n\n\n\n<p>Standardized abstraction is what makes chart data reliable. Two tools anchor the process: a data dictionary and a piloted abstraction form.<\/p>\n\n\n\n<h3>Create a data dictionary<\/h3>\n\n\n\n<p>A data dictionary defines every variable so different abstractors record the same thing the same way.<\/p>\n\n\n\n<ul><li>List each variable with its exact definition and units.<\/li><li>Specify allowed values and codes for each field.<\/li><li>Give decision rules for ambiguous, conflicting, or duplicate entries.<\/li><li>Note the preferred source when a value appears in more than 1 place.<\/li><\/ul>\n\n\n\n<h3>Sample entries from a data dictionary<\/h3>\n\n\n\n<p>Here&#8217;s a sample data dictionary excerpt showing four variables of different types (identifier, date, categorical, continuous). I&#8217;ve split it across two tables to respect the 4-column limit while capturing the fields that matter most.<\/p>\n\n\n\n<h4>Core definitions<\/h4>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Variable name<\/strong><\/td><td><strong>Definition<\/strong><\/td><td><strong>Data type \/ allowed values<\/strong><\/td><\/tr><\/thead><tbody><tr><td>patient_id<\/td><td>Unique study ID assigned at abstraction; not the medical record number<\/td><td>Integer, 4 digits (1001-9999)<\/td><\/tr><tr><td>surgery_date<\/td><td>Date of the index laparoscopic colorectal procedure<\/td><td>Date, YYYY-MM-DD<\/td><\/tr><tr><td>diabetes_status<\/td><td>Documented diabetes diagnosis at time of surgery<\/td><td>0 = No; 1 = Yes; 9 = Not documented<\/td><\/tr><tr><td>hba1c_preop<\/td><td>Most recent HbA1c within 90 days before surgery<\/td><td>Numeric, %, 1 decimal (3.0-20.0)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h4>Source and decision rules<\/h4>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Variable name<\/strong><\/td><td><strong>Source in chart<\/strong><\/td><td><strong>Decision rule for ambiguous or missing entries<\/strong><\/td><\/tr><\/thead><tbody><tr><td>patient_id<\/td><td>Assigned by abstractor<\/td><td>Never reuse an ID; log assignment in the master key<\/td><\/tr><tr><td>surgery_date<\/td><td>Operative note (primary); anesthesia record (secondary)<\/td><td>If the 2 sources conflict, use the operative note<\/td><\/tr><tr><td>diabetes_status<\/td><td>Problem list (primary); discharge summary (secondary)<\/td><td>Code 9 if neither source mentions diabetes; absence is not &#8220;No&#8221;<\/td><\/tr><tr><td>hba1c_preop<\/td><td>Laboratory results<\/td><td>If more than 1 value qualifies, take the closest to surgery; code as missing if none within 90 days<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>A few conventions worth noting from this excerpt:<\/p>\n\n\n\n<ul><li><strong>Separate &#8220;not documented&#8221; from a true negative.<\/strong> diabetes_status uses a distinct code (9) for silence in the chart, so you never mistake absence of evidence for evidence of absence.<\/li><li><strong>Name a primary source and a tiebreaker.<\/strong> Every abstracted variable points to a preferred source and a rule for when sources disagree, which is what keeps 2 abstractors coding the same chart the same way.<\/li><li><strong>Bound every value.<\/strong> Ranges and formats (4 digits, 1 decimal, 3.0-20.0) let you build range and logic checks that flag impossible entries during data collection.<\/li><\/ul>\n\n\n\n<h3>Pilot test the abstraction form<\/h3>\n\n\n\n<p>Test the form on a small sample before full collection to catch problems early.<\/p>\n\n\n\n<ul><li>Abstract 10-20 charts to check clarity and completeness.<\/li><li>Revise unclear fields and add any missing decision rules.<\/li><li>Repeat until abstractors apply the form consistently.<\/li><\/ul>\n\n\n\n<h2><a id=\"_Toc235449451\">How do you ensure reliable data abstraction?<\/a><\/h2>\n\n\n\n<p>Reliable abstraction depends on trained, blinded abstractors working from a shared data dictionary, plus measured agreement between reviewers. Re-abstract a sample of charts to quantify and monitor reliability.<\/p>\n\n\n\n<h3>Train and blind your abstractors<\/h3>\n\n\n\n<ul><li>Train all abstractors on the dictionary and form together.<\/li><li>Blind them to the study hypothesis and to group or outcome status.<\/li><li>Standardize decision rules so judgments do not vary by person.<\/li><li>Monitor performance and retrain if agreement drifts.<\/li><\/ul>\n\n\n\n<h3>Measure inter-rater reliability<\/h3>\n\n\n\n<p>Have 2 or more abstractors independently code an overlapping subset, then quantify agreement:<\/p>\n\n\n\n<ul><li>Use Cohen&#8217;s kappa for categorical variables.<\/li><li>Use the intraclass correlation coefficient (ICC) for continuous variables.<\/li><li>Set an acceptable threshold in advance and report the achieved value.<\/li><\/ul>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Kappa value<\/strong><\/td><td><strong>Strength of agreement<\/strong><\/td><\/tr><\/thead><tbody><tr><td>Less than 0.20<\/td><td>Poor<\/td><\/tr><tr><td>0.21-0.40<\/td><td>Fair<\/td><\/tr><tr><td>0.41-0.60<\/td><td>Moderate<\/td><\/tr><tr><td>0.61-0.80<\/td><td>Substantial<\/td><\/tr><tr><td>0.81-1.00<\/td><td>Almost perfect<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2><a id=\"_Toc235449452\">Collect and manage your data<\/a><\/h2>\n\n\n\n<p>Collect data into a secure, structured database rather than loose spreadsheets. Good data management reduces errors and supports auditing.<\/p>\n\n\n\n<ul><li>Use a purpose-built platform such as REDCap for capture and validation.<\/li><li>Apply range and logic checks to flag impossible values.<\/li><li>Consider double data entry for high-stakes variables.<\/li><li>Treat structured fields and free-text notes differently during extraction.<\/li><li>Verify diagnosis and procedure codes (ICD, CPT), which can be incomplete or inaccurate.<\/li><li>Keep an audit trail of every change to the dataset.<\/li><\/ul>\n\n\n\n<h2><a id=\"_Toc235449453\">How do you handle missing data in chart reviews?<\/a><\/h2>\n\n\n\n<p>First separate not documented from not present: absence in a chart is not proof of absence in the patient. Then classify the missingness and choose a principled handling method.<\/p>\n\n\n\n<p>Missing values fall into 3 broad mechanisms that guide your response:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Mechanism<\/strong><\/td><td><strong>Meaning<\/strong><\/td><td><strong>Common approach<\/strong><\/td><\/tr><\/thead><tbody><tr><td>Missing completely at random<\/td><td>Missingness unrelated to any variable<\/td><td>Complete-case analysis may be acceptable<\/td><\/tr><tr><td>Missing at random<\/td><td>Missingness explained by observed data<\/td><td>Multiple imputation<\/td><\/tr><tr><td>Missing not at random<\/td><td>Missingness tied to the unobserved value<\/td><td>Sensitivity analysis; cautious interpretation<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Whichever method you use, report the amount of missing data and test how much your conclusions depend on it.<\/p>\n\n\n\n<h2><a id=\"_Toc235449454\">Identify and control bias<\/a><\/h2>\n\n\n\n<p>Bias is the central methodological risk in chart reviews. Address it through study design and analysis, not only in the discussion.<\/p>\n\n\n\n<h3>What biases affect chart reviews?<\/h3>\n\n\n\n<p>The main threats are selection bias, information (misclassification) bias, and confounding. Documentation bias is distinctive: chart data were recorded for care, so completeness varies by patient and clinician.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Bias<\/strong><\/td><td><strong>Description<\/strong><\/td><td><strong>Mitigation<\/strong><\/td><\/tr><\/thead><tbody><tr><td>Selection<\/td><td>Sample not representative of the target population<\/td><td>Random or consecutive sampling<\/td><\/tr><tr><td>Information<\/td><td>Inaccurate or inconsistent chart entries<\/td><td>Clear definitions; blinded, trained abstractors<\/td><\/tr><tr><td>Confounding<\/td><td>A third variable distorts the association<\/td><td>Adjust in design or analysis<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3>Strategies to control confounding<\/h3>\n\n\n\n<ul><li>Multivariable regression to adjust for measured confounders.<\/li><li>Propensity-score methods to balance comparison groups.<\/li><li>Stratification or restriction to isolate key subgroups.<\/li><li>Matching to create comparable exposed and unexposed sets.<\/li><\/ul>\n\n\n\n<p>Remember that these methods reduce, but never fully remove, confounding. An RCR can show association, not causation.<\/p>\n\n\n\n<h2><a id=\"_Toc235449455\">Analyze your data<\/a><\/h2>\n\n\n\n<p>Analyze data with methods chosen in advance to match your variables and question:<\/p>\n\n\n\n<ul><li>Summarize with <a href=\"https:\/\/www.editage.com\/blog\/what-are-descriptive-statistics-types-choosing-reporting\/\">descriptive statistics<\/a>: means, medians, and standard deviations.<\/li><li>Inspect the data for outliers and check test assumptions.<\/li><li>Apply inferential tests such as <a href=\"https:\/\/www.editage.com\/blog\/chi-square-test-types-explained-for-biomedical-researchers\/\">chi-square<\/a>, <a href=\"https:\/\/www.editage.com\/blog\/t-test-definition-assumptions-formula-calculation\/\">t-tests<\/a>, or <a href=\"https:\/\/www.editage.com\/blog\/what-is-regression-and-types-of-regression-for-biomedical-researchers\/\">regression<\/a>.<\/li><li>Adjust for confounders using your pre-specified model.<\/li><\/ul>\n\n\n\n<h2><a id=\"_Toc235449456\">Interpret your findings<\/a><\/h2>\n\n\n\n<p>Interpret results in the context of your question and existing evidence, staying within the limits of an observational design:<\/p>\n\n\n\n<ul><li>Compare findings with published literature and guidelines.<\/li><li>Separate statistical significance from clinical importance.<\/li><li>Note that temporal order is often unclear in retrospective data.<\/li><li>Frame conclusions as associations, not proof of cause.<\/li><\/ul>\n\n\n\n<h2><a id=\"_Toc235449457\">Address limitations<\/a><\/h2>\n\n\n\n<p>Acknowledge limitations honestly, and show where your design already reduced them:<\/p>\n\n\n\n<ul><li>Missing or incomplete documentation.<\/li><li>Selection bias from the <a href=\"https:\/\/researcher.life\/blog\/article\/what-is-a-sampling-frame-definition-uses-tips-examples\/\">sampling frame<\/a>.<\/li><li>Residual confounding from unmeasured variables.<\/li><li>Limited <a href=\"https:\/\/www.editage.com\/blog\/what-is-generalizability-definition-examples-tips\/\">generalizability<\/a> from single-center data.<\/li><\/ul>\n\n\n\n<h2><a id=\"_Toc235449458\">Which reporting guidelines should you follow?<\/a><\/h2>\n\n\n\n<p>Follow STROBE for observational studies and RECORD, its extension for routinely-collected health data. RECORD-PE applies to pharmacoepidemiology. These checklists improve transparency and publication odds.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Guideline<\/strong><\/td><td><strong>Scope<\/strong><\/td><\/tr><\/thead><tbody><tr><td>STROBE<\/td><td>General reporting for observational studies<\/td><\/tr><tr><td>RECORD<\/td><td>Studies using routinely-collected health data<\/td><\/tr><tr><td>RECORD-PE<\/td><td>Pharmacoepidemiology using routine data<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2><a id=\"_Toc235449459\">Conclusion<\/a><\/h2>\n\n\n\n<p>A well-conducted retrospective chart review can yield valuable evidence on patient care and public health. Rigor comes from planning: a clear question, ethics approval, a strong abstraction protocol, measured reliability, and transparent reporting.<\/p>\n\n\n\n<p>For help with study design and analysis, consider Editage&#8217;s Statistical Analysis and Review Services.<\/p>\n\n\n\n<h2><a id=\"_Toc235449460\">Frequently Asked Questions<\/a><\/h2>\n\n\n\n<h3>What is the difference between a retrospective chart review and a prospective study?<\/h3>\n\n\n\n<p>A retrospective chart review analyzes data already recorded in the past, while a prospective study collects new data going forward. RCRs are faster and cheaper, but they offer less control over data quality and confounding.<\/p>\n\n\n\n<h3>Do retrospective chart reviews require informed consent?<\/h3>\n\n\n\n<p>Often no. Many RCRs qualify for a waiver of informed consent because they are minimal-risk and use existing, de-identified records. An IRB or ethics committee still must review the study and approve any waiver.<\/p>\n\n\n\n<h3>How many charts are needed for a retrospective chart review?<\/h3>\n\n\n\n<p>There is no fixed number. Base the sample size on a power calculation for your primary outcome, your expected effect size, and the number of eligible records. Always report how you arrived at the final sample.<\/p>\n\n\n\n<h3>What is inter-rater reliability in chart review research?<\/h3>\n\n\n\n<p>Inter-rater reliability measures how consistently 2 or more abstractors record the same data from the same charts. Researchers report it with Cohen&#8217;s kappa for categorical variables or the intraclass correlation coefficient for continuous ones.<\/p>\n\n\n\n<h3>Is a retrospective chart review considered human subjects research?<\/h3>\n\n\n\n<p>Usually yes. Because it uses identifiable or potentially identifiable patient data, an RCR generally counts as human subjects research and needs ethics review, even though patients are not contacted directly.<\/p>\n\n\n\n<h3>What statistical tests are used in retrospective chart reviews?<\/h3>\n\n\n\n<p>Common choices include chi-square tests for proportions, t-tests for group means, and logistic or linear regression to adjust for confounders. The right test depends on the variable types and the research question.<\/p>\n\n\n\n<h3>How do you reduce bias in a retrospective chart review?<\/h3>\n\n\n\n<p>Reduce bias with random or consecutive sampling, clear operational definitions, trained and blinded abstractors, measured inter-rater reliability, and statistical adjustment for confounders such as regression or propensity scores.<\/p>\n","protected":false},"excerpt":{"rendered":"Retrospective chart reviews are an important part of biomedical research. These studies can provide valuable information that can be used to improve patient care and inform future research studies. However, it's important to conduct retrospective chart reviews carefully to ensure the accuracy and validity of your findings. Below are some of the best practices in retrospective chart reviews.","protected":false},"author":2,"featured_media":1466,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_ayudawp_aiss_exclude":false,"_ayudawp_aiss_summary":"A retrospective chart review (RCR) reuses data already in patient records; its credibility is decided before the first chart is opened. A retrospective chart review (RCR) is an observational study that extracts data already recorded in patient charts to answer a research question. A retrospective chart review analyzes data already recorded in the past, while a prospective study collects new data going forward.","_ayudawp_aiss_summary_provider":"extractive","_ayudawp_aiss_summary_hash":"f8a29887763a52669e4296806211e031aadaf761"},"categories":[14],"tags":[23,24],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Retrospective Chart Reviews: Best Practices For Biomedical Researchers | Editage<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.editage.com\/blog\/best-practices-in-retrospective-chart-reviews-for-biomedical-researchers\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Retrospective Chart Reviews: Best Practices For Biomedical Researchers | Editage\" \/>\n<meta property=\"og:description\" content=\"Retrospective chart reviews are an important part of biomedical research. These studies can provide valuable information that can be used to improve patient care and inform future research studies. However, it&#039;s important to conduct retrospective chart reviews carefully to ensure the accuracy and validity of your findings. 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