Ensuring high accuracy of data abstracted from patient charts: the use of a standardized medical record as a training tool

Pan, Li; Fergusson, Dean; Schweitzer, Irwin; Hébert, Paul C.

doi:10.1016/j.jclinepi.2005.02.004

Cited by 58 publications

(42 citation statements)

References 16 publications

(16 reference statements)

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“…A common interpretation of κ states that a value between 0.61 and 0.80 constitutes substantial agreement between raters, while in emergency medicine research, the benchmark is 95% agreement. 18 Ultimately, the published standards from primary care are few and have widely ranging values depending on the variables being measured.…”

Section: In T Er R At Er R El Ia Bil I T Y In Data Col L Ec T Ionmentioning

confidence: 99%

Methods to Achieve High Interrater Reliability in Data Collection From Primary Care Medical Records

Liddy¹,

Wiens²,

Hogg³

2011

The Annals of Family Medicine

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PURPOSEWe assessed interrater reliability (IRR) of chart abstractors within a randomized trial of cardiovascular care in primary care. We report our fi ndings, and outline issues and provide recommendations related to determining sample size, frequency of verifi cation, and minimum thresholds for 2 measures of IRR: the κ statistic and percent agreement. METHODSWe designed a data quality monitoring procedure having 4 parts: use of standardized protocols and forms, extensive training, continuous monitoring of IRR, and a quality improvement feedback mechanism. Four abstractors checked a 5% sample of charts at 3 time points for a predefi ned set of indicators of the quality of care. We set our quality threshold for IRR at a κ of 0.75, a percent agreement of 95%, or both.RESULTS Abstractors reabstracted a sample of charts in 16 of 27 primary care practices, checking a total of 132 charts with 38 indicators per chart. The overall κ across all items was 0.91 (95% confi dence interval, 0.90-0.92) and the overall percent agreement was 94.3%, signifying excellent agreement between abstractors. We gave feedback to the abstractors to highlight items that had a κ of less than 0.70 or a percent agreement less than 95%. No practice had to have its charts abstracted again because of poor quality.CONCLUSIONS A 5% sampling of charts for quality control using IRR analysis yielded κ and agreement levels that met or exceeded our quality thresholds. Using 3 time points during the chart audit phase allows for early quality control as well as ongoing quality monitoring. Our results can be used as a guide and benchmark for other medical chart review studies in primary care.

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Section: In T Er R At Er R El Ia Bil I T Y In Data Col L Ec T Ionmentioning

confidence: 99%

Methods to Achieve High Interrater Reliability in Data Collection From Primary Care Medical Records

Liddy¹,

Wiens²,

Hogg³

2011

The Annals of Family Medicine

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“…Although using medical record data is economical (Polit & Beck, 2004), a major disadvantage is that the data are often retrospective, making it difficult to determine their reliability and accuracy. Although authors have described methods to promote the interrater reliability of data extracted from medical records (Eder, Fullerton, Benroth, & Lindsay, 2005;Luck, Peabody, Dresselhaus, Lee, & Glassman, 2000;Pan, Fergusson, Schweitzer, & Hebert, 2005), these methodologies do not account for bias in the documented medical record data. Terminal digit preference is one such type of bias and has been associated with clinician errors in the documentation of individual blood pressure measurements (Thavarajah, White, & Mansoor, 2003;Wingfield, Cooke, et al, 2002;Wingfield, Freeman, & Bulpitt, 2002) and birth weights (Edouard & Senthilselvan, 1997).…”

Section: Medical Record Versus Researcher Measures Of Height and Weightmentioning

confidence: 99%

Medical Record Versus Researcher Measures of Height and Weight

DiMaria‐Ghalili

2006

Biological Research For Nursing

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This study examined the extent of agreement between medical record and researcher measures of height and weight in adults and assessed the clinical significance of any resulting differences in body mass index (BMI) (kg/m(2)) according to the categorizations of underweight, normal weight, and overweight. Medical record and researcher measurements for height (n = 85), preoperative weight (n = 84), and postoperative weight (n = 65) in older patients undergoing elective coronary artery bypass grafting were used for analysis. Researcher measurements of height and weight were obtained by the same person and were compared to height and weight measurements extracted from the medical record. Bland and Altman's limits of agreement method was used to determine the extent of measurement error between medical record and researcher values. Cohen's kappa was used to assess for clinical significance of the differences in BMI categories based on medical record and researcher measurements. For height, 7% of the values were outside the 95% limits of agreement. For preoperative and postoperative weight, less than 5% of the values were outside the 95% limits of agreement. There were no significant differences in BMI categorization using Cohen's kappa between medical record and researcher measurements. Although there may be some measurement error for height in the medical record, weight measurements may have less error and the amount of measurement error may not be clinically relevant. Height and weight measurements extracted from the medical record are reliable and accurate in patients admitted for elective surgery, as are weight measurements obtained in stable postoperative patients.

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“…Because the information for this study was collected retrospectively from EMRs, the author had to rely exclusively on the information that medical providers recalled and chose to document in their patients' medical charts. Often, medical documentation errors are to be expected and this study cannot determine the validity and reliability of the information documented in the patients' records, which is a common challenge for retrospective chart reviews (146).…”

Section: Limitationsmentioning

confidence: 99%

A Retrospective Chart Review Examination of Demographic, Military, and Psychiatric Differences among Psychiatric Inpatients Admitted for Suicide-Ideation versus Suicide Attempt with an Emphasis on Gender

George¹

2014

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Ensuring high accuracy of data abstracted from patient charts: the use of a standardized medical record as a training tool

Cited by 58 publications

References 16 publications

Methods to Achieve High Interrater Reliability in Data Collection From Primary Care Medical Records

Methods to Achieve High Interrater Reliability in Data Collection From Primary Care Medical Records

Medical Record Versus Researcher Measures of Height and Weight

A Retrospective Chart Review Examination of Demographic, Military, and Psychiatric Differences among Psychiatric Inpatients Admitted for Suicide-Ideation versus Suicide Attempt with an Emphasis on Gender

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