Clinical coding and the quality and integrity of health data

Shepheard, Jennie

doi:10.1177/1833358319874008

Cited by 8 publications

(6 citation statements)

References 11 publications

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“… 3 Furthermore, clinical documentation standards often vary, which can introduce data quality issues. 8 - 10 Privacy requirements or inadequate data governance frameworks can also impede access to these data, limiting their actionability. 11 , 12 …”

Section: Introductionmentioning

confidence: 99%

Developing a Data and Analytics Platform to Enable a Breast Cancer Learning Health System at a Regional Cancer Center

Petch

Kempainnen

Pettengell

et al. 2023

JCO Clinical Cancer Informatics

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PURPOSE This study documents the creation of automated, longitudinal, and prospective data and analytics platform for breast cancer at a regional cancer center. This platform combines principles of data warehousing with natural language processing (NLP) to provide the integrated, timely, meaningful, high-quality, and actionable data required to establish a learning health system. METHODS Data from six hospital information systems and one external data source were integrated on a nightly basis by automated extract/transform/load jobs. Free-text clinical documentation was processed using a commercial NLP engine. RESULTS The platform contains 141 data elements of 7,019 patients with newly diagnosed breast cancer who received care at our regional cancer center from January 1, 2014, to June 3, 2022. Daily updating of the database takes an average of 56 minutes. Evaluation of the tuning of NLP jobs found overall high performance, with an F1 of 1.0 for 19 variables, with a further 16 variables with an F1 of > 0.95. CONCLUSION This study describes how data warehousing combined with NLP can be used to create a prospective data and analytics platform to enable a learning health system. Although upfront time investment required to create the platform was considerable, now that it has been developed, daily data processing is completed automatically in less than an hour.

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Section: Introductionmentioning

confidence: 99%

Developing a Data and Analytics Platform to Enable a Breast Cancer Learning Health System at a Regional Cancer Center

Petch

Kempainnen

Pettengell

et al. 2023

JCO Clinical Cancer Informatics

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“…There is a need for new studies which test novel ways of applying sepsis diagnostic codes, methods of data linkage, and combination coding algorithms to improve the diagnostic accuracy of ICD-10 codes for sepsis. Since coding can be confounded by changing policies, other efforts such as clinical documentation improvement programs, continuous coder training and audits, and diagnosis cover sheets or financial incentives may also be considered (52–55).…”

Section: Discussionmentioning

confidence: 99%

Accuracy of International Classification of Diseases, 10th Revision Codes for Identifying Sepsis: A Systematic Review and Meta-Analysis

Liu

Hadzi-Tosev

Liu

et al. 2022

Critical Care Explorations

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“…Many variables influence the quality of coding practice. In a recent paper, Shepheard mentioned poor quality of clinical documentation, keeping up with clinical case definition, and the necessity of capturing all comorbidity and preexisting conditions as the main obstacles for clinical coders [ 1 ]. The quality of clinical documentation mainly influences coding practice; as Farhan J et al mentioned, "Major problems identified in coding were related to the quality of the medical record documentation rather than to the quality of the coding" [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

A Model for Clinical Coder Satisfaction in Saudi Arabia Based on a Holistic Approach: Clinical, Professional and Organizational Dimensions

Alanazi¹,

Alkhaibari²,

Aldosari³

2023

Cureus

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The quality of clinical coding influences not only hospital revenue but also the quality and efficiency of healthcare services. Assessing the coders' satisfaction is essential to optimizing the quality of clinical coding. This mixed-method study used a qualitative approach to propose the study model while testing the model through a quantitative approach. The relevant variables of the satisfaction model were assessed through a survey targeting clinical coders across the country on a timely basis. Fourteen experts participated in establishing the model with three dimensions: professional, organizational, and clinical. Each dimension has its relevant variables. One hundred eighty-four clinical coders participated in phase two. 34.5% were male, 61% held a diploma, 38% had a bachelor's and above, and 49.7% worked in hospitals having fully electronic health records. We found that organizational and clinical dimensions strongly correlate with coders' satisfaction. Noticeably, the most influencing variables were the availability of coding policies and the computer-assisted coding (CAC) system. The results show that the model explains the satisfaction of the clinical coders, and organizational and clinical-related variables are crucial. Although gender-based differences exist, training (regardless of the training mode), coding policies, and the CAC system substantially influence coders' satisfaction. A significant stream of the literature supports these findings. However, attempting a holistic approach to assess coders' satisfaction and affecting coding quality is the added value of this study. Optimizing clinical coding practice requires organization-wide initiatives and policies to regulate coding practices and standards to promote the quality and timeliness of clinical documentation. Training is indispensable not only for clinical coders, but physicians also need to understand the rationale and value of clinical coding. Better utilization of the outcomes of the coding process and adopting the CAC system are significant drivers to enhance coders' satisfaction.

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Clinical coding and the quality and integrity of health data

Cited by 8 publications

References 11 publications

Developing a Data and Analytics Platform to Enable a Breast Cancer Learning Health System at a Regional Cancer Center

Developing a Data and Analytics Platform to Enable a Breast Cancer Learning Health System at a Regional Cancer Center

Accuracy of International Classification of Diseases, 10th Revision Codes for Identifying Sepsis: A Systematic Review and Meta-Analysis

A Model for Clinical Coder Satisfaction in Saudi Arabia Based on a Holistic Approach: Clinical, Professional and Organizational Dimensions

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