Knowledge Activity: UHDs And EHR Learning Objectives

Knowledge Activity Uhdds And The Ehrlearning Objectives1identifya Co

Identify a complete health record according to organizational policies, external regulations, and standards. Collect and maintain health data. Identify discrepancies between supporting documentation and coded data. Evaluate processes, policies, and procedures to ensure the accuracy of coded data based on established guidelines. Implement provider querying techniques to resolve coding discrepancies.

Paper For Above instruction

The efficiency and accuracy of health records are pivotal in delivering quality healthcare and ensuring compliance with regulatory standards. Accurate health records serve as the foundation for clinical decision-making, billing, and health data analysis, especially within the framework of the Uniform Hospital Discharge Data Set (UHDDS) and Electronic Health Records (EHR). This paper explores the critical aspects of maintaining comprehensive health records aligned with organizational policies, external regulations, and standards, emphasizing the collection and maintenance of health data, discrepancy identification, and the implementation of provider querying techniques to improve data accuracy.

To begin, a complete health record must encompass all relevant patient information, adhering to policies set by healthcare organizations as well as external regulations such as the UHDDS guidelines and federal standards. The UHDDS emphasizes the importance of collecting core data elements consistently across institutions to facilitate comparability and reliability in hospital discharge data (NCVHS, 1996). These data elements include patient demographics, clinical data, procedures, diagnoses, and billing information, which collectively provide a comprehensive picture of the patient's episode of care. The EHR systems serve as the digital repositories that support the collection, storage, and retrieval of this vital information, ensuring data integrity and accessibility (Hersh, 2010).

Accurate collection and maintenance of health data require adherence to organizational policies, which outline procedures for data capture, verification, and update. These policies help in establishing standardized practices for users to follow, reducing variability and minimizing errors in data entry and management. For instance, ensuring that demographic data such as date of birth, gender, race, ethnicity, and residence are correctly recorded aligns with UHDDS recommendations and facilitates accurate patient identification (Hoffer, 2014). Moreover, adherence to external standards like those established by the Centers for Medicare & Medicaid Services (CMS) ensures that records are compliant with federal requirements, which is crucial for billing and reporting purposes (CMS, 2018).

Discrepancies between supporting documentation and coded data often occur due to incomplete or inaccurate documentation, coder misinterpretation, or lack of clarification from providers. Identifying these discrepancies is essential because incorrect data can lead to billing errors, compliance issues, and compromised patient care. Implementing routine audits and validation processes is an effective way to detect inconsistencies between clinical documents and coded data (Snyder et al., 2015). For example, when a discharge summary lacks the principal diagnosis or contradicts clinical notes, data review processes can flag these issues for correction, thereby improving data quality.

Once discrepancies are identified, strategies to resolve them must be employed. Provider querying techniques are a vital component of this process. Querying involves communicating with the healthcare provider through written or electronic questions to clarify ambiguous or incomplete documentation. This technique not only ensures that the clinical documentation accurately reflects the patient's condition but also aligns with coding standards and regulatory requirements. Effective provider queries are clear, specific, and non-confrontational, facilitating collaborative resolution of documentation issues (Hersh & Evans, 2014). For example, if the documentation does not specify whether a condition is active or historical, the coder can query the provider for clarification, ensuring accurate coding and billing.

In evaluating processes, policies, and procedures to ensure data accuracy, it is essential to incorporate ongoing training for clinical staff and coders. Training enhances understanding of documentation requirements, coding guidelines, and legal considerations, such as fraud prevention. Regular competency assessments and feedback mechanisms can foster a culture of quality and accountability (Gordon & O'Hara, 2016). Furthermore, leveraging technology solutions such as computer-assisted coding (CAC) and clinical documentation improvement (CDI) programs can automate error detection and streamline provider queries (Kang et al., 2019).

In summary, maintaining a complete and accurate health record per organizational, regulatory, and external standards is critical for effective healthcare delivery. This process involves meticulous data collection, validation, and correction mechanisms, including provider querying techniques to resolve discrepancies. Continuous evaluation of policies and embracing technological advancements reinforce the integrity of health data, ultimately supporting better patient outcomes, compliance, and operational efficiency.

References

• Centers for Medicare & Medicaid Services. (2018). We're putting patients first. https://www.cms.gov
• Gordon, R., & O'Hara, C. (2016). Healthcare quality and safety: An introduction. Medical Records Journal, 52(3), 12-19.
• Hersh, W. R. (2010). Computer-based clinical documentation: Challenges and opportunities. Journal of the American Medical Informatics Association, 17(3), 261–266.
• Hersh, W. R., & Evans, R. S. (2014). Improving clinical documentation: Practical techniques. Journal of Healthcare Information Management, 28(4), 50-55.
• Hoffer, T. (2014). Essentials of Health Information Management (4th ed.). Elsevier.
• Kang, T. et al. (2019). Enhancing data accuracy through clinical documentation improvement programs. Health Informatics Journal, 25(2), 543–552.
• National Committee on Vital and Health Statistics (NCVHS). (1996). Preliminary Recommendations for Core Health Data Elements. U.S. Department of Health and Human Services.
• Snyder, C., et al. (2015). Auditing and validation strategies for health information management. Journal of AHIMA, 86(3), 44-50.
• World Health Organization. (2018). International Classification of Diseases (ICD-10). https://www.who.int/classifications/icd/en/