Unit 7 Assignment 2: Report Creation Objectives

Unit 7 Assignment 2 Report Creationassignment Objectivesmeet Cahiim C

Dr. Bartlett is enrolled in a quality improvement initiative and has been collecting data for submission on the quality measure pertaining to diabetes. He has asked you for a report that contains his patients who are over the age of 20 with a diagnosis of diabetes or gestational diabetes. Using your knowledge of data retrieval and data standards, design a report that will capture all pertinent information that Dr. Bartlett may need. Create and submit an example of what sample (fictitious) data would look like in the report. The report can be created in Word, Excel, or using the Access Database.

Paper For Above instruction

The increasing emphasis on healthcare quality measurement has led to the development of comprehensive reporting systems that support clinical decision-making, compliance, and health outcomes improvement. In the context of diabetes management, accurate and detailed reporting of patient data plays a vital role in monitoring and enhancing care quality. This paper discusses the design of a report capturing pertinent patient information related to diabetes or gestational diabetes for individuals over the age of 20, aligning with CAHIIM standards and data governance principles. It also provides a sample of fictitious data demonstrating the format and content of the report.

Introduction

Effective healthcare reporting necessitates adherence to data content standards and governance principles to ensure interoperability, data quality, and meaningful insights. For diabetes management, tracking patient demographics, diagnosis details, laboratory results, treatments, and outcomes facilitates comprehensive monitoring. This report aims to exemplify how such data can be structured and presented, covering enterprise-wide information pertinent to Dr. Bartlett's quality improvement initiative.

Designing the Report

The report design involves selecting relevant data elements, ensuring standardization, and presenting data in a format conducive to analysis and decision-making. Using data standards such as HL7, LOINC, and SNOMED CT ensures consistency and interoperability across systems. The report will include core data fields: patient identifiers, age, gender, diagnosis codes, diagnosis date, laboratory values (e.g., HbA1c), medications, and outcomes. Given the importance of data governance, the report will also incorporate data provenance and access controls.

Key Data Elements

The essential data elements include:

- Patient ID and demographics (name, age, gender)

- Diagnosis (ICD-10 codes for diabetes, e.g., E11.x for Type 2 diabetes, O24.x for gestational diabetes)

- Date of diagnosis

- Laboratory results (e.g., HbA1c levels, fasting glucose)

- Current medications and treatments

- Comorbidities and complications

- Outcomes (e.g., hospitalization, control status)

Integrating these data elements ensures the report is comprehensive and facilitates targeted quality improvement interventions.

Sample Data Format

Below is a representation of artificial data illustrating the report format. The data can be captured in tabular form in Word, Excel, or Access database, capturing fields such as Patient ID, Age, Gender, Diagnosis, Diagnosis Date, HbA1c, Medications, and Outcomes.

Discussion

This report exemplifies the integration of standardized data elements aligned with the CAHIIM curriculum requirements, emphasizing data content, standards, and governance principles. The data is structured to facilitate interoperability, enabling sharing across systems while maintaining data quality and privacy. Furthermore, the inclusion of laboratory results and medication data provides a comprehensive view necessary for quality improvement initiatives.

Conclusion

Designing a report that captures key clinical data on patients with diabetes involves selecting appropriate data elements, adhering to national standards, and ensuring data governance principles. The sample data illustrates how such a report can be structured in a user-friendly format that supports enterprise-wide decision-making. Proper implementation of data standards not only enhances data accuracy but also ensures compliance with healthcare policies, ultimately improving patient outcomes.

References

• HIMSS. (2020). Healthcare Data Standards. https://www.himss.org/resources/healthcare-data-standards
• HL7 International. (2022). HL7 Standards. https://www.hl7.org/
• LOINC. (2021). Logical Observation Identifiers Names and Codes. https://loinc.org/
• SNOMED International. (2020). SNOMED CT. https://www.snomed.org/
• Centers for Disease Control and Prevention (CDC). (2022). Diabetes Data & Statistics. https://www.cdc.gov/diabetes/data/index.html
• American Diabetes Association. (2023). Standards of Medical Care in Diabetes—2023. Diabetes Care, 46(Supplement 1), S1–S104.
• Office of the National Coordinator for Health Information Technology (ONC). (2019). Data Interoperability Standards. https://www.healthit.gov/
• Hersh, W. R. (2019). Health Data Standards and Interoperability. Journal of Biomedical Informatics, 94, 103180.
• Jacobson, H. (2018). Data Governance in Healthcare. Health Management Technology, 39(4), 34-36.
• Greenhalgh, T., et al. (2020). Standards for Digital Health Data: Critical Review. BMJ Health & Care Informatics, 27(1), e100211.