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Modernizing Health Information Infrastructure Using AHIMA’s Data Quality Model
Scenario: The flow of patient care must run efficiently and effectively from the point of admission to discharge. Clinical and Administrative health care teams greatly rely on the availability of complete and accurate data to examine, diagnosis, devise a treatment plan, and monitor the patient’s outcome/or response to the prescribed health care plan. As a participating member of the American Health Information Management Association (AHIMA), you were appointed to serve on a global health workforce aimed at developing a Data Quality Management System similar to that of AHIMA’s DQM Model to assist in modernizing health information infrastructures in other countries. The final product to submit is a proposed Health Record Content & Documentation Checklists & Procedures that includes all items in following list. Submit one (1) single Microsoft Word document
Deliverables:
- Compare and contrast the American Health Information Management Association’s (AHIMA’s) Data Quality Management Model (DQM) in comparison to the Canadian Institute for Health Information (CIHI) Data Quality Framework (DQF) aka Six Dimensions of Quality (http://www.ec.gc.ca/inrp-npri/default.asp?lang=En&n=23EAF55A-1)
- Assess the effectiveness of both models by developing two (2) separate data quality checklists based on the AHIMA DQM Model and CIHI Data Quality Framework, to be used, randomly, for the evaluation of a sampling of inpatient health records. The results yielded from the assessment will be used as a tool in the development of a data quality management system. The checklists must assess each data quality characteristic from the two models and include at least two (2) measures to assess each data quality characteristics. A checkbox for each measure, along with a comment box to record any findings, recommendations and/or notes must be included on the checklists.
- In a written one (1) to two (2) page summary, address the following:
- Summarize significant limitations found with either model
- A final recommendation to submit to AHIMA’s global health workforce
- Discuss any concerns in the development and/or use of the data quality checklists
Use the Data Quality checklist, provided below, as a sample to assist you as you develop your data quality checklists.
Sample “Data Quality Checklist:
Data Quality Characteristics: | Data Quality Measure(s): | Select Yes or No if the record meet the data quality measures: | Comments: |
· The patient name and medical record number is included on each form · A data a signature is present throughout the entire record | Yes____ No____ Yes____ No____ |
References & Resources:
Medical Nomenclatures and Vocabularies
Clinical Vocabularies: Essential to the Future of Health Information Management:
Expert Solution Preview
Introduction:
In this assignment, we will be comparing and contrasting AHIMA’s Data Quality Management Model with the Canadian Institute for Health Information’s Data Quality Framework. We will also assess the effectiveness of these models by developing data quality checklists based on each model that will be used to evaluate a sampling of inpatient health records. Finally, we will summarize significant limitations found with either model, provide a final recommendation to submit to AHIMA’s global health workforce, and discuss concerns related to the development and/or use of the data quality checklists.
1. Compare and contrast the AHIMA Data Quality Management Model with the Canadian Institute for Health Information Data Quality Framework.
AHIMA’s Data Quality Management Model focuses on 10 characteristics of data quality, including accuracy, completeness, consistency, and timeliness, among others. The model provides guidelines and standards for healthcare organizations to improve the quality of their data, which is essential for clinical decision-making, research, and population health management.
On the other hand, the Canadian Institute for Health Information’s Data Quality Framework focuses on six dimensions of quality, including relevance, timeliness, accuracy, comparability, coherence, and accessibility. This framework assesses the quality of data at each stage of its life cycle, from collection to reporting, to ensure that it meets the needs of healthcare organizations, policymakers, and other stakeholders.
While both models emphasize the importance of data quality in healthcare, AHIMA’s model provides more detailed guidelines for improvement, whereas the Canadian Institute for Health Information’s framework provides a broader overview of data quality at all stages of the data’s lifecycle.
2. Assess the effectiveness of both models by developing two separate data quality checklists.
We have developed two separate data quality checklists based on AHIMA’s Data Quality Management Model and the Canadian Institute for Health Information’s Data Quality Framework. Both checklists will be used to evaluate a sampling of inpatient health records.
AHIMA’s Data Quality Management Model Checklist:
Data Quality Characteristics:
1. Accuracy
2. Completeness
3. Consistency
4. Currency
5. Definition
6. Granularity
7. Precision
8. Relevance
9. Timeliness
10. Validity
Data Quality Measures:
1. Data dictionary exists
2. Data is checked for errors during collection
3. Procedures are in place for the maintenance of data
4. Documentation standards are consistently used
5. Procedures are in place for the secure transmission of data
CIHI Data Quality Framework Checklist:
Data Quality Dimensions:
1. Relevance
2. Timeliness
3. Accuracy
4. Comparability
5. Coherence
6. Accessibility
Data Quality Measures:
1. Consistent use of standard terminology and coding systems
2. Electronic health records are updated in real time
3. Data is checked for completeness and accuracy during collection
4. Data is compared across different sources and time periods to ensure comparability
5. Procedures are in place to ensure coherence between data sources
6. Health records are easily accessible to authorized persons
3. Summarize significant limitations found with either model.
One limitation of AHIMA’s model is that it focuses heavily on data accuracy and completeness, but doesn’t provide as much guidance on how to ensure comparability or accessibility. Additionally, the model assumes that electronic health records are used, which may not be the case in all healthcare organizations.
The main limitation of CIHI’s framework is that it is very broad, and may not provide as much detail on how to improve data quality as AHIMA’s model does. Additionally, the framework assumes that standard terminology and coding systems are consistently used, but this may not always be the case.
4. A final recommendation to submit to AHIMA’s global health workforce.
Our recommendation is to combine the strengths of both models to create a more comprehensive data quality framework that addresses data accuracy, completeness, comparability, and accessibility. This framework should be adaptable to different healthcare organizations and should provide detailed guidance on how to improve data quality throughout the data’s lifecycle.
5. Discuss any concerns in the development and/or use of the data quality checklists.
One concern with using the data quality checklists is that they may not capture all aspects of data quality that are important to each healthcare organization. Therefore, the checklists should be used in conjunction with other quality improvement measures, such as audits and feedback to ensure that the data is meeting the needs of all stakeholders. Additionally, the checklists should be periodically reviewed and updated to ensure that they remain relevant and useful in improving data quality.
