HIMS 655 Designing and In Service Training Essay
HIMS 655, Designing, Service, Training, Essay
According to the AHIMA Data Quality Model (DQM), the following aspects are considered critical in any training program focused on completeness and accuracy of health records. If I were to create a training program on the importance of data quality, I would emphasize these seven characteristics: (AHIMA, n.d.)
Data Accuracy: The level of assurance that the data is free of errors.
Data Consistency: The consistency of the healthcare data that is consistent across different applications.
- Data Relevancy: The degree to which the data is useful for the purposes that it was collected.
- Data Comprehensiveness: The extent to which the data collected is comprehensive and consistent across all applications.
- Data Currency: The extent to which the data is up-to-date. A datum value is updated if it is current for a specific date in time, and it is outdated if it is incorrect at a later time.
- Data Granularity: The concept of data quality is defined by the level of detail that is required to describe the characteristics of healthcare data.
- Data Timeliness: Up-to-date data is available for a specific time or date.
- In addition to covering the key characteristics of complete and accurate data noted above, I would cover the following management considerations that a HIM professional or department use to assess overall data quality
- Application: The purpose of collecting the data is understood and will answer key questions.
Collection: The process or collecting data is clearly understood and standardized.
Analysis: The way in which HIM translates data into meaning use is understood and valued.
- A study in 2020 on COVID-19 health records indicated that less attention was given to ICD-10 coding than the COVID-19 test results themselves. COVID-19 symptoms were higher in unstructured clinical data than in structure coded data (Binkheder, Asiri, Altowayan, Alshehri, Alzarie, Aldekhyyel, Almaghlouth, & Almulhem, 2021).
- AHIMA (n.d.). Data quality model. http://library.ahima.org/PB/DataQualityModel#.WL2U-hBWDi8
Binkheder, S., Asiri, M., Altowayan, K., Alshehri, T., Alzarie, M., Aldekhyyel, R., Almaghlouth, I., Almulhem, J. (2021). Real-World Evidence of COVID-19 Patients’ Data Quality in the Electronic Health Records. Healthcare, 9(1648), 1648. https://doi-org.ezproxy.umgc.edu/10.3390/healthcare9121648
Its important to have completeness for health records sine the data should all be in one place and available for that information when necessary. Reduction of duplicate testing is important when referring to health records. If health records are unfinished physicians can order duplicate tests and this can be costly.
The cost of resources for the hospital is wasted and it effects the quality of care for patents as well. This can also occupy the patient’s time and lower the quality of care provided. If health records are deemed insufficient, they can be damaging to patients, implying that these records should be thoroughly reviewed.
When it comes to information and data, accuracy is understanding that there should be no mistakes. This is significant since it aids physicians and hospitals in reducing their chances of making errors of any kind. If the data is incorrectly shown, the physician is more likely to hurt the patient or perhaps make a mistake.
Medical blunders can be costly, causing patients money out of pocket or expending their insurance deductibles. Clinical documentation enhancement is the most effective technique to improve the accuracy and completeness of health records (CDI).
This would be beneficial because this type of documentation can assist in the improvement of patient care. Clinical documentation improvement assists hospitals in reimbursements with coding.
Adane, K., Gizachew, M., & Kendie, S. (2019). The role of medical data in efficient patient care delivery: a review. Risk Management and Healthcare Policy, Volume 12, 67–73. https://doi.org/10.2147/rmhp.s179259
AHIMA Work Group. (2013). Integrity of the Healthcare Record: Best Practices for EHR Documentation (2013 update). Journal of AHIMA, 84(8), 58–62 [extended web version]. https://library.ahima.org/doc?oid=300257#.YPpZ2ehKhPY
Hong, C. J., Kaur, M. N., Farrokhyar, F., & Thoma, A. (2015). Accuracy and completeness of electronic medical records obtained from referring physicians in a Hamilton, Ontario, plastic surgery practice: A prospective feasibility study. Plastic Surgery, 23(1), 48–50. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4364140/#:~:text=They%20facilitate%20access%2C%20availability%20and
Rodenberg, H., Shay, L., Sheffield, K., & Dange, Y. (2019). The Expanding Role of Clinical Documentation Improvement Programs in Research and Analytics. Perspectives in Health Information Management, 16(Winter). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6341414/