Jennifer Schneider  For this assignment post, respond by discussing your own experience with that topic and your suggestion(s) for improving the outcome or ensuring that others benefit if it was

     Jennifer Schneider 

For this assignment post, respond by discussing your own experience with that topic and your suggestion(s) for improving the outcome or ensuring that others benefit if it was a positive outcome.

Collecting accurate data is much more complex than one might initially think. Accuracy is defined as, a term used to refer to the extent that the data properly represent the “real-life” objects they are intended to represent, and that the value is valid and correct (Quality Data Improvement, n.d.).  There are so many steps and avenues that data needs to come through before being entered into an EHR. The data needs to first be collected, then validated, then re-checked for updates to previous data collections, lastly the data then needs to load into the EHR database by either a Mass Load spreadsheet, or an individual person. The number of chances of data inaccuracies with human error is vast. Ensuring that this data is accurate imposes quite a task on the HIT professional. It is easy to point out that data collection results in data inaccuracies but it is a much more complex issue to actually find solutions to this problem. A better understanding of the root cause of patient report–medical record discrepancies will be helpful in uncovering ways to prevent them in the future (Weng CY, 2017). Finding ways to collect and enter accurate data into the EHR will be an ongoing problem into the future of EHR systems. 

Data Accuracy is imperative in my workplace. I work as a Nurse on the Transplant Floor and although we use a “nameless” EHR system, we find that that the Intake calculating from all Intake sources (IV fluids, by mouth, etc.) is an extremely important and fairly accurate number. Reporting I/O’s (Intake/Output) on a patient is critical in the setting of freshly transplanted Kidneys. On the contrary we sometimes do find that the system needs “user” data for Intake and Output that is not calculated automatically in the EHR. Some examples of this would be urine output that a patient does not collect in a urinal, drinks the patient had that was not documented in the system by the Nurse or PCT. Occasionally, a simple conversation with the patient will provide the data needed to enter into the EHR but this is not always the case. The end result of this is missed I/O’s and inaccurate totals. I suggest to my Nurses/PCT’s communicating with patients the importance of accurate data for Intake/Output and encouraging the patients to be involved in their care. Most often times we find that patients are receptive and eager to assist in their healthcare, and are more cognizant of their intake/output values.   

REFERENCES:

Quality Improvement: Data Quality Improvement lecture a [Course Resources]. (n.d.). In IFSM 305: Information Systems in Health Care Organizations: Summer 2019. Retrieved from https://content.umuc.edu/file/6aeb7b12-7691-41e6-9998-1f9d4af59f80/1/12-11/comp12_unit11a_lecture_slides.pdf

Weng CY. Data Accuracy in Electronic Medical Record Documentation. JAMA Ophthalmol. 2017;135(3):232–233. doi:10.1001/jamaophthalmol.2016.5562