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Nursing Workflows and the Importance of Data Quality

By Teresa Saxon, R.N. 

Healthcare data quality analysis has many layers of complexity ranging from  discovery of anomalies  to how they are repaired. In simplified terms, poor data quality can be characterized by missing, miscoded or misplaced data in a patient’s record. Data quality is especially important in the nursing field since nurses interact with patients and their health data the most of any healthcare worker. J P Systems’ Teresa Saxon, a Registered Nurse and Health Systems Specialist with our Clinical Data Quality Team, noted the consequences of poor data quality, its impact on patient safety and nursing workflows, barriers to data quality in nursing, and possible solutions.

Poor healthcare data quality can be caused by misentered data, inconsistent data standards between systems, and poor data consumption upon its receipt by an outside party. Nurses are vital in capturing accurate patient information and therefore, play a crucial role in data quality. Data quality can be impacted when healthcare facilities use different standards. If one facility documents a patient’s diagnosis using ICD-10 while another uses a homemade code system, the homemade code system may not be consumable when exchanged resulting in missing patient data and in the case of ICD-10, a diagnosis. Without this critical information, there are gaps in a patient’s record leaving them at a heightened risk for harm, as nurses are unknowingly providing treatment without accurate and up to date information. 

Some of the biggest obstacles to data quality in healthcare and nursing are:

i. The reluctance to update technology and continuing to use outdated systems to gather patient data results in staff handling large amounts of unstructured data with limited retrieval capabilities. Using systems that are not interoperable makes it difficult for patient data to be accurately exchanged among providers.  

ii. Lack of Data Quality training combined with clinician burnout and high staff turnover impact nursing EHR workflows and data uniformity as there are inconsistent and constantly evolving charting requirements. 

As front-line workers, nurses can be the solution for data quality improvement initiatives since nurses review and enter individual patient’s data in their health record. Nurses may document the same information in multiple parts of a patient’s chart, duplicating their efforts. For example, inputting data of the same type, such as neuro checks, in two to four different areas of a patient’s record is a duplication of data. Ideally, to achieve more efficient data documentation, the information should only need to be input once and mapped to the other necessary sections of the record. Enhanced data quality impacts nursing workflows as it reduces redundancy and charting duplication by standardizing and optimizing the patient data. 

Some data presented at a patient’s point of care integrates content from outside records; however, this data may not come from a trustworthy source. Therefore, nurses are then forced to spend their time triaging information through extensive patient interviews and manually review of the information received from outside sources before passing it along to the Health Information Management Department to verify and vet the data so that it can be officially incorporated locally into the patients’ records. Without proper standardization and implementation, this external data reduces the quality of time a nurse spends with their patient. 

JPSys helps solve data quality issues and streamlines nursing workflows by recommending and applying standards and implementation guidelines that ensure the systemization of care and workflows in the EHR with an end goal of clinical data interoperability. This ensures that EHRs and HIEs can communicate and ingest quality data properly. By using HL7’s FHIR and other international message standards and terminologies, the quality of external data can be improved resulting in more accurate patient information for nurses to utilize, thus optimizing patient care and improving patient outcomes.