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The implementation of digital records into healthcare settings has led to significant changes in the way patient information is recorded, stored and used. The result of this can have a wide variety of implications for healthcare professionals, predominantly on what and how information is documented. Nurses and midwives represent the largest health workforce in Australia, with responsibility for collection, entry, and consumption of clinical information (Australian Digital Health Agency [ADHA], 2020), and therefore should have significant input in the design and implementation of electronic records. It is also important for nurses and midwives to understand the processes required for appropriate data and information quality, as set out in the National Nursing and Midwifery Digital Health Capability Framework (ADHA, 2020).

Medical records are designed to create efficiencies within the healthcare setting, by improving access to relevant and centralised information, quality of communication, understanding of instructions (as electronic documentation is easier to read), and improving patient safety using inbuilt algorithms for identifying potential errors or observational deviations (Jedwab et al., 2019). Secondary use of patient data has also been identified to indirectly impact patient outcomes and provide valuable amounts of data to non-direct care staff (Vuokko et al., 2017). To achieve the efficiencies desired, there are many factors that need consideration when implementing or enhancing an electronic medical record (EMR). While EMR processes and structures have improved on paper-based records, some clinicians can lack the knowledge and skill in its application (Akhu-Zaheya et.al., 2018). Due to the increased availability of data, nurses and midwives need to recognise the pivotal role they play in data management and lifecycle of patient data within EMRs.

The differences between traditional paper-based and electronic documentation are numerous and require a rethink on training for documenting within an EMR. Healthcare staff predominantly document historically in progress notes, which is unstructured and requires complex processes to transform data for other purposes. Correct training on the differences between progress notes and coded data fields within electronic systems can assist in enhancing the quality of the data available, for utilisation in and out of an EMR. Coded data fields are essential to increase the interoperability to ancillary applications and reporting to government and funding organisations. Coded data fields provide a great source of information due to the ease of accessibility for reporting and its structured nature.

Education of staff during the roll out phase of EMRs is crucial to obtaining data usable within the EMR and for reporting and quality improvement. Consideration needs to be given to the workflows of staff performing tasks that require data entry. Ensuring definitions and guidelines are provided for information entry to EMRs is critical. For example, coded fields for non-administered medications need to be clearly defined, as ’patient off the ward’ could be due to both a procedure being performed or the patient absconding. Another example – ’medication not given’ – may not provide the granular data required to identify the system issues causing medications to be missed, therefore an opportunity to resolve the underlying issue may be missed.

It is also important to define the roles and responsibilities of staff when discovering incorrect data entry or perceived errors. Entering a patient’s weight in the height field and vice versa, for example, is often easy to recognise in an EMR and fix by re measuring and updating, however, this can be a major problem when creating reports and dealing with the large volumes of data available within EMRs. Report requirements can be quite specific causing the above error to be reported as fact. Imagine creating a simple report looking at body mass index (BMI) or weight trends for an organisation to purchase new bariatric equipment (hoists, beds etc). If weights are underestimated, it could result in an increase in staff injuries as patients are heavier then equipment safe working limits (SWL). If overestimated, it can increase the cost of equipment as the organisation would purchase higher SWL equipment than required.

Secondary data use should be addressed when educating staff on EMR usage. Secondary use of data refers to the ‘non-direct care use of personal health information (PHI) including but not limited to analysis, research, quality/safety measurement, public health, payment, provider certification or accreditation, and marketing and other business including strictly commercial activities’ (Safran et al., 2007). With the greater availability of data, there is an increased desire to make use of it. Previously, researchers requesting data would have been gathered by audits, providing the researcher with an ability to observe workflows and nuances of the data collected. Presently, workflow documents are relied on to understand the processes, and if actual workflows vary from those documented, this can have a dramatic impact on the interpretation of the data and trending calculations. With the increase usage of machine learning, more data points can be utilised to find trends, increasing the impacts from erroneous data.

Reports and dashboards are increasingly being built to consolidate EMR information and improve its usability. Bringing multiple pieces of data together prevents the need of clinicians to click from screen to screen, perform calculations, and compare multiple fields to obtain an accurate picture of a patient’s condition. Changes to EMR systems can take time due to vendor development time and governance and regulatory requirements, however the ability to create inhouse reports from EMR data can reduce this time and improve patient safety. To achieve this, there needs to be reliance on accurate data recording processes and defined workflows.

Nursing and midwifery documentation has a significant impact on the patient’s quality of care and outcomes (Akhu-Zaheya, Al-Maaitah & Hani, 2018). Nurses and midwives should embrace this and look for the desired outcomes of EMR documentation beyond a record of care delivery. Increasing the data literacy of all nurses and midwives, specifically students, and the importance of accurate documentation in the correct fields within EMRs is increasingly important. Generic EMR training should be part of university and TAFE courses, along with education on what data is used for, other than direct patient care. Nurses and midwives strive to provide top quality evidence-informed healthcare, and EMRs provide a tool to achieve and measure this. By understanding the desired outcomes of data and what is required to improve patient care and outcomes, this can influence the data provided in coded fields, decreasing the need for double documentation. Documentation would enable greater insight to patient care, instead of being a described as a chore, and provide a mechanism to prove quality patient outcomes or identify areas to improve the quality of care patients receive. Greater involvement in information technology projects and application development by nurses and midwives will ensure that their perspective is understood and that they have a voice in the development of workflows.

Alan Scanlon CHIA, June 2022

References

Akhu-Zaheya, L., Al-Maaitah, R., Hani, S.B. 2018 Quality of nursing documentation: paper-based health records versus electronic-based health records, Journal of Clinical Nursing 27, e578-e589. Available here.

Australian Digital Health Agency, 2020. National Nursing and Midwifery Digital Health Capability Framework. Australian Government: Sydney, NSW. Available here.

Jedwab, R.M., Chalmers, C., Dobroff N. & Redley, B. 2019 Measuring nursing benefits of an electronic medical record system: a scoping review. Collegian, 26 (5) 562-582. Available here.

Safran, C., Bloomrosen,M., Hammond, W.E., Labkoff, S., Markel-Fox, S., Tang, P.C., Detmer, .E. 2007 Toward a national framework for the secondary use of health data, an American medical informatics association whitepaper, Journal of the American Medical Informatics Association. Available here.

Vuokko, R., Mäkelä-Bengs, P., Hyppönen, H., Lindqvist, M., Doupi, P. 2017 Impacts of structuring the electronic health record: results of a systematic literature review from the perspective of secondary use of patient data. International Journal of Medical Informatics 97, 293–303. Available here.

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