Health data contain information relevant to clinical decision-making. The way they are collected and stored makes it difficult to use them for analysis and quality care assessment. Wound care is no exception and sometimes they may not even contain all the necessary clinical information, with missing or discrepant values occurring or having large datasets with detailed wound information, insights or outcomes that cannot be used for wound care assessment (1).

Nurses spend 13 to 28% of their total shift time recording the care provided and documentation is poor explored or used. Data quality is critical to patient safety, high-quality care, quality assurance, and demonstrating nursing's contribution to patient. No universal standardized terminology has been established and there are several nursing-specific (NANDA, ICNP) and multidisciplinary standardized terminologies approved for clinical practice. Additionally, Nursing Minimum Data Sets (NMDS) and other terminologies are being developed locally to meet specific requirements, leading to disparity in the collection of these data. Interoperability and continuity of data across disciplines and environments are widely recognized, necessitating the consideration of multidisciplinary Standardized Terminologies, such as the Systematized Nomenclature of Medicine – Clinical Terms (SNOMED-CT) (2).

Nurses record data related to wound care aimed at assessing the healing process, which is extremely important for the therapeutic decision. Those data refer to the various stages of the wound care process: prevention, assessment, and treatment; and are based at uniform classified language and tools or scales that describe wound characteristics with systematic form. Uniform classified language and assessment scales or tools are principles of NMDS. Coleman et al.  (2017) developed a generic wound care assessment minimum data set (WCA-MDS) to address the lack of standardization and variable parameters used in wound assessment and enable more consistent wound care practices and help providers and wound managers develop and improve wound care services (3). The core of NMDS of Portuguese nursing is available and is used to automatic produce of clinical indicators for different setting and users (4).  Electronic Health Records (EHR) are considered the ideal tool for evaluating healthcare, monitoring healthcare professional performance due to the availability of stored computerized data, and this feature can enable automated quality assessment, avoiding auditing techniques more expensive and time consuming.

The concept of nursing-sensitive indicators has evolved in recent times, but they allow us to quantify or measure what a nurse does to justify funding and improve practices and results (5). The indicators studied in wound care are based on data reported for nurses, but the indicators used by competent national and international entities are based on other aspects such as quality of care, patient safety, effectiveness of care and epidemiological data. Data quality is relative to its purpose and may be suitable for one case and inappropriate for another use. This situation is also true in clinical databases, so the definition of data quality is important. Data to be of quality must be accurate, completeness, consistent, and timeliness (3). Pruim et al., (2017) describe 48 indicators for wound care and in the pilot study they identified the best 15 indicators, which classify the activities of wound care centers (6).

In the health care context of the National Health Service, there are tools that produce clinical care indicators, based on lists that have not been updated for primary health care since 2017 and for hospital care since the launch of the NMDS (4), in 2007.

Objectives: Compare the wound assessment data record with the data needed to calculate the proposed wound indicators to detect data inaccuracy. To establish nursing minimum data set (NMDS) for wound care to apply in daily nursing practices in hospitals and community settings to measure wound care quality services. To proper a new formulate wound assessment data to assess correctly wounds and to measure the wound care quality by nurses.

Methods: The exploratory study of this issue will allow us to better understand the reality and propose solutions to resolve the situation experienced, to provide better results for patients and satisfaction for nurses.

Compare what is recorded regarding wound care with the data that are necessary to build indicators that are nursing-sensitive care. First step is reviewing literature and find wound care indicators in databases, repositories, and institutional websites. Followed of compare data recorded and indicator calculation needed, to build the wound care MDS to summarize the wound data record needed to produce indicators. Nursing-sensitive indicators were classified using the Donabidean model (structure, process, and outcomes) and indicator sheets were constructed based on the Holmezer matrix.

Results and Conclusions:

The research resulted in the collection of data set and indicators that classify wound care. It was possible to compare a minimum of data specific to wound care, consisting of 6 domains and 37 items, with a list of 47 indicators ideal for measuring activity in a specialized wound center. Thus, from the ideal recording of wound care to the actual and possible recording, considering the standardized languages and information systems used, there is a discrepancy, which boils down to a minimum data set to produce 15 wound care indicators tested in a pilot study. Comparing these works with the data and indicators used in wound care practice in Portugal, the discrepancy is minimal about the data collected, but very poor regarding for nursing-sensitive indicators.

It can be concluded that the information required to be recorded will be necessary for the continuity of care or treatment, which supports financing, and which serves to measure the quality of services provided and the impact on the patient. This information cannot be limited to just the two main types of wounds that have the greatest burden on healthcare systems, which are pressure ulcers (PU) and diabetic foot ulcers (DFU), but to all types of wounds.

Integrated data and knowing its granularity in information systems are our objectives so that the data extraction process is facilitated.

Keywords: Wounds and injuries. Outcome assessment, health care patient. Quality indicators, health care. Data accuracy.

Bibliography:

1. Madu T. Conducting meaningful real-world data analysis in wound care - a guide. Vol. 20, Wound Repair and Regeneration. 2012. p. 93–4.

2. Fennelly, O., Grogan, L., Reed, A., & Hardiker, N. R. (2021). Use of standardized terminologies in clinical practice: A scoping review. In International Journal of Medical Informatics (Vol. 149). Elsevier Ireland Ltd. https://doi.org/10.1016/j.ijmedinf.2021.104431.

3. Coleman, S., Nelson, E. A., Vowden, P., Vowden, K., Adderley, U., Sunderland, L., Harker, J., Conroy, T., Fiori, S., Bezer, N., Holding, E., Atkin, L., Stables, E., Dumville, J., Gavelle, S., Sandoz, H., Moore, K., Chambers, T., Napper, S., & Nixon, J. (2017). Development of a generic wound care assessment minimum data set. Journal of Tissue Viability, 26(4), 226–240. https://doi.org/10.1016/j.jtv.2017.09.007

4. Ordem dos enfermeiros. Resumo Mínimo de Dados e Core de Indicadores de Enfermagem para o Repositório Central de Dados da Saúde [Internet]. Lisboa; 2007 Oct [cited 2023 Jun 5]. Available from: https://www.ordemenfermeiros.pt/arquivo/documentosoficiais/Documents/RMDE_Indicadores-VFOut2007.pdf-

5. Heslop, L., & Lu, S. (2014). Nursing-sensitive indicators: A concept analysis. Journal of Advanced Nursing, 70(11), 2469–2482. https://doi.org/10.1111/jan.12503.

6. Cruz-Correia R, Rodrigues P, Freitas A, Almeida F, Chen R, Costa-Pereira A. Data Quality and Integration Issues in Electronic Health Records. In 2009. Available from: http://www.crcnetbase.com/doi/abs/10.1201/9781420090413-c4.

7. Pruim, L., Wind, A., & van Harten, W. H. (2017). Assessing and comparing the quality of wound centres: a literature review and benchmarking pilot. International Wound Journal, 14(6), 1120–1136. https://doi.org/10.1111/iwj.12768.