Natural language processing and String Metric-assisted Assessment of Semantic Heterogeneity method for capturing and standardizing unstructured nursing activities in a hospital setting: a retrospective study.

Background: Nurses record data in electronic health records (EHRs) using different terminologies and coding systems. The purpose of this study was to identify unstructured free-text nursing activities recorded by nurses in EHRs with natural language processing (NLP) techniques and to map these nursing activities into standard nursing activities using the SMASH method. Study design: A retrospective study using NLP techniques with a unidirectional mapping strategy called SMASH. Methods: The unstructured free-text nursing activities recorded in the Medicine, Neurology and Gastroenterology inpatient units of the Agostino Gemelli IRCCS University Hospital Foundation, Rome, Italy were collected for 6 months in 2018. Data were analyzed by three phases: a) text summarization component with NLP techniques, b) a consensus analysis by four experts to detect the category of word stems, and c) cross-mapping with SMASH. The SMASH method calculated the string comparison, similarity and distance of words through the Levenshtein distance (LD), Jaro-Winker distance and the cross-mapping's cut-offs: map [0.80-1.00] with < 13 LD, partial-map [0.50-0.79] with <13 LD and no map [0.0-0.49] with >13 LD. Results: During the study period, 491 patient records were assessed. 548 different unstructured free-text nursing activities were recorded by nurses. 451 unstructured free-text nursing activities (82.3%) were mapped to standard PAI nursing activities, 47 (8.7%) were partial mapped, while 50 (9.0%) were not mapped. This automated mapping yielded recall of 0.95%, precision of 0.94%, accuracy of 0.91%, F-measure of 0.96. The F-measure indicates good reliability of this automated procedure in cross-mapping. Conclusions: Lexical similarities between unstructured free-text nursing activities and standard nursing activities were found, NLP with the SMASH method is a feasible approach to extract data related to nursing concepts that are not recorded through structured data entry.

Developing a subset of ICNP nursing diagnoses for medical and surgical hospital settings, informed by an Italian nursing conceptual model: a multicenter cross-sectional study.

BACKGROUND: The International Classification for Nursing Practice (ICNP) is designed to facilitate the expression of nursing diagnoses, interventions and outcomes. The development of the ICNP subsets may support nurses by providing appropriate terms for documenting nursing care. This project aimed to develop a subset of ICNP nursing diagnoses oriented by an Italian Nursing Conceptual Model (MPI) to describe nursing clinical data in medical and surgical acute hospital wards. STUDY DESIGN: A subset of ICNP nursing diagnoses was developed based on a literature review and on an expert consensus. A cross-sectional study was conducted in three Northern Italian hospitals to empirically test the subset in target settings. METHODS: In accordance with the guidelines adopted by the International Council of Nursing, the study followed the process for developing an ICNP subset. Twelve expert nurses from clinical settings and nursing education in surgical and medical care participated in a Delphi method to further validate the subset. A cross-mapping process has been implemented and the prevalence of diagnoses was described. Data were collected from healthcare documentation of admitted patients, including, retrospectively, nursing clinical data from the patients' admission date to the time of data collection. RESULTS: Documentation from 476 admitted patients was analysed: 228 were from surgical and 248 from medical wards. 24,142 nursing diagnoses were detected consulting retrospectively each documentation. A total number of 21,401 nursing diagnoses (88%) were fully mapped by the ICNP subset. CONCLUSION: Results showed a high capability of ICNP terminology to describe nursing care in acute medical and surgical areas in Italian hospitals. The identified subset of ICNP diagnoses could be a valuable way to support a computerized documentation system for hospitals using MPI and ICNP. Results could be used to start revising nursing education programs in order to introduce this nursing standardized terminology combining it with the nursing conceptual model in use.