Academic Education

Development and use of a clinical-community nursing information system in a Bachelor's degree program


Purpose: The purposes of this study were, first, to describe the development and implementation of a Clinical-Community Nursing Practice Information System (SIPCE) based upon standardized languages from the International Nursing Diagnostic Association of North America (NANDA-I), the Nursing Intervention Classification (NIC) and the Nursing Outcomes Classification (NOC); and, secondly, to describe SIPCE's data-based services for the academic and research development of students and professors of the nursing program of the Industrial University of Santander (UIS).

Methods: In this paper, the information collected in SIPCE from January 2015 to June 2018 is used. During this time, 11,039 records of care plans that students made during their academic training in different health institutions were collected. The SIPCE software, developed in collaboration with the UIS’s Systems Engineering School, was updated with the latest standardized nursing languages (NANDA-I-NIC-NOC).

Results: The nursing care plans registered in SIPCE were used to identify the main priorities of care, nursing diagnoses, interventions and outcomes. Also, SIPCE enables the evaluation of each student's academic performance and consolidates the students' and professors' work in clinical and community environments.

Conclusions and implications: The SIPCE software helps nursing process documentation, implementation and teaching through the care plans data exploitation obtained from the clinical and community students' training. As a result, the main trends in nursing care using standardized language are shown through the use of this software as an observatory for nursing decision support.


The amount of data derived from nursing work is massive, and the profession is still lacking a worldwide quantification of nursing work (Sensmeier, 2015, 2016). Authors such as Werley and Lang (1988) proposed a Nursing Minimum Data Set (NMDS) that provides data collection essential standards for examining nursing work in care delivery systems (Pruinelli et al., 2016). In addition, nursing organizations such as the International Nursing Diagnostic Association of North America (NANDA-I), the Nursing Intervention Classification (NIC), and the Nursing Outcomes Classification (NOC), which together are known as NNN for their initials have developed Standardized Nursing Languages (SNLs) to unify the labels that nurses use to identify problems, actions and results obtained in the nursing practice. Both NMDS and SNLs set the theoretical framework necessary to organize, systematize and analyze the information related to care (McCormick, et al., 2015; Rutherford, 2008). Unfortunately, despite this knowledge and the fact that nurses invest around 8% to 30% of their time obtaining and manipulating data, nursing care information is not well documented or analyzed (Delaney, et al., 2000).

Nursing bachelor's degree programs can address the lack of documentation of nursing care by offering the training and methodology necessary to document nursing care and conduct its analysis (Stevens & Pickering, 2010). Indeed, there is evidence that SNLs enable documentation and analysis of practice and decision making in novice nurses (Noh & Lee, 2015; Cummings, et al., 2016; Keiffer, 2018). Several studies show that structured nursing care planning, using standardized NNN languages, increases patient satisfaction with care and nurses' job satisfaction, and influences hospital care quality (Marcotullio et al., 2020; Westra, 2008; Dahm, 2008; Müller-Staub, 2009; Müller-Staub, 2010).

However, information on how nursing programs improve the capture and analysis of care plan data during their students' training is still limited. Research studies related to NMDS; standardized languages, such as NANDA-I, NIC and NOC; and nursing records are frequently focused on practicing nurses and not nursing students (Adubi, et al., 2017; Olatubi, et al., 2019).

The above limitations, coupled with the need for student care plan records, became compelling reasons to create a system of registration and storage of nursing care plans that could meet the information needs for the administrative and academic management of the bachelor’s nursing program. In contrast, data analytics and standardized language implementation enable the students to integrate and connect theoretical and practical content in the training practices. In addition, this combination facilitates identifying the primary relationships between NANDA-I, NIC and NOC in building their care plans and carrying out projects during the academic semester.

The system has been developed under continuous delivery methodology, with developers adding more valuable features version to version. At the same time, this methodology has allowed students and teachers to provide feedback for constant application improvement. In the current state, SIPCE incorporates NMDS, the three standardized nursing languages NANDA-I, NIC and NOC (NNN), and the modeling of information based on nursing data. The statistics visualization strengthens and promotes investigating what is recorded in the students’ care plans during their training process.
The present study has two primary purposes: first, to describe the development and implementation of an Information System for Clinical-Community Nursing Practices (SIPCE) based in NNN nursing languages and, secondly, to describe the services based on SIPCE data for academic and research development of students and professors of the nursing program of the Industrial University of Santander (UIS).


Development and implementation of SIPCE

SIPCE is defined as a Clinical-Community Nursing Practices Information System (hereafter, referred to simply as SIPCE). SIPCE has its roots in the professors’ commitment to registering the nursing education process and implementing standardized nursing languages. These interests led to progressively incorporating the NMDS and standardized languages NANDA-I, NIC and NOC in the nursing school curriculum and the students' care plans registration from their training, either in different clinical or community settings.

The teaching and use of standardized language in undergraduate and graduate programs have advantages such as facilitating professional positioning and visibility through the elaboration and implementation of unified concepts. SIPCE has supported individual and collective care training with quality and optimal management of available resources. For scientific disciplines, language classifications are essential because they establish a common language with the terms used in the profession. For example, through the nursing taxonomies NANDA-I, NIC and NOC, the nursing process can be standardized; this is key for electronic decision support systems development. 

The availability of standard codes allows the aggregation and comparison of nursing data, contributing to nursing knowledge advancement. The inclusion of human response data in health care records provides more information about patients, and these standardized languages are compatible with nursing theories. A great strength of NNN is that it describes what nurses bring to the interdisciplinary level so that the benefits of nursing care are visible to all (Gudmundsdottir et al., 2004, Kautz et al., 2008, Stone et al., 2019, Lunney, 2006).

In this age of nursing informatics, care management tools facilitate registration in the different health services. The use of systematized medical histories allows nurses to manage and access nursing information immediately. Organized medical histories boost the implementation of nursing standardized languages by listing diagnostic outcomes and interventions. Such lists are tools required to register what nurses already implement in their nursing training. On the other hand, incorporating care plans into computer programs requires scientific method recognition of the nursing process in the development of the nursing activity.

By 2001, university directives requested a productivity report of the nursing school; this was the opportunity to collaborate with the systems engineering school to design a system to capture and systematize the nursing students' records generated in clinical and non-clinical settings (community, schools and public health institutions). The product of this initiative was the first version of SIPCE.

NANDA-I and NIC labels were part of the first version of SIPCE, which operated off-line and had its installation restricted to computers located in the nursing school. In response to this issue, a physical format for the information to be recorded in SIPCE was created and each student was asked to fill it in immediately after their practices to fulfill the academic requirement. The professors were responsible for collecting all the students' forms and delivering them to the person in charge of typing them into SIPCE.

The second version of SIPCE had the complete list of NOC´s labels. The third version enhanced the user profiles, allowing the differentiation between professors' and students' accounts and giving each user private access to the system. This upgrade positioned the user as individually responsible for their work. It became a routine activity in all nursing students' practices with the supervision of the teachers who validated the care plans in the training sites before their transcription to SIPCE. However, the software was limited to a restricted access server from the system school research group.
The fourth SIPCE version is included in the software ecosystem of the university; this version allows excellent stability of access and real-time consulting of students` academic information. The fifth version includes an enterprise Java update that improves the response times and strength and provides friendlier user interfaces. The contributions from students and professors facilitated the exchange of information and personalized feedback on the students' electronic nursing care plans. The standardized nursing language taxonomies available in this version were completed with the inclusion of NOC indicators, NIC activities, defining characteristics, and risk factors of NANDA-I. In addition, data processing was embedded in SIPCE's system, and statistics visualization was enabled for student and teacher use.

Like Lunney's (2006) vision, SIPCE has revolutionized the teaching of the nursing process by incorporating the use of standardized languages in the nursing process. Unlike the traditional nursing process, the use of standardized languages requires greater attention to developing intellectual, interpersonal and technical competencies for the proper selection of patient diagnoses, outcomes and nursing interventions; SIPCE supports all these requirements. In addition, SIPCE became an observatory of nursing students' performance in practice sites based on NNN and NMDS data.

Information systems and nursing minimum data set elements: SIPCE software is developed under Java enterprise technologies and PostgreSQL databases in compliance with UIS development standards. SIPCE includes various information technologies such as software, databases, communication systems, the internet, and data processing; these technologies enable SIPCE to support the data cycle. From a process perspective, SIPCE captures, transmits, stores, retrieves, manipulates and visualizes the information of bachelor students' care plans in detail (Boell & Cecez-Kecmanovic, 2015). The information capture in SIPCE is organized according to the NMDS, which sets a framework to identify and operationalize a set of data representing core components of nursing practice (Sanson, et al., 2019). SIPCE includes three basic NMDS categories NMDS: first, environment (code of the service, training site, or health institution); second, patient demographics (e.g., age, sex) and third, nursing care (health problems according to International Classification of Diseases ICD-10, standardized languages NNN). The three mentioned components shape the structure of SIPCE´s capture interface.

The SIPCE software was registered with the National Directorate of Copyright (Colombia, Ministry of the Interior) in September 2019 under code 13-75-429 17. The software is licensed for academic and research use for UIS faculty and students.


The characteristic of the fifth version of SIPCE and its functionalities is described throughout this section. The SIPCE system collected 11,039 nursing students’ care records from January 2015 to June 2018. These records were used in this study.


Figure 1 outlines the workflow of the latest version of SIPCE into three significant entities and their interactions from data sources to the decision-making. The first entity enables the students to digitize the patients' cases through the nursing care plans to store them into a shared database using the NMDS and NNN standards. The second entity integrates users and software for operational and data processing; professors control patient information by supervising the NNN labels selected and care plans registered by nursing students before storing them in the SIPCE system. Finally, the third entity is devoted to the communication and report process.

Figure 1. Flow diagram of the information system for clinical-community nursing practices (SIPCE).

It is emphasized that the historical data generated by SIPCE are distributed by semester and rotations. These data are presented to the students before going to the practice training scenarios, which allow a pedagogical experience of introducing the students to the different training scenarios and the essential cognitive and procedural competencies such as the NNN relationships frequently used in the various practice sites. At the end of the training, data are analyzed and contrasted to provide nursing interventions. Data-driven services support the development of undergraduate program research.

Nursing care plan recording interface

The capture of the nursing care plan in SIPCE is done after the students’ practice experiences. In general, in nursing practice, each student assesses the personal, family or community health status and registers the main findings in a paper format with items and questions related to 13 domains of the NANDA-I. Then the students select a maximum of two nursing diagnoses and their related outcomes (NOC) and interventions (NIC). Outcome (NOC) labels are measured through their indicators according to a Likert-type scale in two-time frames: before the implementation of the intervention (initial NOC) and afterward (final NOC). The above information is recorded and revised by the professor after the practice. Once the professor gives the feedback and final approval, each student transcribes the information in the SIPCE student interface, which asks for the following details:

    1. The institution where the practice is carried out, service (clinical, community), ward, name of the pedagogical practice, and student identification.
    2. Type of nursing intervention (individual or group-oriented) this section requests a unique registration identification (a health record number), demographic and temporal information, health status (medical diagnosis) ICD-10, and a nursing assessment.
    3. Document number of the patient. In the case of group practices that do not have a unique number identification, SIPCE generates a number and allows differentiation of the type of records by individual or groups.
    4. Nursing diagnosis NANDA-I, characteristics and related factors, interventions (NIC) and activities, and finally, the outcomes (NOC) and its score.
    5. Care plan: this SIPCE option summarizes the information entered in the previous steps.

Storage and analysis

The SIPCE database stores information generated from the care plan results of students' interactions with the patients in practice. This information is analyzed in an Excel spreadsheet and described in absolute or relative frequencies through tables or figures. For this analysis, the user (coordinators, managers and teachers) validate which part of the dataset is of interest to avoid using nursing assessments with errors or that do not meet academic quality standards. The resulting information can be consulted by variables such as academic semester, sex, rotation, age ranges, institutions, and so forth.

Communication and report

The outputs of SIPCE are described in depth below. Here is a summary:

  • Description of the population´s characteristics, grouped by demographic variables and health problems.
  • Observation of the nursing process by features of the population, academic category (student, group, level of training), place of care (service or healthcare institution), and time (period, year).
  • Prioritization of standardized nursing language lessons according to the frequency of NANDA-I diagnostics, NOC and NIC labels.
  • Quantification of the nursing registry culture.


SIPCE systematically consolidates the students' care plans from the nursing program of the University Industrial of Santander in all practice sites. From 2015 until June 2018, students registered 5,236 NANDA-I, 5,160 NOC, and 5,599 NIC labels. Among the NANDA-I records, the diagnostic label "preparation for knowledge improvement" code (00161) was the most frequent, with 29%; in fifth place, with 6%, was the label "preparation for self-care improvement" (00182). In the case of the NIC taxonomy, the label "teaching: individual" (5606) was the most frequent with 25%; in fifth place was "immunization/vaccination management" (6530) with 6%. Finally, the most frequent label in the NOC taxonomy was "knowledge: cancer threat reduction" (1834) and in fifth place "knowledge: pregnancy prevention" (1821) at 9%. Students and professors use this information according to their needs. Figure 2 presents the top 5 NANDA-I, NOC and NIC labels.

Figure 2. List of most common nursing diagnoses (NANDA-I), interventions (NIC), and outcomes (NOC) in the SIPCE software, from January 2015 to June 2018.

Note. The percentage of total labels 5,236 NANDA-I, 5,160 NOC and 5,599 NIC were recorded in the SIPCE software.

Students use SIPCE information for different purposes during the academic semester. For instance, at the beginning of each semester, students consult SIPCE to anticipate the most frequent health care problems and labels of the NNN taxonomies that will be used. In addition, SIPCE facilitates the association of diagnoses, interventions and outcomes in nursing care plans during the semester. SIPCE suggests NOC and NIC labels according to the NANDA-I diagnosis selected by the student (Johnson et al., 2006). SIPCE also allows the consultation of a complete list of NOC and NIC labels if the student does not find the label suggested useful.

The mandatory registration of SIPCE is a strategy to standardize the nursing process across the nursing program and achieve homogeneity in the teaching methodology of the nursing process. To illustrate, each semester, a student may participate in six different practices under the supervision of professors with diverse backgrounds and nursing process experiences. However, despite this heterogeneity, each student finds the same structure to register the care plan and receive guidance and feedback from each professor under the same nursing process criteria.

In addition, with SIPCE information, professors build case studies and didactic material such as printable NOC booklets. Students can find a list of the most frequent NOC labels, their respective indicators, and related interventions and consult them during their practices. For example, at the top of Figure 3 is the NOC label "anxiety self-management" (1402), on the left side, there is a list of indicators related to this outcome label, and on the right side of the image, there is a list of interventions suitable to be implemented according to outcome label 1402.

Figure 3. An example of a printable NOC booklet on high-risk pregnancy care deployed in the SIPCE software.

Note. A screenshot of the current version of SIPCE software.

Every semester, the professor responsible for coordinating one level of the nursing program uses the information collected in SIPCE to generate a report of the nursing care delivered. This report contains the number of people cared for in each training placement and community and identifies where students and professors performed the practices. Since 2015, the records have increased, for the year 2015, 214 women and 223 men received individual care by nursing students, and 88 people received nursing care in groups. The highest number of records were reported in 2016 with 1258 women and in 2016 year with 561 men. For the year 2018, in the group category, 4149 were recorded (Table 1). These six-monthly reports are also part of the documentation presented by the nursing school during the accreditation process. They are also the inputs to design strategies to strengthen the students’ learning process and improve the response to the needs of the persons, groups, and institutions collaborating with the Bachelor Nursing Program.

Table 1. The number of people cared for by nursing program students from January 2015 to June 2018.


This paper presents the most important landmarks in developing the SIPCE software and shows how the main objective, to systematize and summarize the students’ nursing care plans, was achieved. In addition, SIPCE facilitates the growth of a consultation repository for students and professors. Some of the well-recognized benefits of SIPCE include the recognition of the most frequent care problems, nursing diagnoses, interventions and outcomes. This helps to anticipate the care necessities, standardized nursing language, and competencies required for each level of the nursing program. SIPCE also enables the evaluation of each student’s academic performance and consolidates the students’ and professors’ activities in all training placements.
The implementation and evolution of the SIPCE features make it a tool that facilitates the learning process of nursing students and promotes the use of standardized languages. Significantly, this assists novice students in prioritizing nursing activities in the process of nursing care (Herbert & Connors, 2016). With the information generated from SIPCE records, it is possible to quantify the work performed in all the health care institutions and communities where students are trained. This information may help to differentiate a nurse’s domain of work within interdisciplinary teams. These are significant results that contribute to overcoming the limitation of data manipulation in nursing care (Jones, 2016) and the lack of productivity indicators in the field (Kaye et al., 2017).

One strength in the implementation process of SIPCE has been the leadership of the professors from the nursing and systems engineering schools, who have driven the integration of academic interest with legal and institutional requirements to generate the political will to invest in the design and upgrade of SIPCE. Furthermore, the recognition of SIPCE by external institutions has facilitated the identification of its benefits and justified its adoption as part of the nursing school curriculum.

So far, data registered in SIPCE software has the advantage of being quickly recovered, making SIPCE a data mining resource that is helpful for nursing research. Shortly, SIPCE is expected to support the development of nursing business plans and respond to the social and economic responsibility to measure outcomes of nursing care (Jones, 2016). SIPCE also will enable the consolidation of a standardized database to conduct association and prediction analysis of the NANDA-I-NIC-NOC links; this work will contribute to the front line of the development of the nursing sciences. Big Data, artificial intelligence, telenursing, the Internet of things (IoT), and knowledge management enable new research lines to integrate data and nursing knowledge, build new care models, and offer approaches to predictive and personalized nursing.


The implementation of SIPCE has been an iterative process. SIPCE systematizes nursing care plans and generates quantitative data to analyze nursing students work. Regarding data-based services, the incorporation of care plans into computer programs has led to the institutional recognition of the scientific method of the nursing process in the development of nursing activities. Through the SIPCE database, the use of standardized NNN languages and NMDS data has been evidenced across nursing education training sites.

The academic nursing program has improved its pedagogical, research and practical aspects by analyzing the information stored in SIPCE. Likewise, SIPCE's data generate reports by practice settings and contribute to accreditation reports. Both inform nursing care from academic practices and quantify nursing actions, not only in the clinical context but also in public health environments, contributing to the continued positioning of the nursing profession.

Online Journal of Nursing Informatics

Powered by the HIMSS Foundation and the HIMSS Nursing Informatics Community, the Online Journal of Nursing Informatics is a free, international, peer reviewed publication that is published three times a year and supports all functional areas of nursing informatics.

Read the Latest Edition


Adubi, I. O., Olaogun, A. A., & Adejumo, P. O. (2017). Effect of standardized nursing language continuing education program on nurses’ documentation of care at University College Hospital, Ibadan. Nursing Open, 5(1), 37–44.

Boell, S. K., & Cecez-Kecmanovic, D. (2015). What is an Information System? 2015 48th Hawaii International Conference on System Sciences, 4959–4968.

Cummings, E., Shin, E. H., Mather, C., & Hovenga, E. (2016). Embedding Nursing Informatics Education into an Australian Undergraduate Nursing Degree. Studies in Health Technology and Informatics, 225, 329–333.

Dahm, M.F. and Wadensten, B. (2008) Nurses’ experiences of and opinions about using standardized care plans in electronic health records—A questionnaire study. Journal of Clinical Nursing, 17, 2137-2145.

Delaney, C., Reed, D., & Clarke, M. (2000). Describing patient problems & nursing treatment patterns using nursing minimum data sets (NMDS & NMMDS) & UHDDS repositories. Proceedings. AMIA Symposium, 176–179.

Gudmundsdottir, E., Delaney, C., Thoroddsen, A., & Karlsson, T. (2004). Translation and validation of the Nursing Outcomes Classification labels and definitions for acute care nursing in Iceland. Journal of advanced nursing46(3), 292–302.

Herbert, V. M., & Connors, H. (2016). Integrating an Academic Electronic Health Record: Challenges and Success Strategies. Computers, Informatics, Nursing: CIN, 34(8), 345–354.

Johnson, M., Bulechek, G. & Butcher, H. (2006). NANDA, NOC, and NIC Linkages: Nursing Diagnoses, Outcomes, & Interventions. 2nd ed. Mosby.

Jones, T. L. (2016). Outcome Measurement in Nursing: Imperatives, Ideals, History, and Challenges. Online Journal of Issues in Nursing, 21(2), 1.

Kaye, E. C., Abramson, Z. R., Snaman, J. M., Friebert, S. E., & Baker, J. N. (2017). Productivity in Pediatric Palliative Care: Measuring and Monitoring an Elusive Metric. Journal of Pain and Symptom Management, 53(5), 952–961.

Kautz, D. D., & Van Horn, E. R. (2008). An exemplar of the use of NNN language in developing evidence-based practice guidelines. International Journal of Nursing terminologies and classifications: The official Journal of NANDA International19(1), 14–19.

Keiffer, M. R. (2018). Engaging Nursing Students: Integrating Evidence-Based Inquiry, Informatics, and Clinical Practice. Nursing Education Perspectives, 39(4), 247–249.

Lunney M. (2006). Helping nurses use NANDA, NOC, and NIC: novice to expert. Nurse Educator31(1), 40–46.

Marcotullio, A., Caponnetto, V., La Cerra, C., Toccaceli, A., & Lancia, L. (2020). NANDA-I, NIC, and NOC taxonomies, patients' satisfaction, and nurses' perception of the work environment: an Italian cross-sectional pilot study. Acta bio-medica: Atenei Parmensis, 91(6-S), 85–91.

McCormick K, Sensmeier J, Dykes J, Grace E, Matney S, & Schwartz K. (2015). Exemplars for Advancing Standardized Terminology in Nursing to Achieve Sharable, Comparable Quality Data-Based Upon Evidence. Online Journal of Nursing Informatics (OJNI), 19(2).

Müller-Staub, M., Lunney, M., Lavin, M. A., Needham, I., Odenbreit, M., & van Achterberg, T. (2010). Psychometric properties of Q-DIO, an instrument to measure the quality of documented nursing diagnoses, interventions and outcomes. Pflege, 23(2), 119–128.

Müller-Staub M. (2009). Evaluation of the implementation of nursing diagnoses, interventions, and outcomes. International Journal of Nursing terminologies and classifications: The official Journal of NANDA International, 20(1), 9–15.

Noh, H. K., & Lee, E. (2015). Relationships Among NANDA-I Diagnoses, Nursing Outcomes Classification, and Nursing Interventions Classification by Nursing Students for Patients in Medical-Surgical Units in Korea: Linkages of NANDA-I, NOC, NIC in Korea. International Journal of Nursing Knowledge, 26(1), 43–51.

Olatubi, M. I., Oyediran, O. O., Faremi, F. A., & Salau, O. R. (2019). Knowledge, Perception, and Utilization of Standardized Nursing Language (SNL) (NNN) among Nurses in Three Selected Hospitals in Ondo State, Nigeria: Knowledge, Perception, and Utilization of Standardized Nursing Language. International Journal of Nursing Knowledge, 30(1), 43–48.

Pruinelli, L., Delaney, C. W., Garciannie, A., Caspers, B., & Westra, B. L. (2016). Nursing Management Minimum Data Set: Cost-Effective Tool To Demonstrate the Value of Nurse Staffing in the Big Data Science Era. Nursing Economic$, 34(2), 66–71, 89.

Rutherford Marjorie A. (2008). Standardized Nursing Language: What Does It Mean for Nursing Practice? Online Journal of Issues in Nursing, 13(1).

Sanson, G., Alvaro, R., Cocchieri, A., Vellone, E., Welton, J., Maurici, M., Zega, M., & D'Agostino, F. (2019). Nursing Diagnoses, Interventions, and Activities as Described by a Nursing Minimum Data Set: A Prospective Study in an Oncology Hospital Setting. Cancer nursing, 42(2), E39–E47.

Sensmeier, J. (2015). Big data and the future of nursing knowledge. Nursing Management, 46(4), 22–27; quiz 27–28.

Sensmeier, J. (2016). Understanding the impact of big data on nursing knowledge: Nursing Critical Care, 11(2), 11–13.

Stevens, S., & Pickering, D. (2010). Keeping good nursing records: a guide. Community Eye health, 23(74), 44–45.

Stone, L., Arneil, M., Coventry, L., Casey, V., Moss, S., Cavadino, A., Laing, B., & McCarthy, A. L. (2019). Benchmarking nurse outcomes in Australian Magnet® hospitals: cross-sectional survey. BMC nursing18, 62.

Werley, H.H., Lang, N.M. (1988). Identification of the Nursing Minimum Data Set. Springer.

Westra, B. L., Delaney, C. W., Konicek, D., & Keenan, G. (2008). Nursing standards to support the electronic health record. Nursing Outlook, 56(5), 258–266.e1.

Author Bios:

Zayne Milena Roa-Díaz RN, MSc in epidemiology, PhD student of public health at University of Bern. Her major interests include standardized nursing languages, aging, women´s health, and machine learning methods. She has designed and implemented observational and experimental cardiovascular studies, as well as the validation of nursing diagnoses and clinical scales. She has taught quantitative methods of research in nursing programs.

Emilio Justiniano Carcamo Troconis, B.E. degree in information systems engineering, master’s degree in enterprise management and IT project management, and PhD student in computer science at the Industrial University of Santander. He has experience in software development and Java business technologies. With the IT office of the Industrial University of Santander, he reengineered of SIPCE (Clinical-Community Nursing Information System) software and developed the fourth and fifth versions of the software.

John Anderson García Henao, B.E. in information systems engineering, M.Sc. in information systems engineering, PhD in computer science at the Côte d'Azur University. His research focuses on the design and development of a green intelligence medical system for deploying medical diagnostic tools inside the hospitals with energy-efficient computing clusters. He has specialized in data-driven, representational learning and distributed deep learning methods. He has experience in parallel and distributed programming techniques on heterogeneous system architecture integrating techniques of multi-objective optimization with automatic machine learning methods to reduce the model search space.

Luz Eugenia Ibáñez Alfonso, RN, economist, specialist in chirurgical care, master's degrees in pedagogy and palliative care. A professor of the nursing school of the Industrial University of Santander, she has led the integration of the standardized languages NANDA-I-NOC-NIC in the nursing program curriculum and developed innovative strategies to teach the elaboration of nursing care plans. This effort has made her a pioneer of SIPCE (Clinical-Community Nursing Information System).