José María Santamaría García

Background: Population ageing, multimorbidity, and chronic conditions have increased the demand for person-centred care in Primary Health Care. Current stratification systems rely on diagnoses or resource utilization and fail to capture the multidimensional nature of care complexity, which also depends on functional, social, environmental factors. Objective: To develop a HealthCare Complexity Index using routinely collected electronic health record (EHR) data and the Person-Centred Care Knowledge Model. Methodology: A retrospective, cross-sectional observational study was conducted in Primary Health Care in the Community of Madrid (Spain). Deductive Care Methodology was used to identify functional assessment variables associated with four care dimensions: vulnerability, risk, etiology, and signs/symptoms. A multidisciplinary expert panel selected and validated the variables through iterative consensus rounds. A weighted scoring algorithm, the Care Complexity Index, was designed to quantify care complexity on a 0–100 scale. Relationships between the selected variables and NANDA-I nursing diagnoses were established and validated by an expert in standardized nursing languages. Results: 27 assessment variables were identified and classified into the 4 dimensions of the care model. These variables were integrated into a weighted complexity index with five severity levels ranging from low to critical complexity. Furthermore, 48 NANDA-I nursing diagnoses were linked to the selected variables. Conclusions: The HealthCare Complexity Index provides a standardized, person-centred approach for estimating care complexity using routinely available (EHR) data. It may support early identification of individuals with complex care needs, facilitate individualized care planning, and strengthen clinical decision-making. Future studies should validate its predictive performance and implementation in other clinical practice.

Background: Knowledge management in global health is essential in response to aging populations, increasing morbidity, and rising expectations of care. The Knowledge Model about Person Care promotes health systems organized around individuals rather than diseases. Within this framework, vulnerability—understood as the risk of physical or moral harm—can be assessed through basic care variables (BCV) that determine individuals’ need and capacity for self-care. Primary care health information systems provide an opportunity to operationalize these variables at the population level. Methods: This study applies deductive methodology to extrapolate community-level health indicator data to population-level vulnerability measures. Using the electronic primary care objective monitoring tool (e-SOAP) from the Community of Madrid, we analyzed health and social care indicators derived from primary care clinical information systems. The mathematical architecture of selected indicators was used as an approximation to model-based systems engineering. Results: Primary care indicators enabled the identification and aggregation of community-level data reflecting BCVs. The system supports multi-level analysis (regional, managerial, institutional, and professional), facilitating grouped and anonymized data extraction for future vulnerability assessment. Conclusions: A minimum set of primary care indicators can effectively estimate community vulnerability, supporting person-centered health system management and informed decision-making.

Background: Knowledge management in global health is essential in response to ageing populations, increasing morbidity, and rising expectations of care. The Knowledge Model about Person Care promotes health systems organized around individuals rather than diseases. Within this framework, vulnerability—understood as the risk of physical or moral harm—can be assessed through Basic Care Variables (BVC) that determine individuals’ need and capacity for self-care. Primary care health information systems provide an opportunity to operationalize these variables at the population level. Methods: This study applies Deductive Methodology to extrapolate community-level health indicator data to population-level vulnerability measures. Using the electronic Primary Care Objective Monitoring tool (e-SOAP) from the Community of Madrid, we analyzed health and social care indicators derived from primary care clinical information systems. The mathematical architecture of selected indicators was used as an approximation to Model-Based Systems Engineering. Results: Primary care indicators enabled the identification and aggregation of community-level data reflecting BCV. The system supports multi-level analysis (regional, managerial, institutional, and professional), facilitating grouped and anonymized data extraction for future vulnerability assessment. Conclusion: A minimum set of primary care indicators can effectively estimate community vulnerability, supporting person-centred health system management and informed decision-making.

Gamification and game-based learning provide the opportunity to acquire knowledge and skills on a given subject in a practical and interactive way. They are an innovative teaching methodology that could be used for competence acquisition in a variety of fields. This study focuses on two domains: technology (including robotics) and care. This study evaluates the usability of RobotCareMaker^®, a board game designed to teach care robotics, a branch of robotics oriented towards the study of human care. RobotCareMaker^® consists of 106 elements. The playing cards are the engine of the game and the element of interaction between players. A convenience sample was selected. Usability was evaluated by the System Usability Scale (SUS) questionnaire modified for the game, and three questions about the game experience were used. Using a modified SUS questionnaire, 21 participants rated it with an excellent score of 80.36. Over 90% found the instructions clear and error-free. RobotCareMaker^® allows teachers, professionals, and nursing students to integrate curricular competencies in novel topics such as care robotics. The result suggests that RobotCareMaker^® enhances learning in assistive robotics, improving competencies in education and healthcare.

Background: The COVID-19 pandemic made people face the fact that we are all vulnerable. This vulnerability can be measured through the Basic Variables of Care (BVC) using the Care Vulnerability Index (CVI). Health systems work with indicators that nurses can consult to understand the care and health situation of their population. These indicators provide valuable information on the vulnerability of the population. Objective: to determine the level of community vulnerability of a population group using health indicators from the computerized clinical records of Primary Care. Methods: observational, retrospective study from March 2023 to January 2024, with a sample of 2106 people assigned to a nurse at the Meco Health Centre (Madrid, Spain). Phases: selection of dashboard indicators, linkage to BVC, score assignment, population adjustment and calculation of the CVI. Results: The sample of indicators selects 18 out of 376 included in the Specific Dashboard; each indicator is related to 6 to 10 BCVs, with different rank values. Each score is adjusted by a Vulnerability Correction Factor according to the number of people included in the indicator. Finally, the population-adjusted CVI scores 1.95 points (percentile 37.90). Conclusions: community vulnerability is an essential tool in community health diagnostics and can be measured through health indicators that reflect the care situation of a population group at a given time, including changes in the situation in the face of health crises such as the COVID-19 pandemic.

Background: The postoperative period is the recovery time after surgery and is defined as an individual process whose purpose is to return the person to the state of normality and integrity that they had prior to surgery. Aim: Demonstrate the modification of the level of health of people in the early postoperative period through the development and validation of the Health Index Instrument, which is built from the Nursing Outcomes Classification (NOC) standardized language. Design: The design used a mixed method, which involved a first phase of instrument development and a second phase of instrument validation. Methods: The methods was based on focus group techniques with text analysis techniques, internal validation with a group of care language experts, external validation with a group of clinical nursing experts and a clinical validation with quantitative and qualitative analysis. A panel of experts in Language of Care evaluated the (NOC) labels and their correlation with the 11 Health Variables to construct the instrument. The instrument developed was subjected to external validation with a panel of clinical nurse experts in post-anesthesia care. The clinical validation included a cross-sectional descriptive study in a postoperative unit. The final sample of the cross-sectional descriptive study was 139 cases. Results: Of the 89 NOCs proposed in the preliminary construction phase of the instrument, 36 passed through the first round. Of those 36 NOCs, 25 passed through to the second round with a review performance and 11 directly as approved. The total number of approved NOCs were 4. The results of the research show that there are changes in the global score of the health level and in each health variable. It is observed that there was a significant increase in the scores of the health variables at admission and discharge (p < 0.001). Conclusions: The results of the data analysis show that six groups present a similar pattern of evolution of the health variables. A correlation was found between the time of stay in the unit with the scores obtained in the health variables, the physical functioning, comfort status and the presence of symptoms being particularly significant.

In parallel with the development and design of different technological advances, competencies in nursing have advanced. With the development of robotics, it is expected that nursing robotic competencies will also increase. The aim of this study is to review the competencies in nursing robotics. A review was conducted between January 2017 and December 2023. The search strategy was carried out in the MEDLINE database (through PubMed). This review explores the developmental competencies in nursing robotics and informatics. The data extraction in this review included an intentional search for competencies and learning outcomes in engineering and robotic programs. A total of 340 competencies and program outcomes were reviewed. The synthesis of the data established a total of 17 developmental competencies in nursing robotics based on this knowledge extraction, which we organized into five categories: assessment, diagnosis, planning, intervention (implementation) and evaluation. This review suggests that nursing robotic competencies for the development of care robotics are still scarce, and there is an opportunity for the development of competencies and the definition of new roles in the area of nursing informatics in order to adapt to the new health care demands of society.

Persons that lived through periods of confinement suffered an impact on their physical and mental health. The adaptation of the lifestyle in relation to activity, sleep and social relationships is key to facing these periods of confinement. The aim is to validate a series of care recommendations aimed at being able to maintain an active and healthy confinement, which serves to prepare the population for future health crises. This study is part of a general strategy based on a care recommendation guide for COVID-19. The validation was carried out by a group of experts using the Delphi technique through a questionnaire that uses the Content Validity Index (CVI) and considers high validation those with a score >0.80. A total of 75 care recommendations are proposed: 30 on activity–exercise (CVI = 0.82), 14 on sleep–rest (CVI = 0.83) and 31 on roles–relationships (CVI = 0.83). Additionally, 49 recommendations achieve high validation. The care recommendations integrate a person-centred model, which addresses individual characteristics (age, health status, professional role). An active and healthy confinement requires respecting social distance measures, maintaining a balance between physical activity and sleep, and using technologies to promote social contact, which promote well-being and avoid depression and anxiety.

The COVID-19 crisis accelerated the adoption of technologies. Technological advancement is also expected in robotics applied to any sector, including in healthcare. The aim is to assess the professional perception of care robotics facing COVID-19. This study aimed to (1) select a tool for assessing different aspects of healthcare, (2) analyse the professional perception about the development, usefulness and helpfulness of technologies and robotics in the field of healthcare and (3) evaluate the correlation between the perceived helpfulness of care robotics and the selected tool. We implement five validated clinical tests which integrate 80 items about a person and their clinical situation. From the sample of 46 professionals, 95.65% affirmed that technology was moderately to completely useful for professional performance in the context of the pandemic, lowering to 67.39% when asked only about robotics; 93.48% stated that the inclusion of robotics in at least one health area affected by COVID-19 would have helped them. Finally, the variables extracted from clinical tests corresponded to the most relevant health areas as identified by the professionals. This research shows the potential of care robotics oriented towards healthcare from a care paradigm.

Spiritual and emotional care is an important part of the person, especially in situations such as changes in health or a community coping with a pandemic. However, nurses report scarce university training in this area of care. The aim of the study is to define a catalogue of learning outcomes for spiritual and emotional care for undergraduate nurses. The design used a mixed method for the development and validation of learning outcomes. The first phase designs the catalogue of learning outcomes through a coordinating group and uses a bibliographic search and nursing legislation. The second phase validates the proposal through a group of experts, with a questionnaire using the modified Delphi technique in two rounds. The initial proposal was 75 learning outcomes, of which 17 were eliminated, 36 changed their wording and the experts proposed 7 new ones. The experts validated 65 learning outcomes: 14 for Assessment and diagnosis; 5 for Planning; 17 for Intervention; 4 for Evaluation and quality; 8 for Communication and interpersonal relationship and 17 for Knowledge and intrapersonal development. In conclusion, the academic curriculum can include these learning outcomes to help undergraduate nurses in the process of acquiring knowledge, skills and attitudes in spiritual and emotional care.