1. Background
The development of information technology has influenced various industrial sectors, including healthcare. In Indonesia, information systems have been widely adopted to enhance the effectiveness of healthcare and nursing services. Although several hospitals in the country, including privately owned hospitals, have adopted information systems, not all have fully embraced this transformative technology. In nursing services, many hospitals still rely on conventional manual information systems, resulting in slow service delivery that affects productivity and effectiveness (1, 2). An important innovation in this context is the Mobile Nursing Care Plan Information System (MNCPIS), a digital nursing care information system designed to improve productivity and effectiveness. The system supports nursing activities such as patient assessment, establishment of diagnoses, care planning, documentation of actions, and evaluation. These processes are supported by an integrated diagnostic analysis using an artificial intelligence-based expert system.
MNCPIS is expected to accelerate nursing documentation and improve the efficiency and quality of nursing services. Previous reports have indicated that electronic nursing documentation can reduce the risk of intervention errors, support accountability, and enhance patient safety by minimizing medical errors (3-10). The presumed effects of MNCPIS implementation on productivity and nursing services can be assessed in terms of the simplicity, flexibility, speed, and accuracy of nursing care.
The novelty of this study is the use of MNCPIS, which is based on Indonesian standards for nursing practice. MNCPIS integrates the Indonesian Nursing Diagnosis Standard (INDS), Indonesian Nursing Outcome Standard (INOS), and Indonesian Nursing Intervention Standard (INIS) within a single mobile digital system. To date, this is the first platform to consolidate all three standards into a single mobile-based application. A key innovation is the integration of artificial intelligence through an embedded expert system that enables the automation of nursing diagnoses. This feature is intended to enhance diagnostic accuracy, reduce manual errors, accelerate decision-making, and facilitate more effective planning and evaluation of nursing care (11, 12).
The system also supports real-time nursing documentation, which may improve efficiency and accuracy. By transitioning to a paperless environment, MNCPIS facilitates easier data access for nurses and patients, greater legal validity of documentation, and improved accuracy of nursing records.
2. Objectives
This study aimed to analyze the effect of MNCPIS on the productivity and effectiveness of nursing services in hospitals. The system also provides services based on Indonesian standards for nursing diagnoses, outcomes, and interventions.
3. Methods
3.1. Study Design and Setting
This study employed a quasi-experimental pretest-posttest control-group design without individual- or ward-level randomization to evaluate the impact of MNCPIS on the productivity and effectiveness of nursing services in hospitals across East Java, Indonesia. The study lasted six months, from April 2025 to September 2025.
3.2. Participants and Sampling
The sample size calculation was based on nursing service productivity, which was designated as the primary outcome. Because no previous study had evaluated MNCPIS using the same productivity outcome measure, the sample size parameters were derived from a preliminary pilot study conducted before the main study. The pilot study involved 20 nurses, including 10 in the MNCPIS group and 10 in the conventional documentation group. The pilot study yielded an estimated mean difference of 0.45 in nursing service productivity between the two groups, with an estimated pooled standard deviation of 0.74. Using a two-sided significance level of 5% (alpha = 0.05), statistical power of 80% (beta = 0.20), and the formula for comparing two independent means, the minimum required sample size was 43 participants per group. After allowing for a potential dropout rate of 10%, the sample size was increased to 48 participants per group, resulting in a total sample of 96 nurses.
Participants were recruited using consecutive sampling. The inclusion criteria were staff nurses working in inpatient units, having at least two years of professional experience, being directly involved in nursing care documentation, and providing consent to participate. Nurses holding structural or managerial positions, taking extended leave during the study period, or failing to complete either the pretest or posttest assessments were excluded.
Before recruitment, inpatient wards were assigned nonrandomly to the intervention or control condition in consultation with hospital nursing management. Ward allocation was based on operational feasibility and the need to minimize contamination between groups. Nurses were subsequently assigned to the intervention or control group according to their existing ward units; neither wards nor individual participants were randomly allocated. Nurses working in intervention wards received training and implemented MNCPIS, whereas nurses in control wards continued to use conventional nursing care documentation.
Because allocation was not randomized and participants were aware of the documentation system used, participant blinding was not feasible. However, data analysts or outcome assessors were blinded to group assignment, where applicable, to reduce assessment bias.
Several potential sources of bias were considered. Selection bias may have occurred because participants were recruited from Muhammadiyah hospitals in three cities, which may limit the generalizability of the findings. Performance bias could not be fully eliminated because participants were aware of the documentation system used. Self-reported outcome measures may also have introduced response and social-desirability biases. In addition, contamination was possible if participants in the intervention and control groups exchanged information.
To minimize these risks, identical eligibility criteria, assessment schedules, questionnaire administration procedures, and follow-up periods were applied in both groups. The intervention and control groups were placed in separate wards to reduce contamination. Participants were informed that their individual responses would not be disclosed to hospital management. Data were coded before analysis to reduce analyst-related bias.
3.3. Variables
The independent variable was MNCPIS, and the dependent variables were the productivity and effectiveness of nursing services. Productivity included 13 indicators: discipline, responsibility, teamwork, planning skills, leadership, problem-solving and decision-making skills, compliance, honesty, initiative, self-motivation, analytical thinking, achievement orientation, and innovativeness. Effectiveness included five indicators: adaptability to nursing services, achievement orientation, job satisfaction, job quality, and organizational assessment.
3.4. Data Collection and Instruments
Productivity and nursing service effectiveness were assessed using an 18-item questionnaire consisting of 13 productivity items and five effectiveness items, rated on a five-point Likert scale. Productivity indicators included discipline, responsibility, teamwork, planning skills, leadership, problem-solving and decision-making skills, compliance, honesty, initiative, self-motivation, analytical thinking, achievement orientation, and innovation. Effectiveness was measured using five items assessing adaptability, achievement, job satisfaction, service quality, and organizational assessment.
Each item was rated on a five-point Likert scale ranging from 1 (not at all appropriate) to 5 (highly appropriate). Item scores were summed to obtain total productivity and effectiveness scores, with higher scores indicating greater nursing service productivity and effectiveness. The questionnaire was administered at baseline and after the six-month intervention.
The questionnaire demonstrated acceptable overall internal consistency (Cronbach alpha = 0.784). Instrument validity was evaluated through content and construct validity assessments. Content validity was assessed by five experts in nursing management, clinical nursing, and health information systems. The item-level content validity index ranged from 0.80 to 1.00, while the scale-level content validity index using the averaging approach was 0.94. Construct validity was examined using exploratory factor analysis, with factor loadings for all retained items ranging from 0.62 to 0.88. Domain-specific internal consistency was also high, with Cronbach alpha coefficients of 0.91 for productivity and 0.87 for effectiveness.
3.5. Intervention
Before the intervention, participating nurses signed a written agreement outlining MNCPIS implementation and the expected outcomes. Nurses in the MNCPIS group used the application to document patient care, including assessment, planning, actions, and evaluations, specifically for end-of-life care. Nurses in the Conventional Nursing Care Plan group used traditional manual documentation methods without computerized systems.
Both approaches were implemented over six months. Productivity and effectiveness were assessed across nursing care stages: 1) assessment, 2) diagnosis, 3) planning, 4) implementation, and 5) evaluation. Data collection was conducted using interviews and structured questionnaires.
This study was designed to evaluate the effect of MNCPIS on nursing service productivity and effectiveness rather than to develop a new machine-learning prediction model. Therefore, no data-driven feature-selection algorithm was applied to the outcome questionnaire. Productivity and effectiveness indicators were determined a priori based on the conceptual framework and validated measurement instrument.
MNCPIS underwent functional verification before use in the intervention. Each function, including patient assessment, generation of diagnostic recommendations, nursing care planning, intervention documentation, and evaluation, was tested using predefined test cases. Expected outputs were compared with the outputs generated by the system.
Content validation was performed by a multidisciplinary panel consisting of experts in clinical nursing, nursing diagnosis, and health informatics. The experts assessed whether the system recommendations were consistent with the Indonesian Nursing Diagnosis, Outcome, and Intervention Standards. Disagreements between the system output and expert judgment were reviewed, and decision rules were revised before implementation.
The mobile nursing care plan was compared with the conventional nursing care plan from the first day a nurse recorded patient care until the patient was discharged. After completing nursing care documentation, participants completed the productivity and effectiveness questionnaire. Sociodemographic information, including age, sex, duration of employment, and educational level, was collected by interview.
3.6. Statistical Analysis
Data were analyzed using IBM SPSS Statistics. Questionnaire reliability and validity were evaluated using the analyses described above. Descriptive statistics were used to summarize participant characteristics and outcome scores. Continuous variables are presented as means and standard deviations, whereas categorical variables are presented as frequencies and percentages. Baseline comparability between groups was examined using independent-samples t tests or chi-square tests, as appropriate.
Within-group changes from baseline to post-intervention were examined using paired-samples t tests. The primary intervention effect was evaluated using two-way repeated-measures analysis of variance, with time as the within-subject factor and group as the between-subject factor. The group-by-time interaction was used to determine whether changes over time differed between the MNCPIS and control groups. Effect sizes and 95% confidence intervals were to be reported in addition to P values. Statistical significance was set at P < 0.05.
3.7. Ethical Considerations
The study was approved by the Ethical Review Board of Muhammadiyah University, Surabaya (ERB No. 663/2024). Participation was voluntary, and informed consent was obtained after participants received a detailed explanation of the study. The confidentiality and anonymity of participant data were ensured, and data were used exclusively for research purposes.
4. Results
4.1. Participant Characteristics
A total of 96 nurses participated in this study. Participant characteristics are summarized in Table 1. The mean age was approximately 32 years. The sex distribution was predominantly female in both the intervention (72.92%) and control (62.5%) groups. Regarding educational level, most participants in the intervention group had a diploma (56.25%), whereas most participants in the control group had a bachelor's degree/nursing profession qualification (52.1%). Mean work experience was 12.42 years in the intervention group and 11.23 years in the control group. No statistically significant between-group differences were observed in sex, educational level, age, or years of work experience (P > 0.05).
Table 1.
Participant Characteristics (N = 96) a
| Variables | MNCPIS Group (n = 48) | Conventional Group (n = 48) | P-Value |
|---|---|---|---|
| Gender | 0.278 | ||
| Male | 13 (27.08) | 18 (37.5) | |
| Female | 35 (72.92) | 30 (62.5) | |
| Educational level | 0.551 | ||
| Diploma | 27 (56.25) | 23 (47.9) | |
| Bachelor/Nurse Profession | 20 (41.67) | 25 (52.1) | |
| Master of Nursing | 1 (2.08) | 0 (0.0) | |
| Age | 32.69 ± 8.313 | 33.46 ± 6.785 | 0.808 |
| Duration of work, y | 12.42 ± 7.494 | 11.23 ± 7.625 | 0.444 |
a Values are expressed as No. (%) or mean ± SD.
4.2. Impact of MNCPIS on Nursing Service Productivity
At baseline, nursing service productivity scores were generally comparable between the intervention and control groups. After the intervention, the MNCPIS group had higher mean scores than the control group across all productivity indicators. For example, discipline scores were 4.40 ± 0.869 in the intervention group and 3.85 ± 0.899 in the control group (P = 0.003). Responsibility scores were 4.75 ± 0.484 versus 4.35 ± 0.526 (P < 0.001), and teamwork scores were 4.81 ± 0.394 versus 4.27 ± 0.449 (P < 0.001), respectively. Similar differences were observed for planning skills, leadership, problem-solving and decision-making, obedience, honesty, initiative, self-motivation, analytical thinking, achievement orientation, and innovativeness (Table 2).
Table 2.
Comparison of Nursing Service Productivity Indicators Between the MNCPIS and Conventional Nursing Care Plan Groups (N = 96) a
| Variables | Preintervention | Postintervention | ||||
|---|---|---|---|---|---|---|
| MNCPIS | Control | P-Value | MNCPIS | Control | P-Value | |
| Discipline | 3.77 ± 0.951 | 3.67 ± 1.059 | 0.613 | 4.40 ± 0.869 | 3.85 ± 0.899 | 0.003 |
| Responsibility | 4.27 ± 0.707 | 4.08 ± 0.942 | 0.273 | 4.75 ± 0.484 | 4.35 ± 0.526 | < 0.001 |
| Teamwork | 4.23 ± 0.515 | 4.08 ± 0.794 | 0.289 | 4.81 ± 0.394 | 4.27 ± 0.449 | < 0.001 |
| Planning skills | 4.00 ± 0.684 | 3.92 ± 0.821 | 0.590 | 4.44 ± 0.796 | 4.04 ± 0.651 | 0.009 |
| Leadership | 3.50 ± 0.825 | 3.46 ± 0.874 | 0.811 | 4.17 ± 0.694 | 3.54 ± 0.771 | < 0.001 |
| Problem-solving and decision-making skills | 3.83 ± 0.930 | 3.77 ± 1.016 | 0.754 | 4.65 ± 0.565 | 3.88 ± 0.890 | < 0.001 |
| Obedience | 4.17 ± 0.859 | 4.00 ± 1.072 | 0.403 | 4.83 ± 0.377 | 4.21 ± 0.824 | < 0.001 |
| Honesty | 4.35 ± 0.635 | 4.23 ± 0.881 | 0.427 | 4.83 ± 0.476 | 4.40 ± 0.574 | < 0.001 |
| Initiative | 3.71 ± 0.849 | 3.69 ± 0.879 | 0.906 | 4.44 ± 0.681 | 3.75 ± 0.786 | < 0.001 |
| Self-motivation | 3.38 ± 0.789 | 3.38 ± 0.789 | 1.000 | 3.77 ± 0.905 | 3.42 ± 0.739 | 0.038 |
| Analytical thinking | 3.08 ± 1.048 | 3.08 ± 1.048 | 1.000 | 3.67 ± 1.191 | 3.21 ± 0.967 | 0.041 |
| Achievement orientation | 3.04 ± 0.849 | 2.94 ± 0.861 | 0.552 | 3.46 ± 1.010 | 3.09 ± 0.821 | 0.049 |
| Innovative | 3.04 ± 1.010 | 3.04 ± 1.010 | 1.000 | 3.60 ± 1.216 | 3.10 ± 0.973 | 0.028 |
a Values are expressed as mean ± SD.
4.3. Impact of MNCPIS on Nursing Service Effectiveness
Following the intervention, mean nursing service effectiveness scores were higher in the MNCPIS group than in the control group across all measured domains. The mean adaptability score was 4.94 ± 0.245 in the MNCPIS group compared with 3.40 ± 0.494 in the control group (P < 0.001). Achievement scores were 4.38 ± 0.914 versus 3.50 ± 1.052 (P < 0.001), job satisfaction scores were 4.56 ± 0.848 versus 3.83 ± 1.038 (P < 0.001), quality scores were 4.77 ± 0.660 versus 4.02 ± 0.863 (P < 0.001), and organizational assessment scores were 4.79 ± 0.651 versus 4.10 ± 0.891 (P < 0.001) (Table 3).
Table 3.
Comparison of Nursing Service Effectiveness Between the MNCPIS and Conventional Nursing Care Plan Groups (N = 96) a
| Variables | Preintervention | Postintervention | ||||
|---|---|---|---|---|---|---|
| MNCPIS | Control | P-Value | MNCPIS | Control | P-Value | |
| Ability to adapt | 3.33 ± 0.595 | 3.27 ± 0.676 | 0.632 | 4.94 ± 0.245 | 3.40 ± 0.494 | < 0.001 |
| Achievement | 3.44 ± 1.000 | 3.35 ± 1.139 | 0.715 | 4.38 ± 0.914 | 3.50 ± 1.052 | < 0.001 |
| Job satisfaction | 3.75 ± 1.101 | 3.65 ± 1.176 | 0.655 | 4.56 ± 0.848 | 3.83 ± 1.038 | < 0.001 |
| Quality | 3.94 ± 0.954 | 3.83 ± 1.059 | 0.614 | 4.77 ± 0.660 | 4.02 ± 0.863 | < 0.001 |
| Organizational assessment | 4.04 ± 0.922 | 3.94 ± 1.040 | 0.605 | 4.79 ± 0.651 | 4.10 ± 0.891 | < 0.001 |
a Values are expressed as mean ± SD.
4.4. Pre-Post Changes in Productivity and Effectiveness
As shown in Table 4, the MNCPIS group demonstrated an increase in nursing service productivity from 47.58 ± 7.704 before the intervention to 53.60 ± 6.677 after the intervention, representing a mean increase of 6.02 points. This within-group change was statistically significant (P < 0.001). In contrast, productivity in the conventional nursing care plan group increased by only 0.66 points, from 47.25 ± 8.213 to 47.91 ± 8.447, and this change was not statistically significant (P = 0.838). A similar pattern was observed for nursing service effectiveness. In the MNCPIS group, mean effectiveness increased from 18.50 ± 3.859 before the intervention to 23.43 ± 2.550 after the intervention, corresponding to a mean increase of 4.93 points (P < 0.001). Meanwhile, the conventional nursing care plan group showed an increase of only 0.81 points, from 18.04 ± 4.361 to 18.85 ± 3.451 (P = 0.587). These findings suggest that MNCPIS may improve nursing service productivity and effectiveness compared with conventional nursing care planning.
Table 4.
Pre-Post Intervention Changes in Nursing Service Productivity and Effectiveness Within the Conventional and MNCPIS Groups a
| Variables and Groups | Preintervention | Postintervention | Within-group P-Value |
|---|---|---|---|
| Nursing service productivity | |||
| Conventional Nursing Care Plan | 47.25 ± 8.213 | 47.91 ± 8.447 | 0.838 |
| MNCPIS | 47.58 ± 7.704 | 53.60 ± 6.677 | < 0.001 |
| Nursing service effectiveness | |||
| Conventional Nursing Care Plan | 18.04 ± 4.361 | 18.85 ± 3.451 | 0.587 |
| MNCPIS | 18.50 ± 3.859 | 23.43 ± 2.550 | < 0.001 |
a Values are expressed as mean ± SD.
5. Discussion
This study provides initial evidence regarding the implementation of MNCPIS in Indonesian hospital settings and extends previous investigations (13-15). MNCPIS uses standardized terminology derived from the Indonesian Nursing Diagnosis, Outcome, and Intervention Standards. Nurses use MNCPIS to document nursing care from assessment and nursing diagnosis through planning, implementation, and evaluation. The information system is not only a documentation tool but also includes an assessment system for patient assessment components supported by artificial intelligence. The embedded expert system assists nurses in identifying potential nursing diagnoses based on structured assessment data. During the nursing care planning phase, nurses select and determine action plans based on the Indonesian Nursing Outcome Standard and Indonesian Nursing Intervention Standard. MNCPIS also provides features for recording implementation and nursing evaluation. These functions may support productivity and effectiveness in hospital nursing services (16-19).
The study found that the mean productivity score in the MNCPIS group was 4.283, compared with 3.777 in the conventional nursing care information system group. Similarly, the effectiveness score was 4.680 in the MNCPIS group and 3.770 in the conventional system group. Based on t-test results, statistically significant differences were observed for all measured variables (P < 0.05). These results indicate higher reported productivity and effectiveness among nurses using MNCPIS than among those using the conventional information system.
Productivity in nursing services was reflected in improvements across several indicators. Discipline was reflected by punctuality in duty attendance. Responsibility was reflected by task completion and timeliness. Teamwork was demonstrated through coordination, communication, and respect for opinions and input. Planning skills were demonstrated by the ability to prepare work plans and monitor their implementation. Leadership was demonstrated by decisive and impartial action and by setting an example. Problem-solving and decision-making skills were demonstrated by the ability to formulate relevant and applicable alternative solutions. Compliance included adherence to rules, work procedures, and instructions from supervisors. Honesty was reflected by truthful reporting of work results. Initiative was demonstrated by the ability to make decisions or take necessary actions during task implementation without conflicting with institutional policies. Self-motivation was reflected by the drive to perform tasks with substantial effort while accepting associated consequences. Analytical thinking was represented by the ability to develop complex analyses or plans. Achievement orientation involved setting challenging goals, particularly in nursing services. Innovativeness was reflected by the ability to introduce new initiatives that could improve hospital performance (20).
Effectiveness in nursing services was reflected by improvements in adaptability, achievement orientation, job satisfaction, quality, and organizational assessment. Adaptability was demonstrated by effective coordination and communication with diverse parties and by valuing opinions and input. Achievement orientation was reflected by nurses' ability to set goals in nursing care planning. Job satisfaction was reflected by nurses' satisfaction with their roles and compensation. Quality was reflected by alignment between delivered services and nursing care standards. Organizational assessment was reflected by institutional evaluation of nurses according to nursing care standards (21-24).
MNCPIS may enhance productivity and effectiveness in nursing services because it uses artificial intelligence in the assessment and nursing diagnosis processes, thereby reducing reliance on conventional manual assessment. The system was designed for the Android platform and uses SQLite for data storage while supporting data communication through GSM, Bluetooth, Wi-Fi, and camera functions (25-28). These digital capabilities may support faster service delivery and more accurate data handling.
Compared with conventional documentation, MNCPIS combines mobile access, standardized Indonesian nursing terminology, and rule-based diagnostic support in a single platform. Previous electronic nursing documentation systems have primarily focused on replacing paper records, improving completeness, or increasing data accessibility. MNCPIS extends these functions by linking structured assessment findings with standardized nursing diagnoses, outcomes, and interventions. However, direct superiority over other commercial or hospital-based electronic nursing systems cannot yet be concluded because the present study used conventional documentation rather than another digital system as the comparator.
The results are consistent with the study by Su and Liu (29), who reported that systematically implemented mobile nursing information systems based on human-computer interaction can be used for clinical purposes by responding to patients' conditions in real time, thereby reducing information delays. This finding is also consistent with a study by Bikmoradi et al. (30), which reported that telenursing using information technology affected adherence to treatment plans. Both studies illustrate the potential role of information technology in nursing services.
In addition to supporting productivity and effectiveness, MNCPIS may reduce reliance on paper-based nursing care records, which can be time-consuming to complete. This may allow nurses to allocate more time to direct patient care. From a legal perspective, electronic nursing care records may also improve record security and reduce documentation errors. Real-time use, paperless documentation, and easier data access may benefit both patients and healthcare professionals. Information technology, including artificial intelligence, the Internet of Things, and other innovations, is increasingly used in healthcare to accelerate service delivery, improve productivity and efficiency, and potentially reduce costs (26-28).
The findings indicate that MNCPIS was associated with greater improvements in nursing service productivity and effectiveness than conventional paper-based documentation. The observed benefit was not limited to a single aspect of nursing performance; improvements were identified in discipline, teamwork, planning, problem-solving, compliance, initiative, and adaptability. One possible explanation is that the integration of standardized nursing diagnoses, outcomes, and interventions reduces the time nurses spend searching for terminology and manually linking assessment findings with nursing diagnoses. The expert-system component provides structured diagnostic recommendations, while mobile access allows nurses to document care closer to the point of service. Together, these functions may reduce duplication, improve documentation completeness, and support faster clinical decision-making.
Nevertheless, statistically significant differences should not automatically be interpreted as clinically or operationally meaningful. Practical importance should be assessed using mean changes, effect sizes, confidence intervals, documentation time, completeness of nursing records, user satisfaction, and implementation costs. The present results primarily demonstrate improvements in nurses' reported productivity and service effectiveness and do not establish improvements in patient outcomes, patient safety, length of stay, or hospital costs.
From a practical perspective, hospitals considering MNCPIS implementation should provide adequate mobile devices, stable network access, user training, technical assistance, and integration with existing hospital information systems. Implementation could initially be conducted in selected inpatient wards, followed by evaluation of usability, documentation completeness, system errors, and nurse workload before hospital-wide adoption.
5.1. Study Limitations
The participants were recruited from Muhammadiyah hospitals in three cities; therefore, the findings should be generalized cautiously. Differences in digital infrastructure, staffing levels, management support, and nurses' digital literacy may influence the effectiveness of MNCPIS in other hospitals. In addition, the nonrandom allocation of wards and participants, lack of participant blinding, self-reported outcomes, and possible contamination between groups may have introduced bias. The study also did not establish improvements in patient outcomes, patient safety, length of stay, or hospital costs.
5.2. Conclusions
This study found that MNCPIS was associated with greater productivity and effectiveness in nursing services than conventional nursing care documentation. The system may improve data accuracy, accessibility, and documentation efficiency while supporting standardized assessment, diagnosis, planning, implementation, and evaluation. Wider implementation should be accompanied by adequate infrastructure, training, technical support, and further evaluation of operational and patient-level outcomes.
