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Abstract

Abstract Background: Nursing-sensitive indicators (NSIs) play a crucial role in measuring the quality of care specific to nursing practice. Currently, hospitals monitor several NSIs which may vary between hospitals. Conducting research on NSIs can enhance the monitoring of nursing practice. Aim: The aim is to identify NSIs for hospitals in Jordan. Methods and material: The Delphi approach was utilized to establish a consensus among a panel of national nursing experts (N=60). An initial list of 52 indicators was developed through a rigorous process and subsequently distributed to the panel members. The panelists provided their quantitative responses in three rounds. Consensus was determined based on the following criteria: agreement greater than 51.0%, interquartile range (IQR) below 1.5, standard deviation (SD) below 1, and moderate Kendall’s coefficient of concordance (Kendall’s W). Results: By the conclusion of the third round, a total of 42 indicators achieved group agreement. The agreed-upon indicators consisted of 10 structure, 16 process, and 16 outcome indicators. Conclusion: This study successfully established a consensus and identified a comprehensive set of indicators that capture the distinct contributions of nursing in the hospital setting. The results demonstrate a wide range of agreed-upon indicators across the domains of structure, process, and outcome. These findings are valuable in enhancing the monitoring and evaluation of nursing practice in hospitals. Practical implications: The findings of this study provide a solid foundation for monitoring and reporting the quality of nursing practice in hospitals. Nursing policymakers can utilize these findings to develop policies that promote the voluntary reporting of NSIs.
Review began 04/08/2024
Review ended 04/26/2024
Published 05/01/2024
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Afaneh et al. This is an open access article
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Identifying Nursing-Sensitive Indicators for
Hospitals: A Modified Delphi Approach
Tareq Afaneh , Fathieh Abu-Moghli , Maha Mihdawi
1. Nursing, Bahrain Oncology Center, Al Muharraq, BHR 2. Community Health Nursing, School of Nursing, Jordan
University, Amman, JOR 3. Nursing Research, King Hamad University Hospital, Al Muharraq, BHR
Corresponding author: Maha Mihdawi, maham_1917@yahoo.com
Abstract
Background: Nursing-sensitive indicators (NSIs) play a crucial role in measuring the quality of care specific
to nursing practice. Currently, hospitals monitor several NSIs which may vary between hospitals.
Conducting research on NSIs can enhance the monitoring of nursing practice.
Aim: The aim is to identify NSIs for hospitals in Jordan.
Methods and material: The Delphi approach was utilized to establish a consensus among a panel of national
nursing experts (N=60). An initial list of 52 indicators was developed through a rigorous process and
subsequently distributed to the panel members. The panelists provided their quantitative responses in three
rounds. Consensus was determined based on the following criteria: agreement greater than 51.0%,
interquartile range (IQR) below 1.5, standard deviation (SD) below 1, and moderate Kendall’s coefficient of
concordance (Kendall’s W).
Results: By the conclusion of the third round, a total of 42 indicators achieved group agreement. The agreed-
upon indicators consisted of 10 structure, 16 process, and 16 outcome indicators.
Conclusion: This study successfully established a consensus and identified a comprehensive set of indicators
that capture the distinct contributions of nursing in the hospital setting. The results demonstrate a wide
range of agreed-upon indicators across the domains of structure, process, and outcome. These findings are
valuable in enhancing the monitoring and evaluation of nursing practice in hospitals.
Practical implications: The findings of this study provide a solid foundation for monitoring and reporting
the quality of nursing practice in hospitals. Nursing policymakers can utilize these findings to develop
policies that promote the voluntary reporting of NSIs.
Categories: Quality Improvement
Keywords: delphi method, quality of healthcare, quality indicators, practice, nurse sensitive indicators, nurses
Introduction
The importance of assessing the quality of nursing care and the need for comprehensive measurement
models have been recognized in healthcare. A seminal contribution in this regard is the classic work of
Donabedian [1], who provided a comprehensive and conceptually based model to measure different aspects
of the healthcare system. This model emphasized the measurement of structure, process, and
outcome indicators. Building upon this foundation, Dubois et al. adopted Donabedian's framework to
measure aspects of nursing performance. According to Dubois, nursing performance can be understood as
the outcome of the interaction between three subsystems. The first subsystem involves acquiring, deploying,
and maintaining nursing resources. It is defined as the attributes of the nursing system that affect its ability
to meet healthcare needs, such as nursing staff attributes and the stability of financial resources. The second
subsystem is transforming nursing resources into nursing services, which includes nurses’ activities and the
attributes of the practice environment, such as nursing processes. The third subsystem is producing changes
in patients’ condition, encompassing the health status or events resulting from nursing care, such as patient
safety outcomes, patient functional status, and patient length of stay [2].
Gao et al. [3] used Heslop et al.'s [4] definition of nursing-sensitive quality indicators as a set of principles,
procedures, and assessment scales used to quantify the level of nursing quality and assess nursing outcomes
in clinical nursing practice. Kieft et al. [5] also employed Heslop et al.'s definition of nurse-sensitive
indicators as quantifiable items that monitor or provide an indication of the quality of the nursing care
provided. McNett [6] defined nursing-sensitive indicators (NSIs) as measurement indices that reflect the
quality of nursing care provided in a unit or a hospital. Stalpers et al. [7] defined nurse-sensitive indicators
as outcomes that are relevant, based on nurses’ scope of practice, and for which there is empirical evidence
linking nursing inputs and interventions to the outcome of patient care.
1 2 3
Open Access Original
Article DOI: 10.7759/cureus.59472
How to cite this article
Afaneh T, Abu-Moghli F, Mihdawi M (May 01, 2024) Identifying Nursing-Sensitive Indicators for Hospitals: A Modified Delphi Approach. Cureus
16(5): e59472. DOI 10.7759/cureus.59472
NSIs serve as a tool for systemically assessing the quality of nursing care [5,8]. Monitoring NSIs can identify
significant areas for improving nursing care quality and allow the focus of improvement efforts on priority
areas [9].
The availability of a national database that provides periodic reporting of structure, process, and outcome
NSIs allows for conducting benchmarks with countries of similar socioeconomic development. Furthermore,
NSIs provide measurable evidence for nursing policymaking by establishing linkages between nursing
resources, such as nurse staffing levels and nursing levels of education, and patient outcomes. In light of
these considerations, the present study aims to identify NSIs for hospital use.
Materials And Methods
Design, setting, and participants
The present study employed the Delphi method. The initial description of the Delphi method appeared in the
1950s by RAND Air Force Corporation to predict how technology impacts warfare [10]. The method has lent
itself to nursing and health research for more than three decades. Early applications of the method included
deciding on future research priorities. Later, the applications of the method expanded to include deciding on
policy-making and analysis options, assessing the healthcare outcomes, and determining the relevance of
practice to health-related outcomes [11,12].
The study was conducted in public and private hospitals in Jordan. The healthcare system in Jordan includes
both private and public hospitals. The public sector hospitals include the Ministry of Health (MOH)
Hospitals, the Royal Medical Services (RMS) hospitals, and University Hospitals. The expert panel was
recruited from nurses holding three managerial positions: Directors of Nursing (DONs), nursing shift
supervisors, and nursing quality specialists. There is no general rule for determining the required number of
experts in the Delphi method, as the number of experts varies [13]. The sample size in Delphi ranges from
four to 3,000 participants, with 10 to 100 participants in most healthcare-related studies. The number is
usually determined based on pragmatic considerations [13,14]. The more intellectual diversity invested in a
study, the more knowledge is expected to be generated. Yet, a larger number of experts is linked to
administrative inefficiencies, such as the need for more financial resources and time. The type of required
information is another factor; in-depth information is best achieved by a relatively smaller number of
experts than structured information [15]. This study sought to recruit 40-60 participants who meet the
inclusion criteria: holding a nursing bachelor’s degree as a minimum qualification; ideally holding a
postgraduate qualification in nursing; currently working in a managerial position as DON, nursing
supervisor, or nursing quality specialist, having more than fiveyears of working experience, and willingness
to invest six to 10 weeks in participating in the study. A purposive sampling method was used to recruit the
participants. Participants who met these selection criteria were identified as possessing the required
expertise to contribute to the study.
Data collection
To develop the initial list of NSIs, a search was performed using PubMed, MEDLINE, and CINAHL full text in
the EBSCOhost databases. The subject headings “nurse* sensitive indicators” and “nurse* sensitive
outcomes” were entered. The search included full-text articles published in English from 2009 to
2020. Eligible sources incorporated articles from peer-reviewed journals with any study design.
The first-round survey included three parts: i) study information and instructions, ii) the demographic
characteristics of the panelists, and iii) a list of potential NSIs that was developed by the authors after
reviewing pertinent literature [16]. The order of the identified NSIs in the instrument was based conceptual
definition of the three subsystems of the Nursing Care Performance Framework (NCPF) [2]. This process
resulted in categorizing NSIs into three sections resembling the three subsystems of the NCPF. A five-
point Likert scale ranging from “strongly disagree” to “strongly agree” was implemented to score the level of
agreement.
The authors sent invitation letters to each hospital inviting the DON, nursing supervisors, and nursing
quality specialists to participate in the study. The total number of hospitals that were contacted is 24
hospitals, and 19 hospitals responded. The study included multiple rounds. Each round of the study was
accessible for a duration of two weeks. The time interval between the closure of one round and the opening
of the next round was two weeks.
In round 1, the participants were offered a list of 52 NSIs by individual emails and were asked to suggest
additional NSIs. Potential NSIs resulting from the literature review and participants' input were utilized to
develop the study questionnaire and were included in the subsequent rounds.
During round 2, each participant was asked to determine their level of agreement on a five-point Likert
scale as follows: “strongly disagree” (1), “disagree” (2), “neutral” (3), “agree” (4), and “strongly agree” (5).
The item agreement index is defined as achieving at least 51% of responses as “agree” or “strongly agree,” an
Interquartile Range (IQR) below 1.5, and a Standard Deviation (SD) below 1 [13].
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Two weeks after the closure of round 2, the third round was released. During this round, the participants
received individual feedback on the group response findings of round two through their email addresses to
maintain anonymity. This feedback allowed the participants to assess the group response to each NSI. The
participants were asked to reevaluate all the indicators considering the findings of round 2. A summary of
data collection is presented in Figure 1.
FIGURE 1: Summary of data collection procedure
Data analysis
Participants' responses were transferred to SPSS Statistics 22.0 for Windows (IBM Corp., Armonk, NY) [17].
Mean, median, frequency, IQR, and SD were calculated for each indicator. Responses of “agree” and
“strongly agree” for each indicator were combined to form “agreed.” The percentage of agreed responses for
each indicator was calculated. Indicators that met the three identified criteria (51% or greater of agreed
responses, IQR below 1.5, and SD below 1) in the final round are identified as agreement indicators [13]. The
overall agreement among experts was estimated using Kendall’s coefficient of concordance (Kendall’s W).
Ethical considerations
Institutional review board (IRB) approval was secured prior to contacting the participants from each
hospital. Confidentiality of the responses was assured to the participants, and their responses were kept
confidential. Ethical considerations were explained in the consent form that was obtained from the
participants.
Results
Demographic characteristics
The panel members' ages ranged from 25 and 62 years (M = 40.63, SD = 7.50). The participants included full-
time quality specialists (n=24, 40%), nursing supervisors (n=21, 35%), and directors of nursing (n = 15, 25%).
Their total years of clinical experience ranged from 6 -36 years (M=18.47, SD=7.10). The majority of panel
members (n = 37, 61.7%) held a bachelor’s degree, followed by participants with a master’s degree (n=20,
33.3%), and a PhD (n=3, 5.0%). A summary of the demographic characteristics is presented in Table 1.
2024 Afaneh et al. Cureus 16(5): e59472. DOI 10.7759/cureus.59472 3 of 12
N (%) Min Max Mean (SD) Median
Age 25 62 40.63 (7.50) 40.50
Current position
Director of Nursing 15 (25)
Nursing Supervisor 21 (35)
Quality Specialist 24 (40)
Total Experience (years) 6 36 18.47 (7.10) 18.5
Academic Qualification
Doctorate 3 (5.0)
Master 20 (33.3)
Bachelor 37 (61.7)
Hospital Sector
Public 24 (40)
Private Hospital 36 (60)
Hospital Bed Capacity 42 678 266.27(157.84) 200
TABLE 1: Participants’ characteristics N= (60)
Round 1
The literature review and the input from the participants resulted in the identification of 52 indicators for
inclusion in the subsequent rounds of the study. No additional NSIs were suggested by the participants. The
study encompassed 20 process indicators, 19 outcome indicators, and 13 structure indicators. All the
identified indicators were included in the study. The indicators resulting from round 1 are listed in Table 2.
Indicator
Structure indicators
Working conditions (Employment conditions, Stability, Workload)
Staff maintenance (Retention/turnover)
Nursing staff supply (Quantity/intensity)
Nurse-Bed ratio
Staff maintenance (Absenteeism)
Staff maintenance (Work-related accidents, injuries, illnesses)
Nursing staff supply (Quality/training/experience)
Working conditions (Support resources, Physical facilities, Material resources)
Staff maintenance (Satisfaction at work)
Working Hours Per Patient Days
Nursing staff supply (Patient classification) systems
Economic sustainability (Cost per case-mix or patient-day)
Economic sustainability (Cost of resources)
Process indicators
Communication (Nurse-Patient)
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Professional satisfaction
Deployment of scope of practice
Pain assessment
Nursing work environment characteristics (perceived autonomy, role tension, collaboration)
Job burnout
Collaboration (Nurse-Patient)
Discharge planning
Patient centrality in the nursing care delivery process (Patient/family involvement)
Nursing processes
Inter Unit Work Relations
Patient centrality in the nursing care delivery process (Responsiveness to patients’ needs and expectations)
Restraint application
Health Promotion and Illness Prevention
Problems & symptoms management
Patient centrality in the nursing care delivery process (Continuity, reactivity, timeliness, coordination)
Malnutrition screening
Nurse decision making
Conflict resolution (Nurse-Patient)
Delirium observation
Outcome indicators
Patient Fall
Patient satisfaction
Central line infection rates (Central Line-Associated Bloodstream Infections)
Catheter Associated Urinary Tract Infection
Intra-venous infection (phlebitis)
Patient comfort and quality of life related to care: Hygiene
Hospital acquired Infection
Pressure Injury
Hospital Acquired Pneumonia
Failure to rescue
Patient empowerment: Ability to achieve appropriate self-care
Patient comfort and quality of life related to care: Symptoms management (e.g., pain, nausea, dyspnea, fever)
Medication error
Patient comfort and quality of life related to care: Incontinence
Patient functional status (physical, nutritional)
Patient empowerment: Adoption of health-promoting behaviors
Average Length of Stay
Mortality rate
Re-admissions
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TABLE 2: Initial list of indicators
Round 2
Out of the 52 items entered in round 1, 36 items met the criteria for agreement. The percentages of
agreement ranged from 51% (delirium observation and malnutrition screening) to 97.5% (nurse-patient
communication). The indicators that survived round 2 of the study comprised five structure indicators, 15
process indicators, and 16 outcome indicators. However, there were eight structure indicators, five process
indicators, and three outcome indicators that did not meet the agreement criteria by the end of round 2.
The overall agreement between experts was estimated using Kendall’s coefficient of concordance (Kendall’s
W), which is a non-parametric statistic used to assess agreement among raters. Kendall's W ranges from zero
(no agreement) to one (complete agreement). For round 2 responses, Kendall’s W was found to be .13,
indicating poor agreement among the experts. Based on this finding, the decision was made to proceed to
round 3. These indicators are provided in Table 3.
Indicator Mean Median SD IR % of
agreement
Structure indicators
Nursing staff supply (Quality/training/experience)* 4.33 5 0.95 1 88%
Staff maintenance (Satisfaction at work) 4.22 4 1.03 1 88%
Staff maintenance (Retention/turnover) * 4.18 4 0.97 1 88%
Working conditions (Employment conditions, Stability, Workload) * 4.18 4 0.83 1 87%
Nursing staff supply (Quantity/intensity) 3.98 4 1.1 1 83%
Staff maintenance (Work-related accidents, injuries, illnesses) * 4.03 4 0.9 1 82%
Working Hours Per Patient Days 3.78 4 1.12 0 80%
Nurse-Bed ratio 4.12 4.5 1.17 1 80%
Nursing staff supply (Patient classification) systems 3.82 4 1.03 0 78%
Working conditions(Support resources, Physical facilities, Material resources) 3.85 4 1.15 1 78%
Staff maintenance (Absenteeism)* 3.87 4 0.98 1 77%
Economic sustainability (Cost per case-mix or patient-day) 3.58 4 1.03 1 68%
Economic sustainability (Cost of resources) 3.87 4 1.13 1 57%
Process indicators
Communication (Nurse-Patient) * 4.5 5 0.62 1 97%
Patient centrality in the nursing care delivery process (Patient/family involvement) * 4.18 4 0.7 1 93%
Collaboration (Nurse-Patient) * 4.28 4 0.78 1 92%
Patient centrality in the nursing care delivery process (Continuity, reactivity, timeliness,
coordination) * 4.15 4 0.66 1 92%
Pain assessment* 4.35 5 0.82 1 88%
Nursing processes* 4.22 4 0.74 1 88%
Patient centrality in the nursing care delivery process (Responsiveness to patients’ needs and
expectations) * 4.07 4 0.71 0 88%
Inter Unit Work Relations* 4.1 4 0.8 1 87%
Deployment of scope of practice* 4.12 4 0.87 1 85%
Professional satisfaction* 4.12 4 0.78 1 85%
Discharge planning* 3.95 4 0.93 1 83%
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Nurse decision making 4.08 4 1.03 1 82%
Conflict resolution (Nurse-Patient 4 4 1.03 1 80%
Nursing work environment characteristics (perceived autonomy, role tension, collaboration) * 3.82 4 0.95 0 80%
Restraint application* 3.88 4 0.89 0 78%
Health Promotion and Illness Prevention* 3.88 4 0.87 0 77%
Job burnout* 3.88 4 0.9 0 77%
Problems & symptoms management 3.88 4 0.96 2 75%
Delirium observation 3.28 3.5 1.03 1 50%
Malnutrition screen 3.23 3.5 1.06 2 51%
Outcome indicators
Patient comfort and quality of life related to care: Hygiene* 4.3 4 0.67 1 95%
Pressure Injury* 4.3 4 0.79 1 92%
Patient Fall* 4.43 5 0.85 1 92%
Patient comfort and quality of life related to care: Symptoms management (e.g., pain, nausea,
dyspnea, fever) * 4.15 4 0.78 1 92%
Intra-venous infection (phlebitis)* 4.33 4 0.8 1 90%
Patient satisfaction* 4.15 4 0.8 1 88%
Medication error* 4.3 5 0.94 1 88%
Central line infection rates (Central Line-Associated Bloodstream Infections) * 4.25 4 0.93 1 87%
Catheter Associated Urinary Tract Infection* 4.13 4 0.89 1 87%
Hospital Acquired Pneumonia* 4.05 4 0.91 1 85%
Hospital acquired Infection* 4.22 4 0.83 1 85%
Failure to rescue* 3.78 4 0.96 1 75%
Patient empowerment: Ability to achieve appropriate self-care* 3.8 4 0.84 1 75%
Patient comfort and quality of life related to care: Incontinence* 3.73 4 0.9 1 72%
Patient empowerment: Adoption of health-promoting behaviors* 3.73 4 0.9 1 72%
Patient functional status (physical, nutritional) * 3.63 4 0.88 1 72%
Average Length of Stay 3.67 4 1.13 1 68%
Re-admissions 3.53 4 1.21 2 65%
Mortality rate 3.38 4 1.15 2 55%
TABLE 3: Indicators that achieved agreement by the end of round 2
*Indicators that achieved agreement criteria
Round 3
All indicators that met the agreement in round 2 (36 indicators) were maintained in round 3. Additionally,
six indicators were retained from the 16 dropped indicators in round 2. These retained indicators include
working hours per patient days, nursing staff supply (quantity/intensity), working conditions (support
resources, physical facilities, material resources, staff maintenance), satisfaction at work, nurse-bed ratio,
and problems and symptoms management. As a result, there were 42 indicators categorized as follows: 10
structure indicators, 16 process indicators, and 16 outcome indicators. The percentages of agreement
ranged from 11% (patient re-admissions) to 100% (Nurse-Patient Communication, Professional satisfaction,
Patients Falls, Patient satisfaction, Central line infection rates, Catheter-Associated Urinary Tract Infection,
2024 Afaneh et al. Cureus 16(5): e59472. DOI 10.7759/cureus.59472 7 of 12
Intra-venous infection, Patient comfort and quality of life-related, and Hospital-acquired Infection).
The overall agreement between experts was estimated using Kendall’s coefficient of concordance (Kendall’s
W). For round 3 responses, Kendall’s W was found to be 0.41, indicating moderate agreement between the
experts [18]. Based on these findings, a decision was made to conclude the study at the end of round three
for the following reasons: First, there was stability of responses between round 2 and round 3, as most of the
agreed-upon indicators remained consistent in the two rounds (36/42). Second, a moderate level of
agreement was achieved between experts. Third, the participants showed signs of fatigue, as the dropout
rate increased from 8% in round 2 to 15% in round 3. It was expected that the dropout rate would further
increase if the study proceeded to round 4. A summary of the results is presented in Table 4.
Indicator Mean Median SD IR % of
agreement
Structure indicators
Working conditions (Employment conditions, Stability, Workload) * 4.11 4 0.37 0 98
Staff maintenance (Retention/turnover) * 4.27 4 0.49 1 98
Nursing staff supply (Quantity/intensity) * 4.13 4 0.47 0 95
Nurse-Bed ratio* 4.27 4 0.49 1 95
Staff maintenance (Absenteeism)* 4.02 4 0.41 0 93
Staff maintenance (Work-related accidents, injuries, illnesses) * 4.13 4 0.55 0 91
Nursing staff supply (Quality/training/experience* 4.33 4 0.67 1 89
Working conditions (Support resources, Physical facilities, Material resources) * 3.91 4 0.7 0 82
Staff maintenance (Satisfaction at work) * 3.78 4 0.83 1 71
Working Hours Per Patient Days* 3.47 4 0.69 1 58
Nursing staff supply (Patient classification) systems Ø 3.04 3 0.82 2 35
Economic sustainability (Cost per case-mix or patient-day) Ø 2.96 3 0.86 2 35
Economic sustainability (Cost of resources) Ø 2.87 3 0.7 1 18
Process indicators
Communication (Nurse-Patient) * 4.6 5 0.49 1 100
Professional satisfaction* 4.31 4 0.47 1 100
Deployment of scope of practice* 4.13 4 0.34 0 99
Pain assessment* 4.33 4 0.51 1 98
Nursing work environment characteristics (perceived autonomy, role tension, collaboration) * 4.24 4 0.47 1 98
Job burnout* 4.11 4 0.37 0 98
Collaboration (Nurse-Patient) * 4.25 4 0.55 1 95
Discharge planning* 4.11 4 0.6 0 95
Patient centrality in the nursing care delivery process (Patient/family involvement) * 4.02 4 0.41 0 93
Nursing processes* 3.96 4 0.47 0 91
Inter Unit Work Relations* 4.18 4 0.77 1 89
Patient centrality in the nursing care delivery process (Responsiveness to patients’ needs and
expectations) * 4.02 4 0.49 0 89
Restraint application* 3.93 4 0.42 0 87
Health Promotion and Illness Prevention* 3.91 4 0.44 0 85
Problems & symptoms management* 3.85 4 0.49 0 80
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Patient centrality in the nursing care delivery process (Continuity, reactivity, timeliness,
coordination) *
3.87 4 0.64 0 76
Malnutrition screening Ø 3.29 4 0.81 1 51
Nurse decision making Ø 3.25 3 0.75 1 44
Conflict resolution (Nurse-Patient) Ø 2.93 3 0.92 2 35
Delirium observation Ø 3.07 3 0.57 0 20
Outcome indicators
Patient Fall* 4.45 4 0.5 1 100
Patient satisfaction* 4.31 4 0.47 1 100
Central line infection rates (Central Line-Associated Bloodstream Infections) * 4.36 4 0.49 1 100
Catheter Associated Urinary Tract Infection* 4.31 4 0.47 1 100
Intra-venous infection (phlebitis)* 4.36 4 0.49 1 100
Patient comfort and quality of life related to care: Hygiene* 4.09 4 0.29 0 100
Hospital acquired Infection* 4.15 4 0.36 0 100
Pressure Injury* 4.22 4 0.46 0 98
Hospital Acquired Pneumonia* 4.24 4 0.47 1 98
Failure to rescue* 4 4 0.27 0 96
Patient empowerment: Ability to achieve appropriate self-care* 4.07 4 0.47 0 96
Patient comfort and quality of life related to care: Symptoms management (e.g., pain, nausea,
dyspnea, fever) * 4.04 4 0.47 0 95
Medication error* 4.25 4 0.64 1 93
Patient comfort and quality of life related to care: Incontinence* 4 4 0.38 0 93
Patient functional status (physical, nutritional) * 3.8 4 0.62 0 84
Patient empowerment: Adoption of health-promoting behaviors* 3.96 4 0.67 0 75
Average Length of Stay Ø 3.29 3 0.74 1 45
Mortality rate Ø 3.16 3 0.74 1 36
Re-admissions Ø 2.82 3 0.61 1 11
TABLE 4: Participants responses in round 3.
* Indicators that achieved agreement criteria
Ø Indicators that did not achieve agreement criteria
Discussion
The present study's agreed-upon NSIs in the “structure” category align with commonly reported structure
indicators in the literature. Previous studies by Burston et al. and Xu identified nurse-patient ratio, Nursing
Hours Per Patient Day (NHPPD), education, and years of experience as commonly used structure NSIs
[19,20]. Another study conducted in Iran also highlighted nurses’ education and experience, nurse-to-
patient ratio, and the ratio of Registered Nurses to the nursing staff as important structure indicators [21].
However, none of the structure NSIs related to the economic stability of the nursing system achieved
agreement in this study, which may be attributed to insufficient educational preparation for nurses on fiscal
management and nursing economics [22,23].
The literature describes the relationship between the structure components and the quality of nursing
performance for some NSIs. For example, Kim and Bae conducted a retrospective observational study on
338,369 patients and found significant relationships between nurse staffing levels and six nursing-sensitive
2024 Afaneh et al. Cureus 16(5): e59472. DOI 10.7759/cureus.59472 9 of 12
outcomes [24]. Kouatly et al. [25] assessed the association between nurse staffing levels and patient
outcomes or nursing-sensitive outcomes in in-patient units and found that lower working hours per patient
day were significantly associated with falls, injury falls, and hospital-acquired pressure injuries as well as
central line-associated bloodstream infection (CLABSI) in medical-surgical units. Further research studies
can be conducted to support the association between structural components of the nursing system and
patients' outcomes, which can inform evidence-based decision-making regarding health policy and the
distribution of nursing resources.
Unlike structure indicators, process indicators require active monitoring using various methods. Process
indicators often have limited evidence linking them to outcome indicators [4]. However, the impact of
process Indicators on quality of care should not be underestimated. A study by Rastian et al. revealed a
discrepancy between the quality of care before and after implementing and mentoring the nursing process
[26].
The present study found that more than one-third of the agreed-upon NSIs were outcome indicators (16 out
of 42). This finding is consistent with the understanding of the nurses' primary responsibility in producing
positive patient care outcomes. It also aligns with international findings on outcome indicators [2,19,20].
The NCPF identifies five areas as joint outcome contributions by nursing and other systems: health status,
readmission, length of stay, complications, and mortality. However, in this study, these areas did not
achieve agreement as Nursing-Sensitive Indicators, as reflect higher-level organization-wide key
performance indicators (KPIs) rather than NSIs [27]. The findings of the present study can support the
national strategy for nursing and midwifery by developing an agreed-upon national quality nursing
indicators dataset. Monitoring NSIs helps analyze the impact of nursing performance on patient healthcare
outcomes, provides knowledge to implement evidence-based nursing improvements, and convinces
healthcare policymakers of the impact of nursing practice on health outcomes.
Both mortality rate and readmission rate are considered outcomes for joint contribution by different health
care disciplines. Despite nursing care can affect both mortality and readmission, these two outcomes are not
specific to nursing care and may represent hospital-level KPIs rather than NSIs. Perhaps this is why they
generated low agreement among the participants to be considered NSIs [2].
Despite contributing to the breadth of knowledge about NSIs, the present study has some limitations
inherent in the Delphi method, such as the lack of an agreed statistical cutoff point for agreement among
participants, the absence of criteria for defining experts, and potential validity issues with participants'
responses in the Delphi technique.
Conclusions
This study represents an endeavor to enhance the clarity of NSIs by proposing a scientifically and
conceptually guided agreement on these indicators. Achieving clarity in NSIs is essential to advance the
quantification of the quality of nursing care. NSIs also contribute to researchers’ efforts in providing robust
evidence regarding the impact of the nursing profession on patient healthcare outcomes. The adoption and
implementation of a national database for both mandatory and voluntary reporting of the agreed-upon
structure, process, and outcome of NSIs would provide nursing policymakers with a reliable tool to support
decision-making.
Implications for international practice
Healthcare and nursing policymakers can adopt and implement a nationwide policy for the voluntary and
mandatory monitoring and reporting of the agreed-upon structure, process, and outcome NSIs. These
Indicators can be utilized to measure and benchmark the quality and performance of nursing care at the
national levels, as well as to make comparisons with countries of similar socio-economic status. The
adoption and reporting of this policy can serve broader goals beyond benchmarking healthcare
organizations for improvement. It can also provide evidence of the efficiency of the nursing system by
comparing available resources with patient care outcomes.
Continued nursing research on NSIs is imperative for several reasons. Firstly, measuring quality is a
prerequisite for improving it. Secondly, nursing is a profession with a unique scope of practice, necessitating
a distinctive system for monitoring the quality of provided by nurses. Thirdly, nursing leaders and
policymakers require a valid and reliable tool to measure the quality of nursing care. Lastly, further evidence
is needed to strengthen the position of nursing policymakers in advocating for investment in the nursing
workforce.
Additional Information
Author Contributions
All authors have reviewed the final version to be published and agreed to be accountable for all aspects of the
2024 Afaneh et al. Cureus 16(5): e59472. DOI 10.7759/cureus.59472 10 of 12
work.
Concept and design: Maha Mihdawi, Tareq Afaneh, Fathieh Abu-Moghli
Drafting of the manuscript: Maha Mihdawi, Tareq Afaneh
Acquisition, analysis, or interpretation of data: Tareq Afaneh
Critical review of the manuscript for important intellectual content: Tareq Afaneh, Fathieh Abu-
Moghli
Supervision: Fathieh Abu-Moghli
Disclosures
Human subjects: Consent was obtained or waived by all participants in this study. Animal subjects: All
authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In
compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services
info: All authors have declared that no financial support was received from any organization for the
submitted work. Financial relationships: All authors have declared that they have no financial
relationships at present or within the previous three years with any organizations that might have an
interest in the submitted work. Other relationships: All authors have declared that there are no other
relationships or activities that could appear to have influenced the submitted work.
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... NSIs are the central quality measures used to quantify the quality of nursing care and nursing services' contribution to patients (Gathara et al., 2020). Monitoring NSIs not only identifies areas for enhancing care quality but also directs improvement efforts effectively (Afaneh et al., 2024). Recent systematic reviews highlight improvements in patient outcomes across various settings through focused attention on NSIs (Afaneh et al., 2024;Blume et al., 2021;Eglseer et al., 2021). ...
... Monitoring NSIs not only identifies areas for enhancing care quality but also directs improvement efforts effectively (Afaneh et al., 2024). Recent systematic reviews highlight improvements in patient outcomes across various settings through focused attention on NSIs (Afaneh et al., 2024;Blume et al., 2021;Eglseer et al., 2021). The COVID-19 pandemic further underscored the critical importance of NSIs, revealing shifts in nursing resources and their impact on care quality metrics such as increased rates of nurse burnout and changes in patient outcomes (Reynolds et al., 2023;Ribeiro et al., 2022). ...
... The relevance of this indicator in many aspects of patient care provides an overview of nursing care which makes it valuable to measure general quality improvement. A recent study conducted in Jordan highlighted that more than a third of identified NSIs for the country were outcome indicators (Afaneh et al., 2024). In Malaysia, nurses are primarily responsible for medication administration in the inpatient setting, and prioritizing medication error as an area of concern for NSIs enhances accountability within the profession which fosters improvement in care delivery. ...
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Background key decisions have to be made in healthcare systems and policies often under uncertain conditions or without complete objective evidence. Decisions made on the health system and policy levels affect national and global population, which requires transparency and accountability to ensure the best impact to population's health. Consensus development methods assist complex process of the decision making combining existing evidence and expert opinions. Variety of challenges affect the selection, application and use of the consensus development methods, which requires careful consideration to keep rigour, validity and transparency. Objective To identify and review studies which have used consensus development methods in order to develop national or international policy or framework in health field. Methods A literature review was conducted searching the databases PubMed, EMBASE and PsycINFO. Studies using a consensus development approach to develop tools or frameworks for health system and policy enhancement were eligible for the review. Key elements of consensus development process were extracted and reported using content analysis and narrative synthesis. Results The review included 26 studies in total either in national or international settings. Over 60% of studies extracted did not apply typical consensus development methods; however, stated as consensus meetings instead. Delphi technique was the most used method from the consensus development methods, which often combined with some face-to-face meeting features. Conclusions This review summarised the use of consensus development methods in health system and policy development. The review identified a wide range of variations in the selection, use and application of the methods in studies. For better utilisation and application of the consensus development methods in the field, some standardisation of the methods and reporting would be warranted.
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Objective: Nurse-sensitive indicators (NSIs) are increasingly being developed and used to establish quality of nursing care in Western countries. The objective was to gain insights into the methodological quality of mandatory NSIs in Dutch hospitals, including indicators for pain, wound care, malnutrition and delirium. Design: A descriptive exploratory design was used, starting with desk research into publicly available documents and reports describing the development of the NSIs included in this study. We used the validated Appraisal of Indicators through Research and Evaluation (AIRE) instrument to evaluate the methodological quality. Results: Although the purpose and relevance of each individual NSI have been described, no detailed information about the criteria for selecting these topics is available. It is not clear which specific stakeholders participated and how their input was used. We found no information about the process of collecting and compiling scientific evidence. It is unclear whether and to what extent the usability of NSIs has been tested. Conclusion: The methodological quality of NSIs used in Dutch hospitals is less than optimal in various ways and it is therefore questionable if the indicators are accurate enough to identify changes or improve nursing practice. Our study also provides an example of how the methodological quality of NSIs can be assessed systematically, which is relevant considering the increasing use of NSIs in various countries.