Abstract
OBJECTIVE
The aims of this study were to identify care delivery models (CDMs) currently used in acute care settings and explore sources of variability at the unit, hospital, and system levels.
BACKGROUND
Despite efforts to improve healthcare delivery, acute care settings continue to face challenges such as workforce shortages, inefficiencies, and inconsistent patient outcomes. Care delivery models define how care is organized and delivered, yet current research on CDM innovation is sparse.
METHODS
A multimethod design was used, including a survey of 294 nurse leaders and 34 qualitative interviews. Logistic regression and content analysis identified factors influencing CDM changes.
RESULTS
Primary nursing was the most common CDM (61.6%). Care delivery model changes were driven by staffing shortages, hospital size, and leadership position. A typology of unit-, hospital-, and system-level drivers emerged.
CONCLUSIONS
Care delivery model changes are often reactive rather than strategic or evidence based. Understanding organizational drivers and having aligned metrics can aid in more intentional implementation and evaluation of CDMs.
Healthcare in America presents significant challenges, including high costs, inconsistent outcomes, a shortage of healthcare workers, fragmented care, and suboptimal patient experiences.1 In response to complex healthcare challenges, the Institute for Healthcare Improvement introduced the Triple Aim in 2007, which has since evolved into the Quintuple Aim, focusing on patient experience, population health, cost reduction, clinician well-being, and health equity.2 Despite more than 20 years of calls for improvement, healthcare systems, particularly in hospitals and acute care settings, continue to face challenges. Staff shortages persist, clinical workflows are often not optimized, hospital-acquired infections (HAIs) and medical errors remain prevalent, and clinicians struggle to keep pace with evidence-based practices.
A care delivery model (CDM) refers to how patient care is organized, including the roles and skills of clinicians, the care setting, and the expected outcomes for patients and families.3 There are 4 classic CDMs identified in the literature: functional, individual, team, and primary nursing. The functional nursing model differs from other CDMs by assigning specific tasks such as medication administration or ambulation to staff members, who perform them for all patients during their shift.4,5 In contrast, individual, team, and primary nursing CDMs are person centered. The individual CDM, also known as total patient care, promotes the nurse-patient relationship by assigning 1 nurse complete responsibility for a group of patients during a shift.4,6 The team nursing CDM uses an RN team leader who oversees a team of RNs, LPNs, and nursing assistants, coordinating care for a group of patients and ensuring all team members are knowledgeable about patient needs.3-5 Primary nursing emphasizes continuity of care by assigning a single nurse responsible for assessing, planning, delivering, and evaluating care for 1 or more patients throughout their stay.4,5 This primary nurse holds 24-hour accountability for the patient's care plan, and other nurses follow that plan during subsequent shifts.3-5 More recently, virtual nursing has been added as a CDM, defined as remote use of telecommunication technologies to monitor patients, deliver care, and interpret health data with the goal of supporting their overall health and well-being.3
The choice of CDM is influenced by factors such as previous experience, fiscal responsibility, available resources, and considerations around quality and safety. The effectiveness of a CDM is shaped by multiple variables; a CDM that transcends setting and populations remains elusive.7 The COVID and postpandemic era provided new opportunities for nurse leaders to innovate care delivery, yet research on the impact and effectiveness of these innovations is limited.8
Objective
The purpose of this study was to: 1) identify and describe the CDMs currently in use in acute care settings; and 2) explore sources of variability in these models at the unit, hospital, and system levels.
Methods
Design
Researchers used a multimethod design9 to understand nurse manager (NM), director of nursing (DON), and chief nurse (chief nursing officer [CNO]/chief nurse executive [CNE]) perceptions of changes in CDMs within acute care hospitals, and to elucidate unit-, hospital-, and system-level sources of variability. A multimethod design was chosen to preserve the methodological independence of the study's components. A national survey and individual interviews involved separately recruited nurse leaders and served distinct purposes, broad trend analysis, and in-depth contextual exploration, respectively.
Setting and Sampling
A convenience sample of nurse leaders currently working in acute care with the title/position of NM, DON, or CNO/CNE were included in the study. Registered nurses in a leadership role (NM, DON, CNO/CNE) for a minimum of 6 months, working at least 0.5 full-time equivalent, and able to read and write in English were eligible to participate. Registered nurses who were working outside acute care were excluded. Institutional review board approval was received from the researchers' institutions (University of Kansas Medical Center, University of Iowa, Brigham Young University, and Hackensack Meridian Health) before initiating study activities.
Data Collection
Quantitative Survey
Participants were recruited via electronic newsletter distributed by the Association for Leadership Science in Nursing (ALSN) and American Organization for Nursing Leadership (AONL). Data were collected anonymously via REDCap10 from NMs, DONs, and CNOs/CNEs across the United States. The survey included demographic questions, number of direct reports, hospital zip code, American Nurses Credentialing Center (ANCC) Magnet® or Pathway to Excellence® status, type of unit/division and number of beds, RN vacancy rate, and questions about CDMs used in their organization, with CDM definitions provided.
Qualitative Interviews
A purposive sample11 of NMs, DONs, and CNOs/CNEs who met the inclusion criteria were invited to participate in semistructured individual interviews. Participants were recruited via live presentation at the 2024 AONL Conference, hospital intranet communications, and snowball sampling.11 Interviews took place via Zoom and were recorded and transcribed verbatim. Interview guide questions were developed based on the concepts of CDM (eg, type, characteristics), and CDM innovation, implementation, effectiveness, and sustainability from the organizational theory literature.12 Interview data collection and analysis occurred in an iterative fashion until saturation11 was reached independently across each leader position (NM, DON, and CNO/CNE).
Data Analysis
Quantitative Survey
Descriptive statistics were used to analyze participant demographics and survey responses. Logistic regression was used to determine the likelihood of a change in CDM. The nurse leaders' organizational and demographic variables were used as predictors. Organizational predictors included unit type, hospital/health system size, number of direct nursing reports, number of direct nonnursing reports, ANCC hospital designation status, and US geographic region. Leader demographic predictors included highest level of education, time in current position, and years as an RN.
Qualitative Interviews
Direct content analysis11 was used to analyze the interviews. Three members of the research team (M.L.J., S.W., and B.L.), each with more than 15 years of qualitative research experience, conducted the analysis. A coding framework informed by organization theory12 was used to organize codes by organization level (unit, hospital, and system). Operational definitions for each level were as follows: 1) unit level, the management of a nursing unit within a hospital; 2) hospital level, the operations and management of a single hospital facility; and 3) system level, a healthcare network, including multiple hospitals, clinics, and other facilities, under a single administrative structure. After agreeing on the coding framework, each coder read the raw qualitative data from beginning to end and identified key concepts. In line with inductive qualitative analysis,11 each researcher independently coded a set of the interviews, preliminary codes were assigned, and subcategories were identified. Categories were then sorted into order (axial coding) and categorized with labels. This process supported the dependability of findings. Interviews continued in an iterative fashion until data saturation was achieved. The researchers engaged in peer debriefing to ensure confirmability and concordance of the findings.11
Results
Survey
A total of 462 individuals responded to the survey; 294 responses were retained after removing those that did not meet the inclusion criteria or had missing responses (Table 1). Thirty-five percent of respondents were NMs (n = 102), 33.7% were DONs (n = 99), and 31.6% (n = 93) were CNOs/CNEs. The majority were female (91.5%), White (80.3%), and an RN for 11 to 20 years (33%); had a master's degree (58.8%); were in their current position for 3 to 5 years (27.6%); and were working in an ANCC Magnet organization (46.6%). Among NMs and DONs, the majority had oversight of a medical surgical unit/division (36.2%) or a critical care unit/division (26.6%). Registered nurse vacancy rates ranged from <5% (n = 47, 16%) to 50% or greater (n = 8, 2.7%).
Table 1.
Nurse Leader and Hospital Characteristics
| Total | NM N = 102 (34.7%) |
DON N = 99 (33.7%) |
Chief Nurse (CNO/CNE) N = 93 (31.6%) |
|
|---|---|---|---|---|
| Gender | ||||
| Female | 269 (91.5%) | 89 (87.3%) | 94 (94.9%) | 86 (92.5%) |
| Male | 23 (7.8%) | 11 (10.8%) | 5 (5.1%) | 7 (7.5%) |
| Transgender | 1 (0.3%) | 1 (1%) | ||
| Prefer not to answer | 1 (0.3%) | 1 (1%) | ||
| Age, y | ||||
| 20-29 | 1 (0.3%) | 1 (1%) | ||
| 30-39 | 54 (18.4%) | 23 (22.5%) | 22 (22.2%) | 9 (9.7%) |
| 40-49 | 95 (32.3%) | 40 (39.2%) | 30 (30.3%) | 25 (26.9%) |
| 50-59 | 83 (28.2%) | 24 (23.5%) | 30 (30.3%) | 29 (31.2%) |
| 60-69 | 58 (19.7%) | 13 (12.7%) | 15 (15.2%) | 30 (32.3%) |
| 70 or older | 2 (0.7%) | 1 (1%) | 1 (1%) | |
| Prefer not to answer | 1 (0.3%) | 1 (1%) | ||
| Race | ||||
| Asian | 17 (5.8%) | 13 (12.7%) | 3 (3%) | 1 (1.1%) |
| Black/African American | 18 (6.1%) | 7 (6.9%) | 5 (5.1%) | 6 (6.5%) |
| White | 236 (80.3%) | 74 (72.5%) | 83 (83.8%) | 79 (84.9%) |
| Other | 23 (7.8%) | 8 (7.8%) | 8 (8.1%) | 7 (7.5%) |
| Ethnicity | ||||
| Non-Hispanic | 243 (82.7%) | 77 (75.5%) | 83 (83.8%) | 83 (89.2%) |
| Hispanic | 17 (5.8%) | 9 (8.8%) | 6 (6.1%) | 2 (2.2%) |
| Other | 34 (11.6%) | 16 (15.7%) | 10 (10.1%) | 8 (8.6%) |
| Years as an RN | ||||
| 1-2 | 1 (0.3%) | 1 (1%) | ||
| 3-5 | 2 (0.7%) | 2 (2%) | ||
| 6-10 | 21 (7.1%) | 15 (14.7%) | 6 (6.1%) | |
| 11-20 | 97 (33%) | 43 (42.2%) | 37 (37.4%) | 17 (18.3%) |
| 21-30 | 87 (29.6%) | 25 (24.5%) | 27 (27.3%) | 35 (37.6%) |
| More than 30 | 86 (29.3%) | 17 (16.7%) | 28 (28.3%) | 41 (44.1%) |
| Education level | ||||
| Baccalaureate degree | 52 (17.7%) | 36 (35.3%) | 11 (11.1%) | 5 (5.4%) |
| Master's degree | 173 (58.8%) | 59 (57.8%) | 69 (69.7%) | 45 (48.4%) |
| Doctoral degree | 69 (23.5%) | 7 (6.9%) | 19 (19.2%) | 43 (46.2%) |
| Currently certified | ||||
| Yes | 220 (74.8%) | 66 (64.7%) | 81 (81.8%) | 73 (78.5%) |
| No | 74 (25.2%) | 36 (35.3%) | 18 (18.2%) | 20 (21.5%) |
| Time in current position | ||||
| Less than 6 mo | 10 (3.4%) | 1 (1%) | 4 (4%) | 5 (5.4%) |
| 6-11 mo | 31 (10.5%) | 9 (8.8%) | 14 (14.1%) | 8 (8.6%) |
| 1-2 y | 71 (24.1%) | 23 (22.5%) | 25 (25.3%) | 23 (24.7%) |
| 3-5 y | 81 (27.6%) | 29 (28.4%) | 27 (27.3%) | 25 (26.9%) |
| 6-10 y | 64 (21.8%) | 26 (25.5%) | 17 (17.2%) | 21 (22.6%) |
| 11-20 y | 25 (8.5%) | 9 (8.8%) | 8 (8.1%) | 8 (8.6%) |
| More than 20 y | 12 (4.1%) | 5 (4.9%) | 4 (4%) | 3 (3.2%) |
| Magnet/Pathway designation | ||||
| Magnet | 137 (46.6%) | 46 (45.1%) | 50 (50.5%) | 41 (44.1%) |
| Pathway to Excellence | 17 (5.8%) | 8 (7.8%) | 6 (6.1%) | 3 (3.2%) |
| Not Magnet or Pathway | 128 (43.5%) | 39 (38.2%) | 41 (41.4%) | 48 (51.6%) |
| I am not sure | 12 (4.1%) | 9 (8.8%) | 2 (2%) | 1 (1.1%) |
| No. direct reports | ||||
| Nursing direct reports | 53.6 (121) 20 (0-1500) n = 288 |
55.4 (32.9) 50 (0-139) n = 101 |
50 (88.9) 10 (0-420) n = 96 |
55.3 (192.6) 8 (1-1500) n = 91 |
| Nonnursing direct reports | 103.3 (720.4) 3 (0-9000) n = 260 |
21.7 (63.5) 10 (0-600) n = 93 |
32.6 (101.4) 2 (0-668) n = 79 |
253 (1223.6) 2 (0-9000) n = 88 |
| Type of nursing unit/division | ||||
| Medical-surgical | 72 (36.2%) | 34 (33.3%) | 38 (39.2%) | |
| Critical care | 53 (26.6%) | 24 (23.5%) | 29 (29.9%) | |
| Pediatrics | 22 (11.1%) | 9 (8.8%) | 13 (13.4%) | |
| Mother/baby | 31 (15.6%) | 14 (13.7%) | 17 (17.5%) | |
| Emergency department | 32 (16.1%) | 8 (7.8%) | 24 (24.7%) | |
| Surgical services | 26 (13.1%) | 13 (12.7%) | 13 (13.4%) | |
| Psychiatric | 5 (2.5%) | 1 (1%) | 4 (4.1%) | |
| Other | 80 (40.2%) | 34 (33.3%) | 46 (47.4%) | |
| Current RN vacancy rate | ||||
| Less than 5% | 47 (16%) | 29 (28.4%) | 14 (14.1%) | 4 (4.3%) |
| 5%-9% | 51 (17.3%) | 18 (17.6%) | 19 (19.2%) | 14 (15.1%) |
| 10%-19% | 85 (28.9%) | 18 (17.6%) | 27 (27.3%) | 40 (43%) |
| 20%-29% | 59 (20.1%) | 24 (23.5%) | 15 (15.2%) | 20 (21.5%) |
| 30%-39% | 24 (8.2%) | 6 (5.9%) | 10 (10.1%) | 8 (8.6%) |
| 40%-49% | 4 (1.4%) | 2 (2%) | 2 (2%) | |
| 50% or greater | 8 (2.7%) | 1 (1%) | 4 (4%) | 3 (3.2%) |
| Not sure | 16 (5.4%) | 4 (3.9%) | 8 (8.1%) | 4 (4.3%) |
Respondents were asked about the type(s) of CDMs currently used on their hospital units, whether the CDM(s) had recently changed, and what prompted the change (Table 2). The most reported CDM type was primary nursing (n = 181, 61.6%). Most (n = 213, 72.4%) of the respondents reported that their CDM had not changed recently. Of those who did report a change (n = 81, 27.6%), the most reported factor that prompted the change was overall staffing (n = 71, 87.7%).
Table 2.
Care Delivery Model Characteristics
| Total | NM N = 102 (34.7%) |
DON N = 99 (33.7%) |
Chief Nurse (CNO/CNE) N = 93 (31.6%) |
|
|---|---|---|---|---|
| What care delivery model(s) are you currently using on your unit(s)? (Select all that apply) | ||||
| Functional | 40 (13.6%) | 16 (15.7%) | 13 (13.1%) | 11 (11.8%) |
| Team nursing | 104 (35.4%) | 30 (29.4%) | 32 (32.3%) | 42 (45.2%) |
| Primary nursing | 181 (61.6%) | 63 (61.8%) | 65 (65.7%) | 53 (57%) |
| Virtual nursing | 32 (10.9%) | 4 (3.9%) | 7 (7.1%) | 21 (22.6%) |
| Other | 38 (12.9%) | 7 (6.9%) | 13 (13.1%) | 18 (19.4%) |
| Has your care delivery model changed recently? | ||||
| No | 213 (72.4%) | 90 (88.2%) | 77 (77.8%) | 46 (49.5%) |
| Yes | 81 (27.6%) | 12 (11.8%) | 22 (22.2%) | 47 (50.5%) |
| What prompted the change? (Select all that apply) | ||||
| Overall staffing | 71 (87.7%) | 9 (75%) | 20 (90.9%) | 42 (89.4%) |
| Pandemic | 34 (42%) | 2 (16.7%) | 10 (45.5%) | 22 (46.8%) |
| Patient acuity | 23 (28.4%) | 2 (16.7%) | 7 (31.8%) | 14 (29.8%) |
| Shortage of RNs | 62 (76.5%) | 7 (58.3%) | 17 (77.3%) | 38 (80.9%) |
| Shortage of other providers | 17 (21%) | 1 (8.3%) | 3 (13.6%) | 13 (27.7%) |
| Budget | 24 (29.6%) | 4 (33.3%) | 7 (31.8%) | 13 (27.7%) |
| Innovation | 43 (53.1%) | 3 (25%) | 11 (50%) | 29 (61.7%) |
| Other | 8 (9.9%) | 3 (25%) | 1 (4.5%) | 4 (8.5%) |
Logistic regression was used determine factors associated with likelihood of reporting a change in CDM (Table 3). The CNO/CNE leadership position was a significant predictor. Chief nursing officers/CNEs were 7.66 times more likely to report a change in their CDM than NMs (odds ratio [OR], 7.66; 95% confidence interval [CI], 3.71-15.85; P = 0.001). Chief nursing officers/CNEs were also 3.58 times more likely to report a change in their CDM than DONs (OR, 3.58; 95% CI, 1.92-6.68; P = 0.0001). There was no significant difference between DONs and NMS (P = 0.51). No other personal attributes of the participants, including level of education (P = 0.89), years as an RN (P = 0.56), and time in current position (P = 0.53), were significant. Number of direct reports was set as a dichotomous (low/high) predictor. The likelihood of reporting a change in CDM for nurse leaders with a low number of RN direct reports was 2.6 times more likely than nurse leaders with high numbers of RN direct reports (OR, 2.60; 95% CI, 1.27-5.35; P = 0.009).
Table 3.
Significant Predictors Associated With Likelihood of Reporting a Change in Care Delivery Model(s)
| Predictor | OR | 95% CI | P | |
|---|---|---|---|---|
| Lower | Upper | |||
| Leadership position: CNOs/CNEs vs NMs | 7.66 | 3.71 | 15.85 | 0.001 |
| Leadership position: CNOs/CNEs vs DONs | 3.58 | 1.92 | 6.68 | 0.0001 |
| No. RN direct reports: low vs high | 2.60 | 1.27 | 5.35 | 0.009 |
| Hospital/health system size (no. beds) | 1.10 | 1.03 | 1.17 | 0.002 |
| Hospital/health system region: Midwest vs Northeast | 2.38 | 0.87 | 6.52 | 0.09 |
| Hospital/health system region: Midwest vs West | 3.92 | 1.74 | 8.84 | <0.01 |
| Hospital/health system region: South vs West | 2.61 | 1.21 | 5.64 | 0.01 |
Hospital/health system size was a significant predictor, with larger hospitals/health systems more likely to report a CDM change. For every 10-bed increase, there was a 1.10 times increase in reporting a change in CDM (OR, 1.10; 95% CI, 1.03-1.17; P = 0.002). The region of the United States where the hospital/health system was located was also a significant predictor for many comparisons. Respondents from hospitals/health systems in the Midwest were 2.38 times more likely to report a change in their CDM compared with respondents from hospitals/health systems in the Northeast (OR, 2.38; 95% CI, 0.87-6.52; P = 0.09). Respondents in the Midwest were 3.92 times more likely to report a change than respondents in the West (OR, 3.92; 95% CI, 1.74-8.84; P < 0.01). Respondents in the South were 2.61 times more likely to report a change than respondents in the West (OR, 2.61; 95% CI, 1.21-5.64; P = 0.01). Region was not a significant predictor when comparing hospitals/health systems in the Midwest with those in the South (P = 0.24), those in the Northeast compared with those in the South (P = 0.36), or those in the Northeast with those in the West (P = 0.36). There were no significant differences when comparing rural-urban commuting area codes, indicating that rurality was not a significant predictor (P = 0.23) of change in CDM. Hospital designation (Magnet, Pathway to Excellence, neither) was also not a significant predictor (P = 0.20).
Interviews
Thirty-four nurse leaders from 25 states participated in individual interviews. Interviews were conducted with 10 NMs from 9 states, 15 DONs from 14 states, and 9 CNOs/CNEs from 6 states. Participants represented hospitals/health systems of various sizes, types (eg, academic medical centers, community hospitals), and ANCC designations (eg, Magnet, Pathway to Excellence, neither). Within unit-, hospital-, and system-level categories, several elements emerged as influential in nurse leaders' consideration, planning, and implementation of changes in CDM. Each element has several attributes, which help illustrate the element's substance and scope. A typology was derived to categorize these elements and attributes into unit-, hospital-, and system-level drivers of change, providing a structured framework for understanding their influence (Table 4). Although the unit, hospital, and system drivers, elements, and attributes are portrayed as distinct in the typology, they are likely to have an interactive, dynamic relationship.
Table 4.
Unit, Hospital, and System Level Typology for Driving Change
| Unit-Level Drivers | ||||
|---|---|---|---|---|
| Unit Context | Leader Characteristics | Leader Support | Patient Characteristics | |
| Physical space, including floor layout, number of beds, and infrastructure to support technology-based solutions | NM's personal philosophy and approach to leadership | NM's span of control | Complexity and acuity of patient's needs | |
| Collective capacity for change, given the unit's other challenges and change initiatives | NM's leadership experience and expertise, both generally and specific to care delivery models | NM's supporting personnel—such as assistant NMs—with consideration for their experience, expertise, and leadership abilities | Care needs requiring highly specialized staff and/or care coordination | |
| Collective experience with and level of investment in current care delivery model(s) | NM's critical and creative thinking | The quality and availability of systems, tools, and resources that support the NM's efforts | High-acuity needs requiring specializedclinical equipment and/or monitoring | |
| Current and historical approaches to staffing | NM's knowledge and understanding of technology-based solutions | Changing care delivery needs | ||
| Availability of qualified staff to meet unit needs | ||||
| Hospital-Level Drivers | ||||
| Local Context | Empowerment | Leadership Support | Services | |
| Availability of qualified staff to meet hospital needs | Collaborative governance structure | CNO/CNE's buy-in and support for care delivery change | Local supply and demand for services | |
| Affordability of local area for potential staff | Flexible parameters for making hiring decisions | Executive team's buy-in and support for care delivery change | Recognized expertise in providing care for patients with complex and/or high-acuity needs | |
| Local area's desirability and livability for potential staff | Hospital systems and initiatives conducive to a skilled, healthy, engaged workforce | Executive-level vision for care delivery innovation | Ability to provide care requiring highly specialized staff and/or care coordination | |
| Local accessibility of education and training programs relevant to needed staff roles | Available resources to support change initiatives | Ability to serve patients with needs requiring specialized clinical equipment and/or monitoring | ||
| Laws, regulations, and policies pertaining to hospital operations | Hospital systems and culture conducive to interdisciplinary collaboration | |||
| Allowance for unit-level variation in care delivery models | ||||
| System-Level Drivers | ||||
| Internal Forces | External Forces | Bureaucracy | ||
| System-level futuristic mindset | Emerging evidence and professional sentiments regarding care delivery models | Processes for making decisions with system-level implications, such as increasing the number of beds | ||
| System-level vision for care delivery innovation | Development and availability of technology and tools to support care delivery model change | Parameters and processes for getting buy-in and approval for changes from financial executives | ||
| System-level culture | Care delivery models being used in nearby hospital systems | Number of executives needed to approve system-wide changes | ||
| System-level resources available and used to incentivize innovation | Supply and demand for the system's services | System-level allowance for hospital-level variation in care delivery models | ||
| Laws, regulations, and policies pertaining to health system operations | ||||
Unit-Level Drivers
At the unit level, 4 primary elements associated with CDM changes were identified: unit context, leader characteristics, leader support, and patient characteristics. An illustrative example of a unit-level driver was a 16-bed hospital unit that had recently adopted a universal bed model, in which patients remain on a single unit as their care needs transition from intensive care to rehabilitation. The decision to adopt this CDM was driven by their patient population's exceptionally complex, specialized needs (patient characteristics). According to participants, meeting those needs required a model in which staff with specialized training could be concentrated within 1 unit (unit characteristics). The NM explained, “…the gross healthcare population doesn't quite understand how to manage these patients… [Because of] how critical my patients are and how specialized their [needs] are…they're just they're not easy anymore. Their ability to decompensate at any time [makes] peer support integral.” The NM leading this change benefited from previous experience working in a hospital unit that used a similar model (leader characteristics), as well as assistance from 2 assistant managers and a full-time nurse educator (leader support).
Hospital-Level Drivers
At the hospital level, 4 primary elements associated with CDM changes were identified: local context, empowerment, leader support, and services. An illustrative example of a hospital-level driver was a hospital unit providing behavioral health services that was in the process of reintroducing LPNs into their CDM. For patient safety reasons, it was critical to have qualified staff who can ensure patients receive therapy, medication management, and frequent safety checks (services). Hiring permanent staff to fill these needs had been difficult, in part, because the hospital was in a rural area with a cost of living making it difficult for potential staff to afford living nearby (local context). The hospital had tried to resolve immediate staffing needs by contracting with travel nurses and offering sign-on bonuses, and to address longer-term affordability issues by subsidizing employees' housing expenses. The NM explained, “We have $20,000 sign on bonuses for nursing…and subsidized housing…[Yet], within a year, half of those we hire leave…because it's too expensive for them to settle down [here].” The hospital moved toward incorporating LPNs into their CDM, with a subsidized educational pathway to help them earn associate and baccalaureate degrees (leadership support). In this particular case, the hospital staff were unionized, so the process of changing the CDM had additional complexity (local context, empowerment).
System-Level Drivers
At the system level, 3 primary elements associated with CDM changes were identified: internal forces, external forces, and bureaucracy. An illustrative example of a system-level driver was a healthcare system where, in response to a challenge from the chief executive officer, the nursing leadership team developed their own virtual nursing program (internal forces, bureaucracy). Using iPads from the COVID pandemic, they launched a pilot on 2 units in 2022, which has since expanded to 17 units across 2 hospitals (external forces). A director summarized, “We're now live with 510 beds…about 41% of our beds. So the ROI has been strong for us, because the virtual nurse, the distinction is the virtual nurses taking care of one patient at a time. And it's not that frenetic care that we can often see with a clinical bedside nurse. So patient experience scores have improved.” In addition, the nurse leaders established a virtual acute care network, enabling nurse leaders nationwide to collaborate and share best practices (internal forces).
Discussion
Similar to previous research,7 results of this study indicate that the choice of CDM is influenced by factors such as previous experience and available resources. This study expands what is currently known about CDM selection and innovation by identifying that CNOs/CNEs are more likely to report a change in CDM compared with DONs and NMs. Chief nursing officers/CNEs oversee care delivery across the entire hospital/health system, rather than a single division or unit. As such, they may have been more aware of CDM changes within the hospital or across the hospital system than DONs or NMs. Whereas leadership position was a significant predictor of CDM change, other characteristics of the nurse leader such as level of education, years as an RN, and time in current position were not significant.
Nurse leaders in this study who worked in larger hospitals/health systems and had fewer direct reports were significantly more likely to report a change in CDM than nurse leaders working in smaller hospitals/health systems and with more direct reports. In addition, respondents in this study identified overall staffing as the primary reason for making a CDM change. This result supports previous research7 on the importance of available resources in the type of CDM used. Larger hospitals may be more likely to change their CDM due to greater service demands, staffing challenges, and external pressures such as level 1 trauma center obligations. This study is, to our knowledge, the first to identify that the geographic region of the hospital/health system can be an important predictor of CDM change. Future research should examine geographic region as a potential moderator/mediator of CDM innovation.
The typology that emerged from this study elucidates that leaders at each level of the organization experience distinct elements and attributes associated with their role. However, the elements and attributes pertinent at each level of the organization effect and are affected by those pertinent at the other 2 levels. This finding underscores the importance of coordinating change across all 3 levels. For example, an NM seeking resources (leader support) must work with the CNO to determine whether the needed resources are available within the system (internal forces). Similarly, a CNO hoping to implement emerging technology into care delivery (external forces) must have a clear sense of the NMs' ability to lead change (leader characteristics) and the unit's capacity for change (unit context). The typology provides a framework for individuals at their respective levels to consider before implementing a CDM change. Future research should examine the effectiveness of using the typology in planning, implementing, and evaluating CDM change.
Implications for Nurse Leaders
Strategic Thinking
This study revealed a lack of clarity regarding what constitutes a CDM. The nursing discipline and nurse leaders must take a proactive approach to redefine care delivery. This involves critically analyzing care delivery and influencing factors, anticipating workforce and patient care opportunities, and creating strategic, actionable plans to implement effective and meaningful CDMs. This will require critical thinking, creativity, and foresight to make informed decisions and adapt to new patient care needs and environmental circumstances.
Conflicting Metrics and Indicators
Results of this study indicate that many CDM changes are driven by immediate necessity and lack an evidence-based approach. Differing perceptions of what hospitals/health systems are measuring may result in the inability to track progress and missed opportunities for improvement. Aligning CDM metrics across units, hospitals, and systems provides opportunity to achieve goals and prioritize shared objectives throughout the organization. For example, many hospitals/health systems have implemented virtual nursing to enhance efficiency. Developing standardized metrics should reduce subjectivity and provide clear indicators of virtual nursing effectiveness. There is a critical need for accountability across nurse leader roles to ensure meaningful improvements by collectively identifying metrics and aligning opportunities to address inefficiencies or barriers to changes in care delivery.
Communication and Alignment
Findings from this study illuminate a discrepancy in the perception of CDM changes among leaders based on their leadership position. This finding may indicate a lack of effective communication between NMs, DONs, and CNOs/CNEs. Nurse leaders at all organizational levels should clearly define a comprehensive communication process to convey the plan and ensure all constituents understand the intended meaning. This process requires active listening, explicit language, and consideration of multiple perspectives (NMs, DONs, and CNOs/CNEs). It also calls for interrelatedness across units, the hospital, and the system for effective care delivery. This will require that individuals within units, hospitals, and systems clearly understand their responsibilities, how their work contributes to the overall organizational goals, and how their role interacts with others. This understanding ensures that everyone works cohesively toward the same clearly defined objectives, minimizing confusion or disconnection, and maximizing efficiency and effectiveness in patient care delivery.
Limitations
A convenience sample of nurse leaders was used for this study. As such, those who chose to participate may differ from those who did not. Invitation to participate in the survey occurred through electronic newsletters distributed by partnering organizations (ALSN and AONL). As such, a response rate was not able to be determined. Because the research team did not have a way to assess directly whether an organization changed their CDM, the researchers relied on nurse leader perceptions about whether their organizations changed their CDM. Because of the anonymous nature of the survey, it was not possible to link NMs, DONs, and CNOs/CNEs to their specific organizations. As such, determining whether there were any consistencies or discrepancies in reporting of changes in CDMs between leaders within the same organization was not possible.
Conclusions
Nursing care delivery is essential to achieving the Quintuple Aim.2 Despite decades of research and advocacy, staff shortages persist, workflows remain inefficient, and HAIs and medical errors continue. Changes in CDMs continue to be largely driven by immediate necessity rather than innovation and evidence. Organizational factors (eg, hospital size, number of RN direct reports, and geographic region) and leadership position (CNO/CNE) are significant predictors of CDM change. To validate the effectiveness of CDMs, CNOs/CNEs must be able to articulate the model of CDM they have adopted and the outcomes that will be used to measure success. Nurse leaders at all levels must adopt a proactive, evidence-based decision-making mindset and coordinate communication across unit, hospital, and system levels to drive innovations in CDMs and deliver on the promise of safe, high-quality, cost-effective, and inclusive care.
Footnotes
The authors declare no conflicts of interest.
Funding for this study was provided by the Association for Leadership Science in Nursing and American Organization for Nursing Leadership Foundation.
Contributor Information
Heather V. Nelson-Brantley, Email: hvnelson@uab.edu.
Bret Lyman, Email: bret-lyman@byu.edu.
Esther Chipps, Email: Esther.Chipps@osumc.edu.
Susan H. Weaver, Email: sweave29@gmail.com.
Amany Farag, Email: amany-farag@uiowa.edu.
Joel M. Moore, Email: Joel.Moore@mercyone.org.
Loraine T. Sinnott, Email: sinnott.5@osu.edu.
M. Lindell Joseph, Email: maria-joseph@uiowa.edu.
References
- 1.Jones CH, Dolsten M. Healthcare on the brink: navigating the challenges of an aging society in the United States. Npj. Aging. 2024;10(1):22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Nundy S, Cooper LA, Mate KS. The quintuple aim for health care improvement: a new imperative to advance health equity. JAMA. 2022;327(6):521–522. [DOI] [PubMed] [Google Scholar]
- 3.Parchment J. Professional practice models. In: Huber DL, Joseph ML, eds. Leadership & Nursing Care Management. 7th ed. St Louis, MO: Elsevier; 2021:268–288. [Google Scholar]
- 4.Parreira P, Santos-Costa P, Neri M, Marques A, Queirós P, Salgueiro-Oliveira A. Work methods for nursing care delivery. Int J Environ Res Public Health. 2021;18(4):2088. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Tiedeman ME, Lookinland S. Traditional models of care delivery: what have we learned? J Nurs Adm. 2004;34(6):291–297. [DOI] [PubMed] [Google Scholar]
- 6.Fairbrother G, Chiarella M, Braithwaite J. Models of care choices in today's nursing workplace: where does team nursing sit? Aust Health Rev. 2015;39(5):489–493. [DOI] [PubMed] [Google Scholar]
- 7.Prentice D, Moore J, Desai Y. Nursing care delivery models and outcomes: a literature review. Nurs Forum. 2021;56:971–979. [DOI] [PubMed] [Google Scholar]
- 8.Weaver SH, Lyman B, Nelson-Brantley HV, Joseph ML. Transforming care delivery models: insights and innovations from nurse leaders. Nurse Lead. 2025;23(2):163–169. [Google Scholar]
- 9.Anguera MT, Blanco-Villaseñor A, Losada JL, Sánchez-Algarra P, Onwuegbuzie AJ. Revisiting the difference between mixed methods and multimethods: is it all in the name? Qual Quant. 2018;52:2757–2770. [Google Scholar]
- 10.REDCap . About. https://projectredcap.org/about/. Accessed March 14, 2025.
- 11.Patton MQ. Qualitative Research & Evaluation Methods. 4th ed. Thousand Oaks, CA: SAGE Publications, Inc; 2015. [Google Scholar]
- 12.Parry KW. Leadership and organizational theory. In: Bryman A, Collinson D, Grint K, Jackson B, Uhl-Bien M, eds. The Sage Handbook of Leadership. London, England: SAGE Publications Ltd; 2011:53–70. [Google Scholar]