Barriers To The APN Roles In Healthcare Change

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INTERDISCIPLINARY collabora-tion is an emerging mandateto decrease fragmentation ofcare delivery in U.S. hospi- tals. Higher mortality rates (Estabrooks, Midodzi, Cummings, Ricker, & Giovannetti, 2005) and longer lengths of hospital stay (Zwarenstein, Goldman, & Reeves, 2009) have been found in environ- ments where collaboration is lim- ited or not present. As many as 98,000 people die in hospitals each year as a result of medical errors which may be traced to lack of collaboration and disjointed care. Beyond the cost of human lives, billions of dollars are spent annually for additional care re – sulting from medical errors (Kohn, Corrigan, & Donaldson, 2000). The aim of this study was to determine if a care delivery model based on collaboration and coordination of care using the CareGraph® would improve patient outcomes.

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To provide high-quality care and meet public expectations with limited resources, collaboration has become a necessity. In a land- mark study, Knaus, Draper, Wagner, and Zimmerman (1986) found that hospitals where collaboration was present reported a mortality rate 41% lower than the predicted number of deaths. Hospitals where there was little to no collaboration exceeded predicted mortality by as much as 58%. Collaborative

relationships have also been tied to reduced costs for the health care system (Zwarenstein et al., 2009). Although empirical evi- dence in support of collaboration in the health care environment is available in the literature, there is little evidence on how to create this environment (Tschannen, 2004). The main structural ele- ments necessary for collaboration in an acute care environment in – clude a culture where relation- ships are valued, health care pro- fessionals communicate effective- ly, and respect is shared among all parties. A model of care delivery consistent with these cultural val- ues and focused on patient safety is paramount.

A Midwestern health care sys- tem designed an innovative model of care delivery where collabora- tion was purposefully woven into the structures and processes to effect positive change in patient and organizational outcomes. Called the Clinical Integration Model (CIM) (Zander, 2007), sev- eral of the health system hospitals adopted it while others chose to stay with a traditional primary care model. Comparing hospitals within the health system provides an opportunity to determine if there is a difference in survival, length of stay (LOS), and cost for patients receiving care in facilities utilizing the CIM and those receiv-

EXECUTIVE SUMMARY The current lack of collabora-

tive care is contributing to high- er mortality rates and longer hospital stays in the United States.

A method for improving collabo- ration among health profession- als for patients with congestive heart failure, the Clinical Integration Model (CIM), was implemented.

The CIM utilized a process tool called the CareGraph® to priori- tize care for the interdisciplinary team.

The CareGraph was used to focus communication and treat- ment strategies of health pro- fessionals on the patient rather than the discipline or specific task.

Hospitals who used the collab- orative model demonstrated shorter lengths of stay and cost per case.

Cheryl McKay K. Lynn Wieck

Evaluation of a Collaborative Care Model for Hospitalized Patients

CHERYL McKAY, PhD, CNS, RN, com- pleted this work as part of her doctoral education at the University of Texas at Tyler. She is presently Nurse Executive, Healthier Populations, OrionHealth, Santa Monica, CA.

K. LYNN WIECK, PhD, RN, FAAN, is Mary Coulter Dowdy Distinguished Nursing Professor, University of Texas at Tyler.

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ing care in facilities utilizing a pri- mary care model.

Collaboration in Health Care Collaboration, as defined by

the American Nurses’ Association (ANA) (2010), is a partnership based on trust with shared power, recognition, and acceptance of separate and combined practice spheres of activity and responsi- bility. Collaboration also includes mutual safeguarding of the legiti- mate interests of each party and a commonality of goals. The key components of shared power, recognition and acceptance, and common goals are relevant to many of the definitions found in the literature (Fewster-Thuente & Velsor-Friedrich, 2008; Petri, 2010). These components are essential for a collaborative process and can be operationalized in an acute care setting.

A number of factors have affected the ability of health care organizations to provide a collabo- rative environment including the educational system and profes- sionalization of health care practi- tioners. Studying determinants of successful collaboration, San Martin- Rodriguez, Beaulieu, D’Amour, and Ferrada-Videla (2005) found health care practitioners develop a strong professional identification through education. This strong profession- al identification often limits know – ledge of other professionals within the team and is considered a main obstacle to collaboration. The dynamics of professionalization lead to further differentiation of health care professionals (D’Amour & Oandasan, 2005) and potential conflict hindering the develop- ment of true collaborative rela- tionships.

Collaboration in health care affects patient survival and de – creases adverse patient outcomes. Knaus and colleagues (1986) found hospitals where collabora- tion was present reported a signif- icant decrease in mortality rates (Chi square=62.9, df 12; p<0.0001, r=0.83). Hospitals where there

was little to no perceived collabo- ration exceeded predicted mortal- ity. Positive collaborative relations have also been tied to a decrease in failure to rescue. Boyle (2004) evaluated unit-level characteris- tics and the impact on patient out- comes and found a negative corre- lation between collaboration and failure to rescue (r= -0.53). High levels of perceived collaboration were linked to early detection of change in clinical condition and appropriate intervention leading to a decrease in failure to rescue.

Collaborative environments can positively affect health system outcomes. Ovretveit (2011) evalu- ated the impact of clinical coordi- nation and collaboration and found when collaboration and coor – dination were present, patients ex – perienced a shorter LOS with lower costs to the health care insti- tution. Additionally, Zwarenstein and co-authors (2009) evaluated multiple studies to determine the impact of interprofessional collab- oration and found 80% of the stud- ies demonstrated decreased LOS and cost savings to the health care institutions.

Barriers to Collaboration in Health Care

The barriers to collaboration are rooted in the hierarchal and long-established structures of most health care organizations and are difficult to change. The nurse- physician relationship is one example of an established hierar- chal relationship that has been a barrier to true collaboration in health care facilities. Hojat and colleagues (2001) conducted a cross-cultural study evaluating nurse-physician attitudes toward collaboration and found nurses in both the United States and Mexico expressed more positive attitudes toward collaboration than their physician counterparts (p<0.01). As a possible solution, the authors recommended inter-professional education to improve nurse-physi – cian collaboration.

Empirically the link between collaboration and improved pa – tient and system outcomes has been demonstrated, but there re – mains a gap in the literature on how to create a collaborative envi- ronment. This study begins to fill the gap by looking at a large scale change of care delivery based on essential collaborative structures and processes and its impact at the patient, hospital, and system levels.

Theoretical Framework The Donabedian Model (1966)

is proposed as a way of providing essential structures and processes for collaboration in the health care setting. The model was used to provide a comprehensive struc- ture to move from inputs through the process of care delivery, and conclude with the outcomes for this study.

In accordance with the Dona – bedian Structure, Process, Out – come Model (see Figure 1), struc- ture refers to the environment in which care is provided. Structure encompasses the work environ- ment, availability of equipment and supplies, and type of unit. These structural elements tend to be relatively permanent in nature and are often thought of as key determinants to quality (Donabedian, 1988). Process elements are more flexible and readily changeable. Process encompasses the things health care workers do or fail to do which shape patient outcomes (Montalvo & Dunton, 2007). Out – comes are the changes in patients’ health attributable to their care (Montalvo & Dunton, 2007). Ac – cording to Donabedian (1988), changes in structures and process- es of care are required to optimize patient outcomes.

The Structure, Process, Out – come Model proposes the context (structure) in which the interven- tion (process) occurs has an influ- ence on the outcomes. Collab – oration is seen as the process that occurs within a specific context leading to the measured results or

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outcomes. The process of collabo- ration not only requires health care providers to communicate effectively and trust each other, it also requires a multidisciplinary model of care delivery. The Donabedian Model provides a useful structure for studying pro – cesses and outcomes of care and was used to guide this study.

Clinical Integration Model for Interdisciplinary Collaboration

This clinical effectiveness study utilized the implementation of a new approach to patient care delivery and documentation based on bringing health professionals together as partners in care called the CIM. This collaborative ap – proach was manifested by a new

method for organizing and chart- ing activities that was integrated, consistent, and goal-directed rather than discipline-specific. The focus changed from the task to the patient as the center of care. This model of care delivery was designed with a specific goal of interweaving collaborative struc- tures and processes into care. The

Figure 1. Donabedian Structure, Process, Outcome Model (Adapted)

SOURCE: Adapted from Donabedian, 1966.

Modified Donabedian Model for Clinical Integration Program

Structure Patient diagnosis

Core measure compliance Type of unit

Outcomes Patient survival Length of stay Cost per case

Clinical Integration Model

Patient admitted. CareGraph completed. Top three

problems and discharge

goals identified.

Does patient need complex

care?

Does patient need complex

care?

Continue interdisciplinary

care coordination. Focus on top

three problems.

Patient discharged

from complex care team meetings.

Patient discharged

from hospital with goals

met.

Process Clinical Integration Model or

Traditional care delivery model Information exchange

NO NO

Patient progressing

toward discharge

goals.

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drivers for change within this health system were based on an average LOS that was heading in an upward direction, fragmenta- tion of care delivery, increasing complexity of patient conditions, and increasing costs.

To confirm and chronicle changes in the structures and pro – cesses, the hallmarks of the collab- orative environment included development of a process tool, the CareGraph; focus on the same patient-centered goals; and care coordination around patient needs. Other organizational changes in – cluded provision of essential unit- based staff, clarification of roles among caregivers, and communi- cation of expectations.

Guided by the model, the CareGraph process tool was devel- oped (Center for Case Manage – ment, 2004) (see Figure 2). The tool provides a mechanism for multiple disciplines to speak the same language, focus on the same patient-centered goals, coordinate workflow around patient needs, and document integrated care notes. The CareGraph is imple- mented by the nurse caring for the patient and updated daily. The nurse meets formally with the entire care team three times a week in care coordination rounds to discuss problem foci and pro- gression of care. Any patient

stalled in progression toward opti- mal outcomes is referred to the complex care team, which meets twice weekly and is led by a case manager and hospitalist (see Figure 1). Other operational changes included the provision of unit-based case managers, social workers, and educators. Physi – cians and other allied health prac- titioners were readily available to all nursing staff. The CareGraph serves as the common communi- cation link between these disci- plines.

The well-defined structure and process changes implemented with the CIM provide essential elements for a collaborative, well- coordinated care delivery model. Health care providers have the ability to provide care consistent with the objectives of ANA’s (2010) Social Policy Statement to safeguard patients’ interests and develop common goals with struc- tured communication.

Variables Operational definitions of the

three variables for the proposed study are found in Table 1. Input, or structure variables, used in this study were the number of patients admitted to each of the participat- ing health system hospitals with the diagnosis of congestive heart failure (CHF). Type of patient pop-

ulation, CHF, served as the main structural variable for this study. The model of care delivery, CIM or traditional care delivery model, served as the process variable. The hospitals that implemented the CIM served as the intervention hospitals. The control hospitals continued to deliver traditional care. The outcomes measured to evaluate change after implement- ing the CIM are survival, length of stay, and cost per case for patients with CHF.

Research Design and Methods The purpose of this study was

to determine if there is a differ- ence in survival, LOS, and cost per case in the CHF population in facilities using the Clinical Inte – gration Model compared to those using a traditional care delivery model. A retrospective nonran- domized comparative design us – ing a convenience sample over a time-limited period was used to evaluate patient survival, LOS, and cost per case for patients with the same diagnosis in a large hos- pital system in the Midwestern United States. Inclusion criteria was adult patients (> age 18) admitted during specified dates to one of the health system hospitals chosen for this study with the pri- mary diagnosis of CHF (DRGs 291, 292, and 293). All health system

Figure 2. CareGraph Example of Wound/Skin Category

Admit Baseline

Date Date Date

Wound/Skin: (Identify focus__________________________________)

4 – Has large gaping wound that requires packing or complex dressing change taking >30 minutes >3 times/day

4 4 4

3 – Has draining wound with/without packing or complex dressing change < 3 times/day or unable to apply wound vac

3 3 3

2 – Has draining wound with/without packing or constant re-enforcement or requires wound vac

2 2 2

1 – Has reddened area with skin intact or simple dressing/open to air 1 1 1

0 – Has intact skin/wound/incision 0 0 0

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hospitals have electronic medical records and central billing sys- tems which allowed for capturing of data elements. A pre-imple- mentation, post-implementation design was used to evaluate patient and hospital-level out- comes.

Sample After approval of the institu-

tional review boards from the University of Texas at Tyler and the health system hospitals, a sample of patients CHF (DRG’s 291, 292 and 293) admitted to the participating acute care facilities within the health system were uti- lized to assess patient and hospital outcomes of survival, LOS, and cost per case. The CHF population was chosen because it is a relative- ly homogenous group. The patient characteristics, unit characteris- tics, and treatment plans were more consistent using a single diagnosis.

Patients with heart failure were selected as a means to con- trol variables. These patients are treated using standardized evi- denced-based guidelines devel- oped using core performance measures by the Joint Commission in an effort to improve consisten- cy and quality of care for this pop-

ulation among all hospitals. Four key quality indicators for heart failure treatment were developed and are required for all patients with CHF. The first standard requires all patients discharged from hospitals with the primary diagnosis of heart failure to have left ventricular function assessed before or during hospitalization (Kfourny et al., 2008). The second requires physicians to prescribe an angio tensin-converting enzyme inhibi tor or an angiotensin recep- tor blocker, depending on patient tolerance, for all patients with left- ventricular dysfunction. The third includes providing the patient with self-management instruc- tions on tracking weight, low sodi- um diet, reporting of symptoms, and followup care. Finally, smok- ing cessation counseling for smok- ers was mandated.

Major threats to internal valid- ity for a study with a control group have been addressed in the design with use of a homogenous group, the CHF population, and pre/post evaluation. Knowing the exact dates for implementation or non- implementation of the CIM with use of a control group allows com- parison of groups. In addition, each intervention hospital was matched with a hospital of similar

size and service availability with- in the health system to account for potential historical influence. Multiple outcome measures have also been added to increase valid- ity; and demographics for the geo- graphic area demonstrate the abil- ity to obtain a representative sam- ple relative to gender.

Recruitment/Setting For this study, an extant data-

base was used to access survival, LOS, and total cost data for the participating hospitals. A conven- ience sample of the CHF popula- tion from Hospital A (338 beds) and Hospital B (139 beds) were used as the intervention group. These two hospitals are located in close proximity to each other with the same upper management staff, and both had implemented the CIM. Both hospitals offer full serv- ices with cardiology a major serv- ice line. These hospitals service over 300,000 people in the area and total over 300 admissions for CHF per year. Hospital C (373 beds) was chosen from the health system as a comparison to Hospit – al A, and Hospital D (148 beds) was compared to Hospital B. These two hospitals admit a simi- lar number of patients with CHF and are both full-service facilities of like size, with cardiology con- stituting a major portion of admis- sions. The number of people served by these two facilities is roughly 300,000 (U.S. Census Bureau, 2010). Essential care ele- ments for the CHF population are rendered using core measure crite- ria at each hospital with compli- ance greater than 92%.

Procedures To analyze the impact of the

CIM on hospital outcomes, data were extracted from the health system database for survival, LOS, and cost per case for the CHF pop- ulation from the participating hos- pitals. The time frame is based on Roger’s Theory of Diffusion of Innovation (2003), which states that full diffusion of an innovation

Table 1. Conceptual and Operational Definitions of Study Variables

Variable Conceptual Definition Operational Definition

Structure The environment in which care is provided.*

Inpatient acute care units where patients with CHF (DRGs 291, 292 and 293) receive care.

Process Care provided by health profes- sionals working in a partnership based on trust with shared power, recognition, and accept- ance of separate and combined practice spheres of activity and responsibility. **

Integrated practice approach by various providers indicated by the Clinical Integration Model using the CareGraph tool as opposed to a traditional care delivery model with traditional charting.

Outcome The changes in patients’ health attributable to care.***

Survival Length of stay in days Cost per case (direct cost)

SOURCES: *Donabedian, 1988; **ANA, 2010; ***adapted from Montalvo and Dunton, 2007

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and cultural adherence would oc – cur at approximately 12 months. All outcome data were accessed using the TSI/Eclipses relational database. It is a closed-loop data – set with data extracted and used for cost accounting purposes as well as clinical performance im – provement.

Results The initial data set yielded

1,192 cases after data cleaning and time referencing. Descriptive sta- tistics for each of the primary out- come variables (survival, LOS, cost) were determined using the Statistical Package for Social Sciences (SPSS) version 17 and visually inspected. Outlier cases were not eliminated as they are indicative of the variability in patients or care.

Hospital A, the initial hospital adopting the CIM, accounted for 487 cases totaling 41% of the pop- ulation; the smaller hospital adop – ting the CIM (Hospital B) account- ed for only 5% of the population with 61 cases. Hospital C, the largest control hospital, had 512

cases or 45% of the population, and Hospital D had 9% of the cases. Therefore, 46% of the cases were from intervention hospitals while 54% were from controls.

Overall, 97% of the patients were discharged to another level of care while 3% died during their hospital stay. All four hospitals were evaluated for patient sur- vival using the chi-square statistic. Greater than 20% of the expected counts were less than 5; therefore, the intervention hospitals and control hospitals were combined for further evaluation of mortality. Crosstabs demonstrated an actual mortality equal to the expected mortality for both groups with a minimum expected count of 18.92. For the 1,192 cases evaluat- ed, there was not a significant dif- ference in survival between the patients admitted to the interven- tion hospitals and those admitted to the control hospitals (c2 (1) = 0.001, p=0.979).

A one-way analysis of vari- ance (ANOVA) was conducted to evaluate the effect of the CIM on LOS and cost. Unequal group

sizes and violation of homogene- ity of variance required evaluation using Welch’s F statistic (Field, 2009). There was a significant dif- ference between groups for LOS, F(3, 245)=5.78, p=0.001 and cost F(3,226)=21.70, p=0.000. Post hoc evaluation of differences using the Games-Howell procedure reveal – ed a shorter LOS for both the inter- vention hospitals (A and B) rela- tive to the largest control hospital (C). This difference did not extend to the smaller control hospital (see Table 2). Additionally, the larger intervention hospital (Hospital A) had a significantly lower cost than the other participating hospitals in caring for the CHF population (see Table 2).

Discussion This study found positive

effects for the hospitals that adopt- ed the CIM. The greatest effect appears to be the ability to manage cost. The post hoc evaluation demonstrated a lower cost for the large intervention hospital com- pared to both control hospitals and the smaller intervention hos-

Table 2. Post-Hoc Evaluation of Length of Stay and Cost Between Control and Intervention Hospitals

Using Games-Howell Procedure

Variable: Length of Stay Variable: Cost

Hospital Facility for Comparison

Mean Difference Significance

Mean Difference Significance

C Large control

D 0.83* 0.012 $886.00 0.113

A 0.76** 0.004 $2,063.00** 0.000

B 0.96* 0.035 $390.00 0.374

D Small control

C -0.83* 0.012 $-886.00 0.113

A -0.07 0.994 $1,177.00** 0.007

B 0.13 0.986 $-496.00 0.818

A Large

intervention

C -0.76** 0.004 $-2,063.00** 0.000

D 0.07 0.994 $-1,177.00** 0.007

B 0.20 0.941 $-1,673.00** 0.005

B Small

intervention

C -0.96* 0.035 $-390.00 0.874

D -0.13 0.986 $496.00 0.818

A -0.20 0.941 $1,673.00** 0.005

*p<0.05; **p<0.01

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pital. Operationally, the cost sav- ings may be manifested to a greater degree as volume increases or may be due to the degree of dif- fusion of the CIM. The degree of effect of the CIM on LOS was apparent when comparing the intervention hospitals to the large control hospital; both had statisti- cally shorter LOS. This is consis- tent with current research where Ovretveit (2011) evaluated the impact of clinical coordination and collaboration and found that when collaboration and coordina- tion were present, patients experi- enced a shorter LOS with lower costs to the health care institution.

The positive effects of the CIM on LOS and cost did not extend to patient survival. The effects of using evidence-based practice in treating the CHF population may have greater impact on patient sur- vival than use of the CIM since none of the participating hospitals had mortality rates greater than expected. In addition, all hospitals had at least 92% compliance on all components of The Joint Commission (2012) core measure requirements.

Limitations in design come from not knowing the exact diffu- sion curve or temporal persistence of the Clinical Integration Model. Selection bias may also play a role due to geographic limitations, homogenous groups, and inclu- siveness. Since there is no reliable method for collecting or accessing actual collaboration data, there is no way to implicitly tie findings to length, strength, or penetration of collaboration activities establish – ed with use of the CIM.

Study Implications and Recommendations

Results of this study demon- strate the effectiveness of the Clinical Integration Model in the management of hospital costs. In the current cost-conscious health environment, this study has impli- cations for the multidisciplinary health care team seeking to improve patient outcomes and

optimize efforts to contain run- away costs. The CIM supports the use of a collaborative model for the acute care setting in decreasing LOS for patients with CHF. Further, use of the Caregraph as a multidisciplinary communication and documentation method to confirm the extent and effective- ness of a collaborative approach to patient care is translatable to many hospital and health care settings. This research provides administra- tors and clinicians with a pathway for taking steps toward creating a more collaborative practice model to save money and provide effec- tive care in the local health econo- my. It is also clear from this research that responsibility for col- laborative care needs to be defined clearly in work des criptions and other policies and procedures.

The CareGraph instrument is transferrable into almost all clini- cal areas where care is delivered by a multidisciplinary team. It is recommended that the collabora- tive model supplemented by the CareGraph be used with other populations beyond CHF. Further testing of this collaborative model in other clinical settings and with larger samples is also needed. Another essential step includes studies focusing on the satisfac- tion levels reported by care providers and recipients. The effect of the CIM and use of the CareGraph on the reduction of errors also needs to be explored. With the current focus on collabo- rative care as well as control of health care costs, comparative studies of care delivery models may contribute to solutions for the health delivery challenges of the 21st century. $

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