Organizational predictors of coordination in inpatient medicine.

OctoberYDecember & 2014

279

Organizational predictors of coordination in inpatient medicine Nathalie McIntosh Mark Meterko James F. Burgess Jr Joseph D. Restuccia Anand Kartha Peter Kaboli Martin Charns Background: As the care of hospitalized patients becomes more complex, intraprofessional coordination among nurses and among physicians, and interprofessional coordination between these groups are likely to play an increasingly important role in the provision of hospital care. Purpose: The purpose of this study was to identify the independent effects of organizational factors on provider ratings of overall coordination in inpatient medicine (OCIM). Methodology/Approach: This was an exploratory cross-sectional, descriptive study. Primary data were collected between June 2010 and September 2011 through surveys of inpatient medicine nurse managers, physicians, and chiefs of medicine at 36 Veterans Health Administration medical centers. Secondary data from the 2011 Veterans Health Administration national survey of nurses were also used. Individual-level data were aggregated and analyzed at the facility level. Multivariate linear regression models were used to assess the relationship between 55 organizational factors and provider ratings of OCIM. Key words: Inpatient medicine, interprofessional coordination, intraprofessional coordination, multidisciplinary, organizational factors Nathalie McIntosh, PhD, is Project Manager, Center for Healthcare Organization and Implementation Research, VA Boston Healthcare System, Massachusetts. E-mail: [email protected]. Mark Meterko, PhD, is Manager Methodology & Survey Unit and Senior Investigator, Center for Healthcare Organization and Implementation Research, VA Boston Healthcare System, Massachusetts, and Research Associate Professor, Department of Health Policy & Management, Boston University School of Public Health, Massachusetts. James F. Burgess, Jr., PhD, is Senior Investigator, Center for Healthcare Organization and Implementation Research, VA Boston Healthcare System, Massachusetts, and Professor, Department of Health Policy and Management, Boston University School of Public Health, Massachusetts. Joseph D. Restuccia, DrPH, is Senior Investigator, Center for Healthcare Organization and Implementation Research, VA Boston Healthcare System, Massachusetts, and Professor of Health Care and Operations Management, Boston University School of Management, Massachusetts. Anand Kartha, MD, is Physician, VA Boston Healthcare System Medical Service, Massachusetts. Peter Kaboli, MD, is Physician, Comprehensive Access and Delivery Research and Evaluation Center, Iowa City VA Healthcare System, and Associate Professor, Department of Internal Medicine, University of Iowa Carver College of Medicine. Martin Charns, DBA, is Director, Center for Healthcare Organization and Implementation Research, VA Boston Healthcare System, Massachusetts, and Professor, Department of Health Policy & Management, Boston University School of Public Health, Massachusetts. This study is based on work supported by the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Health Services Research and Development Service (HSR&D IIR 08-067). The authors have disclosed that they have no significant relationship with, or financial interest in, any commercial companies pertaining to this article. DOI: 10.1097/HMR.0000000000000004 Health Care Manage Rev, 2014, 39(4), 279Y292 In the public domain

Copyright © 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.

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Health Care Management Review


Findings: Organizational factors that were common across models and associated with better provider ratings of OCIM included provider perceptions that the goals of senior leadership are aligned with those of the inpatient service and that the facility is committed to the highest quality of patient care, having resources and staff that enable clinicians to do their jobs, and use of strategies that enhance interactions and communication among and between nurses and physicians. Practice Implications: To improve intraprofessional and interprofessional coordination and, consequently, patient care, facilities should consider making patient care quality a more important strategic organizational priority; ensuring that providers have the staffing, training, supplies, and other resources they need to do their jobs; and implementing strategies that improve interprofessional communication and working relationships, such as multidisciplinary rounding.

Introduction Hospitalized patients are increasingly more medically complicated and require the care of multiple provider types (O’Brien-Pallas, Murphy, Shamian, Li, & Hayes, 2010). Having multiple providers involved in the care of individual patients creates interdependent work relationships between them, and for care to be effective, this work should be structured to address interdependencies, that is, coordination (Lawrence & Lorsch, 1967). As a result, coordination, especially among (intraprofessional) and between (interprofessional) nurses and physicians who constitute the main providers of inpatient care, is playing an increasingly important role in the provision of care to hospitalized patients (Eldar, 2005). Coordination is defined as the conscious activity to assemble and synchronize differentiated work efforts so that they function harmoniously in attaining an organizational goal (Haimann, Scott, & Connor, 1978). For inpatient medicine, this goal is patient care. Coordination is a central component of teamwork (Manser, 2009) and has communication at its core (Eldar, 2005, Van Beuzekom, Akerboom, & Boer, 2007). In settings such as inpatient care, where there is reciprocal interdependence (i.e., where interdependent members affect each other’s work) and/or uncertainty of tasks, communication becomes a central component of coordination (Galbraith, 1973). In medical settings, coordination has been positively associated with patient outcomes (Daley et al., 1997; Gittell et al., 2000; Hinami et al., 2010; Kaissi, Johnson, & Kirschbaum, 2003; Young et al., 1997, 1998; Zwarenstein, Goldman, & Reeves, 2009), patient safety (Firth-Cozens, 2001; Kaissi et al., 2003; O’Leary et al., 2011), the quality of patient care (Rosenstein & O’Daniel, 2005; Young et al., 1998), provider job satisfaction (Chang, Ma, Chiu, Lin, & Lee, 2009; O’Brien-Pallas et al., 2010; Zangaro & Soeken, 2007), and patient satisfaction (Hickson & Entman, 2008). In addition, it has been negatively associated with patient lengths of stay (Halter, 2006), hospital-acquired infections (Huber, 2010), and medical errors (Kaissi et al., 2003). The Institute

of Medicine has recommended that nurses and physicians improve coordination to reduce medical errors and increase patient safety (Institute of Medicine, 2004). Many factors are hypothesized to facilitate coordination among and between provider types. In particular, improving communication within and between professions has been associated with better working relationships and better coordination (Dayton & Henriksen, 2007; Frankel, Gardner, Maynard, & Kelly, 2007). Less is known, however, about how other factors, particularly organizational characteristics, are associated with coordination between and within different provider types. Among the facility characteristics with potential to affect coordination are those structures, polices, and/or processes that influence nurseYphysician interactions (e.g., attending physician [ATT] availability); alignment between different organizational units (e.g., performance evaluation structures); and nurse and physician working conditions (e.g., facility support for continuing education). These facility characteristics are important because they have the potential to be modified by managerial interventions. The objective of this study was to answer the question: What organizational factors are associated with coordination in inpatient care? To produce more effective intraprofessional and interprofessional working teams that optimize patient care through coordinated efforts, it is important to better understand what organizational factors influence coordination. Operationally, this study involved the identification of organizational factors that predicted perceptions of coordination by nurse managers (NMs), ATTs, and chiefs of medicine (COMs), including both factors unique to a provider type and common across provider types. Knowing what these factors are may help hospitals design better structures, policies, and processes to improve coordination. The strength of this exploratory study is that it examined 55 organizational factors, including integrative structures (e.g., hospitalist presence) and coordinative processes (e.g., use of multidisciplinary rounds) at multiple levels (i.e., the organizational level and the team level) to assess what


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Predictors of Inpatient Coordination

factors were most likely to be associated with coordination across provider types (i.e., at the organizational level) using an iterative block regression design process for identifying the most important organizational factors.

Theory/Conceptual Framework The study conceptual framework was derived from organizational theory and was informed by two strands of work in that tradition: (1) Charns and Young’s (2011) discussion of how macrolevel approaches to coordination (i.e., facility structures and processes) are associated with microlevel processes (e.g., enhanced communication) and (2) Charns and Tewksbury’s (1993) structural approaches to coordination (integration), as adapted to health care from Galbraith’s work on organization design (Galbraith, 1973). The framework, as shown in Figure 1, specifies macrolevel organizational factors within four domains hypothesized, given evidence in the literature, to influence provider coordination: nurse and physician interactions, alignment of nurse and physician interests, nurse and physician work conditions, and facility characteristics. In the first domain, nurseYphysician (RNYMD) interactions, we included those strategies that promote opportunities for nurses and physicians to form working relationships or that create work situations where the same nurses and physicians work together over time. These strategies are hypothesized to exert their positive influence by enhancing communication and coordination of efforts and reducing misunderstandings. For example, studies show that participating in multidisciplinary rounds improves information exchange between nurses and physicians (Beuscart-Zephir, Pelayo, Anceaux, Maxwell, & Guerlinger, 2007; Dayton &

Henriksen, 2007) and is associated with decreased patient lengths of stay and lower hospital costs (Albert, Sherman, & Backus, 2010; Ettner et al., 2006). Nurses and physicians who work together over time have a more developed shared understanding of the work and their roles. As a result, they are less likely, compared with those who work infrequently with each other, to misunderstand or make erroneous assumptions when working together. Other factors that may affect nurseYphysician interactions by offering more opportunities to communicate formally and informally, form stronger working relationships, and/or better coordinate efforts include physician availability on the medicine unit, the time physicians spend on direct patient care, the presence of hospitalists, having a closed intensive care unit (ICU), and geographic localization of patients (i.e., placing patients on different units according to the treating specialty). Second, we also hypothesized that factors that promote alignment of nurse and physician organizational units would be associated with higher perceptions of coordination. If the unit is part of a hospital service line where both nurses and physicians are evaluated by the same manager and/or if physicians are partly compensated based on team performance goals, nurses and physicians may have stronger common interests that give them incentives to function more efficiently as a team (Charns & Young, 2011; Wageman & Baker, 1997). This is supported by work showing that alignment of interests is associated with improved patient outcomes (Martin, Ummenhjofer, Manser, & Spirig, 2010) and enhances other processes including implementing evidencebased practices (VanDeusen Lukas et al., 2010) and quality improvement activities (Cohen et al., 2008). Third, we hypothesized that factors that produce positive nurse and physician work conditions would be associated with higher ratings of coordination. Such factors include

Figure 1

Conceptual framework


282


adequate nurse and physician staffing, facility support for adequate resources (e.g., supplies and continuing education), and use of standardized clinical protocols that facilitate a common understanding of care processes (i.e., who should do what and when). There is evidence suggesting that positive work environments are associated with better intraprofessional and interprofessional working relationships (Shen, Chiu, Lee, Hu, & Chang, 2010) and that the use of standardized protocols enhances coordination in some situations (Young et al., 1997). These factors allow staff to focus on working together to optimize patient care rather than struggling with the lack of human or other resources. Fourth, we hypothesized that facility characteristics (e.g., size, level of complexity/coordination needs; Lawrence & Lorsch, 1967) would be associated with ratings of coordination, with larger, more complex facilities having lower coordination ratings. In the private sector, larger facilities may devote more resources to information systems to meet their coordination challenges. In the Veterans Health Administration (VHA), however, all facilities share the same information system so that larger facilities, relative to smaller ones, may have more unmet coordination needs. Taking these four hypotheses together, our overall hypothesis was that all of these macrolevel factors would be related to coordination. In this exploratory study, our goal was to identify which of these factors were consistent predictors of coordination across provider types using facility-level models emerging from our iterative block design process.


dition, a single mailing of a paper version of the survey was sent to nonresponding COMs and ATTs. For ATTs, the mailing was sent to their academic affiliate address, if known. These mailings were to mitigate possible access barriers; the Web survey was housed behind the VHA firewall and only accessible from a VHA computer. Email addresses for NMs and COMs were identified using VHA email directory information. ATT email addresses were identified using medicine unit work schedules available on VHA Web sites, and their academic affiliate addresses were obtained from publicly available information on the Internet. In addition, secondary data from the 2011 Veterans Administration Nursing Outcome Database (VANOD) were used to include data from nurses doing direct patient care in inpatient medicine and mixed medicalYsurgical units. The annual VANOD survey of all VHA registered nurses measures nurse perceptions of their practice environment, including staffing and resource adequacy, and the quality of nurseYphysician relationships.

Variables and Measures: Independent Variables Domains outlined in the conceptual framework (see Figure 1) were operationalized by creating 55 variables using items from OFIM and VANOD surveys as well as facility data. Facility complexity scores were based on the VHA 2011 Facility Complexity Level Model, which considers hospital volume, patient severity, teaching, research, and ICU capability (Carney, West, Neily, Mills, & Bagian, 2010).

Methods This was an exploratory, cross-sectional, descriptive study of 36 VHA inpatient medicine services selected to provide a sampling of facilities by region and size. Nine medical centers were selected from each of four geographical regions of the United States: East, Central, South, and West. Of the nine facilities in each region, two were large (Q200 beds), four were medium (100Y199 beds), and three were small (G100 beds). The study was approved by the VHA Boston Healthcare System Institutional Review Board.

Variables and Measures: Dependent Variables The dependent variables were provider ratings of overall coordination in inpatient medicine (OCIM). NMs, ATTs, and COMs were asked ‘‘How would you rate inpatient coordination overall?’’ Response options were poor, fair, good, very good, and excellent. This resulted in having three dependent variables: an OCIM rating by each provider type.

Data Analysis Surveys Survey data were obtained from three primary sources: NMs of inpatient medicine or mixed medicalYsurgical units, medicine service ATTs, and COMs. These surveys were part of a larger study and are referred to as the organizational factors in inpatient medicine (OFIM) surveys. Invitations to participate in the study were sent to potential survey respondents in each of the primary source categories between June 2010 and September 2011 via an email that contained a link to the appropriate electronic survey. Up to four email reminders were sent to nonrespondents. In ad-

Analyses were performed using SAS version 9.1. Responses were collected at the individual level and aggregated and analyzed at the facility level. A facility-level approach was used because coordination is a group-level phenomenon involving multiple individuals, not single actors, and policy is made at the facility level. OFIM survey Likert-type scales were standardized to a mean of 50 and a standard deviation of 10 (i.e., converted to T scores) because constructs were measured using different numbers of response choices in different respondent groups (e.g., 5-point vs. 4-point Likerttype scales; Jaeger, 1990). Descriptive statistics were used


283


to summarize the data and to assess the heterogeneity of distributions. Hierarchical modeling nesting survey respondents within facilities was not possible because of the small number of providers in each facility. The methodology used created multivariate linear regression models that controlled for important facility-level differences and used sensitivity analyzes to assess whether between-facility differences were significant. The goal was to create three multivariate linear regression models, one model for each provider rating of OCIM (i.e., NM, ATT, COM). Model building proceeded in three steps of an iterative block regression design process. Our sample size was 36, and power calculations using an a priori sample size calculator for multiple regression showed that a reasonable power (0.70) could be achieved with up to five independent variables in each final model (Soper, 2013). First, bivariate correlations were computed between each organizational variable and each of the three dependent variables. Only those variables correlated at p values e .10 with a dependent variable were retained. Second, the potential predictors thus identified for each dependent variable were then included as a block in a linear regression model predicting that dependent variable. Variables demonstrating collinearity (variance inflation factor Q 5.0) were deleted in an iterative fashion until none of the predictors remaining in the model exceeded the collinearity threshold. Third, each model was screened for the predictors retained after Step 2 on the basis of their strength of relationship to the dependent variable. Specifically, predictors with regression coefficients with p values 9 .05 were deleted in an iterative fashion until all variables remaining were significant at p e .05. This process resulted in final multivariate linear regression models for each of the three dependent variables.

Sensitivity Analyses: Testing for Between-Facility Differences Because hierarchical modeling that would nest survey respondents within facilities was not possible given the small number of providers within each facility, variables representing the standard deviations of the facility-level scores for predictors in the final models were created and included in the final multivariate linear regression models to assess whether between-facility differences were significant (Benge, Pastorek, & Thornton, 2009). If between-facility differences were significant, it would suggest that a hierarchical nested model rather than the approach taken may have been a more appropriate methodology. Of the six standard deviation variables tested, only one was significant (ATT facility provides appropriate continuing education to do my job; p = .03). This suggests that controlling for facility-specific variables was generally adequate to address facility differences in the models.

Findings Survey Respondents COMs in all 36 selected VHA facilities responded to the survey, as did 80% (N = 67) of NMs and 57% (N = 474) of ATTs. All facilities were represented by data from all three respondent types. The response rate represented by staff nurses in the 2011 National VANOD Survey Database (N = 1002) is unknown because denominator counts for the specific subgroup of nurses used (i.e., inpatient nurses in medicine and mixed medicalYsurgical units) are not known. However, the total number of nurses per facility is known, making it possible to compute the overall VANOD response rate (51%). Relevant data were available for variables from the VANOD survey in all 36 facilities.

Descriptive Statistics: Coordination (Dependent) Variables Table 1 presents the descriptive statistics for the facilitylevel aggregate scores of the study dependent variables, with complete data for all variables. Correlations among the three dependent variables revealed moderately strong relationships between NM and ATT ratings of OCIM (r = .37) and between COMs and ATT ratings of OCIM (r = .45). The correlation between NM and COM ratings of OCIM, however, was essentially zero (r = j.02).

Descriptive Statistics: Organizational (Independent) Variables Table 2 presents descriptive statistics of the 55 organizational variables examined. These data showed heterogeneity at the facility level for all items. For example, among the perception variables measured using Likert-type scales, facility maximum scores were, on average, two times facility

Table 1

Descriptive statistics of ratings of overall coordination in inpatient medicine Variable

n Mean (SD) Min Max

Ratings of overall coordination in inpatient medicine Nurse managers 36 50.2 (8.4) 37.1 74.4 Attending physicians 36 50.7 (5.1) 41.8 66.8 Chiefs of medicine 36 50.0 (10.0) 21.3 68.8 Note. SD = standard deviation; Min = minimum; Max = maximum.


284



Table 2

Descriptive statistics of the 55 organizational factors examined Domain and variable RNYMD interactions Multidisciplinary rounds

Attending availability

Hospitalist presence

Patient geographic localization

RNYMD interactions ICU closed Physician team type

Attending physician work schedule

Definition/item

n

Mean (SD)

Min

Max

When physicians do their rounds, what % of the time did nurses on your unit round with them? (NM) Rate the use of multidisciplinary rounding on the unit (NM).a Rate the use of multidisciplinary rounding on the unit (ATT).a Rate the use of multidisciplinary rounding on the unit (COM).a Agreement with ’’attending physicians are available to nursing staff on the inpatient service when nurses need them‘‘ (ATT).b Agreement with ’’attending physicians are available to nursing staff on the inpatient service when nurses need them‘‘ (NM).b What is the percentage of patients on the inpatient medicine service who are admitted by hospitalists (COM)? What is the percentage of patients assigned to your unit based on the patient’s medical condition (NM)? What is the percentage of patients assigned to your unit based on the patient’s medicine attending/team (NM)? What is the percentage of patients assigned to your unit based on bed availability (NM)?

36

31.7 (29.7)

0

100

35

49.4 (9.5)

25.4

62.2

36

50.0 (4.2)

39.8

59.3

33

50.0 (10)

28.3

62.4

36

50.2 (4.2)

42.7

61.2

35

49.5 (6.6)

36.6

61.2

35

39.1 (36.1)

0

100

36

53.7 (24.8)

12.5

100

33

28.9 (22.5)

0

100

32

30.9 (22.2)

0

87.5

36

0.5 (0.5)

0

1

36

0.6 0.2 0.2 3.3

0 0 0 1

1 1 1 5

Whether a facility has an open or closed MICU: 1= closed, 0 = open Academic model Nonacademic model Mixed model What is the typical number of days for a tour on the team before a handoff occurs from one staff physician to another? 1 = 1Y5 days, 2 = 6Y10 days, 3 = 11Y15 days, 4 = 16Y21 days, and 5 = greater than 21 days (if there was 91 tour type at a facility, the longest tour type was assigned [COM]).

34

(0.5) (0.4) (0.4) (1.2)

(continues)


285


Table 2

Continued Domain and variable Attending physician effort in inpatient care

Alignment of RN and MD organizational units Performance evaluations

Alignment

Definition/item

n

Mean (SD)

Min

Max

What is your average percent time spent providing direct patient care on the inpatient medicine service (ATT)?

35

38.9 (27.5)

7.6

86.3

Who completes the performance evaluation of hospitalist attendings? 1 = COM only; 2 = COM with input from service line manager; 3 = COM and service line manager, equal input; 4 = service line manager with input from COM; 5 = service line manager only (COM). Who completes the performance evaluation of nonhospitalist attendings? 1 = COM only; 2 = COM with input from service line manager; 3 = COM and service line manager, equal input; 4 = service line manager with input from COM; 5 = service line manager only (COM) Who completes the performance evaluations of nurse managers? 1 = COM only; 2 = COM with input from service line manager; 3 = COM and service line manager, equal input; 4 = service line manager with input from COM; 5 = service line manager only (COM) Who contributes to or completes the chief of medicine performance review? 1 = COM only; 2 = COM with input from service line manager; 3 = COM and service line manager, equal input; 4 = service line manager with input from COM; 5 = service line manager only (COM) Rating of shared governance by nurses and physicians (ATT).c Rating of shared governance by nurses and physicians (NM).c Agreement with ’’goals of senior leadership and the inpatient medicine service are aligned‘‘ (ATT).b Agreement with ’’goals of senior leadership and the inpatient medicine service are aligned‘‘ (NM).b Agreement with ’’goals of senior leadership and the inpatient medicine service are aligned‘‘ (COM).b Agreement between goals of senior leadership and those of the inpatient medicine service (ATT).b

26

2.2 (1.5)

1

5

32

2.3 (2.0)

1

5

36

4.8 (0.7)

1

5

35

1.4 (1.1)

1

5

34

49.8 (4.6)

37.6

56.9

35

48.2 (8.8)

33.8

63.2

36

49.3 (7.5)

33.1

69.3

35

3.2 (0.9)

1

5

33

50 (10)

19.4

67.5

36

49.5 (6.2)

32.9

59.6

(continues)


286



Table 2

Continued Domain and variable

Physician compensation

RN and MD work conditions Physician patient volume

RN nurse staffing

Physician staffing

Facility support

Definition/item

n

Mean (SD)

Min

Max

Agreement between goals of senior leadership and those of the inpatient medicineservice (NM).b Agreement between goals of senior leadership and those of the inpatient medicine service (COM).b Agreement with ‘‘nurses have opportunities to participate in strategic planning with regard to inpatient medicine services‘‘ (NM).b Agreement with ‘‘opportunity for staff nurses to participate in policy decisions‘‘ (RN).c Is some portion of inpatient-medicine staff physician compensation team performance based? 1 = yes and 0 = no (COM).

35

50.6 (9.5)

22.6

72.6

33

50 (10)

37.4

65.1

35

49.9 (8.8)

30.0

64.3

36

2.3 (0.4)

1.3

3.0

36

0.5 (0.5)

0

1

36

2.9 (0.6)

1.7

4

36

3.2 (0.7)

2

5.2

36

50.2 (5.8)

36.8

61.0

35

49.7 (9.2)

35.5

68.3

36

2.1 (0.5)

1.3

3.0

36

2.0 (0.4)

1.3

3.3

36

49.1 (6.6)

31.1

60.4

35

50.0 (7.7)

33.8

62.5

36

49.8 (5.6)

32.4

60.0

35

51.2 (8.1)

31.5

66.6

36

49.1 (5.6)

30.7

60.1

On average, what was the total number of patients you admitted over a 7-day week? 1 = less than 5; 2 = 5Y14; 3 = 15Y24; 4 = 25Y35; 5 = greater than 35 (ATT). On average, what was your daily census over a 7-day week? 1 = less than 5; 2 = 5Y9; 3 = 10Y14; 4 = 15Y19; 5 = 20Y24; 6 = greater than 24 (ATT). Rating of ’’there are enough RNs to provide quality care‘‘ (ATT).a Rating of ’’there are enough RNs to provide quality care‘‘ (NM).a Rating of ’’there are enough RNs to provide quality care‘‘ (RN).a Agreement with ’’enough staff to get the work done‘‘ (RN).c Agreement with ’’there is adequate physician staffing in inpatient medicine‘‘ (ATT).b Agreement with ’’there is adequate physician staffing in inpatient medicine‘‘ (NM).b Agreement with ’’employees in my work group have the supplies, materials, and equipment to perform their jobs well‘‘ (ATT).b Agreement with ’’employees in my work group have the supplies, materials, and equipment to perform their jobs well‘‘ (NM).b Agreement with ’’my facility provides appropriate continuing education and training to do my job‘‘ (ATT).b

(continues)


287


Table 2

Continued Domain and variable

Facility leadership

Facility standardization of care

Facility characteristics Facility size

Facility complexity

Definition/item

n

Mean (SD)

Min

Max

Agreement with ’’my facility provides appropriate continuing education and training to do my job‘‘ (NM).b Agreement with ’’adequate support services allow me to spend time with my patients‘‘ (RN).c Agreement with ’’active staff development or continuing education programs for nurses‘‘ (RN).c Agreement with ’’enough time and opportunity to discuss patient care problems with other nurses‘‘ (RN).c Agreement with ’’administration that listens and responds to employee concerns‘‘ (RN).c

35

49.4 (9.4)

25.7

61.6

Agreement with ’’a clear sense of direction exists among the senior leadership‘‘ (ATT).b Agreement with ’’a clear sense of direction exists among the senior leadership‘‘ (NM).b Agreement with ’’a clear sense of direction exists among the senior leadership‘‘ (COM).b Agreement with ’’facility is committed to the highest patient care‘‘ (ATT).b Agreement with ’’facility is committed to the highest patient care‘‘ (NM).b Agreement with ’’facility is committed to the highest patient care‘‘ (COM).b Rating of extent of use of evidence-based practice guidelines or clinical pathways (COM).b Rating of extent of use of planned care for chronic conditions (COM).b

36

1 = small, e100 medicalYsurgical beds; 2 = medium, 100Y199 medicalYsurgical beds; 3 = large, Q200 medicalYsurgical beds 1 = most complex, 2 = between most and least complex, 3 = least complex

36

2.2 (0.4)

1.3

3

36

2.7 (0.4)

2

4

35

2.5 (0.4)

1.8

3.1

36

2.2 (0.4)

1.3

3

49.3 (7.0)

31.0

68.5

35

49.8 (8.8)

27.4

66.5

33

50 (10)

30.4

64.0

36

49.8 (5.9)

33.1

61.7

35

50.1 (7.4)

25.4

59.6

33

50 (10)

19.8

58.1

33

50 (10)

31.8

67.8

33

50 (10)

33.5

63.7

36

1.9 (0.8)

1

3

36

0.7 (0.5) 0.2 (0.4) 0.1 (0.3)

0 0 0

1 1 1

Note. SD = standard deviation; Min = minimum; Max = maximum; MICU = medical intensive care unit; NM = nurse manager; ATT = attending physician; COM = chief of medicine; RNYMD = registered nurseYmedical doctor (physician). a

Ratings were done with a 4-point Likert-type scale in the NM and ATT surveys and a 5-point Likert-type scale in the COM survey or VANOD survey.

b

Ratings were done with a 5-point Likert-type scale.

c

Rating were done with a 4-point Likert-type scale.

minimum scores. Substantial heterogeneity was likewise noted in responses to the variables measured as percentages, with the average range being 92 percentage points. There was also variation in facility’s structural and operational characteristics. In terms of team model type, 61%

of facilities had teams with only academic models, 19% had teams with only nonacademic models, and 19% had both types of teams. Half of the facilities had open ICUs, and a little over half had team performance-based compensation for physicians. The typical number of days for an ATT’s


288



tour of duty ranged from 1Y5 to 921 days, the average number of patients an ATT admitted over 7 days ranged from 5Y14 to 25Y35, and ATTs’ average daily census over 7 days ranged from 5Y9 to 20Y24. There was less variation in facility complexity across facilities: 70% had complexity ratings of 1, 22% had ratings of 2, and 8% had ratings of 3.

Correlations Among Organizational Variables We calculated correlation coefficients between all 55 study variables representing organizational characteristics: 48 were correlated at 0.50 or higher with at least one other organizational variable. Of the possible 1,485 pairs, 97 were involved in such correlations.

Multivariate Linear Regression Models Table 3 presents results from the three multivariate linear regression models. Organizational factors were associated strongly with OCIM ratings; the adjusted model r2s ranged

from 0.44 (NM ratings of OCIM) to 0.58 (ATT ratings of OCIM). Variables from all three domains of organizational factors (RN and MD interaction, alignment, and RN and MD work conditions) were significant predictors in the NM model. In the ATT model, no alignment domain predictors were identified as significant, and in the COM model, the RNYMD work condition domain was not represented. We noted that there were organizational variables that were not significant predictors in the final regression models but were significant in bivariate analyses with the dependent variables and were strongly correlated (at r Q .50) with one or more predictors in the final models. Given the strong correlations between these variables and predictor variables, collinearity could have been a factor in their removal from final models. If this was the case, predictor variables retained in the final models may represent these deleted ‘‘potential predictors.’’ Specifically, in the NM model, two such potential predictors correlated strongly with the predictor ‘‘physician perceptions that the goals of senior leadership and the inpatient service were aligned’’: (1) ‘‘physician perceptions that there were enough nurses to

Table 3

Multivariate linear regression models Outcome variable: Nurse manager OCIM ratings Variable

Domain of organizational factor

Std "

SE

p

ATT: Goals of senior leadership and the inpatient medicine service are aligned. NM: Employees in my workgroup have the supplies to do their jobs. COM: MICU open r2 = .49, adj. r2 = .44, n = 35

Alignment

0.4415

0.15

.003

RN and MD work conditions

0.3701

0.14

.001

RNYMD interaction

0.3857

2.22

.007

Variable


Std "

SE

p

ATT: use of multidisciplinary rounding on the service. NM: Percent patients assigned to unit based on patient’s medical condition. ATT: Facility provides CE to do my job. Facility complexity r2 = .63, adj. r2 = .58, n = 36

RNYMD interaction RNYMD interaction

0.2866 0.2810

0.15 0.03

.02 .03

RN and MD work conditions Facility characteristic

0.5844 0.4454

0.12 1.04

.0001 .002

Variable


Std "

SE

p

ATT: Use of multidisciplinary rounding on the service. COM: If portion of inpatient medicine staff physician compensation is team performance based. r2 = .60, adj. r2 = .57, n = 36

RNYMD interaction Alignment

0.5558 0.4710

0.27 2.20

Outcome variable: Attending physician OCIM ratings

Outcome variable: Chief of medicine OCIM ratings

G.0001 .0002

Note. SE = standard error; NM = nurse manager; ATT = attending physician; COM = chief of medicine; RN = registered nurse; OCIM = overall coordination in inpatient medicine; MICU = medical intensive care unit; CE = continuing education; RNYMD = registered nurseYmedical doctor (physician); adj = adjusted.


289


provide quality care’’ (r = .69) and (2) ‘‘physician ratings of a clear sense of direction existing among senior leadership’’ (r = .97). In the ATT model, four potential predictors correlated strongly with the predictor ‘‘physician perceptions that the facility provides appropriate continuing education to do my job’’: (1) ‘‘physician perceptions that goals of senior leadership and the inpatient service are aligned’’ (r = .65), (2) ‘‘physician perceptions that there is adequate physician staffing in inpatient medicine’’ (r = .59), (3) ‘‘physician perceptions that there is a clear sense of direction among the senior leadership’’ (r = .59), and (4) ‘‘physician perceptions that the facility is committed to the highest quality of patient care’’ (r = .62). Therefore, to arrive at a more complete picture of the organizational factors associated with provider perceptions of coordination, we elected to include these ‘‘potential predictors’’ in our discussion of factors associated with perceptions of coordination. For the NM model, three of the five variables in this more inclusive listing of predictors and potential predictors of OCIM were from the domain of organizational factors that influence nurse and physician working conditions. These include nurse staffing, facility support of nurses regarding resources and supplies to do their jobs, and perceptions that the facility leadership has a clear direction. One was from the domain of organizational factors that influence alignment of different organizational units (i.e., ratings of the extent to which the goals of senior leadership and those of the inpatient medicine service are aligned). The last was from the domain of organizational factors that influence nurse and physician interaction (i.e., having closed ICUs). For the ATT model, all organizational factor domains were represented: those that influence nurseYphysician interactions (e.g., use of multidisciplinary rounding, geographic localization of patients), those that influence alignment (e.g., alignment between goals of senior leadership and the inpatient medicine service), those that influence nurse and physician working conditions (e.g., staffing, training, and continuing education; facility commitment to patient care; and leadership or having a clear sense of direction), and those that relate to facility characteristics (e.g., facility complexity). Of the eight variables, four were factors that influence working conditions, two influence nurseYphysician interactions, and one was related to alignment and facility characteristics. For the COM model, organizational factor domains that influence nurseYphysician interactions (e.g., multidisciplinary rounding) and alignment (e.g., portion of inpatient medicine staff physician compensation is team performance based) were represented. We found commonalities among organizational characteristics that influence provider perceptions of inpatient coordination across providers (see Figure 2). In both the NM and ATT models, alignment of the goals of leadership (i.e., the VA Quad) with the inpatient service and a clear sense of direction among senior leadership were predictors.

This suggests that, if leadership effectively communicates their vision and this vision is aligned with the goals of the inpatient service (assuming that these goals are to provide quality patient care), this will in turn promote work environments that support processes of care that lead to higher quality of patient care, including greater coordination. Our findings also showed that provision of support resources was significant. For both NMs and ATTs, facility support sufficient to allow providers to do their jobs was a predictor of greater perceived coordination. Specifically, NMs endorsed adequate nurse staffing and adequate supplies for nurses, and physicians endorsed adequate physician staffing and appropriate training. In underresourced facilities, providers are more likely to be overworked (Gunnarsdottir, Clarke, Rafferty, & Nutbeam, 2009; Rosenstein & O’Daniel, 2005; Van Bogaert, Meulemans, Clarke, & Van de Heyning, 2009) and may have less slack time to be proactive rather than reactive in efforts to provide coordinated patient care. Furthermore, organizational factors that influence the interactions between nurses and physicians were important, particularly in the physician models. The use of multidisciplinary rounds and geographic localization of patients on units have been found to increase formal and informal communication around patient care (Beuscart-Zephir et al., 2007; Dayton & Henriksen, 2007). The net result may be to establish more stable interprofessional care teams so that communication and shared cognition are enhanced (Cowan et al., 2006). Shared cognition, also referred to as shared knowledge, mental models, or understanding (Dayton & Henriksen, 2007; Van Beuzekom et al., 2007), is the taskand team-related knowledge held by interdependent team members and their collective understanding of their situation. The ability of interdependent team members to have shared cognition is an important driver of team effectiveness and is critical to allowing teams to react to changing task conditions in a compatible and coordinated way (Salas, Cooke, & Rosen, 2008). Studies of the use of multidisciplinary rounding have shown that it is an effective way to promote information exchange and collective orientation (O’Leary et al., 2011). There were a few differences between provider models regarding what organizational factors were predictors of their perceptions of coordination. This was expected given the differences in correlation coefficients among provider ratings of OCIM, especially the essentially zero correlation between NM and COM ratings. This disparity may reflect differences in roles and training. For instance, factors that influence nurseYphysician interactions did not figure prominently in the expanded list of predictors of the NM model (predictors and potential predictors), whereas they did in the ATT and COM expanded models. The only factor in this domain in the NM model was having an open ICU (i.e., where ATTs follow their patients into the ICU rather than hand them off to an intensivist who specializes in intensive care). It is unclear why this variable would predict NM ratings of inpatient coordination. It could be argued that open


290


Figure 2

Significant relationships between macrolevel factors and microlevel processes across provider models


share an information system that may not adequately address the increased coordination needs of its larger, complex facilities. Finally, having a portion of physician compensation based on team-performance, an organizational factor that influences alignment between different organizational units, was a significant predictor only in the COM model.

Practice Implications

ICUs take ATTs away from the medicine unit, which may in turn result in more fragmented medicine unit nurseYphysician interaction and, as a result, poorer communication and coordination. However, one could also argue that ATTs who follow their patients into the ICU may provide continuity and closure for nurses who originally took care of the patient. Closed ICUs have been associated with more efficient patient care and higher nurse confidence in physicians’ judgments in the ICU (Lipschik & Kelley, 2001), but little is known about open/closed ICUs and coordination of care in inpatient medicine. Thus, the reason for this negative association between a closed ICU and coordination remains unclear. Another example of differential predictors among provider types was the finding that facility complexity, which is strongly correlated with facility size, was associated with coordination only in the ATT model. Specifically, lower facility complexity was associated with higher ratings of coordination. This was not surprising because larger, more complex facilities that are composed of more, or larger, interdependent units are associated with greater coordination challenges (Galbraith, 1973; Lawrence & Lorsch, 1967). This may be particularly true in the VHA where all facilities

Improving coordination between interdependent providers, especially between those from different professional cultures (e.g., nurses and physicians), is challenging. Different professions may perceive their roles and the work goals differently, and this creates barriers to interdependent members sharing a common understanding of the work to be done. In addition, the situation is complicated by the current health care environment in which resources are being shifted from hospitals to primary care. As a result, hospitals are struggling to provide quality care with less resources, and administrators must justify even more clearly than they have in the past how resources are used. Our results suggest that, to improve intraprofessional and interprofessional coordination and, consequently, patient care, facilities should consider making patient care quality a more important strategic organizational priority; ensuring that providers have the staffing, training, supplies, and other resources they need to do their jobs; and implementing strategies that improve interprofessional communication and working relationships, such as multidisciplinary rounding. Although these facets of the inpatient medicine service generally fall under the control of the facility and can be changed with planning and effort, the facility cost of each strategy should be factored into any decision, and options that may require greater planning and effort, but not more financial resources (e.g., improving interprofessional communication via implementing multidisciplinary rounding), might be prioritized.

Limitations This study has several potential limitations. First, generalizability may be limited to VHA inpatient medicine units. The VHA is a public health care delivery system that cares for a unique population of patient and uses an integrated information system across all facilities. Results may not pertain to the inpatient medicine units in the private sector. In particular, the findings related to the size and complexity of a facility may be less applicable given the greater variance of size in investment in information technology that may offset coordination needs in the private sector. Second, NM ratings of coordination may not represent all nurses; nurses who work with physicians to provide direct patient care may experience and perceive coordination differently from NMs. Third, perceptions of coordination and some organizational factor data came from the same


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surveys. This may introduce same source bias. However, at least one predictor in each model was from a different survey source than the dependent variable. Finally, significant predictors in the models may be proxies for other organizational variables, which were excluded from final models because of collinearity with predictors. To mitigate this potential shortcoming, we gave potential proxy factors consideration in our interpretation of the models. In general, these potential predictors are consistent in content with significant predictors and reinforced trends represented by the latter.

Conclusion Better provider ratings of coordination in inpatient medicine were associated with an organization’s commitment to high-quality patient care, adequate staffing and resources enabling providers to do their jobs, and strategies that enhance interactions and communication between nurses and physicians. Thus, to improve intraprofessional and interprofessional coordination and, ultimately, patient care, facility senior leaders should consider the following actions drawn from the three principal conclusions of this work: First, make patient care quality an important strategic organizational priority, and then, emphasize this commitment. Second, ensure that providers have adequate staffing, training, supplies, and other resources necessary to do their jobs. Third, implement strategies that improve interprofessional communication or working relationships, such as multidisciplinary rounding and/or patient geographic localization. In addition, future research should focus on developing validated scales measuring each of these constructs to gain a more sophisticated understanding of how these macrolevel factors influence coordination. Acknowledgments

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