Table 2 Summary of the six studies included in this review

Effken et al. (2011) [45] USA

Study objectives: Identifying nursing unit communication patterns associated with patient safety and quality outcomes

Research questions: Can ORA’s visualizations be used to identify patient care unit network communication patterns that affect patient safety and quality outcomes?Do unit network characteristics differ by shift?What network characteristics measured by ORA metrics are related to specific safety and quality measures?

Study design: Observational;cross-sectional

Data collection method(s): Organizational network Analysis questionnaire, previously collected survey with patient outcome data

Critical appraisal: ++

Type/number of HCW: Nursing staff, number not stated

Patient outcomes: Adverse drug events, Falls, Symptom management difference, Symptom management capacity, Simple self-care management, Complex self-care management

Study findings: Demonstrated utility of ORA software for healthcare research and relationship of nursing unit communication patterns to patient safety and outcomes. Differences between day and night shift communication networks. Found more communication not always associated with better patient outcomes, specifically falls and adverse drug events

Limitations: Small and homogenous sample size. Only nursing staff. Limited to weekday shifts. Some of the patient outcomes (falls and adverse drug events) were infrequent events

Lindberg et al. (2013) [47] USA

Study objectives:

Evaluate how intervention affected adherence to infection prevention protocols, patient outcomes, and dialysis center social networks

Research questions:

Does a package of interventions including membership to a collaborative emphasizing positive deviance change HCW collaboration, infection prevention, and innovation networks?

Do patient outcomes change?

Study design:

Experimental pre-post intervention

Longitudinal

Mixed-methods

SNA retrospective

Data collection method(s):

Survey, focus group discussions, observation, and patient data extraction

Critical appraisal: +

Type/number of HCW: Multidisciplinary staff at an outpatient hemodialysis facilitySNA 51 identified (46 completed each of the 2 surveys 90%)FGD 16

Patient outcomes: Infection rates in patients

Study findings: There were changes in all three networks following the implementation of the package of interventions.For collaboration: centralization and reach increased, connectivity decreased, and no change in inclusion.For bloodstream infection (BSI): prevention reach increased, the others did not change significantly.For innovation: inclusion and reach increased, reach decreased, and centralization did not change.Qualitative data supports the noted changes in network with staff looking to each other for innovations in infection prevention, working more as a cohesive team.Patient outcomes improved, with lower incidence of BSIs, although they were a relatively rare event.

Limitations: Results are based on one dialysis center and may not be generalizable to other centers. SNA was based on a retrospective survey and might have been subject to recall bias.The results of the time series analysis are limited as access-related bloodstream infections (AR-BSIs) are a relatively rare outcome and there were a small number of time points between interventions.Researchers were unable to stratify AR-BSIs by access type before 2009.

Alexander et al. (2015) [43] USA

Study objectives:

To evaluate how differences in IT sophistication in nursing homes impact communication and use of technology and associations with skin care and pressure ulcers

Research questions:

What communication strategies do nursing home staff use to provide care to residents at risk of skin breakdown and pressure ulcers?

What evidence-based pressure ulcer preventions are used by nursing home staff with diverse IT sophistication?

What social networks of CNAs enhance or interrupt workflow and have positive or negative effects on nursing work?

Study design:

Observational mixed-method case studies

Data collection method(s):

For SNA, observation of communication among HCWs was documented using a structured field note guide.

Critical appraisal: +

Type/number of HCW: Nursing staff, FGD, 21;SNA, nurses at 2 nursing homes, 1386 observations (unit of analysis was not based on number of HCWs)

Patient outcomes: Incidence of pressure ulcers

Study findings: High IT sophistication lead to more diverse locations for HCW interactions. Low IT sophistication required more face to face interaction in more centralized locations within the nursing home. Patient outcomes captured were more or less equivalent between the two facilities.

Limitations: The study focused on observations only during the day shift. Individual RNs/LPNs and CNAs were not uniquely identified during observations, and the analysis lumped them together as RNs/LPNs and CNAs, respectively. Two nursing homes with a specific degree of IT sophistication were compared, rather than following any change due to the introduction of IT sophistication. Confounding variables offer an opportunity for increasing bias in the results. Generalizability may not be appropriate as this study was an in-depth analysis of two nursing homes in one state––Missouri.

Creswick and Westbrook (2015) [44] Australia

Study objectives:

Determine if there are network property differences in prescription advice-seeking associated with prescription errors

Research questions:

1. Identify and measure from whom hospital clinical staff seek medication advice on a weekly basis

2. Quantify the use of other sources of medication information, assess the difference in medication advice-seeking patterns across professional groups

3.Examine network characteristics in relation to prescribing error rates

Study design:

Observational

Cross-sectional

Data collection method(s): Questionnaire for SNA and clinical audit

Critical appraisal: ++

Type/number of HCW: Multidisciplinary: physicians, nurses, and allied health professionals101 participants

Patient outcomes: Prescription error rates

Study findings: Limited interprofessional advice-seeking overall (particularly between physicians and nurses). Hubs of advice provisions include pharmacists, junior physicians, and senior nurses. Senior physicians are not involved in these advice exchange networks. The ward with the stronger (denser) advice-seeking network had lower rates of procedural and clinical prescribing errors.

Limitations: Limited in scope (only two wards).No psychometric assessments. Networks only examined at one point in time.

Mundt et al. (2015) [48] USA

Study objectives:

To understand what team communication structures contribute to alcohol-related utilization of care and medical costs.

Research questions:

What primary care team communication networks are associated with alcohol-related utilization of care and medical costs for primary care patients?

Study design:

Observational

Retrospective

Data collection method(s): Questionnaire administered in person, electronic health record extractions

Critical appraisal: ++

Type/number of HCW: Multidisciplinary: physicians, physician assistants, nurse practitioners, registered nurses, medical assistants, licensed practical nurses, laboratory technicians, radiology technicians, clinic managers, medical receptionists, and other patient care staff.One hundred sixty HCWs were invited, 155 took part; 31 care teams

Patient outcomes: Alcohol-related emergency department visits,Hospital days and associated costs

Study findings: Teams’ variations in communication patterns (face to face and through electronic health record) are associated with statistically significant differences in alcohol-related patient utilization and medical costs in their patient panels. Excessive alcohol-using patients may fair better if they are cared for by teams with RNs who interact with more team members including LPNs/MAs and by teams whose frequent daily face-to-face communication to the primary care practitioner has been streamlined to a smaller number of team members.

Limitations: Only six practices in limited geography included. No information on content of communication. No information on frequency and quality of alcohol services delivered. Unclear rationale for type of communication method. Increased risk of type I error. Study may underestimate full impact given underreporting of alcohol-related diagnoses in electronic health record.

Hossain and Guan (2012) [46] United States

Study objectives:

To understand coordination in an emergency department through measures ofperformance and quality

Research questions:

Test the following hypotheses:

Performance of coordination in the emergency department is influenced by the social network.

Performance of coordination in the emergency department is influenced by the centrality of the network.

Performance of coordination in the emergency department is influenced by the density of the network.

Performance of coordination in the emergency department is influences by the degree of connections in the network.

Study design:

Observational

Cross-sectional

Data collection method(s): National Hospital Ambulatory Medical Care Survey (NHAMCS), patient record surveys selected from emergency departments

Critical appraisal: ++

Type/number of HCW: Multidisciplinary: emergency department hospital staff. Staff included in patient reports from 359 emergency departments

Patient outcomes: Length of visit, wait time to see physician, revisits within 72 h, deaths within emergency department, and left before seeing physician

Study findings: Coordination and the social network are heavily related within the emergency department. Specifically, as emergency department network density increases, number of patients waiting over triage time decreases but does not influence average wait times. As degree of connection increases, the wait time for patients increases. No evidence of connection between quality of service and death and the social networks. Quality of coordination in emergency department is influenced by centrality of the network. As communication in emergency department increases, the number of patients revisiting decreases.

Limitations: NHAMCS dataset is incomplete, contains less than 40 surveys of each emergency department, which is less than the assumed volume of patients in a 3-month period.