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Table 6 Analysis of studies’ research questions and study methods used

From: Use of social network analysis methods to study professional advice and performance among healthcare providers: a systematic review

Study Objectives/research questions Research question categories Methods
Effken et al. Identify nursing unit communication patterns associated with patient safety and quality outcomes1. Can ORA’s visualizations be used to identify patient care unit network communication patterns that affect patient safety and quality outcomes?2. Do unit network characteristics differ by shift?3. What network characteristics measured by ORA metrics are related to specific safety and quality measures? 1. Descriptive
2. Descriptive
3. Relational
Design: observational, cross-sectional Data collection: Organizational Network Analysis questionnaire, patient outcome survey Analyses: correlations(Spearman Rho) calculated between SNA metrics and patient outcomes
Lindberg et al. Determine if intervention changed adherence to infection prevention protocols, patient outcomes and dialysis center social networks 1. Does a package of interventions including membership to a collaborative emphasizing positive deviance change HCW collaboration, infection prevention and innovation networks? 2. Do patient outcomes change? 1. Causal
2. Causal
Design: experimental, longitudinal, mixed methods, pre-post intervention Data collection: survey, FGD, observation, patient data extraction Analyses: quantitative: Pearson X2 and Fisher’s exact test, t test Reported p valuesQualitative analysis: reflexive observation and contextual analysis
Alexander et al. Evaluate how differences in IT sophistication in nursing homes impact communication and use of technology related to skin care and pressure ulcers. 1. What communication strategies do nursing home staff use to provide care to residents at risk of skin breakdown and pressure ulcers? 2. What evidence-based pressure ulcer preventions are used by nursing home staff with diverse IT sophistication? 3. What social networks of CNAs enhance or interrupt workflow and have positive or negative effects on nursing work? 1. Descriptive
2. Descriptive
3. Relational
Design: observational mixed methods, case studies Data collection: observation, previously collected survey Analysis: quantitative: calculated highest and lowest ITS NH and patient outcomes from survey data in an earlier study Qualitative: axial coding, themes developed using human factors theory
Creswick and Westbrook Determine if there are network property differences in prescription advice-seeking associated with prescription errors 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 1. Descriptive
2. Descriptive
3. Relational
Design: observational, cross-sectional Data collection: network survey and clinical audit Analyses: chi-squared with p values
Mundt et al. To understand what team communication structures contribute to alcohol-related utilization of care and medical costs 1. What primary care team communication networks are associated with alcohol-related utilization of care and medical costs for primary care patients? 1. Relational Design: observational, cross-sectional, retrospective Data collection: network survey, electronic health record extractions Analyses: linear modeling (GLMM) and sensitivity analyses
Hossain and Guan To understand coordination in an emergency department through measures of performance and quality 1. Is performance of coordination in the ED influenced by the social network? 2. Is performance of coordination in the ED influenced by the centrality of the network? 3. Is performance of coordination in the ED influenced by the density of the network? 4. Is performance of coordination in the ED influenced by the degree of connections in the network? 1. Causal
2. Causal
3. Causal
4. Causal
Design: observational, cross-sectional Data collection: survey extraction Analyses: multiple linear regression, p values and r values reported