In healthcare, web-based data entry has taken center stage regarding patient medical information. Where in the past physicians and nurses entered information in paper charts, now there is a shift towards electronic health records (EHR). By entering medical information during the encounter into an EHR, it mitigates recall bias and immediately makes the data available across treatment teams.
Moreover, because EHR data is entered in a standard format, it allows hospital coders greater accuracy and precision when reporting performance and quality statistics at the state and federal levels.
Parallel to the consolidation of processes reported by Benítez et al., healthcare has also streamlined parts of data analysis and visualization. Federal entities such as the Agency for Healthcare Quality and Research (AHRQ) have developed algorithms that allow organizations to analyze information reported to the Centers for Medicare and Medicaid Services (CMS). This is significant because all hospitals that treat Medicare or Medicaid patients must report this data to CMS.
In healthcare, quality measures used to assess quality and performance are constantly under question and the criteria used to gather and assess social network data should face similar scrutiny (Glance, Osler, Mukamel, & Dick, 2008).
It is through the process of verifying each question and measure that we arrive at a standardized instrument that can be used by others. Standardization allows comparisons to be made across studies over time developing the body of knowledge needed to arrive at insights and correlations (Cicchetti, 1994).
With the rise of social network analysis, do you believe the standardization process will become more and more salient?
The need for standardization points towards the inherent need in social network analysis for high quality data to understand centrality. Once we have the data, we must understand the importance of node centrality measures as they are able to realistically represent human interactions. In one study, social networks among students of a karate class, dolphins, and the neural network of a nematode were analyzed.
The networks were studied for which measure of centrality really conveyed influence. Their results identified key nodes as ones with high eigenvector and eccentricity centralities (Batool & Niazi, 2014). Other factors of centrality considered include degree and closeness but these were not found to be significant. The findings are consistent to our practicum exercise on crime networks as nodes high in eigenvector values were put forward as being high value targets.
The use of eigenvector values to understand influence is an intuitive one. Alter nodes high in influence, power or resources being connected to an ego node confers power by associations.
Eccentricity takes into account the maximal distance to other nodes from a central node This is different from closeness because we are measuring how far a node is from one another, not how close.
Given the difficulty in realistically representing social networks, node centrality must be considered in the context of other factors. For example, it was found in cases of complicated grief, node centrality and a node’s expected influence (EI) were both strongly correlated with network influence. Here the EI was calculated as a measure of feelings of emptiness and emotional pain (Robinaugh, Millner, & McNally, 2016). In fact, the EI was found to be more significant than node centrality in terms of influence. It signifies the need to understand the data versus simply measuring centrality.
The significance of EI points towards the work necessary in social network analysis to comprehensively explain behaviors and trends. Node centrality is a strong starting point for understanding interactions, but the social network must be analyzed as a whole to explain observations.
How could you practice beginner’s mind in social network analysis?
Batool, K., & Niazi, M. A. (2014). Towards a methodology for validation of centrality measures in complex networks. PLoS ONE. https://doi.org/10.1371/journal.pone.0090283
Benítez, J. A., Labra, J. E., Quiroga, E., Martín, V., García, I., Marqués-Sánchez, P., & Benavides, C. (2017). A Web-Based Tool for Automatic Data Collection, Curation, and Visualization of Complex Healthcare Survey Studies including Social Network Analysis. Computational and Mathematical Methods in Medicine. https://doi.org/10.1155/2017/2579848
Cicchetti, D. V. (1994). Guidelines, Criteria, and Rules of Thumb for Evaluating Normed and Standardized Assessment Instruments in Psychology. Psychological Assessment. https://doi.org/10.1037/1040-35220.127.116.114
Glance, L. G., Osler, T. M., Mukamel, D. B., & Dick, A. W. (2008). Impact of the present-on-admission indicator on hospital quality measurement: Experience with the agency for healthcare research and quality (AHRQ) inpatient quality indicators. Medical Care. https://doi.org/10.1097/MLR.0b013e318158aed6
Robinaugh, D. J., Millner, A. J., & McNally, R. J. (2016). Identifying highly influential nodes in the complicated grief network. Journal of Abnormal Psychology. https://doi.org/10.1037/abn0000181