The use of Social Network Analysis (SNA) may help define the best use of patient communications to improve outcomes and lower costs. In my first post on SNA, I documented some of the high-level measurements used to analyze data flow between individuals and groups. I covered a few key terms within the categories of size, connections, distributions and segmentation. Let’s skip the size and segmentation elements and focus on the connections and distributions categories to highlight how complex these discussions can get.
To frame the discussion, I’m pulling the following terms from the Wikipedia entry for SNA. Though not ideal, these terms are truly spread out all over the Internet. I couldn’t find any other sources to document the components in summary format.
So many definitions!
Connections. These terms focus on the relationships between individuals or groups, both good and bad.
Homophily: The extent to which actors form ties with similar versus dissimilar others. Similarity can be defined by gender, race, age, occupation, educational achievement, status, values or any other salient characteristic.[26] Homophily is also referred to as assortativity.
Multiplexity: The number of content-forms contained in a tie.[27] For example, two people who are friends and also work together would have a multiplexity of 2.[28] Multiplexity has been associated with relationship strength and can also comprise overlap of positive and negative network ties.[8]
Mutuality/Reciprocity: The extent to which two actors reciprocate each other’s friendship or other interaction.[29]
Network Closure: A measure of the completeness of relational triads. An individual’s assumption of network closure (i.e. that their friends are also friends) is called transitivity. Transitivity is an outcome of the individual or situational trait of Need for Cognitive Closure.[30]
Propinquity: The tendency for actors to have more ties with geographically close others.
Distributions: the following terms refer to the distribution of connections between individuals and groups.
Bridge: An individual whose weak ties fill a structural hole, providing the only link between two individuals or clusters. It also includes the shortest route when a longer one is unfeasible due to a high risk of message distortion or delivery failure.[31]
Centrality: Centrality refers to a group of metrics that aim to quantify the “importance” or “influence” (in a variety of senses) of a particular node (or group) within a network.[32][33][34][35] Examples of common methods of measuring “centrality” include betweenness centrality,[36]closeness centrality, eigenvector centrality, alpha centrality, and degree centrality.[37]
Density: The proportion of direct ties in a network relative to the total number possible.[38][39]
Distance: The minimum number of ties required to connect two particular actors, as popularized by Stanley Milgram‘s small world experimentand the idea of ‘six degrees of separation’.
Structural holes: The absence of ties between two parts of a network. Finding and exploiting a structural hole can give an entrepreneur a competitive advantage. This concept was developed by sociologist Ronald Burt, and is sometimes referred to as an alternate conception of social capital.
Tie Strength: Defined by the linear combination of time, emotional intensity, intimacy and reciprocity (i.e. mutuality).[31] Strong ties are associated with homophily, propinquity and transitivity, while weak ties are associated with bridges.
The slippery slope
I documented two studies in my third post on SNA to analyze clinical networks who treat chronically ill patients. However, they only focused on network density and strength of ties between physicians to see if there was a positive correlation with reduced cost of care or adverse events. There was.
I’m more interested in whether we can analyze the flow of information to patients via coordinated communications, and whether improved outcomes and lower costs can result. Do we even have the capability to examine patient communications utilizing SNA tools? Maybe. If you take a look at all the terms listed above, there are an incredible number of elements to try and document.
If you wanted to explore patient communications, you’d have to fence off the research to examine specific elements of the network or information flow. I must point out, most of the work in existing studies appears to represent the easiest scope: patients who access all care through the same healthcare system. However, this isn’t representative of all patients who access care for their chronic illness. Any research might have a mix of the following scenarios:
- Scenario 1: Examine patient communications for a population that goes to the same healthcare system for all their treatment. In this case, every patient in the test group uses the same physician group and accesses ALL care including primary care, specialists and therapists. All patient data is on the same EHR system and all communications are created within the same system.
- Scenario 2: Examine patient communications for a population that goes to the same primary care physician practice for regular treatment but uses different groups for specialists and therapists. Patient data is on a couple of EHR systems and communications are more fragmented.
- Scenario 3: Examine patient communications for a geographic group of chronically ill patients who live in the same area, but all access different primary care, specialist and therapist groups. Patient data is on multiple EHR systems and communication is highly fragmented.
Where to focus?
If the local healthcare system is large enough to encompass ALL clinical personnel for a specific patient population, you’ll likely find it easier to conduct SNA research on patient communications as most come from the same system. The difficulty arises as you head towards Scenario 3 where a chronically ill patient accesses clinical care from a variety of unconnected healthcare providers.
Can we focus on specific elements of communications and the flow of information to chronically ill populations? Maybe. Consider the following:
- Patient Communication Preferences: Comparing chronically ill patient outcomes in healthcare providers who share patient communication preferences for language, format and timing versus healthcare providers who don’t.
- One on One Interactions: Comparing the communication options and outcomes for chronically ill patients who speak English as a second language versus those who speak English as their primary language.
- Health Literacy: Comparing patient outcomes and costs in healthcare systems that coordinate audience-appropriate information and communications for chronically ill patients with limited health literacy skills versus health systems who don’t.
Using SNA to study patient communications may provide real value in situations where the clinical support is fragmented. There is no question the flow of information is compromised in these situations. And true coordination may not even be possible. But research may show us ways to improve data flow in these situations which, in turn, allows us to identify communication opportunities to improve patient health literacy.
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