About 4 years ago I started to ask questions about the ways that social networks and social learning might be used to understand and support healthcare quality. The reality is that, at least in the US, healthcare quality is broadly considered to be sub-optimal. In some reports the US ranks as low as 16th among developed countries in the provision of high quality healthcare — though it consistently ranks first in costs per patient. But when explored more closely, our challenge turns out to be more about the variation in care.
About 4 years ago I started to ask questions about the ways that social networks and social learning might be used to understand and support healthcare quality. The reality is that, at least in the US, healthcare quality is broadly considered to be sub-optimal. In some reports the US ranks as low as 16th among developed countries in the provision of high quality healthcare — though it consistently ranks first in costs per patient. But when explored more closely, our challenge turns out to be more about the variation in care. In many areas in this country healthcare quality is unsurpassed, and it is for this reasons that royalty and heads of state from around the globe come to the US for care.
Variation in healthcare quality in the US can be demonstrated in countless ways, from measuring general wellness, to obesity, from readmission rates, to vaccination rates, and from infant mortality, to general life expectancy. And variation can be demonstrated at the regional level, through state-wide reporting, and even in more focused data that describe 2-fold, 4-fold, and 6-fold variations in outcomes at a local metropolitan level.
To me the variations in care are, at their root, a problem of information flow — best practices in one region are not being shared and implemented in other regions. In many ways and for many reasons, in healthcare many still believe that knowledge is power. This belief leads to structural and functional restrictions to information flow and as a result healthcare quality (and patients) suffer.
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In a new report in JAMA, Nicholas Christakis and Bruce Landon have explored how social networks of physician are formed around shared-patient loads – the goal is to understand how these networks form and what factors are associated with different types of shared-patient networks. In many ways this study gives rise to a new competency in medicine, that of applying the science of social networks to healthcare quality improvement…and in doing so this landmark report provides marvelous support for the ideas in my book: #SocialQI: Simple Solutions for Improving Your Healthcare.
Here is what Landon and Christakis have found:
Context Physicians are embedded in informal networks that result from their sharing of patients, information, and behaviors.
Objectives To identify professional networks among physicians, examine how such networks vary across geographic regions, and determine factors associated with physician connections.
Design, Setting, and Participants Using methods adopted from social network analysis, Medicare administrative data from 2006 were used to study 4 586 044 Medicare beneficiaries seen by 68 288 physicians practicing in 51 hospital referral regions (HRRs). Distinct networks depicting connections between physicians (defined based on shared patients) were constructed for each of the 51 HRRs.
Main Outcomes Measures Variation in network characteristics across HRRs and factors associated with physicians being connected.
Results The number of physicians per HRR ranged from 135 in Minot, North Dakota, to 8197 in Boston, Massachusetts. There was substantial variation in network characteristics across HRRs. For example, the mean (SD) adjusted degree (number of other physicians each physician was connected to per 100 Medicare beneficiaries) across all HRRs was 27.3 (range, 11.7-54.4); also, primary care physician relative centrality (how central primary care physicians were in the network relative to other physicians) ranged from 0.19 to 1.06, suggesting that primary care physicians were more than 5 times more central in some markets than in others. Physicians with ties to each other were far more likely to be based at the same hospital (69.2% of unconnected physician pairs vs 96.0% of connected physician pairs; adjusted rate ratio, 0.12 [95% CI, 0.12-0.12]; P < .001), and were in closer geographic proximity (mean office distance of 21.1 km for those with connections vs 38.7 km for those without connections, P < .001). Connected physicians also had more similar patient panels in terms of the race or illness burden than unconnected physicians. For instance, connected physician pairs had an average difference of 8.8 points in the percentage of black patients in their 2 patient panels compared with a difference of 14.0 percentage points for unconnected physician pairs (P < .001).
Conclusions Network characteristics vary across geographic areas. Physicians tend to share patients with other physicians with similar physician-level and patient-panel characteristics.
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Though I will admit the science can be a bit dense and even overwhelming at first, this is not to be unexpected when methods from other disciplines are effectively applied in new ways. But please take the time to read the article, remain open to the credibility of the research methods even if they seem unfamiliar…and remain open to the findings. Because whether we are familiar with the science or not, as the authors say,
“Decades of subsequent research demonstrating both small- and large-area variations in care suggest that local norms play an important role in determining practice patterns and that, in aggregate, such norms and customs might account for a large proportion of the variability that exists in health care“
These ‘share-patient networks’ are not formal networks per se and these physicians may not be connected in any other ways — we might see this as both an opportunity and a threat to moving from this research to broader practical application to drive change. Our next goal must be to influence these networks in a way that amplifies their impact and flattens the perverse variations in care that remain, especially in regions where these networks are weakly established and largely unexplored.
“The potential influence of informal networks of physicians on decision making has been understudied despite the potential importance of these networks in day-to-day practice. In addition, understanding more about physicians’ predilections to form relationships with colleagues could be important for identifying levers to influence how physicians exchange information with one another.”
To me this is one of the most fundamental impacts of social media: We now have access to new channels for information flow and learning that dramatically lessen the burden of building networks and managing professional relationship. If we develop the skill set that allows us the study these systems and if we each develop the skill set to leverage these systems, we may have a pretty good chance of improving healthcare quality in this country before the next time we (or our loved ones) are given the title, ‘patient’.
All the best,
Brian
