App Review Series: Doximity.

Doximity, Inc. has done an excellent job combining their massive medical network with in app features that attempt to streamline the workflow of all healthcare professionals. From easy communication with colleagues, access to a treasure trove of relevant information, and the ability to call patients without the fear of giving away one's private cell phone, all from a clean and simple UI, Doximity is truly an essential tool in the workplace. Unfortunately, the Doximity app cannot be accessed unless you are a healthcare professional. While the application is intended to be a means of communication with colleagues, the app lacks proper messaging features that could allow for consistent workday communication with team members. Fortunately, the application is built on feedback from users, so any desired features are likely already coming down the pipeline. In creating this application, Doximity, Inc. sets out to tackle one of the biggest issues facing healthcare workers and the patients they treat: miscommunication and handoff errors. The Doximity application aims to do this by creating a platform that enables quick and easy communication between physicians, HIPPA-compatible document transfer, and a streamlined service to securely contact patients and colleagues.

A physician-initiated intervention to increase colorectal cancer screening in Chinese patients.

BACKGROUND: Among Chinese American individuals, only approximately 42% of cases of colorectal cancer (CRC) are diagnosed at an early stage, possibly because these patients are less likely than non-Hispanic white individuals to undergo CRC screening. METHODS: Primary care physicians (PCPs) were recruited from a local independent practice association serving Chinese Americans and randomized into early-intervention and delayed-intervention groups. PCPs in the early-intervention group received continuing medical education (CME), and their patients received an intervention mailer, consisting of a letter with the PCP's recommendation, a bilingual educational booklet, and a fecal occult blood test (FOBT) kit in year 1. PCPs in the delayed-intervention group received no CME, and their patients received the mailers in year 2. RESULTS: A total of 20 PCPs were assigned to the early-intervention and 22 PCPs to the delayed-intervention group. A total of 3120 patients of these participating PCPs who had undergone CRC screening that was due during the study period were included. A total of 915 mailers were sent in year 1 and 830 mailers were sent in year 2. FOBT screening rates increased from 26.7% at baseline to 58.5% in year 1 in the early-intervention group versus 19.6% at baseline to 22.2% in year 1 in the delayed-intervention group (P<.0001). The overall effect size of the mailer intervention with or without CME was estimated as a difference of 26.6 percentage points (95% confidence interval, 22.0-31.2 percentage points) from baseline compared with usual care. The intervention was found to have no impact on rates of colonoscopy or sigmoidoscopy. CONCLUSIONS: The results of the current pilot study demonstrated that a mailer including educational materials and FOBT kits can increase CRC screening rates with or without CME for the PCPs. Cancer 2018;124:1568-75. © 2018 American Cancer Society.

Analysis of the U.S. patient referral network.

In this paper, we analyze the US Patient Referral Network (also called the Shared Patient Network) and various subnetworks for the years 2009 to 2015. In these networks, two physicians are linked if a patient encounters both of them within a specified time interval, according to the data made available by the Centers for Medicare and Medicaid Services. We find power law distributions on most state-level data as well as a core-periphery structure. On a national and state level, we discover a so-called small-world structure as well as a "gravity law" of the type found in some large-scale economic networks. Some physicians play the role of hubs for interstate referral. Strong correlations between certain network statistics with health care system statistics at both the state and national levels are discovered. The patterns in the referral network evinced using several statistical analyses involving key metrics derived from the network illustrate the potential for using network analysis to provide new insights into the health care system and opportunities or mechanisms for catalyzing improvements.

Estimating the impact of physician risky-prescribing on the network structure underlying physician shared-patient relationships.

Social network analysis and shared-patient physician networks have become effective ways of studying physician collaborations. Assortative mixing or "homophily" is the network phenomenon whereby the propensity for similar individuals to form ties is greater than for dissimilar individuals. Motivated by the public health concern of risky-prescribing among older patients in the United States, we develop network models and tests involving novel network measures to study whether there is evidence of geographic homophily in prescribing and deprescribing in the specific shared-patient network of physicians linked to the US state of Ohio in 2014. Evidence of homophily in risky-prescribing would imply that prescribing behaviors help shape physician networks and could inform interventions to reduce risky-prescribing (e.g., should interventions target groups of physicians or select physicians at random). Furthermore, if such effects varied depending on the structural features of a physician's position in the network (e.g., by whether or not they are involved in cliques - groups of actors that are fully connected to each other - such as closed triangles in the case of three actors), this would further strengthen the case for targeting of select physicians for interventions. Using accompanying Medicare Part D data, we converted patient longitudinal prescription receipts into novel measures of the intensity of each physician's risky-prescribing. Exponential random graph models were used to simultaneously estimate the importance of homophily in prescribing and deprescribing in the network beyond the characteristics of physician specialty (or other metadata) and network-derived features. In addition, novel network measures were introduced to allow homophily to be characterized in relation to specific triadic (three-actor) structural configurations in the network with associated non-parametric randomization tests to evaluate their statistical significance in the network against the null hypothesis of no such phenomena. We found physician homophily in prescribing and deprescribing in both the state-wide and multiple HRR sub-networks, and that the level of homophily varied across HRRs. We also found that physicians exhibited within-triad homophily in risky-prescribing, with the prevalence of homophilic triads significantly higher than expected by chance absent homophily. These results may explain why communities of prescribers emerge and evolve, helping to justify group-level prescriber interventions. The methodology could be applied to arbitrary shared-patient networks and even more generally to other kinds of network data that underlies other kinds of social phenomena.

Estimating the impact of physician risky-prescribing on the network structure underlying physician shared-patient relationships.

Social network analysis and shared-patient physician networks have become effective ways of studying physician collaborations. Assortative mixing or "homophily" is the network phenomenon whereby the propensity for similar individuals to form ties is greater than for dissimilar individuals. Motivated by the public health concern of risky-prescribing among older patients in the United States, we develop network models and tests involving novel network measures to study whether there is evidence of homophily in prescribing and deprescribing in the specific shared-patient network of physicians linked to the US state of Ohio in 2014. Evidence of homophily in risky-prescribing would imply that prescribing behaviors help shape physician networks and would suggest strategies for interventions seeking to reduce risky-prescribing (e.g., strategies to directly reduce risky prescribing might be most effective if applied as group interventions to risky prescribing physicians connected through the network and the connections between these physicians could be targeted by tie dissolution interventions as an indirect way of reducing risky prescribing). Furthermore, if such effects varied depending on the structural features of a physician's position in the network (e.g., by whether or not they are involved in cliques-groups of actors that are fully connected to each other-such as closed triangles in the case of three actors), this would further strengthen the case for targeting groups of physicians involved in risky prescribing and the network connections between them for interventions. Using accompanying Medicare Part D data, we converted patient longitudinal prescription receipts into novel measures of the intensity of each physician's risky-prescribing. Exponential random graph models were used to simultaneously estimate the importance of homophily in prescribing and deprescribing in the network beyond the characteristics of physician specialty (or other metadata) and network-derived features. In addition, novel network measures were introduced to allow homophily to be characterized in relation to specific triadic (three-actor) structural configurations in the network with associated non-parametric randomization tests to evaluate their statistical significance in the network against the null hypothesis of no such phenomena. We found physician homophily in prescribing and deprescribing. We also found that physicians exhibited within-triad homophily in risky-prescribing, with the prevalence of homophilic triads significantly higher than expected by chance absent homophily. These results may explain why communities of prescribers emerge and evolve, helping to justify group-level prescriber interventions. The methodology may be applied, adapted or generalized to study homophily and its generalizations on other network and attribute combinations involving analogous shared-patient networks and more generally using other kinds of network data underlying other kinds of social phenomena.

The influence of strong and weak ties in physician peer networks on new drug adoption.

Physicians interact and exchange information through various social networks. Understanding peer effects through different networks can help accelerate new medical technology and innovative treatment adoption. In this research, we measure the influence of strong-tie and weak-tie connections on new drug adoption and study the overlap between advice-discussion and patient-sharing network. We construct two physician networks with strong and weak ties from peer nomination surveys and commercial medical claims data. We design a dynamic system to define peer adoption status and build patient-level hierarchical logistic models to measure the peer influence on new product adoption for treating new-to-therapy patients. Our results show that A strong-tie early adoption peer has six times more influence on new drug adoption than a weak-tie peer. Weak tie peers collectively exert as much or higher influence than strong-tie peers because of the larger network size. In the case of inaccessibility to strong-tie data, researchers can still reliably use the influence of the weak tie data only even though they will lose the effect of the omitted strong ties.

The evolution of ScotRIGHT: The first pan-Scotland physician trainee research network.

Scotland has a distinguished track record in foundational clinical research. From the completion of the first clinical trial undertaken in scurvy to cloning the world's first whole mammal, Scottish basic and clinical research is world leading. More recently, challenges in access to research skills, funding and programmes by clinical trainees led to the development of alternatives to these typical avenues of accessing research opportunities. Trainee networks evolved to meet the needs of trainees looking to access projects and collaboratives beyond audit and quality improvement commonly performed during structured training. These networks have enjoyed enormous success and have succeeded in progressing projects which have impactful outputs for patients, and improving clinical services. Here, we describe the foundation of the first pan-Scotland physician trainee research network; Scottish Trainees Research In Gastroterology and Hepatology (ScotRIGHT). We outline the foundational efforts, requisites and foundations required to develop a research network.

Optimal Physician Shared-Patient Networks and the Diffusion of Medical Technologies.

Social network analysis has created a productive framework for the analysis of the histories of patient-physician interactions and physician collaboration. Notable is the construction of networks based on the data of "referral paths" - sequences of patient-specific temporally linked physician visits - in this case, culled from a large set of Medicare claims data in the United States. Network constructions depend on a range of choices regarding the underlying data. In this paper we introduce the use of a five-factor experiment that produces 80 distinct projections of the bipartite patient-physician mixing matrix to a unipartite physician network derived from the referral path data, which is further analyzed at the level of the 2,219 hospitals in the final analytic sample. We summarize the networks of physicians within a given hospital using a range of directed and undirected network features (quantities that summarize structural properties of the network such as its size, density, and reciprocity). The different projections and their underlying factors are evaluated in terms of the heterogeneity of the network features across the hospitals. We also evaluate the projections relative to their ability to improve the predictive accuracy of a model estimating a hospital's adoption of implantable cardiac defibrillators, a novel cardiac intervention. Because it optimizes the knowledge learned about the overall and interactive effects of the factors, we anticipate that the factorial design setting for network analysis may be useful more generally as a methodological advance in network analysis.

Physician network position and patient outcomes following implantable cardioverter defibrillator therapy.

OBJECTIVE: To evaluate two novel measures of physician network centrality and their associations with implantable cardioverter defibrillator (ICD) procedure volume and health outcomes. DATA SOURCES: Medicare claims and the National Cardiovascular Data Registry data from 2007 to 2011. STUDY DESIGN: We constructed a national cardiovascular disease patient-sharing physician network and used network analysis to characterize physician network centrality with two measures: within-hospital degree centrality (number of connections within a hospital) and across-hospital degree centrality (number of connections across hospitals). The primary outcome was risk-adjusted 2-year case fatality. Hierarchical logistic regression estimated the effects of physician's within-hospital and across-hospital degree centrality on case fatality. We included 105 109 ICD therapy patients and 3474 ICD implanting physicians in our analyses. PRINCIPAL FINDINGS: After controlling for other physician and hospital characteristics, we observed greater risk-adjusted case fatality among patients treated by physicians in the highest across-hospital degree tertile compared to lowest tertile (OR [95% CI] = 1.10 [1.04-1.16], P = 0.001) and lowest tertile volume physicians compared with highest volume (OR [95% CI] = 0.90 [0.84-0.95], P < 0.001). Physician's within-hospital degree tertile was inversely associated with case fatality but not statistically significant. CONCLUSIONS: Degree centrality measures capture information independent of procedure volume and raise questions about the quality of physicians with networks that predict worse health outcomes.