Adoption of a High-Impact Innovation in a Homogeneous Population

Adoption of innovations, whether new ideas, technologies, or products, is crucially important to knowledge societies. The landmark studies of adoption dealt with innovations having great societal impact (such as antibiotics or hybrid crops) but where determining the utility of the innovation was straightforward (such as fewer side effects or greater yield). Recent large-scale studies of adoption were conducted within heterogeneous populations and focused on products with little societal impact. Here, we focus on a case with great practical significance: adoption by small groups of highly trained individuals of innovations with large societal impact but for which it is impractical to determine the true utility of the innovation. Specifically, we study experimentally the adoption by critical care physicians of a diagnostic assay that complements current protocols for the diagnosis of life-threatening bacterial infections and for which a physician cannot estimate the true accuracy of the assay based on personal experience. We show through computational modeling of the experiment that infection-spreading models—which have been formalized as generalized contagion processes—are not consistent with the experimental data, while a model inspired by opinion models is able to reproduce the empirical data. Our modeling approach enables us to investigate the efficacy of different intervention schemes on the rate and robustness of innovation adoption in the real world. While our study is focused on critical care physicians, our findings have implications for other settings in education, research, and business, where small groups of highly qualified peers make decisions about the adoption of innovations whose utility is difficult if not impossible to gauge.

Popular Summary

Adoption of innovations, such as new technologies or best practices, is critically important in multiple settings, ranging from medicine to marketing. While social network analysis has recently emerged as a key approach in the study of adoption, most recent research has focused on low-impact innovations and heterogeneous populations. We combine an experimental framework with computational modeling to study the adoption of a high-impact medical innovation among a homogeneous group of critical care physicians.

We study 36 pulmonary and critical care physicians in the medical intensive-care unit at Northwestern Memorial Hospital in Chicago. We focus on the adoption of a new assay that is a marker of bacterial infections; this assay yields results 20 times faster than microbial cultures, the current standard of care. In our study, initially all but two physicians were unaware that the assay was available. Significantly, the physicians were unable to estimate the new assay’s efficacy independently but had to trust the advice of their colleagues when deciding whether or not to adopt it. We conduct computer modeling of the adoption process and find that the spread of the innovation does not occur as we would expect for a contagion process. Our analysis enables us to reject the popular generalized contagion model of adoption in favor of a bidirectional persuasion process inspired by opinion models. The primary difference between our persuasion model and generalized contagion models is that our model allows individuals to persuade each other to become for or against an innovation. Over the course of our experiment, which lasted 244 days, 20 physicians met our definition of becoming an adopter (56% of the study population).

This persuasion model may be used to design interventions that hold the promise of increasing the adoption of best practices.

Figure 1

Experimental conditions of innovation adoption in the medical ICU at Northwestern Memorial Hospital. (a) Schematic of attending and fellow physician schedules. On weekdays (shifts 1 and 2), each of two ICU teams include one attending physician (blue), one fellow (green), and several residents (not pictured). On weekends (shifts 3 and 4), each team has one attending physician, but a single fellow is assigned to both. A third team is established on 1/1/2012 (not pictured). (b) Cumulative fraction of microbial cultures (dashed black line) and PCT assays (solid black line) ordered during the experiment. Cumulative microbial culture orders increase linearly ($y=0.004x$, $\text{correlation}\text{coefficient}=0.997$, solid green line), as expected for the gold standard for diagnosing bacterial infection. A quadratic increase in cumulative PCT orders ($y=-2\times {10}^{-4}+1.6\times {10}^{-5}{x}^2$, $\text{correlation}\text{coefficient}=0.979$, solid red line) supports the hypothesis that adoption of the PCT assay increases over time. (c) Rate of adoption of the PCT assay among the physicians in the study. Two physicians are adopters prior to the start of the experiment. Adoption of the PCT assay increases over time (solid line) but is slower than the increase in the number of distinct physicians who have already been on shift (dashed line). The data suggest that adoption requires exposure over multiple shifts before adoption occurs. (d) Distribution of adopters according to number of exposures by a unique physician prior to adoption. An exposure is defined as being on shift with a unique adopter. The empirical distribution (striped bars) is compared with the expected distribution if the data were generated by a Poisson process (white bars) and with the expected distribution according to Rogers’s categorization of adopters (black bars). In Rogers’s categorization, we classify innovators and early adopters, early majority, late majority, and laggards as physicians who adopted after zero, one, two, and three prior exposures, respectively. (Rogers’s framework does not consider nonadopters.)

Figure 2

Computational modeling of the adoption experiment. (a) Schematic of the adoption interaction dynamics between two physicians in the study. Each physician enters each interaction with a belief level $B_i$ in the innovation with range [0, 1] represented as a dial. If $B_i\ge B_{\text{adopt}}$, the physician is an adopter (red dial). We assume an adopter can expose other physicians to knowledge about the innovation (the PCT assay) only during a shared work shift. As a result of the interaction, physicians may change their opinion. (b) Mean number of adopters over time for the different adoption models and their corresponding best-fit parameters (contagion with multiple doses $P_{\text{inf}}=1$, $P_{\text{imm}}=0$, and $\text{dose}=0.2$; contagion with a single dose $P_{\text{inf}}=1$ and $P_{\text{imm}}=0$; and persuasion $B_{\text{adopt}}=0.5$, $P=0.1$, $M=0$, and $R=0$) determined for 1000 simulations compared to experimental data (black line). (c) To test the prediction power of the models, we fit the contagion (with single and multiple doses) and persuasion models to the data for a training period (gray area; $T_{\text{training}}=125$ days is illustrated). We determine the best-fit set of parameter values (contagion with a single dose $P_{\text{inf}}=0.1$ and $P_{\text{imm}}=0$; contagion with multiple doses $P_{\text{inf}}=0.1$, $P_{\text{imm}}=0$, and $\text{dose}=0.4$; and persuasion $B_{\text{adopt}}=0.5$ (fixed), $R=0$ (fixed), $P=0.1$, and $M=0$). We then use these optimal parameter values and the empirical conditions at the end of $T_{\text{training}}$ as new initial conditions to simulate the models for the remainder of the experiment ($244-T_{\text{training}}$), using 1000 independent realizations of each model. (d) Distribution of the final number of adopters predicted by the three models considered. We indicate the experimentally observed final number of adopters with the arrow. These data show that the persuasion model is the only one consistent with the experimental data. See the main text and Table 1 for more details on the model comparisons and performance.

Figure 3

Using the persuasion model to predict the impact of a prompting intervention. (a) Effect of a prompting intervention (see the main text) on the dynamics of innovation adoption for the persuasion model with best-fit parameters and the experimental work-shift schedule and initial conditions. The intervention (dashed line) accelerates adoption during the simulation, leading to a significantly higher mean final number of adopters compared to the model without intervention (solid line) (22.8 [3.0] vs 20.8 [2.3] final adopters, $p<0.01$, student’s $t$ test). (b) Number of additional adopters created by the prompting intervention on the persuasion model as a function of prompt magnitude $\mathrm{\Delta }/B_{\text{adopt}}$ on two different days during the experiment. (c) Distribution of the final number of adopters in the persuasion model with (dashed line) and without (solid line) intervention ($p<0.01$, Kolmogorov-Smirnov test). (d) Distribution of intervention days obtained for the 1000 realizations of the persuasion model with prompting intervention. Inset: Exploded view of the intervention day distribution. For all four panels, the length of the adoption pause is five days. For (a), (b), and (d), the prompting intervention impact is ${\mathrm{\Delta }}_I=0.8B_{\text{adopt}}$. See Supplemental Material Fig. 8 for details [24] of other cases.