Team Learning for Healthcare Quality Improvement

Abstract

In organized healthcare quality improvement collaboratives (QICs), teams of practitioners from different hospitals exchange information on clinical practices with the aim of improving health outcomes at their own institutions. However, what works in one hospital may not work in others with different local contexts because of nonlinear interactions among various demographics, treatments, and practices. In previous studies of collaborations where the goal is a collective problem solving, teams of diverse individuals have been shown to outperform teams of similar individuals. However, when the purpose of collaboration is knowledge diffusion in complex environments, it is not clear whether team diversity will help or hinder effective learning. In this paper, we first use an agent-based model of QICs to show that teams comprising similar individuals outperform those with more diverse individuals under nearly all conditions, and that this advantage increases with the complexity of the landscape and level of noise in assessing performance. Examination of data from a network of real hospitals provides encouraging evidence of a high degree of similarity in clinical practices, especially within teams of hospitals engaging in QIC teams. However, our model also suggests that groups of similar hospitals could benefit from larger teams and more open sharing of details on clinical outcomes than is currently the norm. To facilitate this, we propose a secure virtual collaboration system that would allow hospitals to efficiently identify potentially better practices in use at other institutions similar to theirs without any institutions having to sacrifice the privacy of their own data. Our results may also have implications for other types of data-driven diffusive learning such as in personalized medicine and evolutionary search in noisy, complex combinatorial optimization problems.

Keywords: Agent-based modeling; collaborative learning; healthcare quality; knowledge diffusion; quality improvement; team collaboration; team learning.

FIGURE 1

Representative histograms of proportions of pairwise nHDs of M = 51 agents, each with N = 93 features, for a) a dataset of real hospitals with binarized practices as features, b) clustered synthetic random agents with binary features, generated by MakeSnakingCluster with K = 10 and d = 13, c) scattered synthetic random agents with binary features.

FIGURE 2

Illustration of one instance of a population created with the MakeSnakingCluster algorithm in 2-D real-valued feature space (M = 50, K = 10 and d = 13), where numbered open circles represent core individuals in each step, b) Illustration of the population shown in a, divided into T = 5 teams picked by the PickSimilarTeams algorithm, where each team is shown by a unique color and shape combination.

FIGURE 3

Mean probability of patient survival on 100 random landscapes at each of 100 trial steps, using 40 patients per trial on landscapes with a) 0, b) 495, and c) 2475 two-feature interactions.

FIGURE 4

Mean probability of patient survival over 100 trial steps, averaged over 100 random landscapes, shown as a function of the frequency of team reformation, a) No two-feature interactions in the fitness landscapes and no noise in trials, b) No two-feature interactions in the fitness landscapes and noise in trials (40 patients per trial), c) 2475 two-feature interactions in the fitness landscapes and no noise in trials, and d) 2475 two-feature interactions in the fitness landscapes and noise in trials (40 patients per trial).

FIGURE 5

The effect of a single team reformation at trial step 50, starting from an initially scattered population on a complex landscape with 2475 two-feature interactions when fitness evaluation is noise-free and the team reformation is homophilous (black lines) or when fitness evaluation is noisy (only 40 patients per trial) and team reformation is random (red lines), a) Mean probability of patient survival on 100 random landscapes; b) within-team nHD.

FIGURE 6

Within-team nHD for each of the ten teams (colored lines) over 500 trial steps starting from the same populations shown in Fig. 7. Since teams are reformed homophilously after each trial step, each colored line does not necessarily represent the same set of ten agents in different trial steps. The level of noise in fitness evaluation varies between the three panels: a) no noise, b) low noise, with 320 patients per trial, and c) high noise, with only 40 patients per trial.

FIGURE 7

Within-population nHD for one initially clustered population over 500 trial steps on a landscape with no feature interactions, with homophilous team reformation after each trial step. The level of noise in fitness evaluation varies between the three lines, as indicated.

FIGURE 8

Mean probability of patent survival (averaged over 100 trial steps and 100 random landscapes, each with 495 two-feature interactions), shown as a function of the number of patients in each trial. Note that increasing the number of cases decreases the noise in the fitness function. The fitness in the no noise case is computed using Eq. (1) directly rather than using Bernoulli trials, and therefore represents the asymptotic value for an infinite number of patients.

FIGURE 9

Mean Probability of survival on 100 random landscapes with 495 two-feature interactions as a function of the number of trial steps between team reformation. From top to bottom tabu is 2, 5 and 10, respectively. From left to right is noise-free or high noise (40 patients per trial), respectively.

FIGURE 10

Mean fitnesses on 100 random landscapes with 495 two-feature interactions and clustered initial populations, over different team sizes and the amount of information team members have regarding their teammates’ fitnesses. The horizontal line denotes the performance of random searchers.

FIGURE 11

a) Number of the hospitals in the VON network from 1990 till 2010 that either participated in NICQ collaboratives (dark blue), have complete records but didn’t participate in NICQs (light blue), or didn’t participate in NICQs and have incomplete records (dark red), b) More detailed view of the number of hospitals in NICQ collaboratives from 1995 till 2010. Each color represents the years that each given hospital first joined a NICQ focus group. X-axis labels only show the starting years of the six multi-year NICQ collaboratives.

FIGURE 12

a) Mean pairwise within subpopulation normalized Euclidean Distances (nEDs) (i.e., subpopulation closeness) in the NICQ and non-NICQ subpopulations, where nEDs are calculated for either 20 practices or 15 outcomes, b) Average of the mean pairwise normalized within team EDs for either randomly formed teams, real NICQ teams or homophilous teams (picked by PickSimilar algorithm). Euclidean Distances between individuals are calculated for either 20 practices or 15 outcomes. X-axis labels only show the starting years of 5 NICQ collaboratives in 1999–2010.