Intersectoral collaboration at a decentralized level: information flows in child welfare and healthcare networks

As needs of families with social and behavioral health problems often exceed the expertise and possibilities of a single professional, service or organization, cross-service collaboration is indispensable to adequately meeting those needs [1‐3]. It is for that reason important that organizations within the child welfare and healthcare service system collaborate sufficiently, for instance by sharing resources such as staff, equipment, information about clients’ conditions and effective treatment. Otherwise, the risk that these families receive an inadequate treatment or fall through the organizational cracks of that system is considerable [4, 5]. To this end, there has been - in the past 10 years - a progressive focus on organizing integrated care through collaborating in cross-sectoral service delivery networks [6‐11]. Unfortunately, service fragmentation and service duplication remain persistent problems in child welfare and healthcare service delivery systems [4, 5].

A crucial means to overcome these problems is information exchange between the organizations that constitute a network of welfare and healthcare services [2, 5, 9, 12‐16]. There is strong evidence that sharing information - including case reports and substantive expertise - in an accessible and comprehensible way is an important facilitator to provide integrated care [5, 8, 17]. Information exchange between organizations is vital for a shared understanding of families’ needs, a timely response and inter-professional collaboration within a welfare and healthcare service system [2, 17]. Therefore, to get a grip on these key processes and to ultimately achieve an effectively operating care network, insight into the flow of information is essential not only for public management scholars building theory on networks, but also for network managers and public policy officials [4, 18]. One way of achieving a better understanding of information flows is by analyzing the structure of information exchange relationships [19]. In knowledge networks literature, the focus is frequently on the structural properties of networks [20].

Within a network, different structures can exist. Resource dependency theory argues that organizations in a network will interact with those other network members that control access to the resources they need [21]. The type of resource being considered in the interactions affects the structural properties of networks, because it influences the intrinsic characteristics of organizations. These intrinsic characteristics, such as resource dependency and remit of activity, determine the position and role played by organizations in the interorganizational network [22]. As a result, structures of network relationships can be explained by the tangibility of resources being exchanged in the network [23‐25]. Examples of resources are staff, equipment, influence, reputation, referrals, and information. The more tangible the resources that are exchanged in a network, the more likely it is that the structure of relationships based on that resource will be centralized around one or a small number of key organizations, as this organization controls (or these organizations control) access to these resources [25]. The exchange of intangible resources, on the other hand, tends to be diffused among several organizations in the network [25]. This distinction in terms of tangibility also applies for the nature of information, ranging from tangible, material information (contracts, directives, commissions, and invoices) to intangible, knowledge-based information (verbal case reports, interprofessional consultation regarding clients’ conditions and effective treatment), more referred to as the tacit–explicit dimension of knowledge [20]. Therefore, we expect that there will be different structures of information exchange within a network. However, given the limited prior research on this topic [26], it is unclear whether such structures of information exchange exist within networks and if so, to what extent networks differ amongst each other in this respect.

In addition, networks are not static but dynamic systems [27‐29]. Consequently, it is to be expected that information exchange relationships are continuously evolving, as information exchange is one of the key processes in a network [30, 31]. As a network system matures over time, relationships may become more cemented and robust [32, 33]. Such stability of network relationships turns out to be a major factor in explaining network effectiveness regarding client services [34]. Conversely, flexibility is important for ensuring rapid network responses in ways that meet changing families’ needs [35]. However, studies applying longitudinal network analyses in the field of (child) welfare and healthcare services are scarce [4, 26, 36, 37]. Therefore, it is unclear how structures of a network vary over time and whether the relations between the individual organizations, i.e., the internal network dynamics, remain the same over time.

Hence, this study explores and compares the development of structures of information exchange in networks over time, concerning both material and knowledge-based information. The research questions are: 1) To what extent can structures of respectively material and knowledge-based information exchange be distinguished in child welfare and healthcare networks? and 2) To what extent do these overall structures change over time and is that pattern similar to the internal network dynamics?

As Table 3 shows, the material and knowledge-based information exchange structures are clearly distinguishable per full network. In all three networks, the knowledge-based information structure has more than twice as many relations (ties) between organizations as well as a larger overall connectedness (density) compared to the material information structures. In addition, except for Network II in 2019, the exchange of material information takes place in a more centralized structure than the exchange of knowledge-based information, as the degree centralization scores for the material information structures are higher. In 2019, the knowledge-based information structure of Network III had a relatively high density score (.28 the highest score) coupled with a relatively low degree centralization score (.46 the lowest score).

To test the significance of the differences between the material and knowledge-based information exchange structures and the significance of the differences over time, we conducted the same analyses focusing on only the organizations that are members of the networks in both years (respectively 119, 65 and 71 organizations in Network I, II and III). Table 4 presents, per network, the results of the compare density procedure of two types of information exchange structures. For all three networks in both years, there is a significant difference between the densities of the material and knowledge-based information exchange structures. Over time, there was no change in density for material information exchange per network. For knowledge-based information exchange, only the density in Network I increased statistically significantly (from .15 to .19).

Table 5 presents the degree centralization scores for the three networks, focusing on only the organizations that are members of the networks in both years. For material information exchange, in Network II and III, there was just a small change in network degree centralization from 2018 to 2019. In Network I the degree centralization of the material information structure increased from .72 to .86. Once again, for all three knowledge-based information exchange structures, the degree centralization scores changed over time. In Network I, there is a large increase of degree centralization (from .54 to .75). Network II also saw an increase in degree centralization (from .60 to .76), but the knowledge-based information exchange in Network III became more diffused, as the degree centralization score decreased from .62 to .47.

Beside the changes in the overall structures, the internal network dynamics were examined by calculating the overlap between the structures in both years. Table 6 presents the results of the QAP correlation procedure. There are statistically significant correlations between both material and knowledge-based information exchange structures over time. In Network I, 42% of the knowledge-based information exchange relations between organizations within this structure in 2019 were the same as those in 2018. For Network II and Network III, that is respectively 45 and 50% of the relations. For material information exchange, the sizes of the significant correlation are smaller, ranging from 22 to 39% of the relations.

This study shows that in child welfare and healthcare networks, different structures of information exchange can be distinguished, comprising material and knowledge-based information. The overall connectedness (density) of the studied structures of the networks is quite similar, but the way in which the involvement is structured – degree centralization – turns out to differ between the networks. Over time, the overall connectedness of those structures appears to be stable, but the internal dynamics reveals a major change in relationships between organizations in the networks.

Our findings regarding the first research question of this study generally are consistent with results of earlier research on resource tangibility [24, 25]. Based on the global measures of density and degree centralization, the difference in information tangibility distinguishes significant different structures in the networks. The exchange of knowledge-based information (verbal case reports, interprofessional consultation regarding clients’ conditions and effective treatment) clearly takes place in a more connected and less centralized structure than the exchange of material information (contracts, directives, commissions, invoices). The three studied child welfare and healthcare networks generally show the same pattern. Further analysis of the degree centrality scores per organization shows that the structures of relationships based on material information exchange are centralized around one organization, while for knowledge-based information exchange the relations are centralized around a group of five to six key organizations. This means that in the exchange of material information one organization plays a central role, while in the exchange of knowledge-based information five to six central organizations are closely involved. This structural pattern can be explained by the functions of the organizations in the network that are involved in exchanging material or knowledge-based information. Just the gatekeeper or the municipal government’s procurement and contracting department plays a central role in the exchange of material information. Gatekeepers are organizations that are legally authorized to commission child and youth services covered by the Child and Youth Act. By contrast, the exchange of knowledge-based information involves five to six organizations with various tasks (gatekeeper, signaling and providing services). Thus, despite the relatively high degree centralization scores of the knowledge-based information exchange structures in Network I and II in 2019 (resp. 0.75 and 0.76), the exchange of knowledge-based information is diffused among several functions in the network.

According to resource dependency theory, whoever has control over resources has power over those who need these resources [21]. Based on this logic, we expect that the presence of two highly different information exchange structures within a network could potentially have consequences for the governance of the network, as these different structures influence the power and control mechanisms in the network [34, 49]. Network managers should acknowledge that the diffused exchange of knowledge-based information among several organizations in the network indicates high levels of professional autonomy. That requires a different approach than the highly centralized material information exchange, suggesting a high level of administrative control over the organizations in the network [24]. To further explore to what extent the power and control structure may be influenced by different structures in a network, further research should examine which organizations fulfill a key role and linking-pin position within these structures of the network.

Our findings regarding the second research question of this study stand out. Comparing the networks over time, we found that the information exchange relationships within the networks are not very cemented. With a loss of more than a half of the relations, the relationships between the organizations in the network are not very stable over time. The material information exchange relationships changed significantly; in 2019, only 22 to 39% of these relations were the same as in 2018. This is notable, as the number of material information exchange relations per network are relatively low and with a high degree centralization. As a network matures over time, knowledge and information about network members, especially regarding core organizations, will spread and relationships become more cemented [32]. For that reason, it is to be expected that the highly centralized material exchange relations are relatively easy to stabilize. On the other hand, one should consider that any change in an originally low number of relations will already imply a relatively large loss of relations.

Based on the finding that while the overall connectedness of the networks is relatively stable, the relationships between organizations and the way in which these relationships are distributed change considerably over time, we argue that time matters for child welfare and healthcare networks. Apparently, information exchange structures need more than 3 years to regroup after a major shakeup like a decentralization of the child welfare and healthcare system: a period previously indicated as sufficient time for networks to stabilize [59]. The found instability of relations within the network is relevant, as the welfare and healthcare state reforms were precisely meant to strengthen the relations between the different child welfare and healthcare services [60‐62]. In addition, it is known from business and industry sectors that loss of relations is an important factor for social networks, as it leads to a loss of social capital and ultimately affects service sustainability [63]. Accordingly, it is very important to understand the loss of information exchange relationships, especially knowledge-based information exchange relations, since stability in such relations is crucial for interprofessional collaboration and integrated care [2]. To examine whether the time required to stabilize is longer for information exchange relationships or whether these relationships are always flexible, further research should be longitudinal with several measuring points in time.

Our study emphasizes that child welfare and healthcare networks can be defined as complex collaborations with very different information flows, as it provides empirical evidence of the existence of and differences between structures and dynamics of both material and knowledge-based information exchange relationships. Due to the scarcity of longitudinal comparative whole network research in the field and despite the limitations, the strength of this study is a deeper understanding of structures within networks. The discovery of the contrast between the major internal dynamics and the stable overall connectedness has implications for network policy and management. It has implications for what to expect of interprofessional collaboration and the delivery of integrated care, which has been one of the main goals of the decentralization [60‐62]. An important point of concern for network managers and public officials is that stability of information exchange relationships is not at all a matter of course. Due to this impermanence of relationships, integrated care cannot be guaranteed, and for that reason, management strategies to build and preserve internal stability should be considered [65].