Background: Clinical decision support systems (CDSS) have the potential to improve patient safety and reduce costs in primary care. However, CDSS adoption remains limited due to development and implementation challenges. CDSSs are complex interventions involving multiple interacting components that require technological innovation and behavioral and organizational change. Additionally, the primary care context is considered a complex system with high care demand, fragmented structures, and many independent yet interdependent organizations. Established determinant frameworks for implementing and scaling up complex health care interventions support the identification of implementation determinants. However, they offer limited guidance on the underlying processes of these determinants, such as the implementation processes involved in complex interorganizational collaboration in primary care. Objective: This study examined how an interorganizational collaboration in Dutch primary care ( []) achieved an iterative CDSS development and implementation. We aimed to identify the mechanisms that supported the collaboration in overcoming challenges. Methods: We performed an exploratory process-level case study. Data were collected through 15 semistructured interviews. The nonadoption, abandonment, scale-up, spread, and sustainability framework was used to ensure comprehensive topic coverage during the interviews, but not as an analytical framework. We triangulated the interviews with internal and external documents and expert input. Using a thematic, inductive approach, we developed a chronological overview of the collaboration and identified mechanisms offering insights into how GzGr navigated complexity in the development and implementation of CDSS. Results: We identified two mechanisms: (1) enacting an interorganizational value model and (2) iterative, co-creative experimentation. First, GzGr was driven by a coalition of the willing (ie, individuals willing to take an extra step), with shared goals that prioritized collective benefit while respecting organizational values. They established shared principles that translated the broad GzGr mission into concrete CDSS development choices, while also guiding strategic expansion by involving mission-aligned, innovative organizations. Second, after initial prototypes, GzGr established an iterative learning and improvement experimentation for both the technology and the collaboration. This process allowed for rapid feedback, validation of added value, and ongoing refinement. Additionally, this experimentation approached the development and implementation phase as a continuous process involving multistakeholders, supporting both the technology and the collaboration. Conclusions: This study identified 2 mechanisms that sustained interorganizational collaboration and CDSS development. These mechanisms connected collaborative and technical changes across people, technology, and organizational levels, enabling technological viability, stakeholder value, and multilevel support. The mechanisms operated both within and between organizations through iterative cycles of development and implementation. Practical implications include involving multilevel, innovative, and influential stakeholders; maintaining alignment through an orchestrating actor; and adopting an iterative approach between development and implementation. Our findings extend existing determinant frameworks by offering process-level insights into how such mechanisms help overcome challenges in the development and implementation of CDSS within interorganizational collaborations.

Last year, the Department of Health and Human Services published a sweeping document that described the agency’s approach to real-world data. Historically, health and biomedical data has been intentionally manufactured, the output of carefully designed clinical trials. But in a digitized world, it can instead be mined — and patients’ interactions with the health care system are the natural resource.

The Living HHS Open Data Plan, published in July, proposed treating data more like we do other natural resources. “At the core” of the plan, it reads, “lies the concept that data is a ‘public utility’ for good that powers scientific advancement, innovation, and progress.” Patients should have access to that utility, HHS argued, but it should also be easier to leverage for research, safety monitoring, and other uses in the public interest. 

On Thursday, a group of researchers, former agency officials, and health data companies continued that call in a policy forum published in Science. If health data is to be treated like a public utility, they write, it should be similarly governed. Like electricity, the system would have to involve customers, local distribution companies, transmission companies, generators, and the government. 

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