Artifacts, Actors, and Interactions in the Cross-Project Coordination Practices of Open-Source Communities

While there has been some research on coordination in FLOSS, such research has focused on coordination within a project or within a group. The area of cross-project coordination, where shared goals are tenuous or non-existent, has been under-researched. This paper explores the question of how multiple projects working on a single piece of existing software in the FLOSS environment can coordinate. Using the Ordering Systems lens, we examine this question via a cross-case analysis of four projects performed on the open source game Jagged Alliance 2 (JA2) in the forum Bear’s Pit. Our main findings are that: (1) Ongoing cross-project ordering systems are influenced by the materiality of development artifacts. (2) The emergent trajectory of cross-project ordering systems is influenced by affordances that emerge from the interaction between the goals and desires of the project team building the development artifact, and the materiality of the development artifact. (3) When two parties need to coordinate in the ordering system, all or almost all coordination effort can be borne by a single party. Furthermore, over time, emergent FLOSS projects bear more coordination effort than stable, mature projects

Multiparty Sensemaking: A Technology-Vendor Selection Case Study

Information system (IS) procurement history is replete with poorly executed, multimillion dollar procurement decisions. Yet we barely understand what effective IS procurement should look like. IS procurement is highly challenging, as it requires the client to simultaneously select a technology and vendor. This paper explores the technology-vendor selection process through the sensemaking perspective. Our study develops a sensemaking model for technology-vendor selection that connects the multiple rounds of client-vendor communicative actions with the client\u27s sensemaking process. We show how the client reconciles fragmented and sometimes conflicting cues and information through three intertwined cycles: immediate, retrospective, and decision. Sensebreaking occurs as a separate process (and not a communicative action) when disruptive cues occur persistently and from different vendors over multiple rounds of sensemaking. We derive a set of critical factors, on the basis of the sensemaking perspective, for selecting an appropriate vendor and technical solution. These insights in turn help explain many poorly executed IS procurement decisions

Boundary Organization Practices for Collaboration in Enterprise Integration

Multiparty collaboration is particularly challenging in large-scale enterprise integration (EI) implementations as diverse organizational subunits and external consultants need to work together to enact transformational change. We argue from prior research that a boundary organization -- a formal organizational space that enables participants with divergent interests to collaborate -- provides a relatively durable structure to manage emergent tensions and conflicts among stakeholders in EI projects. To better understand how a boundary organization enables ongoing, dynamic, multiparty collaboration, we introduce the concept of boundary organization practices to describe practices enacted as part of the boundary organization to define the working rules and arrangements for diverse stakeholders to work together.We conducted a longitudinal exploratory case study of a multiyear EI implementation in a large logistics organization. Our analysis of the longitudinal data led us to identify three sets of boundary organization practices (i.e., organizing to negotiate, to contain, and to sustain). These sets of practices emerged to address the divergence that arose in each of the three phases (designing, realizing, and leveraging) of EI implementation. We also find artifacts to be an integral part of the boundary organization practices as they motivated, coordinated, and enabled collaboration among diverse stakeholders

Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare

Sepsis is a leading cause of death in hospitals. Early prediction and diagnosis of sepsis, which is critical in reducing mortality, is challenging as many of its signs and symptoms are similar to other less critical conditions. We develop an artificial intelligence algorithm, SERA algorithm, which uses both structured data and unstructured clinical notes to predict and diagnose sepsis. We test this algorithm with independent, clinical notes and achieve high predictive accuracy 12 hours before the onset of sepsis (AUC 0.94, sensitivity 0.87 and specificity 0.87). We compare the SERA algorithm against physician predictions and show the algorithm’s potential to increase the early detection of sepsis by up to 32% and reduce false positives by up to 17%. Mining unstructured clinical notes is shown to improve the algorithm’s accuracy compared to using only clinical measures for early warning 12 to 48 hours before the onset of sepsis.Ministry of Education (MOE)Published versionThis work is supported by the Ministry of Education, Singapore, under the Social Science Research Council Thematic Grant. Grant number: MOE2017-SSRTG-030

Prediction of Readmission in Geriatric Patients From Clinical Notes: Retrospective Text Mining Study

10.2196/26486Journal of Medical Internet Research2310e26486-e2648