Using a co-evolutionary IS-alignment approach to understand EMR implementations

Research output: Chapter in Book/Report/Conference proceedingConference Article in proceedingAcademicpeer-review

Abstract

Electronic Medical Records (EMRs) are repositories of electronic medical histories of patients, main-tained over time. Hospital operations and EMRs typically become interdependent, due to the inclusion of medical workflow- and administrative process support as core functionalities. Hence, it is profoundly challenging to effectively enable complex, multi-stakeholder clinical processes, enhance patient care, and align EMRs with hospital strategies, goals, and needs. In this study, we build upon co-evolutionary IS-alignment (COISA) theories and argue that current approaches to business-IT alignment in hospitals should be reconceptualised, particularly regarding modern EMR implementations. In this effort, we respond to the call for more empirical research on business-IT co-evolution. We unfold how COISA manifests during EMR implementations using a multiple case study method. This method allows us to get a rich understanding of the complex social phenomena that emerge during EMR implementations. Outcomes show that COISA manifests in all three cases, involving different stakeholder groups, but in different localities and intensities. These findings suggest that COISA is a suitable framework to de-scribe and understand EMR implementations and that different configurations of interaction patterns can lead to comparable results. This understanding enables EMR practitioners to more effectively iden-tify improvement areas in dealing with internal and external complexity.
Original languageEnglish
Title of host publicationProceedings of the 27th European Conference on Information Systems (ECIS)
Place of PublicationStockholm & Uppsala, Sweden
PublisherAssociation for Information Systems
Number of pages15
ISBN (Electronic)9781733632508
Publication statusPublished - Jun 2019

Keywords

  • co-evolutionary is-alignment (COISA)
  • complexity science
  • electronic medical records (EMR)
  • hospitals

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