General medicine at the frontline of acute care delivery: Comparison with family medicine hospitalist model implementation in Singapore

Abstract

Introduction: This mixed methods study seeks to assess the clinical outcomes and qualitative insights associated with the pragmatic implementation of a general medicine (GM) physician-led inpatient care model, first at Alexandra Hospital (AH) and then at Sengkang General Hospital (SKH), and to compare them with findings reported in an earlier implementation of the family medicine (FM) hospitalist model in Singapore. Methods: Anonymous quantitative demographic and clinical data including length of stay, 30-day readmission rate, inpatient mortality rate and gross cost of hospitalisation bills were extracted from the hospital information system. Comparative analyses with the FM hospitalist model and usual care were made. Secondary qualitative data that were gathered focused on increased understanding of the proposed model, its perceived challenges and future opportunities for its implementation. Results: The adapted GM care model implemented first at AH and then at SKH seemed to suggest that such a model run by physicians from various backgrounds was capable of producing similarly superior outcomes when compared with the FM hospitalist model piloted in 2011, which was juxtaposed with usual care. With regard to qualitative insights, three findings were reported: (a) perception of and mindset in relation to generalists, which illustrates the barriers to implementing GM; (b) education and training of generalists, which underlines the current lack of adequate supply of GM specialists; and (c) operational issues of care model implementation, which highlights the current mismatches between the prevailing healthcare philosophy and the requirements for successful implementation of the GM care model. Conclusion: The success of a GM care model hinges on how it is operationalised. With clear protocols, definitions, and a high level of protocol compliance by healthcare team members, the intended outcomes show promise for replication at other interested sites. © The Author(s) 2020.