Leveraging inter-organizational networks to scale up a sepsis recovery program: results from an application of the Making Optimal Decisions for Intervention Flexibility during Implementation (MODIFI) method
Abstract Background Nearly two million adults in the United States are hospitalized with sepsis yearly, with survivors facing complications that result in high rates of hospital readmission and mortality after discharge. We demonstrated improved outcomes following discharge among sepsis survivors wh...
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2025-05-01
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| Series: | Implementation Science Communications |
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| Online Access: | https://doi.org/10.1186/s43058-025-00743-8 |
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| author | Cheyenne R. Wagi Marc A. Kowalkowski Stephanie P. Taylor Aliza Randazzo Asha Ganesan Amit Khanal Sarah A. Birken |
| author_facet | Cheyenne R. Wagi Marc A. Kowalkowski Stephanie P. Taylor Aliza Randazzo Asha Ganesan Amit Khanal Sarah A. Birken |
| author_sort | Cheyenne R. Wagi |
| collection | DOAJ |
| description | Abstract Background Nearly two million adults in the United States are hospitalized with sepsis yearly, with survivors facing complications that result in high rates of hospital readmission and mortality after discharge. We demonstrated improved outcomes following discharge among sepsis survivors who participated in the Sepsis Transition And Recovery (STAR) program; however, important differences among hospitals require STAR’s adaptation to facilitate its implementation and ensure its effectiveness in new settings. Purpose The purpose of this study was to adapt STAR to hospitals with diverse characteristics. Methods We used the Making Optimal Decisions for Intervention Flexibility during Implementation (MODIFI) approach. We identified STAR core functions (i.e., effectiveness-driving features) using semi-structured key informant interviews (n = 7). We identified adaptations using semi-structured interviews with clinicians and leaders with expertise and oversight of resources related to transitions of care after sepsis hospitalization (n = 7) from four hospitals that systematically differed from the hospitals in which we originally found STAR to be effective. Results Network theory, which proposes that performance improves with more efficient flow of information within and across hospitals, underlays STAR’s eleven core functions. Adaptation included specific points-of-contact, communication preferences, and methods for achieving buy-in. We used proposed adaptations to tailor STAR protocols to each hospital. Conclusions We used MODIFI, a state-of-the-science method, to adapt a program that was effective in promoting transition and recovery in sepsis survivors to facilitate its scale-up to diverse hospitals. Future studies will assess STAR’s implementation and effectiveness in diverse hospitals. |
| format | Article |
| id | doaj-art-86dc767ebe364b52a36e723ca19009f6 |
| institution | DOAJ |
| issn | 2662-2211 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Implementation Science Communications |
| spelling | doaj-art-86dc767ebe364b52a36e723ca19009f62025-08-20T03:09:20ZengBMCImplementation Science Communications2662-22112025-05-016111210.1186/s43058-025-00743-8Leveraging inter-organizational networks to scale up a sepsis recovery program: results from an application of the Making Optimal Decisions for Intervention Flexibility during Implementation (MODIFI) methodCheyenne R. Wagi0Marc A. Kowalkowski1Stephanie P. Taylor2Aliza Randazzo3Asha Ganesan4Amit Khanal5Sarah A. Birken6Department of Implementation Science, Wake Forest University School of MedicineDepartment of Internal Medicine, Section on Hospital Medicine Wake Forest University School of Medicine Center for Health System SciencesDivision of Hospital Medicine University of MichiganDepartment of Implementation Science, Wake Forest University School of MedicineCenter for Health System Sciences Atrium HealthDepartment of Internal Medicine, Section on Hospital Medicine Wake Forest University School of MedicineDepartment of Implementation Science, Wake Forest University School of MedicineAbstract Background Nearly two million adults in the United States are hospitalized with sepsis yearly, with survivors facing complications that result in high rates of hospital readmission and mortality after discharge. We demonstrated improved outcomes following discharge among sepsis survivors who participated in the Sepsis Transition And Recovery (STAR) program; however, important differences among hospitals require STAR’s adaptation to facilitate its implementation and ensure its effectiveness in new settings. Purpose The purpose of this study was to adapt STAR to hospitals with diverse characteristics. Methods We used the Making Optimal Decisions for Intervention Flexibility during Implementation (MODIFI) approach. We identified STAR core functions (i.e., effectiveness-driving features) using semi-structured key informant interviews (n = 7). We identified adaptations using semi-structured interviews with clinicians and leaders with expertise and oversight of resources related to transitions of care after sepsis hospitalization (n = 7) from four hospitals that systematically differed from the hospitals in which we originally found STAR to be effective. Results Network theory, which proposes that performance improves with more efficient flow of information within and across hospitals, underlays STAR’s eleven core functions. Adaptation included specific points-of-contact, communication preferences, and methods for achieving buy-in. We used proposed adaptations to tailor STAR protocols to each hospital. Conclusions We used MODIFI, a state-of-the-science method, to adapt a program that was effective in promoting transition and recovery in sepsis survivors to facilitate its scale-up to diverse hospitals. Future studies will assess STAR’s implementation and effectiveness in diverse hospitals.https://doi.org/10.1186/s43058-025-00743-8SepsisEvidence-Based InterventionMODIFI for Implementation ScienceNetwork TheoryCore FunctionsAdaptations |
| spellingShingle | Cheyenne R. Wagi Marc A. Kowalkowski Stephanie P. Taylor Aliza Randazzo Asha Ganesan Amit Khanal Sarah A. Birken Leveraging inter-organizational networks to scale up a sepsis recovery program: results from an application of the Making Optimal Decisions for Intervention Flexibility during Implementation (MODIFI) method Implementation Science Communications Sepsis Evidence-Based Intervention MODIFI for Implementation Science Network Theory Core Functions Adaptations |
| title | Leveraging inter-organizational networks to scale up a sepsis recovery program: results from an application of the Making Optimal Decisions for Intervention Flexibility during Implementation (MODIFI) method |
| title_full | Leveraging inter-organizational networks to scale up a sepsis recovery program: results from an application of the Making Optimal Decisions for Intervention Flexibility during Implementation (MODIFI) method |
| title_fullStr | Leveraging inter-organizational networks to scale up a sepsis recovery program: results from an application of the Making Optimal Decisions for Intervention Flexibility during Implementation (MODIFI) method |
| title_full_unstemmed | Leveraging inter-organizational networks to scale up a sepsis recovery program: results from an application of the Making Optimal Decisions for Intervention Flexibility during Implementation (MODIFI) method |
| title_short | Leveraging inter-organizational networks to scale up a sepsis recovery program: results from an application of the Making Optimal Decisions for Intervention Flexibility during Implementation (MODIFI) method |
| title_sort | leveraging inter organizational networks to scale up a sepsis recovery program results from an application of the making optimal decisions for intervention flexibility during implementation modifi method |
| topic | Sepsis Evidence-Based Intervention MODIFI for Implementation Science Network Theory Core Functions Adaptations |
| url | https://doi.org/10.1186/s43058-025-00743-8 |
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