Leah L Zullig1,2,3,4, Heather E Whitson5,4,6,7,8, Susan N Hastings1,5,4,6,7, Chris Beadles4,9, Julia Kravchenko4,10, Igor Akushevich4,11, Matthew L Maciejewski12,13,14. 1. Center for Health Services Research in Primary Care, Durham Veterans Affairs Medical Center, 411 West Chapel Hill Street, Suite 600, Durham, NC, 27701, USA. 2. Division of General Internal Medicine, Department of Medicine, Duke University Medical Center, Durham, NC, USA. 3. Duke Cancer Institute, Duke University Medical Center, Durham, NC, USA. 4. Ambulatory Care Service, Durham Veterans Affairs Medical Center, Durham, NC, USA. 5. Geriatrics Research, Education and Clinical Center, Durham Veterans Affairs Medical Center, Durham, NC, USA. 6. Division of Geriatrics, Department of Medicine, Duke University, Durham, NC, USA. 7. Center for the Study of Aging and Human Development, Duke University, Durham, NC, USA. 8. Depart ment of Ophthalmology, Duke University Medical Center, Durham, NC, USA. 9. RTI, Chapel Hill, NC, USA. 10. Department of Surgery, Duke University Medical Center, Durham, NC, USA. 11. Social Science Research Institute, Durham, NC, USA. 12. Center for Health Services Research in Primary Care, Durham Veterans Affairs Medical Center, 411 West Chapel Hill Street, Suite 600, Durham, NC, 27701, USA. Matthew.Maciejewski@va.gov. 13. Division of General Internal Medicine, Department of Medicine, Duke University Medical Center, Durham, NC, USA. Matthew.Maciejewski@va.gov. 14. Ambulatory Care Service, Durham Veterans Affairs Medical Center, Durham, NC, USA. Matthew.Maciejewski@va.gov.
Abstract
BACKGROUND: Patient complexity is often operationalized by counting multiple chronic conditions (MCC) without considering contextual factors that can affect patient risk for adverse outcomes. OBJECTIVE: Our objective was to develop a conceptual model of complexity addressing gaps identified in a review of published conceptual models. DATA SOURCES: We searched for English-language MEDLINE papers published between 1 January 2004 and 16 January 2014. Two reviewers independently evaluated abstracts and all authors contributed to the development of the conceptual model in an iterative process. RESULTS: From 1606 identified abstracts, six conceptual models were selected. One additional model was identified through reference review. Each model had strengths, but several constructs were not fully considered: 1) contextual factors; 2) dynamics of complexity; 3) patients' preferences; 4) acute health shocks; and 5) resilience. Our Cycle of Complexity model illustrates relationships between acute shocks and medical events, healthcare access and utilization, workload and capacity, and patient preferences in the context of interpersonal, organizational, and community factors. CONCLUSIONS/IMPLICATIONS: This model may inform studies on the etiology of and changes in complexity, the relationship between complexity and patient outcomes, and intervention development to improve modifiable elements of complex patients.
BACKGROUND:Patient complexity is often operationalized by counting multiple chronic conditions (MCC) without considering contextual factors that can affect patient risk for adverse outcomes. OBJECTIVE: Our objective was to develop a conceptual model of complexity addressing gaps identified in a review of published conceptual models. DATA SOURCES: We searched for English-language MEDLINE papers published between 1 January 2004 and 16 January 2014. Two reviewers independently evaluated abstracts and all authors contributed to the development of the conceptual model in an iterative process. RESULTS: From 1606 identified abstracts, six conceptual models were selected. One additional model was identified through reference review. Each model had strengths, but several constructs were not fully considered: 1) contextual factors; 2) dynamics of complexity; 3) patients' preferences; 4) acute health shocks; and 5) resilience. Our Cycle of Complexity model illustrates relationships between acute shocks and medical events, healthcare access and utilization, workload and capacity, and patient preferences in the context of interpersonal, organizational, and community factors. CONCLUSIONS/IMPLICATIONS: This model may inform studies on the etiology of and changes in complexity, the relationship between complexity and patient outcomes, and intervention development to improve modifiable elements of complex patients.
Authors: María Vallet-Regí; Miguel Manzano; Leocadio Rodriguez-Mañas; Marta Checa López; Matti Aapro; Lodovico Balducci Journal: Oncologist Date: 2017-02-20
Authors: Dan V Blalock; Matthew L Maciejewski; Donna M Zulman; Valerie A Smith; Janet Grubber; Ann-Marie Rosland; Hollis J Weidenbacher; Liberty Greene; Leah L Zullig; Heather E Whitson; Susan N Hastings; Anna Hung Journal: Med Care Date: 2021-05-01 Impact factor: 3.178
Authors: Leah Tuzzio; Andrew L Berry; Kathy Gleason; Jennifer Barrow; Elizabeth A Bayliss; Marlaine Figueroa Gray; Thomas Delate; Zoe Bermet; Connie S Uratsu; Richard W Grant; James D Ralston Journal: Health Serv Res Date: 2021-08-25 Impact factor: 3.734