Skip to main content

Interdisciplinary Team Meetings in Practice: an Observational Study of IDTs, Sensemaking Around Care Transitions, and Readmission Rates

Abstract

Background

Interdisciplinary teams (IDTs) have been implemented to improve collaboration in hospital care, but their impact on patient outcomes, including readmissions, has been mixed. These mixed results might be rooted in differences in organization of IDT meetings between hospitals, as well as variation in IDT characteristics and function. We hypothesize that relationships between IDT members are an important team characteristic, influencing IDT function in terms of how members make sense of what is happening with patients, a process called sensemaking

Objective

(1) To describe how IDT meetings are organized in practice, (2) assess differences in IDT member relationships and sensemaking during patient discussions, and (3) explore their potential association with risk-stratified readmission rates (RSRRs).

Design

Observational, explanatory convergent mixed-methods case-comparison study of IDT meetings in 10 Veterans Affairs hospitals.

Participants

Clinicians participating in IDTs and facility leadership.

Approach

Three-person teams observed and recorded IDT meetings during week-long visits. We used observational data to characterize relationships and sensemaking during IDT patient discussions. To assess sensemaking, we used 2 frameworks that reflected sensemaking around each patient’s situation generally, and around care transitions specifically. We examined the association between IDT relationships and sensemaking, and RSRRs.

Key Results

We observed variability in IDT organization, characteristics, and function across 10 hospitals. This variability was greater between hospitals than between teams at the same hospital. Relationship characteristics and both types of sensemaking were all significantly, positively correlated. General sensemaking regarding each patient was significantly negatively associated with RSRR (− 0.65, p = 0.044).

Conclusions

IDTs vary not only in how they are organized, but also in team relationships and sensemaking. Though our design does not allow for inferences of causation, these differences may be associated with hospital readmission rates.

This is a preview of subscription content, access via your institution.

Figure 1

References

  1. Kripilani S, Theobald CN, Anctil B, Vasilevskis EE. Reducing hospital readmission: current strategies and future directions. Annu Rev Med 2014. 65:471-85.

    Article  Google Scholar 

  2. Goncalves-Bradley DC, Lannin NA, Clemson LM, Cameron ID, Shepperd S. Discharge planning from hospital. Cochrane Database Syst Rev 2016(1). https://doi.org/10.1002/14651858.CD000313.pub5

  3. The Joint Commission. Transitions of care. The need for a more effective approach to continuing patient care. Hot Topics in Health Care. 2012. Joint Commission Center for Transforming Healthcare.

  4. Gonzalo JD, Kuperman E, Lehman E, Haidet P. Bedside interprofessional rounds: perceptions of benefits and barriers by internal medicine nursing staff, attending physicians, and house staff physicians. J Hosp Med 2014. 9(10):646-51.

    Article  Google Scholar 

  5. Pannick S, Beveridge I, Wachter RM, Sevdalis N. Improving the quality and safety of care on the medical ward: a review and synthesis of the evidence base. Eur J Intern Med. 2014;25(10):874–887.

    Article  Google Scholar 

  6. Bhamidipati VS, Elliott DJ, Justice EM, Belleh E, Sonnad SS, Robinson EJ. Structure and outcomes of inter-disciplinary rounds in hospitalized medicine patients: a systematic review and suggested taxonomny. J Hosp Med. 2016;11:513–523.

    Article  Google Scholar 

  7. Ratelle JT, Sawatsky AP, Kashiwagi DT, Schouten WM, Erwin PJ, Gonzalo JD, Beckman TJ, West CP. Implementing bedside rounds to improve patient-centered outcomes: a systematic review. BMJ Qual Saf 2019;28:317-326.

    Article  Google Scholar 

  8. O’Leary KJ, Johnson JK, Auerbach AD. Do interdisciplinary rounds improve patient outcomes? Only if they improve teamwork. J Hosp Med 2016;11(7)524-525.

    Article  Google Scholar 

  9. Leykum LK, O'Leary K. Annals for hospitalists inpatient notes - sensemaking—fostering a shared understanding in clinical teams. Ann Intern Med. 2017;167:HO2–HO3. https://doi.org/10.7326/M17-1829

    Article  PubMed  Google Scholar 

  10. Leykum LK, Chesser H, Lanham HJ, Carla P, Palmer R, Ratcliffe T, Reisinger H, Agar M, Pugh J. The association between sensemaking during team rounds and hospitalized patient outcomes. J Gen Intern Med 2015;30(12):1821-7

    Article  Google Scholar 

  11. Blatt R, Christianson MK, Sutcliffe KM, Rosenthal MM. A sensemaking lens on reliability. J Organ Behav. 2006;27(7):897-917.

    Article  Google Scholar 

  12. Lanham HJ, McDaniel Jr RR, Crabtree BF, Miller WL, Stange KC, Tallia AF, Nutting PA. How improving practice relationships among clinicians and nonclinicians can improve quality in primary care. Jt Comm J Qual Patient Saf. 2009;35(9):457-AP2.

    PubMed  PubMed Central  Google Scholar 

  13. McAllister C, Leykum LK, Lanham H, Reisinger HS, Kohn JL, Palmer R, Pezzia C, Agar M, Parchman M, Pugh J, McDaniel RR. Relationship with inpatient physician housestaff teams and their association with hospitalized patient outcomes. J Hosp Med 2014;9:764-771.

    Article  Google Scholar 

  14. Lanham HJ, Palmer RF, Leykum LK, McDaniel Jr RR, Nutting PA, Stange KC, Crabtree BF, Miller WL, Jaén CR. Trust and reflection in primary care practice redesign. Health Serv Res. 2016;51(4):1489-514.

    Article  Google Scholar 

  15. Leykum LK, Lanham HJ, Pugh JA, Parchman M, Anderson RA, Crabtree BF, Nutting PA, Miller WL, Stange KC, McDaniel RR. Manifestations and implications of uncertainty for improving healthcare systems: an analysis of observational and interventional studies grounded in complexity science. Implement Sci. 2014;9:165. https://doi.org/10.1186/s13012-014-0165-1

    Article  PubMed  PubMed Central  Google Scholar 

  16. Ghaferi AA, Birkmeyer JD, Dimick JB. Complications, failure to rescue, and mortality with major inpatient surgery in medicare patients. Ann Surg. 2009. 350(6):1029-34.

    Article  Google Scholar 

  17. Palinkas LA, Aarons GA, Horwitz S, Chamberlain P, Hurlburt M, Landsverk J. Mixed methods designs in implementation research. Adm Policy Ment Health. 2011. 38:44-53.

    Article  Google Scholar 

  18. Eisenhardt KM. Building theories from case study research. Acad Manag Rev. 1989;14(4):532-50.

    Article  Google Scholar 

  19. Penney LS, Leykum LK, Noel P, Finley EP, Lanham HJ, Pugh J. Protocol for a mixed methods study of hospital readmissions: sensemaking in Veterans Health Administration healthcare system in the USA. BMJ Open 2018:8(4):e020169. https://doi.org/10.1136/bmjopen-2017-020169.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Forman J, Damschroder L. Qualitative Content Analysis. In: Empirical Methods for Bioethics: a Primer 2007 Dec 5. Emerald Group Publishing Limited.

  21. Pelled LH, Eisenhardt KM, Xin KR. Exploring the black box: an analysis of work group diversity, conflict and performance. Adm Sci Q. 1999;44(1):1-28.

    Article  Google Scholar 

  22. Kilduff M, Angelmar R, Mehra A. Top management-team diversity and firm performance: examining the role of cognitions. Organ Sci. 2000;11(1):21-34.

    Article  Google Scholar 

  23. Martins LL, Schilpzand MC, Kirkman BL, Ivanaj S, Ivanaj V. A contingency view of the effects of cognitive diversity on team performance: the moderating roles of team psychological safety and relationship conflict. Small Group Res. 2013;44(2):96-126.

    Article  Google Scholar 

  24. Weick KE, Roberts KH. Collective mind in organizations: heedful interrelating on flight decks. Adm Sci Q. 1993 1:357-81.

    Article  Google Scholar 

  25. Weick KE. The collapse of sensemaking in organizations: the Mann Gulch disaster. Adm Sci Q. 1993 1:628-52.

    Article  Google Scholar 

  26. Sutcliffe KM, Lewton E, Rosenthan MM. Communication failures: an insidious contributor to medical mistakes. Acad Med. 2005;79:186–95.

    Article  Google Scholar 

  27. Vogus TJ, Sutcliffe KM. The Safety Organizing Scale: development and validation of a behavioral measure of safety culture in hospital nursing units. Med Care. 2007 1:46-54.

    Article  Google Scholar 

  28. Maitlis S, Christianson M. Sensemaking in organizations: taking stock and moving forward. Acad Manag Ann. 2014;8(1). https://doi.org/10.5465/19416520.2014.873177.

  29. Weick KE. Sensemaking in organizations. Sage, 1995.

    Google Scholar 

  30. The 8P Screening Tool: identifying your patient’s risk for adverse events after discharge. Society of Hospital Medicine Project Boost. https://www.hospitalmedicine.org/globalassets/clinical-topics/clinical-pdf/8ps_riskassess-1.pdf. Accessed 31 Jul 2022.

  31. Hansen LO, Greenwald JL, Budnitz T, Howell E, Halasyamani L, Maynard G, Vidyarthi A, Coleman EA, Williams MV. Project BOOST: effectiveness of a multihospital effort to reduce rehospitalization. J Hosp Med 2013;8:421-427.

    Article  Google Scholar 

  32. Barrett M, Raetzman S, Andrews R. Overview of key readmission measures and methods. 2012. HCUP Methods Series Report #2012-04. ONLINE December 20, 2012. U.S. Agency for Healthcare Research and Quality. Available: http://www.hcup- us.ahrq.gov/reports/methods/methods.jsp. Accessed 31 Jul 2022.

  33. VA Hospital-Wide 30-day Readmission (HWR) Cube, a product of the Veterans Health Administration Support Service Center (VSSC) VSSC URL: (URL: https://bioffice.pa.cdw.va.gov/default.aspx?bookid=99284cd4-f909-4eee-aa8e-49065f12afeb|ispasFalse|report04a80ff9-b3fd-4223-a583-47d3859101d1|ws1|wsb0|isDisabledAnalyticsFalse|isDashboardPanelOnTrue). (Note: the Cube can also be accessed by going to the Quality of Care page on the VSSC website (URL: https://vssc.med.va.gov/VSSCMainApp/products.aspx?PgmArea=82) and then clicking on Product Name: Hospital-Wide 30-day Readmission Cube.)

  34. Pugh, J., Penney, L.S., Noël, P.H. et al. Evidence based processes to prevent readmissions: more is better, a ten-site observational study. BMC Health Serv Res 21, 189 (2021). https://doi.org/10.1186/s12913-021-06193-x

    Article  PubMed  PubMed Central  Google Scholar 

  35. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna; 2018. https://www.R-project.org/.

    Google Scholar 

Download references

Acknowledgements

Contributors: We acknowledge the contributions of the Department of Veterans Affairs employees who participated in this study, and the directors and chiefs of staff who supported this work. We also thank Musarrat Nahid for her work on this study.

Funding

This work was funded by the Department of Affairs Health Services Research & Development Services Grant #HSR1-031-12W. The views expressed do not reflect the position of the Department of Veterans Affairs.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Luci K. Leykum MD, MBA, MSc.

Ethics declarations

Conflict of Interest

The authors declare that they do not have a conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Prior presentation: None

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 41.8 kb)

ESM 2

(DOCX 41.8 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Leykum, L.K., Noël, P.H., Penney, L.S. et al. Interdisciplinary Team Meetings in Practice: an Observational Study of IDTs, Sensemaking Around Care Transitions, and Readmission Rates. J GEN INTERN MED (2022). https://doi.org/10.1007/s11606-022-07744-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11606-022-07744-6

KEY Words

  • care transitions
  • readmissions
  • interdisciplinary teams
  • sensemaking