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IIR 13-040 – HSR Study

IIR 13-040
Sensemaking in VHA Health Care Systems: A Focus on Readmissions
Jacqueline A Pugh, MD BA
South Texas Health Care System, San Antonio, TX
San Antonio, TX
Funding Period: July 2014 - September 2018
Effective healthcare delivery requires managing interdependencies between multiple providers across diverse professions, as well as between organizational units with unique purposes and work. For the patient navigating the boundaries between units, it should feel like a well-constructed continuum. Providers in each part should communicate and coordinate with patients and each other for the explicit purpose of making sense of health issues and creating a shared understanding to help patients achieve wellness. The VHA is theoretically positioned to deliver integrated care along such a continuum. Despite this, VHA's performance has been similar to or worse than the Medicare population with regard to outcomes that reflect complex interdependencies, such as readmissions. Poor outcomes may be related to difficulties managing the complex interdependencies among organizational units in VHA and to a lack of effective sensemaking among individuals regarding how best to coordinate Veteran needs.

This study used qualitative methods and agent-based modeling to study readmissions as an exemplar of processes requiring a high level of interdependencies and sensemaking. By studying VHA facilities that have or have not successfully improved early readmissions over a 4 year period of time, our goal was to improve our understanding of both readmissions processes and the sensemaking within the organization needed to implement complex, integrated change.

Using reduction of early readmissions as an example of a task that requires management of complex organizational interdependencies: 1) Elucidate how sensemaking unfolds across professional cultures and organizational units to reduce readmissions, the origins of which cannot easily be attributed to any one single organizational or patient level failure; 2) Build an agent-based model based on results from the qualitative data to gain further insights into the interdependencies and relative effects of the various agents and their actions on readmission rates.

Mixed methods case studies of 6 facilities with significant improvement and 4 facilities with a significant worsening in their readmission rates from FY07 through FY11 and a minimum of 100 admissions per month were conducted. 314 (21-41 per site) semi-structured interviews with multiple levels of leadership and frontline staff: facility, inpatient services (Medicine/Hospital Medicine, Surgery, Psychiatry), Primary Care, and Geriatrics with a focus on frontline staff involved in discharge planning and inpatient/outpatient or vice/versa hand-offs were obtained. Interviews probed on the construct of sensemaking described by Weick, Sutcliffe and others. Direct observations were made of the work of inpatient teams around discharge planning (>60 observations), the work of any discharge coordinators or advocates, and committees dealing with discharges to identify care transition processes and how groups related and made sense with one another. We observed over 1200 patient care discussions at 44 Interdisciplinary team (IDTs) meetings across the 10 VAMCs. Based on data from the case studies, an agent-based model is being built to better understand how sensemaking emerges to create lower readmission rates from relationships between: physicians; physicians and nurse coordinators and/social workers; leadership and inpatient teams; and leadership and efforts to support patient needs after discharge.

The study was undertaken during a time when all VA facilities had a downtrend in readmission rates. Even so, facilities that had more evidence-based care transition processes to reduce readmissions in place were more likely to have a lower risk-adjusted readmission rate in the 18 months surrounding the site visit (R2=.54, P<.02). We observed a high degree of variability in how programs were structured and resourced, even for programs that were mandated or standardized across the VA. Some of this variability demonstrated local program adaptation and innovation. Sensemaking related to causes of readmissions clustered in 3 domains: (1)patients/families, (2) internal facility characteristics and (3) factors external to the VA facility. We observed variability in how individuals within a subset of VAMCs made sense of the need to improve care transitions and readmissions and their and their organization's ability to impact readmissions. The association of this variability with readmissions is being explored. Some of the observed variability occurred between front-line providers and leadership, and some occurred between providers of different professions. We also observed variability between facilities in the degree to which readmissions were attributed to patients and their behaviors. Staff and providers at some facilities exhibited more nuanced recognition of the severity of diseases contributing to readmissions as well as the complexity of many patients' social situations. Sensemaking about internal to VA causes of readmissions varied across sites, with some focusing on lack of resources and care transition gaps, others focusing on poor quality hand-offs, and others on process failures. Attributions of readmissions to lack of external/nonVA resources were rarely expressed. Surprisingly few facilities had true bridging programs and those that did, focused on moderate- high-risk patients.

Our findings suggest important associations between how individuals make sense of the causes of readmission, and their local implementation of care transitions programs and interpretation of local readmission rates. Our results could form the basis for interventions to change how individuals make sense of care transitions and readmissions as a strategy for improvement. In addition to usual dissemination routes external to the VA, we will (a) propose cyberseminars, (b) work with GEC and the FIX Committee to disseminate findings to their leadership and providers, and (c) incorporate our findings into the work of the newly funded Dole Center of Excellence The agent based model will be made available to all VAMCs as a tool to identify areas of highest impact for readmission reduction efforts. Innovative programs observed will be explored for replication. Methodologic impact includes lessons learned from building agent based models from extensive qualitative data.

External Links for this Project

NIH Reporter

Grant Number: I01HX001343-01

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Journal Articles

  1. Pugh J, Penney LS, Noël PH, Neller S, Mader M, Finley EP, Lanham HJ, Leykum L. Evidence based processes to prevent readmissions: more is better, a ten-site observational study. BMC health services research. 2021 Mar 1; 21(1):189. [view]
  2. Penney LS, Nahid M, Leykum LK, Lanham HJ, Noël PH, Finley EP, Pugh J. Interventions to reduce readmissions: can complex adaptive system theory explain the heterogeneity in effectiveness? A systematic review. BMC health services research. 2018 Nov 26; 18(1):894. [view]
  3. Penney LS, Leykum LK, Noël 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 Apr 7; 8(4):e020169. [view]

DRA: Health Systems
DRE: Prevention, Prognosis
Keywords: none
MeSH Terms: none

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