Poor care coordination is pervasive and a principal cause of avoidable morbidity, mortality, resource use, and patient and health care team dissatisfaction. Veterans are in particular need of optimal care coordination, given that many suffer from multimorbid conditions, mental health problems, and a challenging socioeconomic environment. Furthermore, in the era of the Choice Act, coordination of VA with non-VA care is increasingly challenging and important. Multidisciplinary care teams, such as the Veterans Administration's (VA) Patient Aligned Care Teams (PACTs), have been proposed as one of multiple strategies to improve care coordination in the primary care setting. For such a strategy to succeed, PACT members must excel at the act of coordinating, i.e., working collectively on interdependent tasks to deliver evidence-based care that could not be accomplished as effectively by a single provider. However, the ability to monitor team coordination is still in its infancy. A clear understanding of the objectives and standards of coordination, as well as the information needs at the point of care, are essential to successfully coordinating care.
Our objective is to determine the point-of-care information PACT members need to successfully coordinate care, via three aims: 1. Develop measurable criteria for effective coordination in PACTs, prioritized and weighted by contribution to overall quality of care. 2. Using the criteria developed in Aim 1, determine the specific information needed at the point of care to improve coordination and recommend point-of-care aids for delivering the needed information. 3. Assess the effect of adopting the aforementioned coordination criteria on PACT clinicians' coordination behaviors.
AIM 1: An intact primary care clinic from VISN 12 consisting of up to 11 clinical personnel participated in a series of structured, facilitated focus groups following the Productivity Measurement and Enhancement System protocol (ProMES, a structured methodology for developing performance measures and assessing productivity) (Pritchard, Weaver & Ashwood, 2011) to (a) identify primary care coordination objectives, (b) develop/identify indicators of effective coordination based on the identified objectives, and (c) prioritize said indicators according to their value to the organization. Structured notes were collected at each focus group, compiled, and organized to extract the aforementioned products (objectives, indicators, priorities).
AIM 2: After completion of Aim 1, the same participants from Aim 1 participated in three traditional focus groups to identify what information was needed at the point of care to improve performance in each indicator developed in Aim 1. Structured field notes from the focus groups were content-analyzed to identify organizing and recurring themes.
AIM 3: 34 PACTs representing four facilities from VISNs 12 and 16 participated in a controlled trial testing the effectiveness of a team-based audit-and-feedback intervention at improving performance on the coordination indicators developed in Aim 1. The intervention consisted of a monthly, ProMES-style feedback report that included raw performance scores and standardized effectiveness scores for each indicator (displayed in order of priority) as well as an overall composite. Each month the report was followed by facilitated team debriefs following a Team Dimensional Training (TDT)-style protocol (Smith-Jentsch, Zeisig, & Acton, 1998). An additional 34 matched control teams from five VA facilities were followed passively. Outcome measure data were collected for the intervention teams from multiple VA data sources including the corporate data warehouse (CDW) and VSSC. Matching outcome data were collected for the control teams for five of the seven resulting coordination measures, as control arm data were unavailable for two of the measures (see final set of indicators determined in Aim 1, below). Multilevel growth curve models were used to predict 4 outcome variables and a composite of those variables using time, study arm, and their interaction as predictors. Due to noticeable differences in variability between the control and treatment arms, mixed-effect location scale models (Hedeker, Mermelstein & Demirtas, 2008) were used to predict the variance of the outcomes. In addition, subgroup analyses were conducted among the intervention teams only to examine whether change in outcomes over time varied by two moderators: 1) number of facilitations and 2) the average number of teams to which a PACT's team members are assigned (referred to henceforth as average team assignment level). Additional multilevel growth curve models were conducted to predict all 7 individual outcomes as well as a composite of the 7 outcomes. Time, each moderator (either average team assignment level or number of facilitations), and the time by moderator interaction were included as predictors. All models contained random intercepts and slopes at both the team and facility levels and controlled for respective baseline scores. Models with both intervention and control teams also controlled for average team assignment level.
AIM 1: The design team initially developed 16 indicators of good coordination that indicate the extent to which 3 coordination objectives have been met:
1.Support and foster Veteran engagement in their wellness by being patient-centered
2.Optimize Communication between and/or within PACTs and Management Staff
3.Ensure Quality and Efficient Care is provided to the Veteran
The final set of indicators, after refinement and greenlighting by facility leadership, comprised 7 measures indicative of performance on two objectives (1 and 3, above), listed here in decreasing order of value to improving coordination:
1.Percentage of patient appointments that start on time
2.Score on patient satisfaction survey (intervention arm only)
3.Clinical reminder completion (intervention arm only)
4.Timely recall scheduling
5.Reliance on ER Care by Current PACT patients
6.My Health-E Vet (MHV) secure messaging enrollment
7.Education offerings utilization
Notably, the three highest-value indicators are all indicators of objective 3, supporting others' assertions that good coordination is essential to high quality care.
AIM 2: Point-of-care information needs centered on two main areas: Patient information (current contact information, current health summary, and assessment of patient computer literacy), and a need for common understanding between both patients and PACTs (e.g., knowing what options are available, and knowing why certain procedures/process matter) as well as within the PACT (e.g., feedback to PACTS re patient satisfaction.)
Recommendations from focus group participants for improving coordination at the point of care included adjustments to the workflow, direct feedback to all PACT members (not just the provider), training and education for both patient and staff regarding available services and options and "the whys and wherefores" of processes, dedicated resources (staff, equipment), and improvements to patient-related communications. Findings identified numerous process changes needed to improve coordination as measured by the aforementioned indicators. Common areas where change needs were identified included: (a) Improvements to IT and changes to CPRS programming to facilitate information, (b) training/education both for patients and for PACT members on selected aspects of coordination, and (c) management buy-in to facilitate suggested changes.
AIM 3: Initial results revealed no differences between treatment and control arms in terms of improvement in each individual outcome (note only 4 outcomes were shared by all 68 teams) and the composite of the four outcomes. However, this may have been partly due to the large amount of variability in the matched controls, which may have masked any improvement observed in the treatment group. Follow-up subgroup analyses of only the treatment arm examining number of facilitations and the average team assignment level as moderators of improvement over time revealed more promise. Specifically, clinical reminder completion significantly increased over time, yet only among PACTs who attended a greater (+1 SD) number of facilitations (b = 0.70, p < 0.0001) and only among PACTs with lower (-1SD) average team assignment level (b = 0.84, p < 0.0001). Additionally, there was a significant increase in ER utilization over time, yet only among teams attending fewer (-1 SD) facilitations (b = 0.28, p < 0.0001); teams attending a greater (+1 SD) number of facilitations did not experience the increase in ER utilization over time (b = -0.01, p = 0.89). Conversely, although MHV secure messaging enrollment significantly increased over time, this effect was only found among PACTs who attended fewer (-1SD) facilitations (b = 0.13, p = 0.01) and only among PACTs with higher (+1SD) average team assignment level (b = 0.20, p < 0.001). Teams attending a greater (+1 SD) number of facilitations had no change in MHV secure messaging enrollment over time (b = -0.04, p = 0.40) and those with lower (-1 SD) average team assignment level showed a decrease in MHV secure messaging enrollment over time (b = -0.12, p = 0.004).
Taken together, these results suggest consistency of dosing, rather than within-team level of attendance, is a more critical factor to ensuring implementation fidelity and effectiveness of this team-based audit and feedback intervention.
This project has identified a set of practical, feasible, and prioritized behavioral measures of care coordination in PACT settings, which in conjunction with regular feedback, can help PACTs pinpoint areas for improvement. These measures have the added benefit of already existing or being readily calculated from existing VA data sources. Importantly, the identified measures and the recommendations for point-of-care tools and improvements relate to processes and improvements in communication and team functioning generally, rather than any condition-specific initiative. These tools may be of even greater important in the era of Choice. Finally, the project also helps advance the science of implementation of team-based coordination tools, by identifying elements of coordination that are important regardless of clinical condition or disease, and highlighting the importance of dose consistency in team-based feedback interventions for coordination.
- Hysong SJ, Amspoker AB, Hughes AM, Lester HF, Svojse EK, Khan K, Mehta P, Petersen LA. Improving team coordination in primary-care settings via multifaceted team-based feedback: a case-control study. BJGP open. 2021 Feb 9.
- Hysong SJ, Francis J, Petersen LA. Motivating and engaging frontline providers in measuring and improving team clinical performance. BMJ quality & safety. 2019 May 1; 28(5):405-411.
- Weaver SJ, Che XX, Petersen LA, Hysong SJ. Unpacking Care Coordination Through a Multiteam System Lens: A Conceptual Framework and Systematic Review. Medical care. 2018 Mar 1; 56(3):247-259.
- Hysong SJ, Che X, Weaver SJ, Petersen LA. Study protocol: identifying and delivering point-of-care information to improve care coordination. Implementation science : IS. 2015 Oct 19; 10:145.
- Hysong SJ, Woodard L, Garvin JH, Murawsky J, Petersen LA. Publishing protocols for partnered research. Journal of general internal medicine. 2014 Dec 1; 29 Suppl 4:820-4.
- Hysong SJ, SoRelle R, Tran TT, Hughes AM. Prevalence and Effectiveness of Evidence-Based Audit and Feedback Strategies for Improving Clinical Quality in Primary Care Settings. Poster session presented at: AcademyHealth Annual Research Meeting; 2016 Jun 26; Boston, MA.