Healthcare-The Journal of Delivery Science and Innovation最新文献

Inpatient capacity constraints have been a pervasive challenge for hospitals throughout the COVID-19 pandemic. The Mayo Clinic Health System — Southwest Minnesota region primarily serves patients in rural southwestern Minnesota and part of Iowa and consists of 1 postacute care hospital, 1 tertiary care medical center, and 3 critical access hospitals. The main hub, Mayo Clinic Health System in Mankato, Minnesota, has a pediatric unit with dedicated pediatric hospitalists. To address the growing demand for adult inpatient beds at the height of the pandemic, the pediatric unit was opened to allow adult patients to be admitted when necessary. For several months, adult inpatient capacity exceeded 90%, which decreased the number of available pediatric (vs adult) beds throughout Minnesota, particularly in rural communities. Data for the health system showed that children were most affected because transfers to the next available hospitals for pediatric cases were 55 miles away or more. To address this gap, the hospital team successfully trialed a pediatric bed prioritization guideline that reduced pediatric transfers by 40%. This was accomplished by prioritizing the last remaining inpatient bed on the pediatric unit for pediatric patients only. This process not only reduced pediatric transfers but also increased unique patient admissions because of an average lower length of stay for pediatric patients compared with adult patients.

Background

There is growing interest in the contributions of embedded, learning health system (LHS), research within healthcare delivery systems. We examined the organization of LHS research units and conditions affecting their contributions to system improvement and learning.

Methods

We conducted 12 key-informant and 44 semi-structured interviews in six delivery systems engaged in LHS research. Using rapid qualitative analysis, we identified themes and compared: successful versus challenging projects; LHS units and other research units in the same system; and LHS units in different systems.

Results

LHS units operate both independently and as subunits within larger research centers. Contributions of LHS units to improvements and learning are influenced by alignment of facilitating factors within units, within the broader system, and between unit and host system. Key alignment factors were availability of internal (system) funding directing researchers’ work toward system priorities; researchers’ skills and experiences that fit a system’s operational needs; LHS unit subculture supporting system improvement and collaboration with clinicians and other internal stakeholders; applications of external funding to system priorities; and executive leadership for system-wide learning. Mutual understanding and collaboration between researchers, clinicians, and leaders was fostered through direct consultation between LHS unit leaders and system executives and engagement of researchers in clinical and operational activities.

Conclusions

Embedded researchers face significant challenges to contributing to system improvement and learning. Nevertheless, when appropriately led, organized, and supported by internal funding, they may learn to collaborate effectively with clinicians and system leaders in advancing care delivery toward the learning health system ideal.

Background

Primary care intensive management programs utilize interdisciplinary care teams to comprehensively meet the complex care needs of patients at high risk for hospitalization. The mixed evidence on the effectiveness of these programs focuses on average treatment effects that may mask heterogeneous treatment effects (HTEs) among subgroups of patients. We test for HTEs by patients’ demographic, economic, and social characteristics.

Methods

Retrospective analysis of a VA randomized quality improvement trial. 3995 primary care patients at high risk for hospitalization were randomized to primary care intensive management (n = 1761) or usual primary care (n = 1731). We estimated HTEs on ED and hospital utilization one year after randomization using model-based recursive partitioning and a pre-versus post-with control group framework. Splitting variables included administratively collected demographic characteristics, travel distance, copay exemption, risk score for future hospitalizations, history of hospital discharge against medical advice, homelessness, and multiple residence ZIP codes.

Results

There were no average or heterogeneous treatment effects of intensive management one year after enrollment. The recursive partitioning algorithm identified variation in effects by risk score, homelessness, and whether the patient had multiple residences in a year. Within each distinct subgroup, the effect of intensive management was not statistically significant.

Conclusions

Primary care intensive management did not affect acute care use of high-risk patients on average or differentially for patients defined by various demographic, economic, and social characteristics.

Implications

Reducing acute care use for high-risk patients is complex, and more work is required to identify patients positioned to benefit from intensive management programs.

This article describes the implementation of an equity-focused strategy to increase the uptake of COVID-19 vaccination among communities of color and in traditionally underserved geographic areas using mobile health clinics (MHCs). The MHC Vaccination Program was implemented through a large integrated healthcare system in North Carolina using a grassroots development and engagement strategy along with a robust model for data-informed decision support to prioritize vulnerable communities. Several valuable lessons from this work can replicated for future outreach initiatives and community-based programming:

•Health systems can no longer operate under the assumption that community members will come to them, particularly those experiencing compounding social and economic challenges. The MHC model had to be a proactive outreach to community members, rather than a responsive delivery mechanism.

•Barriers to access included financial, legal, and logistical challenges, in addition to mistrust among historically underserved and marginalized communities.

•A MHC model can be adaptable and responsive to data-informed decision-making approaches for targeted service delivery.

•A MHC model is not a one-dimensional solution to access, but part of a broader strategy to create diverse points of entry into the healthcare system that fall within the rhythm of life of community members.

Background

Collaborative care models (CoCM) that integrate mental health and primary care improve outcomes and could help address racial and ethnic mental health disparities. We examined whether use of these programs differs by race/ethnicity.

Methods

This retrospective study examined two CoCM interventions implemented across primary care clinics in a large health system in Massachusetts: 1) a primary care-based behavioral health program for depression or anxiety (IMPACT model) and 2) referral to community-based specialty care services (Resource-finding). Outcomes included enrollment, non-completion, and symptom screening rates, and discharge status for Black, Hispanic and White patients referred for CoCM, 2017–2019.

Results

Black and Hispanic vs. White patients referred to CoCM (n = 17,280) were more likely to live in high poverty ZIP codes (34% and 40% vs. 9%). Rates of program enrollment, non-completion, and symptom screening were similar across groups (e.g., 76%, 77%, and 75% of Black, Hispanic, and White patients enrolled). Hispanic vs. White patients were more likely to be enrolled in IMPACT (56%) vs. Resource-finding (43%). Among those completing IMPACT, Hispanic vs. White patients were more likely to be stepped to psychiatry vs. discharged to their primary care provider (51% vs. 20%, aOR = 1.55, 95% CI: 1.02–2.35).

Conclusions

Black and Hispanic patients referred to CoCM were similarly likely to use the program as White patients. Hispanic patients completing IMPACT were more frequently referred to psychiatry.

Implications

These results highlight the promise of CoCMs for engaging minority populations in mental healthcare. Hispanic patients may benefit from additional intervention or earlier linkage to specialty care.

Background

Provision of team-based primary care (PC) is associated with improved care quality, but limited empirical evidence guides practices on how to optimize team functioning. We examined how evidence-based quality improvement (EBQI) was used to change PC team processes. EBQI activities were supported by research-clinical partnerships and included multilevel stakeholder engagement, external facilitation, technical support, formative feedback, QI training, local QI development and across-site collaboration to share proven practices.

Methods

We used a comparative case study in two VA medical centers (Sites A and B) that engaged in EBQI between 2014 and 2016. We analyzed multiple qualitative data sources: baseline and follow-up interviews with key stakeholders and provider team (“teamlet”) members (n = 64), and EBQI meeting notes, reports, and supporting materials.

Results

Site A's QI project entailed engaging in structured daily huddles using a huddle checklist and developing a protocol clarifying team member roles and responsibilities; Site B initiated weekly virtual team meetings that spanned two practice locations. Respondents from both sites perceived these projects as improving team structure and staffing, team communications, role clarity, staff voice and personhood, accountability, and ultimately, overall team functioning over time.

Conclusion

EBQI enabled local QI teams and other stakeholders to develop and implement innovations to improve PC team processes and characteristics in ways that improved teamlet members’ perceptions of team functioning.

Implications

EBQI's multi-level approach may empower staff and facilitate innovation by and within teams, making it an effective implementation strategy for addressing unique practice-based challenges and supporting improvements in team functioning across varied clinical settings.

Level of evidence

VI.

Background

Continuous “rolling” tobacco group treatments may help reduce cessation disparities by increasing access among underserved people who smoke cigarettes. We evaluated the implementation of a rolling enrollment adaptation of an evidence-based tobacco treatment group intervention, Courage to Quit®-Rolling (CTQ®-R).

Methods

The 4-session CTQ®-R incorporating psychoeducation, motivational enhancement, and cognitive behavioral skills was evaluated by examining feasibility and preliminary program outcomes with a pre-post design using the SQUIRE method in a sample of 289 primarily low-income, Black people who smoke. Feasibility was measured by examining program retention. Paired t-tests evaluated changes in behavioral intentions and knowledge about smoking cessation and differences in average daily cigarettes smoked from first to last session attended.

Results

CTQ-R was feasible to implement in an urban medical center program enrolling primarily low-income Black people who smoke, with 52% attending at least 2 sessions and 24% completing the full program. Participants demonstrated improvements in knowledge of smoking cessation strategies and confidence in quitting (ps < .004). Preliminary effectiveness analyses showed a 30% reduction in average daily cigarette use, with group completers reporting greater reduction than non-completers.

Conclusions

CTQ®-R is feasible and showed preliminary effectiveness for increasing knowledge about stop smoking skills and reducing cigarette smoking.

Implications

A rolling enrollment smoking group treatment is feasible and may be effective among people who smoke who face historical and systemic barriers to tobacco treatment engagement. Evaluation in other settings and over longer periods of time is needed.

Background

Medication price transparency tools are increasingly available, but data on their use, and their potential effects on prescribing behavior, patient out of pocket (OOP) costs, and clinician workflow integration, is limited.

Objective

To describe the implementation experiences with real-time prescription benefit (RTPB) tools at 5 large academic medical centers and their early impact on prescription ordering.

Design

and Participants: In this cross-sectional study, we systematically collected information on the characteristics of RTPB tools through discussions with key stakeholders at each of the five organizations. Quantitative encounter data, prescriptions written, and RTPB alerts/estimates and prescription adjustment rates were obtained at each organization in the first three months after “go-live” of the RTPB system(s) between 2019 and 2020.

Main measures

Implementation characteristics, prescription orders, cost estimate retrieval rates, and prescription adjustment rates.

Key results

Differences were noted with respect to implementation characteristics related to RTPB tools. All of the organizations with the exception of one chose to display OOP cost estimates and suggested alternative prescriptions automatically. Differences were also noted with respect to a patient cost threshold for automatic display. In the first three months after “go-live,” RTPB estimate retrieval rates varied greatly across the five organizations, ranging from 8% to 60% of outpatient prescriptions. The prescription adjustment rate was lower, ranging from 0.1% to 4.9% of all prescriptions ordered.

Conclusions

In this study reporting on the early experiences with RTPB tools across five academic medical centers, we found variability in implementation characteristics and population coverage. In addition RTPB estimate retrieval rates were highly variable across the five organizations, while rates of prescription adjustment ranged from low to modest.

The opioid overdose epidemic has caused over 600,000 deaths in the U.S. since 1999. Public access naloxone programs show great potential as a strategy for reducing opioid overdose-related deaths. However, their implementation within public transit stations, often characterized as opioid overdose hotspots, has been limited, partly because of a lack of understanding in how to structure such programs. Here, we propose a comprehensive framework for implementing public access naloxone programs at public transit stations to curb opioid overdose-related deaths. The framework, tailored to local contexts, relies on coordination between local public health organizations to provide naloxone at public access points and bystander training, local academic institutions to oversee program evaluation, and public transit organizations to manage naloxone maintenance. We use the city of Cambridge, Massachusetts as a case study to demonstrate how it and other municipalities may implement such an initiative.