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

Patients with serious mental illness often lack access to mental health support services. Whether new payment models facilitate access to these services is unknown. We conducted a national survey of accountable care organizations(ACOs) and find that fewer than 50% of ACOs surveyed reported that they have the ability to offer or refer patients to supported employment, family psychoeducation, assertive community treatment and illness, management and recovery services. These findings suggest that even among organizations that are early adopters of payment and delivery reforms -- those most likely to lead innovations in population health -- access to these services is limited.

Of the 3 million older adults seeking fall-related emergency care each year, nearly one-third visited the Emergency Department (ED) in the previous 6 months. ED providers have a great opportunity to refer patients for fall prevention services at these initial visits, but lack feasible tools for identifying those at highest-risk. Existing fall screening tools have been poorly adopted due to ED staff/provider burden and lack of workflow integration. To address this, we developed an automated clinical decision support (CDS) system for identifying and referring older adult ED patients at risk of future falls.

We engaged an interdisciplinary design team (ED providers, health services researchers, information technology/predictive analytics professionals, and outpatient Falls Clinic staff) to collaboratively develop a system that successfully met user requirements and integrated seamlessly into existing ED workflows. Our rapid-cycle development and evaluation process employed a novel combination of human-centered design, implementation science, and patient experience strategies, facilitating simultaneous design of the CDS tool and intervention implementation strategies. This included defining system requirements, systematically identifying and resolving usability problems, assessing barriers and facilitators to implementation (e.g., data accessibility, lack of time, high patient volumes, appointment availability) from multiple vantage points, and refining protocols for communicating with referred patients at discharge. ED physician, nurse, and patient stakeholders were also engaged through online surveys and user testing.

Successful CDS design and implementation required integration of multiple new technologies and processes into existing workflows, necessitating interdisciplinary collaboration from the onset. By using this iterative approach, we were able to design and implement an intervention meeting all project goals. Processes used in this Clinical-IT-Research partnership can be applied to other use cases involving automated risk-stratification, CDS development, and EHR-facilitated care coordination.

Patients experiencing homelessness are among the most disadvantaged in our society, suffering from poor health outcomes and exhibiting disproportionately high hospital utilization and spending. However, to date, hospitals have only scantily devoted time or resources to the housing coordination aspect of homelessness. Implementing better systems to coordinate housing for patients experiencing homelessness may improve health outcomes and reduce health care utilization for this population. This objective is now more important than ever as the economic impact of COVID-19 is expected to exacerbate the homelessness crisis. Ensuring that patients are properly connected to temporary or permanent housing is valuable to patient health, health care system metrics such as excess spending and utilization, and provider performance under Accountable Care Organizations or other risk-bearing payment models. Here, we propose a health systems-based housing coordination framework that may improve care delivery for patients experiencing homelessness. This framework relies on the coordination between dedicated hospital-based housing navigators who can identity patients experiencing homelessness and outpatient housing navigators equipped to coordinate short- and long-term housing specifically for patients experiencing homelessness who frequently interact with the health care system.

During the early months of the COVID-19 pandemic, when health systems were overwhelmed with surging hospitalizations and a novel virus, many ambulatory patients diagnosed with COVID-19 lacked guidance and support as they convalesced at home. This case report offers insight into the implementation of a telehealth service utilizing third- and fourth-year medical students to provide follow-up to ambulatory patients diagnosed with COVID-19. The service was evaluated using medical student surveys and retrospective chart review to assess the clinical and social needs of patients during the spring of 2020. Students assessed symptoms for 416 patients with COVID-19 from April 8 to May 20 and provided clinical information and resources. Eighteen percent of these patients sought higher levels of medical care, in part from student referrals. Three key implementation lessons from this experience that may be relevant for others include: 1) Vulnerable patient populations face unique stressors exacerbated by the pandemic and may benefit from intensive follow-up after COVID-19 diagnosis to address both medical and social needs; 2) Medical students can play value-added roles in providing patient education to prevent the spread of COVID-19, assisting patients with escalating care or resource connection, and providing emotional support to those who have lost loved ones; 3) Continuous re-assessment of the intervention was important to address evolving patient needs during the COVID-19 outbreak. Future work should focus on identifying high-risk patient populations and tailoring follow-up interventions to meet the unique needs of these patient populations.

Background

Mental health specialists and care managers facilitate comprehensive care provision within medical homes. Despite implementation challenges, mental health integration is thought to improve patient-centered primary care.

Objectives

To examine the relationship between primary care patient experience and mental health integration.

Research design

Cross-sectional surveys from 168 primary care clinicians (PCPs) (n = 226) matched with assigned patients’ surveys (n = 1734) in one Veterans Health Administration (VA) region, fiscal years 2012–2013. Multilevel regression models examined patient experience and mental health integration, adjusting for patient and PCP characteristics.

Measures

Patient experience outcomes were (1) experience with PCP and (2) receipt of comprehensive care, such as talked about “stress”. Independent variables represented mental health integration— (1) PCP-rated communication with mental health and (2) proportion of clinic patients who saw integrated specialists.

Results

50% and 43% of patients rated their PCPs 10/10 and reported receiving comprehensive care, respectively. Neither patient experience or receipt of comprehensive care was significantly associated with PCP's ratings of communication with mental health, nor with proportion of clinic patients who saw integrated specialists. Among a subsample of patients who rated their mental health as poor/fair, however, we detected an association between proportion of clinic patients who saw integrated specialists and patient experience (odds ratio = 1.05, 95% confidence interval = 1.01–1.09, p = .01).

Conclusions

No association was observed between mental health integration and primary care patients’ reported care experiences, but a significant association existed among patients who reported poor/fair mental health. More research is needed to understand patient experiences with regard to care model implementation.

Background

In response to the COVID-19 pandemic, telemedicine utilization has increased dramatically, yet most institutions lack a standardized approach to determine how much to invest in these programs.

Methods

We used the Quadruple Aim to evaluate the operational impact of CardioClick, a program replacing in-person follow-up visits with video visits in a preventive cardiology clinic. We examined data for 134 patients enrolled in CardioClick with 181 video follow-up visits and 276 patients enrolled in the clinic's traditional prevention program with 694 in-person follow-up visits.

Results

Patients in CardioClick and the cohort receiving in-person care were similar in terms of age (43 vs 45 years), gender balance (74% vs 79% male), and baseline clinical characteristics. Video follow-up visits were shorter than in-person visits in terms of clinician time (median 22 vs 30 min) and total clinic time (median 22 vs 68 min). Video visits were more likely to end on time than in-person visits (71 vs 11%, p < .001). Physicians more often completed video visit documentation on the day of the visit (56 vs 42%, p = .002).

Conclusions

Implementation of video follow-up visits in a preventive cardiology clinic was associated with operational improvements in the areas of efficiency, patient experience, and clinician experience. These benefits in three domains of the Quadruple Aim justify expanded use of telemedicine at our institution.

Implications

The Quadruple Aim provides a framework to evaluate telemedicine programs recently implemented in many health systems.

Level of evidence

Level III (retrospective comparative study).

Introduction

Effective research-operational partnerships require that researchers ask questions targeting top clinical operational priorities. However, disconnects exist between healthcare researchers and operational leadership that result in significant delays between discovery and implementation of breakthroughs in healthcare.

Objective

Using the Veterans Health Administration Quality Enhancement Research Initiative (QUERI) Partnered Evaluation Initiative (PEI) as a case study, we identified relationship characteristics of collaborative research projects designed to address the research/operations gap.

Methods

An interview guide was developed focusing on areas identified as essential for effective research/operational partnerships from previous research. Investigators (N = 14) and their operational leadership partners (N = 14) representing 16 PEIs were interviewed by phone related to the characteristics of their partnerships. All investigators had had advanced degrees in fields related to healthcare delivery and administration and were affiliated with VHA research institutes. All operational partners served in national leadership roles within VHA operational offices. Detailed interview notes collected from interviews were coded and themes identified using thematic analysis.

Results

Eight relationship themes were identified: leadership support, shared understanding, investment, trust, agreement on products, mutual benefit, adaptability, and collaboration. Most operational and investigator partners discussed the importance of leadership support, shared understanding, investment, trust and product agreement, suggesting that these may be more essential than other areas in supporting effective operations/research collaborations. One theme (mutual benefit) was mentioned by most investigators but only some operations partners, pointing to potential differences related to this area between the two groups. Facilitators of effective collaboration included obtaining formal leadership support, developing a shared understanding of partner priorities and needs, ongoing discussions about resource needs, expanding collaborations beyond the initial project, having a clearly defined plan, planning for flexibility, plans for regular communication, and active participation in project meetings.

Conclusions

Partnership characteristics that facilitate effective collaboration include leadership support, shared understanding of planned work, investment, trust, and product agreement. Future research should assess the overall impact of partnered approaches to healthcare improvement within other large healthcare systems.

Importance

The randomization process is considered among the most important components of a randomized control trial (RCTs) and a core advantage of RCTs. Proper randomization should eliminate most population biases, in which some populations, or members of a population are more likely to be selected or not selected than others, such that similar comparison groups are produced to evaluate treatments.^4,5

Objective

To assess the methodologic quality of the descriptions of randomization methods used to allocate participants to comparison groups in randomized controlled trials.

Evidence review

A cross-sectional review of phase 3 clinical trials reported in Clinicaltrials.gov.

Beginning at all records available (n = 345,278) we included studies only listed for stage 3 RCTs in the U.S. National Library of Medicine database. A total of 1528 protocols were identified as of June 1, 2020. Exclusion criteria involved no protocol listed or non-randomized studies, of which 517 were excluded. There were 693 text articles excluded due to unclear methods of randomization. Inclusion criteria involved randomization methods based on “A review of randomization methods in clinical trials” by Berger and Antsygina.1

Each study protocol was extracted to identify the randomization methods described by three independent reviewers. Classification of randomization methods described in the study protocols for randomized clinical trials.

Findings

Only 20.8 % of the study protocols described a method for randomly assigning participants to groups. Of this subset that defined protocols, the Permuted-Block Design was used most often (85.9 %). More than three quarters of all study protocols (77.7 %) provided incomplete descriptions about the type of randomization method (i.e. no protocol, n/a, unclear).

Conclusions

and Relevance:Proper randomization is required to generate unbiased comparison groups in controlled trials, yet the majority of study protocols for RCTs currently in Clinicaltrials.gov provide inadequate or unacceptable information regarding their randomization methods.

Measuring “home time,” number of days not in facility-based care, with medical claims is a promising approach to assess person-centered outcomes on a population level. Generally, spending more time at home matches long-term care preferences and improves quality of life. However, existing “home time” measures have not incorporated key stakeholder perspectives. We sought to understand how patients and family caregivers value time spent in diverse facility-based health care settings (Emergency Department, Nursing Home, Post-Acute Care/Skilled Nursing, Inpatient Hospital) to help determine whether various settings have different effects on quality of life and thus merit different weighting in a “home time” measure. We conducted three focus groups among patients and family caregivers within the U.S. Veterans Health Care System. We identified themes pertaining to patients’ quality of life in each of the four facility-based care settings. Discussions about both emergency department and post-acute/skilled nursing care reflected loss of personal control, counterbalanced by temporary stay. Inpatient hospital care evoked discussion about greater loss of personal control due to the intensity of care. Nursing homes ultimately signified decline. These findings illuminate differences in quality of life across health-care settings and help justify the need for different weights in a measure of “home time.”