JOURNAL OF AMBULATORY CARE MANAGEMENT最新文献

Background: Individuals with substance use disorders (SUDs) have high rates of medical and mental health comorbidities, health care utilization, and costs. Improving the capacity of care management programs to meet the needs of those with SUDs could benefit Medicaid accountable care organizations, but there are few reports of strategies to do so.

Program approach: The Boston Medical Center Health System Complex Care Management (CCM) program aims to build trusting relationships with the highest-risk patients across five Massachusetts Medicaid accountable care organizations and work with those individuals to achieve their health-related goals. Given that more than half of CCM-enrolled patients have been diagnosed with a SUD, CCM has adjusted its staffing models, workforce recruitment and development, patient identification and engagement, approach to health-related social needs, and community partnerships to better meet the needs of patients with an SUD. We have developed strategies to recruit diverse staff; train them on SUDs, harm reduction, motivational interviewing, and trauma-informed care; embed staff members in inpatient and community settings; and ensure teams have access to behavioral health staff for additional support as needed. We have also built partnerships with community, governmental, and health care organizations to ensure patients receive appropriately comprehensive care despite complex social, medical, and behavioral health needs.

Outcomes: Although most patients with a SUD agree to enroll in CCM when outreached, our data indicate room to improve the rates at which we reach these individuals and sustain engagement. Preliminary pre-post data showed that CCM-enrolled patients had lower rates of overdose and higher rates of engagement with outpatient care for SUDs, mental health, and medications for opioid use disorder.

Conclusion: Our experience demonstrates that with appropriate training and tools, care management staff perform vital work to improve outcomes for individuals with SUDs, although more detailed research is needed to assess the effectiveness of specific intervention components.

Background: Implementation of evidence-based practice (EBP) is associated with professionalism; EBP knowledge and implementation of EBP increases nurse's professional values and professionalism.

Methods: A pre- and post-implementation project assessed the impact of online EBP competency education and the use of EBP mentors on EBP knowledge and professionalism.

Results: Slight improvement in scores for knowledge about EBP competencies and professional values were demonstrated. Total competency average score improved from a mean of 2.12 pre- to 2.56 post- (p = .003).

Conclusions: Online EBP education can be utilized for ambulatory care nurses where there is a lack of clinical resources to support time outside of the clinic for education and training.

The focus of this quality improvement project was to implement a team-based care model with a team including three physicians and one nurse practitioner (NP). The project included implementation of a team-based care model by utilizing a standardized NP schedule template and a team-based schedule workflow with the goal to improve quality of chronic care management; increase annual wellness visits and total number of patients seen; and increase effective NP utilization in an ambulatory care setting. The team-based care model implementation was associated with a statistically significant increase in the number of annual wellness visits completed (p < .001).

Objective: Increasing proportions of adverse maternal health outcomes occur in the 12-month postpartum period and could be addressed in outpatient settings. Our objective was to develop and test an algorithm to support a population health tool to identify high-risk prenatal patients served by federally qualified health centers (FQHCs).

Methods: We leveraged human-centered design to develop and test the population health tool and algorithm. We conducted focus groups and a literature search to identify risk criteria for the tool. To evaluate the tool, we conducted structured interviews and predictive modeling to compare the recall between the original tool and the refined algorithm. The population health tool was initially tested using electronic health record (EHR) data at six pilot FQHCs. To test the model's predictive capacity, we expanded to 18 FQHCs. Focus group participants included FQHC clinicians and staff. Data to evaluate the population health tool were queried from prenatal patients receiving care at participating FQHCs. The primary outcomes were adverse outcomes addressed in outpatient settings and health care utilization within 12 months postpartum.

Results: Two focus groups (N = 7) were conducted to inform the implementation. In follow-up interviews (n = 6), users highlighted the tool's utility for identifying high-risk patients. In the predictive models (N = 82,829), the adverse outcome recall increased by 16%, but the algorithm only correctly predicted 42% of adverse outcomes experienced. The postpartum visit recall increased by 45%, with the algorithm correctly predicting 96% of visits utilized.

Conclusion: Results of this project highlight the importance of a deep understanding of EHR data capture and the involvement of clinicians when developing, testing, and evaluating interventions aimed at optimizing care for vulnerable patient populations. Future research should incorporate inpatient, outpatient, and social determinants data to develop a more comprehensive understanding of maternal health risk in the postpartum period.

Background: Despite widespread interruptions to health care in 2020, little evidence demonstrates how populations at highest risk for poor outcomes fared across a comprehensive scope of services.

Methods: Among a predominantly low-income population of individuals ages 65 and older with diabetes (n = 4,187), we measured health care service use in Medicare and Medicaid claims data from 2018 to 2020. Stratified analyses included individuals with Alzheimer disease and related dementias (ADRD, n = 1,025), individuals who used Medicaid-funded home- and community-based services (HCBS, n = 264), and long-term nursing home services (n = 365).

Results: Relative to 2018-2019, adjusted quarterly rates of evaluation and management visits dropped by 26% (95% confidence interval [CI]: 23%-28%) in Q2 2020 and remained 7% lower (95% CI: 4%-10%) in Q4 2020. Persistent declines occurred for inpatient discharges and emergency room visits (relative risk Q4 2020 vs. 2018-2019: 0.87 [95% CI: 0.76-0.99] and 0.77 [95% CI: 0.69-0.87], respectively). Insulin fills declined in later 2020 (relative risk Q4 2020 vs. 2018-2019: 0.87 [95% CI: 0.79-0.95]) while annual wellness visits rebounded (relative risk Q4 2020 vs. 2018-2019: 1.19 [95% CI: 1.06-1.34]). Individuals who used Medicaid-funded HCBS or long-term nursing home services before the pandemic had large declines in evaluation and management visits (relative risk Q4 2020 vs. 2018-2019: 0.80 [95% CI: 0.69-0.93] and 0.63 [95% CI: 0.43-0.94], respectively). Nursing home residents also had notable declines in insulin fills (relative risk Q4 2020 vs. 2018-2019: 0.73, 95% CI: 0.55-0.96). Individuals with ADRD had increased skilled nursing facility admissions (relative ratio Q3 2020 vs. 2018-2019 1.60, 95% CI: 1.21-2.13). Telehealth usage in 2020 did not differ based on ADRD diagnosis.

Conclusions: Extended disruptions in routine care highlight opportunities to improve support for older adults with diabetes.

Objective: The objective of this study was to estimate the association between an area's competition among Federally Qualified Health Centers (FQHCs) and cancer screening rates for cervical, breast, and colorectal cancers at an FQHC.

Methods: The study employed 2 secondary datasets between 2020 and 2022-the Health Resources and Services Administration's Uniform Data System and Unmet Need Score-to conduct a multivariable regression analysis on FQHCs' cervical, breast, and colorectal cancer screening rates in relation to the area's FQHC competition, measured as the Herfindahl-Hirschman Index, accounting for various FQHC-level and zip code-level control variables.

Results: The results indicated a significant negative association between the area's competition among FQHCs and cervical and colorectal cancer screening rates (Coef. = -.051, P < .01 and Coef. = -0.045, P < .01, respectively). There was no significant relationship found between breast cancer screening and the area's FQHC competition. Positive relationships were observed between screening rates, total cost per patient at an FQHC, and the number of FQHC patients.

Conclusions: The study highlights the complexities of FQHC competition, showing that while competition may be associated with service improvements in other health care contexts, its impact on preventive care delivery in the FQHC setting may be more nuanced. Enhancing outreach, care coordination, and addressing resource limitations are key for FQHCs to boost cancer screening rates and improve health outcomes for vulnerable populations. Future research should explore competition's nuanced role further and identify strategies to mitigate its potential adverse effects on care quality.

Objective: The rapid evolution of artificial intelligence (AI) presents opportunities and challenges for health systems, especially safety-net providers like Federally Qualified Health Centers (FQHCs). Safety-net systems may need help with structures and processes for assessing AI applications. To address this need, this article describes Moses-Weitzman Health System's (MWHS) initial steps toward establishing an AI program that defines intentional and informed AI use.

Approach: MWHS established two AI-focused workgroups: one of senior leaders and a cross-departmental group, providing a collaborative space for exploring potential applications, creating guidelines, and discussing concerns. With limited existing templates, MWHS crafted an AI policy emphasizing transparency, privacy, and security, outlining the criteria for implementing AI tools that interact with patient data and ensuring compliance with current regulations. Current AI-related projects focus on automating routine tasks, and research interests include evidence frameworks for making decisions about adopting AI tools and evaluating ambient listening technologies.

Findings: Lessons learned in building our AI program are that effective implementation requires tech-savvy leadership, cross-department collaboration, and cautious differentiation between general automation and generative AI. Challenges include the need for agile budgeting, careful vendor vetting, and safe testing environments to assess AI benefits and risks responsibly.

Conclusions and action steps: MWHS's AI program underscores a cautious but proactive approach to AI, aiming to balance innovation with operational and ethical considerations, and offers a model for other safety-net systems beginning their AI journeys.

Background and objective: The medical home is a primary care model that offers comprehensive, uninterrupted, and family-centered care to children regardless of their special health care needs. Although the overall contribution of medical home is well-studied, the differential influence of its components on health care utilization and unmet needs, particularly for children and youth with special health care needs (CYSHCN) remains insufficiently understood. This study examined the medical home component(s) that can best predict emergency and preventive visits, and unmet needs in CYSHCN.

Methods: This study analyzed 2016-2022 National Survey of Children's Health data from 64 553 caregivers of CYSHCN aged 0-17 years using the Rao-Scott Chi-square test and multivariable logistic regression.

Results: Findings show that receiving care within an overall medical home was associated with lower odds of emergency visits [adjusted odds ratio (aOR): 0.77 (95% confidence interval: 0.70-0.84)] and unmet needs [aOR = 0.29 (0.24-0.34)], and higher odds of preventive dental visits [aOR = 1.40 (1.25-1.57)]. Having a usual source for sick care and receiving effective care coordination was associated with reduced odds of emergency visits (aOR = 0.74 [0.64-0.84] and aOR = 0.85 [0.77-0.95], respectively). Receiving family-centered care [aOR = 0.45 (0.38-0.54)], no problems getting needed referral [aOR = 0.58 (0.47-0.72)], and getting effective care coordination when needed [aOR = 0.32 (0.26-0.39)] were associated with lower odds of unmet needs. The odds of utilizing preventive medical and dental visits was higher with having a personal doctor or nurse [aOR = 1.42(1.12-1.80) and aOR = 1.41 (1.20-1.65), respectively] and receiving family-centered care [aOR = 1.59 (1.18-2.12) and aOR = 1.44 (1.22-1.71), respectively].

Conclusions: The study highlights the importance of considering the differential contribution of medical home components for reducing emergency visits, addressing unmet needs, and improving preventive care, providing insights to enhance health care delivery for CYSHCN.