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

Background

United States healthcare has increasingly transitioned to outpatient care delivery. The degree to which Academic Medical Centers (AMCs) have been able to shift surgical procedures from inpatient to outpatient settings despite higher patient complexity is unknown.

Methods

This observational study used a 20% sample of fee-for-service Medicare beneficiaries age 65 and older undergoing eight elective procedures from 2011 to 2018 to model trends in procedure site (hospital outpatient vs. inpatient) and 30-day standardized Medicare costs, overall and by hospital teaching status.

Results

Of the 1,222,845 procedures, 15.9% occurred at AMCs. There was a 2.42% per-year adjusted increase (95% CI 2.39%–2.45%; p < .001) in proportion of outpatient hospital procedures, from 68.9% in 2011 to 85.4% in 2018. Adjusted 30-day standardized costs declined from $18,122 to $14,353, (-$560/year, 95% CI -$573 to -$547; p < .001). Patients at AMCs had more chronic conditions and higher predicted annual mortality. AMCs had a lower proportion of outpatient procedures in all years compared to non-AMCs, a difference that was statistically significant but small in magnitude. AMCs had higher costs compared to non-AMCs and a lesser decline over time (p < .001 for the interaction). AMCs and non-AMCs saw a similar decline in 30-day mortality.

Conclusions

There has been a substantial shift toward outpatient procedures among Medicare beneficiaries with a decrease in total 30-day Medicare spending as well as 30-day mortality. Despite a higher complexity population, AMCs shifted procedures to the outpatient hospital setting at a similar rate as non-AMCs.

Implications

The trend toward outpatient procedural care and lower spending has been observed broadly across AMCs and non-AMCs, suggesting that Medicare beneficiaries have benefited from more efficient delivery of procedural care across academic and community hospitals.

This case report describes the development of information and communication technology (ICT) for a large scale, federally funded demonstration healthcare Program designed to treat low-income children and adolescents with chronic medical conditions. The ICT developers faced the challenge of supporting a Program with many components to treat pediatric patients with one or more chronic health conditions. The Program's ICT provided means and materials to train and monitor Community Health Workers (CHWs) and the Care Coordination Team (CCT) and to provide disease-specific information to patients and caregivers. The Program ICT was organized into five components: (1) Data Storage Systems, (2) Care Coordination Software, (3) On-line Patient Education, (4) a Social Services Referral component, and (5) Patient Engagement software. The average cost of providing care services to the engaged population utilizing the ICT was $7.39 per member per month (PMPM) and $20.33 PMPM for the subset of children who received direct outreach and services. A description of the Program's ICT development, functioning, strengths, and weaknesses is presented.

As a US-based health care system, Mayo Clinic faced considerable challenges opening a new affiliated outpatient facility in the UK at the beginning of the COVID-19 pandemic, which severely affected patient volumes and staffing. As COVID-19 restrictions were eased, the clinic had to prioritize gradual improvements to reestablish service while using resources responsibly. To assist in understanding the current state and to isolate challenges, we elected to develop a service blueprint. We describe how we did this during the COVID-19 pandemic with the use of both face-to-face and virtual services. In many industries, service blueprints are used to help with the design, delivery, and management of new and established services. Although they share some features with value stream mapping, service blueprints often focus on human tasks and the customer's service experience, while value stream maps emphasize information or product flows and capabilities. Several themes for prioritized improvement efforts were identified for future work. In addition, the service blueprint workshops led to a much better understanding of how each person's work affected the other team members and the patient experience. We learned that service blueprints are an efficient way to identify and anticipate critical operational interdependencies and team dynamics that will affect the patient experience when building new clinical services.

Background

Population health management tools (PHMTs) embedded within electronic health records (EHR) could improve management of high-risk patients and reduce costs associated with potentially avoidable emergency department visits or hospitalizations. Adoption of PHMTs across the Veterans Health Administration (VA) has been variable and previous research suggests that understaffed primary care (PC) teams might not be using the tools.

Methods

We conducted a retrospective content analysis of open-text responses (n = 1804) from the VA’s 2018 national primary care personnel survey to, 1) identify system-level and individual-level factors associated with why clinicians are not using the tools, and 2) to document clinicians’ recommendations to improve tool adoption.

Results

We found three themes pertaining to low adoption and/or tool use: 1) IT burden and administrative tasks (e.g., manually mailing letters to patients), 2) staffing shortages (e.g., nurses covering multiple teams), and 3) no training or difficulty using the tools (e.g., not knowing how to access the tools or use the data). Frontline clinician recommendations included automating some tasks, reconfiguring team roles to shift administrative work away from providers and nurses, consolidating PHMTs into a centralized, easily accessible repository, and providing training.

Conclusions

Healthcare system-level factors (staffing) and individual-level factors (lack of training) can limit adoption of PHMTs that could be useful for reducing costs and improving patient outcomes. Future research, including qualitative interviews with clinicians who use/don’t use the tools, could help develop interventions to address barriers to adoption.

Implications

Shifting more administrative tasks to clerical staff would free up clinician time for population health management but may not be possible for understaffed PC teams. Additionally, healthcare systems may be able to increase PHMT use by making them more easily accessible through the electronic health record and providing training in their use.

Background

When a physician determines that a patient needs radiation therapy (RT), they submit an RT order to a prior authorization program which assesses guideline-concordance. A rule-based clinical decision support system (CDSS) evaluates whether the order is appropriate or potentially non-indicated. If potentially non-indicated, a board-certified oncologist discusses the order with the ordering physician. After discussion, the order is authorized, modified, withdrawn, or recommended for denial. Although patient race is not captured during ordering, bias prior to and during ordering, or during the discussion, may influence outcomes. This study evaluated if associations existed between race and order determinations by the CDSS and by the overall prior authorization program.

Methods

RT orders placed in 2019, pertaining to patients with Medicare Advantage health plans from one national organization, were analyzed. The association between race and prior authorization outcomes was examined for RT orders for all cancers, and then separately for breast, lung, and prostate cancers. Analyses controlled for the patient’s age, urbanicity, and the median income in the patient’s ZIP code. Adjusted analyses were conducted on unmatched and racially-matched samples.

Results

Of the 10,145 patients included in the sample, 8,061 (79.5%) were White and 2,084 (20.5%) were Black. Race was not found to have a significant association with CDSS or prior authorization outcomes in any of the analyses.

Conclusions

CDSS and prior authorization outcomes suggested similar rates of clinical appropriateness of orders for patients, regardless of race.

Implications

Prior authorization utilizing rule-based CDSS was capable of enforcing guidelines without introducing racial bias.

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

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.