Patterns of interactions among ICU interprofessional teams: A prospective patient-shift-level survey approach.

BACKGROUND: The Awakening, Breathing Coordination, Delirium monitoring and Early mobility bundle (ABCDE) is associated with lower mortality for intensive care unit (ICU) patients. However, efforts to improve ABCDE are variably successful, possibly due to lack of clarity about who are the team members interacting when caring for each patient, each shift. Lack of patient shift-level information regarding who is interacting with whom limits the ability to tailor interventions to the specific ICU team to improve ABCDE. OBJECTIVE: Determine the number and types of individuals (i.e., clinicians and family members) interacting in the care of mechanically ventilated (MV) patients, as reported by the patients' assigned physician, nurse, and respiratory therapist (RT) each shift, using a network science lens. METHODS: We conducted a prospective, patient-shift-level survey in 2 medical ICUs. For each patient, we surveyed the assigned physician, nurse, and RT each day and night shift about who they interacted with when providing ABCDE for each patient-shift. We determined the number and types of interactions, reported by physicians, nurses, and RTs and day versus night shift. RESULTS: From 1558 surveys from 404 clinicians who cared for 169 patients over 166 shifts (65% response rate), clinicians reported interacting with 2.6 individuals each shift (physicians: 2.65, nurses: 3.33, RTs: 1.86); this was fewer on night shift compared to day shift (1.99 versus 3.02). Most frequent interactions were with the bedside nurse, attending, resident, intern, and RT; family member interactions were reported in less than 1 in 5 surveys (12.2% of physician surveys, 19.7% of nurse surveys, 4.9% of RT surveys). INTERPRETATION: Clinicians reported interacting with 3-4 clinicians each shift, and fewer on nights. Nurses interacted with the most clincians and family members. Interventions targeting shift-level teams, focusing on nurses and family members, may be a way to improve ABCDE delivery and ICU teamwork.

It's Time! The Path for Nursing Reimbursement Reform.

Since the COVID-19 pandemic, nurses and nurse leaders are increasingly vocal about chronic understaffing and the impact the staffing crisis continues to have on nurses' well-being and patient outcomes. The American Nurses Association's Nurse Staffing Task Force addressed the importance of staffing standards as a critically needed step toward improving patient and population health outcomes. Against the backdrop of ongoing nursing shortages, hospital leaders have been hesitant to embrace staffing ratios, expressing concerns about their ability to hire and retain sufficient nursing staff, as operational revenue margins remain thin and nursing labor is costly. This article explicates structural issues within the current nursing reimbursement model that harms hospitals' business case for investments in nurse staffing and work environments. We argue that nurses must advocate for nursing reimbursement reform to increase the nursing workforce and improve nurse staffing and work environments. Such reform is necessary to support sustained hospital investments, financial philosophies, and approaches to meaningfully address and improve nurse staffing.

Shift-Level Team Familiarity Is Associated with Improved Outcomes in Mechanically Ventilated Adults.

RATIONALE: Organizing intensive care unit (ICU) interprofessional teams is a high priority due to workforce needs, but the role of interprofessional familiarity remains unexplored. OBJECTIVE: Determine if mechanically ventilated patients cared for by teams with greater familiarity have improved outcomes, such as lower mortality, shorter duration of mechanical ventilation (MV), and greater spontaneous breathing trial (SBT) implementation. METHODS: We used electronic health records data of 5 ICUs in an academic medical center to map interprofessional teams and their ICU networks, measuring team familiarity as network coreness and mean team value. We used patient-level regression models to link team familiarity with patient outcomes, accounting for patient/unit factors. We also performed a split-sample analysis by using 2018 team familiarity data to predict 2019 outcomes. MEASUREMENTS: Team familiarity was measured as the average number of patients shared by each clinician with all other clinicians in the ICU (i.e., coreness) and the average number of patients shared by any two members of the team (i.e., mean team value). MAIN RESULTS: Among 4,485 encounters, unadjusted mortality was 12.9%, average duration of MV was 2.32 days and SBT implementation was 89%; average team coreness was 467.2 (SD = 96.15) and average mean team value was 87.02 (SD=42.42). A standard-deviation increase in team coreness was significantly associated with a 4.5% greater probability of SBT implementation, 23% shorter MV duration, and 3.8% lower probability of dying; mean team value was significantly associated with lower mortality. Split-sample results were attenuated but congruent in direction and interpretation. CONCLUSIONS: Interprofessional familiarity was associated with improved outcomes; assignment models that prioritize familiarity might be a novel solution.

Racial and Ethnic Disparities in Home Health Referral Among Adult Medicare Patients.

BACKGROUND: Home health care (HHC) services following hospital discharge provide essential continuity of care to mitigate risks of posthospitalization adverse outcomes and readmissions, yet patients from racial and ethnic minority groups are less likely to receive HHC visits. OBJECTIVE: To examine how the association of nurse assessments of patients' readiness for discharge with referral to HHC services at the time of hospital discharge differs by race and ethnic minority group. RESEARCH DESIGN: Secondary data analysis from a multisite study of the implementation of discharge readiness assessments in 31 US hospitals (READI Randomized Clinical Trial: 09/15/2014-03/31/2017), using linear and logistic models adjusted for patient demographic/clinical characteristics and hospital fixed effects. SUBJECTS: All Medicare patients in the study's intervention arm (n=14,684). MEASURES: Patient's race/ethnicity and discharge disposition code for referral to HHC (vs. home) from electronic health records. Patient's Readiness for Hospital Discharge Scale (RHDS) score (0-10 scale) assessed by the discharging nurse on the day of discharge. RESULTS: Adjusted RHDS scores were similar for non-Hispanic White (8.21; 95% CI: 8.18-8.24), non-Hispanic Black (8.20; 95% CI: 8.12-8.28), Hispanic (7.92; 95% CI: 7.81-8.02), and other race/ethnicity patients (8.09; 95% CI: 8.01-8.17). Non-Hispanic Black patients with low RHDS scores (6 or less) were less likely than non-Hispanic White patients to be discharged with an HHC referral (Black: 26.8%, 95% CI: 23.3-30.3; White: 32.6%, 95% CI: 31.1-34.1). CONCLUSIONS: Despite similar RHDS scores, Black patients were less likely to be discharged with HHC. A better understanding of root causes is needed to address systemic structural injustice in health care settings.

Using machine-learning methods to predict in-hospital mortality through the Elixhauser index: A Medicare data analysis.

Accurate in-hospital mortality prediction can reflect the prognosis of patients, help guide allocation of clinical resources, and help clinicians make the right care decisions. There are limitations to using traditional logistic regression models when assessing the model performance of comorbidity measures to predict in-hospital mortality. Meanwhile, the use of novel machine-learning methods is growing rapidly. In 2021, the Agency for Healthcare Research and Quality published new guidelines for using the Present-on-Admission (POA) indicator from the International Classification of Diseases, Tenth Revision, for coding comorbidities to predict in-hospital mortality from the Elixhauser's comorbidity measurement method. We compared the model performance of logistic regression, elastic net model, and artificial neural network (ANN) to predict in-hospital mortality from Elixhauser's measures under the updated POA guidelines. In this retrospective analysis, 1,810,106 adult Medicare inpatient admissions from six US states admitted after September 23, 2017, and discharged before April 11, 2019 were extracted from the Centers for Medicare and Medicaid Services data warehouse. The POA indicator was used to distinguish pre-existing comorbidities from complications that occurred during hospitalization. All models performed well (C-statistics >0.77). Elastic net method generated a parsimonious model, in which there were five fewer comorbidities selected to predict in-hospital mortality with similar predictive power compared to the logistic regression model. ANN had the highest C-statistics compared to the other two models (0.800 vs. 0.791 and 0.791). Elastic net model and AAN can be applied successfully to predict in-hospital mortality.

Part 6. Education for value-informed nursing practice.

With the ongoing transition to value-based health care, a strong command of foundational economic concepts, like cost and value, and the ability to thoughtfully engage in value-informed nursing practice have become essential for the future of the nursing profession. Earlier in this six-part series, we explained value-informed nursing practice, its historical, economic, and ethical foundation, its promise for an environmentally responsible, innovation-driven future health care, and why its adoption requires a reframing of some of the nursing's professional norms and behaviors. This paper concludes the series with one of the most important issues-education for value-informed nursing practice. We begin by setting forth our vision of how nursing students will learn and apply value informed nursing practice, consider challenges that nurse educators will face, and offer some suggestions for engraining value into the consciousness of the nursing profession.

Part 4: Value-informed nursing practice depends on nursing innovation.

With the adoption of value-based payments which tie reimbursement to patient outcomes and costs, days when nursing is viewed primarily as a cost to hospitals will soon be over. Already the backbone of high-quality care delivery and patient outcomes, nurses are becoming key drivers of health care organizations' financial outcomes, too. The first three articles published in this 6-part series on value-informed nursing practice-practice that considers both the outcomes and the cost of producing the outcomes-described what value-informed nursing practice means, its economic, policy, and ethical impetuses, and how value-informed nursing practice helps improve environmental sustainability of health systems. Here, in Part 4, we focus on the importance of nursing innovation in implementing value-informed nursing practice. We begin by discussing how innovation is connected to value and then examine the false dichotomy, perceived by many, between innovation and evidence-based care. Following this, we examine how health care organizations and systems can support nursing innovation, before concluding with recommendations for nursing educators.

Healthy Lifetime (HL): An Internet-Based Behavioral Health Coaching Protocol for Older Adults.

By 2060, the number of Americans 65 years and older will more than double, comprising nearly one-quarter of the population in the United States. While there are many advantages to living longer, a byproduct of aging is also a growing incidence of chronic illness and functional health limitations associated with a concurrent rise in chronic disease and disability that impair independent living in the community. We describe a personalized, behavioral health coaching protocol for early intervention that is delivered online to enhance a participant's independent functioning and to increase their self-care capacity with a goal to maintain independent living throughout aging. The electronic platform provides secure access to fillable surveys, health tracking, "just in time" communication with coaches and scheduling of two-way videos launched from the platform site. The 2-month protocol used two-way video conferencing which allowed high fidelity communication to sustain a complex behavioral intervention. Participants indicate high satisfaction with the intervention, the use of the platform, and the technology. While many health systems across the U.S. have ramped up virtual delivery of care in a proactive manner with now more than 70% of out-patient visits conducted through virtual delivery modes in some health systems, there remains much unevenness in this capability across the U.S. Our approach is to create a stable, interoperable, virtual outreach system for personalized professional health coaching that is complementary to medically oriented services that supports the health and functioning of participants as they age.

Value-informed nursing practice is needed to make our healthcare systems more environmentally sustainable.

In this 3rd part of our 6-part series on value-informed nursing practice-practice that focuses on both achieving desired patient outcomes and minimizing the use of costly resources to achieve these outcomes-we focus on the importance of nurses in improving environmental outcomes and reducing costly environmental waste. We also propose how nursing education needs to change to prepare the next generation of nurses to effectively address environmental problems through providing value-informed nursing practice.

Value-informed nursing practice: What is it and how to make it a reality.

Nurses make decisions about the use of costly resources in countless care delivery settings 24 hours a day. Consequently, nurses are inseparably connected to not only the quality and safety of care, but to the cost-of-care as well. This article is Part 1 of a 6-part series on value-informed nursing practice. It describes the concept of 'value-informed nursing practice'-practice that focuses not only on outcomes, but also on the cost of care-as a new way to envision nursing practice.

Lives saved and lost in the first six month of the US COVID-19 pandemic: A retrospective cost-benefit analysis.

In the beginning of the COVID-19 US epidemic in March 2020, sweeping lockdowns and other aggressive measures were put in place and retained in many states until end of August of 2020; the ensuing economic downturn has led many to question the wisdom of the early COVID-19 policy measures in the US. This study's objective was to evaluate the cost and benefit of the US COVID-19-mitigating policy intervention during the first six month of the pandemic in terms of COVID-19 mortality potentially averted, versus mortality potentially attributable to the economic downturn. We conducted a synthesis-based retrospective cost-benefit analysis of the full complex of US federal, state, and local COVID-19-mitigating measures, including lockdowns and all other COVID-19-mitigating measures, against the counterfactual scenario involving no public health intervention. We derived parameter estimates from a rapid review and synthesis of recent epidemiologic studies and economic literature on regulation-attributable mortality. According to our estimates, the policy intervention saved 866,350-1,711,150 lives (4,886,214-9,650,886 quality-adjusted life-years), while mortality attributable to the economic downturn was 57,922-245,055 lives (2,093,811-8,858,444 life-years). We conclude that the number of lives saved by the spring-summer lockdowns and other COVID-19-mitigation was greater than the number of lives potentially lost due to the economic downturn. However, the net impact on quality-adjusted life expectancy is ambiguous.

Nonlinear association of nurse staffing and readmissions uncovered in machine learning analysis.

OBJECTIVE: Several studies of nurse staffing and patient outcomes found a curvilinear or U-shaped relationship, with benefits from additional nurse staffing diminishing or reversing at high staffing levels. This study examined potential diminishing returns to nurse staffing and the existence of a "tipping point" or the level of staffing after which higher nurse staffing no longer improves and may worsen readmissions. DATA SOURCES/STUDY SETTING: The Readiness Evaluation And Discharge Interventions (READI) study database of over 130,000 adult (18+) inpatient discharges from 62 medical, surgical, and medical-surgical (noncritical care) units from 31 United States (US) hospitals during October 2014-March 2017. STUDY DESIGN: Observational cross-sectional study using a fully nonparametric random forest machine learning method. Primary exposure was nurse hours per patient day (HPPD) broken down by registered nurses (nonovertime and overtime) and nonlicensed nursing personnel. The outcome was 30-day all-cause same-hospital readmission. Partial dependence plots were used to visualize the pattern of predicted patient readmission risk along a range of unit staffing levels, holding all other patient characteristics and hospital and unit structural variables constant. DATA COLLECTION/EXTRACTION METHODS: Secondary analysis of the READI data. Missing values were imputed using the missing forest algorithm in R. PRINCIPAL FINDINGS: Partial dependence plots were U-shaped, showing the readmission risk first declining and then rising with additional nurse staffing. The tipping points were at 6.95 and 0.21 HPPD for registered nurse staffing (nonovertime and overtime, respectively) and 2.91 HPPD of nonlicensed nursing personnel. CONCLUSIONS: The U-shaped association was consistent with diminishing returns to nurse staffing suggesting that incremental gains in readmission reduction from additional nurse staffing taper off and could reverse at high staffing levels. If confirmed in future causal analyses across multiple outcomes, accompanying investments in infrastructure and human resources may be needed to maximize nursing performance outcomes at higher levels of nurse staffing.

The Effect of Reduced Access to Chiropractic Care on Medical Service Use for Spine Conditions Among Older Adults.

OBJECTIVE: The purpose of this study was to examine the extent to which access to chiropractic care affects medical service use among older adults with spine conditions. METHODS: We used Medicare claims data to identify a cohort of 39,278 older adult chiropractic care users who relocated during 2010-2014 and thus experienced a change in geographic access to chiropractic care. National Plan and Provider Enumeration System data were used to determine chiropractor per population ratios across the United States. A reduction in access to chiropractic care was defined as decreasing 1 quintile or more in chiropractor per population ratio after relocation. Using a difference-in-difference analysis (before versus after relocation), we compared the use of medical services among those who experienced a reduction in access to chiropractic care versus those who did not. RESULTS: Among those who experienced a reduction in access to chiropractic care (versus those who did not), we observed an increase in the rate of visits to primary care physicians for spine conditions (an annual increase of 32.3 visits, 95% CI: 1.4-63.1 per 1,000) and rate of spine surgeries (an annual increase of 5.5 surgeries, 95% CI: 1.3-9.8 per 1,000). Considering the mean cost of a visit to a primary care physician and spine surgery, a reduction in access to chiropractic care was associated with an additional cost of $114,967 per 1,000 beneficiaries on medical services ($391 million nationally). CONCLUSIONS: Among older adults, reduced access to chiropractic care is associated with an increase in the use of some medical services for spine conditions.