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PURPOSE: Older patients with acute leukemia (AL) have a high symptom burden and poor prognosis. Although integration of palliative care (PC) with oncologic care has been shown to improve quality-of-life and end-of-life care in patients with AL, the malignant hematologists at our tertiary care hospital make limited use of PC services and do so late in the disease course. Using the Plan-Do-Study-Act (PDSA) methodology, we aimed to increase early PC utilization by older patients with newly diagnosed AL. METHODS: We instituted the following standardized criteria to trigger inpatient PC consultation: (1) age 70 years and older and (2) new AL diagnosis within 8 weeks. PC consultations were tracked during sequential PDSA cycles in 2021 and compared with baseline rates in 2019. We also assessed the frequency of subsequent PC encounters in patients who received a triggered inpatient PC consult. RESULTS: The baseline PC consultation rate before our intervention was 55%. This increased to 77% and 80% during PDSA cycles 1 and 2, respectively. The median time from diagnosis to first PC consult decreased from 49 days to 7 days. Among patients who received a triggered PC consult, 43% had no subsequent inpatient or outpatient PC encounter after discharge. CONCLUSION: Although standardized PC consultation criteria led to earlier PC consultation in older patients with AL, it did not result in sustained PC follow-up throughout the disease trajectory. Future PDSA cycles will focus on identifying strategies to maintain the integration of PC with oncologic care over time, particularly in the ambulatory setting.

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BACKGROUND: The Caregiver Advise Record Enable (CARE) Act is a state level law that requires hospitals to identify and educate caregivers ("family members or friends") upon discharge. OBJECTIVE: This study examined the association between the implementation of the CARE Act in a Pennsylvania health system and health service utilization (ie, reducing hospital readmission, emergency department [ED] visits, and mortality) for older adults with diabetes. METHODS: The key elements of the CARE Act were implemented and applied to the patients discharged to home. The data between May and October 2017 were pulled from inpatient electronic health records. Likelihood-ratio chi-square tests and multivariate logistic regression models were used for statistical analysis. RESULTS: The sample consisted of 2591 older inpatients with diabetes with a mean age of 74.6 (SD 7.1) years. Of the 2591 patients, 46.1% (n=1194) were female, 86.9% (n=2251) were White, 97.4% (n=2523) had type 2 diabetes, and 69.5% (n=1801) identified a caregiver. Of the 1801 caregivers identified, 399 (22.2%) received discharge education and training. We compared the differences in health service utilization between pre- and postimplementation of the CARE Act; however, no significance was found. No significant differences were detected from the bivariate analyses in any outcomes between individuals who identified a caregiver and those who declined to identify a caregiver. After adjusting for risk factors (multivariate analysis), those who identified a caregiver (12.2%, 219/1801) was associated with higher rates of 30-day hospital readmission than those who declined to identify a caregiver (9.9%, 78/790; odds ratio [OR] 1.38, 95% CI 1.04-1.87; P=.02). Significantly lower rates were detected in 7-day readmission (P=.02), as well as 7-day (P=.03) and 30-day (P=.01) ED visits, among patients with diabetes whose identified caregiver received education and training than those whose identified caregiver did not receive education and training in the bivariate analyses. However, after adjusting for risk factors, no significance was found in 7-day readmission (OR 0.53, 95% CI 0.27-1.05; P=.07), 7-day ED visit (OR 0.63, 95% CI 0.38-1.03; P=.07), and 30-day ED visit (OR 0.73, 95% CI 0.52-1.02; P=.07). No significant associations were found for other outcomes (ie, 30-day readmission and 7-day and 30-day mortality) in both the bivariate and multivariate analyses. CONCLUSIONS: Our study found that the implementation of the CARE Act was associated with certain health service utilization. The identification of caregivers was associated with higher rates of 30-day hospital readmission in the multivariate analysis, whereas having identified caregivers who received discharge education was associated with lower rates of readmission and ED visit in the bivariate analysis.

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BACKGROUND: Inpatient addiction medicine consultation services (AMCS) have grown rapidly, but there is limited research of their impact on patient outcomes. OBJECTIVE: To examine whether AMCS is associated with all-cause mortality and hospital utilization post-discharge. DESIGN: This was a propensity-score-matchedcase-control study from 2018 to 2020. PARTICIPANTS: The intervention group included patients referred to the AMCS from October 2018 to March 2020. Matched control participants included patients hospitalized from October 2017 to September 2018 at an urban academic hospital with a large suburban and rural catchment area. MAIN MEASURES: The effect of treatment was estimated as the difference between the proportion of subjects experiencing the event (7-day and 30-day readmission, emergency department visits, and mortality within 90 days) for each group in the matched sample. KEY RESULTS: There were 711 patients in the intervention group and 2172 patients in the control group. The most common substance use disorders among the intervention group were primary alcohol use disorder (n=181; 25.5%) and primary opioid use disorder (n=175, 24.6%) with over a third with polysubstance use (n=257, 36.1%). Intervention patients showed a reduction in 90-day mortality post-hospital discharge (average treatment effect [ATE]: -2.35%, 95% CI: -3.57, -1.13; p-value <0.001) compared to propensity-matched controls. We found a statistically significant reduction in 7-day hospital readmission by 2.15% (95% CI: -3.65, -0.65; p=0.005) and a nonsignificant reduction in 30-day readmission (ATE: -2.38%, 95% CI: -5.20, 0.45; p=0.099). There was a statistically significant increase in 30-day emergency department visits (ATE: 5.32%, 95% CI: 2.19, 8.46; 0.001) compared to matched controls. CONCLUSIONS: There was a reduction in 90-day all-cause mortality for the AMCS intervention group compared to matched controls, although the impact on hospital utilization was mixed. AMCS are systems interventions that are effective tools to improve patient health and reduce all-cause mortality.

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OBJECTIVES: In the United States, people with serious illness often experience gaps and discontinuity in care. Gaps are frequently exacerbated by limited mobility, need for social support, and challenges managing multiple comorbidities. The Advanced Illness Care (AIC) Program provides nurse practitioner-led, home-based care for people with serious or complex chronic illnesses that specifically targets palliative care needs and coordinates with patients' primary care and specialty health care providers. We sought to investigate the effect of the AIC Program on hospital encounters [hospitalizations and emergency department (ED) visits], hospice conversion, and mortality. DESIGN: Retrospective nearest-neighbor matching. SETTING AND PARTICIPANTS: Patients in AIC who had ≥1 inpatient stay within the 60 days prior to AIC enrollment to fee-for-service Medicare controls at 9 hospitals within one health system. METHODS: We matched on demographic characteristics and comorbidities, with exact matches for diagnosis-related group and home health enrollment. Outcomes were hospital encounters (30- and 90-day ED visits and hospitalizations), hospice conversion, and 30- and 90-day mortality. RESULTS: We included 110 patients enrolled in the AIC Program matched to 371 controls. AIC enrollees were mean age 77.0, 40.9% male, and 79.1% white. Compared with controls, AIC enrollees had a higher likelihood of ED visits at 30 [15.1 percentage points, confidence interval (CI) 4.9, 25.3; P = .004] and 90 days (27.8 percentage points, CI 16.0, 39.6; P < .001); decreased likelihood of hospitalization at 30 days (11.4 percentage points, CI -17.7, -5.0; P < .001); and a higher likelihood of converting to hospice (22.4 percentage points, CI 11.4, 33.3; P < .001). CONCLUSIONS: The AIC Program provides care and coordination that the home-based serious illness population may not otherwise receive. IMPLICATIONS: By identifying and addressing care needs and gaps in care early, patients may avoid unnecessary hospitalizations and receive timely hospice services as they approach the end of life.

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BACKGROUND: Processes for transferring patients to higher acuity facilities lack a standardized approach to prognostication, increasing the risk for low value care that imposes significant burdens on patients and their families with unclear benefits. We sought to develop a rapid and feasible tool for predicting mortality using variables readily available at the time of hospital transfer. METHODS AND FINDINGS: All work was carried out at a single, large, multi-hospital integrated healthcare system. We used a retrospective cohort for model development consisting of patients aged 18 years or older transferred into the healthcare system from another hospital, hospice, skilled nursing or other healthcare facility with an admission priority of direct emergency admit. The cohort was randomly divided into training and test sets to develop first a 54-variable, and then a 14-variable gradient boosting model to predict the primary outcome of all cause in-hospital mortality. Secondary outcomes included 30-day and 90-day mortality and transition to comfort measures only or hospice care. For model validation, we used a prospective cohort consisting of all patients transferred to a single, tertiary care hospital from one of the 3 referring hospitals, excluding patients transferred for myocardial infarction or maternal labor and delivery. Prospective validation was performed by using a web-based tool to calculate the risk of mortality at the time of transfer. Observed outcomes were compared to predicted outcomes to assess model performance. The development cohort included 20,985 patients with 1,937 (9.2%) in-hospital mortalities, 2,884 (13.7%) 30-day mortalities, and 3,899 (18.6%) 90-day mortalities. The 14-variable gradient boosting model effectively predicted in-hospital, 30-day and 90-day mortality (c = 0.903 [95% CI:0.891-0.916]), c = 0.877 [95% CI:0.864-0.890]), and c = 0.869 [95% CI:0.857-0.881], respectively). The tool was proven feasible and valid for bedside implementation in a prospective cohort of 679 sequentially transferred patients for whom the bedside nurse calculated a SafeNET score at the time of transfer, taking only 4-5 minutes per patient with discrimination consistent with the development sample for in-hospital, 30-day and 90-day mortality (c = 0.836 [95%CI: 0.751-0.921], 0.815 [95% CI: 0.730-0.900], and 0.794 [95% CI: 0.725-0.864], respectively). CONCLUSIONS: The SafeNET algorithm is feasible and valid for real-time, bedside mortality risk prediction at the time of hospital transfer. Work is ongoing to build pathways triggered by this score that direct needed resources to the patients at greatest risk of poor outcomes.

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BACKGROUND: With an aging population, elderly patients with multiple comorbidities are more frequently undergoing spine surgery and may be at increased risk for complications. Objective measurement of frailty may predict the incidence of postoperative adverse events. OBJECTIVE: To investigate the associations between preoperative frailty and postoperative spine surgery outcomes including mortality, length of stay, readmission, surgical site infection, and venous thromboembolic disease. METHODS: As part of a system-wide quality improvement initiative, frailty assessment was added to the routine assessment of patients considering spine surgery beginning in July 2016. Frailty was assessed with the Risk Analysis Index (RAI), and patients were categorized as nonfrail (RAI 0-29) or prefrail/frail (RAI ≥ 30). Comparisons between nonfrail and prefrail/frail patients were analyzed using Fisher's exact test for categorical data or by Wilcoxon rank sum tests for continuous data. RESULTS: From August 2016 through September 2018, 668 patients (age of 59.5 ± 13.3 yr) had a preoperative RAI score recorded and underwent scheduled spine surgery. Prefrail and frail patients suffered comparatively higher rates of mortality at 90 d (1.9% vs 0.2%, P < .05) and 1 yr (5.1% vs 1.2%, P < .01) from the procedure date. They also had longer in-hospital length of stay (LOS) (3.9 d ± 3.6 vs 3.1 d ± 2.8, P < .001) and higher rates of 60 d (14.6% vs 8.2%, P < .05) and 90 d (15.8% vs 9.8%, P < .05) readmissions. CONCLUSION: Preoperative frailty, as measured by the RAI, was associated with an increased risk of readmission and 90-d and 1-yr mortality following spine surgery. The RAI can be used to stratify spine patients and inform preoperative surgical decision making.

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BACKGROUND: Over the past 5 y, robotic surgery has expanded within general surgery, especially in regard to hernia repairs. We aimed to evaluate the outcomes of the early experience of over 300 consecutive robotic inguinal hernia repairs performed in an academic multihospital system. METHODS: Consecutive robotic inguinal hernia repairs performed between December 2015 and June 2017 were analyzed. Retrospective chart review was performed, and hospital records were queried. Descriptive statistics were performed. A surgical learning curve case study is presented, breakdown of operative time is delineated, and review of the literature performed. RESULTS: Over a period of 19 mo, 335 robotic inguinal hernia repairs were performed across seven hospitals by 18 surgeons. The mean patient age was 59 y (standard deviation [SD] 14), 93% were male, and the mean body mass index was 27 (SD 4.6). Bilateral hernia repairs were performed on 131 patients (39%). The mean operative time was 102 min (SD 38) and a resident or fellow trainee was present in the operating room for 119 cases (36%). Minor postoperative complications occurred in 54 patients (16%), including 14 with urinary retention (4.2%) and 13 with scrotal swelling (3.9%). The learning curve of the first adopted surgeon was 11-12 cases. CONCLUSIONS: In the largest case series of robotic inguinal hernia repairs to date reporting short-term outcomes, early experience in an academic multihospital system produced safe outcomes including no open conversions, reoperations, and one readmission. In addition, the learning curve is manageable showing improvement in operating time with experience.

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OBJECTIVES: To compare rates of 30- and 90-day hospital readmissions and observation or emergency department (ED) returns of older adults using the University of Pittsburgh Medical Center (UPMC) Health Plan Home Transitions (HT) with those of Medicare fee-for-service (FFS) controls without HT. DESIGN: Retrospective cohort study. SETTING: Analysis of home health and hospital records from 8 UPMC hospitals in Allegheny County, Pennsylvania, from July 1, 2015, to April 30, 2017. PARTICIPANTS: HT program participants (n=1,900) and controls (n=1,300). INTERVENTION: HT is a care transitions program aimed at preventing readmission that identifies older adults at risk of readmission using a robust inclusion algorithm; deploys a multidisciplinary care team, including a nurse practitioner (NP), a social worker (SW), or both; and provides a multimodal service including personalized care planning, education, treatment, monitoring, and communication facilitation. MEASUREMENT: We used multivariable logistic regression to determine the effects of HT on the odds of hospital readmission and observation or ED return, controlling for index admission participant characteristics and home health process measures. RESULTS: The adjusted odds of 30-day readmission was 0.31 (95% confidence interval (CI) = 0.11-0.87, P = .03) and of 90-day readmission was 0.47 (95% CI=CI = 0.26-0.85, P = .01), for participants at medium risk of readmission in HT who received a team visit. The adjusted odds of 30-day readmission was 0.29 (95% CI = 0.10-0.83, P = .02) for participants at high risk of readmission in HT who received a team visit. The adjusted odds of 30-day observation or ED return was 1.90 (95% CI = 1.28-2.82, P = .001) for participants at medium risk of readmission in HT who received a team visit. CONCLUSION: The HT program may be associated with lower odds of 30- and 90-day hospital readmission and counterbalancing higher odds of observation or ED return. J Am Geriatr Soc 67:156-163, 2019.

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BACKGROUND: While proficiency-based robotic training has been shown to enhance skill acquisition, no studies have shown that training leads to improved outcomes or quality measures. METHODS: Board-certified general surgeons participated in an optional proficiency-based robotic training curriculum and outcomes from robotic hernia cases were analyzed. Multivariable analysis was performed for operative times to adjust for patient and surgical variables. RESULTS: Six out of 16 (38%) surgeons completed training and 210 robotic hernia cases were analyzed. Longer operative times were associated with bilateral repairs (observed-to-expected operative time ratio [OTR] = 1.41, p < 0.001) and incarceration (OTR = 1.24, p = 0.006), while female patients (OTR = 0.87, p = 0.001) and increasing chronologic case order (OTR = 0.94, p < 0.001) were associated with shorter operative times. Surgeons who completed robotic training achieved shorter OTRs than those who did not (p = 0.03). Comparing non-risk adjusted hospital costs, trainees had an average of $1207 in savings (20% reduction) per robotic hernia case. CONCLUSIONS: A structured proficiency-based robotics training curriculum is an effective way to reduce operative times and costs.

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