Effect of Automated Real-Time Feedback on Early-Sepsis Care: A Pragmatic Clinical Trial.

OBJECTIVES: To determine if a real-time monitoring system with automated clinician alerts improves 3-hour sepsis bundle adherence. DESIGN: Prospective, pragmatic clinical trial. Allocation alternated every 7 days. SETTING: Quaternary hospital from December 1, 2020 to November 30, 2021. PATIENTS: Adult emergency department or inpatients meeting objective sepsis criteria triggered an electronic medical record (EMR)-embedded best practice advisory. Enrollment occurred when clinicians acknowledged the advisory indicating they felt sepsis was likely. INTERVENTION: Real-time automated EMR monitoring identified suspected sepsis patients with incomplete bundle measures within 1-hour of completion deadlines and generated reminder pages. Clinicians responsible for intervention group patients received reminder pages; no pages were sent for controls. The primary analysis cohort was the subset of enrolled patients at risk of bundle nonadherent care that had reminder pages generated. MEASUREMENTS AND MAIN RESULTS: The primary outcome was orders for all 3-hour bundle elements within guideline time limits. Secondary outcomes included guideline-adherent delivery of all 3-hour bundle elements, 28-day mortality, antibiotic discontinuation within 48-hours, and pathogen recovery from any culture within 7 days of time-zero. Among 3,269 enrolled patients, 1,377 had reminder pages generated and were included in the primary analysis. There were 670 (48.7%) at-risk patients randomized to paging alerts and 707 (51.3%) to control. Bundle-adherent orders were placed for 198 intervention patients (29.6%) versus 149 (21.1%) controls (difference: 8.5%; 95% CI, 3.9-13.1%; p = 0.0003). Bundle-adherent care was delivered for 152 (22.7%) intervention versus 121 (17.1%) control patients (difference: 5.6%; 95% CI, 1.4-9.8%; p = 0.0095). Mortality was similar between groups (8.4% vs 8.3%), as were early antibiotic discontinuation (35.1% vs 33.4%) and pan-culture negativity (69.0% vs 68.2%). CONCLUSIONS: Real-time monitoring and paging alerts significantly increased orders for and delivery of guideline-adherent care for suspected sepsis patients at risk of 3-hour bundle nonadherence. The trial was underpowered to determine whether adherence affected mortality. Despite enrolling patients with clinically suspected sepsis, early antibiotic discontinuation and pan-culture negativity were common, highlighting challenges in identifying appropriate patients for sepsis bundle application.

Impact of a Repatriation Program Between Quaternary and Community Hospitals.

BACKGROUND: Capacity challenges at quaternary hospitals cause delays or denials in patient transfers from community hospitals that can compromise quality and safety. Repatriation is an innovative approach to increase capacity at the quaternary hospital by transferring a patient back to their originating community hospital after the quaternary portion of their care is completed. METHODS: A repatriation program was implemented at a large quaternary care teaching hospital over a one-year period (2020 to 2021). The authors characterized the rate of successful repatriation and associated patient characteristics, determined the impact on quaternary hospital capacity in terms of bed days saved, and estimated the resultant number of backfilled admissions that could be accommodated. The research team also monitored the rate of readmissions for repatriations back to the quaternary hospital. RESULTS: Overall, 215 repatriations were attempted, and 103 (47.5%) were successful. The most common diagnoses were sepsis (13, 12.6%), stroke (12, 11.7%), intracranial bleed (10, 9.7%), gastrointestinal perforation/obstruction (9, 8.7%), and trauma (9, 8.7%). The median length of stay at the quaternary hospital was 13 days (interquartile range [IQR] 7-20) and 12 days (IQR 4-26) at the community hospital. There were 2,842 bed days saved at the quaternary hospital, with a backfill opportunity of 431 admissions. The readmission rate to the quaternary hospital was 1.9%. CONCLUSION: By dynamically matching patient need with hospital capability at different phases of the patient's care, Repatriation can save bed days at the quaternary hospital, creating capacity to improve access for patients needing timely transfer. The low observed readmission rate suggests that repatriation is safe.

Impact of a hospital policy to redistribute admission flow across clinical services for capacity relief during COVID-19 surges.

BACKGROUND: Increased hospital admissions due to COVID-19 place a disproportionate strain on inpatient general medicine service (GMS) capacity compared to other services. OBJECTIVE: To study the impact on capacity and safety of a hospital-wide policy to redistribute admissions from GMS to non-GMS based on admitting diagnosis during surge periods. DESIGN, SETTING, AND PARTICIPANTS: Retrospective case-controlled study at a large teaching hospital. The intervention included adult patients admitted to general care wards during two surge periods (January-February 2021 and 2022) whose admission diagnosis was impacted by the policy. The control cohort included admissions during a matched number of days preceding the intervention. MAIN OUTCOMES AND MEASURES: Capacity measures included average daily admissions and hospital census occupied on GMS. Safety measures included length of stay (LOS) and adverse outcomes (death, rapid response, floor-to-intensive care unit transfer, and 30-day readmission). RESULTS: In the control cohort, there were 365 encounters with 299 (81.9%) GMS admissions and 66 (18.1%) non-GMS versus the intervention with 384 encounters, including 94 (24.5%) GMS admissions and 290 (75.5%) non-GMS (p < .001). The average GMS census decreased from 17.9 and 21.5 during control periods to 5.5 and 8.5 during intervention periods. An interrupted time series analysis confirmed a decrease in GMS daily admissions (p < .001) and average daily hospital census (p = .014; p < .001). There were no significant differences in LOS (5.9 vs. 5.9 days, p = .059) or adverse outcomes (53, 14.5% vs. 63, 16.4%; p = .482). CONCLUSION: Admission redistribution based on diagnosis is a safe lever to reduce capacity strain on GMS during COVID-19 surges.

The Gap Between Daily Hospital Bed Supply and Demand: Design, Implementation, and Impact of Data-Driven Pre-Noon Discharge Targets.

BACKGROUND: Hospitals have sought to increase pre-noon discharges to improve capacity, although evidence is mixed on the impact of these initiatives. Past interventions have not quantified the daily gap between morning bed supply and demand. The authors quantified this gap and applied the pre-noon data to target a pre-noon discharge initiative. METHODS: The study was conducted at a large hospital and included adult and pediatric medical/surgical wards. The researchers calculated the difference between the average cumulative bed requests and transfers in for each hour of the day in 2018, the year prior to the intervention. In 2019 an intervention on six adult general medical and two surgical wards was implemented. Eight intervention and 14 nonintervention wards were compared to determine the change in average cumulative pre-noon discharges. The change in average hospital length of stay (LOS) and 30-day readmissions was also calculated. RESULTS: The average daily cumulative gap by noon between bed supply and demand across all general care wards was 32.1 beds (per ward average, 1.3 beds). On intervention wards, mean pre-noon discharges increased from 4.7 to 6.7 (p < 0.0000) compared with the nonintervention wards 14.0 vs. 14.6 (p = 0.19877). On intervention wards, average LOS decreased from 6.9 to 6.4 days (p < 0.001) and readmission rates were 14.3% vs 13.9% (p = 0.3490). CONCLUSION: The gap between daily hospital bed supply and demand can be quantified and applied to create pre-noon discharge targets. In an intervention using these targets, researchers observed an increase in morning discharges, a decrease in LOS, and no significant change in readmissions.

The Power of Modeling in Emergency Preparedness for COVID-19: A Moonshot Moment for Hospitals.

Before coronavirus disease 2019 (COVID-19), few hospitals had fully tested emergency surge plans. Uncertainty in the timing and degree of surge complicates planning efforts, putting hospitals at risk of being overwhelmed. Many lack access to hospital-specific, data-driven projections of future patient demand to guide operational planning. Our hospital experienced one of the largest surges in New England. We developed statistical models to project hospitalizations during the first wave of the pandemic. We describe how we used these models to meet key planning objectives. To build the models successfully, we emphasize the criticality of having a team that combines data scientists with frontline operational and clinical leadership. While modeling was a cornerstone of our response, models currently available to most hospitals are built outside of their institution and are difficult to translate to their environment for operational planning. Creating data-driven, hospital-specific, and operationally relevant surge targets and activation triggers should be a major objective of all health systems.

Potential impact of hospital at home on postoperative readmissions.

OBJECTIVES: Hospital at home (HAH) is a health care delivery model that substitutes hospital-level services in the home for inpatient hospitalizations. HAH has been shown to be safe and effective for medical patients but has not been investigated in surgical readmissions. We estimated the potential impact of an HAH program for patients readmitted within 60 days postoperatively and described the characteristics of eligible patients to aid in the design of future programs. STUDY DESIGN: This was a cross-sectional study of 60-day postoperative readmissions at a tertiary care center in 2018. METHODS: We identified the number of readmissions that may have been eligible for HAH, collected descriptive information, and estimated the financial margin that could have been generated had eligible readmissions been diverted to HAH. RESULTS: There were 2366 readmissions within 60 days of surgery in 2018. A total of 731 readmissions met inclusion criteria for HAH (30.1%), accounting for 4152 bed days. Of these readmissions, the most common diagnoses were infection, gastrointestinal complications, and cardiac complications. Patients' home addresses were within 16 miles of the hospital in 447 cases (61.1%). Avoidance of these readmissions and use of the beds for new admissions represented a potential backfill margin of $8.8 million, not incorporating the cost of HAH. CONCLUSIONS: Many 60-day postoperative readmissions may be amenable to HAH enrollment, representing a significant opportunity to improve patient experience and generate hospital revenue. This is of particular interest in the post-COVID-19 era. To maximize their impact, HAH programs should tailor clinical and operational services to this population.

COVID-19 Level-Loading: Transferring Emergency Department Patients to a Partner Academic Medical Center Within a Healthcare System.

COVID-19 continues to challenge bed capacity and the ability of hospitals to provide quality care for patients around the country. However, the COVID-19 pandemic at a given point in time does not impact all hospitals equally-even within a single healthcare system, one hospital may be caring for patients in the hallways, while another has available inpatient beds. Here, we demonstrate a program to level-load COVID-19 patients between 2 academic medical centers in a healthcare system by transferring patients at the time of admission from the emergency department of one institution directly to an inpatient bed of the other institution. Over 42 days, 50 patients were transferred which saved 432 bed-days at the home academic medical center without any adverse events during transfer or upgrades to the ICU within the first 24 hours of admission. Programs like this can expand a healthcare system's ability to allocate personnel and resources efficiently for patients and maximize the quality of care delivered even during a pandemic.

Design and Implementation of a Real-time Monitoring Platform for Optimal Sepsis Care in an Emergency Department: Observational Cohort Study.

BACKGROUND: Sepsis is the leading cause of death in US hospitals. Compliance with bundled care, specifically serial lactates, blood cultures, and antibiotics, improves outcomes but is often delayed or missed altogether in a busy practice environment. OBJECTIVE: This study aims to design, implement, and validate a novel monitoring and alerting platform that provides real-time feedback to frontline emergency department (ED) providers regarding adherence to bundled care. METHODS: This single-center, prospective, observational study was conducted in three phases: the design and technical development phase to build an initial version of the platform; the pilot phase to test and refine the platform in the clinical setting; and the postpilot rollout phase to fully implement the study intervention. RESULTS: During the design and technical development, study team members and stakeholders identified the criteria for patient inclusion, selected bundle measures from the Center for Medicare and Medicaid Sepsis Core Measure for alerting, and defined alert thresholds, message content, delivery mechanisms, and recipients. Additional refinements were made based on 70 provider survey results during the pilot phase, including removing alerts for vasopressor initiation and modifying text in the pages to facilitate patient identification. During the 48 days of the postpilot rollout phase, 15,770 ED encounters were tracked and 711 patient encounters were included in the active monitoring cohort. In total, 634 pages were sent at a rate of 0.98 per attending physician shift. Overall, 38.3% (272/711) patients had at least one page. The missing bundle elements that triggered alerts included: antibiotics 41.6% (136/327), repeat lactate 32.4% (106/327), blood cultures 20.8% (68/327), and initial lactate 5.2% (17/327). Of the missing Sepsis Core Measures elements for which a page was sent, 38.2% (125/327) were successfully completed on time. CONCLUSIONS: A real-time sepsis care monitoring and alerting platform was created for the ED environment. The high proportion of patients with at least one alert suggested the significant potential for such a platform to improve care, whereas the overall number of alerts per clinician suggested a low risk of alarm fatigue. The study intervention warrants a more rigorous evaluation to ensure that the added alerts lead to better outcomes for patients with sepsis.

A Remote Surveillance Platform to Monitor General Care Ward Surgical Patients for Acute Physiologic Deterioration.

BACKGROUND: The traditional paradigm of hospital surgical ward care consists of episodic bedside visits by providers with periodic perusals of the patient's electronic health record (EHR). Vital signs and laboratory results are directly pushed to the EHR but not to providers themselves. Results that require intervention may not be recognized for hours. Remote surveillance programs continuously monitor electronic data and provide automatic alerts that can be routed to multidisciplinary providers. Such programs have not been explored in surgical general care wards. METHODS: We performed a quality improvement observational study of otolaryngology and ophthalmology patients on a general care ward from October 2017 to March 2019 during nighttime hours (17:00-07:00). The study was initiated due to the loss of on-site anesthesiology resources that historically helped respond to acute physiologic deterioration events. We implemented a remote surveillance software program to continuously monitor patients for severe vital signs and laboratory abnormalities and automatically alert the ward team and a remote critical care anesthesiology team. The primary end point was the true positive rate, defined as the proportion of alerts that were associated with a downstream action that changed the care of the patient. This was determined using systematic chart review. The secondary end point, as a measure of alarm fatigue, was the average number of alerts per clinician shift. RESULTS: The software monitored 3926 hospital visits and analyzed 1,560,999 vitals signs and 16,635 laboratories. It generated 151 alerts, averaging 2.6 alerts per week. Of these, 143 (94.7%) were numerically accurate and 8 (5.3%) were inaccurate. Hypoxemia with oxygen saturation <88% was the most common etiology (92, 63%) followed by tachycardia >130 beats per minute (19, 13.3%). Among the accurate alerts, 133 (88.1%) were true positives with an associated clinical action. Actions included a change in management 113 (67.7%), new diagnostic test 26 (15.6%), change in discharge planning 20 (12.0%), and change in level of care to the intensive care unit (ICU) 8 (4.8%). As a measure of alarm fatigue, there were 0.4 alerts per clinician shift. CONCLUSIONS: In a surgical general care ward, a remote surveillance software program that continually and automatically monitors physiologic data streams from the EHR and alerts multidisciplinary providers for severe derangements provided highly actionable alarms at a rate that is unlikely to cause alarm fatigue. Such programs are feasible and could be used to change the paradigm of monitoring.

Design and Performance of a COVID-19 Hospital Recovery Model.

To determine the accuracy of a predictive model for inpatient occupancy that was implemented at a large New England hospital to aid hospital recovery planning from the COVID-19 surge. Background: During recovery from COVID surges, hospitals must plan for multiple patient populations vying for inpatient capacity, so that they maintain access for emergency department (ED) patients while enabling time-sensitive scheduled procedures to go forward. To guide pandemic recovery planning, we implemented a model to predict hospital occupancy for COVID and non-COVID patients. Methods: At a quaternary care hospital in New England, we included hospitalizations from March 10 to July 12, 2020 and subdivided them into COVID, non-COVID nonscheduled (NCNS), and non-COVID scheduled operating room (OR) hospitalizations. For the recovery period from May 25 to July 12, the model made daily hospital occupancy predictions for each population. The primary outcome was the daily mean absolute percentage error (MAPE) and mean absolute error (MAE) when comparing the predicted versus actual occupancy. Results: There were 444 COVID, 5637 NCNS, and 1218 non-COVID scheduled OR hospitalizations during the recovery period. For all populations, the MAPE and MAE for total occupancy were 2.8% or 22.3 hospitalizations per day; for general care, 2.6% or 17.8 hospitalizations per day; and for intensive care unit, 9.7% or 11.0 hospitalizations per day. Conclusions: The model was accurate in predicting hospital occupancy during the recovery period. Such models may aid hospital recovery planning so that enough capacity is maintained to care for ED hospitalizations while ensuring scheduled procedures can efficiently return.

Health systems as venture capital investors in digital health: 2011-2019.

Provider health systems as venture capital investors in digital health are uniquely positioned in the industry. Little is known about the volume or characteristics of their investments and how these compare to other investors. From 2011 to 2019, we found that health systems made 184 investments in 105 companies. Compared with other investors, they were more likely to invest in companies focused on workflow, on-demand health services, and data infrastructure/interoperability.

Direct admission to improve timely access to care for patients requiring transfer to a level 1 trauma center.

BACKGROUND: Emergency departments (EDs) at level 1 trauma centers are often overcrowded and deny ED-to-ED transfers from lower-tiered centers. Lack of access to timely level 1 care is associated with increased mortality. We evaluated the feasibility of a direct admission (DA) protocol as a method to increase timely access to a level 1 trauma center during periods of ED overcrowding. METHODS: During periods of ED overcrowding between 1 May and 31 December 2019, we admitted patients from referring EDs directly to the intensive care unit (ICU) or inpatient ward using the DA protocol. In a prospective comparative study design, we compared their outcomes to patients during the same period who were admitted through the ED when the ED was not overcrowded. RESULTS: During periods of ED overcrowding, transfer was requested and clinically accepted for 28 patients, of which 23 (82.1%, age 63±20.3 years, men 52.2% men) were successfully admitted via the DA protocol. Five (17.9%) were not successfully transferred due to lack of available inpatient beds. During periods when the ED was not overcrowded, 106 patients (age 62.8±23.1 years, men 52.8%) were admitted via the ED. There were no morbidity or mortality events attributed to the DA process. Time to patient arrival was 2.7 hours (95% CI 2.3 to 3.1) in the DA cohort and 1.9 hours (95% CI 1.5 to 2.4) in the ED-to-ED cohort (p=0.104). Up-triage to the ICU within 24 hours was performed in only one patient (4.3%). In-hospital mortality did not differ (3 (13%) vs. 8 (7.6%), p=0.392). DISCUSSION: The DA pathway is a feasible method to safely transfer patients from a referring ED to a higher-care trauma center when its ED is overcrowded. LEVEL OF EVIDENCE: Level III, care management.

Development and Validation of a Machine Learning Model to Aid Discharge Processes for Inpatient Surgical Care.

Importance: Inpatient overcrowding is associated with delays in care, including the deferral of surgical care until beds are available to accommodate postoperative patients. Timely patient discharge is critical to address inpatient overcrowding and requires coordination among surgeons, nurses, case managers, and others. This is difficult to achieve without early identification and systemwide transparency of discharge candidates and their respective barriers to discharge. Objective: To validate the performance of a clinically interpretable feedforward neural network model that could improve the discharge process by predicting which patients would be discharged within 24 hours and their clinical and nonclinical barriers. Design, Setting, and Participants: This prognostic study included adult patients discharged from inpatient surgical care from May 1, 2016, to August 31, 2017, at a quaternary care teaching hospital. Model performance was assessed with standard cross-validation techniques. The model's performance was compared with a baseline model using historical procedure median length of stay to predict discharges. In prospective cohort analysis, the feedforward neural network model was used to make predictions on general surgical care floors with 63 beds. If patients were not discharged when predicted, the causes of delay were recorded. Main Outcomes and Measures: The primary outcome was the out-of-sample area under the receiver operating characteristic curve of the model. Secondary outcomes included the causes of discharge delay and the number of avoidable bed-days. Results: The model was trained on 15 201 patients (median [interquartile range] age, 60 [46-70] years; 7623 [50.1%] men) discharged from inpatient surgical care. The estimated out-of-sample area under the receiver operating characteristic curve of the model was 0.840 (SD, 0.008; 95% CI, 0.839-0.844). Compared with the baseline model, the neural network model had higher sensitivity (52.5% vs 56.6%) and specificity (51.7% vs 82.6%). The neural network model identified 65 barriers to discharge. In the prospective study of 605 patients, causes of delays included clinical barriers (41 patients [30.1%]), variation in clinical practice (30 patients [22.1%]), and nonclinical reasons (65 patients [47.8%]). Summing patients who were not discharged owing to variation in clinical practice and nonclinical reasons, 128 bed-days, or 1.2 beds per day, were classified as avoidable. Conclusions and Relevance: This cohort study found that a neural network model could predict daily inpatient surgical care discharges and their barriers. The model identified systemic causes of discharge delays. Such models should be studied for their ability to increase the timeliness of discharges.

Remote Surveillance Technologies: Realizing the Aim of Right Patient, Right Data, Right Time.

The convergence of multiple recent developments in health care information technology and monitoring devices has made possible the creation of remote patient surveillance systems that increase the timeliness and quality of patient care. More convenient, less invasive monitoring devices, including patches, wearables, and biosensors, now allow for continuous physiological data to be gleaned from patients in a variety of care settings across the perioperative experience. These data can be bound into a single data repository, creating so-called data lakes. The high volume and diversity of data in these repositories must be processed into standard formats that can be queried in real time. These data can then be used by sophisticated prediction algorithms currently under development, enabling the early recognition of patterns of clinical deterioration otherwise undetectable to humans. Improved predictions can reduce alarm fatigue. In addition, data are now automatically queriable on a real-time basis such that they can be fed back to clinicians in a time frame that allows for meaningful intervention. These advancements are key components of successful remote surveillance systems. Anesthesiologists have the opportunity to be at the forefront of remote surveillance in the care they provide in the operating room, postanesthesia care unit, and intensive care unit, while also expanding their scope to include high-risk preoperative and postoperative patients on the general care wards. These systems hold the promise of enabling anesthesiologists to detect and intervene upon changes in the clinical status of the patient before adverse events have occurred. Importantly, however, significant barriers still exist to the effective deployment of these technologies and their study in impacting patient outcomes. Studies demonstrating the impact of remote surveillance on patient outcomes are limited. Critical to the impact of the technology are strategies of implementation, including who should receive and respond to alerts and how they should respond. Moreover, the lack of cost-effectiveness data and the uncertainty of whether clinical activities surrounding these technologies will be financially reimbursed remain significant challenges to future scale and sustainability. This narrative review will discuss the evolving technical components of remote surveillance systems, the clinical use cases relevant to the anesthesiologist's practice, the existing evidence for their impact on patients, the barriers that exist to their effective implementation and study, and important considerations regarding sustainability and cost-effectiveness.

Variation in surgical quality measure adherence within hospital referral regions: do publicly reported surgical quality measures distinguish among hospitals that patients are likely to compare?

OBJECTIVE: To determine whether surgical quality measures that Medicare publicly reports provide a basis for patients to choose a hospital from within their geographic region. DATA SOURCE: The Department of Health and Human Services' public reporting website, http://www.medicare.gov/hospitalcompare. STUDY DESIGN: We identified hospitals (n = 2,953) reporting adherence rates to the quality measures intended to reduce surgical site infections (Surgical Care Improvement Project, 1-3) in 2012. We defined regions within which patients were likely to compare hospitals using the hospital referral regions (HRRs) from the Dartmouth Atlas of Health Care Project. We described distributions of reported SCIP adherence within each HRR, including medians, interquartile ranges (IQRs), skewness, and outliers. PRINCIPAL FINDINGS: Ninety-seven percent of HRRs had median SCIP-1 scores ≥95 percent. In 93 percent of HRRs, half of the hospitals in the HRR were within 5 percent of the median hospital's score. In 62 percent of HRRs, hospitals were skewed toward the higher rates (negative skewness). Seven percent of HRRs demonstrated positive skewness. Only 1 percent had a positive outlier. SCIP-2 and SCIP-3 demonstrated similar distributions. CONCLUSIONS: Publicly reported quality measures for surgical site infection prevention do not distinguish the majority of hospitals that patients are likely to choose from when selecting a surgical provider. More studies are needed to improve public reporting's ability to positively impact patient decision making.

Hospital variation in noninvasive positive pressure ventilation for acute decompensated heart failure.

BACKGROUND: Although noninvasive positive pressure ventilation (NIPPV) for patients with acute decompensated heart failure was introduced almost 20 years ago, the variation in its use among hospitals remains unknown. We sought to define hospital practice patterns of NIPPV use for acute decompensated heart failure and their relationship with intubation and mortality. METHODS AND RESULTS: We conducted a cross-sectional study using a database maintained by Premier, Inc., that includes a date-stamped log of all billed items for hospitalizations at >400 hospitals. We examined hospitalizations for acute decompensated heart failure in this database from 2005 to 2010 and included hospitals with annual average volume of >25 such hospitalizations. We identified 384 hospitals that encompassed 524 430 hospitalizations (median annual average volume: 206). We used hierarchical logistic regression models to calculate hospital-level outcomes: risk-standardized NIPPV rate, risk-standardized intubation rate, and in-hospital risk-standardized mortality rate. We grouped hospitals into quartiles by risk-standardized NIPPV rate and compared risk-standardized mortality rates and risk-standardized intubation rates across quartiles. Median risk-standardized NIPPV rate was 6.2% (interquartile range, 2.8%-9.3%; 5th percentile, 0.2%; 95th percentile, 14.8%). There was no clear pattern of risk-standardized mortality rates across quartiles. The bottom quartile of hospitals had higher risk-standardized intubation rate (11.4%) than each of the other quartiles (9.0%, 9.7%, and 9.1%; P<0.02 for all comparisons). CONCLUSIONS: Substantial variation exists among hospitals in the use of NIPPV for acute decompensated heart failure without evidence for differences in mortality. There may be a threshold effect in relation to intubation rates, with the lowest users of NIPPV having higher intubation rates.

Hospital variation in the use of noninvasive cardiac imaging and its association with downstream testing, interventions, and outcomes.

IMPORTANCE Current guidelines allow substantial discretion in use of noninvasive cardiac imaging for patients without acute myocardial infarction (AMI) who are being evaluated for ischemia. Imaging use may affect downstream testing and outcomes. OBJECTIVE To characterize hospital variation in use of noninvasive cardiac imaging and the association of imaging use with downstream testing, interventions, and outcomes. DESIGN, SETTING, AND PARTICIPANTS Cross-sectional study of hospitals using 2010 administrative data from Premier, Inc, including patients with suspected ischemia on initial evaluation who were seen in the emergency department, observation unit, or inpatient ward; received at least 1 cardiac biomarker test on day 0 or 1; and had a principal discharge diagnosis for a common cause of chest discomfort, a sign or symptom of cardiac ischemia, and/or a comorbidity associated with coronary disease. We excluded patients with AMI. MAIN OUTCOMES AND MEASURES At each hospital, the proportion of patients who received noninvasive imaging to identify cardiac ischemia and the subsequent rates of admission, coronary angiography, and revascularization procedures. RESULTS We identified 549,078 patients at 224 hospitals. The median (interquartile range) hospital noninvasive imaging rate was 19.8% (10.9%-27.7%); range, 0.2% to 55.7%. Median hospital imaging rates by quartile were Q1, 6.0%; Q2, 15.9%; Q3, 23.5%; Q4, 34.8%. Compared with Q1, Q4 hospitals had higher rates of admission (Q1, 32.1% vs Q4, 40.0%), downstream coronary angiogram (Q1, 1.2% vs Q4, 4.9%), and revascularization procedures (Q1, 0.5% vs Q4, 1.9%). Hospitals in Q4 had a lower yield of revascularization for noninvasive imaging (Q1, 7.6% vs Q4, 5.4%) and for angiograms (Q1, 41.2% vs Q4, 38.8%). P <.001 for all comparisons. Readmission rates to the same hospital for AMI within 2 months were not different by quartiles (P = .51). Approximately 23% of variation in imaging use was attributable to the behavior of individual hospitals. CONCLUSIONS AND RELEVANCE Hospitals vary in their use of noninvasive cardiac imaging in patients with suspected ischemia who do not have AMI. Hospitals with higher imaging rates did not have substantially different rates of therapeutic interventions or lower readmission rates for AMI but were more likely to admit patients and perform angiography.

Variation exists in rates of admission to intensive care units for heart failure patients across hospitals in the United States.

BACKGROUND: Despite increasing attention on reducing relatively costly hospital practices while maintaining the quality of care, few studies have examined how hospitals use the intensive care unit (ICU), a high-cost setting, for patients admitted with heart failure (HF). We characterized hospital patterns of ICU admission for patients with HF and determined their association with the use of ICU-level therapies and patient outcomes. METHODS AND RESULTS: We identified 166 224 HF discharges from 341 hospitals in the 2009-2010 Premier Perspective database. We excluded hospitals with <25 HF admissions, patients <18 years old, and transfers. We defined ICU as including medical ICU, coronary ICU, and surgical ICU. We calculated the percent of patients admitted directly to an ICU. We compared hospitals in the top quartile (high ICU admission) with the remaining quartiles. The median percentage of ICU admission was 10% (interquartile range, 6%-16%; range, 0%-88%). In top-quartile hospitals, treatments requiring an ICU were used less often; the percentage of ICU days receiving mechanical ventilation was 6% for the top quartile versus 15% for the others; noninvasive positive pressure ventilation, 8% versus 19%; vasopressors and/or inotropes, 9% versus 16%; vasodilators, 6% versus 12%; and any of these interventions, 26% versus 51%. Overall HF in-hospital risk-standardized mortality was similar (3.4% versus 3.5%; P=0.2). CONCLUSIONS: ICU admission rates for HF varied markedly across hospitals and lacked association with in-hospital risk-standardized mortality. Greater ICU use correlated with fewer patients receiving ICU interventions. Judicious ICU use could reduce resource consumption without diminishing patient outcomes.