The Safety of Outpatient Health Care : Review of Electronic Health Records.

BACKGROUND: Despite considerable emphasis on delivering safe care, substantial patient harm occurs. Although most care occurs in the outpatient setting, knowledge of outpatient adverse events (AEs) remains limited. OBJECTIVE: To measure AEs in the outpatient setting. DESIGN: Retrospective review of the electronic health record (EHR). SETTING: 11 outpatient sites in Massachusetts in 2018. PATIENTS: 3103 patients who received outpatient care. MEASUREMENTS: Using a trigger method, nurse reviewers identified possible AEs and physicians adjudicated them, ranked severity, and assessed preventability. Generalized estimating equations were used to assess the association of having at least 1 AE with age, sex, race, and primary insurance. Variation in AE rates was analyzed across sites. RESULTS: The 3103 patients (mean age, 52 years) were more often female (59.8%), White (75.1%), English speakers (90.8%), and privately insured (70.4%) and had a mean of 4 outpatient encounters in 2018. Overall, 7.0% (95% CI, 4.6% to 9.3%) of patients had at least 1 AE (8.6 events per 100 patients annually). Adverse drug events were the most common AE (63.8%), followed by health care-associated infections (14.8%) and surgical or procedural events (14.2%). Severity was serious in 17.4% of AEs, life-threatening in 2.1%, and never fatal. Overall, 23.2% of AEs were preventable. Having at least 1 AE was less often associated with ages 18 to 44 years than with ages 65 to 84 years (standardized risk difference, -0.05 [CI, -0.09 to -0.02]) and more often associated with Black race than with Asian race (standardized risk difference, 0.09 [CI, 0.01 to 0.17]). Across study sites, 1.8% to 23.6% of patients had at least 1 AE and clinical category of AEs varied substantially. LIMITATION: Retrospective EHR review may miss AEs. CONCLUSION: Outpatient harm was relatively common and often serious. Adverse drug events were most frequent. Rates were higher among older adults. Interventions to curtail outpatient harm are urgently needed. PRIMARY FUNDING SOURCE: Controlled Risk Insurance Company and the Risk Management Foundation of the Harvard Medical Institutions.

Patient Safety Indicators During the Initial COVID-19 Pandemic Surge in the United States.

OBJECTIVE: The COVID-19 pandemic presented a challenge to inpatient safety. It is unknown whether there were spillover effects due to COVID-19 into non-COVID-19 care and safety. We sought to evaluate the changes in inpatient Agency for Healthcare Research and Quality patient safety indicators (PSIs) in the United States before and during the first surge of the pandemic among patients admitted without COVID-19. METHODS: We analyzed trends in PSIs from January 2019 to June 2020 in patients without COVID-19 using data from IBM MarketScan Commercial Database. We included members of employer-sponsored or Medicare supplemental health plans with inpatient, non-COVID-19 admissions. The primary outcomes were risk-adjusted composite and individual PSIs. RESULTS: We analyzed 1,869,430 patients admitted without COVID-19. Among patients without COVID-19, the composite PSI score was not significantly different when comparing the first surge (Q2 2020) to the prepandemic period (e.g., Q2 2020 score of 2.46 [95% confidence interval {CI}, 2.34-2.58] versus Q1 2020 score of 2.37 [95% CI, 2.27-2.46]; P = 0.22). Individual PSIs for these patients during Q2 2020 were also not significantly different, except in-hospital fall with hip fracture (e.g., Q2 2020 was 3.42 [95% CI, 3.34-3.49] versus Q4 2019 was 2.45 [95% CI, 2.40-2.50]; P = 0.01). CONCLUSIONS: The first surge of COVID-19 was not associated with worse inpatient safety for patients without COVID-19, highlighting the ability of the healthcare system to respond to the initial surge of the pandemic.

Hospital-Level Care at Home for Acutely Ill Adults in Rural Settings: Proof of Concept.

Residents in rural areas face barriers to accessing acute care. Rural home hospital (RHH) or delivery of acute care at home could represent an important clinical care model. This study assessed the feasibility and acceptability of RHH as a substitute to traditional hospital care. Patients were cared for by a remote RHH attending physician and an RHH registered nurse deployed to the home. The study team conducted daily check-ins with RHH clinicians to assess workflows for completion. Surveys assessed patient experience and qualitative interviews assessed perceived acceptability, safety, and quality of care. We completed qualitative analysis of the interviews and coded qualitative data into domains and subdomains through an iterative process. RHH was successfully deployed to three acutely ill patients in rural Utah. RHH admission, daily care, and discharge processes were accomplished for each patient. From qualitative analysis, we identified four domains: (1) Perceived comfort level during RHH admission, (2) Perceived safety during RHH admission, (3) Perceived quality of care during RHH admission, and (4) Perception of RHH workflows. We found acute care was delivered to rural homes with satisfactory patient and clinician experience. Team dynamics, technology build, robust clinical and operational workflows, and care coordination were important to a successful admission. Learnings from this study can inform program design and training for RHH teams and startup for larger RHH evaluation. Home hospital care is expanding rapidly in the United States and RHH could represent an important clinical care model.

Automation of Functional Mobility Assessments at Home Using a Multimodal Sensor System Integrating Inertial Measurement Units and Computer Vision (IMU-Vision).

OBJECTIVE: Functional movement assessments are routinely used to evaluate and track changes in mobility. The objective of this study was to evaluate a multimodal movement monitoring system developed for autonomous, home-based, functional movement assessment. METHODS: Fifty frail and prefrail adults were recruited from the Brigham and Women's Hospital at Home program to evaluate the feasibility and accuracy of applying the multimodal movement monitoring system to autonomously recognize and score functional activities collected in the home. Study subjects completed sit-to-stand, standing balance (Romberg, semitandem, and tandem), and walking test activities in likeness to the Short Physical Performance Battery. Test activities were identified and scored manually and by the multimodal movement monitoring system's activity recognition and scoring algorithms, which were previously trained on lab-based biomechanical data to integrate wearable inertial measurement unit (IMU) and external red-blue-green-depth vision data. Feasibility was quantified as the proportion of completed tests that were analyzable. Accuracy was quantified as the degree of agreement between the actual and system-identified activities. In an exploratory analysis of a subset of functional activity data, the accuracy of a preliminary activity-scoring algorithm was also evaluated. RESULTS: Activity recognition by the IMU-vision system had good feasibility and high accuracy. Of 271 test activities collected in the home, 217 (80%) were analyzable by the activity-recognition algorithm, which overall correctly identified 206 (95%) of the analyzable activities: 100% of walking, 97% of balance, and 82% of sit-to-stand activities (χ2(2) = 19.9). In the subset of 152 tests suitable for activity scoring, automatic and manual scores showed substantial agreement (Kw = 0.76 [0.69, 0.83]). CONCLUSIONS: Autonomous recognition and scoring of home-based functional activities is enabled by a multimodal movement monitoring system that integrates inertial measurement unit and vision data. Further algorithm training with ecologically valid data and a kitted system that is independently usable by patients are needed before fully autonomous, functional movement assessment is realizable. IMPACT: Functional movement assessments that can be administered in the home without a clinician present have the potential to democratize these evaluations and improve care access.

Single-lead arrhythmia detection through machine learning: cross-sectional evaluation of a novel algorithm using real-world data.

BACKGROUND: Computer-assisted interpretation of single-lead ECG is the preliminary method for clinicians to flag and further evaluate an arrhythmia of clinical importance for acutely ill patients. Critical scrutiny of novel detection algorithms is lacking, particularly in external real-world data sets. This study's objective was to evaluate a hybrid machine learning model's ability to classify eight arrhythmias from a single-lead ECG signal from acutely ill patients. METHODS: This cross-sectional external retrospective evaluation of a previously trained hybrid machine learning model against an ECG reading team in the setting of home hospital care (acute care delivered at home substituting for traditional hospital care) draws from patients admitted at two hospitals in Boston, Massachusetts, USA between 12 June 2017 and 23 November 2019. We calculated classifier statistics for each arrhythmia, all arrhythmias and strips where the model identified normal sinus rhythm. RESULTS: The model analysed 2 680 162 min of single-lead ECG data from 423 patients and identified 691 478 arrhythmias. Patients had a mean age of 70 years (SD, 18), 60% were female and 45% were white. For any arrhythmia, the model had a sensitivity of 98%, a specificity of 100%, an accuracy of 98%, a positive predictive value of 100%, a negative predictive value of 93% and an F1 Score of 99%. Performance was best for pause (F1 Score, 99%) and worst for paroxysmal supraventricular tachycardia (F1 Score, 92%). The model's false positive rate for any arrhythmia was 0.2%, ranging from 0.4% for pause to 7.2% for paroxysmal supraventricular tachycardia. The false negative rate for any arrhythmia was 1.9%. CONCLUSIONS: A hybrid machine learning model was effective at classifying common cardiac arrhythmias from a single-lead ECG in real-world data.

Upper extremity deep venous thrombosis after BNT162b2 mRNA COVID-19 vaccine case report.

We describe a case of an otherwise healthy 60-year-old female patient who presented 6 days after receipt of the second dose of the BNT162b2 mRNA COVID-19 (Pfizer/BioNTech) vaccine and was found to have upper extremity deep venous thrombosis.

The Diagnostic and Triage Accuracy of the GPT-3 Artificial Intelligence Model.

Importance: Artificial intelligence (AI) applications in health care have been effective in many areas of medicine, but they are often trained for a single task using labeled data, making deployment and generalizability challenging. Whether a general-purpose AI language model can perform diagnosis and triage is unknown. Objective: Compare the general-purpose Generative Pre-trained Transformer 3 (GPT-3) AI model's diagnostic and triage performance to attending physicians and lay adults who use the Internet. Design: We compared the accuracy of GPT-3's diagnostic and triage ability for 48 validated case vignettes of both common (e.g., viral illness) and severe (e.g., heart attack) conditions to lay people and practicing physicians. Finally, we examined how well calibrated GPT-3's confidence was for diagnosis and triage. Setting and Participants: The GPT-3 model, a nationally representative sample of lay people, and practicing physicians. Exposure: Validated case vignettes (<60 words; <6th grade reading level). Main Outcomes and Measures: Correct diagnosis, correct triage. Results: Among all cases, GPT-3 replied with the correct diagnosis in its top 3 for 88% (95% CI, 75% to 94%) of cases, compared to 54% (95% CI, 53% to 55%) for lay individuals (p<0.001) and 96% (95% CI, 94% to 97%) for physicians (p=0.0354). GPT-3 triaged (71% correct; 95% CI, 57% to 82%) similarly to lay individuals (74%; 95% CI, 73% to 75%; p=0.73); both were significantly worse than physicians (91%; 95% CI, 89% to 93%; p<0.001). As measured by the Brier score, GPT-3 confidence in its top prediction was reasonably well-calibrated for diagnosis (Brier score = 0.18) and triage (Brier score = 0.22). Conclusions and Relevance: A general-purpose AI language model without any content-specific training could perform diagnosis at levels close to, but below physicians and better than lay individuals. The model was performed less well on triage, where its performance was closer to that of lay individuals.

The Safety of Inpatient Health Care.

BACKGROUND: Adverse events during hospitalization are a major cause of patient harm, as documented in the 1991 Harvard Medical Practice Study. Patient safety has changed substantially in the decades since that study was conducted, and a more current assessment of harm during hospitalization is warranted. METHODS: We conducted a retrospective cohort study to assess the frequency, preventability, and severity of patient harm in a random sample of admissions from 11 Massachusetts hospitals during the 2018 calendar year. The occurrence of adverse events was assessed with the use of a trigger method (identification of information in a medical record that was previously shown to be associated with adverse events) and from review of medical records. Trained nurses reviewed records and identified admissions with possible adverse events that were then adjudicated by physicians, who confirmed the presence and characteristics of the adverse events. RESULTS: In a random sample of 2809 admissions, we identified at least one adverse event in 23.6%. Among 978 adverse events, 222 (22.7%) were judged to be preventable and 316 (32.3%) had a severity level of serious (i.e., caused harm that resulted in substantial intervention or prolonged recovery) or higher. A preventable adverse event occurred in 191 (6.8%) of all admissions, and a preventable adverse event with a severity level of serious or higher occurred in 29 (1.0%). There were seven deaths, one of which was deemed to be preventable. Adverse drug events were the most common adverse events (accounting for 39.0% of all events), followed by surgical or other procedural events (30.4%), patient-care events (which were defined as events associated with nursing care, including falls and pressure ulcers) (15.0%), and health care-associated infections (11.9%). CONCLUSIONS: Adverse events were identified in nearly one in four admissions, and approximately one fourth of the events were preventable. These findings underscore the importance of patient safety and the need for continuing improvement. (Funded by the Controlled Risk Insurance Company and the Risk Management Foundation of the Harvard Medical Institutions.).

Hospital-Level Care at Home for Patients With Acute Respiratory Disease: A Descriptive Analysis.

BACKGROUND: Home hospital (HH) care is hospital-level substitutive care delivered at home for acutely ill patients who traditionally would be cared for in the hospital. Despite HH care programs operating successfully for years and scientific evidence of similar or better outcomes compared with bricks-and-mortar care, HH care outcomes in the United States for respiratory disease have not been evaluated. RESEARCH QUESTION: Do outcomes differ between patients admitted to HH care with acute respiratory illness vs those with other acute general medical conditions? STUDY DESIGN AND METHODS: This was a retrospective evaluation of prospectively collected data of patients admitted to HH care (2017-2021). We compared patients requiring admission with respiratory disease (asthma exacerbation [26%], acute exacerbation of COPD [33%], and non-COVID-19 pneumonia [41%]) to all other patients admitted to HH care. During HH care, patients received two nurse and one physician visit daily, IV medications, advanced respiratory therapies, and continuous heart and respiratory rate monitoring. Main outcomes were acute and postacute health care use and safety. RESULTS: We analyzed 1,031 patients; 24% were admitted for respiratory disease. Patients with and without respiratory disease were similar: mean age, 68 ± 17 years, 62% women, and 48% White. Patients with respiratory disease more often were active smokers (21% vs 9%; P < .001). Eighty percent of patients showed an FEV1 to FVC ratio of ≤ 70; 28% showed a severe or very severe obstructive pattern (n = 118). During HH care, patients with respiratory disease showed less health care use: length of stay (mean, 3.4 vs 4.6 days), laboratory orders (median, 0 vs 2), IV medication (43% vs 73%), and specialist consultation (2% vs 7%; P < .001 for all). Ninety-six percent of patients completed the full admission at home with no mortality in the respiratory group. Within 30 days of discharge, both groups showed similar readmission, ED presentation, and mortality rates. INTERPRETATION: HH care is as safe and effective for patients with acute respiratory disease as for those with other acute general medical conditions. If scaled, it can generate significant high-value capacity for health systems and communities, with opportunities to advance the complexity of care delivered.

A national qualitative study of Hospital-at-Home implementation under the CMS Acute Hospital Care at Home waiver.

BACKGROUND: The Centers for Medicare & Medicaid Services (CMS) announced the Acute Hospital Care at Home (AHCaH) waiver program in November 2020 to help expand hospital capacity to cope with the COVID-19 pandemic. The AHCaH waived the 24/7 on-site nursing requirement and enabled hospitals to obtain full hospital-level diagnosis-related group (DRG) reimbursement for providing Hospital-at-Home (HaH) care. This study sought to describe AHCaH implementation processes and strategies at the national level and identify challenges and facilitators to launching or adapting a HaH to meet waiver requirements. METHODS: We conducted semi-structured interviews to explore barriers and facilitators of HaH implementation. The analysis was informed by the Exploration, Preparation, Implementation, and Sustainment (EPIS) implementation framework. Interviews were audio recorded for transcription and thematic coding. PRINCIPAL FINDINGS: We interviewed a sample of clinical leaders (N = 18; clinical/medical directors, operational and program managers) from 14 new and pre-existing U.S. HaH programs diverse by size, urbanicity, and geography. Participants were enthusiastic about the AHCaH waiver. Participants described barriers and facilitators at planning and implementation stages within three overarching themes influencing waiver program implementation: 1) institutional value and assets; 2) program components, such as electronic health records, vendors, pharmacy, and patient monitoring; and 3) patient enrollment, including eligibility and geographic limits. CONCLUSIONS: Implementation of AHCaH waiver is a complex process that requires building components in compliance with the requirements to extend the hospital into the home, in coordination with internal and external partners. The study identified barriers that potential adopters and proponents should consider alongside the strategies that some organizations have found useful. Clarity regarding the waiver's future may expedite HaH model dissemination and ensure longevity of this valuable model of care delivery.

Remote vs In-home Physician Visits for Hospital-Level Care at Home: A Randomized Clinical Trial.

Importance: Home hospital care is the substitutive provision of home-based acute care services usually associated with a traditional inpatient hospital. Many home hospital models require a physician to see patients at home daily, which may hinder scalability. Whether remote physician visits can safely substitute for most in-home visits is unknown. Objective: To compare remote and in-home physician care. Design, Setting, and Participants: This randomized clinical trial assessed 172 adult patients at an academic medical center and community hospital who required hospital-level care for select acute conditions, including infection, heart failure, chronic obstructive pulmonary disease, and asthma, between August 3, 2019, and March 26, 2020; follow-up ended April 26, 2020. Interventions: All patients received acute care at home, including in-home nurse or paramedic visits, intravenous medications, remote monitoring, and point-of-care testing. Patients were randomized to receive physician care remotely (initial in-home visit followed by daily video visit facilitated by the home hospital nurse) vs in-home care (daily in-home physician visit). In the remote care group, the physician could choose to see the patient at home beyond the first visit if it was felt to be medically necessary. Main Outcomes and Measures: The primary outcome was the number of adverse events, compared using multivariable Poisson regression at a noninferiority threshold of 10 events per 100 patients. Adverse events included a fall, pressure injury, and delirium. Secondary outcomes included the Picker Patient Experience Questionnaire 15 score (scale of 0-15, with 0 indicating worst patient experience and 15 indicating best patient experience) and 30-day readmission rates. Results: A total of 172 patients (84 receiving remote care and 88 receiving in-home physician care [control group]) were randomized; enrollment was terminated early because of COVID-19. The mean (SD) age was 69.3 (18.0) years, 97 patients (56.4%) were female, 77 (45.0%) were White, and 42 (24.4%) lived alone. Mean adjusted adverse event count was 6.8 per 100 patients for remote care patients vs 3.9 per 100 patients for control patients, for a difference of 2.8 (95% CI, -3.3 to 8.9), supporting noninferiority. For remote care vs control patients, the mean adjusted Picker Patient Experience Questionnaire 15 score difference was -0.22 (95% CI, -1.00 to 0.56), supporting noninferiority. The mean adjusted 30-day readmission absolute rate difference was 2.28% (95% CI, -3.23% to 7.79%), which was inconclusive. Of patients in the remote group, 16 (19.0%) required in-home visits beyond the first visit. Conclusions and Relevance: In this study, remote physician visits were noninferior to in-home physician visits during home hospital care for adverse events and patient experience, although in-home physician care was necessary to support many patients receiving remote care. Our findings may allow for a more efficient, scalable home hospital approach but require further research. Trial Registration: ClinicalTrials.gov Identifier: NCT04080570.

The Association of the First Surge of the COVID-19 Pandemic with the High- and Low-Value Outpatient Care Delivered to Adults in the USA.

BACKGROUND: The first surge of the COVID-19 pandemic entirely altered healthcare delivery. Whether this also altered the receipt of high- and low-value care is unknown. OBJECTIVE: To test the association between the April through June 2020 surge of COVID-19 and various high- and low-value care measures to determine how the delivery of care changed. DESIGN: Difference in differences analysis, examining the difference in quality measures between the April through June 2020 surge quarter and the January through March 2020 quarter with the same 2 quarters' difference the year prior. PARTICIPANTS: Adults in the MarketScan® Commercial Database and Medicare Supplemental Database. MAIN MEASURES: Fifteen low-value and 16 high-value quality measures aggregated into 8 clinical quality composites (4 of these low-value). KEY RESULTS: We analyzed 9,352,569 adults. Mean age was 44 years (SD, 15.03), 52% were female, and 75% were employed. Receipt of nearly every type of low-value care decreased during the surge. For example, low-value cancer screening decreased 0.86% (95% CI, -1.03 to -0.69). Use of opioid medications for back and neck pain (DiD +0.94 [95% CI, +0.82 to +1.07]) and use of opioid medications for headache (DiD +0.38 [95% CI, 0.07 to 0.69]) were the only two measures to increase. Nearly all high-value care measures also decreased. For example, high-value diabetes care decreased 9.75% (95% CI, -10.79 to -8.71). CONCLUSIONS: The first COVID-19 surge was associated with receipt of less low-value care and substantially less high-value care for most measures, with the notable exception of increases in low-value opioid use.

Acute home-based care for patients with cancer to avoid, substitute, and follow emergency department visits: a conceptual framework using Porter's Five Forces.

Background: Patients with cancer constitute a large and increasing segment of patients who receive unscheduled hospital-based care due to treatment-related symptoms and disease progression. The initial hospital-based touchpoint for these unscheduled hospitalizations is often the emergency department. Traditional models of emergency department and inpatient hospital-based care are saturated and incapable of scaling to accommodate the future, increased needs projected for this population. New models of care are necessary to address this gap. Acute home-based care is a promising tool potentially providing patient-centric, efficient care to eligible patients. Methods: We applied Porter's Five Forces framework that addresses the bargaining power of buyers and suppliers, threat of substitutes and new entrants, and industry rivalries plus the sixth force of regulation to clarify the factors that will promote or challenge the adoption of a home-based cancer care referral model before or following emergency department visits. Exploring this framework provides insights into the complexities of scaling an acute home-based cancer care model and highlights ways for health systems including hospitals, emergency departments, physician groups, and individual emergency physicians and oncologists to optimize their roles in this emerging model of care. Results: We found that current workforce shortages, as well as workflow, infrastructure, and regulatory complexities, pose major challenges that unless carefully addressed may restrict the growth of acute home-based cancer care. Additional uncertainties persist around appropriate payment models and the competitive landscape. Key promoting factors include the recognized need in the cancer community and among payers for new models to decrease unscheduled hospitalizations and emergency department visits as well as the uptake of home-based and technology-enabled solutions during the COVID-19 pandemic. A better understanding of these forces helps to clarify the risks and opportunities as new entrants build their programs. Conclusions: Acute home-based cancer care is a promising tool to complement traditional outpatient clinics, emergency departments, and inpatient hospital-based models of cancer care. New technologies and policies increasingly enable a broader scope of cancer care in the home setting.

eQTL Set-Based Association Analysis Identifies Novel Susceptibility Loci for Barrett Esophagus and Esophageal Adenocarcinoma.

BACKGROUND: Over 20 susceptibility single-nucleotide polymorphisms (SNP) have been identified for esophageal adenocarcinoma (EAC) and its precursor, Barrett esophagus (BE), explaining a small portion of heritability. METHODS: Using genetic data from 4,323 BE and 4,116 EAC patients aggregated by international consortia including the Barrett's and Esophageal Adenocarcinoma Consortium (BEACON), we conducted a comprehensive transcriptome-wide association study (TWAS) for BE/EAC, leveraging Genotype Tissue Expression (GTEx) gene-expression data from six tissue types of plausible relevance to EAC etiology: mucosa and muscularis from the esophagus, gastroesophageal (GE) junction, stomach, whole blood, and visceral adipose. Two analytical approaches were taken: standard TWAS using the predicted gene expression from local expression quantitative trait loci (eQTL), and set-based SKAT association using selected eQTLs that predict the gene expression. RESULTS: Although the standard approach did not identify significant signals, the eQTL set-based approach identified eight novel associations, three of which were validated in independent external data (eQTL SNP sets for EXOC3, ZNF641, and HSP90AA1). CONCLUSIONS: This study identified novel genetic susceptibility loci for EAC and BE using an eQTL set-based genetic association approach. IMPACT: This study expanded the pool of genetic susceptibility loci for EAC and BE, suggesting the potential of the eQTL set-based genetic association approach as an alternative method for TWAS analysis.

Association of the Patient Protection and Affordable Care Act With Ambulatory Quality, Patient Experience, Utilization, and Cost, 2014-2016.

Importance: The Patient Protection and Affordable Care Act (ACA) expanded Medicaid eligibility at the discretion of states to US individuals earning up to 138% of the federal poverty level (FPL) and made private insurance subsidies available to most individuals earning up to 400% of the FPL. Its national impact remains debated. Objective: To determine the association of the ACA with ambulatory quality, patient experience, utilization, and cost. Design, Setting, and Participants: This cross-sectional study used difference-in-differences (DiD) analyses comparing outcomes before (2011-2013) and after (2014-2016) ACA implementation for US adults aged 18 to 64 years with income below and greater than or equal to 400% of the FPL. Participants were respondents to the Medical Expenditure Panel Survey, a nationally representative annual survey. Data analysis was performed from January 2021 to March 2022. Exposures: ACA implementation. Main Outcomes and Measures: For quality and experience, this study examined previously published composites based on individual measures, including high-value care composites (eg, preventive testing) and low-value care composites (eg, inappropriate imaging), an overall patient experience rating, a physician communication composite, and an access-to-care composite. For utilization, outpatient, emergency, and inpatient encounters and prescribed medicines were examined. Overall and out-of-pocket expenditures were analyzed for cost. Results: The total sample included 123 171 individuals (mean [SD] age, 39.9 [13.4] years; 65 034 women [52.8%]). After ACA implementation, adults with income less than 400% of the FPL received increased high-value care (diagnostic and preventive testing) compared with adults with income 400% or higher of the FPL (change from 70% to 72% vs change from 84% to 84%; adjusted DiD, 1.20%; 95% CI, 0.18% to 2.21%; P = .02) with no difference in any other quality composites. Individuals with income less than 400% of the FPL had larger improvements in experience, communication, and access composites compared with those with income greater than or equal to 400% of the FPL (global rating of health, change from 69% to 73% vs change from 79% to 81%; adjusted DiD, 2.12%; 95% CI, 0.18% to 4.05%; P = .03). There were no differences in utilization or cost, except that receipt of primary care increased for those with lower income vs those with higher income (change from 65% to 66% vs change from 80% to 77%; adjusted DiD, 2.97%; 95% CI, 1.18% to 4.77%; P = .001) and total out-of-pocket expenditures decreased for those with lower income vs those with higher income (change from $504 to $439 vs from $757 to $769; adjusted DiD, -$105.50; 95% CI, -$167.80 to -$43.20; P = .001). Conclusions and Relevance: In this cross-sectional national study, the ACA was associated with improved patient experience, communication, and access and decreased out-of-pocket expenditures, but little or no change in quality, utilization, and total cost.

Differences in End-of-Life Care between COVID-19 Inpatient Decedents with English Proficiency and Limited English Proficiency.

Background: Patients with limited English proficiency (LEP) experience lower quality end-of-life (EOL) care. This inequity may have been exacerbated during the COVID-19 pandemic. Objective: Compare health care utilization, EOL, and palliative care outcomes between COVID-19 decedents with and without LEP during the pandemic's first wave in Massachusetts. Methods: Retrospective cohort study of adult inpatients who died from COVID-19 between February 18, 2020 and May 18, 2020 at two academic and four community hospitals within a greater Boston health care system. We performed multivariable regression adjusting for patient sociodemographic variables and hospital characteristics. Primary outcome was place of death (intensive care unit [ICU] vs. non-ICU). Secondary outcomes included hospital and ICU length of stay and time to initial palliative care consultation. Results: Among 337 patients, 89 (26.4%) had LEP and 248 (73.6%) were English proficient. Patients with LEP were less often white (24 [27.0%] vs. 193 [77.8%]; p < 0.001); were more often Hispanic or Latinx (40 [45.0%] vs. 13 [5.2%]; p < 0.001); and less often had a medical order for life-sustaining treatment (MOLST) on admission (15 [16.9%] vs. 120 [48.4%]; p < 0.001) versus patients with English proficiency. In the multivariable analyses, LEP was not independently associated with ICU death, ICU length of stay, or time to palliative care consultation, but was independently associated with increased hospital length of stay (mean difference 4.12 days; 95% CI, 1.72-6.53; p < 0.001). Conclusions: Inpatient COVID-19 decedents with LEP were not at increased risk of an ICU death, but were associated with an increased hospital length of stay versus inpatient COVID-19 decedents with English proficiency.

Skilled Nursing Facility Care at Home for Adults Discharged From the Hospital: A Pilot Randomized Controlled Trial.

OBJECTIVES: The ability to deliver SNF-level care at home is unclear. We sought to demonstrate the feasibility of rehabilitation at home (RAH) via a pilot randomized controlled trial. METHODS: Daily care from a home health aide, certified nursing assistant, nurse, and physician. Core technologies included remote PT, automated medication dispensing, and continuous monitoring. Primary outcome: episode cost. RESULTS: We randomized 10 patients. Home patients' episode cost a median $8404 (IQR, $2697) versus $9215 (IQR, $5702). LOS for both was 14 days. Home patients' ADLs improved between admission and 30-days post-discharge by median 4 (IQR, 5) versus 1 (IQR, 2). Home patients' median Picker patient experience score was 12/14 (IQR, 2) versus 7/14 (IQR, 3). DISCUSSION: A RAH pilot compared favorably to traditional SNF, with trends toward lower cost, greater functional status improvement, and better patient experience. This delivery innovation could reimagine how we deliver post-acute care but requires replication. CLINICALTRIALS.GOV REGISTRATION: NCT04048590.