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Background: Effective population management of patients with diabetes requires timely recognition. Current case-finding algorithms can accurately detect patients with diabetes, but lack real-time identification. We sought to develop and validate an automated, real-time diabetes case-finding algorithm to identify patients with diabetes at the earliest possible date. Methods. The source population included 160,872 unique patients from a large public hospital system between January 2009 and April 2011. A diabetes case-finding algorithm was iteratively derived using chart review and subsequently validated (n = 343) in a stratified random sample of patients, using data extracted from the electronic health records (EHR). A point-based algorithm using encounter diagnoses, clinical history, pharmacy data, and laboratory results was used to identify diabetes cases. The date when accumulated points reached a specified threshold equated to the diagnosis date. Physician chart review served as the gold standard. Results: The electronic model had a sensitivity of 97%, specificity of 90%, positive predictive value of 90%, and negative predictive value of 96% for the identification of patients with diabetes. The kappa score for agreement between the model and physician for the diagnosis date allowing for a 3-month delay was 0.97, where 78.4% of cases had exact agreement on the precise date. Conclusions: A diabetes case-finding algorithm using data exclusively extracted from a comprehensive EHR can accurately identify patients with diabetes at the earliest possible date within a healthcare system. The real-time capability may enable proactive disease management. © 2013 Makam et al.; licensee BioMed Central Ltd.

Objectives: With the increase in illicit fentanyl use in the USA, hospitals face challenges managing opioid withdrawal and opioid use disorder (OUD). To improve opioid withdrawal and OUD treatment among hospitalized patients with daily fentanyl use, we developed a rapid methadone titration (RMT) protocol. We describe development, implementation, and outcomes during the first 12 weeks., Methods: We analyzed electronic health record data of hospitalizations seen by the Addiction Consult Team (ACT) for methadone initiation between 9/11/23 and 12/3/23. Adults aged 18-64 reporting daily fentanyl use, desiring methadone, and without end-stage organ damage or critical illness were RMT eligible. We characterized patients who received RMT, abstracting demographic and clinical characteristics, adverse events graded by the Common Terminology Criteria for Adverse Events (CTCAE), and methadone and additional full agonist opioid (FAO) dosing. Our primary outcome was adverse events. Secondary outcomes included median time to 100 mg of methadone, FAO dosing, and self-directed discharge., Results: ACT assessed 55 hospitalizations representing 47 patients for RMT eligibility. Among these, 19 (34.5%) hospitalizations representing 17 patients were eligible for and received RMT. Four (21.2%) hospitalizations that received RMT had sedation events, and all were mild or moderate grade by CTCAE. Hospitalizations achieved a median methadone dose of 100 mg by day 6, with FAO doses peaking on day 5. One (5.3%) hospitalization had a self-directed discharge., Conclusions: With careful patient selection and ACT evaluation, a RMT protocol for hospitalized patients with fentanyl use disorder experienced few adverse events other than mild-moderate sedation, even among those receiving FAO and those with concurrent substance use disorders., Competing Interests: Declarations:. Conflict of Interest:: The authors declare that they do not have a conflict of interest., (© 2025. The Author(s).)

Background: Fentanyl use leads to increased opioid tolerance in people with opioid use disorder, complicating management of opioid withdrawal syndrome. While accepted as gold standard, methadone and buprenorphine may be insufficient to treat acute opioid withdrawal. Short-acting full agonist opioids (SAFAO) may improve treatment in the acute care setting., Aim: Characterize use of SAFAO to treat opioid withdrawal syndrome for inpatients., Setting: Urban safety-net hospital., Participants: Inpatients with opioid use disorder., Program Description: Addiction Consult Team offers SAFAO to manage opioid withdrawal syndrome., Program Evaluation: We performed retrospective chart review of hospitalizations (n = 124) for patients with opioid use disorder who received SAFAO between March and June 2023. Patients received methadone or buprenorphine during 94% of hospitalizations. On average, 278 mg (SD 407 mg) oral morphine equivalents (OMEs) of SAFAO were administered daily during the first hospitalization week. Daily Clinical Opiate Withdrawal Scale scores and SAFAO OMEs were inversely correlated (Spearman rank correlation = - 0.96, p = 0.003). Five hospitalizations exhibited adverse events (AEs)., Discussion: Use of methadone/buprenorphine did not alleviate the need for SAFAO, suggesting that SAFAO administration may be an important intervention for opioid withdrawal. Use of escalating OMEs of SAFAO was associated with reduced withdrawal severity. This practice was well-tolerated with few AEs., Competing Interests: Declarations. Conflict of interest: The authors declare that they do not have a conflict of interest., (© 2025. The Author(s).)

Background: Minority racial and ethnic populations have the highest prevalence of type 2 diabetes mellitus but lower use of sodium-glucose co-transporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1ra), novel medications that reduce morbidity and mortality. Observed disparities may be due to differences in insurance coverage, which have variable cost-sharing, prior authorization, and formulary restrictions that influence medication access., Objective: To assess whether racial/ethnic differences in SGLT2i and GLP1ra use differ by payer., Design: Cross-sectional analysis of 2018 and 2019 Medical Expenditure Panel Survey data., Participants: Adults ≥ 18 years old with diabetes., Main Measures: We defined insurance as private, Medicare, or Medicaid using ≥ 7 months of coverage in the calendar year. We defined race/ethnicity as White (non-Hispanic) vs non-White (including Hispanic). The primary outcome was use of ≥ 1 SGLT2i or GLP1ra medication. We used multivariable logistic regression to assess the interaction between payer and race/ethnicity adjusted for cardiovascular, socioeconomic, and healthcare access factors., Key Results: We included 4997 adults, representing 24.8 million US adults annually with diabetes (mean age 63.6 years, 48.8% female, 38.8% non-White; 33.5% private insurance, 56.8% Medicare, 9.8% Medicaid). In our fully adjusted model, White individuals with private insurance had significantly more medication use versus non-White individuals (16.1% vs 8.3%, p < 0.001), which was similar for Medicare beneficiaries but more attenuated (14.7% vs 11.0%, p = 0.04). Medication rates were similar among Medicaid beneficiaries (10.0% vs 9.0%, p = 0.74)., Conclusions: Racial/ethnic disparities in novel diabetes medications were the largest among those with private insurance. There was no disparity among Medicaid enrollees, but overall prescription rates were the lowest. Given that disparities vary considerably by payer, differences in insurance coverage may account for the observed disparities in SGLT2i and GLP1ra use. Future studies are needed to assess racial/ethnic differences in novel diabetes use by insurance formulary restrictions and out-of-pocket cost-sharing., Competing Interests: Declarations: Conflict of Interest: The authors declare that they do not have a conflict of interest., (© 2024. The Author(s).)

Introduction: The COVID-19 pandemic led to a dramatic increase in telemedicine use for direct patient care. Inequities in device/internet access can limit the extent to which patients can engage with telemedicine care and exacerbate health disparities. In this review, we examined existing literature on interventions designed to improve patient telemedicine access by providing digital devices including tablets, smartphones, and computers and/or internet connectivity., Methods: In this systematic scoping review, we searched four databases for peer-reviewed studies published 1/1/2000-10/19/2021 that described healthcare interventions that provided patients with devices and/or internet connectivity and reported outcomes related to telemedicine access and/or usage. Data extraction elements included: study population, setting, intervention design, details on device/connectivity provision, and outcomes evaluated., Results: Twelve articles reflecting seven unique interventions met inclusion criteria. Ten articles examined telemedicine utilization (83%) and reported improved patient show rates/utilization. Seven articles examined patient satisfaction with the interventions (58%) and reported positive experiences. Fewer articles examined health outcomes (17%; 2/12) though these also demonstrated positive results. Across included studies, study quality was low. There were no controlled trials, and the most rigorously designed studies ( n  = 4) involved pre/post-intervention assessments., Discussion: Findings from this review indicate that providing material technology supports to patients can facilitate telemedicine access, is acceptable to patients and clinicians, and can contribute to improved health outcomes. The low number and quality of existing studies limits the strength of this evidence. Future research should explore interventions that can increase equitable access to telemedicine services., Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display&#95;record.php?RecordID=183442, identifier, PROSPERO: CRD42020183442., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Bell, Gottlieb, Lyles, Nguyen, Ackerman and De Marchis.)

Digital health has the potential to expand health care and improve outcomes for patients-particularly for those with challenges to accessing in-person care. The acceleration of digital health (and particularly telemedicine) prompted by the Coronavirus-19 (COVID-19) pandemic facilitated continuity of care in some settings but left many health systems ill-prepared to address digital uptake among patients from underserved backgrounds, who already experience health disparities. As use of digital health grows and the digital divide threatens to widen, healthcare systems must develop approaches to evaluate patients' needs for digital health inclusion, and consequentially equip patients with the resources needed to access the benefits of digital health. However, this is particularly challenging given the absence of any standardized, validated multilingual screening instrument to assess patients' readiness for digital healthcare that is feasible to administer in already under-resourced health systems. This perspective is structured as follows: (1) the need for digital health exclusion risk screening, (2) our convening as a group of stakeholders, (3) our review of the known digital health screening tools and our assessment, (4) formative work with patients regarding their perceptions on language and concepts in the digital health screening tools, and (5) conclusion with recommendations for digital health advocates generated by this collaborative of digital health researchers and operations leaders. There is a need to develop a brief, effective tool to screen for digital health use that can be widely implemented in diverse populations. We include lessons learned from our experiences in developing and testing risk of digital health exclusion screening questions in our respective health systems (e.g., patient perception of questions and response options). Because we recognize that health systems across the country may be facing similar challenges and questions, this perspective aims to inform ongoing efforts in developing health system digital exclusion screening tools and advocate for their role in advancing digital health equity., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Hernandez, Khoong, Kanwar, Lopez-Solano, Rodriguez, De Marchis, Nguyen and Casillas.)

Objectives: Understanding the long-term effects of severe COVID-19 illness on survivors is essential for effective pandemic recovery planning. Therefore, we investigated impairments among hospitalized adults discharged to long-term acute care hospitals (LTACHs) for prolonged severe COVID-19 illness who survived 1 year., Design: The Recovery After Transfer to an LTACH for COVID-19 (RAFT COVID) study was a national, multicenter, prospective longitudinal cohort study., Setting and Patients: We included hospitalized English-speaking adults transferred to one of nine LTACHs in the United States between March 2020 and February 2021 and completed a survey., Interventions: None., Measurements and Main Results: Validated instruments for impairments and free response questions about recovering. Among 282 potentially eligible participants who provided permission to be contacted, 156 (55.3%) participated (median age, 65; 38.5% female; 61.3% in good prior health; median length of stay of 57 d; 77% mechanically ventilated for a median of 26 d; 42% had a tracheostomy). Approximately two-thirds (64%) had a persistent impairment, including physical (57%), respiratory (49%; 19% on supplemental oxygen), psychiatric (24%), and cognitive impairments (15%). Nearly half (47%) had two or more impairment types. Participants also experienced persistent debility from hospital-acquired complications, including mononeuropathies and pressure ulcers. Participants described protracted recovery, attributing improvements to exercise/rehabilitation, support, and time. While considered life-altering with 78.7% not returning to their usual health, participants expressed gratitude for recovering; 99% returned home and 60% of previously employed individuals returned to work., Conclusions: Nearly two-thirds of survivors of among the most prolonged severe COVID-19 illness had persistent impairments at 1 year that resembled post-intensive care syndrome after critical illness plus debility from hospital-acquired complications., Competing Interests: Dr. Makam’s institution received funding from the University of California, San Francisco Research Evaluation and Allocation Committee Carson and Hampton Research Funds and the National Association of Long Term Hospitals (NALTH). Drs. Makam’s and Covinsky’s institutions received funding from the National Institute on Aging. Drs. Makam, Ely, Jackson, and Convinsky received support for article research from the National Institutes of Health. Dr. Prettyman disclosed that he is President of the NALTH. Dr. Espejo’s institution received funding from the Donaghue Foundation. Dr. Votto received funding from the NALTH; he disclosed that he is the Chief Medical Officer of NALTH. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2024 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

Importance: More than 70 000 Medicare beneficiaries receive care in long-term acute care hospitals (LTCHs) annually for prolonged acute illness. However, little is known about long-term functional and cognitive outcomes of middle-aged and older adults after hospitalization in an LTCH., Objective: To describe survival, functional, and cognitive status after LTCH hospitalization and to identify factors associated with an adverse outcome., Design, Setting, and Participants: This retrospective cohort study included middle-aged and older adults enrolled in the Health and Retirement Study (HRS) with linked fee-for-service Medicare claims. Included participants were aged 50 years or older with an LTCH admission between January 1, 2003, and December 31, 2016, with HRS interviews available before admission. Data were analyzed between November 1, 2021, and June 30, 2023., Main Outcomes and Measures: Function and cognition were ascertained from HRS interviews conducted every 2 years. The primary outcome was death or severe impairment in the 2.5 years after LTCH hospitalization, defined as dependencies in 2 or more activities of daily living (ADLs) or dementia. Multivariable logistic regression was performed to evaluate associations with a priori selected risk factors including pre-LTCH survival prognosis (Lee index score), pre-LTCH impairment status, and illness severity characterized by receipt of mechanical ventilation and prolonged intensive care unit stay of 3 days or longer., Results: This study included 396 participants, with a median age of 75 (IQR, 68-82) years. Of the participants, 201 (51%) were women, 125 (28%) had severe impairment, and 318 (80%) died or survived with severe impairment (functional, cognitive, or both) within 2.5 years of LTCH hospitalization. After accounting for acute illness characteristics, prehospitalization survival prognosis as determined by the Lee index score and severe baseline impairment (functional, cognitive, or both) were associated with an increased likelihood of death or severe impairment in the 2.5 years after LTCH hospitalization (adjusted odds ratio [AOR], 3.2 [95% CI, 1.7 to 6.0] for a 5-point increase in Lee index score; and AOR, 4.5 [95% CI, 1.3 to 15.4] for severe vs no impairment)., Conclusions and Relevance: In this cohort study, 4 of 5 middle-aged and older adults died or survived with severe impairment within 2.5 years of LTCH hospitalization. Better preadmission survival prognosis and functional and cognitive status were associated with lower risk of an adverse outcome, and these findings should inform decision-making for older adults with prolonged acute illness.

Importance: Harm reduction is associated with improved health outcomes among people who use substances. As overdose deaths persist, hospitals are recognizing the need for harm reduction services; however, little is known about the outcomes of hospital-based harm reduction for patients and staff., Objective: To evaluate patient and staff perspectives on the impact and challenges of a hospital-based harm reduction program offering safer use education and supplies at discharge., Design, Setting, and Participants: This qualitative study consisted of 40-minute semistructured interviews with hospitalized patients receiving harm reduction services and hospital staff at an urban, safety-net hospital in California from October 2022 to March 2023. Purposive sampling allowed inclusion of diverse patient racial and ethnic identities, substance use disorders (SUDs), and staff roles., Exposure: Receipt of harm reduction education and/or supplies (eg, syringes, pipes, naloxone, and test strips) from an addiction consult team, or providing care for patients receiving these services., Main Outcomes and Measures: Interviews were analyzed using thematic analysis to identify key themes., Results: A total of 40 participants completed interviews, including 20 patients (mean [SD] age, 43 [13] years; 1 American Indian or Alaska Native [5%], 1 Asian and Pacific Islander [5%], 6 Black [30%]; 6 Latine [30%]; and 6 White [30%]) and 20 staff (mean [SD] age 37 [8] years). Patients were diagnosed with a variety of SUDs (7 patients with opioid and stimulant use disorder [35%]; 7 patients with stimulant use disorder [35%]; 3 patients with opioid use disorder [15%]; and 3 patients with alcohol use disorder [15%]). A total of 3 themes were identified; respondents reported that harm reduction programs (1) expanded access to harm reduction education and supplies, particularly for ethnically and racially minoritized populations; (2) built trust by improving the patient care experience and increasing engagement; and (3) catalyzed culture change by helping destigmatize care for individuals who planned to continue using substances and increasing staff fulfillment. Black and Latine patients, those who primarily used stimulants, and those with limited English proficiency (LEP) reported learning new harm reduction strategies. Program challenges included hesitancy regarding regulations, limited SUD education among staff, remaining stigma, and the need for careful assessment of patient goals., Conclusions and Relevance: In this qualitative study, patients and staff believed that integrating harm reduction services into hospital care increased access for populations unfamiliar with harm reduction, improved trust, and reduced stigma. These findings suggest that efforts to increase access to harm reduction services for Black, Latine, and LEP populations, including those who use stimulants, are especially needed.

Importance: Telehealth implementation associated with the COVID-19 public health emergency (PHE) affected patient-clinical team interactions in numerous ways. Yet, studies have narrowly examined billed patient-clinician visits rather than including visits with other team members (eg, pharmacists) or between-visit interactions., Objective: To evaluate rates of change over time in visits (in-person, telehealth) and between-visit interactions (telephone calls, patient portal messages) overall and by key patient characteristics., Design, Setting, and Participants: This retrospective cohort study included adults with diabetes receiving primary care at urban academic (University of California San Francisco [UCSF]) and safety-net (San Francisco Health Network [SFHN]) health care systems. Encounters from April 2019 to March 2021 were analyzed., Exposure: Telehealth implementation over 3 periods: pre-PHE (April 2019 to March 2020), strict shelter-in-place (April to June 2020), and hybrid-PHE (July 2020 to March 2021)., Main Outcomes and Measures: The main outcomes were rates of change in monthly mean number of total encounters, visits with any health care team member, visits with billing clinicians, and between-visit interactions. Key patient-level characteristics were age, race and ethnicity, language, and neighborhood socioeconomic status (nSES)., Results: Of 15 148 patients (4976 UCSF; 8975 SFHN) included, 2464 (16%) were 75 years or older, 7734 (51%) were female patients, 9823 (65%) self-identified as racially or ethnically minoritized, 6223 (41%) had a non-English language preference, and 4618 (31%) lived in the lowest nSES quintile. After accounting for changes to care delivery through an interrupted time-series analysis, total encounters increased in the hybrid-PHE period (UCSF: 2.3% per patient/mo; 95% CI, 1.6%-2.9% per patient/mo; SFHN: 1.8% per patient/mo, 95% CI, 1.3%-2.2% per patient/mo), associated primarily with growth in between-visit interactions (UCSF: 3.1% per patient/mo, 95% CI, 2.3%-3.8% per patient/mo; SFHN: 2.9% per patient/mo, 95% CI, 2.3%-3.4% per patient/mo). In contrast, rates of visits were stable during the hybrid-PHE period. Although there were fewer differences in visit use by key patient-level characteristics during the hybrid-PHE period, pre-PHE differences in between-visit interactions persisted during the hybrid-PHE period at SFHN. Asian and Chinese-speaking patients at SFHN had fewer monthly mean between-visit interactions compared with White patients (0.46 [95% CI, 0.42-0.50] vs 0.59 [95% CI, 0.53-0.66] between-visit interactions/patient/mo; P < .001) and English-speaking patients (0.52 [95% CI, 0.47-0.58] vs 0.61 [95% CI, 0.56-0.66] between-visit interactions/patient/mo; P = .03)., Conclusions and Relevance: In this study, pre-PHE growth in overall patient-clinician encounters persisted after PHE-related telehealth implementation, driven in both periods by between-visit interactions. Differential utilization based on patient characteristics was observed, which may indicate disparities. The implications for health care team workload and patient outcomes are unknown, particularly regarding between-visit interactions. Therefore, to comprehensively understand care utilization for patients with chronic diseases, research should expand beyond billed visits.

Current digital health approaches have not engaged diverse end users or reduced health or health care inequities, despite their promise to deliver more tailored and personalized support to individuals at the right time and the right place. To achieve digital health equity, we must refocus our attention on the current state of digital health uptake and use across the policy, system, community, individual, and intervention levels. We focus here on ( a ) outlining a multilevel framework underlying digital health equity; ( b ) summarizingfive types of interventions/programs (with example studies) that hold promise for advancing digital health equity; and ( c ) recommending future steps for improving policy, practice, and research in this space.

Background: Hospitalizations by patients who do not meet acute inpatient criteria are common and overburden healthcare systems. Studies have characterized these alternate levels of care (ALC) but have not delineated prolonged (pALC) versus short ALC (sALC) stays., Objective: To descriptively compare pALC and sALC hospitalizations-groups we hypothesize have unique needs., Designs, Settings, and Participants: A retrospective study of hospitalizations from March-April 2018 at an academic safety-net hospital., Main Outcome and Measures: Levels of care for pALC (>3 days) and sALC (1-3 days) were determined using InterQual©, an industry standard utilization review tool for determining the clinical appropriateness of hospitalization. We examined sociodemographic and clinical characteristics., Results: Of 2365 hospitalizations, 215 (9.1%) were pALC, 277 (11.7%) were sALC, and 1873 (79.2%) had no ALC days. There were 17,683 hospital days included, and 28.3% (n = 5006) were considered ALC. Compared to patients with sALC, those with pALC were older and more likely to be publicly insured, experience homelessness, and have substance use or psychiatric comorbidities. Patients with pALC were more likely to be admitted for care meeting inpatient criteria (89.3% vs. 66.8%, p < .001), had significantly more ALC days (median 8 vs. 1 day, p < .001), and were less likely to be discharged to the community (p < .001)., Conclusions: Patients with prolonged ALC stays were more likely to be admitted for acute care, had greater psychosocial complexity, significantly longer lengths of stay, and unique discharge needs. Given the complexity and needs for hospitalizations with pALC days, intensive interdisciplinary coordination and resource mobilization are necessary., (© 2023 Society of Hospital Medicine.)

Background: Acute healthcare utilization attributed to alcohol use disorders (AUD) and other substance use disorders (SUD) is rising., Objective: To describe the prevalence and characteristics of emergency department (ED) visits and hospitalizations made by adults with AUD or SUD., Design, Setting, and Participants: Observational study with retrospective analysis of the National Hospital Ambulatory Medical Care Survey (2014 to 2018), a nationally representative survey of acute care visits with information on the presence of AUD or SUD abstracted from the medical chart., Main Measures: Outcome measured as the presence of AUD or SUD., Key Results: From 2014 to 2018, the annual average prevalence of AUD or SUD was 9.4% of ED visits (9.3 million visits) and 11.9% hospitalizations (1.4 million hospitalizations). Both estimates increased over time (30% and 57% relative increase for ED visits and hospitalizations, respectively, from 2014 to 2018). ED visits and hospitalizations from individuals with AUD or SUD, compared to individuals with neither AUD nor SUD, had higher percentages of Medicaid insurance (ED visits: AUD: 33.1%, SUD: 35.0%, neither: 24.4%; hospitalizations: AUD: 30.7%, SUD: 36.3%, neither: 14.8%); homelessness (ED visits: AUD: 6.2%, SUD 4.4%, neither 0.4%; hospitalizations: AUD: 5.9%, SUD 7.3%, neither: 0.4%); coexisting depression (ED visits: AUD: 26.3%, SUD 24.7%, neither 10.5%; hospitalizations: AUD: 33.5%, SUD 35.3%, neither: 13.9%); and injury/trauma (ED visits: AUD: 51.3%, SUD 36.3%, neither: 26.4%; hospitalizations: AUD: 31.8%, SUD: 23.8%, neither: 15.0%)., Conclusions: In this nationally representative study, 1 in 11 ED visits and 1 in 9 hospitalizations were made by adults with AUD or SUD, and both increased over time. These estimates are higher or similar than previous national estimates using claims data. This highlights the importance of identifying opportunities to address AUD and SUD in acute care settings in tandem with other medical concerns, particularly among visits presenting with injury, trauma, or coexisting depression., (© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

In the present study, the photocatalytic activity of Ti-SBA-15/C 3 N 4 catalysts was investigated to degrade 2,4-Dichlorophenoxyacetic acid (2,4-D) herbicides in water under visible light irradiation. The catalysts were synthesized via a simple hydrothermal method and characterized by various analytical techniques, including SAXS, N 2 adsorption-desorption isotherms, Zeta potential, PL, FT-IR, XRF, TGA, and UV-DRS. Our study indicated that the 2.5Ti-SBA-15/C 3 N 4 had higher efficiency in the degradation of 2,4-D than Ti-SBA-15 and C 3 N 4 . The decomposition of 2,4-D reached 60% under 180 minutes of visible light irradiation at room temperature on 2.5Ti-SBA-15/C 3 N 4 . Moreover, the degradation of 2,4-D on Ti-SBA-15/C 3 N 4 was pseudo-first-order kinetics with the highest rate constant (0.00484 min -1 ), which was much higher than that obtained for other photocatalysts reported recently. Furthermore, the catalyst can be reused at least two times for photodegradation of 2,4-D solution under visible light irradiation within a slight decrease in catalytic activity., Competing Interests: The authors declare that there are no conflicts of interest., (Copyright © 2022 Hoa T. T. Duong et al.)

Background: It is not known which social determinants of health (SDOH) impact 30-day readmission after a heart failure (HF) hospitalization among older adults. We examined the association of 9 individual SDOH with 30-day readmission after an HF hospitalization., Methods and Results: Using the REGARDS study (Reasons for Geographic and Racial Differences in Stroke), we included Medicare beneficiaries who were discharged alive after an HF hospitalization between 2003 and 2014. We assessed 9 SDOH based on the Healthy People 2030 Framework: race, education, income, social isolation, social network, residential poverty, Health Professional Shortage Area, rural residence, and state public health infrastructure. The primary outcome was 30-day all-cause readmission. For each SDOH, we calculated incidence per 1000 person-years and multivariable-adjusted hazard ratios of readmission. Among 690 participants, the median age was 76 years at hospitalization (interquartile range, 71-82), 44.3% were women, 35.5% were Black, 23.5% had low educational attainment, 63.0% had low income, 21.0% had zip code-level poverty, 43.5% resided in Health Professional Shortage Areas, 39.3% lived in states with poor public health infrastructure, 13.1% were socially isolated, 13.3% had poor social networks, and 10.2% lived in rural areas. The 30-day readmission rate was 22.4%. In an unadjusted analysis, only Health Professional Shortage Area was significantly associated with 30-day readmission; in a fully adjusted analysis, none of the 9 SDOH were individually associated with 30-day readmission., Conclusions: In this modestly sized national cohort, although prevalent, none of the SDOH were associated with 30-day readmission after an HF hospitalization. Policies or interventions that only target individual SDOH to reduce readmissions after HF hospitalizations may not be sufficient to prevent readmission among older adults.

Background: Electronic health record (EHR)-based readmission risk prediction models can be automated in real-time but have modest discrimination and may be missing important readmission risk factors. Clinician predictions of readmissions may incorporate information unavailable in the EHR, but the comparative usefulness is unknown. We sought to compare clinicians versus a validated EHR-based prediction model in predicting 30-day hospital readmissions., Methods: We conducted a prospective survey of internal medicine clinicians in an urban safety-net hospital. Clinicians prospectively predicted patients' 30-day readmission risk on 5-point Likert scales, subsequently dichotomized into low- vs. high-risk. We compared human with machine predictions using discrimination, net reclassification, and diagnostic test characteristics. Observed readmissions were ascertained from a regional hospitalization database. We also developed and assessed a "human-plus-machine" logistic regression model incorporating both human and machine predictions., Results: We included 1183 hospitalizations from 106 clinicians, with a readmission rate of 20.8%. Both clinicians and the EHR model had similar discrimination (C-statistic 0.66 vs. 0.66, p = 0.91). Clinicians had higher specificity (79.0% vs. 48.9%, p < 0.001) but lower sensitivity (43.9 vs. 75.2%, p < 0.001) than EHR model predictions. Compared with machine, human was better at reclassifying non-readmissions (non-event NRI + 30.1%) but worse at reclassifying readmissions (event NRI - 31.3%). A human-plus-machine approach best optimized discrimination (C-statistic 0.70, 95% CI 0.67-0.74), sensitivity (65.5%), and specificity (66.7%)., Conclusion: Clinicians had similar discrimination but higher specificity and lower sensitivity than EHR model predictions. Human-plus-machine was better than either alone. Readmission risk prediction strategies should incorporate clinician assessments to optimize the accuracy of readmission predictions., (© 2021. Society of General Internal Medicine.)

Objectives: Goals of care discussions are a vital component of patient care, but Internal Medicine residents receive limited training in these skills. Existing curricula often require simulated patients or faculty development, limiting implementation in many residency programs. Thus, we developed and implemented a curriculum leveraging existing educational resources with the goal of improving resident attitudes and confidence in conducting goals of care discussions in training settings., Methods: We developed cases and a detailed faculty guide for small-group discussion with three exercises to standardize the learner experience and minimize the need for faculty development. Exercises introduced established communication skill frameworks including SPIKES (setting, perception, invitation, knowledge, empathy, summary) and REMAP (reframe, emotion, map, align, propose a plan) for how to break bad news, respond to strong emotions, and conduct a goals of care discussion. Participants were 163 Internal Medicine postgraduate year 1, -2, and -3 residents at a large urban academic institution, where residency-wide curriculum is delivered in weekly half-day didactic sessions during the course of 5 weeks. Primary outcomes were resident self-reported confidence with goals of care communication skills., Results: A total of 109 (response rate 67%) of residents reported improvement in overall confidence in goals of care discussion skills (3.6 ± 0.9 vs 4.1 ± 0.6, P < 0.001), responding to emotions (3.5 ± 0.9 vs 3.9 ± 0.6, P = 0.004), making care recommendations (3.5 ± 1.0 vs 3.9 ± 0.7, P < 0.001), and quickly conducting a code status discussion (3.6 ± 1.0 vs 4.0 ± 0.7, P < 0.001). Residents also expressed an increased desire for supervision and feedback to further develop these skills., Conclusions: This goals of care communication curriculum improves resident confidence and requires minimal resources. It may be ideal for programs that have limited access to simulated patients and/or faculty trained in communication skill simulation, but desire enhanced education on this important aspect of patient-doctor communication and high-quality patient care. Future studies measuring clinical outcomes and changes in learner behavior as a result of this intervention are needed. Ongoing observation and feedback on these skills will be important to solidify learning and sustain impact.

Across the USA, morbidity and mortality from substance use are rising as reflected by increases in acute care hospitalisations for substance use complications and substance-related deaths. Patients with substance use disorders (SUD) have long and costly hospitalisations and higher readmission rates compared to those without SUD. Hospitalisation presents an opportunity to diagnose and treat individuals with SUD and connect them to ongoing care. However, SUD care often remains unaddressed by hospital providers due to lack of a systems approach and addiction medicine knowledge, and is compounded by stigma. We present a blueprint to launching an interprofessional inpatient addiction care team embedded in the hospital medicine division of an urban, safety-net integrated health system. We describe key factors for successful implementation including: (1) demonstrating the scope and impact of SUD in our health system via a needs assessment; (2) aligning improvement areas with health system leadership priorities; (3) involving executive leadership to create goal and initiative alignment; and (4) obtaining seed funding for a pilot programme from our Medicaid health plan partner. We also present challenges and lessons learnt., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Long-term acute care hospital (LTACH) use varies considerably across the U.S., which may reflect uncertainty about the effectiveness of LTACHs vs. skilled nursing facilities (SNF), the principal post-acute care alternative. Given that LTACHs provide more intensive care and thus receive over triple the reimbursement of SNFs for comparable diagnoses, we sought to compare outcomes and spending between LTACH versus SNF transfer., Methods: Using Medicare claims linked to electronic health record (EHR) data from six Texas Hospitals between 2009 and 2010, we conducted a retrospective cohort study of patients hospitalized on a medicine service in a high-LTACH use region and discharged to either an LTACH or SNF and followed for one year. The primary outcomes included mortality, 60-day recovery without inpatient care, days at home, and healthcare spending RESULTS: Of 3503 patients, 18% were transferred to an LTACH. Patients transferred to LTACHs were younger (median 71 vs. 82 years), less likely to be female (50.5 vs 66.6%) and white (69.0 vs. 84.1%), but were sicker (24.3 vs. 14.2% for prolonged intensive care unit stay; median diagnosis resource intensity weight of 2.03 vs. 1.38). In unadjusted analyses, patients transferred to an LTACH vs. SNF were less likely to survive (59.1 vs. 65.0%) or recover (62.5 vs 66.0%), and spent fewer days at home (186 vs. 200). Adjusting for demographic and clinical confounders available in Medicare claims and EHR data, LTACH transfer was not significantly associated with differences in mortality (HR, 1.12, 95% CI, 0.94-1.33), recovery (SHR, 1.07, 0.93-1.23), and days spent at home (IRR, 0.96, 0.83-1.10), but was associated with greater Medicare spending ($16,689 for one year, 95% CI, $12,216-$21,162)., Conclusion: LTACH transfer for Medicare beneficiaries is associated with similar clinical outcomes but with higher healthcare spending compared to SNF transfer. LTACH use should be reserved for patients who require complex inpatient care and cannot be cared for in SNFs.

Background: Despite focus on preventing 30-day readmissions, early readmissions (within 7 days of discharge) may be more preventable than later readmissions (8-30 days). We assessed how well a previously validated 30-day EHR-based readmission prediction model predicts 7-day readmissions and compared differences in strength of predictors., Methods: We conducted an observational study on adult hospitalizations from 6 diverse hospitals in North Texas using a 50-50 split-sample derivation and validation approach. We re-derived model coefficients for the same predictors as in the original 30-day model to optimize prediction of 7-day readmissions. We then compared the discrimination and calibration of the 7-day model to the 30-day model to assess model performance. To examine the changes in the point estimates between the two models, we evaluated the percent changes in coefficients., Results: Of 32,922 index hospitalizations among unique patients, 4.4% had a 7-day admission and 12.7% had a 30-day readmission. Our original 30-day model had modestly lower discrimination for predicting 7-day vs. any 30-day readmission (C-statistic of 0.66 vs. 0.69, p ≤ 0.001). Our re-derived 7-day model had similar discrimination (C-statistic of 0.66, p = 0.38), but improved calibration. For the re-derived 7-day model, discharge day factors were more predictive of early readmissions, while baseline characteristics were less predictive., Conclusion: A previously validated 30-day readmission model can also be used as a stopgap to predict 7-day readmissions as model performance did not substantially change. However, strength of predictors differed between the 7-day and 30-day model; characteristics at discharge were more predictive of 7-day readmissions, while baseline characteristics were less predictive. Improvements in predicting early 7-day readmissions will likely require new risk factors proximal to day of discharge.

Respiratory rate (RR) is a predictor of adverse outcomes. However, RRs are inaccurately measured in the hospital. We conducted a quality improvement (QI) initiative using plan-do-study-act methodology on one inpatient unit of a safety-net hospital to improve RR accuracy. We added time-keeping devices to vital sign carts and retrained patient-care assistants on a newly modified workflow that included concomitant RR measurement during automated blood pressure measurement. The median RR was 18 (interquartile range [IQR] 18-20) preintervention versus 14 (IQR 15-20) postintervention. RR accuracy, defined as ±2 breaths of gold-standard measurements, increased from 36% preintervention to 58% postintervention (P < .01). The median time for vital signs decreased from 2:36 minutes (IQR, 2:04-3:20) to 1:55 minutes (IQR, 1:40-2:22; P < .01). The intervention was associated with a 7.8% reduced incidence of tachypnea-specific systemic inflammatory response syndrome (SIRS = 2 points with RR > 20; 95% CI, -13.5% to -2.2%). Our interdisciplinary, low-cost, low-tech QI initiative improved the accuracy and efficiency of RR measurement.

Objectives: Long-term acute care (LTAC) hospitals provide extended complex post-acute care to more than 120 000 Medicare beneficiaries annually, with the goal of helping patients to regain independence and recover. Because little is known about patients' long-term outcomes, we sought to examine the clinical course after LTAC admission., Design: Nationally representative 5-year cohort study using 5% Medicare data from 2009 to 2013., Setting: LTAC hospitals., Participants: Hospitalized Medicare fee-for-service beneficiaries 65 years of age or older who were transferred to an LTAC hospital., Measurements: Mortality, recovery (defined as achieving 60 consecutive days alive without inpatient care), time spent in an inpatient facility following LTAC hospital admission, receipt of an artificial life-prolonging procedure (feeding tube, tracheostomy, hemodialysis), and palliative care physician consultation., Results: Of 14 072 hospitalized older adults transferred to an LTAC hospital, median survival was 8.3 months, and 1- and 5-year survival rates were 45% and 18%, respectively. Following LTAC admission, 53% never achieved a 60-day recovery. The median time of their remaining life a patient spent as an inpatient after LTAC admission was 65.6% (interquartile range = 21.4%-100%). More than one-third (36.9%) died in an inpatient setting, never returning home after the LTAC admission. During the preceding hospitalization and index LTAC admission, 30.9% received an artificial life-prolonging procedure, and 1% had a palliative care physician consultation., Conclusion: Hospitalized older adults transferred to LTAC hospitals have poor survival, spend most of their remaining life as an inpatient, and frequently undergo life-prolonging procedures. This prognostic understanding is essential to inform goals of care discussions and prioritize healthcare needs for hospitalized older adults admitted to LTAC hospitals. Given the exceedingly low rates of palliative care consultations, future research is needed to examine unmet palliative care needs in this population. J Am Geriatr Soc 67:2282-2288, 2019., (© 2019 The American Geriatrics Society.)

Background: Over the past decade, nearly half of internal medicine residencies have implemented block clinic scheduling; however, the effects on residency-related outcomes are unknown. The authors systematically reviewed the impact of block versus traditional ambulatory scheduling on residency-related outcomes, including (1) resident satisfaction, (2) resident-perceived conflict between inpatient and outpatient responsibilities, (3) ambulatory training time, (4) continuity of care, (5) patient satisfaction, and (6) patient health outcomes., Method: The authors reviewed the following databases: Ovid MEDLINE, Ovid MEDLINE InProcess, EBSCO CINAHL, EBSCO ERIC, and the Cochrane Library from inception through March 2017 and included studies of residency programs comparing block to traditional scheduling with at least one outcome of interest. Two authors independently extracted data on setting, participants, schedule design, and the outcomes of interest., Results: Of 8139 studies, 11 studies of fair to moderate methodologic quality were included in the final analysis. Overall, block scheduling was associated with marked improvements in resident satisfaction (n = 7 studies, effect size range - 0.3 to + 0.9), resident-perceived conflict between inpatient and outpatient responsibilities (n = 5, effect size range + 0.3 to + 2.6), and available ambulatory training time (n = 5). Larger improvements occurred in programs implementing short (1 week) ambulatory blocks. However, block scheduling may result in worse physician continuity (n = 4). Block scheduling had inconsistent effects on patient continuity (n = 4), satisfaction (n = 3), and health outcomes (n = 3)., Discussion: Although block scheduling improves resident satisfaction, conflict between inpatient and outpatient responsibilities, and ambulatory training time, there may be important tradeoffs with worse care continuity.

Importance: In 40 of 50 US states, scheduled dialysis is withheld from undocumented immigrants with end-stage renal disease (ESRD); instead, they receive intermittent emergency-only dialysis to treat life-threatening manifestations of ESRD. However, the comparative effectiveness of scheduled dialysis vs emergency-only dialysis and the influence of treatment on health outcomes, utilization, and costs is uncertain., Objective: To compare the effectiveness of scheduled vs emergency-only dialysis with regard to health outcomes, utilization, and costs in undocumented immigrants with ESRD., Design, Setting, and Participants: Observational cohort study of 181 eligible adults with ESRD receiving emergency-only dialysis in Dallas, Texas, who became newly eligible and applied for private commercial health insurance in February 2015; 105 received coverage and were enrolled in scheduled dialysis; 76 were not enrolled in insurance for nonclinical reasons (eg, lack of capacity at a participating outpatient dialysis center) and remained uninsured, receiving emergency-only dialysis. We examined data on eligible persons during a 6-month period prior to enrollment (baseline period, August 1, 2014-January 31, 2015) until 12 months after enrollment (follow-up period, March 1, 2015-February 29, 2016), with an intervening 1-month washout period (February 2015). All participants were undocumented immigrants; self-reported data on immigration status was collected from Parkland Hospital electronic health records., Exposures: Enrollment in private health insurance coverage and scheduled dialysis., Main Outcomes and Measures: We used enrollment in health insurance and scheduled dialysis to estimate the influence of scheduled dialysis on 1-year mortality, utilization, and health care costs, using a propensity score-adjusted, intention-to-treat approach, including time-to-event analyses for mortality, difference-in-differences (DiD) negative binomial regression analyses for utilization, and DiD gamma generalized linear regression for health care costs., Results: Of 181 eligible adults with ESRD, 105 (65 men, 40 women; mean age, 45 years) received scheduled dialysis and 76 (38 men, 38 women; mean age, 52 years) received emergency-only dialysis. Compared with emergency-only dialysis, scheduled dialysis was significantly associated with reduced mortality (3% vs 17%, P = .001; absolute risk reduction, 14%; number needed to treat, 7; adjusted hazard ratio, 4.6; 95% CI, 1.2-18.2; P = .03), adjusted emergency department visits (-5.2 vs +1.1 visits/mo; DiD, -6.2; P < .001), adjusted hospitalizations (-2.1 vs -0.5 hospitalizations/6 months; DiD, -1.6; P < .001), adjusted hospital days (-9.2 vs +0.8 days/6 months; DiD, -9.9; P = .007), and adjusted costs (-$4316 vs +$1452 per person per month; DiD, -$5768; P < .001)., Conclusions and Relevance: In this study, scheduled dialysis was significantly associated with reduced 1-year mortality, health care utilization, and costs compared with emergency-only dialysis. Scheduled dialysis should be the universal standard of care for all individuals with ESRD in the United States.

Objective: To assess the projected effect of the Centers for Medicare and Medicaid Services new site-neutral payment policy, which aims to decrease unnecessary long-term acute care hospital (LTACH) admissions by reducing reimbursements for less-ill individuals by 2020., Design: Observational., Setting: National 5% Medicare data (2011-12)., Measurements: We examined the proportion of site-neutral LTACH admissions. Regional LTACH market supply was defined as LTACH beds per 100,000 residents, categorized according to tertile. We conducted a hospital-level analysis to compare the projected effect of site-neutral payment on "propensity score" matched high- and low-LTACH-use hospitals., Results: Forty-one percent of LTACH admissions would be subjected to site-neutral payment. The proportion of site-neutral admissions was large, varied considerably according to LTACH (median 40%, interquartile range 22-60%), and was only modestly greater with greater market supply (Pearson correlation coefficient=0.23, p<.001; coefficient of determination=0.10). The site-neutral payment policy would affect 47% of admissions from the highest-supply regions, versus 30% from the lowest-supply regions (p<.001); and 43% from high-use hospitals versus 36% from propensity score-matched low-use hospitals (p<.001)., Conclusion: A considerable proportion of LTACH admissions will be subjected to lower site-neutral payments. Although the policy will disproportionately affect high-use regions and hospitals, it will also affect nearly one-third of the current LTACH population from low-use hospitals and regions. As such, the site-neutral payment policy may limit LTACH access in existing LTAC-scarce markets, with potential adverse implications for recovery of hospitalized older adults. J Am Geriatr Soc 66:2104-2111, 2018., (© 2018, Copyright the Authors Journal compilation © 2018, The American Geriatrics Society.)

Objectives: To determine why non-mechanically ventilated hospitalized older adults are transferred to long-term acute care (LTAC) hospitals rather than remaining in the hospital., Design: Observational cohort., Setting: National Medicare data., Participants: Non-mechanically ventilated hospitalized adults aged 65 and older with fee-for-service Medicare in 2012 who were transferred to an LTAC hospital (n=1,831) or had a prolonged hospitalization without transfer (average hospital length of stay or longer of those transferred to an LTAC hospital) and had one of the 50 most common hospital diagnoses leading to LTAC transfer (N=12,875)., Measurements: We assessed predictors of transfer using a multilevel model, adjusting for patient-, hospital-, and hospital referral region (HRR)-level factors. We estimated proportions of variance at each level and adjusted hospital- and HRR-specific LTAC transfer rates using sequential models., Results: The strongest predictor of transfer was being hospitalized near an LTAC hospital (<1.4 vs > 33.6 miles, adjusted odds ratio=6.2, 95% confidence interval (CI)=4.2-9.1). After adjusting for case mix, differences between hospitals explained 15.4% of the variation in LTAC use and differences between regions explained 27.8%. Case mix-adjusted LTAC use was high in the South, where many HRRs had rates between 20.3% and 53.1%, whereas many HRRs were less than 5.4% in the Pacific Northwest, North, and New England. From our fully adjusted model, the median adjusted hospital LTAC transfer rate was 7.2% (interquartile range 2.8-17.5%), with substantial within-region variation (intraclass coefficient=0.25, 95% CI=0.21-0.30)., Conclusions: Nearly half of the variation in LTAC use is independent of illness severity and is explained by which hospital and what region the individual was hospitalized in. Because of the greater fragmentation of care and Medicare spending with LTAC transfers (because LTAC hospitals generate a separate bundled payment from the hospital), greater attention is needed to define the optimal role of LTAC hospitals in caring for older adults. J Am Geriatr Soc 66:2112-2119, 2018., (© 2018, Copyright the Authors Journal compilation © 2018, The American Geriatrics Society.)

Background: Readmissions after hospitalization for acute myocardial infarction (AMI) are common. However, the few currently available AMI readmission risk prediction models have poor-to-modest predictive ability and are not readily actionable in real time. We sought to develop an actionable and accurate AMI readmission risk prediction model to identify high-risk patients as early as possible during hospitalization., Methods and Results: We used electronic health record data from consecutive AMI hospitalizations from 6 hospitals in north Texas from 2009 to 2010 to derive and validate models predicting all-cause nonelective 30-day readmissions, using stepwise backward selection and 5-fold cross-validation. Of 826 patients hospitalized with AMI, 13% had a 30-day readmission. The first-day AMI model (the AMI "READMITS" score) included 7 predictors: renal function, elevated brain natriuretic peptide, age, diabetes mellitus, nonmale sex, intervention with timely percutaneous coronary intervention, and low systolic blood pressure, had an optimism-corrected C-statistic of 0.73 (95% confidence interval, 0.71-0.74) and was well calibrated. The full-stay AMI model, which included 3 additional predictors (use of intravenous diuretics, anemia on discharge, and discharge to postacute care), had an optimism-corrected C-statistic of 0.75 (95% confidence interval, 0.74-0.76) with minimally improved net reclassification and calibration. Both AMI models outperformed corresponding multicondition readmission models., Conclusions: The parsimonious AMI READMITS score enables early prospective identification of high-risk AMI patients for targeted readmissions reduction interventions within the first 24 hours of hospitalization. A full-stay AMI readmission model only modestly outperformed the AMI READMITS score in terms of discrimination, but surprisingly did not meaningfully improve reclassification., (© 2018 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.)

Importance: Despite providing an overlapping level of care, it is unknown why hospitalized older adults are transferred to long-term acute care hospitals (LTACs) vs less costly skilled nursing facilities (SNFs) for postacute care., Objective: To examine factors associated with variation in LTAC vs SNF transfer among hospitalized older adults., Design, Setting, and Participants: We conducted this retrospective observational cohort study of hospitalized older adults (≥65 years) transferred to an LTAC vs SNF during fiscal year 2012 using national 5% Medicare data., Main Outcomes and Measures: Predictors of LTAC transfer were assessed using a multilevel mixed-effects model adjusting for patient-, hospital-, and region-level factors. We estimated variation partition coefficients and adjusted hospital- and region-specific LTAC transfer rates using sequential models., Results: Among 65 525 hospitalized older adults (42 461 [64.8%] women; 39 908 [60.9%] ≥85 years) transferred to an LTAC or SNF, 3093 (4.7%) were transferred to an LTAC. We identified 29 patient-, 3 hospital-, and 5 region-level independent predictors. The strongest predictors of LTAC transfer were receiving a tracheostomy (adjusted odds ration [aOR], 23.8; 95% CI, 15.8-35.9) and being hospitalized in close proximity to an LTAC (0-2 vs >42 miles; aOR, 8.4, 95% CI, 6.1-11.5). After adjusting for case-mix, differences between patients explained 52.1% (95% CI, 47.7%-56.5%) of the variation in LTAC use. The remainder was attributable to hospital (15.0%; 95% CI, 12.3%-17.6%), and regional differences (32.9%; 95% CI, 27.6%-38.3%). Case-mix adjusted LTAC use was very high in the South (17%-37%) compared with the Pacific Northwest, North, and Northeast (<2.2%). From the full multilevel model, the median adjusted hospital LTAC transfer rate was 2.1% (10th-90th percentile, 0.24%-10.8%). Even within a region, adjusted hospital LTAC transfer rates varied substantially (intraclass correlation coefficient [ICC], 0.26; 95% CI, 0.23-0.30)., Conclusions and Relevance: Although many patient-level factors were associated with LTAC use, half of the variation in LTAC vs SNF transfer is independent of patients' illness severity or clinical complexity, and is explained by where the patient was hospitalized and in what region, with far greater use in the South. Even among hospitals in regions with similar LTAC access, there was considerable variation in LTAC use. Given the higher expense associated with LTACs vs SNFs, greater attention is needed to define the optimal role of LTACs in the postacute care of older adults.

Background: Hospitals are subject to federal financial penalties for excessive 30-day hospital readmissions for acute myocardial infarction (AMI). Prospectively identifying patients hospitalized with AMI at high risk for readmission could help prevent 30-day readmissions by enabling targeted interventions. However, the performance of AMI-specific readmission risk prediction models is unknown., Methods and Results: We systematically searched the published literature through March 2017 for studies of risk prediction models for 30-day hospital readmission among adults with AMI. We identified 11 studies of 18 unique risk prediction models across diverse settings primarily in the United States, of which 16 models were specific to AMI. The median overall observed all-cause 30-day readmission rate across studies was 16.3% (range, 10.6%-21.0%). Six models were based on administrative data; 4 on electronic health record data; 3 on clinical hospital data; and 5 on cardiac registry data. Models included 7 to 37 predictors, of which demographics, comorbidities, and utilization metrics were the most frequently included domains. Most models, including the Centers for Medicare and Medicaid Services AMI administrative model, had modest discrimination (median C statistic, 0.65; range, 0.53-0.79). Of the 16 reported AMI-specific models, only 8 models were assessed in a validation cohort, limiting generalizability. Observed risk-stratified readmission rates ranged from 3.0% among the lowest-risk individuals to 43.0% among the highest-risk individuals, suggesting good risk stratification across all models., Conclusions: Current AMI-specific readmission risk prediction models have modest predictive ability and uncertain generalizability given methodological limitations. No existing models provide actionable information in real time to enable early identification and risk-stratification of patients with AMI before hospital discharge, a functionality needed to optimize the potential effectiveness of readmission reduction interventions., (© 2018 American Heart Association, Inc.)

Background: Respiratory rate (RR) is an independent predictor of adverse outcomes and an integral component of many risk prediction scores for hospitalised adults. Yet, it is unclear if RR is recorded accurately. We sought to assess the potential accuracy of RR by analysing the distribution and variation as a proxy, since RR should be normally distributed if recorded accurately., Methods: We conducted a descriptive observational study of electronic health record data from consecutive hospitalisations from 2009 to 2010 from six diverse hospitals. We assessed the distribution of the maximum RR on admission, using heart rate (HR) as a comparison since this is objectively measured. We assessed RR patterns among selected subgroups expected to have greater physiological variation using the coefficient of variation (CV=SD/mean)., Results: Among 36 966 hospitalisations, recorded RR was not normally distributed (p<0.001), but right skewed (skewness=3.99) with values clustered at 18 and 20 (kurtosis=23.9). In contrast, HR was relatively normally distributed. Patients with a cardiopulmonary diagnosis or hypoxia only had modestly greater variation (CV increase of 2%-6%). Among 1318 patients transferred from the ward to the intensive care unit (n=1318), RR variation the day preceding transfer was similar to that observed on admission (CV 0.24 vs 0.26), even for those transferred with respiratory failure (CV 0.25)., Conclusions: The observed patterns suggest that RR is inaccurately recorded, even among those with cardiopulmonary compromise, and represents a 'spot' estimate with values of 18 and 20 breaths per minute representing 'normal.' While spot estimates may potentially be adequate to indicate clinical stability, inaccurate RR may alternatively lead to misclassification of disease severity, potentially jeopardising patient safety. Thus, we recommend greater training for hospital personnel to accurately record RR., Competing Interests: Competing interests: None declared., (© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)

Background: Although hypothesized to be a hazard of hospitalization, it is unclear whether hospital-acquired anemia (HAA) is associated with increased adverse outcomes following discharge., Objective: To examine the incidence, predictors, and postdischarge outcomes associated with HAA., Design: Observational cohort study using electronic health record data., Subjects: Consecutive medicine discharges between November 1, 2009 and October 30, 2010 from 6 Texas hospitals, including safety-net, teaching, and nonteaching sites. Patients with anemia on admission or missing hematocrit values at admission or discharge were excluded., Measures: HAA was defined using the last hematocrit value prior to discharge and categorized by severity. The primary outcome was a composite of 30-day mortality and nonelective readmission., Results: Among 11,309 patients, one-third developed HAA (21.6% with mild HAA; 10.1% with moderate HAA; and 1.4% with severe HAA). The 2 strongest potentially modifiable predictors of developing moderate or severe HAA were length of stay (adjusted odds ratio [OR], 1.26 per day; 95% confidence interval [CI], 1.23-1.29) and receipt of a major procedure (adjusted OR, 5.09; 95% CI, 3.79-6.82). Patients without HAA had a 9.7% incidence for the composite outcome versus 16.4% for those with severe HAA. Severe HAA was independently associated with a 39% increase in the odds for 30-day readmission or death (95% CI, 1.09-1.78). Most patients with severe HAA (85%) underwent a major procedure, had a discharge diagnosis of hemorrhage, and/or a discharge diagnosis of hemorrhagic disorder., Conclusions: Severe HAA is associated with increased odds for 30-day mortality and readmission after discharge; however, it is uncertain whether severe HAA is preventable. Journal of Hospital Medicine 2017;12:317-322.

Objective: To examine adherence to guideline-concordant hypertension treatment practices at community health centers (CHCs) compared with private physicians' offices., Data Sources/study Setting: National Ambulatory Medical Care Survey from 2006 to 2010., Study Design: We examined four guideline-concordant treatment practices: initiation of a new medication for uncontrolled hypertension, use of fixed-dose combination drugs for patients on multiple antihypertensive medications, use of thiazide diuretics among patients with uncontrolled hypertension on ≥3 antihypertensive medications, and use of aldosterone antagonist for resistant hypertension, comparing use at CHC with private physicians' offices overall and by payer group., Data Collection/extraction Methods: We identified visits of nonpregnant adults with hypertension at CHCs and private physicians' offices., Principal Findings: Medicaid patients at CHCs were as likely as privately insured individuals to receive a new medication for uncontrolled hypertension (AOR 1.0, 95 percent CI: 0.6-1.9), whereas Medicaid patients at private physicians' offices were less likely to receive a new medication (AOR 0.3, 95 percent CI: 0.1-0.6). Use of fixed-dose combination drugs was lower at CHCs (AOR 0.6, 95 percent CI: 0.4-0.9). Thiazide use for patients was similar in both settings (AOR 0.8, 95 percent CI: 0.4-1.7). Use of aldosterone antagonists was too rare (2.1 percent at CHCs and 1.5 percent at private clinics) to allow for statistically reliable comparisons., Conclusions: Increasing physician use of fixed-dose combination drugs may be particularly helpful in improving hypertension control at CHCs where there are higher rates of uncontrolled hypertension., (© Health Research and Educational Trust.)

Background: Readmissions after hospitalization for pneumonia are common, but the few risk-prediction models have poor to modest predictive ability. Data routinely collected in the electronic health record (EHR) may improve prediction., Objective: To develop pneumonia-specific readmission risk-prediction models using EHR data from the first day and from the entire hospital stay ("full stay")., Design: Observational cohort study using stepwise-backward selection and cross-validation., Subjects: Consecutive pneumonia hospitalizations from 6 diverse hospitals in north Texas from 2009-2010., Measures: All-cause nonelective 30-day readmissions, ascertained from 75 regional hospitals., Results: Of 1463 patients, 13.6% were readmitted. The first-day pneumonia-specific model included sociodemographic factors, prior hospitalizations, thrombocytosis, and a modified pneumonia severity index; the full-stay model included disposition status, vital sign instabilities on discharge, and an updated pneumonia severity index calculated using values from the day of discharge as additional predictors. The full-stay pneumonia-specific model outperformed the first-day model (C statistic 0.731 vs 0.695; P = 0.02; net reclassification index = 0.08). Compared to a validated multi-condition readmission model, the Centers for Medicare and Medicaid Services pneumonia model, and 2 commonly used pneumonia severity of illness scores, the full-stay pneumonia-specific model had better discrimination (C statistic range 0.604-0.681; P < 0.01 for all comparisons), predicted a broader range of risk, and better reclassified individuals by their true risk (net reclassification index range, 0.09-0.18)., Conclusions: EHR data collected from the entire hospitalization can accurately predict readmission risk among patients hospitalized for pneumonia. This approach outperforms a first-day pneumonia-specific model, the Centers for Medicare and Medicaid Services pneumonia model, and 2 commonly used pneumonia severity of illness scores. Journal of Hospital Medicine 2017;12:209-216., (© 2017 Society of Hospital Medicine)

Overtreatment is pervasive in medicine and leads to potential patient harms and excessive costs in health care. Although evidence-based medicine is often derided as practice by rote algorithmic medicine, the appropriate application of key evidence-based medicine principles in clinical decision making is fundamental to preventing overtreatment and promoting high-value, individualized patient-centered care. Specifically, this article discusses the importance of (1) using absolute rather than relative estimates of benefits to inform treatment decisions; (2) considering the time horizon to benefit of treatments; (3) balancing potential harms and benefits; and (4) using shared decision making by physicians to incorporate the patient's values and preferences into treatment decisions. Here, we illustrate the application of these principles to considering the decision of whether or not to recommend intensive glycemic control to patients to minimize microvascular and cardiovascular complications in type 2 diabetes mellitus. Through this lens, this example will illustrate how an evidence-based medicine approach can be used to individualize glycemic goals and prevent overtreatment, and can serve as a template for applying evidence-based medicine to inform treatment decisions for other conditions to optimize health and individualize patient care., (© 2017 American Heart Association, Inc.)

Background: Vital sign instability on discharge could be a clinically objective means of assessing readiness and safety for discharge; however, the association between vital sign instability on discharge and post-hospital outcomes is unclear., Objective: To assess the association between vital sign instability at hospital discharge and post-discharge adverse outcomes., Design: Multi-center observational cohort study using electronic health record data. Abnormalities in temperature, heart rate, blood pressure, respiratory rate, and oxygen saturation were assessed within 24 hours of discharge. We used logistic regression adjusted for predictors of 30-day death and readmission., Participants: Adults (≥18 years) with a hospitalization to any medicine service in 2009-2010 at six hospitals (safety-net, community, teaching, and non-teaching) in north Texas., Main Measures: Death or non-elective readmission within 30 days after discharge., Key Results: Of 32,835 individuals, 18.7 % were discharged with one or more vital sign instabilities. Overall, 12.8 % of individuals with no instabilities on discharge died or were readmitted, compared to 16.9 % with one instability, 21.2 % with two instabilities, and 26.0 % with three or more instabilities (p < 0.001). The presence of any (≥1) instability was associated with higher risk-adjusted odds of either death or readmission (AOR 1.36, 95 % CI 1.26-1.48), and was more strongly associated with death (AOR 2.31, 95 % CI 1.91-2.79). Individuals with three or more instabilities had nearly fourfold increased odds of death (AOR 3.91, 95 % CI 1.69-9.06) and increased odds of 30-day readmission (AOR 1.36, 95 % 0.81-2.30) compared to individuals with no instabilities. Having two or more vital sign instabilities at discharge had a positive predictive value of 22 % and positive likelihood ratio of 1.8 for 30-day death or readmission., Conclusions: Vital sign instability on discharge is associated with increased risk-adjusted rates of 30-day mortality and readmission. These simple vital sign criteria could be used to assess safety for discharge, and to reduce 30-day mortality and readmissions., Competing Interests: The authors declare that they do not have a conflict of interest.

Introduction: Food insecurity is negatively associated with health; however, health needs may differ among people participating in food assistance programs. Our objectives were to characterize differences in health among people receiving different types of food assistance and summarize strategies for targeted recruitment and outreach of various food insecure populations., Methods: We examined health status, behaviors, and health care access associated with food insecurity and receipt of food assistance among US adults aged 20 years or older using data from participants (N = 16,934) of the National Health and Nutrition Examination Survey from 2005 through 2010., Results: Food insecurity affected 19.3% of US adults (95% confidence interval, 17.9%-20.7%). People who were food insecure reported poorer health and less health care access than those who were food secure (P < .001 for all). Among those who were food insecure, 58.0% received no assistance, 20.3% received only Supplemental Nutrition Assistance Program (SNAP) benefits, 9.7% received only food bank assistance, and 12.0% received both SNAP and food bank assistance. We observed an inverse relationship between receipt of food assistance and health and health behaviors among the food insecure. Receipt of both (SNAP and food bank assistance) was associated with the poorest health; receiving no assistance was associated with the best health. For example, functional limitations were twice as prevalent among people receiving both types of food assistance than among those receiving none., Conclusion: Receipt of food assistance is an overlooked factor associated with health and has the potential to shape future chronic disease prevention efforts among the food insecure.