Lessons learned from a multi-site collaborative working toward a digital health use screening tool.

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.

One-Year Recovery Among Survivors of Prolonged Severe COVID-19: A National Multicenter Cohort.

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.

Patient and Staff Perspectives on the Impacts and Challenges of Hospital-Based Harm Reduction.

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.

Multilevel Determinants of Digital Health Equity: A Literature Synthesis to Advance the Field.

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.

National Prevalence of Alcohol and Other Substance Use Disorders Among Emergency Department Visits and Hospitalizations: NHAMCS 2014-2018.

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.

Adapting Existing Resources for Serious Illness Communication Skills Training for Internal Medicine Residents.

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.

Man vs. Machine: Comparing Physician vs. Electronic Health Record-Based Model Predictions for 30-Day Hospital Readmissions.

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.

Comparative effectiveness of long-term acute care hospital versus skilled nursing facility transfer.

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.

Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?

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.

The Clinical Course after Long-Term Acute Care Hospital Admission among Older Medicare Beneficiaries.

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.

Improving Respiratory Rate Accuracy in the Hospital: A Quality Improvement Initiative.

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.

Association of Scheduled vs Emergency-Only Dialysis With Health Outcomes and Costs in Undocumented Immigrants With End-stage Renal Disease.

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.

Long-Term Acute Care Hospital Use of Non-Mechanically Ventilated Hospitalized Older Adults.

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.