Where Do Real-Time Prescription Benefit Tools Fit in the Landscape of High US Prescription Medication Costs? A Narrative Review.

The problem of unaffordable prescription medications in the United States is complex and can result in poor patient adherence to therapy, worse clinical outcomes, and high costs to the healthcare system. While providers are aware of the financial burden of healthcare for patients, there is a lack of actionable price transparency at the point of prescribing. Real-time prescription benefit (RTPB) tools are new electronic clinical decision support tools that retrieve patient- and medication-specific out-of-pocket cost information and display it to clinicians at the point of prescribing. The rise in US healthcare costs has been a major driver for efforts to increase medication price transparency, and mandates from the Centers for Medicare & Medicaid Services for Medicare Part D sponsors to adopt RTPB tools may spur integration of such tools into electronic health records. Although multiple factors affect the implementation of RTPB tools, there is limited evidence on outcomes. Further research will be needed to understand the impact of RTPB tools on end results such as prescribing behavior, out-of-pocket medication costs for patients, and adherence to pharmacologic treatment. We review the terminology and concepts essential in understanding the landscape of RTPB tools, implementation considerations, barriers to adoption, and directions for future research that will be important to patients, prescribers, health systems, and insurers.

Smoking Status, Nicotine Medication, Vaccination, and COVID-19 Hospital Outcomes: Findings from the COVID EHR Cohort at the University of Wisconsin (CEC-UW) Study.

INTRODUCTION: Available evidence is mixed concerning associations between smoking status and COVID-19 clinical outcomes. Effects of nicotine replacement therapy (NRT) and vaccination status on COVID-19 outcomes in smokers are unknown. METHODS: Electronic health record data from 104 590 COVID-19 patients hospitalized February 1, 2020 to September 30, 2021 in 21 U.S. health systems were analyzed to assess associations of smoking status, in-hospital NRT prescription, and vaccination status with in-hospital death and ICU admission. RESULTS: Current (n = 7764) and never smokers (n = 57 454) did not differ on outcomes after adjustment for age, sex, race, ethnicity, insurance, body mass index, and comorbidities. Former (vs never) smokers (n = 33 101) had higher adjusted odds of death (aOR, 1.11; 95% CI, 1.06-1.17) and ICU admission (aOR, 1.07; 95% CI, 1.04-1.11). Among current smokers, NRT prescription was associated with reduced mortality (aOR, 0.64; 95% CI, 0.50-0.82). Vaccination effects were significantly moderated by smoking status; vaccination was more strongly associated with reduced mortality among current (aOR, 0.29; 95% CI, 0.16-0.66) and former smokers (aOR, 0.47; 95% CI, 0.39-0.57) than for never smokers (aOR, 0.67; 95% CI, 0.57, 0.79). Vaccination was associated with reduced ICU admission more strongly among former (aOR, 0.74; 95% CI, 0.66-0.83) than never smokers (aOR, 0.87; 95% CI, 0.79-0.97). CONCLUSIONS: Former but not current smokers hospitalized with COVID-19 are at higher risk for severe outcomes. SARS-CoV-2 vaccination is associated with better hospital outcomes in COVID-19 patients, especially current and former smokers. NRT during COVID-19 hospitalization may reduce mortality for current smokers. IMPLICATIONS: Prior findings regarding associations between smoking and severe COVID-19 disease outcomes have been inconsistent. This large cohort study suggests potential beneficial effects of nicotine replacement therapy on COVID-19 outcomes in current smokers and outsized benefits of SARS-CoV-2 vaccination in current and former smokers. Such findings may influence clinical practice and prevention efforts and motivate additional research that explores mechanisms for these effects.

Creation and Validation of an Automated Registry for Outpatient Parenteral Antibiotics.

Existing outpatient parenteral antibiotic therapy (OPAT) registries are resource intensive, and OPAT programs struggle to produce objective data to show the value of their work. We describe the building and validation of an automated OPAT registry within our electronic medical record and provide objective data on the value of the program. Variables and outcomes include age, sex, race, ethnicity, primary insurance payor, antibiotic names, infection syndromes treated, discharge disposition, 30-day all-cause readmission and death rates, complications, and an estimate of the hospital days saved. Records for 146 OPAT episodes were reviewed manually to validate the registry. Data were displayed in a dashboard within the electronic medical record. Over the 4-year time frame, our registry collected 3956 unique patients who completed 4710 episodes (approximately 1200 episodes per year). A total of 400 complications during OPAT were identified. All variables had an accuracy of >90% on validation. The OPAT program resulted in a reduction in hospital length of stay by 88 820 days, or roughly 22 000 days per year. We intend our registry to serve as a blueprint for similar OPAT programs with limited administrative resources. Wider application of our system would allow for easier aggregation and comparisons of OPAT practice and address the lack interinstitutional standardization of OPAT data and outcomes.

Implementation and evaluation of an electronic health record-integrated app for postpartum monitoring of hypertensive disorders of pregnancy using patient-contributed data collection.

Remote monitoring of women experiencing hypertensive disorders of pregnancy (HDP) can provide timely life-saving data, particularly if these data are integrated into existing patient and clinical workflows. This pilot intervention of a smartphone application (app) for postpartum monitoring of hypertensive disorders integrates patient-contributed data into electronic health records (EHRs) to support monitoring and clinical decision-making. Results from the evaluation of the pilot highlight the resources needed when implementing the app, challenges for integrating an app into the EHR, and the usability and utility of the HDP monitoring app for patient and clinician users. The implementation team's key observations included the importance of a local clinical champion, more robust patient involvement and support for the remote patient monitoring program, an impetus for EHR developers to adopt data integration standards, and a need to expand the capabilities of the standards to support interventions using patient-contributed data.

Implementing real-time prescription benefit tools: Early experiences from 5 academic medical centers.

BACKGROUND: Medication price transparency tools are increasingly available, but data on their use, and their potential effects on prescribing behavior, patient out of pocket (OOP) costs, and clinician workflow integration, is limited. OBJECTIVE: To describe the implementation experiences with real-time prescription benefit (RTPB) tools at 5 large academic medical centers and their early impact on prescription ordering. DESIGN: and Participants: In this cross-sectional study, we systematically collected information on the characteristics of RTPB tools through discussions with key stakeholders at each of the five organizations. Quantitative encounter data, prescriptions written, and RTPB alerts/estimates and prescription adjustment rates were obtained at each organization in the first three months after "go-live" of the RTPB system(s) between 2019 and 2020. MAIN MEASURES: Implementation characteristics, prescription orders, cost estimate retrieval rates, and prescription adjustment rates. KEY RESULTS: Differences were noted with respect to implementation characteristics related to RTPB tools. All of the organizations with the exception of one chose to display OOP cost estimates and suggested alternative prescriptions automatically. Differences were also noted with respect to a patient cost threshold for automatic display. In the first three months after "go-live," RTPB estimate retrieval rates varied greatly across the five organizations, ranging from 8% to 60% of outpatient prescriptions. The prescription adjustment rate was lower, ranging from 0.1% to 4.9% of all prescriptions ordered. CONCLUSIONS: In this study reporting on the early experiences with RTPB tools across five academic medical centers, we found variability in implementation characteristics and population coverage. In addition RTPB estimate retrieval rates were highly variable across the five organizations, while rates of prescription adjustment ranged from low to modest.

Colchicine and high-intensity rosuvastatin in the treatment of non-critically ill patients hospitalised with COVID-19: a randomised clinical trial.

OBJECTIVE: To evaluate the effect of colchicine and high-intensity rosuvastatin in addition to standard of care on the progression of COVID-19 disease in hospitalised patients. DESIGN: A pragmatic, open-label, multicentre, randomised controlled trial conducted from October 2020 to September 2021. Follow-up was conducted at 30 and 60 days. The electronic medical record was used at all stages of the trial including screening, enrolment, randomisation, event ascertainment and follow-up. SETTING: Four centres in the Yale New Haven Health System. PARTICIPANTS: Non-critically ill hospitalised patients with COVID-19. INTERVENTIONS: Patients were randomised 1:1 to either colchicine plus high-intensity rosuvastatin in addition to standard of care versus standard of care alone. Assigned treatment was continued for the duration of index hospitalisation or 30 days, whichever was shorter. PRIMARY AND SECONDARY OUTCOME MEASURES: The prespecified primary endpoint was progression to severe COVID-19 disease (new high-flow or non-invasive ventilation, mechanical ventilation, need for vasopressors, renal replacement therapy or extracorporeal membrane oxygenation, or death) or arterial/venous thromboembolic events (ischaemic stroke, myocardial infarction, deep venous thrombosis or pulmonary embolism) evaluated at 30 days. RESULTS: Among the 250 patients randomised in this trial (125 to each arm), the median age was 61 years, 44% were women, 15% were Black and 26% were Hispanic/Latino. As part of the standard of care, patients received remdesivir (87%), dexamethasone (92%), tocilizumab (18%), baricitinib (2%), prophylactic/therapeutic anticoagulation (98%) and aspirin (91%). The trial was terminated early by the data and safety monitoring board for futility. No patients were lost to follow-up due to electronic medical record follow-up. There was no significant difference in the primary endpoint at 30 days between the active arm and standard of care arm (15.2% vs 8.8%, respectively, p=0.17). CONCLUSIONS: In this small, open-label, randomised trial of non-critically ill hospitalised patients with COVID-19, the combination of colchicine and rosuvastatin in addition to standard of care did not appear to reduce the risk of progression of COVID-19 disease or thromboembolic events, although the trial was underpowered due to a lower-than-expected event rate. The trial leveraged the power of electronic medical records for efficiency and improved follow-up and demonstrates the utility of incorporating electronic medical records into future trials. TRIAL REGISTRATION: NCT04472611.

Relations of Current and Past Cancer with Severe Outcomes among 104,590 Hospitalized COVID-19 Patients: The COVID EHR Cohort at the University of Wisconsin.

BACKGROUND: There is mixed evidence about the relations of current versus past cancer with severe COVID-19 outcomes and how they vary by patient and cancer characteristics. METHODS: Electronic health record data of 104,590 adult hospitalized patients with COVID-19 were obtained from 21 United States health systems from February 2020 through September 2021. In-hospital mortality and ICU admission were predicted from current and past cancer diagnoses. Moderation by patient characteristics, vaccination status, cancer type, and year of the pandemic was examined. RESULTS: 6.8% of the patients had current (n = 7,141) and 6.5% had past (n = 6,749) cancer diagnoses. Current cancer predicted both severe outcomes but past cancer did not; adjusted odds ratios (aOR) for mortality were 1.58 [95% confidence interval (CI), 1.46-1.70] and 1.04 (95% CI, 0.96-1.13), respectively. Mortality rates decreased over the pandemic but the incremental risk of current cancer persisted, with the increment being larger among younger vs. older patients. Prior COVID-19 vaccination reduced mortality generally and among those with current cancer (aOR, 0.69; 95% CI, 0.53-0.90). CONCLUSIONS: Current cancer, especially among younger patients, posed a substantially increased risk for death and ICU admission among patients with COVID-19; prior COVID-19 vaccination mitigated the risk associated with current cancer. Past history of cancer was not associated with higher risks for severe COVID-19 outcomes for most cancer types. IMPACT: This study clarifies the characteristics that modify the risk associated with cancer on severe COVID-19 outcomes across the first 20 months of the COVID-19 pandemic. See related commentary by Egan et al., p. 3.

Experiences with Cyclical User-Centered Design for Patient and Clinician Facing Medication Reconciliation mHealth Applications.

Accurate medication lists are essential data required to make clinically informed decisions. Obtaining a comprehensive, up-to-date medication list is difficult for clinicians. Patients have limited input into reviewing and reconciling their own medication data. Ideally, a medication list would comprise a 360-degree view of all prescribed, dispensed, purchased medications and would seamlessly connect patients and providers to medication data from multiple sources. While an ideal medication list would capture every aspect of medication management, in reality a Best-Possible Medication History (BPMH) is a more achievable goal. In an effort to realize a BPMH and to facilitate the goals of the State of Connecticut's Office of Health Strategy's Medication Reconciliation and Polypharmacy Committee (MRPC), we engaged stakeholders (patients, clinicians, advocates) in focus-groups and interviews to solicit feedback on the user interface requirements for a BPMH. Feedback was obtained via facilitated discussions that occurred in-person, via virtual meetings, and through online surveys.

Alerting Clinicians to 1-Year Mortality Risk in Patients Hospitalized With Heart Failure: The REVEAL-HF Randomized Clinical Trial.

Importance: Heart failure is a major cause of morbidity and mortality worldwide. The use of risk scores has the potential to improve targeted use of interventions by clinicians that improve patient outcomes, but this hypothesis has not been tested in a randomized trial. Objective: To evaluate whether prognostic information in heart failure translates into improved decisions about initiation and intensity of treatment, more appropriate end-of-life care, and a subsequent reduction in rates of hospitalization or death. Design, Setting, and Participants: This was a pragmatic, multicenter, electronic health record-based, randomized clinical trial across the Yale New Haven Health System, comprising small community hospitals and large tertiary care centers. Patients hospitalized for heart failure who had N-terminal pro-brain natriuretic peptide (NT-proBNP) levels of greater than 500 pg/mL and received intravenous diuretics within 24 hours of admission were automatically randomly assigned to the alert (intervention) or usual-care groups. Interventions: The alert group had their risk of 1-year mortality calculated using an algorithm that was derived and validated using similar historic patients in the electronic health record. This estimate, including a categorical risk assessment, was presented to clinicians while they were interacting with a patient's electronic health record. Main Outcomes and Measures: The primary outcome was a composite of 30-day hospital readmissions and all-cause mortality at 1 year. Results: Between November 27, 2019, through March 7, 2021, 3124 patients were randomly assigned to the alert (1590 [50.9%]) or usual-care (1534 [49.1%]) group. The alert group had a median (IQR) age of 76.5 (65-86) years, and 796 were female patients (50.1%). Patients from the following race and ethnicity groups were included: 13 Asian (0.8%), 324 Black (20.4%), 136 Hispanic (8.6%), 1448 non-Hispanic (91.1%), 1126 White (70.8%), 6 other ethnicity (0.4%), and 127 other race (8.0%). The usual-care group had a median (IQR) age of 77 (65-86) years, and 788 were female patients (51.4%). Patients from the following race and ethnicity groups were included: 11 Asian (1.4%), 298 Black (19.4%), 162 Hispanic (10.6%), 1359 non-Hispanic (88.6%), 1077 White (70.2%), 13 other ethnicity (0.9%), and 137 other race (8.9%). Median (IQR) NT-proBNP levels were 3826 (1692-8241) pg/mL in the alert group and 3867 (1663-8917) pg/mL in the usual-care group. A total of 284 patients (17.9%) and 270 patients (17.6%) were admitted to the intensive care unit in the alert and usual-care groups, respectively. A total of 367 patients (23.1%) and 359 patients (23.4%) had a left ventricular ejection fraction of 40% or less in the alert and usual-care groups, respectively. The model achieved an area under the curve of 0.74 in the trial population. The primary outcome occurred in 619 patients (38.9%) in the alert group and 603 patients (39.3%) in the usual-care group (P = .89). There were no significant differences between study groups in the prescription of heart failure medications at discharge, the placement of an implantable cardioverter-defibrillator, or referral to palliative care. Conclusions and Relevance: Provision of 1-year mortality estimates during heart failure hospitalization did not affect hospitalization or mortality, nor did it affect clinical decision-making. Trial Registration: ClinicalTrials.gov Identifier NCT03845660.

Integrating a Patient Engagement App into an Electronic Health Record-Enabled Workflow Using Interoperability Standards.

BACKGROUND: Patient use of mobile health applications is increasing. To promote patient-centered care, data from these apps must be integrated into clinician workflows within the electronic health record (EHR). Health Level 7 Fast Healthcare Interoperability Resources (FHIR) offers a standards-based application programming interface (API) that may support such integration. OBJECTIVE: We aimed to use interoperability standards to integrate a patient mobile application (coronavirus 2019 [COVID-19] Tracker) with an EHR. The COVID-19 Tracker engages patients by sending introductory and reminder text messages, collecting vital signs and symptom data from COVID-19 patients, and providing actionable guidance if concerning issues are identified. This case report explored the use of FHIR APIs to integrate the app into EHR-enabled clinical workflows. METHODS: The authors used notes from project meetings and from semistructured discussions among the application development team to track the design and implementation processes. Seven points of integration between the application and the EHR were identified, and approaches using FHIR to perform these integrations were delineated. RESULTS: Although this clinical decision support integration project benefited from its standards-based approach, many challenges were encountered. These were due to (1) partial implementation of the FHIR standard in the EHR, particularly, components needed for patient engagement applications; (2) limited experience with the adoption of FHIR standards; and (3) gaps in the current FHIR standard. Alternative approaches, often not based on interoperability standards, were developed to overcome these limitations. CONCLUSION: Despite the challenges encountered due to the early stages of FHIR development and adoption, FHIR standards provide a promising mechanism for overcoming longstanding barriers and facilitating the integration of patient engagement apps with EHRs. To accelerate the integration of apps into clinical workflows, additional components of the FHIR standard must be implemented within the EHR and other clinical systems. Continued expansion of available FHIR resources will help with tighter workflow integration.

Accuracy of the Veterans Health Administration COVID-19 (VACO) Index for predicting short-term mortality among 1307 US academic medical centre inpatients and 427 224 US Medicare patients.

BACKGROUND: The Veterans Health Administration COVID-19 (VACO) Index predicts 30-day all-cause mortality in patients with COVID-19 using age, sex and pre-existing comorbidity diagnoses. The VACO Index was initially developed and validated in a nationwide cohort of US veterans-we now assess its accuracy in an academic medical centre and a nationwide US Medicare cohort. METHODS: With measures and weights previously derived and validated in US national Veterans Health Administration (VA) inpatients and outpatients (n=13 323), we evaluated the accuracy of the VACO Index for estimating 30-day all-cause mortality using area under the receiver operating characteristic curve (AUC) and calibration plots of predicted versus observed mortality in inpatients at a single US academic medical centre (n=1307) and in Medicare inpatients and outpatients aged 65+ (n=427 224). RESULTS: 30-day mortality varied by data source: VA 8.5%, academic medical centre 17.5%, Medicare 16.0%. The VACO Index demonstrated similar discrimination in VA (AUC=0.82) and academic medical centre inpatient population (AUC=0.80), and when restricted to patients aged 65+ in VA (AUC=0.69) and Medicare inpatient and outpatient data (AUC=0.67). The Index modestly overestimated risk in VA and Medicare data and underestimated risk in Yale New Haven Hospital data. CONCLUSIONS: The VACO Index estimates risk of short-term mortality across a wide variety of patients with COVID-19 using data available prior to or at the time of diagnosis. The VACO Index could help inform primary and booster vaccination prioritisation, and indicate who among outpatients testing positive for SARS-CoV-2 should receive greater clinical attention or scarce treatments.

The first 20 months of the COVID-19 pandemic: Mortality, intubation and ICU rates among 104,590 patients hospitalized at 21 United States health systems.

MAIN OBJECTIVE: There is limited information on how patient outcomes have changed during the COVID-19 pandemic. This study characterizes changes in mortality, intubation, and ICU admission rates during the first 20 months of the pandemic. STUDY DESIGN AND METHODS: University of Wisconsin researchers collected and harmonized electronic health record data from 1.1 million COVID-19 patients across 21 United States health systems from February 2020 through September 2021. The analysis comprised data from 104,590 adult hospitalized COVID-19 patients. Inclusion criteria for the analysis were: (1) age 18 years or older; (2) COVID-19 ICD-10 diagnosis during hospitalization and/or a positive COVID-19 PCR test in a 14-day window (+/- 7 days of hospital admission); and (3) health system contact prior to COVID-19 hospitalization. Outcomes assessed were: (1) mortality (primary), (2) endotracheal intubation, and (3) ICU admission. RESULTS AND SIGNIFICANCE: The 104,590 hospitalized participants had a mean age of 61.7 years and were 50.4% female, 24% Black, and 56.8% White. Overall risk-standardized mortality (adjusted for age, sex, race, ethnicity, body mass index, insurance status and medical comorbidities) declined from 16% of hospitalized COVID-19 patients (95% CI: 16% to 17%) early in the pandemic (February-April 2020) to 9% (CI: 9% to 10%) later (July-September 2021). Among subpopulations, males (vs. females), those on Medicare (vs. those on commercial insurance), the severely obese (vs. normal weight), and those aged 60 and older (vs. younger individuals) had especially high mortality rates both early and late in the pandemic. ICU admission and intubation rates also declined across these 20 months. CONCLUSIONS: Mortality, intubation, and ICU admission rates improved markedly over the first 20 months of the pandemic among adult hospitalized COVID-19 patients although gains varied by subpopulation. These data provide important information on the course of COVID-19 and identify hospitalized patient groups at heightened risk for negative outcomes. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04506528 (https://clinicaltrials.gov/ct2/show/NCT04506528).

REVeAL-HF: Design and Rationale of a Pragmatic Randomized Controlled Trial Embedded Within Routine Clinical Practice.

Heart failure (HF) is one of the most common causes of hospitalization in the United States and carries a significant risk of morbidity and mortality. Use of evidence-based interventions may improve outcomes, but their use is encumbered in part by limitations in accurate prognostication. The REVeAL-HF (Risk EValuation And its Impact on ClinicAL Decision Making and Outcomes in Heart Failure) trial is the first to definitively evaluate the impact of knowledge about prognosis on clinical decision making and patient outcomes. The REVeAL-HF trial is a pragmatic, completely electronic, randomized controlled trial that has completed enrollment of 3,124 adults hospitalized for HF, defined as having an N-terminal pro-B-type natriuretic peptide level of >500 pg/ml and receiving intravenous diuretic agents within 24 h of admission. Patients randomized to the intervention had their risk of 1-year mortality generated with information in the electronic health record and presented to their providers, who had the option to give feedback on their impression of this risk assessment. The authors are examining the impact of this information on clinical decision-making (use of HF pharmacotherapies, referral to electrophysiology, palliative care referral, and referral for advanced therapies like heart transplantation or mechanical circulatory support) and patient outcomes (length of stay, post-discharge 30-day rehospitalizations, and 1-year mortality). The REVeAL-HF trial will definitively examine whether knowledge about prognosis in HF has an impact on clinical decision making and patient outcomes. It will also examine the relationship between calculated, perceived, and real risk of mortality in this patient population. (Risk EValuation And Its Impact on ClinicAL Decision Making and Outcomes in Heart Failure [REVeAL-HF]; NCT03845660).

The potential impact of triage protocols on racial disparities in clinical outcomes among COVID-positive patients in a large academic healthcare system.

BACKGROUND: The COVID-19 pandemic has had a devastating impact in the United States, particularly for Black populations, and has heavily burdened the healthcare system. Hospitals have created protocols to allocate limited resources, but there is concern that these protocols will exacerbate disparities. The sequential organ failure assessment (SOFA) score is a tool often used in triage protocols. In these protocols, patients with higher SOFA scores are denied resources based on the assumption that they have worse clinical outcomes. The purpose of this study was to assess whether using SOFA score as a triage tool among COVID-positive patients would exacerbate racial disparities in clinical outcomes. METHODS: We analyzed data from a retrospective cohort of hospitalized COVID-positive patients in the Yale-New Haven Health System. We examined associations between race/ethnicity and peak overall/24-hour SOFA score, in-hospital mortality, and ICU admission. Other predictors of interest were age, sex, primary language, and insurance status. We used one-way ANOVA and chi-square tests to assess differences in SOFA score across racial/ethnic groups and linear and logistic regression to assess differences in clinical outcomes by sociodemographic characteristics. RESULTS: Our final sample included 2,554 patients. Black patients had higher SOFA scores compared to patients of other races. However, Black patients did not have significantly greater in-hospital mortality or ICU admission compared to patients of other races. CONCLUSION: While Black patients in this sample of hospitalized COVID-positive patients had higher SOFA scores compared to patients of other races, this did not translate to higher in-hospital mortality or ICU admission. Results demonstrate that if SOFA score had been used to allocate care, Black COVID patients would have been denied care despite having similar clinical outcomes to white patients. Therefore, using SOFA score to allocate resources has the potential to exacerbate racial inequities by disproportionately denying care to Black patients and should not be used to determine access to care. Healthcare systems must develop and use COVID-19 triage protocols that prioritize equity.

Racial disparities in the SOFA score among patients hospitalized with COVID-19.

BACKGROUND: Sequential Organ Failure Assessment (SOFA) score predicts probability of in-hospital mortality. Many crisis standards of care suggest the use of SOFA scores to allocate medical resources during the COVID-19 pandemic. RESEARCH QUESTION: Are SOFA scores elevated among Non-Hispanic Black and Hispanic patients hospitalized with COVID-19, compared to Non-Hispanic White patients? STUDY DESIGN AND METHODS: Retrospective cohort study conducted in Yale New Haven Health System, including 5 hospitals with total of 2681 beds. Study population drawn from consecutive patients aged ≥18 admitted with COVID-19 from March 29th to August 1st, 2020. Patients excluded from the analysis if not their first admission with COVID-19, if they did not have SOFA score recorded within 24 hours of admission, if race and ethnicity data were not Non-Hispanic Black, Non-Hispanic White, or Hispanic, or if they had other missing data. The primary outcome was SOFA score, with peak score within 24 hours of admission dichotomized as <6 or ≥6. RESULTS: Of 2982 patients admitted with COVID-19, 2320 met inclusion criteria and were analyzed, of whom 1058 (45.6%) were Non-Hispanic White, 645 (27.8%) were Hispanic, and 617 (26.6%) were Non-Hispanic Black. Median age was 65.0 and 1226 (52.8%) were female. In univariate logistic screen and in full multivariate model, Non-Hispanic Black patients but not Hispanic patients had greater odds of an elevated SOFA score ≥6 when compared to Non-Hispanic White patients (OR 1.49, 95%CI 1.11-1.99). INTERPRETATION: Given current unequal patterns in social determinants of health, US crisis standards of care utilizing the SOFA score to allocate medical resources would be more likely to deny these resources to Non-Hispanic Black patients.

Design and rationale of the colchicine/statin for the prevention of COVID-19 complications (COLSTAT) trial.

BACKGROUND: Despite improvement in the standard of care (SOC) for hospitalized COVID-19 patients, rates of morbidity and mortality remain high. There continues to be a need for easily available and cost-effective treatments. Colchicine and rosuvastatin are both safe and well-studied medications with anti-inflammatory and other pleiotropic effects that may provide additional benefits to hospitalized COVID-19 patients. METHODS AND RESULTS: The Colchicine/Statin for the Prevention of COVID-19 Complications (COLSTAT) trial is a pragmatic, open-label, multicenter, randomized trial comparing the combination of colchicine and rosuvastatin in addition to SOC to SOC alone in hospitalized COVID-19 patients. Four centers in the Yale New Haven Health network will enroll a total of 466 patients with 1:1 randomization. The trial will utilize the electronic health record (Epic® Systems, Verona, Wisconsin, USA) at all stages including screening, randomization, intervention, event ascertainment, and follow-up. The primary endpoint is the 30-day composite of progression to severe COVID-19 disease as defined by the World Health Organization ordinal scale of clinical improvement and arterial/venous thromboembolic events. The secondary powered endpoint is the 30-day composite of death, respiratory failure requiring intubation, and myocardial injury. CONCLUSIONS: The COLSTAT trial will provide evidence on the efficacy of repurposing colchicine and rosuvastatin for the treatment of hospitalized COVID-19 patients. Moreover, it is designed to be a pragmatic trial that will demonstrate the power of using electronic health records to improve efficiency and enrollment in clinical trials in an adapting landscape. CLINICAL TRIAL REGISTRATION: NCT04472611 (https://clinicaltrials.gov/ct2/show/NCT04472611).

COVID-19 infections and outcomes in a live registry of heart failure patients across an integrated health care system.

BACKGROUND: Patients with comorbid conditions have a higher risk of mortality with SARS-CoV-2 (COVID-19) infection, but the impact on heart failure patients living near a disease hotspot is unknown. Therefore, we sought to characterize the prevalence and outcomes of COVID-19 in a live registry of heart failure patients across an integrated health care system in Connecticut. METHODS: In this retrospective analysis, the Yale Heart Failure Registry (NCT04237701) that includes 26,703 patients with heart failure across a 6-hospital integrated health care system in Connecticut was queried on April 16th, 2020 for all patients tested for COVID-19. Sociodemographic and geospatial data as well as, clinical management, respiratory failure, and patient mortality were obtained via the real-time registry. Data on COVID-19 specific care was extracted by retrospective chart review. RESULTS: COVID-19 testing was performed on 900 symptomatic patients, comprising 3.4% of the Yale Heart Failure Registry (N = 26,703). Overall, 206 (23%) were COVID- 19+. As compared to COVID-19-, these patients were more likely to be older, black, have hypertension, coronary artery disease, and were less likely to be on renin angiotensin blockers (P<0.05, all). COVID-19- patients tended to be more diffusely spread across the state whereas COVID-19+ were largely clustered around urban centers. 20% of COVID-19+ patients died, and age was associated with increased risk of death [OR 1.92 95% CI (1.33-2.78); P<0.001]. Among COVID-19+ patients who were ≥85 years of age rates of hospitalization were 87%, rates of death 36%, and continuing hospitalization 62% at time of manuscript preparation. CONCLUSIONS: In this real-world snapshot of COVID-19 infection among a large cohort of heart failure patients, we found that a small proportion had undergone testing. Patients found to be COVID-19+ tended to be black with multiple comorbidities and clustered around lower socioeconomic status communities. Elderly COVID-19+ patients were very likely to be admitted to the hospital and experience high rates of mortality.

Evaluating the Impact of a New Smartphone Texting Tool on Patient Care in Obstetrics, an Emergent Healthcare Setting.

BACKGROUND: Hospitals across the country are investing millions of dollars to adopt new Health Insurance Portability and Accountability Act (HIPAA)-compliant secure text messaging systems. However, in nearly all cases, these implementations are occurring without evaluation of their impact on patient care. OBJECTIVE: To evaluate perceived impact on patient care and workflow of new text messaging system implemented in obstetrics at Yale-New Haven Hospital and to inform guidelines for future implementations in emergent settings. METHODS: A new HIPAA-compliant texting system was implemented in obstetrics in 2016. Before implementation of the new system, residents and nurses were surveyed on perceived effect of communication system (pagers with text receiving, service mobile phones, personal cell phones) on clinical workflow and patient care using 5-point Likert scale and open-ended questions. Following roll-out (1 and 6 months), both teams were surveyed with same questions. Results were compared using Wilcoxon-Mann-Whitney test (0-1 months and then 0-6 months). Open-ended question results were qualitatively compared for recurrent unifying themes. RESULTS: In both nursing and resident domains, 1 month after implementation, the new communication system was perceived to significantly improve efficiency and patient care across all metrics. After 6 months, this effect decayed in nearly all categories (including efficiency, real-time communication, and knowledge of covering provider). The exception was nurse's knowledge of which resident to contact and resident's timely evaluation of patient, for which we observed sustained improvements. System shortcomings identified included interrupted connection (i.e., dropped calls), dysfunctional and inaccurate alert system, and unclear identification of the covering provider. CONCLUSION: A new text-messaging-based communication system may improve efficiency and patient care in emergent settings, but system shortcomings can substantially erode potential benefits over time. We recommend implementers evaluate new systems for a set of specific functional requirements to increase probability of sustained improvement and decrease risk of poor patient outcomes.

Analysis of medication therapy discontinuation orders in new electronic prescriptions and opportunities for implementing CancelRx.

Objective: To illustrate the need for wider implementation of the CancelRx message by quantifying and characterizing the inappropriate usage of new electronic prescription (NewRx) messages for communicating discontinuation instructions to pharmacies. Materials and Methods: A retrospective analysis on a nationally representative random sample of 1 400 000 NewRx messages transmitted over 7 days to identify e-prescriptions containing medication discontinuation instructions in NewRx text fields. A vocabulary of search terms signifying cancellation instructions was formulated and then iteratively refined. True-positives were subsequently identified programmatically and through manual reviews. Two independent reviewers identified incidences in which these instructions were associated with high-alert or look-alike-sound-like (LASA) medications. Results: We identified 9735 (0.7% of the total) NewRx messages containing prescription cancellation instructions with 78.5% observed in the Notes field; 35.3% of identified NewRxs were associated with high-alert or LASA medications. The most prevalent cancellation instruction types were medication strength or dosage changes (39.3%) and alternative therapy replacement orders (39.0%). Discussion: While the incidence of prescribers using the NewRx to transmit cancellation instructions was low, their transmission in NewRx fields not intended to accommodate such information can produce significant potential patient safety concerns, such as duplicate or inaccurate therapies. These findings reveal the need for wider industry adoption of the CancelRx message by electronic health record (EHR) and pharmacy systems, along with clearer guidance and improved end-user training, particularly as states increasingly mandate electronic prescribing of controlled substances. Conclusion: Encouraging the use of CancelRx and reducing the misuse of NewRx fields would reduce workflow disruptions and unnecessary risks to patient safety.