1
|
von Falkenhausen AS, Geipel S, Gail A, Scherer C, Stockhausen S, Sams LE, Becker F, Doldi PM, Lemmermöhle E, de Villèle P, Schleef M, Becker M, Lauterbach M, Massberg S, Kääb S, Sinner MF. Telemedical management of symptomatic COVID-19 outpatients. ERJ Open Res 2024; 10:00277-2024. [PMID: 39135664 PMCID: PMC11317893 DOI: 10.1183/23120541.00277-2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Accepted: 04/16/2024] [Indexed: 08/15/2024] Open
Abstract
Background COVID-19 remains a challenge to individual health and healthcare resources worldwide. Telemedical surveillance might minimise hospitalisation and direct patient-physician contacts. Yet, randomised clinical trials evaluating telemedical management of COVID-19 patients are lacking. Methods COVID-SMART is a randomised, open-label, controlled clinical trial investigating whether telemedicine reduces the primary end-point of hospitalisation or any unscheduled utilisation of an emergency medical service within 30 days of follow-up. Key secondary end-points included mortality and primary end-point components. We enrolled acutely infected SARS-CoV-2 patients suitable for outpatient care. All presented with ≥1 risk factor for an adverse COVID-19 course. Patients were randomised 1:1 into a control group receiving standard of care and an intervention group receiving smartphone-based assessment of oxygen saturation, heart rate and electrocardiogram, and telemedical counselling via a 24/7 emergency hotline. Results Of 607 enrolled patients (mean±sd age 46.7±13.5 years), 304 were randomised into the intervention and 303 into the control group. The primary end-point occurred in 6.9% (n=21) of the intervention and in 9.6% (n=29) of the control group (hazard ratio (HR) 0.72, 95% confidence interval (CI) 0.41-1.26; p=0.24). No deaths occurred during follow-up. Fewer intervention group participants utilised outpatient-based emergency medical services (HR 0.43, 95% CI 0.20-0.90; p=0.03). Conclusions COVID-SMART is the first randomised clinical trial assessing the benefit of telemedicine in an acute respiratory infectious disease. Whereas telemedical management did not reduce the primary end-point of hospitalisation, fewer intervention group patients used outpatient-based emergency services, suggesting a potential benefit for less-acutely infected individuals.
Collapse
Affiliation(s)
- Aenne S. von Falkenhausen
- Department of Medicine I, LMU University Hospital, LMU Munich, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), partner site: Munich Heart Alliance, Munich, Germany
| | - Scott Geipel
- Department of Medicine I, LMU University Hospital, LMU Munich, Munich, Germany
| | - Antonia Gail
- Department of Medicine I, LMU University Hospital, LMU Munich, Munich, Germany
| | - Clemens Scherer
- Department of Medicine I, LMU University Hospital, LMU Munich, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), partner site: Munich Heart Alliance, Munich, Germany
| | - Sven Stockhausen
- Department of Medicine I, LMU University Hospital, LMU Munich, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), partner site: Munich Heart Alliance, Munich, Germany
| | - Lauren E. Sams
- Department of Medicine I, LMU University Hospital, LMU Munich, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), partner site: Munich Heart Alliance, Munich, Germany
| | - Finn Becker
- Department of Medicine I, LMU University Hospital, LMU Munich, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), partner site: Munich Heart Alliance, Munich, Germany
| | - Philipp M. Doldi
- Department of Medicine I, LMU University Hospital, LMU Munich, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), partner site: Munich Heart Alliance, Munich, Germany
| | - Eric Lemmermöhle
- Department of Medicine I, LMU University Hospital, LMU Munich, Munich, Germany
| | | | | | | | | | - Steffen Massberg
- Department of Medicine I, LMU University Hospital, LMU Munich, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), partner site: Munich Heart Alliance, Munich, Germany
| | - Stefan Kääb
- Department of Medicine I, LMU University Hospital, LMU Munich, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), partner site: Munich Heart Alliance, Munich, Germany
- These authors share senior authorship
| | - Moritz F. Sinner
- Department of Medicine I, LMU University Hospital, LMU Munich, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), partner site: Munich Heart Alliance, Munich, Germany
- These authors share senior authorship
| |
Collapse
|
2
|
Abbasi MH, Yuan K, Kasner SE, McPartland E, Owens KC, Sloane KL. Text Message-Based Assessment of 90-Day Modified Rankin Scale After Stroke. J Am Heart Assoc 2024; 13:e033301. [PMID: 38686866 PMCID: PMC11179808 DOI: 10.1161/jaha.123.033301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 03/21/2024] [Indexed: 05/02/2024]
Abstract
BACKGROUND The modified Rankin Scale (mRS) is commonly used to measure disability after stroke, traditionally assessed through telephone or in-person evaluation. Here, we investigated the validity of mRS assessment through an automated text messaging system based on the simplified mRS questionnaire as an alternative method to traditional methods of assessment. METHODS AND RESULTS A total of 250 patients admitted to 3 hospitals within the University of Pennsylvania Health System with ischemic or hemorrhagic stroke were enrolled. Participants received automated text messages sent 48 hours before their outpatient appointment at about 90 days after stroke. The mRS scores were assigned on the basis of participant responses to 2 to 4 text questions eliciting yes/no responses. The mRS was then evaluated in person or by telephone interview for comparison. Responses were compared with κ. A total of 142 patients (57%) completed the study. The spontaneous response rate to text messages was 46.5% and up to 72% with an additional direct in-person or phone call reminder. Agreement was substantial (quadratic-weighted κ=0.87 [95% CI, 0.83-0.89]) between responses derived from the automated text messaging and traditional interviews. Agreement for distinguishing functional independence (mRS 0-1) from dependence (mRS 2-5) was substantial (unweighted κ=0.79 [95% CI, 0.69-0.90]). CONCLUSIONS An automated text messaging system is a feasible method for remotely obtaining the mRS after stroke and a potential alternative to traditional in-person or telephone assessment. Further studies are needed to evaluate the generalizability of text message-based approaches to stroke outcome measurement.
Collapse
Affiliation(s)
| | - Kristy Yuan
- Department of NeurologyUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPAUSA
| | - Scott E. Kasner
- Department of NeurologyUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPAUSA
| | - Ellen McPartland
- Department of NeurologyUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPAUSA
| | - Karrima C. Owens
- Department of NeurologyUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPAUSA
| | - Kelly L. Sloane
- Department of NeurologyUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPAUSA
- Department of Physical Medicine and RehabilitationUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPAUSA
| |
Collapse
|
3
|
Friedman AB, Delgado MK, Weissman GE. Artificial Intelligence for Emergency Care Triage-Much Promise, but Still Much to Learn. JAMA Netw Open 2024; 7:e248857. [PMID: 38713470 DOI: 10.1001/jamanetworkopen.2024.8857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/08/2024] Open
Affiliation(s)
- Ari B Friedman
- Department of Emergency Medicine, University of Pennsylvania, Philadelphia
- Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia
- The Leonard Davis Institute, University of Pennsylvania, Philadelphia
| | - M Kit Delgado
- Department of Emergency Medicine, University of Pennsylvania, Philadelphia
- The Leonard Davis Institute, University of Pennsylvania, Philadelphia
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia
- Center for Health Care Transformation and Innovation, University of Pennsylvania, Philadelphia
| | - Gary E Weissman
- The Leonard Davis Institute, University of Pennsylvania, Philadelphia
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia
- Palliative and Advanced Illness Research Center, University of Pennsylvania Perelman School of Medicine, Philadelphia
- Pulmonary, Allergy, and Critical Care Division, University of Pennsylvania Perelman School of Medicine, Philadelphia
| |
Collapse
|
4
|
Bruce C, Pinn-Kirkland T, Meyers A, Javaluyas E, Osborn J, Kelkar S, Bruchhaus L, McLaury K, Sauceda K, Carr K, Garcia C, Arabie LA, Williams T, Vozzella G, Nisar T, Schwartz RL, Sasangohar F. Investigating patient engagement associations between a postdischarge texting programme and patient experience, readmission and revisit rates outcomes. BMJ Open 2024; 14:e079775. [PMID: 38485169 PMCID: PMC10941103 DOI: 10.1136/bmjopen-2023-079775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 02/20/2024] [Indexed: 03/17/2024] Open
Abstract
OBJECTIVES This study aimed (1) to examine the association between patient engagement with a bidirectional, semiautomated postdischarge texting programme and Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey outcomes, readmissions and revisit rates in a large health system and (2) to describe operational and clinical flow considerations for implementing a postdischarge texting programme. SETTING The study involved 1 main academic hospital (beds: 2500+) and 6 community hospitals (beds: 190-400, averaging 300 beds per hospital) in Houston, Texas. METHODS Retrospective, observational cohort study between non-engaged patients (responded with 0-2 incoming text messages) and engaged patients (responded with 3+ incoming, patient-initiated text messages) between December 2022 and May 2023. We used the two-tailed t-test for continuous variables and χ2 test for categorical variables to compare the baseline characteristics between the two cohorts. For the binary outcomes, such as the revisit (1=yes, vs 0=no) and readmissions (1=yes vs 0=no), we constructed mixed effect logistic regression models with the random effects to account for repeated measurements from the hospitals. For the continuous outcome, such as the case mix index (CMI), a generalised linear quantile mixed effect model was built. All tests for significance were two tailed, using an alpha level of 0.05, and 95% CIs were provided. Significance tests were performed to evaluate the CMI and readmissions and revisit rates. RESULTS From 78 883 patients who were contacted over the course of this pilot implementation, 49 222 (62.4%) responded, with 39 442 (50%) responded with 3+ incoming text messages. The engaged cohort had higher HCAHPS scores in all domains compared with the non-engaged cohort. The engaged cohort used significantly fewer 30-day acute care resources, experiencing 29% fewer overall readmissions and 20% fewer revisit rates (23% less likely to revisit) and were 27% less likely to be readmitted. The results were statistically significant for all but two hospitals. CONCLUSIONS This study builds on the few postdischarge texting studies, and also builds on the patient engagement literature, finding that patient engagement with postdischarge texting can be associated with fewer acute care resources. To our knowledge, this is the only study that documented an association between a text-based postdischarge programme and HCAHPS scores, perhaps owing to the bidirectionality and ease with which patients could interact with nurses. Future research should explore the texting paradigms to evaluate their associated outcomes in a variety of postdischarge applications.
Collapse
Affiliation(s)
- Courtenay Bruce
- System Patient Experience, Houston Methodist, Houston, Texas, USA
| | - Theresa Pinn-Kirkland
- Houston Methodist Physicians Alliance for Quality, Houston Methodist, Houston, Texas, USA
| | - Adam Meyers
- Houston Methodist Physician Organization, Houston Methodist, Houston, Texas, USA
| | | | - John Osborn
- System Quality & Patient Safety, Houston Methodist, Houston, Texas, USA
| | - Sayali Kelkar
- System Quality & Patient Safety, Houston Methodist, Houston, Texas, USA
| | - Lindsey Bruchhaus
- Department of Guest Relations and Patient Experience, Houston Methodist The Woodlands, The Woodlands, Texas, USA
| | - Kristen McLaury
- Department of Guest Relations and Patient Experience, Houston Methodist The Woodlands, The Woodlands, Texas, USA
| | - Katherine Sauceda
- Department of Guest Relations and Patient Experience, Houston Methodist Sugar Land Hospital, Sugar Land, Texas, USA
| | - Karen Carr
- Department of Guest Relations and Patient Experience, Houston Methodist Sugar Land Hospital, Sugar Land, Texas, USA
| | - Claudia Garcia
- Department of Guest Relations and Patient Experience, Houston Methodist Baytown, Houston, Texas, USA
| | | | - Terrell Williams
- System Patient Experience, Houston Methodist, Houston, Texas, USA
| | - Gail Vozzella
- Department of Nursing, Houston Methodist, Houston, Texas, USA
| | - Tariq Nisar
- Center for Health Data Science & Analytics, Houston Methodist, Houston, Texas, USA
| | - Roberta L Schwartz
- Houston Methodist Academic Institute, Houston Methodist, Houston, Texas, USA
| | - Farzan Sasangohar
- Industrial and Systems Engineering, Texas A&M University System, College Station, Texas, USA
| |
Collapse
|
5
|
Yadav KN, Hemmons J, Snider CK, Patel A, Childs M, Delgado MK. Association between patient-reported onset-to-door time and mortality in patients hospitalized with COVID-19 disease. Am J Emerg Med 2024; 77:169-176. [PMID: 38157591 DOI: 10.1016/j.ajem.2023.11.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 11/17/2023] [Accepted: 11/23/2023] [Indexed: 01/03/2024] Open
Abstract
INTRODUCTION Timely hospital presentation and treatment are critical for recovery from coronavirus disease (COVID-19). However, the relationship between symptom onset-to-door time and key clinical outcomes, such as inpatient mortality, has been poorly understood due to the difficulty of retrospectively measuring symptom onset in observational data. This study examines the association between patient-reported symptom onset-to-door time (ODT) and mortality among patients hospitalized and treated for COVID-19 disease. METHODS We conducted a retrospective cohort study of emergency department (ED) encounters of patients with COVID-19 disease who were hospitalized and received remdesivir and/or dexamethasone between March 1, 2020, and March 1, 2022. The exposure was patient-reported ODT in days. The outcome of interest was inpatient mortality, including referral to hospice care. We used multivariable logistic regression to examine the association between ODT and mortality while adjusting for patient characteristics, hospital sites, and seasonality. We tested whether severe illness on hospital presentation modified the association between ODT and mortality. Severe illness was defined by Emergency Severity Index triage level 1 or 2 and hypoxia (SpO2 < 94%). RESULTS Of the 3451 ED hospitalizations included, 439 (12.7%) resulted in mortality, and 1693 (49.1%) involved patients with severe illness on hospital presentation. Greater ODT was significantly associated with lower odds of inpatient mortality (adjusted odds ratio (AOR) = 0.96, 95% CI = 0.93-1.00, P = 0.023). There was a statistically significant interaction between ODT and severe illness at hospital arrival on mortality, suggesting the negative association between ODT and mortality specifically pertained to patients who were not severely ill upon ED presentation (AOR = 0.93, 95% CI = 0.87-1.00, P = 0.035). The adjusted probability of mortality was significantly lower for non-severely ill, hospitalized patients who presented on days 8-14 (5.2%-3.3%) versus days 0-3 (9.4%-7.5%) after symptom onset. CONCLUSION More days between symptom onset and hospital arrival were associated with lower mortality among hospitalized patients treated for COVID-19 disease, particularly if they did not have severe illness at ED presentation. However, onset-to-door time was not associated with mortality among hospitalized patients with severe illness at ED presentation. Collectively, these results suggest that non-severely ill COVID-19 patients who require hospitalization are less likely to decompensate with each passing day without severe illness. These findings may continue to guide clinical care delivery for hospitalized COVID-19 patients.
Collapse
Affiliation(s)
- Kuldeep N Yadav
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America.
| | - Jessica Hemmons
- Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America.
| | - Christopher K Snider
- Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America.
| | - Arjun Patel
- Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America.
| | - Maya Childs
- Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America.
| | - M Kit Delgado
- Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America.
| |
Collapse
|
6
|
Myers LC, Lawson BL, Escobar GJ, Daly KA, Chen YFI, Dlott R, Lee C, Liu V. Evaluation of an outreach programme for patients with COVID-19 in an integrated healthcare delivery system: a retrospective cohort study. BMJ Open 2024; 14:e073622. [PMID: 38191255 PMCID: PMC10806839 DOI: 10.1136/bmjopen-2023-073622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 11/30/2023] [Indexed: 01/10/2024] Open
Abstract
OBJECTIVES In the first year of the COVID-19 pandemic, health systems implemented programmes to manage outpatients with COVID-19. The goal was to expedite patients' referral to acute care and prevent overcrowding of medical centres. We sought to evaluate the impact of such a programme, the COVID-19 Home Care Team (CHCT) programme. DESIGN Retrospective cohort. SETTING Kaiser Permanente Northern California. PARTICIPANTS Adult members before COVID-19 vaccine availability (1 February 2020-31 January 2021) with positive SARS-CoV-2 tests. INTERVENTION Virtual programme to track and treat patients with 'CHCT programme'. OUTCOMES The outcomes were (1) COVID-19-related emergency department visit, (2) COVID-19-related hospitalisation and (3) inpatient mortality or 30-day hospice referral. MEASURES We estimated the average effect comparing patients who were and were not treated by CHCT. We estimated propensity scores using an ensemble super learner (random forest, XGBoost, generalised additive model and multivariate adaptive regression splines) and augmented inverse probability weighting. RESULTS There were 98 585 patients with COVID-19. The majority were followed by CHCT (n=80 067, 81.2%). Patients followed by CHCT were older (mean age 43.9 vs 41.6 years, p<0.001) and more comorbid with COmorbidity Point Score, V.2, score ≥65 (1.7% vs 1.1%, p<0.001). Unadjusted analyses showed more COVID-19-related emergency department visits (9.5% vs 8.5%, p<0.001) and hospitalisations (3.9% vs 3.2%, p<0.001) in patients followed by CHCT but lower inpatient death or 30-day hospice referral (0.3% vs 0.5%, p<0.001). After weighting, there were higher rates of COVID-19-related emergency department visits (estimated intervention effect -0.8%, 95% CI -1.4% to -0.3%) and hospitalisation (-0.5%, 95% CI -0.9% to -0.1%) but lower inpatient mortality or 30-day hospice referral (-0.5%, 95% CI -0.7% to -0.3%) in patients followed by CHCT. CONCLUSIONS Despite CHCT following older patients with higher comorbidity burden, there appeared to be a protective effect. Patients followed by CHCT were more likely to present to acute care and less likely to die inpatient.
Collapse
Affiliation(s)
- Laura C Myers
- Division of Research, Kaiser Permanente, Oakland, California, USA
- The Permanente Medical Group Inc, Oakland, California, USA
| | - Brian L Lawson
- Division of Research, Kaiser Permanente, Oakland, California, USA
| | - Gabriel J Escobar
- Division of Research, Kaiser Permanente, Oakland, California, USA
- The Permanente Medical Group Inc, Oakland, California, USA
| | - Kathleen A Daly
- Division of Research, Kaiser Permanente, Oakland, California, USA
| | | | - Richard Dlott
- The Permanente Medical Group Inc, Oakland, California, USA
| | - Catherine Lee
- Division of Research, Kaiser Permanente, Oakland, California, USA
| | - Vincent Liu
- Division of Research, Kaiser Permanente, Oakland, California, USA
- The Permanente Medical Group Inc, Oakland, California, USA
| |
Collapse
|
7
|
Sarsembayev B, Madyarov V, Kuzikeev M, Kurakbayev E, Utegaliev T. Coronavirus infection and systemic inflammatory reaction syndrome. POLSKI MERKURIUSZ LEKARSKI : ORGAN POLSKIEGO TOWARZYSTWA LEKARSKIEGO 2024; 52:60-66. [PMID: 38518235 DOI: 10.36740/merkur202401110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
Abstract
OBJECTIVE Aim: The purpose of this study was a clinical approbation of the Kometad drug (international non-proprietary name sodium colistimethate), an antibiotic from the polymyxin group in patients with severe course of confirmed сoronavirus infection in the intensive care unit of the Branch of the I. Zhekenova Municipal Clinical Infectious Diseases Hospital.. PATIENTS AND METHODS Materials and Methods: The methodology is based on both theoretical and empirical methods of scientific cognition. During the study, the features of the Coronavirus infection and the inflammatory reaction syndrome were considered, which became quite a big problem during the pandemic. RESULTS Results: The main indications for the tested drug and the consequences of its use for one age group were also determined. CONCLUSION Conclusions: The conclusion was made about the positive dynamics of the patients' health status, and recommendations were given for further research in this area. The practical significance of this study lies in the first clinical approbation of the Kometad drug, which can be used in medicine to reduce the severity of the systemic inflammatory reaction syndrome and improve the patient's health as a result of the disease of Coronavirus infection, after further clinical trials of the drug with different age groups of patients.
Collapse
Affiliation(s)
| | | | - Marat Kuzikeev
- KAZAKH RUSSIAN MEDICAL UNIVERSITY, ALMATY, REPUBLIC OF KAZAKHSTAN
| | - Edil Kurakbayev
- KAZAKHSTAN MEDICAL UNIVERSITY "HIGHER SCHOOL OF PUBLIC HEALTH", ALMATY, REPUBLIC OF KAZAKHSTAN
| | - Timur Utegaliev
- MANGYSTAU REGIONAL MULTIDISCIPLINARY HOSPITAL, AKTAU, REPUBLIC OF KAZAKHSTAN
| |
Collapse
|
8
|
Sener T, Haenen W, Smits P, Hans GH. Large-scale real-life implementation of technology-enabled care to maximize hospitals' medical surge preparedness during future infectious disease outbreaks and winter seasons: a viewpoint. Front Public Health 2023; 11:1149247. [PMID: 37621607 PMCID: PMC10446840 DOI: 10.3389/fpubh.2023.1149247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 07/24/2023] [Indexed: 08/26/2023] Open
Abstract
Hospitals can be overburdened with large numbers of patients with severe infectious conditions during infectious disease outbreaks. Such outbreaks or epidemics put tremendous pressure on the admission capacity of care facilities in the concerned region, negatively affecting the elective program within these facilities. Such situations have been observed during the recent waves of the coronavirus disease pandemic. Owing to the imminent threat of a "tripledemic" by new variants of the coronavirus disease (such as the new Omicron XBB.1.16 strain), influenza, and respiratory syncytial virus during future winter seasons, healthcare agencies should take decisive steps to safeguard hospitals' surge capacity while continuing to provide optimal and safe care to a potentially large number of patients in their trusted home environment. Preparedness of health systems for infectious diseases will require dynamic interaction between a continuous assessment of region-wide available hospital capacity and programs for intensive home treatment of patients who can spread the disease. In this viewpoint, we describe an innovative, dynamic coupling system between hospital surge capacity and cascading activation of a nationwide system for remote patient monitoring. This approach was developed using the multi-criteria decision analysis methodology, considering previously published real-life experiences on remote patient monitoring.
Collapse
Affiliation(s)
- Talia Sener
- Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Winne Haenen
- Federal Public Service for Health, Food Chain Safety and Environment, Brussels, Belgium
| | - Patrick Smits
- Cell Crisis Preparedness, Agentschap Zorg en Gezondheid, Brussels, Belgium
| | - Guy H. Hans
- Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
- Chief Medical Officer, Antwerp University Hospital (UZA), Edegem, Belgium
| |
Collapse
|
9
|
Majoor K, Vorselaars AD. Home monitoring of coronavirus disease 2019 patients in different phases of disease. Curr Opin Pulm Med 2023; 29:293-301. [PMID: 37158218 PMCID: PMC10241420 DOI: 10.1097/mcp.0000000000000964] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
PURPOSE OF REVIEW Various home monitoring programs have emerged through the COVID-19 pandemic in different phases of COVID-19 disease. RECENT FINDINGS The prehospital monitoring of COVID-19-positive patients detects early deterioration. Hospital care at home provides early discharge with oxygen to empty hospital beds for other patients. Home monitoring during recovery can be used for rehabilitation and detection of potential relapses. General goals of home monitoring in COVID-19 are early detection of deterioration and prompt escalation of care such as emergency department presentation, medical advice, medication prescription and mental support. Due to the innovations of vaccination and treatment changes, such as dexamethasone and tocilizumab, the challenge for the healthcare system has shifted from large numbers of admitted COVID-19 patients to lower numbers of admitted patients with specific risk profiles (such as immunocompromised). This also changes the field of home monitoring in COVID-19. Efficacy and cost-effectiveness of home monitoring interventions depend on the costs of the intervention (use of devices, apps and medical staff) and the proposed patient group (depending on risk factors and disease severity). SUMMARY Patient satisfaction of COVID-19 home monitoring programs was mostly high. Home monitoring programs for COVID-19 should be ready to be re-escalated in case of a new global pandemic.
Collapse
Affiliation(s)
| | - Adriane D.M. Vorselaars
- Division of Heart and Lungs, University Medical Center Utrecht
- Interstitial Lung Diseases Center of Excellence, Department of Pulmonology, St Antonius Hospital, Nieuwegein, The Netherlands
| |
Collapse
|
10
|
Chaiyachati KH, Shea JA, Ward M, Nelson MN, Ghosh M, Reilly J, Kelly S, Chisholm DL, Barbati Z, Hemmons JE, Abdel-Rahman D, Ebert JP, Xiong RA, Snider CK, Lee KC, Friedman AB, Meisel ZF, Kilaru AS, Asch DA, Delgado MK, Morgan AU. Patient and clinician perspectives of a remote monitoring program for COVID-19 and lessons for future programs. BMC Health Serv Res 2023; 23:698. [PMID: 37370059 PMCID: PMC10304230 DOI: 10.1186/s12913-023-09684-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 06/11/2023] [Indexed: 06/29/2023] Open
Abstract
COVID Watch is a remote patient monitoring program implemented during the pandemic to support home dwelling patients with COVID-19. The program conferred a large survival advantage. We conducted semi-structured interviews of 85 patients and clinicians using COVID Watch to understand how to design such programs even better. Patients and clinicians found COVID Watch to be comforting and beneficial, but both groups desired more clarity about the purpose and timing of enrollment and alternatives to text-messages to adapt to patients' preferences as these may have limited engagement and enrollment among marginalized patient populations. Because inclusiveness and equity are important elements of programmatic success, future programs will need flexible and multi-channel human-to-human communication pathways for complex clinical interactions or for patients who do not desire tech-first approaches.
Collapse
Affiliation(s)
- Krisda H Chaiyachati
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA.
- Center for Health Care Innovation, University of Pennsylvania Health System, Philadelphia, PA, USA.
| | - Judy A Shea
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Michaela Ward
- Mixed Methods Research Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Maria N Nelson
- Mixed Methods Research Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Medha Ghosh
- Center for Emergency Care Policy and Research, Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Julianne Reilly
- Center for Emergency Care Policy and Research, Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sheila Kelly
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Deena L Chisholm
- Center for Emergency Care Policy and Research, Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Zoe Barbati
- Mixed Methods Research Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jessica E Hemmons
- Center for Emergency Care Policy and Research, Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dina Abdel-Rahman
- Center for Emergency Care Policy and Research, Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jeffrey P Ebert
- Center for Emergency Care Policy and Research, Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruiying A Xiong
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Emergency Care Policy and Research, Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- The Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher K Snider
- Center for Health Care Innovation, University of Pennsylvania Health System, Philadelphia, PA, USA
| | - Kathleen C Lee
- Comcast NBCUniversal in Philadelphia, PA, Philadelphia, USA
| | - Ari B Friedman
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA
- The Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Zachary F Meisel
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Emergency Care Policy and Research, Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Injury Science Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Austin S Kilaru
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Emergency Care Policy and Research, Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Asch
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Health Care Innovation, University of Pennsylvania Health System, Philadelphia, PA, USA
- The Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - M Kit Delgado
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Health Care Innovation, University of Pennsylvania Health System, Philadelphia, PA, USA
- Center for Emergency Care Policy and Research, Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- The Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Anna U Morgan
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Health Care Innovation, University of Pennsylvania Health System, Philadelphia, PA, USA
| |
Collapse
|
11
|
Jaulmes L, Yordanov Y, Descamps A, Durand-Zaleski I, Dinh A, Jourdain P, Dechartres A. Effectiveness and Medicoeconomic Evaluation of Home Monitoring of Patients With Mild COVID-19: Covidom Cohort Study. J Med Internet Res 2023; 25:e43980. [PMID: 37134021 PMCID: PMC10337320 DOI: 10.2196/43980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 03/03/2023] [Accepted: 03/20/2023] [Indexed: 03/22/2023] Open
Abstract
BACKGROUND Covidom was a telemonitoring solution for home monitoring of patients with mild to moderate COVID-19, deployed in March 2020 in the Greater Paris area in France to alleviate the burden on the health care system. The Covidom solution included a free mobile application with daily monitoring questionnaires and a regional control center to quickly handle patient alerts, including dispatching emergency medical services when necessary. OBJECTIVE This study aimed to provide an overall evaluation of the Covidom solution 18 months after its inception in terms of effectiveness, safety, and cost. METHODS Our primary outcome was to measure effectiveness using the number of handled alerts, response escalation, and patient-reported medical contacts outside of Covidom. Then, we analyzed the safety of Covidom by assessing its ability to detect clinical worsening, defined as hospitalization or death, and the number of patients with clinical worsening without any preceding alert. We evaluated the cost of Covidom and compared the cost of hospitalization for Covidom and non-Covidom patients with mild COVID-19 cases seen in the emergency departments of the largest network of hospitals in the Greater Paris area (Assistance Publique-Hôpitaux de Paris). Finally, we reported on user satisfaction. RESULTS Of the 60,073 patients monitored by Covidom, the regional control center handled 285,496 alerts and dispatched emergency medical services 518 times. Of the 13,204 respondents who responded to either of the follow-up questionnaires, 65.8% (n=8690) reported having sought medical care outside the Covidom solution during their monitoring period. Of the 947 patients who experienced clinical worsening while adhering to daily monitoring, only 35 (3.7%) did not previously trigger alerts (35 were hospitalized, including 1 who died). The average cost of Covidom was €54 (US $1=€0.8614) per patient, and the cost of hospitalization for COVID-19 worsening was significantly lower in Covidom than in non-Covidom patients with mild COVID-19 cases seen in the emergency departments of Assistance Publique-Hôpitaux de Paris. The patients who responded to the satisfaction questionnaire had a median rating of 9 (out of 10) for the likelihood of recommending Covidom. CONCLUSIONS Covidom may have contributed to alleviating the pressure on the health care system in the initial months of the pandemic, although its impact was lower than anticipated, with a substantial number of patients having consulted outside of Covidom. Covidom seems to be safe for home monitoring of patients with mild to moderate COVID-19.
Collapse
Affiliation(s)
- Luc Jaulmes
- Centre de pharmaco-épidémiologie de l'APHP, Dépt. de Santé Publique, Hôpital Pitié Salpêtrière, Sorbonne Université, AP-HP, Paris, France
| | - Youri Yordanov
- Sorbonne Université, AP-HP, Hôpital Saint Antoine, Service d'Accueil des Urgences, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, UMR-S 1136, Paris, France
| | - Alexandre Descamps
- CIC Cochin Pasteur, INSERM CIC 1417, Université Paris Cité, AP-HP, Paris, France
| | - Isabelle Durand-Zaleski
- Institut Pierre Louis d'Epidémiologie et de Santé Publique, INSERM, Université Paris Est, AP-HP, Paris, France
- URC Eco, Hôpital de l'Hôtel Dieu, DRCI, AP-HP, Paris, France
| | - Aurélien Dinh
- Infectious Disease department, University Hospital R. Poincaré, UVSQ, AP-HP, Garches, France
| | - Patrick Jourdain
- INSERM U999, CHU Bicêtre AP-HP, Université Paris-Saclay, AP-HP, Gif-sur-Yvette, France
| | - Agnès Dechartres
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, AP-HP. Sorbonne Université, Hôpital Pitié Salpêtrière, Département de Santé Publique, centre de pharmaco-épidémiologie de l'APHP, F75013, Paris, France
| |
Collapse
|
12
|
Haddad TC, Coffey JD, Deng Y, Glasgow AE, Christopherson LA, Sangaralingham LR, Bell SJ, Shah VP, Pritchett JC, Orenstein R, Speicher LL, Maniaci MJ, Ganesh R, Borah BJ. Impact of a High-Risk, Ambulatory COVID-19 Remote Patient Monitoring Program on Utilization, Cost, and Mortality. Mayo Clin Proc 2022; 97:2215-2225. [PMID: 36464463 PMCID: PMC9444887 DOI: 10.1016/j.mayocp.2022.08.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 08/30/2022] [Indexed: 12/15/2022]
Abstract
OBJECTIVE To evaluate care utilization, cost, and mortality among high-risk patients enrolled in a coronavirus disease 2019 (COVID-19) remote patient monitoring (RPM) program. METHODS This retrospective analysis included patients diagnosed with COVID-19 at risk for severe disease who enrolled in the RPM program between March 2020 and October 2021. The program included in-home technology for symptom and physiologic data monitoring with centralized care management. Propensity score matching established matched cohorts of RPM-engaged (defined as ≥1 RPM technology interactions) and non-engaged patients using a logistic regression model of 59 baseline characteristics. Billing codes and the electronic death certificate system were used for data abstraction from the electronic health record and reporting of care utilization and mortality endpoints. RESULTS Among 5796 RPM-enrolled patients, 80.0% engaged with the technology. Following matching, 1128 pairs of RPM-engaged and non-engaged patients comprised the analysis cohorts. Mean patient age was 63.3 years, 50.9% of patients were female, and 81.9% were non-Hispanic White. Patients who were RPM-engaged experienced significantly lower rates of 30-day, all-cause hospitalization (13.7% vs 18.0%, P=.01), prolonged hospitalization (3.5% vs 6.7%, P=.001), intensive care unit admission (2.3% vs 4.2%, P=.01), and mortality (0.5% vs 1.7%; odds ratio, 0.31; 95% CI, 0.12 to 0.78; P=.01), as well as cost of care ($2306.33 USD vs $3565.97 USD, P=0.04), than those enrolled in RPM but non-engaged. CONCLUSION High-risk COVID-19 patients enrolled and engaged in an RPM program experienced lower rates of hospitalization, intensive care unit admission, mortality, and cost than those enrolled and non-engaged. These findings translate to improved hospital bed access and patient outcomes.
Collapse
Affiliation(s)
- Tufia C Haddad
- Center for Digital Health, Mayo Clinic, Rochester, MN, USA; Department of Oncology, Mayo Clinic, Rochester, MN, USA.
| | | | - Yihong Deng
- Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Amy E Glasgow
- Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | | | | | - Sarah J Bell
- Center for Digital Health, Mayo Clinic, Rochester, MN, USA; Department of Nursing, Mayo Clinic, Rochester, MN, USA
| | - Vishal P Shah
- Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA; Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Joshua C Pritchett
- Department of Oncology, Mayo Clinic, Rochester, MN, USA; Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | | | | | - Michael J Maniaci
- Center for Digital Health, Mayo Clinic, Rochester, MN, USA; Department of Medicine, Mayo Clinic, Jacksonville, FL, USA
| | | | - Bijan J Borah
- Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA; Division of Health Care Delivery Science, Mayo Clinic, Rochester, MN, USA
| |
Collapse
|
13
|
Chaiyachati K, Shea J, Ward M, Nelson M, Ghosh M, Reilly J, Kelly S, Chisholm D, Barbati Z, Hemmons J, Abdel-Rahman D, Ebert J, Xiong R, Snider C, Lee K, Friedman A, Meisel Z, Kilaru A, Asch D, Delgado MK, Morgan A. Patient and clinician perspectives of a remote monitoring program for COVID-19 and lessons for future programs. RESEARCH SQUARE 2022:rs.3.rs-2234197. [PMID: 36451877 PMCID: PMC9709795 DOI: 10.21203/rs.3.rs-2234197/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
COVID Watch is a remote patient monitoring program implemented during the pandemic to support home dwelling patients with COVID-19. The program conferred a large survival advantage. We conducted semi-structured interviews of 85 patients and clinicians using COVID Watch to understand how to design such programs even better. Patients and clinicians found COVID Watch to be comforting and beneficial, but both groups desired more clarity about the purpose and timing of enrollment and alternatives to text-messages to adapt to patients’ preferences as these may have limited engagement and enrollment among marginalized patient populations. Because inclusiveness and equity are important elements of programmatic success, future programs will need flexible and multi-channel human-to-human communication pathways for complex clinical interactions or patients who do not desire tech-first approaches.
Collapse
|
14
|
Chen AT, Muralidharan M, Friedman AB. Algorithms Identifying Low Acuity Emergency Department Visits: A Review and Validation Study. Health Serv Res 2022; 57:979-989. [PMID: 35619335 PMCID: PMC9264468 DOI: 10.1111/1475-6773.14011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
OBJECTIVE To characterize and validate the landscape of algorithms that use International Classification of Disease (ICD) codes to identify low acuity emergency department (ED) visits. DATA SOURCES Publicly available ED data from the National Hospital Ambulatory Medical Care Survey (NHAMCS). STUDY DESIGN We systematically searched for studies that specify algorithms consisting of ICD codes that identify preventable or low acuity ED visits. We classified ED visits in NHAMCS according to these algorithms and compared agreement using the Jaccard index. We then evaluated the performance of each algorithm using positive predictive value (PPV) and sensitivity, with the reference group specified using low acuity composite (LAC) criteria consisting of both triage and clinical components. In sensitivity analyses, we repeated our primary analysis using only triage or only clinical criteria for reference. DATA COLLECTION We used 2011-2017 NHAMCS data, totaling 163,576 observations before survey weighting and after dropping observations missing a primary diagnosis. We translated ICD-9 codes (years 2011-2015) to ICD-10 using a standard crosswalk. PRINCIPAL FINDINGS We identified 15 papers with an original list of ICD codes used to identify preventable or low acuity ED presentations. These papers were published between 1992 and 2020, cited an average of 310 (SD 360) times, and included 968 (SD 1175) codes. Pairwise Jaccard similarity indices (0 = no overlap, 1 = perfect congruence) ranged from 0.01 to 0.82, with mean 0.20 (SD 0.13). When validated against the LAC reference group, the algorithms had an average PPV of 0.308 (95% CI [0.253, 0.364]) and sensitivity of 0.183 (95% CI [0.111, 0.256]). Overall, 2.1% of visits identified as low acuity by the algorithms died prehospital or in the ED, or needed surgery, critical care, or cardiac catheterization. CONCLUSIONS Existing algorithms that identify low acuity ED visits lack congruence and are imperfect predictors of visit acuity.
Collapse
Affiliation(s)
- Angela T Chen
- Health Care Management Department, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Madhavi Muralidharan
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Ari B Friedman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| |
Collapse
|
15
|
Lee KC, Morgan AU, Chaiyachati KH, Asch DA, Xiong RA, Do D, Kilaru AS, Lam D, Parambath A, Friedman AB, Meisel ZF, Snider CK, Chisholm DL, Kelly S, Hemmons JE, Abdel-Rahman D, Ebert J, Ghosh M, Reilly J, O'Malley CJ, Hahn L, Mannion NM, Huffenberger AM, McGinley S, Balachandran M, Khan N, Shea JA, Mitra N, Delgado MK. Pulse Oximetry for Monitoring Patients with Covid-19 at Home - A Pragmatic, Randomized Trial. N Engl J Med 2022; 386:1857-1859. [PMID: 35385625 PMCID: PMC9006781 DOI: 10.1056/nejmc2201541] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Kathleen C Lee
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Anna U Morgan
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | | | - David A Asch
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Ruiying A Xiong
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - David Do
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Austin S Kilaru
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Doreen Lam
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Andrew Parambath
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Ari B Friedman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Zachary F Meisel
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | | | - Deena L Chisholm
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Sheila Kelly
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Jessica E Hemmons
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Dina Abdel-Rahman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Jeffrey Ebert
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Medha Ghosh
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Julianne Reilly
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | | | - Lauren Hahn
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Nancy M Mannion
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Ann M Huffenberger
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Susan McGinley
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Mohan Balachandran
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Neda Khan
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Judy A Shea
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Nandita Mitra
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - M Kit Delgado
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| |
Collapse
|
16
|
Emery A, Houchens N, Gupta A. Quality and Safety in the Literature: May 2022. BMJ Qual Saf 2022; 31:409-414. [PMID: 35440499 DOI: 10.1136/bmjqs-2022-014848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 02/16/2022] [Indexed: 12/15/2022]
Affiliation(s)
- Albert Emery
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Nathan Houchens
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA.,Medicine Service, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan, USA
| | - Ashwin Gupta
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA.,Medicine Service, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan, USA
| |
Collapse
|