Integrated Genomic and Social Network Analyses of SARS-CoV-2 Transmission in the Healthcare Setting.

BACKGROUND: Infection prevention (IP) measures are designed to mitigate the transmission of pathogens in healthcare. Using large-scale viral genomic and social network analyses, we determined if IP measures used during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic were adequate in protecting healthcare workers (HCWs) and patients from acquiring SARS-CoV-2. METHODS: We performed retrospective cross-sectional analyses of viral genomics from all available SARS-CoV-2 viral samples collected at UC San Diego Health and social network analysis using the electronic medical record to derive temporospatial overlap of infections among related viromes and supplemented with contact tracing data. The outcome measure was any instance of healthcare transmission, defined as cases with closely related viral genomes and epidemiological connection within the healthcare setting during the infection window. Between November 2020 through January 2022, 12 933 viral genomes were obtained from 35 666 patients and HCWs. RESULTS: Among 5112 SARS-CoV-2 viral samples sequenced from the second and third waves of SARS-CoV-2 (pre-Omicron), 291 pairs were derived from persons with a plausible healthcare overlap. Of these, 34 pairs (12%) were phylogenetically linked: 19 attributable to household and 14 to healthcare transmission. During the Omicron wave, 2106 contact pairs among 7821 sequences resulted in 120 (6%) related pairs among 32 clusters, of which 10 were consistent with healthcare transmission. Transmission was more likely to occur in shared spaces in the older hospital compared with the newer hospital (2.54 vs 0.63 transmission events per 1000 admissions, P < .001). CONCLUSIONS: IP strategies were effective at identifying and preventing healthcare SARS-CoV-2 transmission.

Longitudinal assessment of the impact of COVID-19 infection on mask-wearing behaviors.

BACKGROUND: Wearing a mask was a crucial component in slowing the COVID-19 pandemic. However, little is known about the intersectionality between mask usage, risk perception, and infection. The purpose of this study was to investigate whether risk perceptions and masking behaviors are associated with contracting SARS-CoV-2 and how contracting SARS-CoV-2 subsequently changes masking behaviors in specific situations. METHODS: This cohort study utilized survey data from the UC San Diego ZAP COVID-19 study (n = 1,230) to evaluate the risk of contracting SARS-CoV-2 in relation to baseline risk perceptions and masking behaviors in various situations and how contracting SARS-CoV-2 affects subsequent masking behavior. RESULTS: We found that more consistent self-reported mask use in indoor public spaces (p = 0.03) and in other people's houses (p = 0.002) was associated with remaining free of SARS-CoV-2 infection. We also found that contracting SARS-CoV-2 was associated with a subsequent increase in mask use in other people's houses (p = 0.01). CONCLUSIONS: Our findings suggest that consistent mask use is correlated with decreased infection and that contracting SARS-CoV-2 may modify mask use behaviors in high-risk situations. These findings may help inform future public health messaging for infectious disease prevention. TRIAL REGISTRATION: This study has not been previously registered as it is an observational study. There was no pre-registration of the analytic plan for the present study.

Optimizing the Implementation of Clinical Predictive Models to Minimize National Costs: Sepsis Case Study.

BACKGROUND: Sepsis costs and incidence vary dramatically across diagnostic categories, warranting a customized approach for implementing predictive models. OBJECTIVE: The aim of this study was to optimize the parameters of a sepsis prediction model within distinct patient groups to minimize the excess cost of sepsis care and analyze the potential effect of factors contributing to end-user response to sepsis alerts on overall model utility. METHODS: We calculated the excess costs of sepsis to the Centers for Medicare and Medicaid Services (CMS) by comparing patients with and without a secondary sepsis diagnosis but with the same primary diagnosis and baseline comorbidities. We optimized the parameters of a sepsis prediction algorithm across different diagnostic categories to minimize these excess costs. At the optima, we evaluated diagnostic odds ratios and analyzed the impact of compliance factors such as noncompliance, treatment efficacy, and tolerance for false alarms on the net benefit of triggering sepsis alerts. RESULTS: Compliance factors significantly contributed to the net benefit of triggering a sepsis alert. However, a customized deployment policy can achieve a significantly higher diagnostic odds ratio and reduced costs of sepsis care. Implementing our optimization routine with powerful predictive models could result in US $4.6 billion in excess cost savings for CMS. CONCLUSIONS: We designed a framework for customizing sepsis alert protocols within different diagnostic categories to minimize excess costs and analyzed model performance as a function of false alarm tolerance and compliance with model recommendations. We provide a framework that CMS policymakers could use to recommend minimum adherence rates to the early recognition and appropriate care of sepsis that is sensitive to hospital department-level incidence rates and national excess costs. Customizing the implementation of clinical predictive models by accounting for various behavioral and economic factors may improve the practical benefit of predictive models.

Telehealth in the COVID-19 Era: A Balancing Act to Avoid Harm.

The telehealth revolution in response to COVID-19 has increased essential health care access during an unprecedented public health crisis. However, virtual patient care can also limit the patient-provider relationship, quality of examination, efficiency of health care delivery, and overall quality of care. As we witness the most rapidly adopted medical trend in modern history, clinicians are beginning to comprehend the many possibilities of telehealth, but its limitations also need to be understood. As outcomes are studied and federal regulations reconsidered, it is important to be precise in the virtual patient encounter approach. Herein, we offer some simple guidelines that could assist health care providers and clinic schedulers in determining the appropriateness of a telehealth visit by considering visit types, patient characteristics, and chief complaint or disease states.

The Impact of Inpatient Telemedicine on Personal Protective Equipment Savings During the COVID-19 Pandemic: Cross-sectional Study.

With the emergence of the COVID-19 pandemic and shortage of adequate personal protective equipment (PPE), hospitals implemented inpatient telemedicine measures to ensure operational readiness and a safe working environment for clinicians. The utility and sustainability of inpatient telemedicine initiatives need to be evaluated as the number of COVID-19 inpatients is expected to continue declining. In this viewpoint, we describe the use of a rapidly deployed inpatient telemedicine workflow at a large academic medical center and discuss the potential impact on PPE savings. In early 2020, videoconferencing software was installed on patient bedside iPads at two academic medical center teaching hospitals. An internal website allowed providers to initiate video calls with patients in any patient room with an activated iPad, including both COVID-19 and non-COVID-19 patients. Patients were encouraged to use telemedicine technology to connect with loved ones via native apps or videoconferencing software. We evaluated the use of telemedicine technology on patients' bedside iPads by monitoring traffic to the internal website. Between May 2020 and March 2021, there were a total of 1240 active users of the Video Visits website (mean 112.7, SD 49.0 connection events per month). Of these, 133 (10.7%) connections were made. Patients initiated 63 (47.4%) video calls with family or friends and sent 37 (27.8%) emails with videoconference connection instructions. Providers initiated a total of 33 (24.8%) video calls with the majority of calls initiated in August (n=22, 67%). There was a low level of adoption of inpatient telemedicine capability by providers and patients. With sufficient availability of PPE, inpatient providers did not find a frequent need to use the bedside telemedicine technology, despite a high census of patients with COVID-19. Compared to providers, patients used videoconferencing capabilities more frequently in September and October 2020. We did not find savings of PPE associated with the use of inpatient telemedicine.

Crowdsourcing Diagnosis for Patients With Undiagnosed Illnesses: An Evaluation of CrowdMed.

BACKGROUND: Despite visits to multiple physicians, many patients remain undiagnosed. A new online program, CrowdMed, aims to leverage the "wisdom of the crowd" by giving patients an opportunity to submit their cases and interact with case solvers to obtain diagnostic possibilities. OBJECTIVE: To describe CrowdMed and provide an independent assessment of its impact. METHODS: Patients submit their cases online to CrowdMed and case solvers sign up to help diagnose patients. Case solvers attempt to solve patients' diagnostic dilemmas and often have an interactive online discussion with patients, including an exchange of additional diagnostic details. At the end, patients receive detailed reports containing diagnostic suggestions to discuss with their physicians and fill out surveys about their outcomes. We independently analyzed data collected from cases between May 2013 and April 2015 to determine patient and case solver characteristics and case outcomes. RESULTS: During the study period, 397 cases were completed. These patients previously visited a median of 5 physicians, incurred a median of US $10,000 in medical expenses, spent a median of 50 hours researching their illnesses online, and had symptoms for a median of 2.6 years. During this period, 357 active case solvers participated, of which 37.9% (132/348) were male and 58.3% (208/357) worked or studied in the medical industry. About half (50.9%, 202/397) of patients were likely to recommend CrowdMed to a friend, 59.6% (233/391) reported that the process gave insights that led them closer to the correct diagnoses, 57% (52/92) reported estimated decreases in medical expenses, and 38% (29/77) reported estimated improvement in school or work productivity. CONCLUSIONS: Some patients with undiagnosed illnesses reported receiving helpful guidance from crowdsourcing their diagnoses during their difficult diagnostic journeys. However, further development and use of crowdsourcing methods to facilitate diagnosis requires long-term evaluation as well as validation to account for patients' ultimate correct diagnoses.

Optimizing care of adults with congenital heart disease in a pediatric cardiovascular ICU using electronic clinical decision support*.

OBJECTIVES: The optimal location for postoperative cardiac care of adults with congenital heart disease is controversial. Some congenital heart surgeons operate on these adults in children's hospitals with postoperative care provided by pediatric critical care teams who may be unfamiliar with adult national performance measures. This study tested the hypothesis that Clinical Decision Support tools integrated into the clinical workflow would facilitate improved compliance with The Joint Commission Surgical Care Improvement Project performance measures in adults recovering from cardiac surgery in a children's hospital. DESIGN: Retrospective chart review comparing compliance pre- and post-Clinical Decision Support intervention for Surgical Care Improvement Project measures addressed in the critical care unit: appropriate cessation of prophylactic antibiotics; controlled blood glucose; urinary catheter removal; and reinitiation of preoperative ß-blocker when indicated. SETTING: Cardiovascular ICU in a quaternary care freestanding children's hospital. PATIENTS: The cohort included 114 adults 18-70 years old recovering from cardiac surgery in our pediatric cardiovascular ICU. INTERVENTIONS: Clinical Decision Support tools including data-triggered alerts, smart documentation forms, and order sets with conditional logic were integrated into the workflow. MEASUREMENTS AND MAIN RESULTS: Compliance with antibiotic discontinuation was 100% pre- and postintervention. Compliance rates improved for glucose control (p = 0.007) and urinary catheter removal (p = 0.05). Documentation of ß-blocker therapy (nonexistent preintervention) was 100% postintervention. Composite compliance for all measures increased from 53% to 84% (p = 0.002). There were no complications related to institution of the Surgical Care Improvement Project measures. There was no in-hospital mortality. CONCLUSIONS: Compliance with the national adult postoperative performance measures can be excellent in a children's hospital with the help of Clinical Decision Support tools. This represents an important step toward providing high-quality care to a growing population of adults with congenital heart disease who may receive care in a pediatric center.

Generative artificial intelligence responses to patient messages in the electronic health record: early lessons learned.

Background: Electronic health record (EHR)-based patient messages can contribute to burnout. Messages with a negative tone are particularly challenging to address. In this perspective, we describe our initial evaluation of large language model (LLM)-generated responses to negative EHR patient messages and contend that using LLMs to generate initial drafts may be feasible, although refinement will be needed. Methods: A retrospective sample (n = 50) of negative patient messages was extracted from a health system EHR, de-identified, and inputted into an LLM (ChatGPT). Qualitative analyses were conducted to compare LLM responses to actual care team responses. Results: Some LLM-generated draft responses varied from human responses in relational connection, informational content, and recommendations for next steps. Occasionally, the LLM draft responses could have potentially escalated emotionally charged conversations. Conclusion: Further work is needed to optimize the use of LLMs for responding to negative patient messages in the EHR.

Implementation of an electronic health record-integrated instant messaging system in an academic health system.

OBJECTIVES: Effective communication amongst healthcare workers simultaneously promotes optimal patient outcomes when present and is deleterious to outcomes when absent. The advent of electronic health record (EHR)-embedded secure instantaneous messaging systems has provided a new conduit for provider communication. This manuscript describes the experience of one academic medical center with deployment of one such system (Secure Chat). METHODS: Data were collected on Secure Chat message volume from June 2017 to April 2023. Significant perideployment events were reviewed chronologically. RESULTS: After the first coronavirus disease 2019 lockdown in March 2020, messaging use increased by over 25 000 messages per month, with 1.2 million messages sent monthly by April 2023. Comparative features of current communication modalities in healthcare were summarized, highlighting the many advantages of Secure Chat. CONCLUSIONS: While EHR-embedded secure instantaneous messaging systems represent a novel and potentially valuable communication medium in healthcare, generally agreed-upon best practices for their implementation are, as of yet, undetermined.

Integrating clinical research into electronic health record workflows to support a learning health system.

Objective: Integrating clinical research into routine clinical care workflows within electronic health record systems (EHRs) can be challenging, expensive, and labor-intensive. This case study presents a large-scale clinical research project conducted entirely within a commercial EHR during the COVID-19 pandemic. Case Report: The UCSD and UCSDH COVID-19 NeutraliZing Antibody Project (ZAP) aimed to evaluate antibody levels to SARS-CoV-2 virus in a large population at an academic medical center and examine the association between antibody levels and subsequent infection diagnosis. Results: The project rapidly and successfully enrolled and consented over 2000 participants, integrating the research trial with standing COVID-19 testing operations, staff, lab, and mobile applications. EHR-integration increased enrollment, ease of scheduling, survey distribution, and return of research results at a low cost by utilizing existing resources. Conclusion: The case study highlights the potential benefits of EHR-integrated clinical research, expanding their reach across multiple health systems and facilitating rapid learning during a global health crisis.

An Automated System for Physician Trainee Procedure Logging via Electronic Health Records.

Importance: Procedural proficiency is a core competency for graduate medical education; however, procedural reporting often relies on manual workflows that are duplicative and generate data whose validity and accuracy are difficult to assess. Failure to accurately gather these data can impede learner progression, delay procedures, and negatively impact patient safety. Objective: To examine accuracy and procedure logging completeness of a system that extracts procedural data from an electronic health record system and uploads these data securely to an application used by many residency programs for accreditation. Design, Setting, and Participants: This quality improvement study of all emergency medicine resident physicians at University of California, San Diego Health was performed from May 23, 2023, to June 25, 2023. Exposures: Automated system for procedure data extraction and upload to a residency management software application. Main Outcomes and Measures: The number of procedures captured by the automated system when running silently compared with manually logged procedures in the same timeframe, as well as accuracy of the data upload. Results: Forty-seven residents participated in the initial silent assessment of the extraction component of the system. During a 1-year period (May 23, 2022, to May 7, 2023), 4291 procedures were manually logged by residents, compared with 7617 procedures captured by the automated system during the same period, representing a 78% increase. During assessment of the upload component of the system (May 8, 2023, to June 25, 2023), a total of 1353 procedures and patient encounters were evaluated, with the system operating with a sensitivity of 97.4%, specificity of 100%, and overall accuracy of 99.5%. Conclusions and Relevance: In this quality improvement study of emergency medicine resident physicians, an automated system demonstrated that reliance on self-reported procedure logging resulted in significant procedural underreporting compared with the use of data obtained at the point of performance. Additionally, this system afforded a degree of reliability and validity heretofore absent from the usual after-the-fact procedure logging workflows while using a novel application programming interface-based approach. To our knowledge, this system constitutes the first generalizable implementation of an automated solution to a problem that has existed in graduate medical education for decades.