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Background and objectivesTelemedicine bridges the gap between care needs and provider availability. The value of telemedicine can be eclipsed by long wait times, especially if patients are stuck in virtual waiting rooms. UCSD Tele-Untethered allows patients to join visits without waiting in virtual waiting rooms. Tele-Untethered uses a text-to-video link to improve clinic flow, decrease virtual waiting room reliance, improve throughput, and potentially improve satisfaction.MethodsThis institutional review board (IRB)-approved quality improvement pilot (IRB #210364QI) included patients seen in a single vascular neurology clinic, within the pilot period, if they had a smartphone/cell phone, and agreed to participate in a flexible approach to telehealth visits. Standard work was disseminated (patient instructions, scripting, and workflows). Patients provided a cell phone number to receive a text link when the provider was ready to see them. Metrics included demographics, volumes, visit rates, percentage seen early/late, time savings, and satisfaction surveys.ResultsOver 2.5 months, 22 patients were scheduled. Of those arriving, 76% were "Tele-Untethered" and 24% were "Standard Telemedicine." Text-for-video link was used for 94% of Tele-Untethered. Fifty-five percent were seen early. There was a 55-minute-per-session time savings.ConclusionThis UCSD Tele-Untethered pilot benefitted patients by allowing scheduling flexibility while not being tied to a "virtual waiting room." It benefited providers as it allowed them to see patients in order/not tied to exact times, improved throughput, and saved time. Even modest time savings for busy providers, coupled with Lean workflows, can provide critical value. High Tele-Untethered uptake and use of verbal check-in highlight that patients expect flexibility and ease of use. As our initial UCSD Tele-Untethered successes included patient flexibility and time savings for patients and providers, it can serve as a model as enterprises strive for optimal care and improved satisfaction. Expansion to other clinic settings is underway with a mantra of "UCSD Tele-Untethered: Your provider can see you now."

Objectives: Toward common methods for system monitoring and evaluation, we proposed a key performance indicator framework and discussed lessons learned while implementing a statewide exposure notification (EN) system in California during the COVID-19 epidemic. Materials and Methods: California deployed the Google Apple Exposure Notification framework, branded CA Notify, on December 10, 2020, to supplement traditional COVID-19 contact tracing programs. For system evaluation, we defined 6 key performance indicators: adoption, retention, sharing of unique codes, identification of potential contacts, behavior change, and impact. We aggregated and analyzed data from December 10, 2020, to July 1, 2021, in compliance with the CA Notify privacy policy. Results: We estimated CA Notify adoption at nearly 11 million smartphone activations during the study period. Among 1 654 201 CA Notify users who received a positive test result for SARS-CoV-2, 446 634 (27%) shared their unique code, leading to ENs for other CA Notify users who were in close proximity to the SARS-CoV-2–positive individual. We identified at least 122 970 CA Notify users as contacts through this process. Contact identification occurred a median of 4 days after symptom onset or specimen collection date of the user who received a positive test result for SARS-CoV-2. Practice Implications: Smartphone-based EN systems are promising new tools to supplement traditional contact tracing and public health interventions, particularly when efficient scaling is not feasible for other approaches. Methods to collect and interpret appropriate measures of system performance must be refined while maintaining trust and privacy.

Long COVID is a chronic condition characterized by symptoms such as fatigue, dyspnea, and cognitive impairment that persist or relapse months after an acute infection with the SARS-CoV-2 virus. Many distinct symptoms have been attributed to Long COVID; however, little is known about the potential clustering of these symptoms and risk factors that may predispose patients to certain clusters. In this study, an electronic survey was sent to patients in the UC San Diego Health (UCSDH) system who tested positive for COVID-19, querying if patients were experiencing symptoms consistent with Long COVID. Based on survey results, along with patient demographics reported in the electronic health record (EHR), linear and logistic regression models were used to examine putative risk factors, and exploratory factor analysis was performed to determine symptom clusters. Among 999 survey respondents, increased odds of Long COVID (n = 421; 42%) and greater Long COVID symptom burden were associated with female sex (OR = 1.73, 99% CI: 1.16–2.58; β = 0.48, 0.22–0.75), COVID-19 hospitalization (OR = 4.51, 2.50–8.43; β = 0.48, 0.17–0.78), and poorer pre-COVID self-rated health (OR = 0.75, 0.57–0.97; β = −0.19, −0.32–−0.07). Over one-fifth of Long COVID patients screened positive for depression and/or anxiety, the latter of which was associated with younger age (OR = 0.96, 0.94–0.99). Factor analysis of 16 self-reported symptoms suggested five symptom clusters—gastrointestinal (GI), musculoskeletal (MSK), neurocognitive (NC), airway (AW), and cardiopulmonary (CP), with older age (β = 0.21, 0.11–0.30) and mixed race (β = 0.27, 0.04–0.51) being associated with greater MSK symptom burden. Greater NC symptom burden was associated with increased odds of depression (OR = 5.86, 2.71–13.8) and anxiety (OR = 2.83, 1.36–6.14). These results can inform clinicians in identifying patients at increased risk for Long COVID-related medical issues, particularly neurocognitive symptoms and symptom clusters, as well as informing health systems to manage operational expectations on a population-health level.

BACKGROUND Recent advancements in machine learning (ML) and the proliferation of healthcare data have led to widespread excitement about using these technologies to improve care. Predictive analytic models in domains such as sepsis, acute kidney injury, respiratory failure, and general deterioration have been proposed to improve the timely administration of life-saving treatments and mitigate expensive downstream complications. It has been argued that a more tailored approach that accounts for implementation constraints that may differ across care settings can further enhance the adoption of such systems. OBJECTIVE 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 Center 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—like non-compliance, 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 $4.6 billion in excess cost savings for CMS. CONCLUSIONS 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.

ObjectiveTo 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.Materials and MethodsWe calculated the excess costs of sepsis 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—like non-compliance, treatment efficacy, and tolerance for false alarms—on the net benefit of triggering sepsis alerts.ResultsCompliance 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 $4.6 billion in excess cost savings for the Medicare program.DiscussionSepsis costs and incidence vary dramatically across diagnostic categories, warranting a customized approach for implementing predictive models. 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.ConclusionCustomizing the implementation of clinical predictive models by accounting for various behavioral and economic factors may improve the practical benefit of predictive models.

ImportanceCyberattacks on health care delivery organizations are increasing in frequency and sophistication. Ransomware infections have been associated with significant operational disruption, but data describing regional associations of these cyberattacks with neighboring hospitals have not been previously reported, to our knowledge.ObjectiveTo examine an institution’s emergency department (ED) patient volume and stroke care metrics during a month-long ransomware attack on a geographically proximal but separate health care delivery organization.Design, Setting, and ParticipantsThis before and after cohort study compares adult and pediatric patient volume and stroke care metrics of 2 US urban academic EDs in the 4 weeks prior to the ransomware attack on May 1, 2021 (April 3-30, 2021), as well as during the attack and recovery (May 1-28, 2021) and 4 weeks after the attack and recovery (May 29 to June 25, 2021). The 2 EDs had a combined mean annual census of more than 70 000 care encounters and 11% of San Diego County’s total acute inpatient discharges. The health care delivery organization targeted by the ransomware constitutes approximately 25% of the regional inpatient discharges.ExposureA month-long ransomware cyberattack on 4 adjacent hospitals.Main Outcomes and MeasuresEmergency department encounter volumes (census), temporal throughput, regional diversion of emergency medical services (EMS), and stroke care metrics.ResultsThis study evaluated 19 857 ED visits at the unaffected ED: 6114 (mean [SD] age, 49.6 [19.3] years; 2931 [47.9%] female patients; 1663 [27.2%] Hispanic, 677 [11.1%] non-Hispanic Black, and 2678 [43.8%] non-Hispanic White patients) in the preattack phase, 7039 (mean [SD] age, 49.8 [19.5] years; 3377 [48.0%] female patients; 1840 [26.1%] Hispanic, 778 [11.1%] non-Hispanic Black, and 3168 [45.0%] non-Hispanic White patients) in the attack and recovery phase, and 6704 (mean [SD] age, 48.8 [19.6] years; 3326 [49.5%] female patients; 1753 [26.1%] Hispanic, 725 [10.8%] non-Hispanic Black, and 3012 [44.9%] non-Hispanic White patients) in the postattack phase. Compared with the preattack phase, during the attack phase, there were significant associated increases in the daily mean (SD) ED census (218.4 [18.9] vs 251.4 [35.2]; P P P = .01), patients leaving without being seen (158 [2.6] vs 360 [5.1]; P P = .03). There were also significant associated increases during the attack phase compared with the preattack phase in median waiting room times (21 minutes [IQR, 7-62 minutes] vs 31 minutes [IQR, 9-89 minutes]; P P P = .01) as well as confirmed strokes (22 vs 47; P = .02).Conclusions and RelevanceThis study found that hospitals adjacent to health care delivery organizations affected by ransomware attacks may see increases in patient census and may experience resource constraints affecting time-sensitive care for conditions such as acute stroke. These findings suggest that targeted hospital cyberattacks may be associated with disruptions of health care delivery at nontargeted hospitals within a community and should be considered a regional disaster.

Resurgence of Covid-19 in Vaccinated Health Workers From March through July 2021, a total of 227 health care workers at UCSD, 57% of whom were fully vaccinated, tested positive for SARS-CoV-2; the ...

Introduction Academic medical centers (AMCs) and community physicians seeking to establish a clinically integrated network (CIN) may benefit from a road map to navigate the opportunities and challenges of such an organizational structure. Creating and participating in a CIN requires careful consideration, investment of time, financial resources, alignment of a new quality infrastructure, shared governance, and vision. Potential Benefits, Challenges, and Regulatory Considerations Potential AMC benefits include geographic clinical expansion, the ability to provide care for a broader population of patients, a mechanism to collaborate with regional physician graduates, and an expansion of available teaching sites for trainees. Potential benefits to community practices include propagation of high-value care, enhanced access to evidence-based protocols and priority measures, preparation for value-based reimbursement structures, and connection to an institution that produces future health care practitioners. Challenges to CIN creation include goal alignment, trust between AMC and community partners, acceptance of common quality measures and benchmarks, access to shared data, and local adoption of quality improvement activities. Quality and Information Technology Considerations At inception the mission was to create an innovative academic-community alliance delivering high-quality, high-value, personalized care. Defining the clinical quality goals, measurement, governance, and improvement strategy, as well as information technology structure and decision making, are described. Future Directions The network continues to grow and now includes more than 350 physicians, in 16 different specialties across 50 different independent medical practices throughout Southern California. We believe this builds a firm foundation for value-based health care.

The impact of statins, angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers (ARBs) on coronavirus disease 2019 (COVID-19) severity and recovery is important given their high prevalence of use among individuals at risk for severe COVID-19. We studied the association between use of statin/angiotensin-converting enzyme inhibitors/ARB in the month before hospital admission, with risk of severe outcome, and with time to severe outcome or disease recovery, among patients hospitalized for COVID-19. We performed a retrospective single-center study of all patients hospitalized at University of California San Diego Health between February 10, 2020 and June 17, 2020 (n = 170 hospitalized for COVID-19, n = 5,281 COVID-negative controls). Logistic regression and competing risks analyses were used to investigate progression to severe disease (death or intensive care unit admission), and time to discharge without severe disease. Severe disease occurred in 53% of COVID-positive inpatients. Median time from hospitalization to severe disease was 2 days; median time to recovery was 7 days. Statin use prior to admission was associated with reduced risk of severe COVID-19 (adjusted OR 0.29, 95%CI 0.11 to 0.71, p < 0.01) and faster time to recovery among those without severe disease (adjusted HR for recovery 2.69, 95%CI 1.36 to 5.33, p < 0.01). The association between statin use and severe disease was smaller in the COVID-negative cohort (p for interaction = 0.07). There was potential evidence of faster time to recovery with ARB use (adjusted HR 1.92, 95%CI 0.81 to 4.56). In conclusion, statin use during the 30 days prior to admission for COVID-19 was associated with a lower risk of developing severe COVID-19, and a faster time to recovery among patients without severe disease.

The growing availability of multi-scale biomedical data sources that can be used to enable research and improve healthcare delivery has brought about what can be described as a healthcare "data age." This new era is defined by the explosive growth in bio-molecular, clinical, and population-level data that can be readily accessed by researchers, clinicians, and decision-makers, and utilized for systems-level approaches to hypothesis generation and testing as well as operational decision-making. However, taking full advantage of these unprecedented opportunities presents an opportunity to revisit the alignment between traditionally academic biomedical informatics (BMI) and operational healthcare information technology (HIT) personnel and activities in academic health systems. While the history of the academic field of BMI includes active engagement in the delivery of operational HIT platforms, in many contemporary settings these efforts have grown distinct. Recent experiences during the COVID-19 pandemic have demonstrated greater coordination of BMI and HIT activities that have allowed organizations to respond to pandemic-related changes more effectively, with demonstrable and positive impact as a result. In this position paper, we discuss the challenges and opportunities associated with driving alignment between BMI and HIT, as viewed from the perspective of a learning healthcare system. In doing so, we hope to illustrate the benefits of coordination between BMI and HIT in terms of the quality, safety, and outcomes of care provided to patients and populations, demonstrating that these two groups can be "better together."

Study Objectives: The surge and long tail of patients in acute respiratory distress during the coronavirus-19 (CoVID19) pandemic has inspired new innovations in diagnosing, treating and dispositioning patients during high census conditions with constrained resources During the first wave of the pandemic, we deployed an artificial intelligence (AI) algorithm for assisted interpretation of chest x-ray for use by radiologists and emergency department (ED) physicians We report first experiences of physician interaction with this novel AI algorithm designed to enhance physician abilities to identify ground glass and consolidation on chest radiographs Methods: Design: We created a fully-automated pipeline into the clinical environment to provide AI augmentation of chest x-rays, utilizing a previously developed deep learning-based AI algorithm Trained with 22,000 annotations by radiologists, the algorithm overlays X-rays with color-coded maps that indicate pneumonia probability This was provided alongside standard chest x-ray images for physicians to use in real-time at the point of care with existing imaging software For this prospective observational study, we developed a 3-point survey to characterize experiences with the tool regarding ease of use and impact on clinical decision-making Setting: Surveys were conducted during a one-month period surrounding the projected CoVID-19 surge locally (April 8-May 9) at two academic hospitals in Southern California A federal declaration of emergency occurred March 13, 2020 and the tool was urgently deployed on March 25 Types of Participants: Emergency medicine resident and attending physicians surveyed in real time by telephone Results: Of the 5,125 total visits and 1,960 chest radiographs obtained in the ED during the study period, 1,855 were analyzed by the algorithm Among these, emergency physicians were surveyed for their experiences on 202 Real-time computation and delivery of the tool took four minutes on average Overall, 86% either strongly agreed or somewhat agreed that the intervention was easy to use in the existing workflow 20% of all respondents reported that the algorithm impacted their clinical decision making In general, resident physicians found the AI implementation easier to use than attendings (Mann Whitney U, p=0 005) Descriptive statistics regarding further impact are summarized below (table 1) Conclusion: This AI technology was rapidly deployed in a large academic health system in the first wave of a global pandemic Surveyed ED physicians found this implementation easy to use within existing workflows Twenty percent of physicians reported that the tool changed clinical decision making, and approximately one third of those found that it impacted diagnostic testing decisions and treatment plans Several physicians reported ordering COVID-19 PCR testing as a direct result of the AI, resulting in positive tests and subsequent quarantining of patients who otherwise might not have been appropriately diagnosed To our knowledge, this is the first published study evaluating the impact of medical imaging AI on clinical decision making in the ED setting and may prove to be a powerful tool during the pandemic response [Formula presented]

As participants in the California Medicaid 1115 waiver, the University of California San Diego Health (UCSDH) used population health informatics tools to address health disparities. This case study describes a modern application of health informatics to improve data capture, describe health disparities through demographic stratification, and drive reliable care through electronic medical record-based registries. We provide a details in our successful approach using (1) standardized collection of race, ethnicity, language, sexual orientation, and gender identity data, (2) stratification of 8 quality measures by demographic profile, and (3) improved quality performance through registries for wellness, social determinants of health, and chronic disease. A strong population health platform paired with executive support, physician leadership, education and training, and workflow redesign can improve the representation of diversity and drive reliable processes for care delivery that improve health equity.

The future of connected health care will involve the collection of patient data or enhancement of clinician workflows through various biosensors and displays found on wearable electronic devices, many of which are marketed directly to consumers. The adoption of wearables in health care is being driven by efforts to reduce health care costs, improve care quality, and increase clinician efficiency. Wearables have significant potential to achieve these goals but are currently limited by lack of widespread integrations into electronic health records, biosensor data collection types, and a lack of scientifically rigorous literature showing benefit.

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.

Value-based, outcome-oriented care supported with innovative technology is the future of surgery. We established a novel fellowship in Perioperative Administration, Quality, and Informatics. The aim is to equip future surgeon scholars with the requisite knowledge base and skillset to serve as institutional leaders capable of transforming surgical healthcare delivery. The model was designed as a project-based, "operations-focused" education with supplemental didactics and mentored by surgical leaders and institutional executives. We describe our initial experience, successes, and challenges such that a similar model may be replicated elsewhere.

Introduction Methods Results Conclusions Digital exposure notification (EN) approaches may offer considerable advantages over traditional contact tracing in speed, scale, efficacy, and confidentiality in pandemic control. We applied the science of learning health systems to test the effect of framing and digital means, email vs Short Message Service (SMS), on EN adoption among patients of an academic health center.We tested three communication approaches of the Apple and Google EN system in a rapid learning cycle involving 15 000 patients pseudorandomly assigned to three groups. The patients in the first group received a 284‐word email that presented EN as a tool that can help slow the spread. The patients in the second group received a 32‐word SMS that described EN as a new tool to help slow the spread (SlowTheSpreadSMS). Patients in the third group received a 47‐word SMS that depicted the system as a new digital tool that can empower them to protect their family and friends (EmpowerSMS). A brief four‐question anonymous survey of adoption was included in a reminder message sent 2 days after the initial outreach.One hundred and sixty people responded to the survey within 1 week: 2.33% from EmpowerSMS, 0.97% from SlowTheSpreadSMS, and 0.53% from emails;29 (41.43%), 24 (41.38%), and 11 (34.38%) reported having adopted EN from each group, respectively. Patient reported barriers to adoption included iOS version incompatibility, privacy concerns, and low trust of government agencies or companies like Apple and Google. Patients recommended that healthcare systems play an active role in disseminating information about this tool. Patients also recommended advertising on social media and providing reassurance about privacy.The EmpowerSMS resulted in relatively more survey responses. Both SMS groups had slightly higher, but not statistically significant EN adoption rates compared to email. Findings from the pilot not only informed operational decision‐making in our health system but also contributed to EN rollout planning in our State. [ABSTRACT FROM AUTHOR] Copyright of Learning Health Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

SummaryObjective: The year 2020 was predominated by the coronavirus disease 2019 (COVID-19) pandemic. The objective of this article is to review the areas in which clinical information systems (CIS) can be and have been utilized to support and enhance the response of healthcare systems to pandemics, focusing on COVID-19.Methods: PubMed/MEDLINE, Google Scholar, the tables of contents of major informatics journals, and the bibliographies of articles were searched for studies pertaining to CIS, pandemics, and COVID-19 through October 2020. The most informative and detailed studies were highlighted, while many others were referenced.Results: CIS were heavily relied upon by health systems and governmental agencies worldwide in response to COVID-19. Technology-based screening tools were developed to assist rapid case identification and appropriate triaging. Clinical care was supported by utilizing the electronic health record (EHR) to onboard frontline providers to new protocols, offer clinical decision support, and improve systems for diagnostic testing. Telehealth became the most rapidly adopted medical trend in recent history and an essential strategy for allowing safe and effective access to medical care. Artificial intelligence and machine learning algorithms were developed to enhance screening, diagnostic imaging, and predictive analytics - though evidence of improved outcomes remains limited. Geographic information systems and big data enabled real-time dashboards vital for epidemic monitoring, hospital preparedness strategies, and health policy decision making. Digital contact tracing systems were implemented to assist a labor-intensive task with the aim of curbing transmission. Large scale data sharing, effective health information exchange, and interoperability of EHRs remain challenges for the informatics community with immense clinical and academic potential. CIS must be used in combination with engaged stakeholders and operational change management in order to meaningfully improve patient outcomes.Conclusion: Managing a pandemic requires widespread, timely, and effective distribution of reliable information. In the past year, CIS and informaticists made prominent and influential contributions in the global response to the COVID-19 pandemic.

ImportancePhysician burnout is an ongoing epidemic; electronic health record (EHR) use has been associated with burnout, and the burden of EHR inbasket messages has grown in the context of the COVID-19 pandemic. Understanding how EHR inbasket messages are associated with physician burnout may uncover new insights for intervention strategies.ObjectiveTo evaluate associations between EHR inbasket message characteristics and physician burnout.Design, Setting, and ParticipantsCross-sectional study in a single academic medical center involving physicians from multiple specialties. Data collection took place April to September 2020, and data were analyzed September to December 2020.ExposuresPhysicians responded to a survey including the validated Mini-Z 5-point burnout scale.Main Outcomes and MeasuresPhysician burnout according to the self-reported burnout scale. A sentiment analysis model was used to calculate sentiment scores for EHR inbasket messages extracted for participating physicians. Multivariable modeling was used to model risk of physician burnout using factors such as message characteristics, physician demographics, and clinical practice characteristics.ResultsOf 609 physicians who responded to the survey, 297 (48.8%) were women, 343 (56.3%) were White, 391 (64.2%) practiced in outpatient settings, and 428 (70.28%) had been in medical practice for 15 years or less. Half (307 [50.4%]) reported burnout (score of 3 or higher). A total of 1 453 245 inbasket messages were extracted, of which 630 828 (43.4%) were patient messages. Among negative messages, common words included medical conditions, expletives and/or profanity, and words related to violence. There were no significant associations between message characteristics (including sentiment scores) and burnout. Odds of burnout were significantly higher among Hispanic/Latino physicians (odds ratio [OR], 3.44; 95% CI, 1.18-10.61; P = .03) and women (OR, 1.60; 95% CI, 1.13-2.27; P = .01), and significantly lower among physicians in clinical practice for more than 15 years (OR, 0.46; 95% CI, 0.30-0.68; P Conclusions and RelevanceIn this cross-sectional study, message characteristics were not associated with physician burnout, but the presence of expletives and violent words represents an opportunity for improving patient engagement, EHR portal design, or filters. Natural language processing represents a novel approach to understanding potential associations between EHR inbasket messages and physician burnout and may also help inform quality improvement initiatives aimed at improving patient experience.

Patients' journeys across the care continuum can be improved with patient-centered technology integrated into the care process. Misaligned financial incentives, change management challenges, and privacy concerns are some of the hurdles that have prevented health systems from deploying technology that engages patients along the care continuum. Despite these sociotechnical challenges, some health care organizations have developed innovative approaches to engaging patients. We describe promising technology-enabled consumer engagement practices at two community-based delivery organizations and two academic medical centers to demonstrate the approaches, sociotechnical challenges, and outcomes associated with their implementation. Leadership commitment and payer policies that align with the quadruple aim-enhancing patient experience, improving population health, reducing costs, and improving the work life of health care providers-would encourage further deployment and lead to greater consumer engagement along the care continuum.

Background Patient–physician communication during clinical encounters is essential to ensure quality of care. Many studies have attempted to improve patient–physician communication. Incorporating patient priorities into agenda setting and medical decision-making are fundamental to patient-centered communication. Efficient and scalable approaches are needed to empower patients to speak up and prepare physicians to respond. Leveraging electronic health records (EHRs) in engaging patients and health care teams has the potential to enhance the integration of patient priorities in clinical encounters. A systematic approach to eliciting and documenting patient priorities before encounters could facilitate effective communication in such encounters. Objective In this paper, we report the design and implementation of a set of EHR tools built into clinical workflows for facilitating patient–physician joint agenda setting and the documentation of patient concerns in the EHRs for ambulatory encounters. Methods We engaged health information technology leaders and users in three health care systems for developing and implementing a set of EHR tools. The goal of these tools is to standardize the elicitation of patient priorities by using a previsit “patient important issue” questionnaire distributed through the patient portal to the EHR. We built additional EHR documentation tools to facilitate patient–staff communication when the staff records the vital signs and the reason for the visit in the EHR while in the examination room, with a simple transmission method for physicians to incorporate patient concerns in EHR notes. Results The study is ongoing. The anticipated completion date for survey data collection is November 2021. A total of 34,037 primary care patients from three health systems (n=26,441; n=5136; and n=2460 separately recruited from each system) used the previsit patient important issue questionnaire in 2020. The adoption of the digital previsit questionnaire during the COVID-19 pandemic was much higher in one health care system because it expanded the use of the questionnaire from physicians participating in trials to all primary care providers midway through the year. It also required the use of this previsit questionnaire for eCheck-ins, which are required for telehealth encounters. Physicians and staff suggested anecdotally that this questionnaire helped patient–clinician communication, particularly during the COVID-19 pandemic. Conclusions EHR tools have the potential to facilitate the integration of patient priorities into agenda setting and documentation in real-world primary care practices. Early results suggest the feasibility and acceptability of such digital tools in three health systems. EHR tools can support patient engagement and clinicians’ work during in-person and telehealth visits. They could potentially exert a sustained influence on patient and clinician communication behaviors in contrast to prior ad hoc educational efforts targeting patients or clinicians. Trial Registration ClinicalTrials.gov NCT03385512; https://clinicaltrials.gov/ct2/show/NCT03385512 International Registered Report Identifier (IRRID) DERR1-10.2196/30431

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.

Large-scale wastewater surveillance has the ability to greatly augment the tracking of infection dynamics especially in communities where the prevalence rates far exceed the testing capacity. However, current methods for viral detection in wastewater are severely lacking in terms of scaling up for high throughput. In the present study, we employed an automated magnetic-bead-based concentration approach for viral detection in sewage that can effectively be scaled up for processing 24 samples in a single 40-min run. The method compared favorably to conventionally used methods for viral wastewater concentrations with higher recovery efficiencies from input sample volumes as low as 10 ml and can enable the processing of over 100 wastewater samples in a day. The sensitivity of the high-throughput protocol was shown to detect 1 asymptomatic individual in a building of 415 residents. Using the high-throughput pipeline, samples from the influent stream of the primary wastewater treatment plant of San Diego County (serving 2.3 million residents) were processed for a period of 13 weeks. Wastewater estimates of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral genome copies in raw untreated wastewater correlated strongly with clinically reported cases by the county, and when used alongside past reported case numbers and temporal information in an autoregressive integrated moving average (ARIMA) model enabled prediction of new reported cases up to 3 weeks in advance. Taken together, the results show that the high-throughput surveillance could greatly ameliorate comprehensive community prevalence assessments by providing robust, rapid estimates. IMPORTANCE Wastewater monitoring has a lot of potential for revealing coronavirus disease 2019 (COVID-19) outbreaks before they happen because the virus is found in the wastewater before people have clinical symptoms. However, application of wastewater-based surveillance has been limited by long processing times specifically at the concentration step. Here we introduce a much faster method of processing the samples and show its robustness by demonstrating direct comparisons with existing methods and showing that we can predict cases in San Diego by a week with excellent accuracy, and 3 weeks with fair accuracy, using city sewage. The automated viral concentration method will greatly alleviate the major bottleneck in wastewater processing by reducing the turnaround time during epidemics.

SARS-CoV-2 Vaccine and Infection in Health Workers After more than 36,500 health care workers at the University of California received at least one dose of vaccine, 71% of 379 workers with positive...

UNSTRUCTURED 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.

Facing the COVID-19 pandemic, healthcare organizations across the world worked expeditiously to prepare for and manage the outbreak. In the modern era, clinical information systems (CIS) and the electronic health record (EHR) are essential tools that can support patient care and enhance healthcare delivery. Researchers out of the University of California, San Diego, were the first to describe in detail the rapid development and implementation of EHR based tools designed specifically to support the management of COVID-19. Drs. Jeff Reeves and Chris Longhurst will describe how CIS are utilized in response to COVID-19, challenges to successful implementation of informatics tools, and the future of informatics in pandemic management.

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.

Objective Clinical decision support (CDS) can contribute to quality and safety. Prior work has shown that errors in CDS systems are common and can lead to unintended consequences. Many CDS systems use Boolean logic, which can be difficult for CDS analysts to specify accurately. We set out to determine the prevalence of certain types of Boolean logic errors in CDS statements. Methods Nine health care organizations extracted Boolean logic statements from their Epic electronic health record (EHR). We developed an open-source software tool, which implemented the Espresso logic minimization algorithm, to identify three classes of logic errors. Results Participating organizations submitted 260,698 logic statements, of which 44,890 were minimized by Espresso. We found errors in 209 of them. Every participating organization had at least two errors, and all organizations reported that they would act on the feedback. Discussion An automated algorithm can readily detect specific categories of Boolean CDS logic errors. These errors represent a minority of CDS errors, but very likely require correction to avoid patient safety issues. This process found only a few errors at each site, but the problem appears to be widespread, affecting all participating organizations. Conclusion Both CDS implementers and EHR vendors should consider implementing similar algorithms as part of the CDS authoring process to reduce the number of errors in their CDS interventions.

Large-scale wastewater surveillance has the ability to greatly augment the tracking of infection dynamics especially in communities where the prevalence rates far exceed the testing capacity. However, current methods for viral detection in wastewater are severely lacking in terms of scaling up for high throughput. In the present study, we employed an automated magnetic-bead based concentration approach for viral detection in sewage that can effectively be scaled up for processing 24 samples in a single 40-minute run. The method compared favorably to conventionally used methods for viral wastewater concentrations with higher recovery efficiencies from input sample volumes as low as 10ml and can enable the processing of over 100 wastewater samples in a day. The sensitivity of the high-throughput protocol was shown to detect cases as low as 2 in a hospital building with a known COVID-19 caseload. Using the high throughput pipeline, samples from the influent stream of the primary wastewater treatment plant of San Diego county (serving 2.3 million residents) were processed for a period of 13 weeks. Wastewater estimates of SARS-CoV-2 viral genome copies in raw untreated wastewater correlated strongly with clinically reported cases by the county, and when used alongside past reported case numbers and temporal information in an autoregressive integrated moving average (ARIMA) model enabled prediction of new reported cases up to 3 weeks in advance. Taken together, the results show that the high-throughput surveillance could greatly ameliorate comprehensive community prevalence assessments by providing robust, rapid estimates.ImportanceWastewater monitoring has a lot of potential for revealing COVID-19 outbreaks before they happen because the virus is found in the wastewater before people have clinical symptoms. However, application of wastewater-based surveillance has been limited by long processing times specifically at the concentration step. Here we introduce a much faster method of processing the samples, and show that its robustness by demonstrating direct comparisons with existing methods and showing that we can predict cases in San Diego by a week with excellent accuracy, and three weeks with fair accuracy, using city sewage. The automated viral concentration method will greatly alleviate the major bottleneck in wastewater processing by reducing the turnaround time during epidemics.

To detail the implementation, benefits and challenges of onboarding campus-based health services onto a health system’s electronic health record. UC San Diego Student Health and Well-Being offers m...

Background: The authors draw upon their experience with a successful, enterprise-level, telemedicine program implementation to present a "How To" paradigm for other academic health centers that wish to rapidly deploy such a program in the setting of the COVID-19 pandemic. The advent of social distancing as essential for decreasing viral transmission has made it challenging to provide medical care. Telemedicine has the potential to medically undistance health care providers while maintaining the quality of care delivered and fulfilling the goal of social distancing. Methods: Rather than simply reporting enterprise telemedicine successes, the authors detail key telemedicine elements essential for rapid deployment of both an ambulatory and inpatient telemedicine solution. Such a deployment requires a multifaceted strategy: (1) determining the appropriateness of telemedicine use, (2) understanding the interface with the electronic health record, (3) knowing the equipment and resources needed, (4) developing a rapid rollout plan, (5) establishing a command center for post go-live support, (6) creating and disseminating reference materials and educational guides, (7) training clinicians, patients, and clinic schedulers, (8) considering billing and credentialing implications, (9) building a robust communications strategy, and (10) measuring key outcomes. Results: Initial results are reported, showing a telemedicine rate increase to 45.8% (58.6% video and telephone) in just the first week of rollout. Over a 5-month period, the enterprise has since conducted over 119,500 ambulatory telemedicine evaluations (a 1,000-fold rate increase from the pre-COVID-19 time period). Conclusion: This article is designed to offer a "How To" potential best practice approach for others wishing to quickly implement a telemedicine program during the COVID-19 pandemic.

UNSTRUCTURED 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.

BACKGROUND: The institution-wide response of the University of California San Diego Health system to the 2019 novel coronavirus disease (COVID-19) pandemic was founded on rapid development of in-house testing capacity, optimization of personal protective equipment usage, expansion of intensive care unit capacity, development of analytic dashboards for monitoring of institutional status, and implementation of an operating room (OR) triage plan that postponed nonessential/elective procedures. We analyzed the impact of this triage plan on the only academic neurosurgery center in San Diego County, California, USA. METHODS: We conducted a de-identified retrospective review of all operative cases and procedures performed by the Department of Neurosurgery from November 24, 2019, through July 6, 2020, a 226-day period. Statistical analysis involved 2-sample z tests assessing daily case totals over the 113-day periods before and after implementation of the OR triage plan on March 16, 2020. RESULTS: The neurosurgical service performed 1429 surgical and interventional radiologic procedures over the study period. There was no statistically significant difference in mean number of daily total cases in the pre-versus post-OR triage plan periods (6.9 vs. 5.8 mean daily cases; 1-tail P = 0.050, 2-tail P = 0.101), a trend reflected by nearly every category of neurosurgical cases. CONCLUSIONS: During the COVID-19 pandemic, the University of California San Diego Department of Neurosurgery maintained an operative volume that was only modestly diminished and continued to meet the essential neurosurgical needs of a large population. Lessons from our experience can guide other departments as they triage neurosurgical cases to meet community needs.

In 2017, 43.9% of US physicians reported symptoms of burnout. Poor electronic health record (EHR) usability and time-consuming data entry contribute to burnout. However, less is known about how modifiable dimensions of EHR use relate to burnout and how these associations vary by medical specialty. Using the KLAS Arch Collaborative’s large-scale nationwide physician (MD/DO) data, we used ordinal logistic regression to analyze associations between self-reported burnout and after-hours charting and organizational EHR support. We examined how these relationships differ by medical specialty, adjusting for confounders. Physicians reporting ≤ 5 hours weekly of after-hours charting were twice as likely to report lower burnout scores compared to those charting ≥6 hours (aOR: 2.43, 95% CI: 2.30, 2.57). Physicians who agree that their organization has done a great job with EHR implementation, training, and support (aOR: 2.14, 95% CI: 2.01, 2.28) were also twice as likely to report lower scores on the burnout survey question compared to those who disagree. Efforts to reduce after-hours charting and improve organizational EHR support could help address physician burnout.