Statewide Real-Time Tracking of Beds and Ventilators During Coronavirus Disease 2019 and Beyond

Digital twins for health: a scoping review

The use of digital twins (DTs) has proliferated across various fields and industries, with a recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds great promise to revolutionize the entire healthcare system, including management and delivery, disease treatment and prevention, and health well-being maintenance, ultimately improving human life. The rapid growth of big data and continuous advancement in data science (DS) and artificial intelligence (AI) have the potential to significantly expedite DT research and development by providing scientific expertise, essential data, and robust cybertechnology infrastructure. Although various DT initiatives have been underway in the industry, government, and military, DT4H is still in its early stages. This paper presents an overview of the current applications of DTs in healthcare, examines consortium research centers and their limitations, and surveys the current landscape of emerging research and development opportunities in healthcare. We envision the emergence of a collaborative global effort among stakeholders to enhance healthcare and improve the quality of life for millions of individuals worldwide through pioneering research and development in the realm of DT technology.

The Minnesota Medical Operations Coordination Center: A COVID-19 Statewide Response to Ensure Access to Critical Care and Medical-Surgical Beds

BACKGROUND

COVID-19 led to unprecedented inpatient capacity challenges, particularly in ICUs, which spurred development of statewide or regional placement centers for coordinating transfer (load-balancing) of adult patients needing intensive care to hospitals with remaining capacity.

RESEARCH QUESTION

Do Medical Operations Coordination Centers (MOCC) augment patient placement during times of severe capacity challenges?

STUDY DESIGN AND METHODS

The Minnesota MOCC was established with a focus on transfer of adult ICU and medical-surgical patients; trauma, cardiac, stroke, burn, and extracorporeal membrane oxygenation cases were excluded. The center operated within one health care system's bed management center, using a dedicated 24/7 telephone number. Major health care systems statewide and two tertiary centers in a neighboring state participated, sharing information on system status, challenges, and strategies. Patient volumes and transfer data were tracked; client satisfaction was evaluated through an anonymous survey.

RESULTS

From August 1, 2020, through March 31, 2022, a total of 5,307 requests were made, 2,008 beds identified, 1,316 requests canceled, and 1,981 requests were unable to be fulfilled. A total of 1,715 patients had COVID-19 (32.3%), and 2,473 were negative or low risk for COVID-19 (46.6%). COVID-19 status was unknown in 1,119 (21.1%). Overall, 760 were patients on ventilators (49.1% COVID-19 positive). The Minnesota Critical Care Coordination Center placed most patients during the fall 2020 surge with the Minnesota Governor's stay-at-home order during the peak. However, during the fall 2021 surge, only 30% of ICU patients and 39% of medical-surgical patients were placed. Indicators characterizing severe surge include the number of Critical Care Coordination Center requests, decreasing placements, longer placement times, and time series analysis showing significant request-acceptance differences.

INTERPRETATION

Implementation of a large-scale Minnesota MOCC program was effective at placing patients during the first COVID-19 pandemic fall 2020 surge and was well regarded by hospitals and health systems. However, under worsening duress of limited resources during the fall 2021 surge, placement of ICU and medical-surgical patients was greatly decreased.

Using Two Statewide Medical Operations Coordination Centers to Load Balance in Pediatric Hospitals During a Severe Respiratory Surge in the United States

OBJECTIVES

Report on the use of two statewide Medical Operations Coordination Centers (MOCCs) to manage a rapid surge in pediatric acute and critical care patient needs.

DESIGN

Brief report.

SETTING

The states of Washington and Oregon during the pediatric respiratory surge in November 2022/December 2022 which overwhelmed existing pediatric acute and critical care hospital capacity.

PATIENTS

Pediatric patients requiring hospitalization in Washington and Oregon.

INTERVENTIONS

Adaptations to the use of two existing statewide MOCCs to provide pediatric patient load balancing through surveillance, modifications of existing referral agreements, coordinated expansion of resources, activation of regional crisis standards of care, and integration of pediatric critical care physicians from Harborview Medical Center as subject matter experts (SMEs).

MEASUREMENTS AND MAIN RESULTS

The Washington and Oregon MOCCs managed 183 pediatric requests from hospitals unable to transfer pediatric patients between November 1, 2022, and December 14, 2022. Sixteen percent of requests were for children younger than 3 months and 37% were for children between 3 months and 1 year; most had acute viral respiratory disease. Requests for children older than 13 years old were primarily intentional drug ingestions. Fifty-eight percent were for critically ill children and 17% originated from critical access hospitals. Washington's SMEs were utilized in nearly a quarter of cases with the disposition changing in 38% of these.

CONCLUSIONS

Washington and Oregon statewide MOCCs have leveraged centralized coordination to effectively load balance a surge in pediatric patients which has overwhelmed existing pediatric hospital resources. Centralized coordination and surveillance informed pediatric hospitals and policy makers of unmet clinical needs and facilitated rapid expansion of clinical capacity and modifications to referral processes. Integration of pediatric SMEs enabled efficient triage of these resources. MOCCs provide an adaptable centralized resource for addressing surge and have been effective in managing overwhelmed pediatric hospital resources in Washington and Oregon.

Engaging Multidisciplinary Clinical Users in the Design of an Artificial Intelligence-Powered Graphical User Interface for Intensive Care Unit Instability Decision Support

BACKGROUND

Critical instability forecast and treatment can be optimized by artificial intelligence (AI)-enabled clinical decision support. It is important that the user-facing display of AI output facilitates clinical thinking and workflow for all disciplines involved in bedside care.

OBJECTIVES

Our objective is to engage multidisciplinary users (physicians, nurse practitioners, physician assistants) in the development of a graphical user interface (GUI) to present an AI-derived risk score.

METHODS

Intensive care unit (ICU) clinicians participated in focus groups seeking input on instability risk forecast presented in a prototype GUI. Two stratified rounds (three focus groups [only nurses, only providers, then combined]) were moderated by a focus group methodologist. After round 1, GUI design changes were made and presented in round 2. Focus groups were recorded, transcribed, and deidentified transcripts independently coded by three researchers. Codes were coalesced into emerging themes.

RESULTS

Twenty-three ICU clinicians participated (11 nurses, 12 medical providers [3 mid-level and 9 physicians]). Six themes emerged: (1) analytics transparency, (2) graphical interpretability, (3) impact on practice, (4) value of trend synthesis of dynamic patient data, (5) decisional weight (weighing AI output during decision-making), and (6) display location (usability, concerns for patient/family GUI view). Nurses emphasized having GUI objective information to support communication and optimal GUI location. While providers emphasized need for recommendation interpretability and concern for impairing trainee critical thinking. All disciplines valued synthesized views of vital signs, interventions, and risk trends but were skeptical of placing decisional weight on AI output until proven trustworthy.

CONCLUSION

Gaining input from all clinical users is important to consider when designing AI-derived GUIs. Results highlight that health care intelligent decisional support systems technologies need to be transparent on how they work, easy to read and interpret, cause little disruption to current workflow, as well as decisional support components need to be used as an adjunct to human decision-making.

Implementation and use of a national electronic dashboard to guide COVID-19 clinical management in Fiji

Problem

From April to September 2021, Fiji experienced a second wave of coronavirus disease (COVID-19) precipitated by the Delta variant of concern, prompting a need to strengthen existing data management of positive COVID-19 cases.

Context

With COVID-19 cases peaking at 1405 a day and many hospital admissions, the need to develop a better way to visualize data became clear.

Action

The Fiji Ministry of Health and Medical Services, the World Health Organization and the United Nations Office for the Coordination of Humanitarian Affairs collaborated to develop an online clinical dashboard to support better visualization of case management data.

Outcome

The dashboard was used across Fiji at national, divisional and local levels for COVID-19 management. At the national level, it provided real-time reports describing the surge pattern, severity and management of COVID-19 cases across the country during daily incident management team meetings. At the divisional level, it gave the divisional directors access to timely information about hospital and community isolation of cases. At the hospital level, the dashboard allowed managers to monitor trends in isolated cases and use of oxygen resources.

Discussion

The dashboard replaced previous paper-based reporting of statistics with delivery of trends and real-time data. The team that developed the tool were situated in different locations and did not meet physically, demonstrating the ease of implementing this online tool in a resource-constrained setting. The dashboard is easy to use and could be used in other Pacific island countries and areas.

Use of Hospital Capacity Command Centers to Improve Patient Flow and Safety: A Scoping Review

OBJECTIVES

Delayed emergency department (ED) and hospital patient throughput is recognized as a critical threat to patient safety. Increasingly, hospitals are investing significantly in deploying command centers, long used in airlines and the military, to proactively manage hospital-wide patient flow. This scoping review characterizes the evidence related to hospital capacity command centers (CCCs) and synthesizes current data regarding their implementation.

METHODS

As no consensus definition exists for CCCs, we characterized them as units (i) involving interdisciplinary, permanently colocated teams, (ii) using real-time data, and (iii) managing 2 or more patient flow functions (e.g., bed management, transfers, discharge planning, etc.), to distinguish CCCs from transfer centers. We undertook a scoping review of the medical and gray literature published through April 2019 related to CCCs meeting these criteria.

RESULTS

We identified 8 eligible articles (including 4 peer-reviewed studies) describing 7 CCCs of varying designs. The most common CCC outcome measures related to transfer volume (n = 5) and ED boarding (n = 4). Several CCCs also monitored patient-level clinical parameters. Although all articles reported performance improvements, heterogeneity in CCC design and evidence quality currently restricts generalizability of findings.

CONCLUSIONS

Numerous anecdotal accounts suggest that CCCs are being widely deployed in an effort to improve hospital patient flow and safety, yet peer-reviewed evidence regarding their design and effectiveness is in its earliest stages. The costs, objectives, and growing deployment of CCCs merit an investment in rigorous research to better measure their processes and outcomes. We propose a standard definition, conceptual framework, research priorities, and reporting standards to guide future investigation of CCCs.

COVID-19 and telehealth in the intensive care unit setting: a survey

Background

Coronavirus disease (COVID-19) has led to changes in how healthcare is delivered. Here, through the administration of surveys, we evaluated telehealth use and views in US intensive care units (ICUs) during the pandemic.

Methods

From June 2020 to July 2021, voluntary, electronic surveys were provided to ICU leaders of Johns Hopkins Medical Institution (JHMI) hospitals, members of the Neurocritical Care Society (NCS) who practice in the US, and Society of Critical Care Medicine (SCCM) members practicing adult medicine.

Results

Response rates to our survey were as follows: 18 of 22 (81.8%) JHMI-based ICU leaders, 22 of 2218 (1.0%) NCS members practicing in the US, and 136 of 13,047 (1.0%) SCCM members. COVID-19 patients were among those cared for in the ICUs of 77.7, 86.4, and 93.4% of respondents, respectively, in April 2020 (defined as the peak of the pandemic). Telehealth technologies were used by 88.9, 77.3, and 75.6% of respondents, respectively, following the start of COVID-19 while only 22.2, 31.8, and 43.7% utilized them prior. The most common telehealth technologies were virtual meeting software and telephone (with no video component). Provider, nurse, and patient communications with the patient’s family constituted the most frequent types of interactions utilizing telehealth. Most common reasons for telehealth use included providing an update on a patient’s condition and conducting a goals of care discussion. 93.8–100.0% of respondents found telehealth technologies valuable in managing patients. Technical issues were noted by 66.7, 50.0, and 63.4% of respondents, respectively.

Conclusions

Telehealth use increased greatly among respondents following the start of COVID-19. In US ICUs, telehealth technologies found diverse uses during the pandemic. Future studies are needed to confirm our findings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-08197-7.

Emergency Department and Intensive Care Unit Overcrowding and Ventilator Shortages in US Hospitals During the COVID-19 Pandemic, 2020-2021

OBJECTIVE

In 2020, the COVID-19 pandemic overburdened the US health care system because of extended and unprecedented patient surges and supply shortages in hospitals. We investigated the extent to which several US hospitals experienced emergency department (ED) and intensive care unit (ICU) overcrowding and ventilator shortages during the COVID-19 pandemic.

METHODS

We analyzed Health Pulse data to assess the extent to which US hospitals reported alerts when experiencing ED overcrowding, ICU overcrowding, and ventilator shortages from March 7, 2020, through April 30, 2021.

RESULTS

Of 625 participating hospitals in 29 states, 393 (63%) reported at least 1 hospital alert during the study period: 246 (63%) reported ED overcrowding, 239 (61%) reported ICU overcrowding, and 48 (12%) reported ventilator shortages. The number of alerts for overcrowding in EDs and ICUs increased as the number of COVID-19 cases surged.

CONCLUSIONS

Timely assessment and communication about critical factors such as ED and ICU overcrowding and ventilator shortages during public health emergencies can guide public health response efforts in supporting federal, state, and local public health agencies.

Toward a Learning Health Care System: A Systematic Review and Evidence-Based Conceptual Framework for Implementation of Clinical Analytics in a Digital Hospital

Objective  A learning health care system (LHS) uses routinely collected data to continuously monitor and improve health care outcomes. Little is reported on the challenges and methods used to implement the analytics underpinning an LHS. Our aim was to systematically review the literature for reports of real-time clinical analytics implementation in digital hospitals and to use these findings to synthesize a conceptual framework for LHS implementation.

Methods  Embase, PubMed, and Web of Science databases were searched for clinical analytics derived from electronic health records in adult inpatient and emergency department settings between 2015 and 2021. Evidence was coded from the final study selection that related to (1) dashboard implementation challenges, (2) methods to overcome implementation challenges, and (3) dashboard assessment and impact. The evidences obtained, together with evidence extracted from relevant prior reviews, were mapped to an existing digital health transformation model to derive a conceptual framework for LHS analytics implementation.

Results  A total of 238 candidate articles were reviewed and 14 met inclusion criteria. From the selected studies, we extracted 37 implementation challenges and 64 methods employed to overcome such challenges. We identified common approaches for evaluating the implementation of clinical dashboards. Six studies assessed clinical process outcomes and only four studies evaluated patient health outcomes. A conceptual framework for implementing the analytics of an LHS was developed.

Conclusion  Health care organizations face diverse challenges when trying to implement real-time data analytics. These challenges have shifted over the past decade. While prior reviews identified fundamental information problems, such as data size and complexity, our review uncovered more postpilot challenges, such as supporting diverse users, workflows, and user-interface screens. Our review identified practical methods to overcome these challenges which have been incorporated into a conceptual framework. It is hoped this framework will support health care organizations deploying near-real-time clinical dashboards and progress toward an LHS.

Patterns in the Pandemic: Disproportionate Patient Burdens Among Regional Hospitals

Study objective

To examine the distribution of hospitalized COVID-19 patients among adult acute care facilities in the Greater Philadelphia area and identify factors associated with hospitals carrying higher burdens of COVID-19 patients.

Methods

In this observational descriptive study, we obtained self-reported daily COVID-19 inpatient censuses from 28 large (>100 beds) adult acute care hospitals in the Greater Philadelphia region during the initial wave of the COVID-19 pandemic (March 23, 2020, to July 28, 2020). We examined hospitals based on their size, location, trauma certification, median household income, and reliance on public insurance. COVID-19 inpatient burdens (ie, beds occupied by COVID-19 patients), relative to overall facility capacity (ie, total beds), were reported and assessed using thresholds established by the Institute of Health Metrics and Evaluation to approximate the stress induced by different COVID-19 levels.

Results

Maximum (ie, peak) daily COVID-19 occupancy averaged 27.5% (SD 11.2%) across the 28 hospitals. However, there was dramatic variation between hospitals, with maximum daily COVID-19 occupancy ranging from 5.7% to 50.0%. Smaller hospitals remained above 20% COVID-19 capacity for longer (small hospital median 27.5 days [interquartile range {IQR}: 4 to 32]; medium hospital median 18.5 days [IQR: 0.5 to 37]; large hospital median 13 days [IQR: 6 to 32]). Trauma centers reached 20% capacity sooner (median 19 days [IQR: 16-25] versus nontrauma median 30 days [IQR: 20 to 128]), remained above 20% capacity for longer (median 31 days [IQR: 11 to 38]; nontrauma median 8 days [IQR: 0 to 30]), and had higher observed burdens relative to their total capacity (median 5.8% [IQR: 2.4% to 8.3%]; nontrauma median 2.5% [IQR: 1.6% to 2.8%]). Urban location was also predictive of higher COVID-19 patient burden (urban median 3.8% [IQR: 2.6% to 6.7%]; suburban median 2.2% [IQR: 1.5% to 2.8%]). Heat map analyses demonstrated that hospitals in lower-income areas and hospitals in areas of higher reliance on public insurance also exhibited substantially higher COVID-19 occupancy and longer periods of higher COVID-19 occupancy.

Conclusion

Substantial discrepancies in COVID-19 inpatient burdens existed among Philadelphia-region adult acute care facilities during the initial COVID-19 surge. Trauma center status, urban location, low household income, and high reliance on public insurance were associated with both higher COVID-19 burdens and longer periods of high occupancy. Improved data collection and centralized sharing of pandemic-specific data between health care facilities may improve resource balancing and patient care during current and future response efforts.

Universal Level Designations for Hospitalized Pediatric Patients in Evacuation

Children comprise approximately 22% of the population in the United States.1 In a widespread disaster such as a hurricane, pandemic, wildfire or major earthquake, children are at least proportionately affected to their share of the population, if not more so. They also have unique vulnerabilities including physical, mental, and developmental differences from adults, which make them more prone to adverse health effects of disasters.2-4 There are about 5000 pediatric critical care beds and 23 000 neonatal intensive care beds out of 900 000 total hospital beds in the United States.5 While no mechanism exists to consistently track pediatric acute care beds nationally (especially in real time), a previous study6 showed a 7% decline in pediatric medical-surgical beds between 2002 and 2011. This study also estimated there are about 30 000 acute care pediatric beds nationally. Finding appropriate hospital resources for the provision of care for pediatric disaster victims is an important concern for those charged with triaging patients in a major event.

Mass Critical Care Surge Response During COVID-19: Implementation of Contingency Strategies - A Preliminary Report of Findings From the Task Force for Mass Critical Care

BACKGROUND

After the publication of a 2014 consensus statement regarding mass critical care during public health emergencies, much has been learned about surge responses and the care of overwhelming numbers of patients during the COVID-19 pandemic. Gaps in prior pandemic planning were identified and require modification in the midst of severe ongoing surges throughout the world.

RESEARCH QUESTION

A subcommittee from The Task Force for Mass Critical Care (TFMCC) investigated the most recent COVID-19 publications coupled with TFMCC members anecdotal experience in order to formulate operational strategies to optimize contingency level care, and prevent crisis care circumstances associated with increased mortality.

STUDY DESIGN AND METHODS

TFMCC adopted a modified version of established rapid guideline methodologies from the World Health Organization and the Guidelines International Network-McMaster Guideline Development Checklist. With a consensus development process incorporating expert opinion to define important questions and extract evidence, the TFMCC developed relevant pandemic surge suggestions in a structured manner, incorporating peer-reviewed literature, "gray" evidence from lay media sources, and anecdotal experiential evidence.

RESULTS

Ten suggestions were identified regarding staffing, load-balancing, communication, and technology. Staffing models are suggested with resilience strategies to support critical care staff. ICU surge strategies and strain indicators are suggested to enhance ICU prioritization tactics to maintain contingency level care and to avoid crisis triage, with early transfer strategies to further load-balance care. We suggest that intensivists and hospitalists be engaged with the incident command structure to ensure two-way communication, situational awareness, and the use of technology to support critical care delivery and families of patients in ICUs.

INTERPRETATION

A subcommittee from the TFMCC offers interim evidence-informed operational strategies to assist hospitals and communities to plan for and respond to surge capacity demands resulting from COVID-19.