Service Workflow Research Articles

Digital Support to Multimodal Community-Based Prehabilitation: Looking for Optimization of Health Value Generation.

Prehabilitation has shown its potential for most intra-cavity surgery patients on enhancing preoperative functional capacity and postoperative outcomes. However, its large-scale implementation is limited by several constrictions, such as: i) unsolved practicalities of the service workflow, ii) challenges associated to change management in collaborative care; iii) insufficient access to prehabilitation; iv) relevant percentage of program drop-outs; v) need for program personalization; and, vi) economical sustainability. Transferability of prehabilitation programs from the hospital setting to the community would potentially provide a new scenario with greater accessibility, as well as offer an opportunity to effectively address the aforementioned issues and, thus, optimize healthcare value generation. A core aspect to take into account for an optimal management of prehabilitation programs is to use proper technological tools enabling: i) customizable and interoperable integrated care pathways facilitating personalization of the service and effective engagement among stakeholders; ii) remote monitoring (i.e. physical activity, physiological signs and patient-reported outcomes and experience measures) to support patient adherence to the program and empowerment for self-management; and, iii) use of health risk assessment supporting decision making for personalized service selection. The current manuscript details a proposal to bring digital innovation to community-based prehabilitation programs. Moreover, this approach has the potential to be adopted by programs supporting long-term management of cancer patients, chronic patients and prevention of multimorbidity in subjects at risk.

The Big Move: Five Mass Transport To Ankara City Hospital

Aim
 It is a rare need for IHT that an existing hospital is scheduled to be closed. In this study, the process of Mass Transport due to the relocation of five different hospitals to a single target hospital is discussed.
 Methods
 Meetings were held with hospital administrators to plan the Mass Transport and an emergency transport action plan was decided. During the planning phase of the Mass Transport, each hospital was examined separately and in-hospital and inter-hospital transfer routes were created. During the Mass Transport, spare ambulances were activated to avoid disrupting the daily workflow of emergency medical services (EMS). In addition, EMS administrations of nearby provinces were asked to send their spare ambulances to Ankara. While 1 driver and 2 paramedics were assigned to a stable patient transport team, 1 driver, 1 paramedic and 1 doctor were assigned to a critical patient transport team.
 Results
 The patients who were transported from the clinics comprised 24.95% (n = 130) of all transported patients. Of all patients transported, 56.81% (n = 296) were males. The mean age of all transported patients was 46.33 ± 26.04 years (min-max; 0 - 93) (46.21 ± 26.45 years for females, 46.43 ± 25.56 years for males).
 
 Conclusion
 Pre-transport planning and measures are crucial for the safe transport of patients, especially in large-scale IHTs. In addition, an effective communication system ensures a trouble-free transport.

Aggregated Capability Assessment (AgCA) For CAIQ Enabled Cross-cloud Federation

Cross-Cloud Federation (CCF) enables resource exchange among multiple, heterogeneous Cloud Service Providers (CSPs) to support the composition of services (workflow) hosted by different providers. CCF participation can either be fixed, or the types of services that can be used are limited to reduce the potential risk of service failure or secure access. Although many service selection approaches have been proposed in literature for cloud computing, their applicability to CCF i.e., cloud-to-cloud interaction, has not been adequately investigated. A key component of this cloud-to-cloud paradigm involves assessing the combined capability of contributing participants within a federation and their connectivity. A novel Aggregated Capability Assessment ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AgCA</i> ) approach based on using the Consensus Assessment Initiative Questionnaire from Cloud Security Alliance is proposed for CCF. The proposed mechanism is implemented as a component of a centralized broker to enhance the quality of the selection process for participants within a federation. Our experimental results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AgCA</i> is a useful tool for partner selection in a dynamic, heterogeneous and multilevel cloud federation.

A Benchmarking application on Workload and Performance forecasting of micro services

Micro services are increasingly understood as the ideal architectural framework for building large cloud applications within and beyond organizational boundaries. These micro services architectures scale up the application, but are expensive to work on, so pay attention to workflow planning and workflow planning. However, this issue is not very clear. In this work, we are developing independent micro services workflows suitable for modeling and prediction methods and designing three-step game models for based applications. Solved the problem of designing micro services based applications to reduce end-to-end delays under user-specific limitations (MAWS-BC) and recommended micro services routing algorithms. The design process and estimation methods are improved and adequate. The experimental results produced by a well-known micro service bank cover a wide variety of statistical analyzes and the production utility of graphic design is shown by a large comparison copy compared to current algorithms.

Efficient Computation Offloading for Service Workflow of Mobile Applications in Mobile Edge Computing

Edge computing has become a promising solution to overcome the user equipment (UE) constraints such as low computing capacity and limited energy. A key edge computing challenge is providing computing services with low service congestion and low latency, but the computing resources of edge servers were limited. User task randomness and network inhomogeneity brought considerable challenges to limited-resource MEC systems. To solve these problems, the presented paper proposed a blocking- and delay-aware schedule strategy for MEC environment service workflow offloading. First, the workflow was modeled in mobile applications and the buffer queue in servers. Then, the server collaboration area was divided through a collaboration area division method based on clustering. Finally, an improved particle swarm optimization scheduling method was utilized to solve this NP-hard problem. Many simulation results verified the effectiveness of the proposed scheme. This method was superior to existing methods, which effectively reduces the blocking probability and execution delay and ensures the quality of the experience of the user.

Characterization of genetic counselor practices in inpatient care settings.

Rapid genomic testing is increasingly used in inpatient settings for diagnostic and treatment purposes. With the expansion of genetic testing in this setting, requests for inpatient genetics consultations have increased. There have been reports of genetic counselors working in inpatient care, though their specific roles are not well described. In this study, we characterized the roles of genetic counselors practicing in inpatient care settings in the United States and Canada. Genetic counselors were recruited via professional organization listservs to complete an online survey. The survey gathered information on participants' roles and workflow of inpatient genetics consultation services at their institution. Responses from 132 participants demonstrate that 50.4% of genetic counselors cover genetics consultations as needed or on a rotating schedule (34.6%). They practice in general pediatric (59.1%), neonatal (42.5%), cancer (28.3%), and/or prenatal (18.9%) specialties, among others. Participants reported working independently (16.1%) or with other providers (54.8%), including geneticists and other attending physicians. The workflow of genetics consultation services varies between institutions in the delivery of consults, members of the inpatient genetics consultation care team, and administrative support. Fifty percent of participants reported having no exposure to inpatients during graduate training, and 87.3% of participants reported receiving no institutional training for their inpatient role. This is the first study to describe roles of genetic counselors in inpatient care. It establishes a foundation for future research on inpatient genetic counseling and genetic counseling outcomes in inpatient services. As demand for genetics expertise in inpatient care grows, genetic counselors can be hired to serve inpatient populations alongside genetics and non-genetics providers.

Geoscience model service integrated workflow for rainstorm waterlogging analysis

ABSTRACT This paper proposed a geoscience model service integrated workflow-based rainstorm waterlogging analysis method to overcome the defects of conventional waterlogging analysis systems. In this research, we studied a general OGC WPS service invoking strategy, an automatic asynchronous invoking mechanism of WPS services in the BPEL workflow, and a distributed waterlogging analysis services integrated workflow to realize the reconstruction of a waterlogging analysis model based on the proposed method. The proposed method can make use of the flexible adjustment capability of the workflow and not only overcomes the inherent defects of conventional geoscience analysis methods but also realizes the integration and calculation of distributed geospatial data, models and computing resources automatically. The method has better construction convenience, execution reliability, extensibility and intelligence potential than a conventional method and has important value for dealing with more natural disasters and environmental challenges.

Automated Analysis of Distributed Tracing: Challenges and Research Directions

Microservice-based architectures are gaining popularity for their benefits in software development. Distributed tracing can be used to help operators maintain observability in this highly distributed context, and find problems such as latency, and analyse their context and root cause. However, exploring and working with distributed tracing data is sometimes difficult due to its complexity and application specificity, volume of information and lack of tools. The most common and general tools available for this kind of data, focus on trace-level human-readable data visualisation. Unfortunately, these tools do not provide good ways to abstract, navigate, filter and analyse tracing data. Additionally, they do not automate or aid with trace analysis, relying on administrators to do it themselves. In this paper we propose using tracing data to extract service metrics, dependency graphs and work-flows with the objective of detecting anomalous services and operation patterns. We implemented and published open source prototype tools to process tracing data, conforming to the OpenTracing standard, and developed anomaly detection methods. We validated our tools and methods against real data provided by a major cloud provider. Results show that there is an underused wealth of actionable information that can be extracted from both metric and morphological aspects derived from tracing. In particular, our tools were able to detect anomalous behaviour and situate it both in terms of involved services, work-flows and time-frame. Furthermore, we identified some limitations of the OpenTracing format—as well as the industry accepted tracing abstractions—, and provide suggestions to test trace quality and enhance the standard.

Computed tomography technologist notes in PACS to radiologists: what are they telling us and how does it increase value?

In the clinical workflow of radiology services, a critical connection exists between technologists and radiologists, yet there is often limited communication between this key link in the chain of patient imaging. Our aim was to quantify and detail the communication between CT technologists and radiologists in our tertiary oncology practice. Using the note function in our EMR, as standard operating procedure, CT technologists are instructed to place pertinent notes for the radiologist relevant to any portion of the patient encounter. Note categories pertain to quality and/or safety: patient limitations (e.g., patient unable to raise arm), protocol confirmation (e.g., rectal contrast given), critical finding communication, scan range considerations, IV issues, reduction in eGFR, oral contrast issues, allergy information, general feedback, equipment malfunction, and radiologist approval information. The percentage of notes within each category were recorded upon review of contiguous abdominal CT scans in July 2018 with the primary outcome measure of overall note volume compared to baseline in July 2016 after which time technologists were educated on the importance of notes and were requested to increase use of this tool. Notes were regularly reviewed to identify practice improvement opportunities. Compared to baseline 2years earlier (8860 CT scans, 812 technologist notes), there was a 32% increase in technologist note volume (10,948 CT scans, 1330 technologist notes), representing an increase of notes from 9.2% of exams to 12.1% of exams (p < .001) and there were 14 related practice improvements. After communicating the importance of CT technologist notes and requesting increased notation frequency, technologist note volume significantly increased and 14 specific case examples of related practice improvement demonstrate the electronic medical record note function to be a robust tool in the management of patient imaging.

Use of Emergency Medical Services: Experience 100 Days after First Case of COVID-19

Background: The spread of the novel coronavirus 2019 (COVID-19), emergency medical services (EMS) work flow to be different from the normal situation such as avoid advanced airways management that perform aerosol generating. However, no studies examining EMS operation have been conducted at Srinagarind Hospital. Objective: To compare EMS operation time and procedures during COVID-19 spreading and routine period. Materials and Methods: This cross-sectional study examined all cases in which EMS were dispatched from Srinagarind Hospital during 13th January to 21st April 2020 compared with last year. Data were collected from the Srinagarind Hospital EMS operation database and hospital information database system. Results: Five hundred forty-five EMS operations were examined. The mean age of the patients in 2020 was 50.4+6.2 years, and 70.9% (n = 195) were male. The average times from 1669 center call receipt to arrival on scene (response time) for 2019 and 2020 were 12.44+4.12 minutes and 7.32+2.40 minutes, respectively (p = 0.016). The nebulizer mask procedure was performed in 16.1% of cases in 2019 group compared with 7.1% in the 2020 group (p&lt;0.001). Conclusion: COVID-19 pandemic in Thailand effect EMS operation in age group of patients, operation time, type of patients, response time and procedures in airway and breathing. Keywords: COVID-19, Emergency medical services, Prehospital emergency care, Response time

PathoFusion: An Open-Source AI Framework for Recognition of Pathomorphological Features and Mapping of Immunohistochemical Data.

Simple SummaryWe present an open-source AI framework for marking, training, and automated recognition of pathological features in whole-slide scans of diagnostic tissue sections. The integrated system permits high-resolution qualitative as well as quantitative morphological analyses of entire histological slides and harbors significant potential to facilitate the microscopic analysis of complex pathomorphological problems and the simultaneous mapping of immunohistochemical markers in routine slide diagnostics.We have developed a platform, termed PathoFusion, which is an integrated system for marking, training, and recognition of pathological features in whole-slide tissue sections. The platform uses a bifocal convolutional neural network (BCNN) which is designed to simultaneously capture both index and contextual feature information from shorter and longer image tiles, respectively. This is analogous to how a microscopist in pathology works, identifying a cancerous morphological feature in the tissue context using first a narrow and then a wider focus, hence bifocal. Adjacent tissue sections obtained from glioblastoma cases were processed for hematoxylin and eosin (H&E) and immunohistochemical (CD276) staining. Image tiles cropped from the digitized images based on markings made by a consultant neuropathologist were used to train the BCNN. PathoFusion demonstrated its ability to recognize malignant neuropathological features autonomously and map immunohistochemical data simultaneously. Our experiments show that PathoFusion achieved areas under the curve (AUCs) of 0.985 ± 0.011 and 0.988 ± 0.001 in patch-level recognition of six typical pathomorphological features and detection of associated immunoreactivity, respectively. On this basis, the system further correlated CD276 immunoreactivity to abnormal tumor vasculature. Corresponding feature distributions and overlaps were visualized by heatmaps, permitting high-resolution qualitative as well as quantitative morphological analyses for entire histological slides. Recognition of more user-defined pathomorphological features can be added to the system and included in future tissue analyses. Integration of PathoFusion with the day-to-day service workflow of a (neuro)pathology department is a goal. The software code for PathoFusion is made publicly available.

Standard electronic health record (EHR) framework for Indian healthcare system

Digitization of health records in public health facility and its instant availability in the form of electronic records anywhere any time health service is yet to be implemented in developing nations like India and other countries. In India, patient care is mainly delivered through 3 levels namely Primary/Community Healthcare Centre (PHC/CHC), Secondary healthcare centre (District Hospital), and Tertiary Healthcare Centre (National level). The healthcare facilities face many challenges in collecting, processing, and storing these data and managing it without compromising security and privacy. Presently, some of the secondary and tertiary care facilities have started implementing healthcare IT application in terms of Hospital Information System, Hospital Management Information System, Electronic Medical Records (EMR) etc. to manage the patient data in electronic format. However, these systems are developed by different vendors by using different programming languages and databases. This approach makes the system unique but the patient details remains in the same hospital and cannot be shared with other hospitals when patient moves from one hospital to other for advanced or specialized treatment. This is because the data is not interoperable and semantic. In the proposed work, a standard secure Electronic Health Record(EHR) framework is developed using standard medical terminology and coding standards. Implementation of EHR framework for Indian health system will improve the work-flow of health services to the population. EHR at all levels of healthcare system enable efficient and continuous care to the patient.

The Cloud-edge-based Dynamic Reconfiguration to Service Workflow for Mobile Ecommerce Environments

The emergence of mobile service composition meets the current needs for real-time eCommerce. However, the requirements for eCommerce, such as safety and timeliness, are becoming increasingly strict. Thus, the cloud-edge hybrid computing model has been introduced to accelerate information processing, especially in a mobile scenario. However, the mobile environment is characterized by limited resource storage and users who frequently move, and these characteristics strongly affect the reliability of service composition running in this environment. Consequently, applications are likely to fail if inappropriate services are invoked. To ensure that the composite service can operate normally, traditional dynamic reconfiguration methods tend to focus on cloud services scheduling. Unfortunately, most of these approaches cannot support timely responses to dynamic changes. In this article, the cloud-edge based dynamic reconfiguration to service workflow for mobile eCommerce environments is proposed. First, the service quality concept is extended. Specifically, the value and cost attributes of a service are considered. The value attribute is used to assess the stability of the service for some time to come, and the cost attribute is the cost of a service invocation. Second, a long short-term memory (LSTM) neural network is used to predict the stability of services, which is related to the calculation of the value attribute. Then, in view of the limited available equipment resources, a method for calculating the cost of calling a service is introduced. Third, candidate services are selected by considering both service stability and the cost of service invocation, thus yielding a dynamic reconfiguration scheme that is more suitable for the cloud-edge environment. Finally, a series of comparative experiments were carried out, and the experimental results prove that the method proposed in this article offers higher stability, less energy consumption, and more accurate service prediction.

EPRO-based individual follow-up care for women treated for early breast cancer: impact on service use and workflows.

The increasing population of breast cancer survivors highlights the need to (re)consider how we utilize available services for survivorship care in oncology clinics. Electronic Patient-Reported Outcomes (ePROs) can be used to identify patients' individual care needs and triage them to the right services. We examined the impact on service use, workflow and workload following the introduction of an ePRO-based individual follow-up (PIFU) for women treated for early breast cancer. A multi-method approach was used. In a pilot randomized controlled trial, the use of consultations, telephone calls, and specialist referrals were systematically recorded. Comparison was done between PIFU and standard follow-up care (SFU). Focus group interviews with nurse navigators evaluated the impact on workflow and workload qualitatively. The 64 women randomized to attend SFU used a mean of 3.8 (95% CI: 3.5-4.1) planned consultations during the 2-year study period compared with a mean of 1.9 consultations (95% CI: 1.4-2.4) for the 60 women randomized to PIFU (P < 0.001). Urgent appointments were more frequent in SFU (mean of 0.47 vs 0.22 per patient, P = 0.03). No statistically significant differences were observed in the use of telephone calls and specialist referrals. The nurse navigators did not experience an increase in their workload, but implementation of PIFU may require a re-structured workflow. The ePRO-based individual follow-up could change organization of care and re-allocate services for those in need of it. ePRO-based individual follow-up could potentially ensure more time for those most in need of face-to-face care.

Integrating FMEA and the Kano Model to Improve the Service Quality of Logistics Centers

This study uses the logistics center of a large organic retail store in Taiwan to analyze service blueprint and workflow, identifying the potential points of failure and thus serving as a basis for quality improvement. The failure mode and effect analysis (FMEA) model is an effective problem prevention methodology that can easily interface with many engineering and reliability methods. The utilized method integrates the failure mode and effect analysis (FMEA) and the Kano model to explore the possible occurrence of failures in the internal workflow and services of the studied logistics center. A two-stage survey was conducted. In the first stage, an investigation was conducted by 20 logistics experts on the FMEA’s key service failures. In the second stage, a questionnaire was filled out by 220 store staff to summarize the logistics service quality factors found in the Kano model. The results show that the degree of attention and satisfaction in the priority improvement items when there were service failures vary among the opinions of different internal employees and customers. The participants jointly believed that the items that need improvement are “Damaged incoming goods” and “A shortfall in the quantities of delivered goods”.

PCN45 Systems Models to Identify Implementation Requirements for Disruptive and Complex Health Technologies: A Conceptual Model Reflecting the Organization of Whole-Genome Sequencing in Lung Cancer

After the value of complex disruptive health technologies is established, health organizations face numerous decisions regarding their implementation, which in turn affect accessibility and affordability. This conceptual paper shows how dynamic simulation modeling and systems science can be utilized in HTA to inform organizational decisions regarding the use of complex disruptive health technologies. Using the case study of the nationwide implementation of Whole Genome Sequencing (WGS) into clinical practice in lung cancer in the Dutch healthcare system, we demonstrated how explicitly modeling of the organization of care provides essential insight into the affordability and accessibility of care. We adopted the principles of systems modeling, and thus also consider the broader system in which WGS may be implemented. The relevant system and actors have been identified in discussion with stakeholders and were implemented as a discrete-event simulation model in AnyLogic. The model included workflows of hospitals, genomic services, and molecular tumor boards, while also considering technical and social aspects of WGS. Analysis of three scenarios, describing differences in the organization of care, was conducted to illustrate the potential consequences to the affordability and accessibility of care. Both intermediate outcomes, such as diagnostic turnaround time, guideline-based treatment decisions, access to WGS, as well as final outcomes, such as the number of patients deceased and cost per patient, were used to assess the consequences of differences in the organization of care related to WGS. We have shown how the organization of care and the way WGS is integrated into clinical practice can influence the potential value of WGS. Our approach is, therefore, useful for determining which system changes are required to accommodate and make optimal use of new health technologies. Hence, our systems approach complements conventional approaches focusing on expected impact in a hypothetical perfect organizational context.

A deep learning approach for collaborative prediction of Web service QoS

Web services are a cornerstone of many crucial domains, including cloud computing and the Internet of Things. In this context, QoS prediction for Web services is a highly important and challenging issue that facilitates the building of value-added processes such as compositions and workflows of services. Current QoS prediction approaches, like collaborative filtering methods, mainly suffer from obstacles related to data sparsity and the cold-start problem. Moreover, previous studies have not conducted in-depth explorations of the impact of the geographical characteristics of services/users and QoS ratings on the prediction problem. To address these difficulties, we propose a deep-learning-based approach for QoS prediction. The main idea consists of combining a matrix factorization model based on a deep autoencoder (DAE) with a clustering technique based on geographical characteristics to improve the effectiveness of prediction. The overall method proceeds as follows: first, we cluster the input QoS data using a self-organizing map that incorporates knowledge of geographical neighborhoods; by doing so, we can reduce the data sparsity while preserving the topology of the input data. In addition, the clustering step effectively overcomes the cold-start problem. Next, we train a DAE that minimizes the squared loss between the ground-truth QoS and the predicted one, for each cluster. Finally, the unknown QoS of a new service is predicted using the trained DAE related to the closest cluster. To evaluate the effectiveness and robustness of our approach, we conducted a comprehensive set of experiments based on a real-world Web service QoS dataset. The experimental results show that our method achieves better prediction performance than several state-of-the-art methods.

Driving patient’s records management process on the healthcare service delivery using records life cycle as a tunnel towards quality patients care

It cannot be overemphasised that healthcare institutions are mandated to ensure that health records are properly filed, archived, stored, disposed and retrievable using appropriate system as required by legislation, and that appropriate security measures are in place to ensure secured health records in the healthcare workflow and storages. The study was partially extracted from the author’s PhD research dissertation completed in 2016, to investigate a typical framework for management of patients records on the healthcare service delivery practice following the records life cycle for quality patients care. This multimethod study used stratified random sampling method to collect quantitative data using questionnaire. Sample was drawn from records management officials in 40 hospitals of Limpopo Province in South Africa. The statistical data in questionnaire was supported with a triangulation of data from observation and document/system analysis to clarify statistical information. The study discovered that medical records were not properly managed at different stages of the records life cycle during healthcare service workflow. The study recommended a strategic framework that may be adopted by the healthcare institutions to improve manageability of their medical information, with an electronic record tracking system for file content management and records movement tracking. This will assist healthcare institutions in taking care of the file content completeness in case they maintain creation of information contained in paper-based records. They will also able to track the location of each file while also keeping track record of the file usage (audit trail) for user accountability.

CWOM: a lightweight cloud-oriented workflow optimisation middleware

In recent years, more and more workflow applications have been developed to execute on various kinds of cloud platforms. Although many cloud-based workflow management and enactment systems have been proposed, there are still many challenges when deploying and executing workflows on various kinds of real-world cloud platform. In this paper, we present a lightweight framework, namely cloud-oriented workflow optimisation middleware (CWOM), which is designed for easing the work of optimising the workflow execution efficiency as well as the work of realising fine-grained workflow management. The proposed CWOM is implemented as a plug-in middleware that can interact with the other workflow services and therefore provide more desirable services for workflow applications. Another advantage of our CWOM is that its services are loosely coupled with other cloud services, which makes it more flexible and configurable in most of the real-world cloud platforms. To investigate the effectiveness of the proposed system, we deploy the prototype implementation of CWOM in a campus-based cloud platform, and the experimental results indicate that it can significantly improve the delivered QoS and enhance the service level offered by conventional workflow solutions.

Getting back to work: A framework and pivot plan to resume elective surgery and procedures after COVID-19

IntroductionThe COVID-19 pandemic has compelled a majority of hospital systems to reduce surgical and procedural volumes in an attempt to preserve resources. Elective surgery and procedures resumption has proven to be a calculated risk between COVID-19 exposure and resource depletion and patient morbidity and mortality from surgical deferral. MethodsWithin a few days of halting elective surgery and procedures, our 7-hospital (2427 in-patient beds, 26,647 inpatient surgeries) healthcare system developed a multidisciplinary Pivot Plan with the primary outcome of a phased resumption of elective surgery and procedures. The plan entailed the integration of our electronic medical record, order entry automatization, perioperative staff utilization, partnering with primary care providers, and a stepwise COVID-19 testing algorithm based on a predetermined hierarchy of case acuity and timeliness of patient care. ResultsThe Pivot Plan was instituted on May 10, 2020. Since then, 22,624 patients have been tested for COVID-19 in anticipation of an elective surgery and procedures; 140 (0.62%) tested positive for COVID-19 and had their procedure deferred. As our testing capability has increased, we have been able to increase our added elective surgery and procedures capacity from 13 cases per day to 531 cases per day. In turn, we have seen the case volume increase by 52%. ConclusionOur academic healthcare system located in one of the initial COVID-19 hotspots in the United States has successfully resumed elective surgery and procedures in part due to a receptive and supportive culture based upon nimbleness, agility, and rapid integration of multiple resources from a cohort of diverse disciplines applied to the perioperative services workflow.