tinyIFD: A High-Throughput Binding Pose Refinement Workflow Through Induced-Fit Ligand Docking.

A critical step in structure-based drug discovery is predicting whether and how a candidate molecule binds to a model of a therapeutic target. However, substantial protein side chain movements prevent current screening methods, such as docking, from accurately predicting the ligand conformations and require expensive refinements to produce viable candidates. We present the development of a high-throughput and flexible ligand pose refinement workflow, called "tinyIFD". The main features of the workflow include the use of specialized high-throughput, small-system MD simulation code mdgx.cuda and an actively learning model zoo approach. We show the application of this workflow on a large test set of diverse protein targets, achieving 66% and 76% success rates for finding a crystal-like pose within the top-2 and top-5 poses, respectively. We also applied this workflow to the SARS-CoV-2 main protease (Mpro) inhibitors, where we demonstrate the benefit of the active learning aspect in this workflow.

An assessment of information needs and workflows for emergency service providers and caregivers of children with medical complexity.

BACKGROUND: The goal of Project Austin, an initiative to improve emergency care for rural children who are medically complex (CMC), is to provide an Emergency Information Form (EIF) to their parents/caregivers, to local Emergency Medical Services, and Emergency Departments. EIFs are standard forms recommended by the American Academy of Pediatrics that provide pre-planned rapid response instructions, including medical conditions, medications, and care recommendations, for emergency providers. Our objective is to describe the workflows and perceived utility of the provided emergency information forms (EIFs) in the acute medical management of CMC. METHODS: We sampled from two key stakeholder groups in the acute management of CMC: four focus groups with emergency medical providers from rural and urban settings and eight key informant interviews with parents/caregivers enrolled in an emergency medical management program for CMC. Transcripts were thematically analyzed in NVivo© by two coders using a content analysis approach. The thematic codes were combined into a codebook and revised the themes present through combining relevant themes and developing of sub-themes until they reached consensus. RESULTS: All parents/caregivers interviewed were enrolled in Project Austin and had an EIF. Emergency medical providers and parents/caregivers supported the usage of EIFs for CMC. Parents/caregivers also felt EIFs made emergency medical providers more prepared for their child. Providers identified that EIFs helped provide individualized care, however they were not confident the data was current and so felt unsure they could rely on the recommendations on the EIF. CONCLUSION: EIFs are an easy way to engage parents, caregivers, and emergency medical providers about the specifics of a care for CMC during an emergency. Timely updates and electronic access to EIFs could improve their value for medical providers.

Monkeypox viral nucleic acids detected using both DNA and RNA extraction workflows.

Molecular methods have been used to detect human pathogens in wastewater with sampling typically performed at wastewater treatment plants (WWTP) and upstream locations within the sewer system. A wastewater-based surveillance (WBS) program was established at the University of Miami (UM) in 2020, which included measurements of SARS-CoV-2 levels in wastewater from its hospital and within the regional WWTP. In addition to the development of a SARS-CoV-2 quantitative PCR (qPCR) assay, qPCR assays to detect other human pathogens of interest were also developed at UM. Here we report on the use of a modified set of reagents published by the CDC to detect nucleic acids of Monkeypox virus (MPXV) which emerged during May of 2022 to become a concern worldwide. Samples collected from the University hospital and from the regional WWTP were processed through DNA and RNA workflows and analyzed by qPCR to detect a segment of the MPXV CrmB gene. Results show positive detections of MPXV nucleic acids in the hospital and wastewater treatment plant wastewater which coincided with clinical cases in the community and mirrored the overall trend of nationwide MPXV cases reported to the CDC. We recommend the expansion of current WBS programs' methods to detect a broader range of pathogens of concern in wastewater and present evidence that viral RNA in human cells infected by a DNA virus can be detected in wastewater.

Underscoring the effect of swab type, workflow, and positive sample order on swab pooling for COVID-19 surveillance testing.

Sample pooling is a promising strategy to facilitate COVID-19 surveillance testing for a larger population in comparison to individual single testing due to resource and time constraints. Increased surveillance testing capacity will reduce the likelihood of outbreaks as the general population is returning to work, school, and other gatherings. We have analyzed the impact of three variables on the effectiveness of pooling test samples: swab type, workflow, and positive sample order. We investigated the performance of several commercially available swabs (Steripack polyester flocked, Puritan nylon flocked, Puritan foam) in comparison to a new injected molded design (Yukon). The bench-top performance of collection swab was conducted with a previously developed anterior nasal cavity tissue model, based on a silk-glycerol sponge to mimic soft tissue mechanics and saturated with a physiologically relevant synthetic nasal fluid spiked with heat-inactivated SARS-CoV-2. Overall, we demonstrated statistically significant differences in performance across the different swab types. A characterization of individual swab uptake (gravimetric analysis) and FITC microparticle release suggests that differences in absorbance and retention drive the observed differences in Ct of the pooled samples. We also proposed two distinct pooling workflows to encompass different community collection modes and analyzed the difference in resulting positive pools as an effect of workflow, swab type, and positive sample order. Overall, swab types with lower volume retention resulted in reduced false negative occurrence, also observed for collection workflows with limited incubation times. Concurrently, positive sample order did have a significant impact on pooling test outcome, particularly in the case of swab type with great volume retention. We demonstrated that the variables investigated here affect the results of pooled COVID-19 testing, and therefore should be considered while designing pooled surveillance testing.

BIRCH: An Automated Workflow for Evaluation, Correction, and Visualization of Batch Effect in Bottom-Up Mass Spectrometry-Based Proteomics Data.

Recent surges in large-scale mass spectrometry (MS)-based proteomics studies demand a concurrent rise in methods to facilitate reliable and reproducible data analysis. Quantification of proteins in MS analysis can be affected by variations in technical factors such as sample preparation and data acquisition conditions leading to batch effects, which adds to noise in the data set. This may in turn affect the effectiveness of any biological conclusions derived from the data. Here we present Batch-effect Identification, Representation, and Correction of Heterogeneous data (BIRCH), a workflow for analysis and correction of batch effect through an automated, versatile, and easy to use web-based tool with the goal of eliminating technical variation. BIRCH also supports diagnosis of the data to check for the presence of batch effects, feasibility of batch correction, and imputation to deal with missing values in the data set. To illustrate the relevance of the tool, we explore two case studies, including an iPSC-derived cell study and a Covid vaccine study to show different context-specific use cases. Ultimately this tool can be used as an extremely powerful approach for eliminating technical bias while retaining biological bias, toward understanding disease mechanisms and potential therapeutics.

COVID-19 and clinic workflow optimization using lean six sigma.

OBJECTIVES: The COVID-19 pandemic has fundamentally changed the workflow of clinics. We applied Lean Six Sigma processes to optimize clinic workflow to reduce patient wait times and improve the patient experience. STUDY DESIGN: Prospective cohort study. METHODS: We implemented (1) pushing most extended wait times to the end of the workflow by rooming the patient directly and (2) using distractions during the waiting process by using educational videos and a timer for physician arrival in the patient exam room. We compared the patient wait times and subcomponents of Press Ganey scores as a surrogate for changes in patient experience and satisfaction from the preimplementation period (n = 277) to the 3-month (September 1, 2020, to November 30, 2020) postimplementation period (n = 218). RESULTS: There was a significant reduction in overall throughput time (38 vs 35 minutes) and wait before rooming (11 vs 8 minutes), and increased physician time with patients (15 vs 17 minutes) (P < .0001 for all). These results corresponded with a significant improvement in Press Ganey subcomponents of (1) waiting time in the exam room before being seen by the care provider, (2) degree to which you were informed about any delays, (3) wait time at clinic (from arriving to leaving), and (4) length of wait before going to an exam room (P < .001 for all). CONCLUSIONS: Simple, inexpensive measures can improve patient engagement and provide a safe setting for patients for clinic visits in the wake of COVID-19. In the future, clinics' common wait areas could be reappropriated to increase the number of clinic exam rooms.

Accelerated CMC workflows to enable speed to clinic in the COVID-19 era: A multi-company view from the biopharmaceutical industry.

The COVID-19 pandemic has placed unprecedented pressure on biopharmaceutical companies to develop efficacious preventative and therapeutic treatments, which is unlikely to abate in the coming years. The importance of fast progress to clinical evaluation for treatments, which tackle unmet medical needs puts strain on traditional product development timelines, which can take years from start to finish. Although previous work has been successful in reducing phase 1 timelines for recombinant antibodies, through utilization of the latest technological advances and acceptance of greater business risk or costs, substantially faster development is likely achievable without increased risk to patients during initial clinical evaluation. To optimize lessons learned from the pandemic and maximize multi-stakeholder (i.e., patients, clinicians, companies, regulatory agencies) benefit, we conducted an industry wide benchmarking survey in September/October 2021. The aims of this survey were to: (i) benchmark current technical practices of key process and product development activities related to manufacturing of therapeutic proteins, (ii) understand the impact of changes implemented in COVID-19 accelerated Ab programs, and whether any such changes can be retained as part of sustainable long-term business practices and (iii) understand whether any accelerative action(s) taken have (negatively) impacted the wider development process. This article provides an in-depth analysis of this data, ultimately highlighting an industry perspective of how biopharmaceutical companies can sustainably adopt new approaches to therapeutic protein development and production.

Carryover Contamination-Controlled Amplicon Sequencing Workflow for Accurate Qualitative and Quantitative Detection of Pathogens: a Case Study on SARS-CoV-2.

Carryover contamination during amplicon sequencing workflow (AMP-Seq) put the accuracy of the high-throughput detection for pathogens at risk. The purpose of this study is to develop a carryover contaminations-controlled AMP-Seq (ccAMP-Seq) workflow to enable accurate qualitative and quantitative detection for pathogens. By using the AMP-Seq workflow to detect SARS-CoV-2, Aerosols, reagents and pipettes were identified as potential sources of contaminations and ccAMP-Seq was then developed. ccAMP-Seq used filter tips and physically isolation of experimental steps to avoid cross contamination, synthetic DNA spike-ins to compete with contaminations and quantify SARS-CoV-2, dUTP/uracil DNA glycosylase system to digest the carryover contaminations, and a new data analysis procedure to remove the sequencing reads from contaminations. Compared to AMP-Seq, the contamination level of ccAMP-Seq was at least 22-folds lower and the detection limit was also about an order of magnitude lower-as low as one copy/reaction. By testing the dilution series of SARS-CoV-2 nucleic acid standard, ccAMP-Seq showed 100% sensitivity and specificity. The high sensitivity of ccAMP-Seq was further confirmed by the detection of SARS-CoV-2 from 62 clinical samples. The consistency between qPCR and ccAMP-Seq was 100% for all the 53 qPCR-positive clinical samples. Seven qPCR-negative clinical samples were found to be positive by ccAMP-Seq, which was confirmed by extra qPCR tests on subsequent samples from the same patients. This study presents a carryover contamination-controlled, accurate qualitative and quantitative amplicon sequencing workflow that addresses the critical problem of pathogen detection for infectious diseases. IMPORTANCE Accuracy, a key indicator of pathogen detection technology, is compromised by carryover contamination in the amplicon sequencing workflow. Taking the detection of SARS-CoV-2 as case, this study presents a new carryover contamination-controlled amplicon sequencing workflow. The new workflow significantly reduces the degree of contamination in the workflow, thereby significantly improving the accuracy and sensitivity of the SARS-CoV-2 detection and empowering the ability of quantitative detection. More importantly, the use of the new workflow is simple and economical. Therefore, the results of this study can be easily applied to other microorganism, which has great significance for improving the detection level of microorganism.

The role of triage in the prevention and control of COVID-19.

OBJECTIVE: To prevent and control public health emergencies, we set up a prescreening and triage workflow and analyzed the effects on coronavirus disease 2019 (COVID-19). METHODS: In accordance with the requirements of the level 1 emergency response of public health emergencies in Shaanxi Province, China, a triage process for COVID-19 was established to guide patients through a 4-level triage process during their hospital visits. The diagnosis of COVID-19 was based on positive COVID-19 nucleic acid testing according to the unified triage standards of the Guidelines for the Diagnosis and Treatment of Novel Coronavirus Pneumonia (Trial version 4),4 issued by the National Health Commission of the People's Republic of China. RESULTS: The screened rate of suspected COVID-19 was 1.63% (4 of 246) in the general fever outpatient clinic and 8.28% (13 of 157) in the COVID-19 outpatient clinic, and they showed a significant difference (P = .00). CONCLUSIONS: The triage procedure effectively screened the patients and identified the high-risk population.

Successful Implementation of Workflow-Embedded Clinical Pathways During the COVID 19 Pandemic.

BACKGROUND AND OBJECTIVES: Clinical pathways have been found effective for improving adherence to evidence-based guidelines, thus providing better patient outcomes. As coronavirus disease-2019 (COVID-19) clinical guidance changed rapidly and evolved, a large hospital system in Colorado established clinical pathways within the electronic health record to guide clinical practice and provide the most up-to-date information to frontline providers. METHODS: On March 12, 2020, a system-wide multidisciplinary committee of specialists in emergency medicine, hospital medicine, surgery, intensive care, infectious disease, pharmacy, care management, virtual health, informatics, and primary care was recruited to develop clinical guidelines for COVID-19 patient care based on the limited available evidence and consensus. These guidelines were organized into novel noninterruptive digitally embedded pathways in the electronic health record (Epic Systems, Verona, Wisconsin) and made available to nurses and providers at all sites of care. Pathway utilization data were analyzed from March 14 to December 31, 2020. Retrospective pathway utilization was stratified by each care setting and compared with Colorado hospitalization rates. This project was designated as a quality improvement initiative. RESULTS: Nine unique pathways were developed, including emergency medicine, ambulatory, inpatient, and surgical care guidelines. Pathway data were analyzed from March 14 to December 31, 2020, and showed that COVID-19 clinical pathways were used 21 099 times. Eighty-one percent of pathway utilization occurred in the emergency department setting, and 92.4% applied embedded testing recommendations. A total of 3474 distinct providers employed these pathways for patient care. CONCLUSIONS: Noninterruptive digitally embedded clinical care pathways were broadly utilized during the early part of the COVID-19 pandemic in Colorado and influenced care across many care settings. This clinical guidance was most highly utilized in the emergency department setting. This shows an opportunity to leverage noninterruptive technology at the point of care to guide clinical decision-making and practice.

Characteristics of telemedicine workflows in nursing homes during the COVID-19 pandemic.

BACKGROUND: The use of telemedicine increased dramatically in nursing homes (NHs) during the COVID-19 pandemic. However, little is known about the actual process of conducting a telemedicine encounter in NHs. The objective of this study was to identify and document the work processes associated with different types of telemedicine encounters conducted in NHs during the COVID-19 pandemic. METHODS: A mixed methods convergent study was utilized. The study was conducted in a convenience sample of two NHs that had newly adopted telemedicine during the COVID-19 pandemic. Participants included NH staff and providers involved in telemedicine encounters conducted in the study NHs. The study involved semi-structured interviews and direct observation of telemedicine encounters and post-encounter interviews with staff and providers involved in telemedicine encounters observed by research staff. The semi-structured interviews were structured using the Systems Engineering Initiative for Patient Safety (SEIPS) model to collect information about telemedicine workflows. A structured checklist was utilized to document steps performed during direct observations of telemedicine encounters. Information from interviews and observations informed the creation of a process map of the NH telemedicine encounter. RESULTS: A total of 17 individuals participated in semi-structured interviews. Fifteen unique telemedicine encounters were observed. A total of 18 post-encounter interviews with 7 unique providers (15 interviews in total) and three NH staff were performed. A 9-step process map of the telemedicine encounter, along with two microprocess maps related to encounter preparation and activities within the telemedicine encounter, were created. Six main processes were identified: encounter planning, family or healthcare authority notification, pre-encounter preparation, pre-encounter huddle, conducting the encounter, and post-encounter follow-up. CONCLUSION: The COVID-19 pandemic changed the delivery of care in NHs and increased reliance on telemedicine services in these facilities. Workflow mapping using the SEIPS model revealed that the NH telemedicine encounter is a complex multi-step process and identified weaknesses related to scheduling, electronic health record interoperability, pre-encounter planning, and post-encounter information exchange, which represent opportunities to improve and enhance the telemedicine encounter process in NHs. Given public acceptance of telemedicine as a care delivery model, expanding the use of telemedicine beyond the COVID-19 pandemic, especially for certain NH telemedicine encounters, could improve quality of care.

New workflow predicts drug targets against SARS-CoV-2 via metabolic changes in infected cells.

COVID-19 is one of the deadliest respiratory diseases, and its emergence caught the pharmaceutical industry off guard. While vaccines have been rapidly developed, treatment options for infected people remain scarce, and COVID-19 poses a substantial global threat. This study presents a novel workflow to predict robust druggable targets against emerging RNA viruses using metabolic networks and information of the viral structure and its genome sequence. For this purpose, we implemented pymCADRE and PREDICATE to create tissue-specific metabolic models, construct viral biomass functions and predict host-based antiviral targets from more than one genome. We observed that pymCADRE reduces the computational time of flux variability analysis for internal optimizations. We applied these tools to create a new metabolic network of primary bronchial epithelial cells infected with SARS-CoV-2 and identified enzymatic reactions with inhibitory effects. The most promising reported targets were from the purine metabolism, while targeting the pyrimidine and carbohydrate metabolisms seemed to be promising approaches to enhance viral inhibition. Finally, we computationally tested the robustness of our targets in all known variants of concern, verifying our targets' inhibitory effects. Since laboratory tests are time-consuming and involve complex readouts to track processes, our workflow focuses on metabolic fluxes within infected cells and is applicable for rapid hypothesis-driven identification of potentially exploitable antivirals concerning various viruses and host cell types.

A hybrid structure determination approach to investigate the druggability of the nucleocapsid protein of SARS-CoV-2.

The pandemic caused by SARS-CoV-2 has called for concerted efforts to generate new insights into the biology of betacoronaviruses to inform drug screening and development. Here, we establish a workflow to determine the RNA recognition and druggability of the nucleocapsid N-protein of SARS-CoV-2, a highly abundant protein crucial for the viral life cycle. We use a synergistic method that combines NMR spectroscopy and protein-RNA cross-linking coupled to mass spectrometry to quickly determine the RNA binding of two RNA recognition domains of the N-protein. Finally, we explore the druggability of these domains by performing an NMR fragment screening. This workflow identified small molecule chemotypes that bind to RNA binding interfaces and that have promising properties for further fragment expansion and drug development.

Gyrolab Immunoassays: Miniaturization, Automation, and Integration into a Rapid Workflow.

Gyrolab® is an open immunoassay platform that automates the complete immunoassay protocol in a microfluidic disc. The column profiles generated with Gyrolab immunoassays are used to gain more information about biomolecular interactions that can be useful in assay development or quantify analytes in samples. Gyrolab immunoassays can be used to cover a broad concentration range and diversity of matrices in applications ranging from biomarker monitoring, pharmacodynamics and pharmacokinetics studies, to bioprocess development in many areas, including therapeutic antibodies, vaccines, and cell and gene therapy.This chapter is an overview of Gyrolab technology, including system components and the assay development workflow, including the process of selecting affinity reagents, Gyrolab Bioaffy CDs, and assay conditions to optimize immunoassays. Two case studies are included. The first involves an assay for the humanized antibody pembrolizumab used in cancer immunotherapy that can generate data for pharmacokinetics studies. The second case study involves quantification of the biomarker and biotherapeutic interleukin-2 (IL-2) in human serum and buffer. IL-2 has been implicated in the cytokine storm associated with COVID-19, and cytokine release syndrome (CRS), which can occur during chimeric antigen receptor T cell (CART) therapy used in treating cancer. These molecules also have therapeutic relevance in combination.

Workload and workflow implications associated with the use of electronic clinical decision support tools used by health professionals in general practice: a scoping review.

BACKGROUND: Electronic clinical decision support tools (eCDS) are increasingly available to assist General Practitioners (GP) with the diagnosis and management of a range of health conditions. It is unclear whether the use of eCDS tools has an impact on GP workload. This scoping review aimed to identify the available evidence on the use of eCDS tools by health professionals in general practice in relation to their impact on workload and workflow. METHODS: A scoping review was carried out using the Arksey and O'Malley methodological framework. The search strategy was developed iteratively, with three main aspects: general practice/primary care contexts, risk assessment/decision support tools, and workload-related factors. Three databases were searched in 2019, and updated in 2021, covering articles published since 2009: Medline (Ovid), HMIC (Ovid) and Web of Science (TR). Double screening was completed by two reviewers, and data extracted from included articles were analysed. RESULTS: The search resulted in 5,594 references, leading to 95 full articles, referring to 87 studies, after screening. Of these, 36 studies were based in the USA, 21 in the UK and 11 in Australia. A further 18 originated from Canada or Europe, with the remaining studies conducted in New Zealand, South Africa and Malaysia. Studies examined the use of eCDS tools and reported some findings related to their impact on workload, including on consultation duration. Most studies were qualitative and exploratory in nature, reporting health professionals' subjective perceptions of consultation duration as opposed to objectively-measured time spent using tools or consultation durations. Other workload-related findings included impacts on cognitive workload, "workflow" and dialogue with patients, and clinicians' experience of "alert fatigue". CONCLUSIONS: The published literature on the impact of eCDS tools in general practice showed that limited efforts have focused on investigating the impact of such tools on workload and workflow. To gain an understanding of this area, further research, including quantitative measurement of consultation durations, would be useful to inform the future design and implementation of eCDS tools.

Aerosol formation during processing of potentially infectious samples on Roche immunochemistry analyzers (cobas e analyzers) and in an end-to-end laboratory workflow to model SARS-CoV-2 infection risk for laboratory operators.

Objectives: To assess aerosol formation during processing of model samples in a simulated real-world laboratory setting, then apply these findings to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to assess the risk of infection to laboratory operators. Design: This study assessed aerosol formation when using cobas e analyzers only and in an end-to-end laboratory workflow. Recombinant hepatitis B surface antigen (HBsAg) was used as a surrogate marker for infectious SARS-CoV-2 viral particles. Using the HBsAg model, air sampling was performed at different positions around the cobas e analyzers and in four scenarios reflecting critical handling and/or transport locations in an end-to-end laboratory workflow. Aerosol formation of HBsAg was quantified using the Elecsys® HBsAg II quant II immunoassay. The model was then applied to SARS-CoV-2. Results: Following application to SARS-CoV-2, mean HBsAg uptake/hour was 1.9 viral particles across the cobas e analyzers and 0.87 viral particles across all tested scenarios in an end-to-end laboratory workflow, corresponding to a maximum inhalation rate of <16 viral particles during an 8-hour shift. Conclusion: Low production of marker-containing aerosol when using cobas e analyzers and in an end-to-end laboratory workflow is consistent with a remote risk of laboratory-acquired SARS-CoV-2 infection for laboratory operators.

A Multiplex PCR and DNA-Sequencing Workflow on Serum for the Diagnosis and Species Identification for Invasive Aspergillosis and Mucormycosis.

There has been significant increase in the use of molecular tools for the diagnosis of invasive aspergillosis (IA) and mucormycosis. However, their range of detection may be too limited as species diversity and coinfections are increasing. Here, we aimed to evaluate a molecular workflow based on a new multiplex PCR assay detecting the whole Aspergillus genus and the Mucorales order followed by a species-specific PCR or a DNA-sequencing approach for IA and/or mucormycosis diagnosis and species identification on serum. Performances of the MycoGENIE Aspergillus spp./Mucorales spp. duplex PCR kit were analyzed on a broad range of fungal strains and on sera from high-risk patients prospectively over a 12-month period. The kit allowed the detection of nine Aspergillus species and 10 Mucorales (eight genera) strains assessed. No cross-reactions between the two targets were observed. Sera from 744 patients were prospectively analyzed, including 35 IA, 16 mucormycosis, and four coinfections. Sensitivity varies from 85.7% (18/21) in probable/proven IA to 28.6% (4/14) in COVID-19-associated pulmonary aspergillosis. PCR-positive samples corresponded to 21 A. fumigatus, one A. flavus, and one A. nidulans infections. All the disseminated mucormycosis were positive in serum (14/14), including the four Aspergillus coinfections, but sensitivity fell to 33.3% (2/6) in localized forms. DNA sequencing allowed Mucorales identification in serum in 15 patients. Remarkably, the most frequent species identified was Rhizomucor pusillus (eight cases), whereas it is barely found in fungal culture. This molecular workflow is a promising approach to improve IA and mucormycosis diagnosis and epidemiology.

Integrating a Patient Engagement App into an Electronic Health Record-Enabled Workflow Using Interoperability Standards.

BACKGROUND: Patient use of mobile health applications is increasing. To promote patient-centered care, data from these apps must be integrated into clinician workflows within the electronic health record (EHR). Health Level 7 Fast Healthcare Interoperability Resources (FHIR) offers a standards-based application programming interface (API) that may support such integration. OBJECTIVE: We aimed to use interoperability standards to integrate a patient mobile application (coronavirus 2019 [COVID-19] Tracker) with an EHR. The COVID-19 Tracker engages patients by sending introductory and reminder text messages, collecting vital signs and symptom data from COVID-19 patients, and providing actionable guidance if concerning issues are identified. This case report explored the use of FHIR APIs to integrate the app into EHR-enabled clinical workflows. METHODS: The authors used notes from project meetings and from semistructured discussions among the application development team to track the design and implementation processes. Seven points of integration between the application and the EHR were identified, and approaches using FHIR to perform these integrations were delineated. RESULTS: Although this clinical decision support integration project benefited from its standards-based approach, many challenges were encountered. These were due to (1) partial implementation of the FHIR standard in the EHR, particularly, components needed for patient engagement applications; (2) limited experience with the adoption of FHIR standards; and (3) gaps in the current FHIR standard. Alternative approaches, often not based on interoperability standards, were developed to overcome these limitations. CONCLUSION: Despite the challenges encountered due to the early stages of FHIR development and adoption, FHIR standards provide a promising mechanism for overcoming longstanding barriers and facilitating the integration of patient engagement apps with EHRs. To accelerate the integration of apps into clinical workflows, additional components of the FHIR standard must be implemented within the EHR and other clinical systems. Continued expansion of available FHIR resources will help with tighter workflow integration.

Insights from incorporating quantum computing into drug design workflows.

MOTIVATION: While many quantum computing (QC) methods promise theoretical advantages over classical counterparts, quantum hardware remains limited. Exploiting near-term QC in computer-aided drug design (CADD) thus requires judicious partitioning between classical and quantum calculations. RESULTS: We present HypaCADD, a hybrid classical-quantum workflow for finding ligands binding to proteins, while accounting for genetic mutations. We explicitly identify modules of our drug-design workflow currently amenable to replacement by QC: non-intuitively, we identify the mutation-impact predictor as the best candidate. HypaCADD thus combines classical docking and molecular dynamics with quantum machine learning (QML) to infer the impact of mutations. We present a case study with the coronavirus (SARS-CoV-2) protease and associated mutants. We map a classical machine-learning module onto QC, using a neural network constructed from qubit-rotation gates. We have implemented this in simulation and on two commercial quantum computers. We find that the QML models can perform on par with, if not better than, classical baselines. In summary, HypaCADD offers a successful strategy for leveraging QC for CADD. AVAILABILITY AND IMPLEMENTATION: Jupyter Notebooks with Python code are freely available for academic use on GitHub: https://www.github.com/hypahub/hypacadd_notebook. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.