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BackgroundTo evaluate the degree to which clinical comorbidities or combinations of comorbidities are associated with SARS-CoV-2 breakthrough infection.Materials and methodsA breakthrough infection was defined as a positive test at least 14 days after a full vaccination regimen. Logistic regression was used to calculate aORs, which were adjusted for age, sex, and race information.ResultsA total of 110,380 patients from the UC CORDS database were included. After adjustment, stage 5 CKD due to hypertension (aOR: 7.33; 95% CI: 4.86-10.69; p

The deployment of predictive analytic algorithms that can safely and seamlessly integrate into existing healthcare workflows remains a significant challenge. Here, we present a scalable, cloud-based, fault-tolerant platform that is capable of extracting and processing electronic health record (EHR) data for any patient at any time following admission and transferring results back into the EHR. This platform has been successfully deployed within the UC San Diego Health system and utilizes interoperable data standards to enable portability.Clinical relevanceThis platform is currently hosting a deep learning model for the early prediction of sepsis that is operational in two emergency departments.

BACKGROUND Older adults are the fastest growing digital technology users. IVAs embodied in smart speakers are popular and they are accessible to people with sensory, physical and cognitive impairments. The geriatrics 5Ms: Medications, Mind, Mobility, what Matters most and Multicomplexity, addresses the complex care needs of older adults. OBJECTIVE We examined data on older adults’ views of how an IVA may address their health-related needs and ascertained their fit into the geriatrics 5Ms framework. METHODS We analyzed qualitative data from interviews among 16 older adults. Two authors independently assigned a subset of the original codes into themes corresponding to the 5Ms. RESULTS We sorted 111 codes into the 5Ms. Representative themes for each of the 5Ms and potential IVA uses are: 1) Medication: difficulty remembering complex medication schedules. Use: medication reminders. 2) Mind: isolation, depression, anxiety and memory loss. Use: companionship, behavioral interventions and memory aids. 3) Mobility: barriers to exercise. Use: reminders, incentives, ideas for exercise. 4) Matters most: participant health concerns (e.g., nutrition). Use: suggesting nutritious foods, and ordering them from local stores. 5) Multicomplexity; managing multimorbidity. Use: symptom tracking and communicating with health care professionals and care partners. CONCLUSIONS Older adult participants’ needs and recommendations for a health-related IVA can be put into a 5Ms framework. Moreover, we identified numerous uses for an IVA utilizing this older adult needs-focused framework. Incorporating the 5Ms framework into IVA design will improve their ability to address health care priorities of older adults and their care partners.

Opioid Overdose Network is an effort to generalize and adapt an existing research data network, the Accrual to Clinical Trials (ACT) Network, to support design of trials for survivors of opioid overdoses presenting to emergency departments (ED). Four institutions (Medical University of South Carolina [MUSC], Dartmouth Medical School [DMS], University of Kentucky [UK], and University of California San Diego [UCSD]) worked to adapt the ACT network. The approach that was taken to enhance the ACT network focused on 4 activities: cloning and extending the ACT infrastructure, developing an e-phenotype and corresponding registry, developing portable natural language processing tools to enhance data capture, and developing automated documentation templates to enhance extended data capture. Overall, initial results suggest that tailoring of existing multipurpose federated research networks to specific tasks is feasible; however, substantial efforts are required for coordination of the subnetwork and development of new tools for extension of available data. The initial output of the project was a new approach to decision support for the prescription of naloxone for home use in the ED, which is under further study within the network.

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

Voice based Intelligent Virtual Assistants (IVAs) promise to improve healthcare management and Quality of Life (QOL) by introducing the paradigm of hands free and eye free interactions. However, there has been little understanding regarding the challenges for designing such systems for older adults, especially when it comes to healthcare related tasks. To tackle this, we consider the processes of care delivery and QOL enhancements for older adults as a collaborative task between patients and providers. By interviewing 16 older adults living independently or semi independently and 5 providers, we identified 12 barriers that older adults might encounter during daily routine and while managing health. We ultimately highlighted key design challenges and opportunities that might be introduced when integrating voice based IVAs into the life of older adults. Our work will benefit practitioners who study and attempt to create full fledged IVA powered smart devices to deliver better care and support an increased QOL for aging populations., 16 pages, The 23rd International ACM SIGACCESS Conference on Computers and Accessibility

ObjectiveOpioid Overdose Network is an effort to generalize and adapt an existing research data network, the Accrual to Clinical Trials (ACT) Network, to support design of trials for survivors of opioid overdoses presenting to emergency departments (ED). Four institutions (Medical University of South Carolina (MUSC), Dartmouth Medical School (DMS), University of Kentucky (UK), and University of California San Diego (UCSD)) worked to adapt ACT network. This paper reports their progress.Materials and MethodsThe approach taken to enhancing ACT network focused on four activities: cloning and extending the ACT infrastructure, developing an e-phenotype and corresponding registry, developing portable natural language processing (NLP) tools to enhance data capture, and developing automated documentation templates to enhance extended data capture.ResultsAll four institutions were able to replicate their i2b2 and Shared Health Research Information Network (SHRINE) infrastructure. A five category e-phenotype model based on ICD-10 coding was developed from prior published work. Ongoing work is refining this via machine learning and artificial intelligence methods. Portable NLP tools, focused at the sentence level, were also developed to identify uncoded opioid overdose related concepts in provider notes.Optimal performance was seen in NLP tools that combined rule-based with deep learning methods (F score, 0.94). A template for ED overdose documentation was developed to improve primary data capture. Interactive prompts to physicians inside ED progress notes were effective in promoting use of the template. The template had good system usability and net promoter scores (0.72 and 0.75, respectively, n=13). Work to design ED trials based on the network’s data is underway.Discussion and ConclusionsOverall, initial results suggest that tailoring of existing multipurpose research networks to specific tasks is feasible; however, substantial efforts are required for coordination of the subnetwork and development of new tools for extension of available data.

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

Acute lymphoblastic leukemia affects both children and adults. Rising costs of cancer care and patient burden contribute to the need to study factors influencing outcomes. This study explored the quality of datasets generated from a clinical research institution. The ‘fit-for-use’ of data prior to examining survival/complications was determined through a systematic approach guided by the Weiskopf et al. 3x3 Data Quality Assessment Framework. Constructs of completeness, correctness, and currency were explored for the data dimensions of patient, variables, and time. There were 11 types of data retrieved. Sufficient data points were found for patient and variable data in each dataset (≥70% of its cells filled with patient level data). Although there was concordance between variables, we found the distribution of lab values and death data to be incorrect. There were missing values for labs ordered and death dates. Our study showed that datasets retrieved can vary, even from the same institution.

We aimed to develop and validate an instrument to detect hospital medication prescribing errors using repurposed clinical decision support system data. Despite significant efforts to eliminate medication prescribing errors, these events remain common in hospitals. Data from clinical decision support systems have not been used to identify prescribing errors as an instrument for physician-level performance. We evaluated medication order alerts generated by a knowledge-based electronic prescribing system occurring in one large academic medical center's acute care facilities for patient encounters between 2009 and 2012. We developed and validated an instrument to detect medication prescribing errors through a clinical expert panel consensus process to assess physician quality of care. Six medication prescribing alert categories were evaluated for inclusion, one of which - dose - was included in the algorithm to detect prescribing errors. The instrument was 93% sensitive (recall), 51% specific, 40% precise, 62% accurate, with an F1 score of 55%, positive predictive value of 96%, and a negative predictive value of 32%. Using repurposed electronic prescribing system data, dose alert overrides can be used to systematically detect medication prescribing errors occurring in an inpatient setting with high sensitivity.

Screening for latent tuberculosis infection is recommended for foreign-born persons in the United States. We used proxy data from electronic health records to determine that 17.5% of foreign-born outpatients attending the UC San Diego Health clinic (San Diego, CA, USA) underwent screening. Ending the global tuberculosis epidemic requires improved screening.

Background Fungal ocular involvement can develop in patients with fungal bloodstream infections and can be vision-threatening. Ocular involvement has become less common in the current era of improved antifungal therapies. Retrospectively determining the prevalence of fungal ocular involvement is important for informing clinical guidelines, such as the need for routine ophthalmologic consultations. However, manual retrospective record review to detect cases is time-consuming. Objective This study aimed to determine the prevalence of fungal ocular involvement in a critical care database using both structured and unstructured electronic health record (EHR) data. Methods We queried microbiology data from 46,467 critical care patients over 12 years (2000-2012) from the Medical Information Mart for Intensive Care III (MIMIC-III) to identify 265 patients with culture-proven fungemia. For each fungemic patient, demographic data, fungal species present in blood culture, and risk factors for fungemia (eg, presence of indwelling catheters, recent major surgery, diabetes, immunosuppressed status) were ascertained. All structured diagnosis codes and free-text narrative notes associated with each patient’s hospitalization were also extracted. Screening for fungal endophthalmitis was performed using two approaches: (1) by querying a wide array of eye- and vision-related diagnosis codes, and (2) by utilizing a custom regular expression pipeline to identify and collate relevant text matches pertaining to fungal ocular involvement. Both approaches were validated using manual record review. The main outcome measure was the documentation of any fungal ocular involvement. Results In total, 265 patients had culture-proven fungemia, with Candida albicans (n=114, 43%) and Candida glabrata (n=74, 28%) being the most common fungal species in blood culture. The in-hospital mortality rate was 121 (46%). In total, 7 patients were identified as having eye- or vision-related diagnosis codes, none of whom had fungal endophthalmitis based on record review. There were 26,830 free-text narrative notes associated with these 265 patients. A regular expression pipeline based on relevant terms yielded possible matches in 683 notes from 108 patients. Subsequent manual record review again demonstrated that no patients had fungal ocular involvement. Therefore, the prevalence of fungal ocular involvement in this cohort was 0%. Conclusions MIMIC-III contained no cases of ocular involvement among fungemic patients, consistent with prior studies reporting low rates of ocular involvement in fungemia. This study demonstrates an application of natural language processing to expedite the review of narrative notes. This approach is highly relevant for ophthalmology, where diagnoses are often based on physical examination findings that are documented within clinical notes.

Fungemia is a life-threatening infection, but predictive models of in-patient mortality in this infection are few. In this study, we developed models predicting all-cause in-hospital mortality among 265 fungemic patients in the Medical Information Mart for Intensive Care (MIMIC-III) database using both structured and unstructured data. Structured data models included multivariable logistic regression, extreme gradient boosting, and stacked ensemble models. Unstructured data models were developed using Amazon Comprehend Medical and BioWordVec embeddings in logistic regression, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). We evaluated models trained on all notes, notes from only the first three days of hospitalization, and models trained on only physician notes. The best-performing structured data model was a multivariable logistic regression model that achieved an accuracy of 0.74 and AUC of 0.76. Liver disease, acute renal failure, and intubation were some of the top features driving prediction in multiple models. CNNs using unstructured data achieved similar performance even when trained with notes from only the first three days of hospitalization. The best-performing unstructured data models used the Amazon Comprehend Medical document classifier and CNNs, achieving accuracy ranging from 0.99–1.00, and AUCs of 1.00. Therefore, unstructured data - particularly notes composed by physicians - offer added predictive value over models based on structured data alone. CLINICAL RELEVANCE: Unstructured data may help identify which patients are at high risk of death and need more aggressive treatment or closer monitoring. This is critically important since mortality rates from fungemia are very high.

BACKGROUND Fungal ocular involvement can develop in patients with fungal bloodstream infections and can be vision-threatening. Ocular involvement has become less common in the current era of improved antifungal therapies. Retrospectively determining the prevalence of fungal ocular involvement is important for informing clinical guidelines, such as the need for routine ophthalmologic consultations. However, manual retrospective record review to detect cases is time-consuming. OBJECTIVE This study aimed to determine the prevalence of fungal ocular involvement in a critical care database using both structured and unstructured electronic health record (EHR) data. METHODS We queried microbiology data from 46,467 critical care patients over 12 years (2000-2012) from the Medical Information Mart for Intensive Care III (MIMIC-III) to identify 265 patients with culture-proven fungemia. For each fungemic patient, demographic data, fungal species present in blood culture, and risk factors for fungemia (eg, presence of indwelling catheters, recent major surgery, diabetes, immunosuppressed status) were ascertained. All structured diagnosis codes and free-text narrative notes associated with each patient’s hospitalization were also extracted. Screening for fungal endophthalmitis was performed using two approaches: (1) by querying a wide array of eye- and vision-related diagnosis codes, and (2) by utilizing a custom regular expression pipeline to identify and collate relevant text matches pertaining to fungal ocular involvement. Both approaches were validated using manual record review. The main outcome measure was the documentation of any fungal ocular involvement. RESULTS In total, 265 patients had culture-proven fungemia, with Candida albicans (n=114, 43%) and Candida glabrata (n=74, 28%) being the most common fungal species in blood culture. The in-hospital mortality rate was 121 (46%). In total, 7 patients were identified as having eye- or vision-related diagnosis codes, none of whom had fungal endophthalmitis based on record review. There were 26,830 free-text narrative notes associated with these 265 patients. A regular expression pipeline based on relevant terms yielded possible matches in 683 notes from 108 patients. Subsequent manual record review again demonstrated that no patients had fungal ocular involvement. Therefore, the prevalence of fungal ocular involvement in this cohort was 0%. CONCLUSIONS MIMIC-III contained no cases of ocular involvement among fungemic patients, consistent with prior studies reporting low rates of ocular involvement in fungemia. This study demonstrates an application of natural language processing to expedite the review of narrative notes. This approach is highly relevant for ophthalmology, where diagnoses are often based on physical examination findings that are documented within clinical notes. CLINICALTRIAL

We currently live in an age of massive personal data creation through activities such as social media, online banking, and healthcare. In healthcare, modern concepts of patient privacy were codified in the 1996 Health Insurance Portability and Accountability Act (HIPAA).6 In 2009, the Health Information Technology for Economic and Clinical Health Act was passed to encourage the adoption of electronic medical records. These laws regulate the privacy of patients' clinical laboratory data. With the generation and collection of large sets of clinical laboratory data, there is a privacy risk. However, this risk is balanced by the use of clinical laboratory data for biomarker discovery and the measurement of clinical outcomes. Recently proposed initiatives such as the Cancer Moonshot and Precision Medicine Initiative (now called “All of Us”) will require the information of millions of patients, including both conventional laboratory and genomic data. In this Q&A, 4 experts explore the current state of data warehouses and health exchanges with a focus on the clinical laboratory. Can you provide an example of a data warehouses or health information exchanges? Toby C. Cornish: Health Data Compass, which went live in 2015, is the enterprise health data warehouse for the University of Colorado (UC) School of Medicine and UCHealth. It is a joint venture by the UC School of Medicine, University of Colorado Medicine, UCHealth, and Children's Hospital Colorado. Laboratory data were added to the data warehouse in July 2016 and initially included over 369 million patient laboratory test orders and results from UCHealth and over 124 million patient laboratory test orders and results from Children's Hospital Colorado. Health Data Compass does not yet contain genomic data, but it is expected to begin housing single-nucleotide polymorphism results from the Colorado Center for Personalized Medicine's Biobank project when that data become available. UCHealth participates …

Background:Pathologic complete response(pCR) after neoadjuvant therapy is an established prognostic biomarker for high-risk breast cancer(BC). Improving pCR rates may identify new therapies that improve survival. I-SPY 2 uses response-adaptive randomization within biomarker subtypes to evaluate novel agents when added to standard neoadjuvant therapy for women with high-risk stage II/III breast cancer; the goal is to identify regimens that have ≥85% Bayesian predictive probability of success (statistical significance) in a 300-patient phase 3 neoadjuvant trial defined by hormone-receptor (HR), HER2 status and MammaPrint (MP). We report the results for Ganetespib, a selective inhibitor of Hsp90 that induces the degradation/deactivation of key drivers of tumor initiation, progression, angiogenesis, and metastasis.Ganetespib + taxanes previously have resulted in a superior therapeutic response compared to monotherapy in multiple solid tumor models including BC. Methods:Women with tumors ≥2.5cm were eligible for screening and participation. MP low/HR+ tumors were ineligible for randomization. QTcF >470msec and HbA1C >8.0% were ineligible. MRI scans (baseline, +3 cycles, following weekly paclitaxel, T, and pre-surgery) were used in a longitudinal statistical model to improve the efficiency of adaptive randomization. Ganetespib was given with weekly T at 150 mg/m2 IV weekly (3 weeks on, 1 off). Patients were premedicated (dexamethasone 10mg and diphenhydramine HCl 25-50 mg, or therapeutic equivalents). Analysis was intention to treat with patients who switched to non-protocol therapy counted as non-pCRs. The Ganetespib regimen was open only to HER2- patients, and eligible for graduation in 3 of 10 pre-defined signatures: HER2-, HR+/HER2- and HR-/HER2-. Results:Ganetespib did not meet the criteria for graduation in the 3 signatures tested. When the maximum sample size was reached, accrual stopped. Ganetespib was assigned to 93 patients; there were 140 controls. We report probabilities of superiority for Ganetespib over control and Bayesian predictive probabilities of success in a neoadjuvant phase 3 trial equally randomized between Ganetespib and control, for the 3 biomarker signatures, using the final pCR data from all patients. Safety data will be presented. SignatureEstimated pCR Rate (95% probability interval)Probability Ganetespib Is Superior to ControlPredictive Probability of Ganetespib Success in a Phase 3 Trial Ganetespib N = 93Control N = 140 All HER2-26% (16%-37%)18% (8%-28%)91%47%HR+/HER2-15% (4%-27%)14% (4%-24%)60%19%HR-/HER2-38% (23%-53%)22% (9%-35%)96%72% Conclusion:The I-SPY 2 adaptive randomization model efficiently evaluates investigational agents in the setting of neoadjuvant BC. The value of I-SPY 2 is that it provides insight as to the regimen's likelihood of success in a phase 3 neoadjuvant study. Although no signature reached the efficacy threshold of 85% likelihood of success in phase 3, we observed the most impact in HR-/HER2- patients, with a 16% improvement in pCR rate. While our data do not support the continued development of Ganetespib alone for neoadjuvant BC, combinations with Ganetespib, which could potentiate its effect, may be worth pursuing in I-SPY 2 or similar trials. Citation Format: Forero A, Yee D, Buxton MB, Symmans WF, Chien AJ, Boughey JC, Elias AD, DeMichele A, Moulder S, Minton S, Kaplan HG, Albain KS, Wallace AM, Haley BB, Isaacs C, Korde LA, Nanda R, Lang JE, Kemmer KA, Hylton NM, Paoloni M, van't Veer L, Lyandres J, Perlmutter J, Hogarth M, Yau C, Sanil A, Berry DA, Esserman LJ. Efficacy of Hsp90 inhibitor ganetespib plus standard neoadjuvant therapy in high-risk breast cancer: Results from the I-SPY 2 trial [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P6-11-02.

Background: I-SPY 2 is a multicenter, phase 2 trial using response-adaptive randomization within biomarker subtypes to evaluate novel agents when added to standard neoadjuvant therapy for women with high-risk stage II/III breast cancer - investigational agent(I) +paclitaxel(T) qwk, doxorubicin & cyclophosphamide(AC) q2-3 wk x 4 vs. T/AC (control arm). The primary endpoint is pathologic complete response (pCR) at surgery. The goal is to identify/graduate regimens that have ≥85% Bayesian predictive probability of success (statistical significance) in a 300-patient phase 3 neoadjuvant trial defined by hormone-receptor (HR) & HER2 status & MammaPrint (MP). Regimens may also leave the trial for futility (< 10% probability of success) or following accrual of maximum sample size (10%< probability of success Methods: Women with tumors ≥2.5cm were eligible for screening. MP low/HR+ and HER2+ tumors were ineligible for randomization. Hemoglobin A1C≥ 8.0% were ineligible. MRI scans (baseline, 3 cycles after start of therapy, at completion of weekly T and prior to surgery) were used in a longitudinal statistical model to improve the efficiency of adaptive randomization. Ganitumab was given at 12mg/kg q2 weeks and metformin at 850mg PO BID, while receiving ganitumab. Analysis was intention to treat with patients who switched to non-protocol therapy counted as non-pCRs. Ganitumab/metformin was open only to HER2- patients, and eligible for graduation in 3 of 10 pre-defined signatures: HER2-, HR+HER2- and HR-HER2-. Results: Ganitumab/metformin did not meet the criteria for graduation in the 3 signatures tested. When the maximum sample size was reached, accrual to this arm stopped. Ganitumab/metformin was assigned to 106 patients; there were 128 controls. We report probabilities of superiority for Ganitumab/metformin over control and Bayesian predictive probabilities of success in a neoadjuvant phase 3 trial equally randomized between Ganitumab/metformin and control, for each of the 3 biomarker signatures, using the final pathological response data from all patients. Safety data will be presented. SignatureEstimated pCR Rate (95% probability interval)Probability Ganitumab/ Metformin Is Superior to ControlPredictive Probability of Success in Phase 3 Ganitumab/ Metformin N = 106Control N = 128 All HER2-22% (13%-31%)16% (10%-23%)89%33%HR+/HER2-14% (4%-24%)12% (4%-19%)66%21%HR-/HER2-32% (17%-46%)21% (11%-32%)91%51% Conclusion: The I-SPY 2 adaptive randomization study estimates the probability that investigational regimens will be successful in a phase 3 neoadjuvant trial. The value of I-SPY 2 is to give insight about the performance of an investigational agent's likelihood of achieving pCR. For Ganitumab/metformin, no subtype came close to the efficacy threshold of 85% likelihood of success in phase 3, and this regimen does not appear to impact upfront reduction of tumor burden. Our data do not support its continued development for the neoadjuvant treatment of breast cancer. Citation Format: Yee D, Paoloni M, van't Veer L, Sanil A, Yau C, Forero A, Chien AJ, Wallace AM, Moulder S, Albain KS, Kaplan HG, Elias AD, Haley BB, Boughey JC, Kemmer KA, Korde LA, Isaacs C, Minton S, Nanda R, DeMichele A, Lang JE, Buxton MB, Hylton NM, Symmans WF, Lyandres J, Hogarth M, Perlmutter J, Esserman LJ, Berry DA. The evaluation of ganitumab/metformin plus standard neoadjuvant therapy in high-risk breast cancer: Results from the I-SPY 2 trial [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P6-11-04.

Several reviews and case reports have described how information derived from the analysis of genomes are currently included in electronic health records (EHRs) for the purposes of supporting clinical decisions. Since the introduction of this new type of information in EHRs is relatively new (for instance, the widespread adoption of EHRs in the United States is just about a decade old), it is not surprising that a myriad of approaches has been attempted, with various degrees of success. EHR systems undergo much customization to fit the needs of health systems; these approaches have been varied and not always generalizable. The intent of this article is to present a high-level view of these approaches, emphasizing the functionality that they are trying to achieve, and not to advocate for specific solutions, which may become obsolete soon after this review is published. We start by broadly defining the end goal of including genomics in EHRs for healthcare and then explaining the various sources of information that need to be linked to arrive at a clinically actionable genomics analysis using a pharmacogenomics example. In addition, we include discussions on open issues and a vision for the next generation systems that integrate whole genome sequencing and EHRs in a seamless fashion.

Background: Radiology reporting is a clinically oriented form of documentation that reflects critical information for patients about their health care processes. Realizing its importance, many medical institutions have started providing radiology reports in patient portals. The gain, however, can be limited because of medical language barriers, which require a way for customizing these reports for patients. The open-access, collaborative consumer health vocabulary (CHV) is a terminology system created for such purposes and can be the basis of lexical simplification processes for clinical notes. Objective: The aim of this study was to examine the comprehensibility and suitability of CHV in simplifying radiology reports for consumers. This was done by characterizing the content coverage and the lexical similarity between the terms in the reports and the CHV-preferred terms. Methods: The overall procedure was divided into the following two main stages: (1) translation and (2) evaluation. The translation process involved using MetaMap to link terms in the reports to CHV concepts. This is followed by replacing the terms with CHV-preferred terms using the concept names and sources table (MRCONSO) in the Unified Medical Language System (UMLS) Metathesaurus. In the second stage, medical terms in the reports and general terms that are used to describe medical phenomena were selected and evaluated by comparing the words in the original reports with the translated ones. The evaluation includes measuring the content coverage, investigating lexical similarity, and finding trends in missing concepts. Results: Of the 792 terms selected from the radiology reports, 695 of them could be mapped directly to CHV concepts, indicating a content coverage of 88.5%. A total of 51 of the concepts (53%, 51/97) that could not be mapped are names of human anatomical structures and regions, followed by 28 anatomical descriptions and pathological variations (29%, 28/97). In addition, 12 radiology techniques and projections represented 12% of the unmapped concepts, whereas the remaining six concepts (6%, 12/97) were physiological descriptions. The rate of lexical similarity between the CHV-preferred terms and the terms in the radiology reports was approximately 72.6%. Conclusions: The CHV covered a high percentage of concepts found in the radiology reports, but unmapped concepts are associated with areas that are commonly found in radiology reporting. CHV terms also showed a high percentage of lexical similarity with terms in the reports, which contain a myriad of medical jargon. This suggests that many CHV terms might not be suitable for lay consumers who would not be facile with radiology-specific vocabulary. Therefore, further patient-centered content changes are needed of the CHV to increase its usefulness and facilitate its integration into consumer-oriented applications. [J Med Internet Res 2017;19(12):e417]

This article describes the patient-centered Scalable National Network for Effectiveness Research (pSCANNER), which is part of the recently formed PCORnet, a national network composed of learning healthcare systems and patient-powered research networks funded by the Patient Centered Outcomes Research Institute (PCORI). It is designed to be a stakeholder-governed federated network that uses a distributed architecture to integrate data from three existing networks covering over 21 million patients in all 50 states: (1) VA Informatics and Computing Infrastructure (VINCI), with data from Veteran Health Administration's 151 inpatient and 909 ambulatory care and community-based outpatient clinics; (2) the University of California Research exchange (UC-ReX) network, with data from UC Davis, Irvine, Los Angeles, San Francisco, and San Diego; and (3) SCANNER, a consortium of UCSD, Tennessee VA, and three federally qualified health systems in the Los Angeles area supplemented with claims and health information exchange data, led by the University of Southern California. Initial use cases will focus on three conditions: (1) congestive heart failure; (2) Kawasaki disease; (3) obesity. Stakeholders, such as patients, clinicians, and health service researchers, will be engaged to prioritize research questions to be answered through the network. We will use a privacy-preserving distributed computation model with synchronous and asynchronous modes. The distributed system will be based on a common data model that allows the construction and evaluation of distributed multivariate models for a variety of statistical analyses.

BackgroundThe heterogeneity of breast cancer makes identifying effective therapies challenging. The I-SPY 2 trial, a multicenter, adaptive phase 2 trial of neoadjuvant therapy for high-risk clinical stage II or III breast cancer, evaluated multiple new agents added to standard chemotherapy to assess the effects on rates of pathological complete response (i.e., absence of residual cancer in the breast or lymph nodes at the time of surgery). MethodsWe used adaptive randomization to compare standard neoadjuvant chemotherapy plus the tyrosine kinase inhibitor neratinib with control. Eligible women were categorized according to eight biomarker subtypes on the basis of human epidermal growth factor receptor 2 (HER2) status, hormone-receptor status, and risk according to a 70-gene profile. Neratinib was evaluated against control with regard to 10 biomarker signatures (prospectively defined combinations of subtypes). The primary end point was pathological complete response. Volume changes on serial magnetic reson...

The Center for Medicare and Medical Services in the United States compares hospital's readmission performance to the facilities across the nation using a 30-day window from the hospital discharge. Heart Failure (HF) is one of the conditions included in the comparison, as it is the most frequent and the most expensive diagnosis for hospitalization. If risk stratification for readmission of HF patients could be carried out at the time of discharge from the index hospitalization, corresponding appropriate post-discharge interventions could be arranged. We, therefore, sought to compare two different risk prediction models using 48 clinical predictors from electronic health records data of 1037 HF patients from one hospital. We used logistic regression and random forest as methods of analyses and found that logistic regression with bagging approach produced better predictive results (C-Statistics: 0.65) when compared to random forest (C-Statistics: 0.61).

Many healthcare systems are moving toward a fully electronic health record (EHR) in order to better manage patient care. Unfortunately, in the United States, many current EHR systems leave much to be desired. Among well-documented criticisms are that they tend to be inflexible, proprietary, nonintuitive, expensive, difficult to maintain and rarely interoperable across health systems. From the clinician's perspective, these flaws sometimes make having an EHR system seem no better than retaining a paper-based system. Open-source software, a great success in other information-intensive industries, is one possible solution to these problems, and may help integrate a functional EHR system into, and across, more health systems and clinics because of the greater potential for local customization. We believe that the advantages of an open-source EHR system outweigh the costs of a more traditional, proprietary EHR system, and recommend that more work be done to advance an interoperable open-source EHR system in the United States. Open-source EHR systems have the potential to improve healthcare in the United States as they have done in many other areas around the world.

© 2015 © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com. Despite recent increased attention to healthcare performance and the burden of disease from cancer, measures of quality of cancer care are not readily available. In 2013, the California HealthCare Foundation convened an expert workgroup to explore the potential for leveraging data in the California Cancer Registry (CCR), one of the world's largest population-based cancer registries, for measuring and improving the quality of cancer care. The workgroup assessed current registry operations, the value to be gained by linking CCR data with health insurance claims or encounter data and clinical data contained in health system electronic health records, and potential barriers to these linkages. The workgroup concluded that: 1) The CCR mandate should be expanded to include use of its data for quality of cancer care measurement and public reporting; and 2) a system should be developed to support linkage of registry data with both claims data and provider electronic health record data.

This article highlights the importance of distributed broadband networks as part of the core infrastructure necessary to deliver academic research and education programs. The authors review recent developments in the field and present the University of California, Davis, environment as a case study of a future virtual regional academic medical center. Achieving the potential benefits of distributed broadband networks through regional health information organizations will involve considerable collaboration between different academic groups and the development and implementation of standardized technological and business approaches. Broadband technologies offer a substantial opportunity to improve the way academic education and research programs are delivered.

The authors developed a Web-based mission-based reporting (MBR) system for their university's (UC Davis's) health system to report faculty members' activities in research and creative work, clinical service, education, and community/university service. They developed the system over several years (1998-2001) in response to a perceived need to better define faculty members' productivity for faculty development, financial management, and program assessment. The goal was to create a measurement tool that could be used by department chairs to counsel faculty on their performances. The MBR system provides measures of effort for each of the university's four missions. Departments or the school can use the output to better define expenditures and allocations of resources. The system provides both a quantitative metric of times spent on various activities within each mission, and a qualitative metric for the effort expended. The authors report the process of developing the MBR system and making it applicable for both clinical and basic science departments, and the mixed success experienced in its implementation. The system appears to depict the activities of most faculty fairly accurately, and chairs of test departments have been generally enthusiastic. However, resistance to general implementation remains, chiefly due to concerns about reliability, validity, and time required for completing the report. The authors conclude that MBR can be useful but will require some streamlining and the elimination of other redundant reporting instruments. A well-defined purpose is required to motivate its use.

Modern pathologists have been challenged to “validate” mouse models of human cancer. Validation requires matching of morphological attributes of the model to human disease. Computers can assist in the validation process. However, adequate controlled, computer-readable vocabularies that can match terms do not currently exist in mouse pathology. Further, current standard diagnostic terminologies do not include the new concepts discussed here such as pathway pathology and mammary intraepithelial neoplasia. The terminologies must be revised and improved to meet the challenge. Human medicine has traditionally used “guilt-by-association” to validate interpretations of disease. Experimental pathology uses experimental verification exemplified by “test-by-transplantation.” Genetically Engineered Mice (GEM) develop unique tumor phenotypes bringing new structural-functional insights and reevaluation of concepts. Novel GEM-related tumors appear in all organ systems but mouse models of human breast cancer are prototypes. For example, mammary tumors induced by Mouse Mammary Tumor Virus (MMTV), chemical, radiation or other carcinogenic stimuli have limited phenotypes. These “spontaneous” or induced mammary tumors have never resembled human breast cancers. GEM tumors created with genes associated with human cancer are strikingly different. GEM tumors have unique histological phenotypes. Depending on the genes, the tumors may: 1) resemble MMTV-induced tumors, 2) display “signature” phenotypes, and 3) mimic human breast cancers. The phenotypes can be placed into structural and functional clusters with shared characteristics leading to the concepts of Pathway Pathology: tumor phenotype reflects the genotype.

An approach and software tools are described for identifying and extracting compound terms (CTs), acronyms and their associated contexts from textual material that is associated with neuroanatomical atlases. A set of simple syntactic rules were appended to the output of a commercially available part of speech (POS) tagger (Qtag v 3.01) that extracts CTs and their associated context from the texts of neuroanatomical atlases. This "hybrid" parser. appears to be highly sensitive and recognized 96% of the potentially germane neuroanatomical CTs and acronyms present in the cat and primate thalamic atlases. A comparison of neuroanatomical CTs and acronymsbetween the cat and primate atlas texts was initially performed using exact-term matching. The implementation of string-matching algorithms significantly improved the identification of relevant terms and acronyms between the two domains. The End Gap Free string matcher identified 98% of CTs and the Needleman Wunsch (NW) string matcher matched 36% of acronyms between the two atlases. Combining several simple grammatical and lexical rules with the POS tagger ("hybrid parser") (1) extracted complex neuroanatomical terms and acronyms from selected cat and primate thalamic atlases and (2) and facilitated the semi-automated generation of a highly granular thalamic terminology. The implementation of string-matching algorithms (1) reconciled terminological errors generated by optical character recognition (OCR) software used to generate the neuroanatomical text information and (2) increased the sensitivity of matching neuroanatomical terms and acronyms between the two neuroanatomical domains that were generated by the "hybrid" parser.

Purpose To describe the utility of school-wide use of mission-based reporting (MBR) for medical school deans and department chairs. Method All faculty members in the University of California, Davis, School of Medicine reported their clinical, creative, teaching, and service activities for 2000–2001 to the MBR system. The authors report on school-wide and department MBR profiles, and profiles by rank and academic series. They validate MBR by comparing individual results with actual merit actions reviewed independently by the school's academic personnel committee. Results A total of 419 faculty members (85%) completed their MBR reports. The average faculty member spent considerably more than 50 hours per week fulfilling the missions of the school, and full professors and faculty members in academic series supported by state funds were the most productive in investigative and creative work. The teaching load was shared equally by all the academic ranks, although the clinician–scholars taught more than did faculty members in the other series. There was an inverse relationship between clinical load and academic rank, with the majority of the clinical work performed by junior faculty members. MBR results compared favorably with the merit review process, although MBR is not expected to replace the traditional peer review system. Conclusion The creation of these graphic profiles and summaries is a valuable feature of MBR that would not have been possible without such quantitative data. The profiles allow monitoring to ensure that workload conforms to established objectives for individuals, departments, academic ranks and series. Finally, the authors discuss future directions for their MBR system.

Objective Develop and evaluate a foundational oncology-specific standard for the communication and coordination of care throughout the cancer journey, with early-stage breast cancer as the use case. Materials and Methods Owing to broad uptake of the Health Level Seven (HL7) Consolidated Clinical Document Architecture (C-CDA) by health information exchanges and large provider organizations, we developed an implementation guide in congruence with C-CDA. The resultant product was balloted through the HL7 process and subsequently implemented by two groups: the Health Story Project (Health Story) and the Athena Breast Health Network (Athena). Results The HL7 Implementation Guide for CDA, Release 2: Clinical Oncology Treatment Plan and Summary, DSTU Release 1 (eCOTPS) was successfully balloted and published as a Draft Standard for Trial Use (DSTU) in October 2013. Health Story successfully implemented the eCOTPS the 2014 meeting of the Healthcare Information and Management Systems Society (HIMSS) in a clinical vignette. During the evaluation and implementation of eCOPS, Athena identified two practical concerns: (1) the need for additional CDA templates specific to their use case; (2) the many-to-many mapping of Athena-defined data elements to eCOTPS. Discussion Early implementation of eCOTPS has demonstrated successful vendor-agnostic transmission of oncology-specific data. The modularity enabled by the C-CDA framework ensures the relatively straightforward expansion of the eCOTPS to include other cancer subtypes. Lessons learned during the process will strengthen future versions of the standard. Conclusion eCOTPS is the first oncology-specific CDA standard to achieve HL7 DSTU status. Oncology standards will improve care throughout the cancer journey by allowing the efficient transmission of reliable, meaningful, and current clinical data between the many involved stakeholders.

The US health care system and its information access models are organized around institutions and providers. Patient-centered functionality is rarely present in prevailing information systems and, if present, it typically does not ideally support shared decision making about important treatment events. We sought to better understand the functional needs of providers and patients around the process of care plan decision making, and used this information to develop a prototype decision support tool, using women with newly diagnosed breast cancer as our clinical scenario. This paper describes the user-centered design process we undertook and the resulting prototype system, the Communication and Care Plan (CCP).

We introduce a novel hybrid telepathology system providing simultaneous transmission of both real-time microscopy and static imagery at a reasonable cost. It is capable of capturing static, true color images at resolution of 1,520 [times ] 1,144 pixels and providing live transmission of real-time video images of the glass slide in Common Intermediate Format (CIF), which can be viewed at 1,024 [times ] 768 pixels or higher. Instant freeze function can be enabled with 4[times ] CIF resolution (1,408 [times ] 1,152 pixels). This system comprises the advantages of real-time transmission and high-resolution static imagery, while direct visual and audio communications are maintained at the same time. It uses standard [lsquo ]off-the-shelf[rsquo ] components with a total cost of around $30,000 to $40,000. We also conclude that differences in telepathology modality and image quality have an impact on diagnostic accuracy and should be considered for discussion and standards development by professional organizations in the field of telepathology. An effort to ascertain the diagnostic limitations of various telepathology modalities is essential to the future use of this modality of pathology practice. H UM P ATHOL 31:829-833.