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The 6th annual Nursing Knowledge Big Data Science Conference held June 13 - 15, 2018 in Minneapolis offers registration a 40% reduced early bird rate.

Renowned for her work in health professional education, and specifically, interprofessional education and continuing education, Dr. Barbara Brandt serves as associate vice president within the University of Minnesota’s Academic Health Center, and she is the director of the National Center for Inte...
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Feasibility Testing of a Wearable Behavioral Aid for Social Learning in Children with Autism
Jena Daniels, Nick Haber, Catalin Voss, Jessey Schwartz, Serena Tamura, Azar Fazel, Aaron Kline, Peter Washington, Jennifer Phillips, Terry Winograd, Carl Feinstein, Dennis P. Wall

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Background Recent advances in computer vision and wearable technology have created an opportunity to introduce mobile therapy systems for autism spectrum disorders (ASD) that can respond to the increasing demand for therapeutic interventions; however, feasibility questions must be answered first.

Objective We studied the feasibility of a prototype therapeutic tool for children with ASD using Google Glass, examining whether children with ASD would wear such a device, if providing the emotion classification will improve emotion recognition, and how emotion recognition differs between ASD participants and neurotypical controls (NC).

Methods We ran a controlled laboratory experiment with 43 children: 23 with ASD and 20 NC. Children identified static facial images on a computer screen with one of 7 emotions in 3 successive batches: the first with no information about emotion provided to the child, the second with the correct classification from the Glass labeling the emotion, and the third again without emotion information. We then trained a logistic regression classifier on the emotion confusion matrices generated by the two information-free batches to predict ASD versus NC.

Results All 43 children were comfortable wearing the Glass. ASD and NC participants who completed the computer task with Glass providing audible emotion labeling (n = 33) showed increased accuracies in emotion labeling, and the logistic regression classifier achieved an accuracy of 72.7%. Further analysis suggests that the ability to recognize surprise, fear, and neutrality may distinguish ASD cases from NC.

Conclusion This feasibility study supports the utility of a wearable device for social affective learning in ASD children and demonstrates subtle differences in how ASD and NC children perform on an emotion recognition task.

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Development and Validation of a Natural Language Processing Tool to Identify Patients Treated for Pneumonia across VA Emergency Departments
B. E. Jones, B. R. South, Y. Shao, C.C. Lu, J. Leng, B. C. Sauer, A. V. Gundlapalli, M. H. Samore, Q. Zeng

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Background Identifying pneumonia using diagnosis codes alone may be insufficient for research on clinical decision making. Natural language processing (NLP) may enable the inclusion of cases missed by diagnosis codes.

Objectives This article (1) develops a NLP tool that identifies the clinical assertion of pneumonia from physician emergency department (ED) notes, and (2) compares classification methods using diagnosis codes versus NLP against a gold standard of manual chart review to identify patients initially treated for pneumonia.

Methods Among a national population of ED visits occurring between 2006 and 2012 across the Veterans Affairs health system, we extracted 811 physician documents containing search terms for pneumonia for training, and 100 random documents for validation. Two reviewers annotated span- and document-level classifications of the clinical assertion of pneumonia. An NLP tool using a support vector machine was trained on the enriched documents. We extracted diagnosis codes assigned in the ED and upon hospital discharge and calculated performance characteristics for diagnosis codes, NLP, and NLP plus diagnosis codes against manual review in training and validation sets.

Results Among the training documents, 51% contained clinical assertions of pneumonia; in the validation set, 9% were classified with pneumonia, of which 100% contained pneumonia search terms. After enriching with search terms, the NLP system alone demonstrated a recall/sensitivity of 0.72 (training) and 0.55 (validation), and a precision/positive predictive value (PPV) of 0.89 (training) and 0.71 (validation). ED-assigned diagnostic codes demonstrated lower recall/sensitivity (0.48 and 0.44) but higher precision/PPV (0.95 in training, 1.0 in validation); the NLP system identified more “possible-treated” cases than diagnostic coding. An approach combining NLP and ED-assigned diagnostic coding classification achieved the best performance (sensitivity 0.89 and PPV 0.80).

Conclusion System-wide application of NLP to clinical text can increase capture of initial diagnostic hypotheses, an important inclusion when studying diagnosis and clinical decision-making under uncertainty.

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A Novel Patient Recruitment Strategy: Patient Selection Directly from the Community through Linkage to Clinical Data
Lindsay P. Zimmerman, Satyender Goel, Shazia Sathar, Charon E. Gladfelter, Alejandra Onate, Lindsey L. Kane, Shelly Sital, Jasmin Phua, Paris Davis, Helen Margellos-Anast, David O. Meltzer, Tamar S. Polonsky, Raj C. Shah, William E. Trick, Faraz S. Ahmad, Abel N. Kho

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Objective This article presents and describes our methods in developing a novel strategy for recruitment of underrepresented, community-based participants, for pragmatic research studies leveraging routinely collected electronic health record (EHR) data.

Methods We designed a new approach for recruiting eligible patients from the community, while also leveraging affiliated health systems to extract clinical data for community participants. The strategy involves methods for data collection, linkage, and tracking. In this workflow, potential participants are identified in the community and surveyed regarding eligibility. These data are then encrypted and deidentified via a hashing algorithm for linkage of the community participant back to a record at a clinical site. The linkage allows for eligibility verification and automated follow-up. Longitudinal data are collected by querying the EHR data and surveying the community participant directly. We discuss this strategy within the context of two national research projects, a clinical trial and an observational cohort study.

Conclusion The community-based recruitment strategy is a novel, low-touch, clinical trial enrollment method to engage a diverse set of participants. Direct outreach to community participants, while utilizing EHR data for clinical information and follow-up, allows for efficient recruitment and follow-up strategies. This new strategy for recruitment links data reported from community participants to clinical data in the EHR and allows for eligibility verification and automated follow-up. The workflow has the potential to improve recruitment efficiency and engage traditionally underrepresented individuals in research.

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Optimizing the User Experience: Identifying Opportunities to Improve Use of an Inpatient Portal
Daniel M. Walker, Terri Menser, Po-Yin Yen, Ann Scheck McAlearney

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Background Patient portals specifically designed for the inpatient setting have significant potential to improve patient care. However, little is known about how the users of this technology, the patients, may interact with the inpatient portals. As a result, hospitals have limited ability to design approaches that support patient use of the portal.

Objectives This study aims to evaluate the user experience associated with an inpatient portal.

Methods We used a Think-Aloud protocol to study user interactions with a commercially available inpatient portal—MyChart Bedside (MCB). Study participants included 19 English-speaking adults over the age of 18 years. In one-on-one sessions, participants narrated their experience using the MCB application and completing eight specific tasks. Recordings were transcribed and coded into three dimensions of the user experience: physical, cognitive, and sociobehavioral.

Results Our analysis of the physical experience highlighted the navigational errors and technical challenges associated with the use of MCB. We also found that issues associated with the cognitive experience included comprehension problems that spurred anxiety and uncertainty. Analysis of the sociobehavioral experience suggested that users have different learning styles and preferences for learning including self-guided, handouts, and in-person training.

Conclusion Inpatient portals may be an effective tool to improve the patient experience in the hospital. Moreover, making this technology available to inpatients may help to foster ongoing use of technology across the care continuum. However, deriving the benefits from the technology requires appropriate support. We identified multiple opportunities for hospital management to intervene. In particular, teaching patients to use the application by making a variety of instructional materials available could help to reduce several identified barriers to use. Additionally, hospitals should be prepared to manage patient anxiety and increased questioning arising from the availability of information in the inpatient portal application.

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http://palliativecare.org.au/…/bedtime-stories-learning-li…/
ACI Associate Editor Terry Hannan publishes book!

As a physician and clinical educator, Dr Terry Hannan believes the art of ‘listening to the patient’ is virtually lost in modern medicine. In Dr Terry Hannan’s latest book, Bedtime Stories, he writes a collection of patient journeys highlighting the depths that can occur during the distress of...
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https://www.thieme-connect.com/…/abs…/10.1055/s-0037-1621705
Efficiency of Emergency Physicians: Insights from an Observational Study using EHR Log Files
Objective With federal mandates and incentives since the turn of this decade, electronic health records (EHR) have been widely adopted and used for clinical care. Over the last several years, we have seen both positive and negative perspectives on its use. Using an analysis of log files of EHR use, we investigated the nature... of EHR use and their effect on an emergency department's (ED) throughput and efficiency.

Methods EHR logs of time spent by attending physicians on EHR-based activities over a 6-week period (n = 2,304 patients) were collected. For each patient encounter, physician activities in the EHR were categorized into four activities: documentation, review, orders, and navigation. Four ED-based performance metrics were also captured: door-to-provider time, door-to-doctor time, door-to-disposition time, and length of stay (LOS). Association between the four EHR-based activities and corresponding ED performance metrics were evaluated.

Results We found positive correlations between physician review of patient charts, and door-to-disposition time (r = 0.43, p < 0.05), and with LOS (r = 0.48, p < 0.05). There were no statistically significant associations between any of the other performance metrics and EHR activities.

Conclusion The results highlight that longer time spent on reviewing information on the EHR is potentially associated with decreased ED throughput efficiency. Balancing these competing goals is often a challenge of physicians, and its implications for patient safety is discussed.

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Clinical Information Systems Integration in New York City's First Mobile Stroke Unit

Background Mobile stroke units (MSUs) reduce time to thrombolytic therapy in acute ischemic stroke. These units are widely used, but the clinical information systems underlying MSU operations are understudied.

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Objective The first MSU on the East Coast of the United States was established at New York Presbyterian Hospital (NYP) in October 2016. We describe our program's 7-month pilot, focusing on the integration of our hospital's clinical information systems into our MSU to support patient care and research efforts.

Methods NYP's MSU was staffed by two paramedics, one radiology technologist, and a vascular neurologist. The unit was equipped with four laptop computers and networking infrastructure enabling all staff to access the hospital intranet and clinical applications during operating hours. A telephone-based registration procedure registered patients from the field into our admit/discharge/transfer system, which interfaced with the institutional electronic health record (EHR). We developed and implemented a computerized physician order entry set in our EHR with prefilled values to permit quick ordering of medications, imaging, and laboratory testing. We also developed and implemented a structured clinician note to facilitate care documentation and clinical data extraction.

Results Our MSU began operating on October 3, 2016. As of April 27, 2017, the MSU transported 49 patients, of whom 16 received tissue plasminogen activator (t-PA). Zero technical problems impacting patient care were reported around registration, order entry, or intranet access. Two onboard network failures occurred, resulting in computed tomography scanner malfunctions, although no patients became ineligible for time-sensitive treatment as a result. Thirteen (26.5%) clinical notes contained at least one incomplete time field.

Conclusion The main technical challenges encountered during the integration of our hospital's clinical information systems into our MSU were onboard network failures and incomplete clinical documentation. Future studies are necessary to determine whether such integrative efforts improve MSU care quality, and which enhancements to information systems will optimize clinical care and research efforts.

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Exploring Data Quality Management within Clinical Trials
Lauren Houston, Yasmine Probst, Ping Yu, Allison Martin

Background Clinical trials are an important research method for improving medical knowledge and patient care. Multiple international and national guidelines stipulate the need for data quality and assurance. Many strategies and interventions are developed to reduce error in trials, including standard operating procedures, personnel training, data monitoring, and de...sign of case report forms. However, guidelines are nonspecific in the nature and extent of necessary methods.

Objective This article gathers information about current data quality tools and procedures used within Australian clinical trial sites, with the aim to develop standard data quality monitoring procedures to ensure data integrity.

Methods Relevant information about data quality management methods and procedures, error levels, data monitoring, staff training, and development were collected. Staff members from 142 clinical trials listed on the National Health and Medical Research Council (NHMRC) clinical trials Web site were invited to complete a short self-reported semiquantitative anonymous online survey.

Results Twenty (14%) clinical trials completed the survey. Results from the survey indicate that procedures to ensure data quality varies among clinical trial sites. Centralized monitoring (65%) was the most common procedure to ensure high-quality data. Ten (50%) trials reported having a data management plan in place and two sites utilized an error acceptance level to minimize discrepancy, set at <5% and 5 to 10%, respectively. The quantity of data variables checked (10–100%), the frequency of visits (once-a-month to annually), and types of variables (100%, critical data or critical and noncritical data audits) for data monitoring varied among respondents. The average time spent on staff training per person was 11.58 hours over a 12-month period and the type of training was diverse.

Conclusion Clinical trial sites are implementing ad hoc methods pragmatically to ensure data quality. Findings highlight the necessity for further research into “standard practice” focusing on developing and implementing publically available data quality monitoring procedures.

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Using EHR Data to Detect Prescribing Errors in Rapidly Discontinued Medication Orders
Jonathan D. Burlison, Robert B. McDaniel, Donald K. Baker, Murad Hasan, Jennifer J. Robertson, Scott C. Howard, James M. Hoffman

Background Previous research developed a new method for locating prescribing errors in rapidly discontinued electronic medication orders. Although effective, the prospective design of that research hinders its feasibility for regular use.

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Objectives Our objectives were to assess a method to retrospectively detect prescribing errors, to characterize the identified errors, and to identify potential improvement opportunities.

Methods Electronically submitted medication orders from 28 randomly selected days that were discontinued within 120 minutes of submission were reviewed and categorized as most likely errors, nonerrors, or not enough information to determine status. Identified errors were evaluated by amount of time elapsed from original submission to discontinuation, error type, staff position, and potential clinical significance. Pearson's chi-square test was used to compare rates of errors across prescriber types.

Results In all, 147 errors were identified in 305 medication orders. The method was most effective for orders that were discontinued within 90 minutes. Duplicate orders were most common; physicians in training had the highest error rate (p < 0.001), and 24 errors were potentially clinically significant. None of the errors were voluntarily reported.

Conclusion It is possible to identify prescribing errors in rapidly discontinued medication orders by using retrospective methods that do not require interrupting prescribers to discuss order details. Future research could validate our methods in different clinical settings. Regular use of this measure could help determine the causes of prescribing errors, track performance, and identify and evaluate interventions to improve prescribing systems and processes.

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CALL FOR ABSTRACTS for the 2018 Council on Clinical Information Technology (COCIT) Program at AAP’s National Conference & Exhibition is open!

Please consider submitting an abstract to present a paper or a poster during the program:
Saturday, November 3
9 AM – 5 PM (podium presentations 9-12; posters 12-1)...
Orlando, FL

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Towards Implementation of OMOP in a German University Hospital Consortium
C. Maier1, L. Lang1, H. Storf2, P. Vormstein2, R. Bieber3, J. Bernarding4, T. Herrmann4, C. Haverkamp5, P. Horki6, J. Laufer7, F. Berger7, G. Höning8, H.W. Fritsch9, J. Schüttler10, T. Ganslandt11, H.U. Prokosch1, M. Sedlmayr1

Background In 2015, the German Federal Ministry of Education and Research initiated a large data integration and data sharing research initiative to improve the reuse of data from... patient care and translational research. The Observational Medical Outcomes Partnership (OMOP) common data model and the Observational Health Data Sciences and Informatics (OHDSI) tools could be used as a core element in this initiative for harmonizing the terminologies used as well as facilitating the federation of research analyses across institutions.

Objective To realize an OMOP/OHDSI-based pilot implementation within a consortium of eight German university hospitals, evaluate the applicability to support data harmonization and sharing among them, and identify potential enhancement requirements.

Methods The vocabularies and terminological mapping required for importing the fact data were prepared, and the process for importing the data from the source files was designed. For eight German university hospitals, a virtual machine preconfigured with the OMOP database and the OHDSI tools as well as the jobs to import the data and conduct the analysis was provided. Last, a federated/distributed query to test the approach was executed.

Results While the mapping of ICD-10 German Modification succeeded with a rate of 98.8% of all terms for diagnoses, the procedures could not be mapped and hence an extension to the OMOP standard terminologies had to be made.

Overall, the data of 3 million inpatients with approximately 26 million conditions, 21 million procedures, and 23 million observations have been imported.

A federated query to identify a cohort of colorectal cancer patients was successfully executed and yielded 16,701 patient cases visualized in a Sunburst plot.

Conclusion OMOP/OHDSI is a viable open source solution for data integration in a German research consortium. Once the terminology problems can be solved, researchers can build on an active community for further development.

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Improving the Accuracy of a Clinical Decision Support System for Cervical Cancer Screening and Surveillance
K.E. Ravikumar1, Kathy L. MacLaughlin2, Marianne R. Scheitel3, Maya Kessler4, Kavishwar B. Wagholikar5, Hongfang Liu1, Rajeev Chaudhry4

Background Clinical decision support systems (CDSS) for cervical cancer prevention are generally limited to identifying patients who are overdue for their next routine/next screening, and they do not provide recommendations for follow-u...p of abnormal results. We previously developed a CDSS to automatically provide follow-up recommendations based on the American Society of Colposcopy and Cervical Pathology (ASCCP) guidelines for women with both previously normal and abnormal test results leveraging information available in the electronic medical record (EMR).
Objective Enhance the CDSS by improving its accuracy and incorporating changes to reflect the latest revision of the guidelines.
Methods After making enhancements to the CDSS, we evaluated the performance of the clinical recommendations on 393 patients selected through stratified sampling from a set of 3,704 patients in a nonclinical setting. We performed chart review of individual patient's record to evaluate the performance of the system. An expert clinician assisted by a resident manually reviewed the recommendation made by the system and verified whether the recommendations were as per the ASCCP guidelines.
Results The recommendation accuracy of the enhanced CDSS improved to 93%, which is a substantial improvement over the 84% reported previously. A detailed analysis of errors is presented in this article. We fixed the errors identified in this evaluation that were amenable to correction to further improve the accuracy of the system. The source code of the updated CDSS is available at https://github.com/ohnlp/MayoNlpPapCdss.
Conclusion We made substantial enhancements to our earlier prototype CDSS with the updated ASCCP guidelines and performed a thorough evaluation in a nonclinical setting to improve the accuracy of the CDSS. The CDSS will be further refined as it is utilized in the practice.

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