JMIR Publications

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  Decision Support for Health...

  Nov 6, 2018

  Assessing the Impacts of Integrated Decision Support Software on Sexual Orientation Recording, Comprehensive Sexual Health Testing, and Detection of...

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  Authors List:

  • Denton Callander  
  • Christopher Bourne  
  • Handan Wand  
  • Mark Stoové  
  • Jane S Hocking  
  • John de Wit  
  • John M Kaldor  
  • Basil Donovan  
  • Catherine Pell  
  • Robert Finlayson  
  • David Baker  
  • Bradley Forssman  
  • BK Tee  
  • Bill Kefalas  
  • Tim Duck  
  • Rebecca Guy  

  Abstract:

  Background: Gay and bisexual men are disproportionately affected by HIV and other sexually transmissible infections (STIs), yet opportunities for sexual health testing of this population are often missed or incomplete in general practice settings. Strategies are needed for improving the uptake and completeness of sexual health testing in this setting. Objectives: The goal of the research was to evaluate the impact of an intervention centered around integrated decision support software and routine data feedback on the collection of sexual orientation data and sexual health testing among gay and bisexual men attending general practice. Methods: A study using before/after and intervention/comparison methods was undertaken to assess the intervention’s impact in 7 purposively sampled Australian general practice clinics located near the urban centers of Sydney and Melbourne. The software was introduced at staggered points between April and August 2012; it used patient records to prompt clinicians to record sexual orientation and accessed pathology testing history to generate prompts when sexual health testing was overdue or incomplete. The software also had a function for querying patient management system databases in order to generate de-identified data extracts, which were used to report regularly to participating clinicians. We calculated summary rate ratios (SRRs) based on quarterly trends and used Poisson regression analyses to assess differences between the 12-month preintervention and 24-month intervention periods as well as between the intervention sites and 4 similar comparison sites that did not receive the intervention. Results: Among 32,276 male patients attending intervention clinics, sexual orientation recording increased 19% (from 3213/6909 [46.50%] to 5136/9110 [56.38%]) during the intervention period (SRR 1.10, 95% CI 1.04-1.11, P<.001) while comprehensive sexual health testing increased by 89% (305/1159 [26.32%] to 690/1413 [48.83%]; SRR 1.38, 95% CI 1.28-1.46, P<.001). Comprehensive testing increased slightly among the 7290 gay and bisexual men attending comparison sites, but the increase was comparatively greater in clinics that received the intervention (SRR 1.12, 95% CI 1.10-1.14, P<.001). In clinics that received the intervention, there was also an increase in detection of chlamydia and gonorrhea that was not observed in the comparison sites. Conclusions: Integrated decision support software and data feedback were associated with modest increases in sexual orientation recording, comprehensive testing among gay and bisexual men, and the detection of STIs. Tests for and detection of chlamydia and gonorrhea were the most dramatically impacted. Decision support software can be used to enhance the delivery of sexual health care in general practice.

  • JMIR Med Inform 2018;6(4):e10808

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  Information Models

  Nov 5, 2018

  Identifying Patients Who Are Likely to Receive Most of Their Care From a Specific Health Care System: Demonstration via Secondary Analysis

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  Authors List:

  • Gang Luo  
  • Peter Tarczy-Hornoch  
  • Adam B Wilcox  
  • E Sally Lee  

  Abstract:

  Background: In the United States, health care is fragmented in numerous distinct health care systems including private, public, and federal organizations like private physician groups and academic medical centers. Many patients have their complete medical data scattered across these several health care systems, with no particular system having complete data on any of them. Several major data analysis tasks such as predictive modeling using historical data are considered impractical on incomplete data. Objective: Our objective was to find a way to enable these analysis tasks for a health care system with incomplete data on many of its patients. Methods: This study presents, to the best of our knowledge, the first method to use a geographic constraint to identify a reasonably large subset of patients who tend to receive most of their care from a given health care system. A data analysis task needing relatively complete data can be conducted on this subset of patients. We demonstrated our method using data from the University of Washington Medicine (UWM) and PreManage data covering the use of all hospitals in Washington State. We compared 10 candidate constraints to optimize the solution. Results: For UWM, the best constraint is that the patient has a UWM primary care physician and lives within 5 miles of at least one UWM hospital. About 16.01% (55,707/348,054) of UWM patients satisfied this constraint. Around 69.38% (10,501/15,135) of their inpatient stays and emergency department visits occurred within UWM in the following 6 months, more than double the corresponding percentage for all UWM patients. Conclusions: Our method can identify a reasonably large subset of patients who tend to receive most of their care from UWM. This enables several major analysis tasks on incomplete medical data that were previously deemed infeasible.

  • JMIR Med Inform 2018;6(4):e12241

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  Secondary Use of Clinical Data for...

  Nov 5, 2018

  Appropriateness of Hospital Admission for Emergency Department Patients with Bronchiolitis: Secondary Analysis

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  Authors List:

  • Gang Luo  
  • Michael D Johnson  
  • Flory L Nkoy  
  • Shan He  
  • Bryan L Stone  

  Abstract:

  Background: Bronchiolitis is the leading cause of hospitalization in children under 2 years of age. Each year in the United States, bronchiolitis results in 287,000 emergency department visits, 32%-40% of which end in hospitalization. Frequently, emergency department disposition decisions (to discharge or hospitalize) are made subjectively because of the lack of evidence and objective criteria for bronchiolitis management, leading to significant practice variation, wasted health care use, and suboptimal outcomes. At present, no operational definition of appropriate hospital admission for emergency department patients with bronchiolitis exists. Yet, such a definition is essential for assessing care quality and building a predictive model to guide and standardize disposition decisions. Our prior work provided a framework of such a definition using 2 concepts, one on safe versus unsafe discharge and another on necessary versus unnecessary hospitalization. Objective: The goal of this study was to determine the 2 threshold values used in the 2 concepts, with 1 value per concept. Methods: Using Intermountain Healthcare data from 2005-2014, we examined distributions of several relevant attributes of emergency department visits by children under 2 years of age for bronchiolitis. Via a data-driven approach, we determined the 2 threshold values. Results: We completed the first operational definition of appropriate hospital admission for emergency department patients with bronchiolitis. Appropriate hospital admissions include actual admissions with exposure to major medical interventions for more than 6 hours, as well as actual emergency department discharges, followed by an emergency department return within 12 hours ending in admission for bronchiolitis. Based on the definition, 0.96% (221/23,125) of the emergency department discharges were deemed unsafe. Moreover, 14.36% (432/3008) of the hospital admissions from the emergency department were deemed unnecessary. Conclusions: Our operational definition can define the prediction target for building a predictive model to guide and improve emergency department disposition decisions for bronchiolitis in the future.

  • JMIR Med Inform 2018;6(4):e10498

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  Electronic Health Records

  Oct 31, 2018

  Impact of Electronic Versus Paper Vital Sign Observations on Length of Stay in Trauma Patients: Stepped-Wedge, Cluster Randomized Controlled Trial

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  Authors List:

  • David CW Wong  
  • Julia Knight  
  • Jacqueline Birks  
  • Lionel Tarassenko  
  • Peter J Watkinson  

  Abstract:

  Background: Electronic recording of vital sign observations (e-Obs) has become increasingly prevalent in hospital care. The evidence of clinical impact for these systems is mixed. Objective: The objective of our study was to assess the effect of e-Obs versus paper documentation (paper) on length of stay (time between trauma unit admission and “fit to discharge”) for trauma patients. Methods: A single-center, randomized stepped-wedge study of e-Obs against paper was conducted in two 26-bed trauma wards at a medium-sized UK teaching hospital. Randomization of the phased intervention order to 12 study areas was computer generated. The primary outcome was length of stay. Results: A total of 1232 patient episodes were randomized (paper: 628, e-Obs: 604). There were 37 deaths in hospital: 21 in the paper arm and 16 in the e-Obs arm. For discharged patients, the median length of stay was 5.4 (range: 0.2-79.0) days on the paper arm and 5.6 (range: 0.1-236.7) days on the e-Obs arm. Competing risks regression analysis for time to discharge showed no difference between the treatment arms (subhazard ratio: 1.05; 95% CI 0.82-1.35; P=.68). A greater proportion of patient episodes contained an Early Warning Score (EWS) ≥3 using the e-Obs system than using paper (subhazard ratio: 1.63; 95% CI 1.28-2.09; P<.001). However, there was no difference in the time to the subsequent observation, “escalation time” (hazard ratio 1.05; 95% CI 0.80-1.38; P=.70). Conclusions: The phased introduction of an e-Obs documentation system was not associated with a change in length of stay. A greater proportion of patient episodes contained an EWS≥3 using the e-Obs system, but this was not associated with a change in “escalation time.” Trial Registration: ISRCTN Registry ISRCTN91040762; http://www.isrctn.com/ISRCTN91040762 (Archived by WebCite at http://www.webcitation.org/72prakGTU)

  • JMIR Med Inform 2018;6(4):e10221

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  Clinical Communication, Electronic...

  Oct 30, 2018

  Benefits and Costs of Digital Consulting in Clinics Serving Young People With Long-Term Conditions: Mixed-Methods Approach

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  Authors List:

  • Sung Wook Kim  
  • Jason Madan  
  • Melina Dritsaki  
  • Carol Bryce  
  • Vera Forjaz  
  • Joe Fraser  
  • Frances Griffiths  
  • Kathryn Hamilton  
  • Caroline Huxley  
  • Jackie Sturt  

  Abstract:

  Background: Since the introduction of digital health technologies in National Health Service (NHS), health professionals are starting to use email, text, and other digital methods to consult with their patients in a timely manner. There is lack of evidence regarding the economic impact of digital consulting in the United Kingdom (UK) NHS. Objective: This study aimed to estimate the direct costs associated with digital consulting as an adjunct to routine care at 18 clinics serving young people aged 16-24 years with long-term conditions. Methods: This study uses both quantitative and qualitative approaches. Semistructured interviews were conducted with 173 clinical team members on the impacts of digital consulting. A structured questionnaire was developed and used for 115 health professionals across 12 health conditions at 18 sites in the United Kingdom to collect data on time and other resources used for digital consulting. A follow-up semistructured interview was conducted with a single senior clinician at each site to clarify the mechanisms through which digital consulting use might lead to outcomes relevant to economic evaluation. We used the two-part model to see the association between the time spent on digital consulting and the job role of staff, type of clinic, and the average length of the working hours using digital consulting. Results: When estimated using the two-part model, consultants spent less time on digital consulting compared with nurses (95.48 minutes; P<.001), physiotherapists (55.3 minutes; P<.001), and psychologists (31.67 minutes; P<.001). Part-time staff spent less time using digital consulting than full-time staff despite insignificant result (P=.15). Time spent on digital consulting differed across sites, and no clear pattern in using digital consulting was found. Health professionals qualitatively identified the following 4 potential economic impacts for the NHS: decreasing adverse events, improving patient well-being, decreasing wait lists, and staff workload. We did not find evidence to suggest that the clinical condition was associated with digital consulting use. Conclusions: Nurses and physiotherapists were the greatest users of digital consulting. Teams appear to use an efficient triage system with the most expensive members digitally consulting less than lower-paid team members. Staff report showed concerns regarding time spent digitally consulting, which implies that direct costs increase. There remain considerable gaps in evidence related to cost-effectiveness of digital consulting, but this study has highlighted important cost-related outcomes for assessment in future cost-effectiveness trials of digital consulting.

  • JMIR Med Inform 2018;6(4):e48

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  Imaging Informatics

  Oct 25, 2018

  A Computerized Method for Measuring Computed Tomography Pulmonary Angiography Yield in the Emergency Department: Validation Study

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  Authors List:

  • Safiya Richardson  
  • Philip Solomon  
  • Alexander O'Connell  
  • Sundas Khan  
  • Jonathan Gong  
  • Alex Makhnevich  
  • Guang Qiu  
  • Meng Zhang  
  • Thomas McGinn  

  Abstract:

  Background: Use of computed tomography pulmonary angiography (CTPA) in the assessment of pulmonary embolism (PE) has markedly increased over the past two decades. While this technology has improved the accuracy of radiological testing for PE, CTPA also carries the risk of substantial iatrogenic harm. Each CTPA carries a 14% risk of contrast-induced nephropathy and a lifetime malignancy risk that can be as high as 2.76%. The appropriate use of CTPA can be estimated by monitoring the CTPA yield, the percentage of tests positive for PE. This is the first study to propose and validate a computerized method for measuring the CTPA yield in the emergency department (ED). Objective: The objective of our study was to assess the validity of a novel computerized method of calculating the CTPA yield in the ED. Methods: The electronic health record databases at two tertiary care academic hospitals were queried for CTPA orders completed in the ED over 1-month periods. These visits were linked with an inpatient admission with a discharge diagnosis of PE based on the International Classification of Diseases codes. The computerized the CTPA yield was calculated as the number of CTPA orders with an associated inpatient discharge diagnosis of PE divided by the total number of orders for completed CTPA. This computerized method was then validated by 2 independent reviewers performing a manual chart review, which included reading the free-text radiology reports for each CTPA. Results: A total of 349 CTPA orders were completed during the 1-month periods at the two institutions. Of them, acute PE was diagnosed on CTPA in 28 studies, with a CTPA yield of 7.7%. The computerized method correctly identified 27 of 28 scans positive for PE. The one discordant scan was tied to a patient who was discharged directly from the ED and, as a result, never received an inpatient discharge diagnosis. Conclusions: This is the first successful validation study of a computerized method for calculating the CTPA yield in the ED. This method for data extraction allows for an accurate determination of the CTPA yield and is more efficient than manual chart review. With this ability, health care systems can monitor the appropriate use of CTPA and the effect of interventions to reduce overuse and decrease preventable iatrogenic harm.

  • JMIR Med Inform 2018;6(4):e44

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  Electronic Health Records

  Oct 23, 2018

  Validation and Testing of Fast Healthcare Interoperability Resources Standards Compliance: Data Analysis

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  Authors List:

  • Jason Walonoski  
  • Robert Scanlon  
  • Conor Dowling  
  • Mario Hyland  
  • Richard Ettema  
  • Steven Posnack  

  Abstract:

  Background: There is wide recognition that the lack of health data interoperability has significant impacts. Traditionally, health data standards are complex and test-driven methods played important roles in achieving interoperability. The Health Level Seven International (HL7) standard Fast Healthcare Interoperability Resources (FHIR) may be a technical solution that aligns with policy, but systems need to be validated and tested. Objective: Our objective is to explore the question of whether or not the regular use of validation and testing tools improves server compliance with the HL7 FHIR specification. Methods: We used two independent validation and testing tools, Crucible and Touchstone, and analyzed the usage and result data to determine their impact on server compliance with the HL7 FHIR specification. Results: The use of validation and testing tools such as Crucible and Touchstone are strongly correlated with increased compliance and “practice makes perfect.” Frequent and thorough testing has clear implications for health data interoperability. Additional data analysis reveals trends over time with respect to vendors, use cases, and FHIR versions. Conclusions: Validation and testing tools can aid in the transition to an interoperable health care infrastructure. Developers that use testing and validation tools tend to produce more compliant FHIR implementations. When it comes to health data interoperability, “practice makes perfect.”

  • JMIR Med Inform 2018;6(4):e10870

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  eHealth Infrastructures

  Oct 22, 2018

  Development of an eHealth System to Capture and Analyze Patient Sensor and Self-Report Data: Mixed-Methods Assessment of Potential Applications to Improve...

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  Authors List:

  • Alexander R Lucas  
  • Michael B Bass  
  • Nan E Rothrock  
  • Mary L O'Connor  
  • Mia R Sorkin  
  • Jason Nawyn  
  • Fahd Albinali  
  • Lynne I Wagner  

  Abstract:

  Background: Capturing and Analyzing Sensor and Self-Report Data for Clinicians and Researchers (COMPASS) is an electronic health (eHealth) platform designed to improve cancer care delivery through passive monitoring of patients’ health status and delivering customizable reports to clinicians. Based on data from sensors and context-driven administration of patient-reported outcome (PRO) measures, key indices of patients’ functional status can be collected between regular clinic visits, supporting clinicians in the delivery of patient care. Objective: The first phase of this project aimed to systematically collect input from oncology providers and patients on potential clinical applications for COMPASS to refine the system. Methods: Ten clinicians representing various oncology specialties and disciplines completed semi-structured interviews designed to solicit clinician input on how COMPASS can best support clinical care delivery. Three cancer patients tested a prototype of COMPASS for 7 days and provided feedback. Interview data were tabulated using thematic content analysis to identify the most clinically relevant objective and PRO domains. Results: Thematic content analysis revealed that clinicians were most interested in monitoring vital statistics, symptoms, and functional status, including the physical activity level (n=9), weight (n=5), fatigue (n=9), sleep quality (n=8), and anxiety (n=7). Patients (2 in active treatment and 1 in remission) reported that they would use such a device, were enthusiastic about their clinicians monitoring their health status, especially the tracking of symptoms, and felt knowing their clinicians were monitoring and reviewing their health status provided valuable reassurance. Patients would, however, like to provide some context to their data. Conclusions: Clinicians and patients both articulated potential benefits of the COMPASS system in improving cancer care. From a clinician standpoint, data need to be easily interpretable and actionable. The fact that patients and clinicians both see potential value in eHealth systems suggests wider adoption and utilization could prove to be a useful tool for improving care delivery.

  • JMIR Med Inform 2018;6(4):e46

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  Decision Support for Health...

  Oct 22, 2018

  Predicting Current Glycated Hemoglobin Values in Adults: Development of an Algorithm From the Electronic Health Record

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  Authors List:

  • Brian J Wells  
  • Kristin M Lenoir  
  • Jose-Franck Diaz-Garelli  
  • Wendell Futrell  
  • Elizabeth Lockerman  
  • Kevin M Pantalone  
  • Michael W Kattan  

  Abstract:

  Background: Electronic, personalized clinical decision support tools to optimize glycated hemoglobin (HbA1c) screening are lacking. Current screening guidelines are based on simple, categorical rules developed for populations of patients. Although personalized diabetes risk calculators have been created, none are designed to predict current glycemic status using structured data commonly available in electronic health records (EHRs). Objective: The goal of this project was to create a mathematical equation for predicting the probability of current elevations in HbA1c (≥5.7%) among patients with no history of hyperglycemia using readily available variables that will allow integration with EHR systems. Methods: The reduced model was compared head-to-head with calculators created by Baan and Griffin. Ten-fold cross-validation was used to calculate the bias-adjusted prediction accuracy of the new model. Statistical analyses were performed in R version 3.2.5 (The R Foundation for Statistical Computing) using the rms (Regression Modeling Strategies) package. Results: The final model to predict an elevated HbA1c based on 22,635 patient records contained the following variables in order from most to least importance according to their impact on the discriminating accuracy of the model: age, body mass index, random glucose, race, serum non–high-density lipoprotein, serum total cholesterol, estimated glomerular filtration rate, and smoking status. The new model achieved a concordance statistic of 0.77 which was statistically significantly better than prior models. The model appeared to be well calibrated according to a plot of the predicted probabilities versus the prevalence of the outcome at different probabilities. Conclusions: The calculator created for predicting the probability of having an elevated HbA1c significantly outperformed the existing calculators. The personalized prediction model presented in this paper could improve the efficiency of HbA1c screening initiatives.

  • JMIR Med Inform 2018;6(4):e10780

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  Decision Support for Health...

  Oct 10, 2018

  Utilization of Electronic Medical Records and Biomedical Literature to Support the Diagnosis of Rare Diseases Using Data Fusion and Collaborative Filtering...

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  Authors List:

  • Feichen Shen  
  • Sijia Liu  
  • Yanshan Wang  
  • Andrew Wen  
  • Liwei Wang  
  • Hongfang Liu  

  Abstract:

  Background: In the United States, a rare disease is characterized as the one affecting no more than 200,000 patients at a certain period. Patients suffering from rare diseases are often either misdiagnosed or left undiagnosed, possibly due to insufficient knowledge or experience with the rare disease on the part of clinical practitioners. With an exponentially growing volume of electronically accessible medical data, a large volume of information on thousands of rare diseases and their potentially associated diagnostic information is buried in electronic medical records (EMRs) and medical literature. Objective: This study aimed to leverage information contained in heterogeneous datasets to assist rare disease diagnosis. Phenotypic information of patients existed in EMRs and biomedical literature could be fully leveraged to speed up diagnosis of diseases. Methods: In our previous work, we advanced the use of a collaborative filtering recommendation system to support rare disease diagnostic decision making based on phenotypes derived solely from EMR data. However, the influence of using heterogeneous data with collaborative filtering was not discussed, which is an essential problem while facing large volumes of data from various resources. In this study, to further investigate the performance of collaborative filtering on heterogeneous datasets, we studied EMR data generated at Mayo Clinic as well as published article abstracts retrieved from the Semantic MEDLINE Database. Specifically, in this study, we designed different data fusion strategies from heterogeneous resources and integrated them with the collaborative filtering model. Results: We evaluated performance of the proposed system using characterizations derived from various combinations of EMR data and literature, as well as with sole EMR data. We extracted nearly 13 million EMRs from the patient cohort generated between 2010 and 2015 at Mayo Clinic and retrieved all article abstracts from the semistructured Semantic MEDLINE Database that were published till the end of 2016. We applied a collaborative filtering model and compared the performance generated by different metrics. Log likelihood ratio similarity combined with k-nearest neighbor on heterogeneous datasets showed the optimal performance in patient recommendation with area under the precision-recall curve (PRAUC) 0.475 (string match), 0.511 (systematized nomenclature of medicine [SNOMED] match), and 0.752 (Genetic and Rare Diseases Information Center [GARD] match). Log likelihood ratio similarity also performed the best with mean average precision 0.465 (string match), 0.5 (SNOMED match), and 0.749 (GARD match). Performance of rare disease prediction was also demonstrated by using the optimal algorithm. Macro-average F-measure for string, SNOMED, and GARD match were 0.32, 0.42, and 0.63, respectively. Conclusions: This study demonstrated potential utilization of heterogeneous datasets in a collaborative filtering model to support rare disease diagnosis. In addition to phenotypic-based analysis, in the future, we plan to further resolve the heterogeneity issue and reduce miscommunication between EMR and literature by mining genotypic information to establish a comprehensive disease-phenotype-gene network for rare disease diagnosis.

  • JMIR Med Inform 2018;6(4):e11301

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  Pharmacovigilance and Surveillance

  Sep 27, 2018

  Extraction and Standardization of Patient Complaints from Electronic Medication Histories for Pharmacovigilance: Natural Language Processing Analysis in...

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  Authors List:

  • Misa Usui  
  • Eiji Aramaki  
  • Tomohide Iwao  
  • Shoko Wakamiya  
  • Tohru Sakamoto  
  • Mayumi Mochizuki  

  Abstract:

  Background: Despite the growing number of studies using natural language processing for pharmacovigilance, there are few reports on manipulating free text patient information in Japanese. Objective: This study aimed to establish a method of extracting and standardizing patient complaints from electronic medication histories accumulated in a Japanese community pharmacy for the detection of possible adverse drug event (ADE) signals. Methods: Subjective information included in electronic medication history data provided by a Japanese pharmacy operating in Hiroshima, Japan from September 1, 2015 to August 31, 2016, was used as patients’ complaints. We formulated search rules based on morphological analysis and daily (nonmedical) speech and developed a system that automatically executes the search rules and annotates free text data with International Classification of Diseases, Tenth Revision (ICD-10) codes. The performance of the system was evaluated through comparisons with data manually annotated by health care workers for a data set of 5000 complaints. Results: Of 5000 complaints, the system annotated 2236 complaints with ICD-10 codes, whereas health care workers annotated 2348 statements. There was a match in the annotation of 1480 complaints between the system and manual work. System performance was .66 regarding precision, .63 in recall, and .65 for the F-measure. Conclusions: Our results suggest that the system may be helpful in extracting and standardizing patients’ speech related to symptoms from massive amounts of free text data, replacing manual work. After improving the extraction accuracy, we expect to utilize this system to detect signals of possible ADEs from patients’ complaints in the future.

  • JMIR Med Inform 2018;6(3):e11021

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  Health Information Exchange

  Sep 25, 2018

  Emergency Physician Use of the Alberta Netcare Portal, a Province-Wide Interoperable Electronic Health Record: Multi-Method Observational Study

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  Authors List:

  • Timothy AD Graham  
  • Mark Ballermann  
  • Eddy Lang  
  • Michael J Bullard  
  • Denise Parsons  
  • Gabriella Mercuur  
  • Pat San Agustin  
  • Samina Ali  

  Abstract:

  Background: The adoption and use of an electronic health record (EHR) can facilitate real-time access to key health information and support improved outcomes. Many Canadian provinces use interoperable EHRs (iEHRs) to facilitate health information exchange, but the clinical use and utility of iEHRs has not been well described. Objective: The aim of this study was to describe the use of a provincial iEHR known as the Alberta Netcare Portal (ANP) in 4 urban Alberta emergency departments. The secondary objectives were to characterize the time spent using the respective electronic tools and identify the aspects that were perceived as most useful by emergency department physicians. Methods: In this study, we have included 4 emergency departments, 2 using paper-based ordering (University of Alberta Hospital [UAH] and Grey Nuns Community Hospital [GNCH]) and 2 using a commercial vendor clinical information system (Peter Lougheed Centre [PLC] and Foothills Medical Centre [FMC]). Structured clinical observations of ANP use and system audit logs analysis were compared at the 4 sites from October 2014 to March 2016. Results: Observers followed 142 physicians for a total of 566 hours over 376 occasions. The median percentage of observed time spent using ANP was 8.5% at UAH (interquartile range, IQR, 3.7%-13.3%), 4.4% at GNCH (IQR 2.4%-4.4%), 4.6% at FMC (IQR 2.4%-7.6%), and 5.1% at PLC (IQR 3.0%-7.7%). By combining administrative and access audit data, the median number of ANP screens (ie, results and reports displayed on a screen) accessed per patient visit were 20 at UAH (IQR 6-67), 9 at GNCH (IQR 4-29), 7 at FMC (IQR 2-18), and 5 at PLC (IQR 2-14). When compared with the structured clinical observations, the statistical analysis of screen access data showed that ANP was used more at UAH than the other sites. Conclusions: This study shows that the iEHR is well utilized at the 4 sites studied, and the usage patterns implied clinical value. Use of the ANP was highest in a paper-based academic center and lower in the centers using a commercial emergency department clinical information system. More study about the clinical impacts of using iEHRs in the Canadian context including longer term impacts on quality of practice and safety are required.

  • JMIR Med Inform 2018;6(3):e10184

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