Commercial Electronic Health Record Research Articles

Staff expectations for the implementation of an electronic health record system: a qualitative study using normalisation process theory

BackgroundGlobal evidence suggests a range of benefits for introducing electronic health record (EHR) systems to improve patient care. However, implementing EHR within healthcare organisations is complex and, in the United Kingdom (UK), uptake has been slow. More research is needed to explore factors influencing successful implementation. This study explored staff expectations for change and outcome following procurement of a commercial EHR system by a large academic acute NHS hospital in the UK.MethodsQualitative interviews were conducted with 14 members of hospital staff who represented a variety of user groups across different specialities within the hospital. The four components of Normalisation Process Theory (Coherence, Cognitive participation, Collective action and Reflexive monitoring) provided a theoretical framework to interpret and report study findings.ResultsHealth professionals had a common understanding for the rationale for EHR implementation (Coherence). There was variation in willingness to engage with and invest time into EHR (Cognitive participation) at an individual, professional and organisational level. Collective action (whether staff feel able to use the EHR) was influenced by context and perceived user-involvement in EHR design and planning of the implementation strategy. When appraising EHR (Reflexive monitoring), staff anticipated short and long-term benefits. Staff perceived that quality and safety of patient care would be improved with EHR implementation, but that these benefits may not be immediate. Some staff perceived that use of the system may negatively impact patient care. The findings indicate that preparedness for EHR use could mitigate perceived threats to the quality and safety of care.ConclusionsHealth professionals looked forward to reaping the benefits from EHR use. Variations in level of engagement suggest early components of the implementation strategy were effective, and that more work was needed to involve users in preparing them for use. A clearer understanding as to how staff groups and services differentially interact with the EHR as they go about their daily work was required. The findings may inform other hospitals and healthcare systems on actions that can be taken prior to EHR implementation to reduce concerns for quality and safety of patient care and improve the chance of successful implementation.

Integrating pharmacogenomics into the electronic health record by implementing genomic indicators.

Pharmacogenomics (PGx) clinical decision support integrated into the electronic health record (EHR) has the potential to provide relevant knowledge to clinicians to enable individualized care. However, past experience implementing PGx clinical decision support into multiple EHR platforms has identified important clinical, procedural, and technical challenges. Commercial EHRs have been widely criticized for the lack of readiness to implement precision medicine. Herein, we share our experiences and lessons learned implementing new EHR functionality charting PGx phenotypes in a unique repository, genomic indicators, instead of using the problem or allergy list. The Gen-Ind has additional features including a brief description of the clinical impact, a hyperlink to the original laboratory report, and links to additional educational resources. The automatic generation of genomic indicators from interfaced PGx test results facilitates implementation and long-term maintenance of PGx data in the EHR and can be used as criteria for synchronous and asynchronous CDS.

Wearable Health Technology and Electronic Health Record Integration: Scoping Review and Future Directions.

BackgroundDue to the adoption of electronic health records (EHRs) and legislation on meaningful use in recent decades, health systems are increasingly interdependent on EHR capabilities, offerings, and innovations to better capture patient data. A novel capability offered by health systems encompasses the integration between EHRs and wearable health technology. Although wearables have the potential to transform patient care, issues such as concerns with patient privacy, system interoperability, and patient data overload pose a challenge to the adoption of wearables by providers.ObjectiveThis study aimed to review the landscape of wearable health technology and data integration to provider EHRs, specifically Epic, because of its prevalence among health systems. The objectives of the study were to (1) identify the current innovations and new directions in the field across start-ups, health systems, and insurance companies and (2) understand the associated challenges to inform future wearable health technology projects at other health organizations.MethodsWe used a scoping process to survey existing efforts through Epic’s Web-based hub and discussion forum, UserWeb, and on the general Web, PubMed, and Google Scholar. We contacted Epic, because of their position as the largest commercial EHR system, for information on published client work in the integration of patient-collected data. Results from our searches had to meet criteria such as publication date and matching relevant search terms.ResultsNumerous health institutions have started to integrate device data into patient portals. We identified the following 10 start-up organizations that have developed, or are in the process of developing, technology to enhance wearable health technology and enable EHR integration for health systems: Overlap, Royal Philips, Vivify Health, Validic, Doximity Dialer, Xealth, Redox, Conversa, Human API, and Glooko. We reported sample start-up partnerships with a total of 16 health systems in addressing challenges of the meaningful use of device data and streamlining provider workflows. We also found 4 insurance companies that encourage the growth and uptake of wearables through health tracking and incentive programs: Oscar Health, United Healthcare, Humana, and John Hancock.ConclusionsThe future design and development of digital technology in this space will rely on continued analysis of best practices, pain points, and potential solutions to mitigate existing challenges. Although this study does not provide a full comprehensive catalog of all wearable health technology initiatives, it is representative of trends and implications for the integration of patient data into the EHR. Our work serves as an initial foundation to provide resources on implementation and workflows around wearable health technology for organizations across the health care industry.

Variation in Physicians' Electronic Health Record Documentation and Potential Patient Harm from That Variation.

Physician-to-physician variation in electronic health record (EHR) documentation not driven by patients' clinical status could be harmful. Measure variation in completion of common clinical documentation domains. Identify perceived causes and effects of variation and strategies to mitigate negative effects. Sequential, explanatory, mixed methods using log data from a commercial EHR vendor and semi-structured interviews with outpatient primary care practices. Quantitative: 170,332 encounters led by 809 physicians in 237 practices. Qualitative: 40 interviewees in 10 practices. Interquartile range (IQR) of the proportion of encounters in which a physician completed documentation, for each documentation category. Multilevel linear regression measured the proportion of variation at the physician level. Five clinical documentation categories had substantial and statistically significant (p < 0.001) variation at the physician level after accounting for state, organization, and practice levels: (1) discussing results (IQR = 50.8%, proportion of variation explained by physician level = 78.1%); (2) assessment and diagnosis (IQR = 60.4%, physician-level variation = 76.0%); (3) problem list (IQR = 73.1%, physician-level variation = 70.1%); (4) review of systems (IQR = 62.3%, physician-level variation = 67.7%); and (5) social history (IQR = 53.3%, physician-level variation = 62.2%). Drivers of variation from interviews included user preferences and EHR designs with multiple places to record similar information. Variation was perceived to create documentation inefficiencies and risk patient harm due to missed or misinterpreted information. Mitigation strategies included targeted user training during EHR implementation and practice meetings focused on documentation standardization. Physician-to-physician variation in EHR documentation impedes effective and safe use of EHRs, but there are potential strategies to mitigate negative consequences.

Electronic but overly eclectic: Disciplined EHR data management is needed to automate MIPS reporting.

e18074 Background: Physician reimbursement for care delivered to Medicare beneficiaries fundamentally changed with the 2015 MACRA legislation, requiring eligible clinicians to report quality measures in the Merit-Based Incentive Payment System (MIPS). To determine whether structured data in electronic health records (EHRs) were adequate to report MIPS results, EHR data ingested by ASCO’s CancerLinQ (CLQ) was analyzed. Methods: Nineteen MIPS measures specified for medical oncology, including 8 shared by other specialties, were retrieved from qpp.cms.gov and systematically evaluated to determine data elements necessary to satisfy each measure. The existence of corresponding data fields and completion of these fields with clinical data was analyzed according to EHR implementation in de-identified and aggregated CLQ data. Results: Five clinician informaticists reviewed the 19 oncology MIPS measures, and identified a consensus list of 52 discrete EHR data elements (DEs) that would be needed. CLQ-processed data from 4 commercial EHR systems implemented at 47 CLQ practices found structured data fields for 84% (43 of 52) of the DE, but fewer than half (46%) of these fields were ever populated and only 32% (17 of 52) of DE were recorded for &gt; 20% of cases. Only 3-5 of 19 MIPS measures could be reliably reported based on data element availability by most practices in this sample set. There were minimal differences between the EHRs ability to encode MIPS DE. Elements most likely to be encoded were those for registration (birthdate, gender), billing (diagnosis, meds), vital signs and smoking status, while those seldom or never encoded related to care plans (tobacco, alcohol, pain management). Other DE rarely encoded were patient events occurring outside the oncology practice (receipt/completion of consultations, dates of hospice enrollment and death), which would be dependent on data exchange between work units and practice entities or, more likely, re-entry by oncology practices. Conclusions: Only a minority of DE required to satisfy MIPS criteria are available as discrete data fields in current EHRs, limiting automated reporting efforts. Improved data quality and completeness is needed to satisfy mandated reporting.

Looking Behind the Curtain: Identifying Factors Contributing to Changes on Care Outcomes During a Large Commercial EHR Implementation.

Objective:To identify factors contributing to changes on quality, productivity, and safety outcomes during a large commercial electronic health record (EHR) implementation and to guide future research.Methods:We conducted a mixed-methods study assessing the impact of a commercial EHR implementation. The method consisted of a quantitative longitudinal evaluation followed by qualitative semi-structured, in-depth interviews with clinical employees from the same implementation. Fourteen interviews were recorded and transcribed. Three authors independently coded interview narratives and via consensus identified factors contributing to changes on 15 outcomes of quality, productivity, and safety.Results:We identified 14 factors that potentially affected the outcomes previously monitored. Our findings demonstrate that several factors related to the implementation (e.g., incomplete data migration), partially related (e.g., intentional decrease in volume of work), and not related (e.g., health insurance changes) may affect outcomes in different ways.Discussion:This is the first study to investigate factors contributing to changes on a broad set of quality, productivity, and safety outcomes during an EHR implementation guided by the results of a large longitudinal evaluation. The diversity of factors identified indicates that the need for organizational adaptation to take full advantage of new technologies is as important for health care as it is for other services sectors.Conclusions:We recommend continuous identification and monitoring of these factors in future evaluations to hopefully increase our understanding of the full impact of health information technology interventions.

Assessing the Safety of Custom Web-Based Clinical Decision Support Systems in Electronic Health Records: A Case Study.

With the widespread adoption of vendor-supplied electronic health record (EHR) systems, clinical decision support (CDS) customization efforts beyond those anticipated by the vendor may require the use of technologies external to the EHR such as web services. Pursuing such customizations, however, is not without risk. Validating the expected behavior of a customized CDS system in the high-volume, complex environment of the live EHR is a challenging problem. This article identifies technology failures that impacted clinical care related to web service-based advanced custom CDS systems embedded in the complex sociotechnical context of a production EHR. In an academic health system's primary care network, we performed an inventory of incidents between January 1, 2008 and December 31, 2016 related to a customized CDS system and performed a targeted review of changes in the CDS source code. Additional feedback on the root cause of individual incidents was obtained through interviews with members of the CDS project teams. We identified five CDS malfunctions that impaired clinical workflow. The mechanisms for these failures are mapped to four characteristics of well-behaved applications: (1) system integrity; (2) data integrity; (3) reliability; and (4) scalability. Over the 9-year period, two malfunctions of the customized CDS significantly impaired clinical workflow for a total of 5 hours. Lesser impacts-loss of individual features with straightforward workarounds-arose from three malfunctions, which affected users on 53 days. Advanced customization of EHRs for the purpose of CDS can present significant risks to clinical workflow. This case study highlights that advanced customization of CDS within a commercial EHR may support care for complex patient populations, but ongoing monitoring and support is required to ensure its safe use.

A Pharmacogenomics Clinical Decision Support Service Based on FHIR and CDS Hooks.

Pharmacogenomics (PGx) is often considered a low-hanging fruit for genomics-electronic health record (EHR) integrations, and many have expressed the notion that drug-gene interaction checking might one day become as much a commodity in EHRs as drug-drug and drug-allergy checking. In addition, the U.S. Office of the National Coordinator has recognized the trend toward storing complete sequencing data outside the EHR in a Genomic Archiving and Communication System (GACS) and has emphasized the need for "pilots that test Fast Healthcare Interoperability Resources (FHIR) Genomics for GACS integration with EHRs." We sought to develop a PGx clinical decision support (CDS) service, leveraging the emerging FHIR and CDS Hooks standards, and based on an assumption that pharmacogene sequencing data would be stored alongside the EHR in a GACS. We developed a PGx CDS service as a functional prototype. The service is triggered by a medication order in the EHR. When evoked, the service looks for relevant genetic data in a GACS and returns corresponding recommendations back to the ordering clinician. Where the patient has no genetic data on file, the service can recommend pretreatment genetic testing where applicable. Overall, we were able to meet our objectives and deploy a functional prototype, interfaced with a commercial EHR. We identified several areas where FHIR or CDS Hooks lacked necessary semantics or have implementation ambiguity. Primary FHIR challenges included multiple ways to say the same thing, which exacerbated the complexity of variant to allele conversion and lack of representation of deoxyribonucleic acid region(s) studied. Primary CDS Hooks challenges included the complexity of executing an authenticated query against one system (GACS) upon being triggered by a different system (the EHR), and limitations in the types of actionable recommendations that can be returned to the EHR. In conclusion, we have found that PGx CDS based on FHIR and CDS Hooks appears to represent a promising means of genomics-EHR integration. More real-world testing along with a set of use-case driven GACS interface requirements will push us closer to the U.S. National Human Genome Research Institute vision of a plug-in PGx app.

Transitioning from one Electronic Health Record (EHR) to Another: A Narrative Literature Review

This paper reports the results of a literature review of health care organizations that have transitioned from one electronic health record (EHR) to another. Ten different EHR to EHR transitions are documented in the academic literature. In eight of the 10 transitions, the health care organization transitioned to Epic, a commercial EHR which is dominating the market for large and medium hospitals and health care systems. The focus of the articles reviewed falls into two main categories: (1) data migration from the old to new EHR and (2) implementation of the new EHR as it relates to patient safety, provider satisfaction, and other measures pre-and post-transition. Several conclusions and recommendations are derived from this review of the literature, which may be informative for healthcare organizations preparing to replace an existing EHR. These recommendations are likely broadly relevant to EHR to EHR transitions, regardless of the new EHR vendor.

Capsule Commentary on Wright et. al.: Reduced Effectiveness of Interruptive Drug-Drug Interaction Alerts After Conversion to a Commercial Electronic Health Record.

Capsule Commentary on Wright et. al.: Reduced Effectiveness of Interruptive Drug-Drug Interaction Alerts After Conversion to a Commercial Electronic Health Record.

Effect of Electronic Health Record–Based Medication Support and Nurse-Led Medication Therapy Management on Hypertension and Medication Self-management

Complex medication regimens pose self-management challenges, particularly among populations with low levels of health literacy. To test medication management tools delivered through a commercial electronic health record (EHR) with and without a nurse-led education intervention. This 3-group cluster randomized clinical trial was performed in community health centers in Chicago, Illinois. Participants included 794 patients with hypertension who self-reported using 3 or more medications concurrently (for any purpose). Data were collected from April 30, 2012, through February 29, 2016, and analyzed by intention to treat. Clinics were randomly assigned to to groups: electronic health record-based medication management tools (medication review sheets at visit check-in, lay medication information sheets printed after visits; EHR-alone group), EHR-based tools plus nurse-led medication management support (EHR plus education group), or usual care. Outcomes at 12 months included systolic blood pressure (primary outcome), medication reconciliation, knowledge of drug indications, understanding of medication instructions and dosing, and self-reported medication adherence. Medication outcomes were assessed for all hypertension prescriptions, all prescriptions to treat chronic disease, and all medications. Among the 794 participants (68.6% women; mean [SD] age, 52.7 [9.6] years), systolic blood pressure at 12 months was greater in the EHR-alone group compared with the usual care group by 3.6 mm Hg (95% CI, 0.3 to 6.9 mm Hg). Systolic blood pressure in the EHR plus education group was not significantly lower compared with the usual care group (difference, -2.0 mm Hg; 95% CI, -5.2 to 1.3 mm Hg) but was lower compared with the EHR-alone group (-5.6 mm Hg; 95% CI, -8.8 to -2.4 mm Hg). At 12 months, hypertension medication reconciliation was improved in the EHR-alone group (adjusted odds ratio [OR], 1.8; 95% CI, 1.1 to 2.9) and the EHR plus education group (adjusted odds ratio [OR], 2.0; 95% CI, 1.3 to 3.3) compared with usual care. Understanding of medication instructions and dosing was greater in the EHR plus education group than the usual care group for hypertension medications (OR, 2.3; 95% CI, 1.1 to 4.8) and all medications combined (OR, 1.7; 95% CI, 1.0 to 2.8). Compared with usual care, the EHR tools alone and EHR plus education interventions did not improve hypertension medication adherence (OR, 0.9; 95% CI, 0.6-1.4 for both) or knowledge of chronic drug indications (OR for EHR tools alone, 1.0 [95% CI, 0.6 to 1.5] and OR for EHR plus education, 1.1 [95% CI, 0.7-1.7]). The study found that EHR tools in isolation improved medication reconciliation but worsened blood pressure. Combining these tools with nurse-led support suggested improved understanding of medication instructions and dosing but did not lower blood pressure compared with usual care. ClinicalTrials.gov identifier: NCT01578577.

Comprehensive methodology to monitor longitudinal change patterns during EHR implementations: a case study at a large health care delivery network

ObjectiveTo test a systematic methodology to monitor longitudinal change patterns on quality, productivity, and safety outcomes during a large-scale commercial Electronic Health Record (EHR) implementation. Materials and MethodsOur method combines an interrupted time-series design with control sites and 41 consensus outcomes including quality (11 measures), productivity (20 measures), and safety (10 measures). The intervention consisted of a phased commercial EHR implementation at a large health care delivery network. Four medium-size hospitals and 39 clinics from 5 geographic regions implementing the new EHR were compared against a parallel control consisting of one medium-size and one large hospital and 10 clinics that had not implemented the new EHR at the time of this study. We collected monthly data from February 2013 to July 2017. ResultsThe proposed methodology was successfully implemented and significant changes were observed in most measured variables. A significant change attributable to the intervention was observed in 12 (29%) measures in three or more regions; in 32 (78%) measures in two or more regions; and in 40 (98%) measures in at least one region. A similar pattern (i.e., same impact in three or more regions) was detected for nine (22%) measures, a mixed pattern (i.e., same impact in two regions, and different impact in other regions) was detected for nine (22%) measures, and an inconsistent pattern (i.e., did not detect the same impact across regions) was detected for 23 (56%) measures. DiscussionUsing a formal methodology to assess changes in a set of consensus measures, we detected various patterns of impact and mixed time-sensitive effects. With an increasing adoption of EHR systems, it is critical for health care organizations to systematically monitor their EHR implementations. The proposed method provides a robust and consistent approach to monitor EHR implementations longitudinally allowing for continuous monitoring after the system becomes stable in order to avoid unexpected effects. ConclusionOur results and methodology can guide the broader medical and informatics communities by informing what and how to continuously monitor EHR impact on quality, productivity, and safety.

Reduced Effectiveness of Interruptive Drug-Drug Interaction Alerts after Conversion to a Commercial Electronic Health Record.

Drug-drug interaction (DDI) alerts in electronic health records (EHRs) can help prevent adverse drug events, but such alerts are frequently overridden, raising concerns about their clinical usefulness and contribution to alert fatigue. To study the effect of conversion to a commercial EHR on DDI alert and acceptance rates. Two before-and-after studies. 3277 clinicians who received a DDI alert in the outpatient setting. Introduction of a new, commercial EHR and subsequent adjustment of DDI alerting criteria. Alert burden and proportion of alerts accepted. Overall interruptive DDI alert burden increased by a factor of 6 from the legacy EHR to the commercial EHR. The acceptance rate for the most severe alerts fell from 100 to 8.4%, and from 29.3 to 7.5% for medium severity alerts (P < 0.001). After disabling the least severe alerts, total DDI alert burden fell by 50.5%, and acceptance of Tier 1 alerts rose from 9.1 to 12.7% (P < 0.01). Changing from a highly tailored DDI alerting system to a more general one as part of an EHR conversion decreased acceptance of DDI alerts and increased alert burden on users. The decrease in acceptance rates cannot be fully explained by differences in the clinical knowledge base, nor can it be fully explained by alert fatigue associated with increased alert burden. Instead, workflow factors probably predominate, including timing of alerts in the prescribing process, lack of differentiation of more and less severe alerts, and features of how users interact with alerts.

Decreasing triage time: effects of implementing a step-wise ESI algorithm in an EHR.

To determine if adapting a widely-used triage scale into a computerized algorithm in an electronic health record (EHR) shortens emergency department (ED) triage time. Before-and-after quasi-experimental study. Urban, tertiary care hospital ED. Consecutive adult patient visits between July 2011 and June 2013. A step-wise algorithm, based on the Emergency Severity Index (ESI-5) was programmed into the triage module of a commercial EHR. Duration of triage (triage interval) for all patients and change in percentage of high acuity patients (ESI 1 and 2) completing triage within 15 min, 12 months before-and-after implementation of the algorithm. Multivariable analysis adjusted for confounders; interrupted time series demonstrated effects over time. Secondary outcomes examined quality metrics and patient flow. About 32 546 patient visits before and 33 032 after the intervention were included. Post-intervention patients were slightly older, census was higher and admission rate slightly increased. Median triage interval was 5.92 min (interquartile ranges, IQR 4.2-8.73) before and 2.8 min (IQR 1.88-4.23) after the intervention (P < 0.001). Adjusted mean triage interval decreased 3.4 min (95% CI: -3.6, -3.2). The proportion of high acuity patients completing triage within 15 min increased from 63.9% (95% CI 62.5, 65.2%) to 75.0% (95% CI 73.8, 76.1). Monthly time series demonstrated immediate and sustained improvement following the intervention. Return visits within 72 h and door-to-balloon time were unchanged. Total length of stay was similar. The computerized triage scale improved speed of triage, allowing more high acuity patients to be seen within recommended timeframes, without notable impact on quality.

Agile Acceptance Test-Driven Development of Clinical Decision Support Advisories: Feasibility of Using Open Source Software.

BackgroundMoving to electronic health records (EHRs) confers substantial benefits but risks unintended consequences. Modern EHRs consist of complex software code with extensive local configurability options, which can introduce defects. Defects in clinical decision support (CDS) tools are surprisingly common. Feasible approaches to prevent and detect defects in EHR configuration, including CDS tools, are needed. In complex software systems, use of test–driven development and automated regression testing promotes reliability. Test–driven development encourages modular, testable design and expanding regression test coverage. Automated regression test suites improve software quality, providing a “safety net” for future software modifications. Each automated acceptance test serves multiple purposes, as requirements (prior to build), acceptance testing (on completion of build), regression testing (once live), and “living” design documentation. Rapid-cycle development or “agile” methods are being successfully applied to CDS development. The agile practice of automated test–driven development is not widely adopted, perhaps because most EHR software code is vendor-developed. However, key CDS advisory configuration design decisions and rules stored in the EHR may prove amenable to automated testing as “executable requirements.”ObjectiveWe aimed to establish feasibility of acceptance test–driven development of clinical decision support advisories in a commonly used EHR, using an open source automated acceptance testing framework (FitNesse).MethodsAcceptance tests were initially constructed as spreadsheet tables to facilitate clinical review. Each table specified one aspect of the CDS advisory’s expected behavior. Table contents were then imported into a test suite in FitNesse, which queried the EHR database to automate testing. Tests and corresponding CDS configuration were migrated together from the development environment to production, with tests becoming part of the production regression test suite.ResultsWe used test–driven development to construct a new CDS tool advising Emergency Department nurses to perform a swallowing assessment prior to administering oral medication to a patient with suspected stroke. Test tables specified desired behavior for (1) applicable clinical settings, (2) triggering action, (3) rule logic, (4) user interface, and (5) system actions in response to user input. Automated test suite results for the “executable requirements” are shown prior to building the CDS alert, during build, and after successful build.ConclusionsAutomated acceptance test–driven development and continuous regression testing of CDS configuration in a commercial EHR proves feasible with open source software. Automated test–driven development offers one potential contribution to achieving high-reliability EHR configuration. Vetting acceptance tests with clinicians elicits their input on crucial configuration details early during initial CDS design and iteratively during rapid-cycle optimization.

Development and evaluation of a novel user interface for reviewing clinical microbiology results.

Microbiology laboratory results are complex and cumbersome to review. We sought to develop a new review tool to improve the ease and accuracy of microbiology results review. We observed and informally interviewed clinicians to determine areas in which existing microbiology review tools were lacking. We developed a new tool that reorganizes microbiology results by time and organism. We conducted a scenario-based usability evaluation to compare the new tool to existing legacy tools, using a balanced block design. The average time-on-task decreased from 45.3 min for the legacy tools to 27.1 min for the new tool (P < .0001). Total errors decreased from 41 with the legacy tools to 19 with the new tool (P = .0068). The average Single Ease Question score was 5.65 (out of 7) for the new tool, compared to 3.78 for the legacy tools (P < .0001). The new tool scored 88 ("Excellent") on the System Usability Scale. The new tool substantially improved efficiency, accuracy, and usability. It was subsequently integrated into the electronic health record and rolled out system-wide. This project provides an example of how clinical and informatics teams can innovative alongside a commercial Electronic Health Record (EHR).

Evaluating the usability of speech recognition to create clinical documentation using a commercial electronic health record

ObjectiveTo conduct a usability study exploring the value of using speech recognition (SR) for clinical documentation tasks within an electronic health record (EHR) system. MethodsThirty-five emergency department clinicians completed a system usability scale (SUS) questionnaire. The study was undertaken after participants undertook randomly allocated clinical documentation tasks using keyboard and mouse (KBM) or SR. SUS scores were analyzed and the results with KBM were compared to SR results. ResultsSignificant difference in SUS scores between EHR system use with and without SR were observed (KBM 67, SR 61; P = 0.045; CI, 0.1 to 12.0). Nineteen of 35 participants scored higher for EHR with KBM, 11 higher for EHR with SR and 5 gave the same score for both. Factor analysis showed no significant difference in scores for the sub-element of usability (EHR with KBM 65, EHR with SR 62; P = 0.255; CI, −2.6 to 9.5). Scores for the sub-element of learnability were significantly different (KBM 72, SR 55; P < 0.001; CI, 9.8 to 23.5). A significant correlation was found between the perceived usability of the two system configurations (EHR with KBM or SR) and the efficiency of documentation (time to document) (P = 0.002; CI, 10.5 to −0.1) but not with safety (number of errors) (P = 0.90; CI, −2.3 to 2.6). DiscussionSR was associated with significantly reduced overall usability scores, even though it is often positioned as ease of use technology. SR was perceived to impose larger costs in terms of learnability via training and support requirements for EHR based documentation when compared to using KBM. Lower usability scores were significantly associated with longer documentation times. ConclusionThe usability of EHR systems with any input modality is an area that requires continued development. The addition of an SR component to an EHR system may cause a significant reduction in terms of perceived usability by clinicians.

A Hybrid Electronic Health Record System Integrating Electronic and Paper-based Records

Objectives: Ordinary electronic health record (EHR) systems have difficulty managing paper-based medical records because digitized paper-based records must be browsed with a viewer other than the main display for the EHR system. Because of this fragmentation of electronic and paper information, it is difficult to combine keyboard/ mouse input with handwriting input in a clinical setting. We developed a new EHR system to address this problem. Methods: We revised a commercial EHR system to function as a progress note system that integrates keyboard/ mouse-based electronic records with digitized paper-based records, both of which can be viewed in a page-turning style. In this system all records, including paper-based records, are arranged in relation to the time they were written, even when they were digitized later at a scanning center. These features of the system allow for combined use of handwritten and keyboard/mouse input, without fragmentation. We investigated the impact of the introduction of the first EHR system in the ophthalmology department at our hospital, which had long resisted implementation of an EHR system. Results: The number of ophthalmology outpatients did not significantly decrease after introduction of the system. The ophthalmologists in our hospital accepted the first EHR system without negative reactions, and a gradual transition from handwritten input to keyboard/mouse input was noted. Conclusions: The present system enables users to produce flexible medical documentation, by using both keyboard/mouse and handwriting inputs, without reducing medical efficiency or safety. This is a cost-efficient, true hybrid digital–analog EHR system.

Using voice to create hospital progress notes: Description of a mobile application and supporting system integrated with a commercial electronic health record

We describe the development and design of a smartphone app-based system to create inpatient progress notes using voice, commercial automatic speech recognition software, with text processing to recognize spoken voice commands and format the note, and integration with a commercial EHR. This new system fits hospital rounding workflow and was used to support a randomized clinical trial testing whether use of voice to create notes improves timeliness of note availability, note quality, and physician satisfaction with the note creation process. The system was used to create 709 notes which were placed in the corresponding patient’s EHR record. The median time from pressing the Send button to appearance of the formatted note in the Inbox was 8.8 min. It was generally very reliable, accepted by physician users, and secure. This approach provides an alternative to use of keyboard and templates to create progress notes and may appeal to physicians who prefer voice to typing.

Implementing electronic health records (EHRs): health care provider perceptions before and after transition from a local basic EHR to a commercial comprehensive EHR.

We assessed changes in the percentage of providers with positive perceptions of electronic health record (EHR) benefit before and after transition from a local basic to a commercial comprehensive EHR. Changes in the percentage of providers with positive perceptions of EHR benefit were captured via a survey of academic health care providers before (baseline) and at 6-12 months (short term) and 12-24 months (long term) after the transition. We analyzed 32 items for the overall group and by practice setting, provider age, and specialty using separate multivariable-adjusted random effects logistic regression models. A total of 223 providers completed all 3 surveys (30% response rate): 85.6% had outpatient practices, 56.5% were >45 years old, and 23.8% were primary care providers. The percentage of providers with positive perceptions significantly increased from baseline to long-term follow-up for patient communication, hospital transitions - access to clinical information, preventive care delivery, preventive care prompt, preventive lab prompt, satisfaction with system reliability, and sharing medical information (P < .05 for each). The percentage of providers with positive perceptions significantly decreased over time for overall satisfaction, productivity, better patient care, clinical decision quality, easy access to patient information, monitoring patients, more time for patients, coordination of care, computer access, adequate resources, and satisfaction with ease of use (P < 0.05 for each). Results varied by subgroup. After a transition to a commercial comprehensive EHR, items with significant increases and significant decreases in the percentage of providers with positive perceptions of EHR benefit were identified, overall and by subgroup.