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Journal articles on the topic 'EHR'

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Published: 4 June 2021
Last updated: 21 February 2023

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1

Rosenberg, Tziporah. "Ehr." Families, Systems, & Health 34, no. 3 (September 2016): 303. http://dx.doi.org/10.1037/fsh0000215.

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2

King, Jason, Ben Smith, and Laurie Williams. "Audit Mechanisms in Electronic Health Record Systems." International Journal of Computational Models and Algorithms in Medicine 3, no. 2 (April 2012): 23–42. http://dx.doi.org/10.4018/jcmam.2012040102.

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Inadequate audit mechanisms may result in undetected misuse of data in software-intensive systems. In the healthcare domain, electronic health record (EHR) systems should log the creating, reading, updating, or deleting of privacy-critical protected health information. The objective of this paper is to assess electronic health record audit mechanisms to determine the current degree of auditing for non-repudiation and to assess whether general audit guidelines adequately address non-repudiation. The authors analyzed the audit mechanisms of two open source EHR systems, OpenEMR and Tolven eCHR, and one proprietary EHR system. The authors base the qualitative assessment on a set of 16 general auditable events and 58 black-box test cases for specific auditable events. The authors find that OpenEMR satisfies 62.5% of the general criteria and passes 63.8% of the black-box test cases. Tolven eCHR and the proprietary EHR system each satisfy less than 19% of the general criteria and pass less than 11% of the black-box test cases.
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Nolan, Matthew, Rizwan Siwani, Haytham Helmi, Brian Pickering, Pablo Moreno-Franco, and Vitaly Herasevich. "Health IT Usability Focus Section: Data Use and Navigation Patterns among Medical ICU Clinicians during Electronic Chart Review." Applied Clinical Informatics 08, no. 04 (2017): 1117–26. http://dx.doi.org/10.4338/aci-2017-06-ra-0110.

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Background A detailed understanding of electronic health record (EHR) workflow patterns and information use is necessary to inform user-centered design of critical care information systems. While developing a longitudinal medical record visualization tool to facilitate electronic chart review (ECR) for medical intensive care unit (MICU) clinicians, we found inadequate research on clinician–EHR interactions. Objective We systematically studied EHR information use and workflow among MICU clinicians to determine the optimal selection and display of core data for a revised EHR interface. Methods We conducted a direct observational study of MICU clinicians performing ECR for unfamiliar patients during their routine daily practice at an academic medical center. Using a customized manual data collection instrument, we unobtrusively recorded the content and sequence of EHR data reviewed by clinicians. Results We performed 32 ECR observations among 24 clinicians. The median (interquartile range [IQR]) chart review duration was 9.2 (7.3–14.7) minutes, with the largest time spent reviewing clinical notes (44.4%), laboratories (13.3%), imaging studies (11.7%), and searching/scrolling (9.4%). Historical vital sign and intake/output data were never viewed in 31% and 59% of observations, respectively. Clinical notes and diagnostic reports were browsed ≥10 years in time for 60% of ECR sessions. Clinicians viewed a median of 7 clinical notes, 2.5 imaging studies, and 1.5 diagnostic studies, typically referencing a select few subtypes. Clinicians browsed a median (IQR) of 26.5 (22.5–37.25) data screens to complete their ECR, demonstrating high variability in navigation patterns and frequent back-and-forth switching between screens. Nonetheless, 47% of ECRs begin with review of clinical notes, which were also the most common navigation destination. Conclusion Electronic chart review centers around the viewing of clinical notes among MICU clinicians. Convoluted workflows and prolonged searching activities indicate room for system improvement. Using study findings, specific design recommendations to enhance usability for critical care information systems are provided.
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Panigrahi, Amrutanshu, Ajit Kumar Nayak, and Rourab Paul. "HealthCare EHR." International Journal of Information Systems and Supply Chain Management 15, no. 3 (July 2022): 1–15. http://dx.doi.org/10.4018/ijisscm.290017.

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Blockchain technology is currently playing a significant role in providing a secure and effective means to share information in a variety of domains, including the financial sector, supply chain management (SCM) in various domains, IoT, and the field of health care systems (HCS). The HCS application's interoperability and security allow patients and vendors to communicate information seamlessly. The absence of such traits reveals the patient's difficulties in gaining access to his or her own health status. As a result, incorporating blockchain technology will eliminate this disadvantage, allowing the HCS to become more effective and efficient. These potential benefits provide a foundation for blockchain technology to be used in various aspects of HCS, such as maintain the patient electronic health record (EHR) and electronic medical records (EMR) for various medical devices, billing, and telemedicine systems, and so on.
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5

Sriram, Indira, Robin Holland, and Steven R. Lowenstein. "I, EHR." Journal of Hospital Medicine, Volume 15, Issue 02 (May 10, 2019): 119–20. http://dx.doi.org/10.12788/jhm.3211.

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6

OʼBrien, Ann, Charlotte Weaver, Theresa (Tess) Settergren, Mary L. Hook, and Catherine H. Ivory. "EHR Documentation." Nursing Administration Quarterly 39, no. 4 (2015): 333–39. http://dx.doi.org/10.1097/naq.0000000000000132.

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7

Thede, Linda Q. "EHR Data." AJN, American Journal of Nursing 120, no. 4 (April 2020): 13. http://dx.doi.org/10.1097/01.naj.0000659948.46046.cd.

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8

Chauhan, Zain, Mohammad Samarah, Kim Unertl, and Martha Jones. "Adoption of Electronic Dental Records: Examining the Influence of Practice Characteristics on Adoption in One State." Applied Clinical Informatics 09, no. 03 (July 2018): 635–45. http://dx.doi.org/10.1055/s-0038-1667331.

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Objective Compared with medicine, less research has focused on adoption rates and factors contributing to the adoption of electronic dental records (EDRs) and certified electronic health records (EHRs) in the field of dentistry. We ran two multivariate models on EDR adoption and certification-capable EHR adoption to determine environmental and organizational factors associated with adoption. Methods We conducted telephone survey of a 10-item questionnaire using disproportionate stratified sampling procedure of 149 dental clinics in Tennessee in 2017 measuring adoption of dental information technology (IT) (EDRs and certification-capable EHRs) and practice characteristics, including region, rurality, specialty, and practice size. We used binomial logistic regression models to determine associations of adoption with predictor variables. Results A total of 77% of surveyed dental clinics in Tennessee had adopted some type of EDR system. When the definitions of certification capable were applied, the adoption rates in dental clinics dropped to 58%. A binomial logistic regression model for the effects of rurality, specialization, and practice size on the likelihood that a clinic would adopt an EHR product was statistically significant (chi-square (3) = 12.41, p = 0.0061). Of the three predictor variables, specialization and practice size were significant: Odds of adopting an EHR is 67% lower for specialists than for general dentists; and clinics with two or more practicing dentists were associated with a much greater likelihood of adopting an EHR-capable system (adjusted odds ratio = 3.09, p = 0.009). Conclusion Findings from this study indicate moderate to high levels of overall dental IT adoption. However, adoption rates in dental clinics do remain lower than those observed in office-based physician practices in medicine. Specialization and practice size were significant predictors of EHR-capable system adoption. Efforts to increase EHR adoption in dentistry should be mindful of potential disparities in smaller practices and between dental specialties and generalists.
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Ziebell, Robert-Christian, Jose Albors-Garrigos, Martin Schultz, Klaus Peter Schoeneberg, and M. Rosario Perello-Marin. "eHR Cloud Transformation." International Journal of Intelligent Information Technologies 15, no. 1 (January 2019): 1–21. http://dx.doi.org/10.4018/ijiit.2019010101.

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The article covers process models for HR IT projects and in particular for HR transformation projects. Based on the authors' experience, an applied process model for HR transformation projects in a cloud-based environment is derived. The article identifies findings applicable to the fields of organisation, business, and IT as well as decisions and critical success factors in the specific context of cloud-based HR solutions.
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10

Chan, Wiley. "P012 The EHR." BMJ Quality & Safety 22, Suppl 1 (August 2013): A5.1—A5. http://dx.doi.org/10.1136/bmjqs-2013-002293.12.

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11

Sensmeier, Joyce E. "Advancing the EHR." Nursing Management (Springhouse) 40, no. 3 (March 2009): 19–23. http://dx.doi.org/10.1097/01.numa.0000347408.27460.a9.

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Gomez, Robin. "EHR upgrade considerations." Nursing Management (Springhouse) 41, no. 12 (December 2010): 35–37. http://dx.doi.org/10.1097/01.numa.0000390464.11624.d6.

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13

East, Thomas D. "The EHR Paradox." Frontiers of Health Services Management 22, no. 2 (2005): 33–35. http://dx.doi.org/10.1097/01974520-200510000-00005.

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14

NOTTE, CHRISTOPHER, and NEIL SKOLNIK. "Affording Your EHR." Family Practice News 41, no. 16 (October 2011): 53. http://dx.doi.org/10.1016/s0300-7073(11)70874-x.

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NOTTE, CHRISTOPHER, and NEIL SKOLNIK. "Affording Your EHR." Internal Medicine News 44, no. 16 (October 2011): 52. http://dx.doi.org/10.1016/s1097-8690(11)70846-0.

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16

Avitzur, Orly. "EHR Bake-offs." Neurology Today 8, no. 24 (January 2008): 15–16. http://dx.doi.org/10.1097/01.nt.0000308765.80460.50.

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17

Rubin, Rita. "Preventing EHR Confusion." JAMA 321, no. 8 (February 26, 2019): 734. http://dx.doi.org/10.1001/jama.2019.0609.

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18

Herout, Jennifer, Jason J. Saleem, Matthew Weinger, Robert W. Grundmeier, Emily S. Patterson, Shilo Anders, and A. Zachary Hettinger. "EHR to EHR Transitions: Establishing and Growing a Knowledge Base." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 513–17. http://dx.doi.org/10.1177/1541931218621117.

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Although numerous healthcare organizations have transitioned from one electronic health record (EHR) to another or are currently planning a transition, there are few documented artifacts, such as published studies or operationalizable resources, that offer guidance on such transitions. This panel seeks to begin a conversation about human factors considerations in EHR transitions from a legacy system. Panel members will discuss current literature and research on the topic as well as experiences with and lessons learned from transitions within their organizations. Panel discussion can be expected to identify new research opportunities, needed resources, and guidance for EHR vendors or healthcare facilities in the midst of or preparing for an EHR transition. Panelists will also lay out systemic issues that need to be addressed at the national policy and regulatory level. This topic is relevant not only to full-scale EHR transitions, but also has applicability for significant EHR version changes.
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Nguyen, Oliver T., Kea Turner, Nate C. Apathy, Tanja Magoc, Karim Hanna, Lisa J. Merlo, Christopher A. Harle, Lindsay A. Thompson, Eta S. Berner, and Sue S. Feldman. "Primary care physicians’ electronic health record proficiency and efficiency behaviors and time interacting with electronic health records: a quantile regression analysis." Journal of the American Medical Informatics Association 29, no. 3 (December 13, 2021): 461–71. http://dx.doi.org/10.1093/jamia/ocab272.

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Abstract Objective This study aimed to understand the association between primary care physician (PCP) proficiency with the electronic health record (EHR) system and time spent interacting with the EHR. Materials and Methods We examined the use of EHR proficiency tools among PCPs at one large academic health system using EHR-derived measures of clinician EHR proficiency and efficiency. Our main predictors were the use of EHR proficiency tools and our outcomes focused on 4 measures assessing time spent in the EHR: (1) total time spent interacting with the EHR, (2) time spent outside scheduled clinical hours, (3) time spent documenting, and (4) time spent on inbox management. We conducted multivariable quantile regression models with fixed effects for physician-level factors and time in order to identify factors that were independently associated with time spent in the EHR. Results Across 441 primary care physicians, we found mixed associations between certain EHR proficiency behaviors and time spent in the EHR. Across EHR activities studied, QuickActions, SmartPhrases, and documentation length were positively associated with increased time spent in the EHR. Models also showed a greater amount of help from team members in note writing was associated with less time spent in the EHR and documenting. Discussion Examining the prevalence of EHR proficiency behaviors may suggest targeted areas for initial and ongoing EHR training. Although documentation behaviors are key areas for training, team-based models for documentation and inbox management require further study. Conclusions A nuanced association exists between physician EHR proficiency and time spent in the EHR.
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Sinha, Amrita, Lindsay A. Stevens, Felice Su, Natalie M. Pageler, and Daniel S. Tawfik. "Measuring Electronic Health Record Use in the Pediatric ICU Using Audit-Logs and Screen Recordings." Applied Clinical Informatics 12, no. 04 (August 2021): 737–44. http://dx.doi.org/10.1055/s-0041-1733851.

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Abstract Background Time spent in the electronic health record (EHR) has been identified as an important unit of measure for health care provider clinical activity. The lack of validation of audit-log based inpatient EHR time may have resulted in underuse of this data in studies focusing on inpatient patient outcomes, provider efficiency, provider satisfaction, etc. This has also led to a dearth of clinically relevant EHR usage metrics consistent with inpatient provider clinical activity. Objective The aim of our study was to validate audit-log based EHR times using observed EHR-times extracted from screen recordings of EHR usage in the inpatient setting. Methods This study was conducted in a 36-bed pediatric intensive care unit (PICU) at Lucile Packard Children's Hospital Stanford between June 11 and July 14, 2020. Attending physicians, fellow physicians, hospitalists, and advanced practice providers with ≥0.5 full-time equivalent (FTE) for the prior four consecutive weeks and at least one EHR session recording were included in the study. Citrix session recording player was used to retrospectively review EHR session recordings that were captured as the provider interacted with the EHR. Results EHR use patterns varied by provider type. Audit-log based total EHR time correlated strongly with both observed total EHR time (r = 0.98, p < 0.001) and observed active EHR time (r = 0.95, p < 0.001). Each minute of audit-log based total EHR time corresponded to 0.95 (0.87–1.02) minutes of observed total EHR time and 0.75 (0.67–0.83) minutes of observed active EHR time. Results were similar when stratified by provider role. Conclusion Our study found inpatient audit-log based EHR time to correlate strongly with observed EHR time among pediatric critical care providers. These findings support the use of audit-log based EHR-time as a surrogate measure for inpatient provider EHR use, providing an opportunity for researchers and other stakeholders to leverage EHR audit-log data in measuring clinical activity and tracking outcomes of workflow improvement efforts longitudinally and across provider groups.
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Mañas-García, Alejandro, José Alberto Maldonado, Mar Marcos, Diego Boscá, and Montserrat Robles. "Augmented EHR: Enrichment of EHR with Contents from Semantic Web Sources." Applied Sciences 11, no. 9 (April 27, 2021): 3978. http://dx.doi.org/10.3390/app11093978.

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This work presents methods to combine data from the Semantic Web into existing EHRs, leading to an augmented EHR. An existing EHR extract is augmented by combining it with additional information from external sources, typically linked data sources. The starting point is a standardized EHR extract described by an archetype. The method consists of combining specific data from the original EHR with contents from the external information source by building a semantic representation, which is used to query the external source. The results are converted into a standardized EHR extract according to an archetype. This work sets the foundations to transform Semantic Web contents into normalized EHR extracts. Finally, to exemplify the approach, the work includes a practical use case in which the summarized EHR is augmented with drug–drug interactions and disease-related treatment information.
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Wang, Q., and R. S. Laramee. "EHR STAR: The State‐Of‐the‐Art in Interactive EHR Visualization." Computer Graphics Forum 41, no. 1 (December 2021): 69–105. http://dx.doi.org/10.1111/cgf.14424.

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23

Csatari, A., and A. Gettinger. "Transitioning from a Legacy EHR to a Commercial, Vendor-supplied, EHR." Applied Clinical Informatics 03, no. 04 (2012): 367–76. http://dx.doi.org/10.4338/aci-2012-04-r-0014.

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Summary Objective: Describe the planning, decisions, and implementation results experienced during the large-scale transition from one EHR to another throughout a large academic health system, which occurred simultaneously throughout both in-patient and all ambulatory settings Methods: Review of internal decision-making documents, interviews with key participants, and data from conversion software Results: Over 7,000 unique users caring for a population of more than 1.2 million patients in both inpatient and outpatient venues and distributed across two states were successfully transitioned to a new EHR simultaneously. Challenges in data conversion were encountered resulting in more work for end-users than desired or anticipated. Users continued to access older information (principally schedules) in the legacy EHR one year later Conclusion: Data conversion from one EHR to another can be unsuccessful due to differences in how EHR’s structure data obtained from underlying feeder applications or databases. Abstraction of only the pertinent clinical content is difficult in the context of transitioning to a new EHR. Clinicians require facile access to legacy content that can be achieved by implanting CCOW compliant solutions. Citation: Gettinger A, Csatari A. Transitioning from a legacy EHR to a commercial, vendor-supplied, EHR. Appl Clin Inf 2012; 3: 367–376http://dx.doi.org/10.4338/ACI-2012-04-R-0014
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Saleem, Jason J., and Jennifer Herout. "Transitioning from one Electronic Health Record (EHR) to Another: A Narrative Literature Review." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 489–93. http://dx.doi.org/10.1177/1541931218621112.

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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.
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Anderson, Jacob, Jason Leubner, and Steven R. Brown. "EHR Overtime: An Analysis of Time Spent After Hours by Family Physicians." Family Medicine 52, no. 2 (February 7, 2020): 135–37. http://dx.doi.org/10.22454/fammed.2020.942762.

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Background and Objectives: Time spent in the electronic health record (EHR), away from direct patient care, is associated with physician burnout. Yet there is a lack of evidence quantifying EHR use among family physicians. The purpose of the study was to describe a method for quantifying habits and duration of use within the electronic health record in family medicine residents and faculty with particular attention paid to time spent after hours. Methods: We audited EHR time for family medicine residents and faculty using an EHR vendor-provided, web-based tracking system. We collected and analyzed the number of patient encounters, total time in the EHR per patient, total time in the EHR after hours by physicians for a 6-month time period. Results: Over the 6-month period reviewed, family medicine trainees and faculty saw between one and 164 patients monthly, spent between 17 and 217 minutes in the EHR per patient, and spent between 0 and 33 hours in the EHR after hours per month. Conclusions: Family medicine residents spend a significant amount of time completing EHR tasks after hours. Objective EHR data can be used by family medicine residency programs to devise interventions to decrease inefficient use of the EHR, decrease after-hours EHR use, and improve well-being.
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Pradanthi, Ines Meiyola, Maya Weka Santi, and Atma Deharja. "Evaluasi Electronic Health Record (EHR) dengan Metode PIECES di Unit Rekam Medis Pusat RSUPN dr. Cipto Mangunkusumo." J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan 1, no. 3 (August 12, 2020): 216–25. http://dx.doi.org/10.25047/j-remi.v1i3.2047.

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National Center General Hospital Dr. Cipto Mangunkusumo is one of the hospitals whose services haveused Hospital Management Information System (SIMRS). The SIMRS used in RSCM is called the ElectronicHealth Record (EHR). In its application, there were still obstacles that made the staff less satisfied with theEHR, one of which was the loading of the EHR. The purpose of this study is to evaluate the EHR in termsof user satisfaction using the PIECES method (performance, information, economic, control, efficiency,service). This type of research is qualitative research, by describing the results of interviews andobservations that have been conducted by researchers and assessed by the PIECES method. Respondentsin this study were 1 filling officer, 1 reporting officer, 1 warehouse officer, and 1 expedition officer. The resultsobtained in this study are that the EHR performance has produced data according to user needs and thereis still a fairly long loading. EHR is able to provide quality information that is easily understood and has beenintegrated with BPJS. that the EHR can produce data according to user needs and the EHR still needs timeif the officer inputs or loads. The EHR has a use value because it is integrated with BPJS Health and alsohas an EHR officer or improvement team. EHR users feel that having an EHR makes it easy for EHR userswhen performing health services such as searching for patient files and making reports. The conclusion isthat the EHR makes it easy for users to do their work. Suggestions given by researchers are to the UMSI toupdate the EHR and perform maintenance on the system and hardware.
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Alsadi, Mohammad, Ali Saleh, Malek Khalil, and Islam Oweidat. "Readiness-Based Implementation of Electronic Health Records." Creative Nursing 28, no. 1 (February 1, 2022): 42–47. http://dx.doi.org/10.1891/cn-2021-0024.

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Electronic health record (EHR) implementation is expanding worldwide to achieve the benefits of that technology, but it is reported in the literature as a “disruptive” change to the work environment in which all health-care workers need to be ready for the change, to enhance adoption and harvest the benefits. Jordan has rolled out a national EHR system. This study explored EHR implementation readiness, levels of realizing the benefits of EHR, and adoption among Jordanian nurses, using a self-report questionnaire at nine governmental hospitals in Jordan. A total of 462 registered nurses participated in the study. Results showed that nurses have moderate levels of readiness for EHR implementation, but higher levels of EHR benefits realization and adoption. All health-care workers’ readiness for EHR implementation must be assessed regularly before, during, and after EHR implementation. Readiness-based roll-out can be used as a strategy in implementing EHR systems. Introducing a large-scale change management program is recommended to assess readiness, guide roll-out plans, enhance EHR implementation readiness, improve benefits realization, and increase EHR adoption levels, to help move health-care systems into the digital era.
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Chen, Lu, Uta Guo, Lijo C. Illipparambil, Matt D. Netherton, Bhairavi Sheshadri, Eric Karu, Stephen J. Peterson, and Parag H. Mehta. "Racing Against the Clock: Internal Medicine Residents' Time Spent On Electronic Health Records." Journal of Graduate Medical Education 8, no. 1 (February 1, 2016): 39–44. http://dx.doi.org/10.4300/jgme-d-15-00240.1.

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ABSTRACT Background Since the late 1980s, resident physicians have spent increasing amounts of time on electronic health record (EHR) data entry and retrieval. Objective longitudinal data measuring time spent on the EHR are lacking. Objective We sought to quantify the time actually spent using the EHR by all first-year internal medicine residents in a single program (N = 41). Methods Active EHR usage data were collected from the audit logs for May, July, and October 2014 and January 2015. Per recommendations from our EHR vendor (Cerner Corporation), active EHR usage time was defined as more than 15 keystrokes, or 3 mouse clicks, or 1700 “mouse miles” per minute. Active EHR usage time was tallied for each patient chart viewed each day and termed an electronic patient record encounter (EPRE). Results In 4 months, 41 interns accumulated 18 322 hours of active EHR usage in more than 33 733 EPREs. Each intern spent on average 112 hours per month on 206 EPREs. Interns spent more time in July compared to January (41 minutes versus 30 minutes per EPRE, P &lt; .001). Time spent on the EHR in January echoed that of the previous May (30 minutes versus 29 minutes, P = .40). Conclusions First-year residents spent a significant amount of time actively using the EHR, achieving maximal proficiency on or before January of the academic year. Decreased time spent on the EHR may reflect greater familiarity with the EHR, growing EHR efficiencies, or other factors.
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Sandoval, Marie B., Mary Val Palumbo, and Vicki Hart. "Electronic Health Record's Effects on the Outpatient Office Visit and Clinical Education." Journal of Innovation in Health Informatics 23, no. 4 (January 24, 2017): 765. http://dx.doi.org/10.14236/jhi.v23i4.151.

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Background: During an office visit, the provider has the important cognitive task of attending to the patient while actively using the electronic health record (EHR). Prior literature suggests that EHR may have a positive effect on simple tasks, but a negative effect on tasks that require complex cognitive processes. No study has examined the provider’s perception of EHR on multiple distinct aspects of the office visit.Methods: We surveyed providers/preceptors regarding their perception of EHR on multiple aspects of the office visit. We summarized their EHR utilization history and their perceptions of the EHR during the visit using descriptive statistics. We tested for associations between time spent using the EHR and distinct aspects of the visit using Chi-square tests of association.Results: In total, 83 providers/preceptors reported use of EHR (response rate 52%). Provider/preceptors reported an overall negative effect of EHR on the patient-provider connection, but an overall positive effect on the review of medications/medical records, communication between providers, review of results with patients and review of follow-up to testing results with patients. The effect of EHR on history taking and teaching students was neutral. We observed no correlation between the provider’s time spent using the EHR and their perception of its effectiveness.Conclusions: Providers reported a positive perception of EHR on aspects of the office visit that involved a single cognitive task. However, providers reported a negative perception of EHR on patient-provider connection, which involves a high degree of cognitive processing.
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Burke, Harry B., Laura L. Sessums, Albert Hoang, Dorothy A. Becher, Paul Fontelo, Fang Liu, Mark Stephens, et al. "Electronic health records improve clinical note quality." Journal of the American Medical Informatics Association 22, no. 1 (October 23, 2014): 199–205. http://dx.doi.org/10.1136/amiajnl-2014-002726.

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Abstract Background and objective The clinical note documents the clinician's information collection, problem assessment, clinical management, and its used for administrative purposes. Electronic health records (EHRs) are being implemented in clinical practices throughout the USA yet it is not known whether they improve the quality of clinical notes. The goal in this study was to determine if EHRs improve the quality of outpatient clinical notes. Materials and methods A five and a half year longitudinal retrospective multicenter quantitative study comparing the quality of handwritten and electronic outpatient clinical visit notes for 100 patients with type 2 diabetes at three time points: 6 months prior to the introduction of the EHR (before-EHR), 6 months after the introduction of the EHR (after-EHR), and 5 years after the introduction of the EHR (5-year-EHR). QNOTE, a validated quantitative instrument, was used to assess the quality of outpatient clinical notes. Its scores can range from a low of 0 to a high of 100. Sixteen primary care physicians with active practices used QNOTE to determine the quality of the 300 patient notes. Results The before-EHR, after-EHR, and 5-year-EHR grand mean scores (SD) were 52.0 (18.4), 61.2 (16.3), and 80.4 (8.9), respectively, and the change in scores for before-EHR to after-EHR and before-EHR to 5-year-EHR were 18% (p&lt;0.0001) and 55% (p&lt;0.0001), respectively. All the element and grand mean quality scores significantly improved over the 5-year time interval. Conclusions The EHR significantly improved the overall quality of the outpatient clinical note and the quality of all its elements, including the core and non-core elements. To our knowledge, this is the first study to demonstrate that the EHR significantly improves the quality of clinical notes.
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DiAngi, Yumi T., Lindsay A. Stevens, Bonnie Halpern – Felsher, Natalie M. Pageler, and Tzielan C. Lee. "Electronic health record (EHR) training program identifies a new tool to quantify the EHR time burden and improves providers’ perceived control over their workload in the EHR." JAMIA Open 2, no. 2 (March 21, 2019): 222–30. http://dx.doi.org/10.1093/jamiaopen/ooz003.

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AbstractObjectiveTo understand if providers who had additional electronic health record (EHR) training improved their satisfaction, decreased personal EHR-use time, and decreased turnaround time on tasks.Materials and MethodsThis pre-post study with no controls evaluated the impact of a supplemental EHR training program on a group of academic and community practice clinicians that previously had go-live group EHR training and 20 months experience using this EHR on self-reported data, calculated EHR time, and vendor-reported metrics.ResultsProviders self-reported significant improvements in their knowledge of efficiency tools in the EHR after training and doubled (significant) their preference list entries (mean pre = 38.1 [65.88], post = 63.5 [90.47], P &lt; .01). Of the 7 EHR satisfaction variables, only 1 self-reported variable significantly improved after training: Control over my workload in the EHR (mean pre = 2.7 [0.96], post = 3.0 [1.04], P &lt; .01). There was no significant decrease in their calculated EHR usage outside of clinic (mean pre = 0.39 [0.77] to post = 0.37 [0.48], P = .73). No significant difference was seen in turnaround time for patient calls (mean pre = 2.3 [2.06] days, post = 1.9 [1.76] days, P = .08) and results (mean before = 4.0 [2.79] days, after = 3.2 [2.33] days, P = .03).DiscussionMultiple sources of data provide a holistic view of the provider experience in the EHR. This study suggests that individualized EHR training can improve the knowledge of EHR tools and satisfaction with their perceived control of EHR workload, however this did not translate into less Clinician Logged-In Outside Clinic (CLOC) time, a calculated metric, nor quicker turnaround on in box tasks. CLOC time emerged as a potential less-costly surrogate metric for provider satisfaction in EHR work than surveying clinicians. Further study is required to understand the cost-benefit of various interventions to decrease CLOC time.ConclusionsThis supplemental EHR training session, 20 months post go-live, where most participants elected to receive 2 or fewer sessions did significantly improve provider satisfaction with perceived control over their workload in the EHR, but it was not effective in decreasing EHR-use time outside of clinic. CLOC time, a calculated metric, could be a practical trackable surrogate for provider satisfaction (inverse correlation) with after-hours time spent in the EHR. Further study into interventions that decrease CLOC time and improve turnaround time to respond to inbox tasks are suggested next steps.
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Micek, Mark A., Brian Arndt, Wen-Jan Tuan, Elizabeth Trowbridge, Shannon M. Dean, Jennifer Lochner, Emmanuel Sampene, and Nancy Pandhi. "Physician Burnout and Timing of Electronic Health Record Use." ACI Open 04, no. 01 (January 2020): e1-e8. http://dx.doi.org/10.1055/s-0039-3401815.

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Abstract Background Rates of burnout among physicians have been high in recent years. The electronic health record (EHR) is implicated as a major cause of burnout. Objective This article aimed to determine the association between physician burnout and timing of EHR use in an academic internal medicine primary care practice. Methods We conducted an observational cohort study using cross-sectional and retrospective data. Participants included primary care physicians in an academic outpatient general internal medicine practice. Burnout was measured with a single-item question via self-reported survey. EHR time was measured using retrospective automated data routinely captured within the institution's EHR. EHR time was separated into four categories: weekday work-hours in-clinic time, weekday work-hours out-of-clinic time, weekday afterhours time, and weekend/holiday after-hours time. Ordinal regression was used to determine the relationship between burnout and EHR time categories. Results EHR use during in-clinic sessions was related to burnout in both bivariate (odds ratio [OR] = 1.04, 95% confidence interval [CI]: 1.01, 1.06; p = 0.007) and adjusted (OR = 1.07, 95% CI: 1.03, 1.1; p = 0.001) analyses. No significant relationships were found between burnout and after-hours EHR use. Conclusion In this small single-institution study, physician burnout was associated with higher levels of in-clinic EHR use but not after-hours EHR use. Improved understanding of the variability of in-clinic EHR use, and the EHR tasks that are particularly burdensome to physicians, could help lead to interventions that better integrate EHR demands with clinical care and potentially reduce burnout. Further studies including more participants from diverse clinical settings are needed to further understand the relationship between burnout and after-hours EHR use.
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Gilleland, Meghan, Katherine Komis, Sonya Chawla, Stephen Fernandez, Mary Fishman, and Michael Adams. "Resident Duty Hours in the Outpatient Electronic Health Record Era: Inaccuracies and Implications." Journal of Graduate Medical Education 6, no. 1 (March 1, 2014): 151–54. http://dx.doi.org/10.4300/jgme-d-13-00061.1.

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Abstract Background The Accreditation Council for Graduate Medical Education expects resident duty hours to be monitored, yet no previous studies have examined the effect of after-hours electronic health record (EHR) use on resident hours or burnout. Objective We assessed internal medicine residents' perceived and actual time spent on after-hours outpatient EHR use and calculated increased duty hours if after-hours EHR use were included; we also assessed its effect on resident burnout. Methods We retrospectively aggregated time spent logged on to the outpatient EHR for residents in a general internal medicine clinic for 13 weeks in 2011. Residents completed a survey on EHR use, which was correlated with objectively recorded data on EHR usage. We compared actual and self-reported EHR time and identified violations that would be generated if these hours were included in reported duty hours. We also correlated resident after-hours EHR use with responses to an internally developed burnout survey. Results The 44 residents in this study overestimated time spent on the ambulatory EHR (they spent 3.03 hours/week on after-hours use compared with a recorded 1.20 hours/week). In total, 190 duty hour violations (mean duration of violation = 37 minutes) would have been generated if after-hours EHR usage were included in residents' reported duty hours. Conclusions Resident estimates of EHR use by residents were not accurate; including after-hours EHR use would increase the number of reported duty hour violations. There was no association between after-hours EHR use and resident burnout.
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Burke, Harry B., Dorothy A. Becher, Albert Hoang, and Ronald W. Gimbel. "The adoption of an electronic health record did not improve A1c values in Type 2 diabetes." Journal of Innovation in Health Informatics 23, no. 1 (April 15, 2016): 433. http://dx.doi.org/10.14236/jhi.v23i1.144.

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Background: A major justification for the clinical adoption of electronic health records (EHRs) was the expectation that it would improve the quality of medical care. No longitudinal study has tested this assumption.Objective: We used hemoglobin A1c, a recognized clinical quality measure directly related to diabetes outcomes, to assess the effect of EHR use on clinical quality.Methods: We performed a five-and-one-half-year multicentre longitudinal retrospective study of the A1c values of 537 type 2 diabetic patients. The same patients had to have been seen on at least three occasions: once approximately six months prior to EHR adoption (before-EHR), once approximately six monthsafter EHR adoption (after-EHR) and once approximately five years after EHR adoption (five-years), for a total of 1,611 notes.Results The overall mean confidence interval (CI) A1c values for the before- EHR, after-EHR and five-years were 7.07 (6.91 – 7.23), 7.33 (7.14 – 7.52) and 7.19 (7.06 – 7.32), respectively. There was a small but significant increase in A1c valuesbetween before-EHR and after-EHR, p = .04; there were no other significant differences. There was a significant decrease in notes missing at least one A1c value, from 42% before-EHR to 16% five-years (p < .001).Conclusion: We found that based on patient’s A1c values, EHRs did not improve the clinical quality of diabetic care in six months and five years after EHR adoption. To our knowledge, this is the first longitudinal study to directly assess the relationshipbetween the use of an EHR and clinical quality.
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Long, Christopher P., Ming Tai-Seale, Robert El-Kareh, Jeffrey E. Lee, and Sally L. Baxter. "Electronic Health Record Use among Ophthalmology Residents while on Call." Journal of Academic Ophthalmology 12, no. 02 (July 2020): e143-e150. http://dx.doi.org/10.1055/s-0040-1716411.

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Abstract Background As electronic health record (EHR) use becomes more widespread, detailed records of how users interact with the EHR, known as EHR audit logs, are being used to characterize the clinical workflows of physicians including residents. After-hours EHR use is of particular interest given its known association with physician burnout. Several studies have analyzed EHR audit logs for residents in other fields, such as internal medicine, but none thus far in ophthalmology. Here, we focused specifically on EHR use during on-call shifts outside of normal clinic hours. Methods In this retrospective study, we analyzed raw EHR audit log data from on-call shifts for 12 ophthalmology residents at a single institution over the course of a calendar year. Data were analyzed to characterize total time spent using the EHR, clinical volume, diagnoses of patients seen on call, and EHR tasks. Results Across all call shifts, the median and interquartile range (IQR) of the time spent logged into the EHR per shift were 88 and 131 minutes, respectively. The median (IQR) unique patient charts accessed per shift was 7 (9) patients. When standardized to per-hour measures, weekday evening shifts were the busiest call shifts with regard to both EHR use time and clinical volume. Total EHR use time and clinical volume were greatest in the summer months (July to September). Chart review comprised a majority (63.4%) of ophthalmology residents' on-call EHR activities. Conclusion In summary, EHR audit logs demonstrate substantial call burden for ophthalmology residents outside of regular clinic hours. These data and future studies can be used to further characterize the clinical exposure and call burden of ophthalmology residents and could potentially have broader implications in the fields of physician burnout and education policy.
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Katamanin, Olivia, and Alex M. Glazer MD. "Dermatologists' Perceptions and Use of Electronic Health Record Systems." SKIN The Journal of Cutaneous Medicine 4, no. 5 (August 29, 2020): 404–7. http://dx.doi.org/10.25251/skin.4.5.2.

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Introduction: Electronic Health Records (EHR) have been adopted and integrated into medical practices over the past 20 years. Many positive and negative implications have been described by physicians using EHR. This study aims to US dermatologists' perceptions and use of EHR within their clinical practice. Methods: A validated survey was administered to US dermatologists at a national educational conference to assess use and perceptions of EHR. Results Seventy-two percent (291/400) of those sampled completed greater than 90% survey and were included in outcome analysis. Eighty-six percent of the participants were currently using or had used EHR. Most dermatologists felt that EHR negatively impacted their workflow efficiency and face-to-face time with patients. A portion of dermatologists thought that EHR improved their documentation. Limitations: Selection bias may have led those with strong beliefs with EHR more likely to complete the entire survey. Conclusion: Despite widespread adoption, most dermatologists have a negative impression of EHR and felt that it interfered with their ability to effectively see patients. Interventions to improve EHR should focus on improving workflow efficiency and maximizing the amount of time dermatologists can spend with patients.
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Melnick, Edward R., Shawn Y. Ong, Allan Fong, Vimig Socrates, Raj M. Ratwani, Bidisha Nath, Michael Simonov, et al. "Characterizing physician EHR use with vendor derived data: a feasibility study and cross-sectional analysis." Journal of the American Medical Informatics Association 28, no. 7 (May 4, 2021): 1383–92. http://dx.doi.org/10.1093/jamia/ocab011.

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Abstract Objective To derive 7 proposed core electronic health record (EHR) use metrics across 2 healthcare systems with different EHR vendor product installations and examine factors associated with EHR time. Materials and Methods A cross-sectional analysis of ambulatory physicians EHR use across the Yale-New Haven and MedStar Health systems was performed for August 2019 using 7 proposed core EHR use metrics normalized to 8 hours of patient scheduled time. Results Five out of 7 proposed metrics could be measured in a population of nonteaching, exclusively ambulatory physicians. Among 573 physicians (Yale-New Haven N = 290, MedStar N = 283) in the analysis, median EHR-Time8 was 5.23 hours. Gender, additional clinical hours scheduled, and certain medical specialties were associated with EHR-Time8 after adjusting for age and health system on multivariable analysis. For every 8 hours of scheduled patient time, the model predicted these differences in EHR time (P &lt; .001, unless otherwise indicated): female physicians +0.58 hours; each additional clinical hour scheduled per month −0.01 hours; practicing cardiology −1.30 hours; medical subspecialties −0.89 hours (except gastroenterology, P = .002); neurology/psychiatry −2.60 hours; obstetrics/gynecology −1.88 hours; pediatrics −1.05 hours (P = .001); sports/physical medicine and rehabilitation −3.25 hours; and surgical specialties −3.65 hours. Conclusions For every 8 hours of scheduled patient time, ambulatory physicians spend more than 5 hours on the EHR. Physician gender, specialty, and number of clinical hours practicing are associated with differences in EHR time. While audit logs remain a powerful tool for understanding physician EHR use, additional transparency, granularity, and standardization of vendor-derived EHR use data definitions are still necessary to standardize EHR use measurement.
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Hernandez-Boussard, Tina, Keri L. Monda, Blai Coll Crespo, and Dan Riskin. "Real world evidence in cardiovascular medicine: ensuring data validity in electronic health record-based studies." Journal of the American Medical Informatics Association 26, no. 11 (August 12, 2019): 1189–94. http://dx.doi.org/10.1093/jamia/ocz119.

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Abstract Objective With growing availability of digital health data and technology, health-related studies are increasingly augmented or implemented using real world data (RWD). Recent federal initiatives promote the use of RWD to make clinical assertions that influence regulatory decision-making. Our objective was to determine whether traditional real world evidence (RWE) techniques in cardiovascular medicine achieve accuracy sufficient for credible clinical assertions, also known as “regulatory-grade” RWE. Design Retrospective observational study using electronic health records (EHR), 2010–2016. Methods A predefined set of clinical concepts was extracted from EHR structured (EHR-S) and unstructured (EHR-U) data using traditional query techniques and artificial intelligence (AI) technologies, respectively. Performance was evaluated against manually annotated cohorts using standard metrics. Accuracy was compared to pre-defined criteria for regulatory-grade. Differences in accuracy were compared using Chi-square test. Results The dataset included 10 840 clinical notes. Individual concept occurrence ranged from 194 for coronary artery bypass graft to 4502 for diabetes mellitus. In EHR-S, average recall and precision were 51.7% and 98.3%, respectively and 95.5% and 95.3% in EHR-U, respectively. For each clinical concept, EHR-S accuracy was below regulatory-grade, while EHR-U met or exceeded criteria, with the exception of medications. Conclusions Identifying an appropriate RWE approach is dependent on cohorts studied and accuracy required. In this study, recall varied greatly between EHR-S and EHR-U. Overall, EHR-S did not meet regulatory grade criteria, while EHR-U did. These results suggest that recall should be routinely measured in EHR-based studes intended for regulatory use. Furthermore, advanced data and technologies may be required to achieve regulatory grade results.
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Ming, Yang, and Tingting Zhang. "Efficient Privacy-Preserving Access Control Scheme in Electronic Health Records System." Sensors 18, no. 10 (October 18, 2018): 3520. http://dx.doi.org/10.3390/s18103520.

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The sharing of electronic health records (EHR) in cloud servers is an increasingly important development that can improve the efficiency of medical systems. However, there are several concerns focusing on the issues of security and privacy in EHR system. The EHR data contains the EHR owner’s sensitive personal information, if these data are obtained by a malicious user, it will not only cause the leakage of patient’s privacy, but also affect the doctor’s diagnosis. It is a very challenging problem for the EHR owner fully controls over own EHR data as well as preserves the privacy of himself. In this paper, we propose a new privacy-preserving access control (PPAC) scheme for EHR. To achieve fine-grained access control of the EHR data, we utilize the attribute-based signcryption (ABSC) mechanism to signcrypt data based on the access policy for the linear secret sharing schemes. Employing the cuckoo filter to hide the access policy, it could protect the EHR owner’s privacy information. In addition, the security analysis shows that the proposed scheme is provably secure under the decisional bilinear Diffie-Hellman exponent assumption and the computational Diffie-Hellman exponent assumption in the standard model. Furthermore, the performance analysis indicates that the proposed scheme achieves low costs of communication and computation compared with the related schemes, meanwhile preserves the EHR owner’s privacy. Therefore, the proposed scheme is better suited to EHR system.
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Abdel-Kader, Khaled, and Manisha Jhamb. "EHR-Based Clinical Trials." Clinical Journal of the American Society of Nephrology 15, no. 7 (February 24, 2020): 1050–52. http://dx.doi.org/10.2215/cjn.11860919.

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Seymour, Tom, Dean Frantsvog, and Tod Graeber. "Electronic Health Records (EHR)." American Journal of Health Sciences (AJHS) 3, no. 3 (July 13, 2012): 201–10. http://dx.doi.org/10.19030/ajhs.v3i3.7139.

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Electronic Health Records are electronic versions of patients’ healthcare records. An electronic health record gathers, creates, and stores the health record electronically. The electronic health record has been slow to be adopted by healthcare providers. The federal government has recently passed legislation requiring the use of electronic records or face monetary penalties. The electronic health record will improve clinical documentation, quality, healthcare utilization tracking, billing and coding, and make health records portable. The core components of an electronic health record include administrative functions, computerized physician order entry, lab systems, radiology systems, pharmacy systems, and clinical documentation. HL7 is the standard communication protocol technology that an electronic health record utilizes. Implementation of software, hardware, and IT networks are important for a successful electronic health record project. The benefits of an electronic health record include a gain in healthcare efficiencies, large gains in quality and safety, and lower healthcare costs for consumers. Electronic health record challenges include costly software packages, system security, patient confidentiality, and unknown future government regulations. Future technologies for electronic health records include bar coding, radio-frequency identification, and speech recognition.
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Balog, Daniel. "Psychiatrists, Rate Your EHR!" Psychiatric News 48, no. 9 (May 3, 2013): 17–24. http://dx.doi.org/10.1176/appi.pn.2013.5a6.

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CARTER, ERNEST. "About That EHR System …" Family Practice News 36, no. 24 (December 2006): 8. http://dx.doi.org/10.1016/s0300-7073(06)74302-x.

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BLACKBURN, MAGGIE. "Small Practices Selecting EHR." Family Practice News 37, no. 7 (April 2007): 52. http://dx.doi.org/10.1016/s0300-7073(07)70453-x.

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WAX, CRAIG M. "EHR: The Grand Illusion." Internal Medicine News 43, no. 5 (March 2010): 14. http://dx.doi.org/10.1016/s1097-8690(10)70254-7.

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Huston, Janis L. "EHR in the UK." Health Care Manager 25, no. 4 (October 2006): 335–40. http://dx.doi.org/10.1097/00126450-200610000-00009.

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WAX, CRAIG M. "EHR: The Grand Illusion." Skin & Allergy News 41, no. 4 (April 2010): 15. http://dx.doi.org/10.1016/s0037-6337(10)70083-8.

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Grams, Ralph. "The Obama EHR Experiment." Journal of Medical Systems 36, no. 2 (July 20, 2010): 951–56. http://dx.doi.org/10.1007/s10916-010-9559-z.

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BROWN, ROBIN, and MICHAEL WOOLERY. "EHR Implementation … In Hindsight." Family Practice News 38, no. 14 (July 2008): 49. http://dx.doi.org/10.1016/s0300-7073(08)70926-5.

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WAX, CRAIG M. "EHR: The Grand Illusion." Family Practice News 40, no. 4 (March 2010): 8. http://dx.doi.org/10.1016/s0300-7073(10)70328-5.

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