Current Electronic Health Record Research Articles

An Efficient Secure Sharing of Electronic Health Records Using IoT-Based Hyperledger Blockchain

Electronic Health Record (EHR) systems are a valuable and effective tool for exchanging medical information about patients between hospitals and other significant healthcare sector stakeholders in order to improve patient diagnosis and treatment around the world. Nevertheless, the majority of the hospital infrastructures that are now in place lack the proper security, trusted access control, and management of privacy and confidentiality concerns that the current EHR systems are supposed to provide. Goal. For various EHR systems, this research proposes a Blockchain-enabled Hyperledger Fabric Architecture as a solution to this delicate issue. The three steps of the suggested system are the secure upload phase, the secure download phase, and authentication. Patient registration, login, and verification make up the authentication step. The administrator grants authorization to read, edit, delete, or revoke the files following user details verification. In the secure upload phase, feature extraction is carried out first, and then a hashed access policy is created from the extracted feature. Next, the hash value is stored in an IoT-based Hyperledger blockchain. The uploaded EHR files are additionally encrypted before being stored on the cloud server. In the secure download step, the physician uses a hashed access policy to send the request to the cloud and decrypts the corresponding files. The experimental findings demonstrate that the system outperformed cutting-edge techniques. The proposed Modified Key Policy Attribute-Based Encryption performs better for the remaining 10 to 25 mb file sizes. This IoT framework compares MKP-ABE with certain efficiency indicators, such as encryption, decryption period, protection level analysis and encrypted memory use, resource use on decryption, upload time, and transfer time, which are present in the KP-ABE, the ECC, RSA, and AES. Here, the IoT device suggested requires 4008 ms for data encryption and 4138 ms for the data decryption.

Pre-pandemic assessment: a decade of progress in electronic health record adoption among U.S. hospitals.

As the COVID-19 pandemic loomed, the adoption of electronic health records (EHRs) in US hospitals became a pivotal concern. This study provides a pre-pandemic assessment, highlighting a decade of progress in EHR adoption from 2009 to 2019, with the last available survey conducted from January to June of 2020. It delves into the current EHR adoption rates, variations across different hospital categories, the influence of major vendors, and the challenges in implementing these systems. The study found that basic EHR adoption surged from 6.6% to 81.2%, while comprehensive systems increased from 3.6% to 63.2%. Despite this growth, the findings point to enduring disparities among hospitals, a concentrated market share by 6 vendors (90%), and significant concerns regarding maintenance costs. These insights provide an invaluable snapshot of the state of EHR adoption at the brink of the pandemic, serving as a benchmark to assess hospitals' readiness to utilize digital infrastructure in health care. The conclusions underscore the necessity for strategic policy interventions to encourage a competitive landscape and guarantee equitable access, ultimately strengthening the health care system's responsiveness to global health crises such as COVID-19.

Nursing assessments of women with hypertensive disorders during labor in an academic medical center in Mississippi: a best practice implementation project

The objective of this project was to promote evidence-based practices regarding the nursing assessment of pregnant women with hypertensive disorders admitted to the intrapartum unit. Hypertension during pregnancy has been associated with adverse maternal and fetal outcomes. Ongoing evaluation and nursing care are key in the prevention of complications resulting from hypertensive disorders in pregnancy. The implementation of this best practice project was guided by the JBI Model of Evidence-based Healthcare and used the JBI Practical Application of Clinical Evidence System and Getting Research into Practice audit and feedback strategy to promote evidence-based practices regarding the nursing assessment of pregnant women with hypertensive disorders admitted to an intrapartum unit. Eight audit criteria that were representative of best-practice recommendations for nursing assessment of pregnant women with hypertensive disorders were used. A baseline audit was conducted, followed by the implementation of multiple strategies that were determined by the key stakeholders. The project was finalized with a follow-up audit to determine a change in compliance with best-practice recommendations. Baseline audits revealed an average 45% compliance rate with the eight best practice audit criteria. An on-site simulation event, which included a nursing assessment of normal and abnormal lung sounds along with the hands-on practice of deep tendon reflexes, was provided by project members. Evidence-based assessment guidelines were presented and reviewed with all participants. Input from the nursing staff was gathered regarding current documentation practices and electronic health record accessibility. As a result, an electronic health record change was requested, and improvements in nursing practice were observed for five of the eight audit criteria. Follow-up audits revealed an average compliance rate of 73% for all eight audit criteria, a 28% improvement. Continuing nursing education and ongoing competency refreshers can affect the quality of client care and outcomes by offering occasions to improve or strengthen clinical expertise and proficiency. For this project, the simulation training event improved nursing staff compliance with best practices.

Assessing the accuracy of electronic health record gender identity and REaL data at an academic medical center

BackgroundCollection of accurate patient race, ethnicity, preferred language (REaL) and gender identity in the electronic health record (EHR) is essential for equitable and inclusive care. Misidentification of these factors limits quality measurement of health outcomes in at-risk populations. Therefore, the aim of our study was to assess the accuracy of REaL and gender identity data at our institution.MethodsA survey was administered to 117 random patients, selected from prior day admissions at a large academic medical center in urban central New York. Patients (or guardians) self-reported REaL and gender identity data, selecting from current EHR options. Variables were coded for the presence or absence of a difference from data recorded in the EHR.ResultsRace was misreported in the EHR for 13% of patients and ethnicity for 6%. For most White and Black patients, race was concordant. However, self-identified data for all multiracial patients were discordant with the EHR. Most Non-Hispanic patients had ethnicity correctly documented. Some Hispanic patients were misidentified. There was a significant association between reporting both a race and an ethnicity which differed from the EHR on chi square analysis (P < 0.001). Of those who reported an alternative ethnicity, 71.4% also reported an alternative race. Gender identity was missing for most patients and 11% of the gender-identity entries present in the EHR were discordant with the patient’s self-identity. Preferred language was 100% concordant with the EHR.ConclusionsAt an academic medical center, multiracial and Hispanic patients were more likely to have their demographics misreported in the EHR, and gender identity data were largely missing. Healthcare systems need strategies that support accurate collection of patients’ self-reported ReAL and gender identity data to improve the future ability to identify and address healthcare disparities.

Parkinson's disease diagnosis codes are insufficiently accurate for electronic health record research and differ by race

BackgroundThere are no evidence-based guidelines for data cleaning of electronic health record (EHR) databases in Parkinson's disease (PD). Previous filtering criteria have primarily used the 9th International Statistical Classification of Diseases and Related Health Problems (ICD) with variable accuracy for true PD cases. Prior studies have not excluded atypical or drug-induced parkinsonism, and little is known about differences in accuracy by race. ObjectiveTo determine if excluding parkinsonism diagnoses improves accuracy of ICD-9 and -10 PD diagnosis codes. MethodsWe included ≥2 instances of an ICD-9 and/or −10 code for PD. We removed any records with at least one code indicating atypical or drug-induced parkinsonism first in all races, and then in Non-Hispanic White and Black patients. We manually reviewed 100 randomly selected charts per group before and after filtering, and performed a test of proportion (null hypothesis 0.5) for confirmed PD. Results5633 records had ≥2 instances of a PD code. 2833 remained after filtering. The rate of true PD cases was low before and after filtering to remove parkinsonism codes (0.55 vs. 0.51, p = 0.84). Accuracy was lowest in Black patients before filtering (0.48, p = 0.69), but filtering had a greater (though modest) impact on accuracy (0.68, p < 0.001). ConclusionsThere was inadequate accuracy of PD diagnosis codes in the largest study of ICD-9 and -10 codes. Accuracy was lowest in Black patients but improved the most with removing other parkinsonism codes. This highlights the limitations of using current real-world EHR data in PD research and need for further study.

Concordance between electronic health record-recorded race/ethnicity and parental report in hospitalized children.

Electronic health records (EHRs) have become an important repository for patient race and ethnicity. Misclassification could negatively affect efforts to monitor and reduce health disparities and structural discrimination. We assessed the concordance of parental reports of race/ethnicity for their hospitalized children with EHR-documenteddemographics. We also aimed to describe parents' preferences on how race/ethnicity should be captured in the hospital's EHR. From December 2021 to May 2022, we conducted a single-center cross-sectional survey of parents of hospitalized children asking to describe their child's race/ethnicity and compared these responses to the race/ethnicity documented in the EHR. Concordance was analyzed with a kappa statistic (κ). Additionally, we queried respondents about their awareness of and preferences for race/ethnicity documentation. Of the 275 participants surveyed (79% response rate), there was 69% agreement (κ = 0.56) for race and 80% agreement (κ = 0.63) for ethnicity between parent report and EHR documentation. Sixty-eight parents (21%) felt that the designated categories poorly represent their child's race/ethnicity. Twenty-two (8%) were uncomfortable with their child's race/ethnicity being displayed on the hospital's EHR. Eighty-nine (32%) preferred a more comprehensive list of race/ethnicity categories. Nonconcordance between EHR-recorded race/ethnicity and parental report exists in the EHR for our hospitalized patients, which has implications for describing patient populations and for understanding racial and ethnic disparities. Current EHR categories may be limited in their ability to capture the complexity of these constructs. Future efforts should focus on ensuring that demographic information in the EHR is accurately collected and appropriately reflects families' preferences.

Research Priorities to Support Women Veterans' Reproductive Health and Health Care Within a Learning Health Care System.

Research Priorities to Support Women Veterans' Reproductive Health and Health Care Within a Learning Health Care System.

An EHR Data Quality Evaluation Approach Based on Medical Knowledge and Text Matching

Introduction: Recently, medical artificial intelligence based on Electronic Health Records (EHR) is a significant research field, and EHR data has been widely used in clinical decision support systems and medical diagnosis systems. However, because EHR are used to record the patient's disease information and are not primarily designed for research and discovery, the utility of EHR for research will be hindered by data quality problems. Therefore, it is a meaningful and challenging task to evaluate the data quality of EHR before they are used in medical artificial intelligence. Most of the current EHR data quality evaluation methods are based on some conventional evaluation indicators, and rarely consider the introduction of clinical evidence.Materials and methods: we propose an EHR data quality evaluation approach based on clinical evidence and a deep text matching model. First, based on the medical knowledge of the particular disease, we establish the list of standard clinical evidence descriptions including typical symptoms and special signs, etc. Then we find the relevant clinical evidence from the EHR based on the text matching model, and finally evaluate the quality of the EHR based on the quantity and quality of the relevant clinical evidence found.Results: The experimental results of more than 1,000 EHR for two types of diseases show that our approach can effectively distinguish high-quality EHR from low-quality EHR, and the high-quality EHR found generally contains sufficient and consistent information related to disease diagnosis.Conclusions: Experiments results on a real-world dataset demonstrate the effectiveness of our EHR data quality evaluation approach based on medical knowledge and text matching.

Blockchain-Based Framework for Interoperable Electronic Health Records for an Improved Healthcare System

The healthcare industry has been transitioning from paper-based medical records to electronic health records (EHRs) in most healthcare facilities. However, the current EHR frameworks face challenges in secure data storage, credibility, and management. Interoperability and user control of personal data are also significant concerns in the healthcare sector. Although block chain technology has emerged as a powerful solution that can offer the properties of immutability, security, and user control on stored records, its potential application in EHR frameworks is not yet fully understood. To address this gap in knowledge, this research aims to provide an interoperable blockchain-based EHR framework that can fulfill the requirements defined by various national and international EHR standards such as HIPAA and HL7. The research method employed is a systematic literature review to explore the current state of the art in the field of EHRs, including blockchain-based implementations of EHRs. The study defines the interoperability issues in the existing blockchain-based EHR frameworks, reviews various national and international standards of EHR, and further defines the interoperability requirements based on these standards. The proposed framework can offer safer methods to interchange health information for the healthcare sector and can provide the properties of immutability, security, and user control on stored records without the need for centralized storage. The contributions of this work include enhancing the understanding of the potential application of blockchain technology in EHR frameworks and proposing an interoperable blockchain-based EHR framework that can fulfill the requirements defined by various national and international EHR standards. Overall, this study has significant implications for the healthcare sector, as it can enhance the secure sharing and storage of electronic health data while ensuring the confidentiality, privacy, and integrity of medical records.

Effectiveness and Challenges: The Current Situation of Electronic Health Records (EHRs) to Improve Health Care Outcomes

Background. In the Philippines, although software for Electronic Health Records (EHRs) is still new, knowledge about EHRs benefits is increasing.Objective: This study investigated the effectiveness of electronic health records to health care outcomes at selected health facilities in the National Capital Region (NCR) and CALABARZON regions of the Philippines. The effectiveness of the current EHRs in government and private health facilities were determined in terms of quality patient care, patient engagement, support team-based care, workload, and data liquidity; and the challenges facing health providers in the implementation of electronic health records were identified.Methods. The research utilized the descriptive, cross-sectional design. Three hundred seven (N=307) health providers from the medical, nursing, and paramedical departments were the study participants. A three-part adapted questionnaire was the main tool used for data collection. Mean, standard deviation, and Mann-Whitney &amp; Kruskal-Wallis’s tests were utilized for data analysis.Results. The results revealed that in the Philippines, EHRs are moderately effective in improving health care outcomes. EHRs effectiveness is primarily achieved through better access and retrieval of health information. EHRs implementation poses major challenges in the Philippines’ health facilities which are central to socio-economic and organizational factors.Conclusions. The more effective the use of EHR systems, the greater the challenges experienced by care providers and health facilities.

Design and Implementation of a Collaborative Clinical Practice and Research Documentation System Using SNOMED-CT and HL7-CDA in the Context of a Pediatric Neurodevelopmental Unit

This paper introduces a prototype for clinical research documentation using the structured information model HL7 CDA and clinical terminology (SNOMED CT). The proposed solution was integrated with the current electronic health record system (EHR-S) and aimed to implement interoperability and structure information, and to create a collaborative platform between clinical and research teams. The framework also aims to overcome the limitations imposed by classical documentation strategies in real-time healthcare encounters that may require fast access to complex information. The solution was developed in the pediatric hospital (HP) of the University Hospital Center of Coimbra (CHUC), a national reference for neurodevelopmental disorders, particularly for autism spectrum disorder (ASD), which is very demanding in terms of longitudinal and cross-sectional data throughput. The platform uses a three-layer approach to reduce components' dependencies and facilitate maintenance, scalability, and security. The system was validated in a real-life context of the neurodevelopmental and autism unit (UNDA) in the HP and assessed based on the functionalities model of EHR-S (EHR-S FM) regarding their successful implementation and comparison with state-of-the-art alternative platforms. A global approach to the clinical history of neurodevelopmental disorders was worked out, providing transparent healthcare data coding and structuring while preserving information quality. Thus, the platform enabled the development of user-defined structured templates and the creation of structured documents with standardized clinical terminology that can be used in many healthcare contexts. Moreover, storing structured data associated with healthcare encounters supports a longitudinal view of the patient's healthcare data and health status over time, which is critical in routine and pediatric research contexts. Additionally, it enables queries on population statistics that are key to supporting the definition of local and global policies, whose importance was recently emphasized by the COVID pandemic.

Use of chosen names in electronic health records to promote transgender inclusivity.

Transgender people have a right to be called by their chosen names. However, the current electronic health record (EHR)-even with dramatic changes in recent years-does not allow for proper documentation to accurately and sensitively capture the experiences of transgender patients. This article suggests that EHRs should be modified to allow for distinctions in legal and chosen name fields, recognize the wide-ranging experiences and needs of transgender patients, and promote inclusive, identity-sensitive health care. Healthcare professionals should insist that technology be used in service of the full humanity of their patients.

Blockchain-based privacy and security preserving in electronic health: a systematic review.

In today's world, health and medicine play an undeniable role in human life. Traditional and current Electronic Health Records (EHR) systems that are used to exchange information between medical stakeholders (patients, physicians, insurance companies, pharmaceuticals, medical researchers, etc.) suffer weaknesses in terms of security and privacy due to having centralized architecture. Blockchain technology ensures the privacy and security of EHR systems thanks to the use of encryption. Moreover, due to its decentralized nature, this technology prevents central failure and central attack points. In this paper, a systematic literature review (SLR) is proposed to analyze the existing Blockchain-based approaches for improving privacy and security in electronic health systems. The research methodology, paper selection process, and the search query are explained. 51 papers returned from our search criteria published between 2018 and Dec 2022 are reviewed. The main ideas, type of Blockchain, evaluation metrics, and used tools of each selected paper are discussed in detail. Finally, future research directions, open challenges, and some issues are discussed.

Quantum Digital Signatures (QDS): A novel approach to safeguarding electronic health records

Electronic Health Records (EHRs) form the backbone of modern healthcare, ensuring seamless access to a patient’s medical history and fostering effective patient-doctor relationships. Their digital nature, however, exposes them to myriad security threats, including unauthorized access, tampering, and fraudulent alterations. Traditional cryptographic techniques, while effective in the current digital landscape, might not stand robust against future threats, especially those posed by quantum computers. This paper introduces Quantum Digital Signatures (QDS) as a cutting-edge approach to address this burgeoning concern. QDS leverages the principles of quantum mechanics to provide an additional layer of security, making it computationally infeasible for adversaries to forge or alter signed messages, including sensitive EHR data. Unlike conventional digital signatures that depend on the presumed difficulty of certain mathematical problems, the security attributes of QDS stem from the fundamental laws of quantum physics, ensuring a higher level of integrity and authenticity. We present a comprehensive overview of the QDS protocol and its integration into current EHR systems. Our comparative analysis with existing cryptographic techniques illuminates the advantages of QDS, especially in preventing undetected alterations and ensuring non-repudiation. Practical deployment challenges, including quantum device imperfections and potential integration bottlenecks, are also addressed. The adoption of Quantum Digital Signatures in safeguarding EHRs ushers in a new era of medical data security. While the large-scale implementation of such quantum technologies is still in its infancy, proactive research and early adoption are pivotal in preparing healthcare systems for future challenges. Through this research, we advocate for the broader integration of quantum cryptographic techniques, such as QDS, to uphold the sanctity and trustworthiness of electronic medical data in the face of evolving digital threats.

Barriers to and recommendations for integrating the age-friendly 4-Ms framework into electronic health records.

The Institute for Healthcare Improvement's 4-Ms framework of care for older adults recommends a multidisciplinary assessment of a patient's Medications, Mentation, Mobility, and What Matters Most. Electronic health record (EHR) systems were developed prior to this emphasis on the 4-Ms. We sought to understand how healthcare providers across the healthcare system perceive their EHRs and to identify any current best practices and ideas for improvement regarding integration of the 4-Ms. Anonymous survey of healthcare providers who care for older adults. The survey aimed to evaluate efficiency, error tolerance, and satisfaction (usefulness and likeability). The survey was distributed through organizational list serves that focus on the care of older adults and through social media. Sixty-six respondents from all geographic segments of the U.S. (n=62) and non-U.S. practices (n=4) responded. Most (82%) were physicians. Respondents used a range of EHRs and 82% had >5 years of experience with their current EHR. Over half of respondents agreed that their EHR had easy to find contact information (56%) and advance directives. Finding a patient's prior cognitive status (26% agreement), goals of care (24%), functional status (14%), and multidisciplinary geriatric assessments (27%) was more difficult. Only 3% were satisfied with how their EHR handles geriatric syndromes. In free text responses, respondents (79%) described three areas that the EHR assists in the care of older adults: screening tied to actions or orders; advance care planning, and medication alerts or review. Common suggestions on how to improve the EHR included incorporating geriatric assessments in notes, establishing a unified place to review the 4-Ms, and creating age-specific best practice alerts. The majority of healthcare providers were not satisfied with how their EHR handles multidisciplinary geriatric assessment and geriatric care. EHR modifications would aide in reporting, communicating, and tracking the 4-Ms in EHRs.

SIMCard: Toward better connected electronic health record visualization

SummaryRecently, several healthcare organizations use decision‐making systems based on electronic health record (EHR) data in order to guarantee patient's safety and improve the quality of healthcare. In essence, the evolutions of Internet of Things (IoT) technologies have been of great help for implementing an integrated and interoperable decision‐making system based on EHR and medical devices (MDs). Those IoT‐based systems allow Clinicians collecting real‐time health data and provide accurate patient's monitoring. Nevertheless, several studies have shown that it is hard to improve the quality of healthcare using the current EHR IoT‐based systems since they do not allow to easily express clinician needs. Interactive visualization tools have been proposed to improve the efficacy and utility of these EHR based systems. However, there is no framework that provides a visual summary of patient data to clinician for planning specific clinical tasks, subsequently evaluating clinician responses, visually exploring EHR data and MDs data, gaining insights, supporting dynamic coordination processes care, and forming and validating hypotheses and risks. This article addresses this problem and introduces SIMCard, an aggregation‐based connected EHR visualization framework for patient monitoring, interpreting and predicting with MDs. The proposed framework aims to synthesize patient's clinical data into a single aggregating model for both EHR and MD conforming to health standard and terminologies. It also allows to link the aggregating model to the relevant medical knowledge in order to provide a connected and dynamic care and preventive plan. Last but not least, it provides an aggregated visualization model capable of displaying graphically a patient's personal data from databases, healthcare devices and sensors to reduce cognitive barriers related to the complexity of medical information and interpretation of health data. To demonstrate the refinement and design of our system and to observe user's actual practice of visualizing and analyzing real‐world dataset, we evaluated our system and compare to existing ones.

Information and Data Visualization Needs among Direct Care Nurses in the Intensive Care Unit

Intensive care unit (ICU) direct care nurses spend 22% of their shift completing tasks within the electronic health record (EHR). Miscommunications and inefficiencies occur, particularly during patient hand-off, placing patient safety at risk. Redesigning how direct care nurses visualize and interact with patient information during hand-off is one opportunity to improve EHR use. A web-based survey was deployed to better understand the information and visualization needs at patient hand-off to inform redesign. A multicenter anonymous web-based survey of direct care ICU nurses was conducted (9-12/2021). Semi-structured interviews with stakeholders informed survey development. The primary outcome was identifying primary EHR data needs at patient hand-off for inclusion in future EHR visualization and interface development. Secondary outcomes included current use of the EHR at patient hand-off, EHR satisfaction, and visualization preferences. Frequencies, means, and medians were calculated for each data item then ranked in descending order to generate proportional quarters using SAS v9.4. In total, 107 direct care ICU nurses completed the survey. The majority (46%, n = 49/107) use the EHR at patient hand-off to verify exchanged verbal information. Sixty-four percent (n = 68/107) indicated that current EHR visualization was insufficient. At the start of an ICU shift, primary EHR data needs included hemodynamics (mean 4.89 ± 0.37, 98%, n = 105), continuous IV medications (4.55 ± 0.73, 93%, n = 99), laboratory results (4.60 ± 0.56, 96%, n = 103), mechanical circulatory support devices (4.62 ± 0.72, 90%, n = 97), code status (4.40 ± 0.85, 59%, n = 108), and ventilation status (4.35 + 0.79, 51%, n = 108). Secondary outcomes included mean EHR satisfaction of 65 (0-100 scale, standard deviation = ± 21) and preferred future EHR user-interfaces to be organized by organ system (53%, n = 57/107) and visualized by tasks/schedule (61%, n = 65/107). We identified information and visualization needs of direct care ICU nurses. The study findings could serve as a baseline toward redesigning an EHR interface.

Allergy Electronic Health Record Documentation: A 2022 Work Group Report of the AAAAI Adverse Reactions to Drugs, Biologicals, and Latex Committee

The allergy section of the electronic health record (EHR) is ideally reviewed and updated by health care workers during routine outpatient visits, emergency room visits, inpatient hospitalizations, and surgical procedures. This EHR section has the potential to help proactively and comprehensively avoid exposures to drugs, contact irritants, foods, and other agents for which, based on an individual's medical history and/or genetics, there is increased risk for adverse outcomes with future exposures. Because clinical decisions are made and clinical decision support is triggered based on allergy details from the EHR, the allergy module needs to provide meaningful, accurate, timely, and comprehensive allergy information. Although the allergy section of the EHR must meet these requirements to guide appropriate clinical decisions and treatment plans, current EHR allergy modules have not achieved this standard. We urge EHR vendors to collaborate with allergists to optimize and modernize allergy documentation. A work group within the Adverse Reactions to Drugs, Biologicals, and Latex Committee of the American Academy of Allergy, Asthma & Immunology was formed to create recommendations for allergy documentation in the EHR. Whereas it is recognized that the term "allergy" is often used incorrectly because most adverse drug reactions (ADRs) are not true immune-mediated hypersensitivity reactions, "allergy" in this article includes allergies and hypersensitivities as well as side effects and intolerances. Our primary objective is to provide guidance for the current state of allergy documentation in the EHR. This guidance includes clarification of the definition of specific ADR types, reconciliation of confirmed ADRs, and removal of disproved or erroneous ADRs. This document includes a proposal for the creation, education, and implementation of a drug allergy labeling system that may allow for more accurate EHR documentation for improved patient safety.

Uncovering Discrepancies in IV Vancomycin Infusion Records between Pump Logs and EHR Documentation.

Infusion start time, completion time, and interruptions are the key data points needed in both area under the concentration-time curve (AUC)- and trough-based vancomycin therapeutic drug monitoring (TDM). However, little is known about the accuracy of documented times of drug infusions compared with automated recorded events in the infusion pump system. A traditional approach of direct observations of infusion practice is resource intensive and impractical to scale. We need a new methodology to leverage the infusion pump event logs to understand the prevalence of timestamp discrepancies as documented in the electronic health records (EHRs). We aimed to analyze timestamp discrepancies between EHR documentation (the information used for clinical decision making) and pump event logs (actual administration process) for vancomycin treatment as it may lead to suboptimal data used for therapeutic decisions. We used process mining to study the conformance between pump event logs and EHR data for a single hospital in the United States from July to December 2016. An algorithm was developed to link records belonging to the same infusions. We analyzed discrepancies in infusion start time, completion time, and interruptions. Of the 1,858 infusions, 19.1% had infusion start time discrepancy more than ± 10 minutes. Of the 487 infusion interruptions, 2.5% lasted for more than 20 minutes before the infusion resumed. 24.2% (312 of 1,287) of 1-hour infusions and 32% (114 of 359) of 2-hour infusions had over 10-minute completion time discrepancy. We believe those discrepancies are inherent part of the current EHR documentation process commonly found in hospitals, not unique to the care facility under study. We demonstrated pump event logs and EHR data can be utilized to study time discrepancies in infusion administration at scale. Such discrepancy should be further investigated at different hospitals to address the prevalence of the problem and improvement effort.

Rebooting the Electronic Health Record

Justifications for the widespread adoption and integration of an electronic health record (EHR) have long leaned on the purported benefits of the technology. However, the performance of the EHR has been underwhelming relative to the promises of immediate access to relevant patient information, clinical decision supports, computerized ordering, and transferable patient data. In this narrative review, we provide an overview of the historical problems and limitations of the EHR, detail the core principles that define agile processes that may overcome the barriers faced by the current EHR, and re-imagine what an integrated, seamless EHR that serves its users and patients might look like. Moving forward, the EHR should be redesigned using a middle-out framework and empowering dual-type champions to maintain the sustainable diffusion of future innovations.