Structured Data Entry in the Electronic Medical Record: Perspectives of Pediatric Specialty Physicians and Surgeons

Triage Accuracy in Pediatrics Using the Emergency Severity Index

INTRODUCTION

Although the Emergency Severity Index is the most widely used tool in the United States to prioritize care for patients who seek emergency care, including children, there are significant deficiencies in the tool's performance. Inaccurate triage has been associated with delayed treatment, unnecessary diagnostic testing, and bias in clinical care. We evaluated the accuracy of the Emergency Severity Index to stratify patient priority based on predicted resource utilization in pediatric emergency department patients and identified covariates influencing performance.

METHODS

This cross-sectional, retrospective study used a data platform that links clinical and research data sets from a single freestanding pediatric hospital in the United States. Chi-square analysis was used to describes rates of over- and undertriage. Mixed effects ordinal logistic regression identified associations between Emergency Severity Index categories assigned at triage and key emergency department resources using discrete data elements and natural language processing of text notes.

RESULTS

We analyzed 304,422 emergency department visits by 153,984 unique individuals in the final analysis; 80% of visits were triaged as lower acuity Emergency Severity Index levels 3 to 5, with the most common level being Emergency Severity Index 4 (43%). Emergency department visits scored Emergency Severity Index levels 3 and 4 were triaged accurately 46% and 38%, respectively. We noted racial differences in overall triage accuracy.

DISCUSSION

Although the plurality of patients was scored as Emergency Severity Index 4, 50% were mistriaged, and there were disparities based on race indicating Emergency Severity Index mistriages pediatric patients. Further study is needed to elucidate the application of the Emergency Severity Indices in pediatrics using a multicenter emergency department population with diverse clinical and demographic characteristics.

Process analysis of the patient pathway for automated data collection: an exemplar using pituitary surgery

Introduction

Automation of routine clinical data shows promise in relieving health systems of the burden associated with manual data collection. Identifying consistent points of documentation in the electronic health record (EHR) provides salient targets to improve data entry quality. Using our pituitary surgery service as an exemplar, we aimed to demonstrate how process mapping can be used to identify reliable areas of documentation in the patient pathway to target structured data entry interventions.

Materials and methods

This mixed methods study was conducted in the largest pituitary centre in the UK. Purposive snowball sampling identified frontline stakeholders for process mapping to produce a patient pathway. The final patient pathway was subsequently validated against a real-world dataset of 50 patients who underwent surgery for pituitary adenoma. Events were categorized by frequency and mapped to the patient pathway to determine critical data points.

Results

Eighteen stakeholders encompassing all members of the multidisciplinary team (MDT) were consulted for process mapping. The commonest events recorded were neurosurgical ward round entries (N = 212, 14.7%), pituitary clinical nurse specialist (CNS) ward round entries (N = 88, 6.12%) and pituitary MDT treatment decisions (N = 88, 6.12%) representing critical data points. Operation notes and neurosurgical ward round entries were present for every patient. 43/44 (97.7%) had a pre-operative pituitary MDT entry, pre-operative clinic letter, a post-operative clinic letter, an admission clerking entry, a discharge summary, and a post-operative histopathology pituitary multidisciplinary (MDT) team entries.

Conclusion

This is the first study to produce a validated patient pathway of patients undergoing pituitary surgery, serving as a comparison to optimise this patient pathway. We have identified salient targets for structured data entry interventions, including mandatory datapoints seen in every admission and have also identified areas to improve documentation adherence, both of which support movement towards automation.

The relationship between perceptions of electronic health record usability and clinical importance of social and environmental determinants of health on provider documentation

Social and environmental determinants of health (SEDH) data in the electronic health record (EHR) can be inaccurate and incomplete. Providers are in a unique position to impact this issue as they both obtain and enter this data, however, the variability in screening and documentation practices currently limits the ability to mobilize SEDH data for secondary uses. This study explores whether providers' perceptions of clinical importance of SEDH or EHR usability influenced data entry by analyzing two relationships: (1) provider charting behavior and clinical consideration of SEDH and (2) provider charting behavior and ease of EHR use in charting. We performed a cross-sectional study using an 11-question electronic survey to assess self-reported practices related to clinical consideration of SEDH elements, EHR usability and SEDH documentation of all staff physicians, identified using administrative listserves, at Penn State Health Hershey Medical Center during September to October 2021. A total of 201 physicians responded to and completed the survey out of a possible 2,478 identified staff physicians (8.1% response rate). A five-point Likert scale from "never" to "always" assessed charting behavior and clinical consideration. Responses were dichotomized as consistent/inconsistent and vital/not vital respectively. EHR usability was assessed as "yes" or "no" responses. Fisher's exact tests assessed the relationship between charting behavior and clinical consideration and to compare charting practices between different SEDHs. Cumulative measures were constructed for consistent charting and ease of charting. A generalized linear mixed model (GLMM) compared SDH and EDH with respect to each cumulative measure and was quantified using odds ratios (OR) and 95% confidence intervals (CI). Our results show that provider documentation frequency of an SEDH is associated with perceived clinical utility as well as ease of charting and that providers were more likely to consistently chart on SDH versus EDH. Nuances in these relationships did exist with one notable example comparing the results of smoking (SDH) to infectious disease outbreaks (EDH). Despite similar percentages of physicians reporting that both smoking and infectious disease outbreaks are vital to care, differences in charting consistency and ease of charting between these two were seen. Taken as a whole, our results suggest that SEDH quality optimization efforts cannot consider physician perceptions and EHR usability as siloed entities and that EHR design should not be the only target for intervention. The associations found in this study provide a starting point to understand the complexity in how clinical utility and EHR usability influence charting consistency of each SEDH element, however, further research is needed to understand how these relationships intersect at various levels in the SEDH data optimization process.

Correctly structured problem lists lead to better and faster clinical decision-making in electronic health records compared to non-curated problem lists: A single-blinded crossover randomized controlled trial

BACKGROUND

Correctly structured problem lists in electronic health records (EHRs) offer major benefits to patient care. Without structured lists, diagnosis information is often scatteredly documented in free text, which may contribute to errors and inefficient information retrieval. This study aims to assess whether EHRs with correctly structured problem lists result in better and faster clinical decision-making compared to non-curated problem lists.

METHODS

Two versions of two patient records (A and B) were created in an EHR training environment: one version included diagnosis information structured and coded on the problem list ("correctly structured problem list"), the other version had missing problem list diagnoses and diagnosis information partly documented in free text ("non-curated problem list"). In this single-blinded crossover randomized controlled trial, healthcare providers, who can prescribe medications, from two Dutch university medical center locations first evaluated a randomized version of patient A, then B. Participants were asked to motivate their answer to two medication prescription questions. One (test) question required information similarly presented in both record versions. The second (comparison) question required information documented on problem lists and/or in notes. The primary outcome measure was the correctness of the motivated answer to the comparison question. Secondary outcome measure was the time to answer and motivate both questions correctly.

RESULTS

As planned, 160 participants enrolled. Two were excluded for not meeting inclusion criteria. Correctly structured problem lists increased providers' ability to answer the comparison question correctly (56.3 % versus 33.5 %, McNemar odds ratio 2.80 (1.65-4.93) 95 %-CI). Median time to answer both questions correctly was significantly lower for EHRs with correctly structured problem lists (Wilcoxon-signed-rank test p = 0.00002, with incorrect answers coded equally at slowest time).

CONCLUSIONS

Correctly structured problem lists lead to better and faster clinical decision-making. Increased structured problem lists usage may be warranted for which implementation policies should be developed.

Overview of approaches to estimate real-world disease progression in lung cancer

BACKGROUND

Randomized clinical trials of novel treatments for solid tumors normally measure disease progression using the Response Evaluation Criteria in Solid Tumors. However, novel, scalable approaches to estimate disease progression using real-world data are needed to advance cancer outcomes research. The purpose of this narrative review is to summarize examples from the existing literature on approaches to estimate real-world disease progression and their relative strengths and limitations, using lung cancer as a case study.

METHODS

A narrative literature review was conducted in PubMed to identify articles that used approaches to estimate real-world disease progression in lung cancer patients. Data abstracted included data source, approach used to estimate real-world progression, and comparison to a selected gold standard (if applicable).

RESULTS

A total of 40 articles were identified from 2008 to 2022. Five approaches to estimate real-world disease progression were identified including manual abstraction of medical records, natural language processing of clinical notes and/or radiology reports, treatment-based algorithms, changes in tumor volume, and delta radiomics-based approaches. The accuracy of these progression approaches were assessed using different methods, including correlations between real-world endpoints and overall survival for manual abstraction (Spearman rank ρ = 0.61-0.84) and area under the curve for natural language processing approaches (area under the curve = 0.86-0.96).

CONCLUSIONS

Real-world disease progression has been measured in several observational studies of lung cancer. However, comparing the accuracy of methods across studies is challenging, in part, because of the lack of a gold standard and the different methods used to evaluate accuracy. Concerted efforts are needed to define a gold standard and quality metrics for real-world data.

Effective and feasible interventions to improve structured EHR data registration and exchange: A concept mapping approach and exploration of practical examples in the Netherlands

BACKGROUND

Data in Electronic Health Records (EHRs) is often poorly structured and standardized, which hampers data reuse. Research described some examples of interventions to increase and improve structured and standardized data, such as guidelines and policies, training and user friendly EHR interfaces. However, little is known about the translation of this knowledge into practical solutions. Our study aimed to specify the most effective and feasible interventions that enable better structured and standardized EHR data registration and described practical examples of successfully implemented interventions.

METHODS

A concept mapping approach was used to determine feasible interventions that were considered to be effective or have been successfully implemented in Dutch hospitals. A focus group was held with Chief Medical Information Officers and Chief Nursing Information Officers. After interventions were determined, multidimensional scaling and cluster analysis were performed to categorize sorted interventions using Groupwisdom™, an online tool for concept mapping. Results are presented as Go-Zone plots and cluster maps. Following, semi-structured interviews were conducted to describe practical examples of successful interventions.

RESULTS

Interventions were classified into seven clusters ranked from highest to lowest perceived effectiveness: (1) education on usefulness and need; (2) strategic and (3) tactical organizational policies; (4) national policy; (5) monitoring and adjusting data (6) structure of and support from the EHR and (7) support in the registration process (EHR independent). Interviewees emphasized the following interventions proven successful in their practice: an enthusiastic ambassador per specialty who is responsible for educating peers by increasing awareness of the direct benefit of structured and standardized data registration; dashboards for continuous feedback on data quality; and EHR functionalities that support (automating) the registration process.

CONCLUSIONS

Our study provided a list of effective and feasible interventions including practical examples of interventions that have been successful. Organizations should continue to share their best practices to learn from and attempted interventions to prevent implementation of ineffective interventions.

Addressing social determinants of health through customization: Quality improvement, telemedicine, and care coordination to serve immigrant families

PURPOSE

The purpose of this project was to establish a pathway for electronic medical record (EMR) customization, utilizing quality improvement methodology, to both identify and address adverse social determinants of health (SDOH) among a diverse spina bifida (SB) population.

METHODS

Starting in September 2020, the four fundamental steps were to (1) facilitate an advisory committee to safeguard the standard clinical protocols, (2) characterize barriers to implementation, (3) evaluate workflow to sustain data entry capture, and (4) manage the technology platform for seamless integration. The SB clinic was the first clinic within the enterprise to rollout the use of an adverse SDOH mitigation activity. A Spanish-speaking interpreter was scheduled for all clinics, as many families were limited in English proficiency.

RESULTS

The customization of the EMR to support an efficient workflow to address SDOH was feasible in a large and diverse urban medical center. Of the 758 patients served in the clinic, a myelomeningocele diagnosis was present in 86% of individuals. While 52% of participants were female, ethnically 52% of individuals served were Latino. Many of these individuals disclosed being recent immigrants to the United States. Often immigration and asylum related issues were at the forefront of the SDOH issues addressed.

CONCLUSION

Given the occurrence of adverse SDOH among individuals with SB, many of whom are new Latin-American immigrants, meaningful clinical efforts are needed to both identify and address the causes of the observed disparities. EMR customization is feasible and can identify and, through social prescriptions, address SDOH to support the provision of safe, high quality, and equitable care for vulnerable and medically complex populations at home and potentially abroad.

A large language model for electronic health records

There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI systems utilizing clinical narratives. However, there are few clinical language models, the largest of which trained in the clinical domain is comparatively small at 110 million parameters (compared with billions of parameters in the general domain). It is not clear how large clinical language models with billions of parameters can help medical AI systems utilize unstructured EHRs. In this study, we develop from scratch a large clinical language model-GatorTron-using >90 billion words of text (including >82 billion words of de-identified clinical text) and systematically evaluate it on five clinical NLP tasks including clinical concept extraction, medical relation extraction, semantic textual similarity, natural language inference (NLI), and medical question answering (MQA). We examine how (1) scaling up the number of parameters and (2) scaling up the size of the training data could benefit these NLP tasks. GatorTron models scale up the clinical language model from 110 million to 8.9 billion parameters and improve five clinical NLP tasks (e.g., 9.6% and 9.5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery. The GatorTron models are publicly available at: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/gatortron_og .

Development of an Electronic Health Record Registry to Facilitate Collection of Commission on Cancer Metrics for Patients Undergoing Surgery for Breast Cancer

PURPOSE

Accurate and efficient data collection is a challenge for quality improvement initiatives and clinical research. We describe the development of a custom electronic health record (EHR)–based registry to automatically extract structured Commission on Cancer axillary surgery-specific metrics from a custom synoptic note template included in the operative reports for patients with breast cancer undergoing surgery.

METHODS

The smart functionality of our enterprise-based EHR system was leveraged to create a custom smart phrase to capture axillary surgery-specific variables. A multidisciplinary team developed structured data elements correlating to each axillary surgery-specific variable. These data elements were then included in a note template for the operative report. Each variable could be aggregated and converted into a single flat database through the EHR's reporting workbench and serve as a live, prospective registry for all users within the EHR.

RESULTS

The final axillary surgery-specific note template in a synoptic format allowed for efficient and easy entry and automatic collection of breast cancer–specific metrics. From initial adoption in February 2021-December 2021, there were 1,254 patients who underwent breast surgery with axillary surgery. The operative notes allowed for automatic capture of metrics from 60.5% (n = 759) of patients. Data capture improved from 37.6% in the initial adoption period of 6 months to 86.2% in the last 5 months.

CONCLUSION

We were able to demonstrate successful implementation of provider-driven structured data entry into EHR systems that permits automatic data capture. The end result is a custom synoptic note template and a real-time, prospective registry of breast cancer–specific Commission on Cancer metrics that are robust enough to use for quality improvement initiatives and clinical research.

What makes administrative data "research-ready"? A systematic review and thematic analysis of published literature

Introduction

Administrative data are a valuable research resource, but are under-utilised in the UK due to governance, technical and other barriers (e.g., the time and effort taken to gain secure data access). In recent years, there has been considerable government investment in making administrative data "research-ready", but there is no definition of what this term means. A common understanding of what constitutes research-ready administrative data is needed to establish clear principles and frameworks for their development and the realisation of their full research potential.

Objective

To define the characteristics of research-ready administrative data based on a systematic review and synthesis of existing literature.

Methods

On 29th June 2021, we systematically searched seven electronic databases for (1) peer-reviewed literature (2) related to research-ready administrative data (3) written in the English language. Following supplementary searches and snowball screening, we conducted a thematic analysis of the identified relevant literature.

Results

Overall, we screened 2,375 records and identified 38 relevant studies published between 2012 and 2021. Most related to administrative data from the UK and US and particularly to health data. The term research-ready was used inconsistently in the literature and there was some conflation with the concept of data being ready for statistical analysis. From the thematic analysis, we identified five defining characteristics of research-ready administrative data: (a) accessible, (b) broad, (c) curated, (d) documented and (e) enhanced for research purposes.

Conclusions

Our proposed characteristics of research-ready administrative data could act as a starting point to help data owners and researchers develop common principles and standards. In the more immediate term, the proposed characteristics are a useful framework for cataloguing existing research-ready administrative databases and relevant resources that can support their development.

Assessing author willingness to enter study information into structured data templates as part of the manuscript submission process: A pilot study

Background

Environmental health and other researchers can benefit from automated or semi-automated summaries of data within published studies as summarizing study methods and results is time and resource intensive. Automated summaries can be designed to identify and extract details of interest pertaining to the study design, population, testing agent/intervention, or outcome (etc.). Much of the data reported across existing publications lack unified structure, standardization and machine-readable formats or may be presented in complex tables which serve as barriers that impede the development of automated data extraction methodologies.

As full automation of data extraction seems unlikely soon, encouraging investigators to submit structured summaries of methods and results in standardized formats with meta-data tagging of content may be of value during the publication process. This would produce machine-readable content to facilitate automated data extraction, establish sharable data repositories, help make research data FAIR, and could improve reporting quality.

Objectives

A pilot study was conducted to assess the feasibility of asking participants to summarize study methods and results using a structured, web-based data extraction model as a potential workflow that could be implemented during the manuscript submission process.

Methods

Eight participants entered study details and data into the Health Assessment Workplace Collaborative (HAWC). Participants were surveyed after the extraction exercise to ascertain 1) whether this extraction exercise will impact their conducting and reporting of future research, 2) the ease of data extraction, including which fields were easiest and relatively more problematic to extract and 3) the amount of time taken to perform data extractions and other related tasks. Investigators then presented participants the potential benefits of providing structured data in the format they were extracting. After this, participants were surveyed about 1) their willingness to provide structured data during the publication process and 2) whether they felt the potential application of structured data entry approaches and their implementation during the journal submission process should continue to be further explored.

Conclusions

Routine provision of structured data that summarizes key information from research studies could reduce the amount of effort required for reusing that data in the future, such as in systematic reviews or agency scientific assessments. Our pilot study suggests that directly asking authors to provide that data, via structured templates, may be a viable approach to achieving this: participants were willing to do so, and the overall process was not prohibitively arduous. We also found some support for the hypothesis that use of study templates may have halo benefits in improving the conduct and completeness of reporting of future research. While limitations in the generalizability of our findings mean that the conditions of success of templates cannot be assumed, further research into how such templates might be designed and implemented does seem to have enough chance of success that it ought to be undertaken.

Demonstration of an Analytic Process using Home Health Care Electronic Health Records: A Case Example Exploring the Prevalence of Patients with a Substance Use History and a Venous Access Device

Electronic health records (EHR) are an important, but underutilized source for home health care research and practice improvement. Although the use of EHR is more efficient than prospective data collection, an analysis of EHR data can be complex and time-consuming. To demonstrate the overall process, we describe a secondary analysis of EHR data that explored the prevalence of home health care patients with a substance use history (SUH) and a venous access device (VAD). We detail our process of EHR data extraction, management, and analysis to assist researchers and clinicians interested in similar work. The example analysis showed that that 10.6% of adult home health care patients had a SUH, 8.8% had a long-term VAD, and 1.3% had both. EHRs can be a valuable data source for home health care research and quality improvement projects, but a systematic and thoughtful strategy is needed to fully leverage their potential.

Which Electronic Health Record System Should We Use? A Systematic Review

The UK government had intended to introduce a comprehensive Electronic Health Record (EHR) system in England by 2020. These EHRs would run across primary, secondary, and social care, linking data in a single digital platform. The objectives of this systematic review were to identify studies that compare EHR in terms of direct comparison between systems and to evaluate them using System and Software Quality Requirements and Evaluation (SQuaRE) ISO/IEC 25010. A systematic review was performed by searching Embase and Ovid MEDLINE databases between 1974 and April 2021. All original studies that appraised EHR systems and their providers were included. The main outcome measures were EHR system comparison and the eight characteristics of SQuaRE: functional suitability, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability. A total of 724 studies were identified using the search criteria. After a review of titles and abstracts, this was filtered down to 40 studies as per the exclusion and inclusion criteria set out in our study. Seven studies compared more than one EHR. The following number of studies looked at the various aspects of the SQuaRE, respectively - 19 studies: functional suitability, performance efficiency: 18 studies, compatibility: 12 studies, usability: 25 studies, reliability: 6 studies, security: 2 studies, maintainability: 16 studies, portability: 13 studies. Epic was the most studied EHR system and one of the most implemented systems in the US market and one of the top ten in the UK. It is difficult to assess which is the most advantageous EHR system when they are assessed by SQuaRE's 8 characteristics for software evaluation.

Physicians' Attitude towards Electronic Medical Record Systems: An Input for Future Implementers

Background

Electronic medical record (EMR) systems offer the potential to improve health care quality by allowing physicians real-time access to patient healthcare information. The endorsement and usage of EMRs by physicians have a significant influence on other user groups in the healthcare system. As a result, the purpose of this study was to examine physicians' attitudes regarding EMRs and identify the elements that may influence their attitudes.

Method

An institutional-based cross-sectional study design supplemented with a qualitative study was conducted from March 1 to April 30, 2018, among a total of 403 physicians. A self-administered questionnaire was used to collect quantitative data. The validity of the prediction bounds for the dependent variable and the validity of the confidence intervals and P values for the parameters were measured with a value of less than 0.05 and 95 percent of confidence interval. For the supplementary qualitative study, data were collected using semistructured in-depth interviews from 11 key informants, and the data were analyzed using thematic analysis.

Result

Physicians' computer literacy (CI: 0.264, 0.713; P: 0001) and computer access at work (CI: 0.141, 0.533, P: 0.001) were shown to be favorable predictors of their attitude towards EMR system adoption. Another conclusion from this study was the inverse relationship between physicians' prior EMR experience and their attitude about the system (CI: -0.517, -0.121; P: 0.002).

Conclusion

According to the findings of this study, physicians' attitudes regarding EMR were found moderate in the studied region. There was a favorable relationship between computer ownership, computer literacy, lack of EMR experience, participation in EMR training, and attitude towards EMR. Improving the aforementioned elements is critical to improving physicians' attitudes regarding EMR.

Evolution of the Cancer Registrar in the Era of Informatics

The cancer registrar reports accurate, complete, and timely abstracted cancer data to various healthcare agencies. The data are used for understanding the incidence of cancer, evaluating the effectiveness of public health efforts in the prevention of new cases and improving patient care outcomes and survival. There are increasing demands placed on registrars for additional data points with real-time submission to reporting agencies. To that end, registrars are increasing the use of informatics to meet the demand. The purpose of this article is the role of the registrar in the collection and reporting of critical cancer data and how registrars are currently using informatics to enhance their work. This article describes how informatics can be leveraged in the future and how registrars play a vital role in meeting the increasing demands placed on them to provide timely, meaningful, and accurate data for the cancer community.

Unified concept and assertion detection using contextual multi-task learning in a clinical decision support system

Assertions, such as negation and speculation, alter the meaning of clinical findings ('concepts') in Electronic Health Records. Accurate assertion detection is vital to the identification of target findings in clinical decision support systems. Diverse clinical concepts and assertion modifiers embedded within longer sentences add to the challenge of error-free detection. Recent approaches leveraging biomedical contextual embeddings lead to standalone concept and assertion models that do not effectively utilize inter-task knowledge transfer. We propose a novel neural model integrating task-specific fine-tuning and multi-task learning in a coherent framework based on the hierarchical relationship between the tasks. We show that such a unified framework enhances both the tasks using several real-world clinical notes' datasets (n2c2 2010, n2c2 2012, NegEx). Concept task performance enhanced by +1.69 F1 on n2c2 2010 and +2.96 F1 on n2c2 2012 compared to standalone baselines. Assertion recognition improved by +2.89 F1 and +3.77 F1, respectively. Negation detection under low-resource settings increased significantly (+2.4 F1, p-value = 3.11E-05, McNemar's test), demonstrating the impact of inter-task knowledge transfer. The integrated architecture enhanced the generalization performance of speculation detection (+2.09 F1). To the best of our knowledge, this model is the first demonstration of a contextual multi-task system for unified detection of concepts and assertions in clinical decision support applications.

Interaction Time with Electronic Health Records: A Systematic Review

BACKGROUND

The amount of time that health care clinicians (physicians and nurses) spend interacting with the electronic health record is not well understood.

OBJECTIVE

This study aimed to evaluate the time that health care providers spend interacting with electronic health records (EHR).

METHODS

Data are retrieved from Ovid MEDLINE(R) and Epub Ahead of Print, In-Process and Other Non-Indexed Citations and Daily, (Ovid) Embase, CINAHL, and SCOPUS.

STUDY ELIGIBILITY CRITERIA

Peer-reviewed studies that describe the use of EHR and include measurement of time either in hours, minutes, or in the percentage of a clinician's workday. Papers were written in English and published between 1990 and 2021.

PARTICIPANTS

All physicians and nurses involved in inpatient and outpatient settings.

STUDY APPRAISAL AND SYNTHESIS METHODS

A narrative synthesis of the results, providing summaries of interaction time with EHR. The studies were rated according to Quality Assessment Tool for Studies with Diverse Designs.

RESULTS

Out of 5,133 de-duplicated references identified through database searching, 18 met inclusion criteria. Most were time-motion studies (50%) that followed by logged-based analysis (44%). Most were conducted in the United States (94%) and examined a clinician workflow in the inpatient settings (83%). The average time was nearly 37% of time of their workday by physicians in both inpatient and outpatient settings and 22% of the workday by nurses in inpatient settings. The studies showed methodological heterogeneity.

CONCLUSION

This systematic review evaluates the time that health care providers spend interacting with EHR. Interaction time with EHR varies depending on clinicians' roles and clinical settings, computer systems, and users' experience. The average time spent by physicians on EHR exceeded one-third of their workday. The finding is a possible indicator that the EHR has room for usability, functionality improvement, and workflow optimization.

Examining Individual Transition from Healthcare to Information Technology Roles Using the Theory of Planned Behavior

Many health information management (HIM) positions, including coders and transcriptionists, are evolving due to the widespread adoption of electronic health records (EHR) and other automated entry systems. Thus, focus for roles associated with those positions are changing and new positions to manage and manipulate the data collected in the new systems. This study seeks to identify which factors influence HIM professionals' decision to transition from a traditional HIM role to an information technology (IT) position. An online survey was sent to these individuals to determine which factors influenced their decision to consider a transition from healthcare roles to information technology using the theory of planned behavior. In other words, this study explored whether these individuals were influenced by attitudes, normative beliefs, and self-efficacy to consider transitioning from healthcare roles to information technology positions. In order to better understand whether education played a role in this behavior, an additional element, education efficacy was added. The findings revealed that these health information management professionals are not considering a transition from healthcare positions to IT roles.

Investigating Evaluation Frameworks for Electronic Health Record: A Literature Review

BACKGROUND: There are various electronic health records (EHRs) evaluation frameworks with multiple dimensions and numerous sets of evaluation measures, while the coverage rate of evaluation measures in a common framework varies in different studies. AIM: This study provides a literature review of the current EHR evaluation frameworks and a model for measuring the coverage rate of evaluation measures in EHR frameworks. METHODS: The current study was a comprehensive literature review and a critical appraisal study. The study was conducted in three phases. In Phase 1, a literature review of EHR evaluation frameworks was conducted. In Phase 2, a three-level hierarchical structure was developed, which includes three aspects, 12 dimensions, and 110 evaluation measures. Subsequently, evaluation measures in the identified studies were categorized based on the hierarchical structure. In Phase 3, relative frequency (RF) of evaluation measures in different dimensions and aspects for each of the identified studies were determined and categorized as follows: Appropriate, moderate, and low coverage. RESULTS: Out of a total of 8276 retrieved articles, 62 studies were considered relevant. The RF range in the second and third level of the hierarchical structure was between 8.6%–91.94% and 0.2%–61%, respectively. “Ease of use” and “system quality” were the most frequent evaluation measure and dimension. Our results indicate that identified studies cover at least one and at most nine evaluation dimensions and current evaluation frameworks focus more on the technology aspect. Almost in all identified studies, evaluation measures related to the technology aspect were covered. However, evaluation measures related to human and organization aspects were covered in 68% and 84% of the identified studies, respectively. CONCLUSION: In this study, we systematically reviewed all literature presenting any type of EHR evaluation framework and analyzed and discussed their aspects and features. We believe that the findings of this study can help researchers to review and adopt the EHR evaluation frameworks for their own particular field of usage.

Think globally, act locally: Quality improvement as a catalyst for COVID-19 related care during the transitional years

The COVID-19 pandemic has posed distinctive challenges to adolescents and young adults living with spina bifida, especially those from ethic minority populations. With this public health challenge in mind, developing a customized electronic health record to leverage registry data to promote and quantify COVID-19 vaccination uptake among this population is feasible. We provide a brief description of our activities in customizing an electronic health record to track vaccination uptake among adolescents and young adults with spina bifida (AYASB); and the lessons learned, in hopeful support of those scaling-up vaccination delivery across the globe for AYASB as they transition to adult-centered care. Thus, as providers think globally and act locally, COVID-19 immunization efforts can be implemented while providing culturally appropriate transition policies and services for individuals with neurodevelopmental disabilities.

Laboratory information system requirements to manage the COVID-19 pandemic: A report from the Belgian national reference testing center

Objective

The study sought to describe the development, implementation, and requirements of laboratory information system (LIS) functionality to manage test ordering, registration, sample flow, and result reporting during the coronavirus disease 2019 (COVID-19) pandemic.

Materials and Methods

Our large (>12 000 000 tests/y) academic hospital laboratory is the Belgian National Reference Center for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing. We have performed a moving total of >25 000 SARS-CoV-2 polymerase chain reaction tests in parallel to standard routine testing since the start of the outbreak. A LIS implementation team dedicated to develop tools to remove the bottlenecks, primarily situated in the pre- and postanalytical phases, was established early in the crisis.

Results

We outline the design, implementation, and requirements of LIS functionality related to managing increased test demand during the COVID-19 crisis, including tools for test ordering, standardized order sets integrated into a computerized provider order entry module, notifications on shipping requirements, automated triaging based on digital metadata forms, and the establishment of databases with contact details of other laboratories and primary care physicians to enable automated reporting. We also describe our approach to data mining and reporting of actionable daily summary statistics to governing bodies and other policymakers.

Conclusions

Rapidly developed, agile extendable LIS functionality and its meaningful use alleviates the administrative burden on laboratory personnel and improves turnaround time of SARS-CoV-2 testing. It will be important to maintain an environment that is conducive for the rapid adoption of meaningful LIS tools after the COVID-19 crisis.

Trends and Perceptions of Electronic Health Record Usage among Plastic Surgeons

Background

Electronic health records (EHRs) should help physicians stay organized, improve patient safety, and facilitate communication with both patients and fellow healthcare providers. However, few studies have directly evaluated physician satisfaction with EHR and its perceived impact on patient care. This study assessed trends and perceptions of EHR within the American plastic surgery community.

Methods

An Institutional Review Board-approved survey that assessed demographics, patterns of EHR use, and attitudes toward EHR was deployed by the American Society of Plastic Surgeons Member Survey Research Services. Statistical analyses were performed using Stata 14.2 and QDA Miner Lite software (Version 2.0; Provalis, Montreal, Canada). Significance level was P < 0.05.

Results

Among plastic surgeons who use EHR, EPIC Systems software (Epic, Verona, Wisc.) was the most common vendor, with users noting a net positive effect on the quality of care they provided to patients. Younger age and less years of experience were correlated with a more positive attitude toward EHR. Positive attitude was closely linked to shared responsibility among support staff over data entry, whereas negative attitude was tightly tied to the perceived time wasted because of EHR, followed by poor technical support and design.

Conclusions

EHR use among plastic surgeons was more common in academic-associated specialties and larger practice groups. Overall, age and practice type had weak associations with perceptions of EHR usage. On average, there were slightly more positive perceptions of EHR usage than negative. The most commonly perceived issues with EHR were wasted time and barriers to user-friendliness. These findings suggest the need for greater physician involvement in EHR optimization.

Unlocking the Potential of Electronic Health Records for Health Research

Electronic health records (EHRs), originally designed to facilitate health care delivery, are becoming a valuable data source for health research. EHR systems have two components, both of which have various components, and points of data entry, management, and analysis. The “front end” refers to where the data are entered, primarily by healthcare workers (e.g. physicians and nurses). The second component of EHR systems is the electronic data warehouse, or “back-end,” where the data are stored in a relational database. EHR data elements can be of many types, which can be categorized as structured, unstructured free-text, and imaging data. The Sunrise Clinical Manager (SCM) EHR is one example of an inpatient EHR system, which covers the city of Calgary (Alberta, Canada). This system, under the management of Alberta Health Services, is now being explored for research use. The purpose of the present paper is to describe the SCM EHR for research purposes, showing how this generalizes to EHRs in general. We further discuss advantages, challenges (e.g. potential bias and data quality issues), analytical capacities, and requirements associated with using EHRs in a health research context.

Documentation of work ability data in occupational health records

Background

In Finland, patient health records are structured in the same way. Patient data entries are grouped using national headings and each data entry must have at least one heading.

Aims

To determine the use of national headings for the documentation of work ability data and to gather the experience of professionals on usefulness, ease of use and usability of national headings in occupational health services (OHSs).

Methods

An electronic questionnaire and a semi-structured themed interview were used to collect data. Data were analysed using SPSS Statistics 24 and interview material was analysed by deductive content analysis using ATLAS.ti.

Results

A total of 359 people completed the questionnaire. Most of the work ability data were documented using the headings history, plan and current status. More than half of respondents felt that using national headings improved quality and allowed greater control. Almost all respondents thought that learning to use national headings was easy. During the interviews (n = 19), all respondents felt that use of national headings improved the quality of documentation. However, more than half stated that national headings were not well suited to documentation of work ability data.

Conclusion

These results can be used to develop national documentation standards, as well as electronic health records, to support healthcare professionals' interactions with working-age patients. Earlier studies of national headings in OHSs were not found.

"Minimally invasive research?" Use of the electronic health record to facilitate research in pediatric urology

BACKGROUND

The electronic health record (EHR) was designed as a clinical and administrative tool to improve clinical patient care. Electronic healthcare systems have been successfully adopted across the world through use of government mandates and incentives.

METHODS

Using electronic health record, health information system, electronic medical record, health information systems, research, outcomes, pediatric, surgery, and urology as initial search terms, the literature focusing on clinical documentation data capture and the EHR as a potential resource for research related to clinical outcomes, quality improvement, and comparative effectiveness was reviewed. Relevant articles were supplemented by secondary review of article references as well as seminal articles in the field as identified by the senior author.

FINDINGS

US federal funding agencies, including the Agency for Healthcare Research and Quality, the Patient-Centered Outcomes Research Institute, the National Institutes of Health, and the Food and Drug Administration have recognized the EHR's role supporting research. The main approached to using EHR data include enhanced lists, direct data extraction, structured data entry, and unstructured data entry. The EHR's potential to facilitate research, overcoming cost and time burdens associated with traditional data collection, has not resulted in widespread use of EHR-based research tools.

CONCLUSION

There are strengths and weaknesses for all existing methodologies of using EHR data to support research. Collaboration is needed to identify the method that best suits the institution for incorporation of research-oriented data collection into routine pediatric urologic clinical practice.

Using structured data entry systems in the electronic medical record to collect clinical data for quality and research: Can we efficiently serve multiple needs for complex patients with spina bifida?

PURPOSE

The era of the electronic health record (EHR) generates the ability to systematically collect and record innumerable data for complex procedures such as videourodynamic studies (VUDS). We developed a Structured Data Entry System (SDES) that would serve as a way to better standardize VUDS for both quality improvement and research capabilities.

METHODS

A working group convened to design a SDES form for VUDS in a flow sheet format in our hospital's EHR, allowing for easy integration of the information into the clinical encounter note and for weekly export of data to clinicians in spreadsheet form.

RESULTS

Analysis of weekly VUDS data revealed that entries were missing in 3% of cells in all SDES forms completed. The availability of the data in an Excel spreadsheet allows for easy manipulation, calculation of clinical variables, and streamlined analysis in figures or graphs to identify patients at the highest risk.

CONCLUSION

Designing and implementing a SDES based on a flowsheet that can allow data to be placed seamlessly in the clinical record and to be integrated into a searchable database for quality improvement and research purposes allows one to harness the true potential of the EHR.