An electronic health record based on structured narrative

System interoperability and data linkage in the era of health information management: A bibliometric analysis

Background: Across the world, health data generation is growing exponentially. The continuous rise of new and diversified technology to obtain and handle health data places health information management and governance under pressure. Lack of data linkage and interoperability between systems undermines best efforts to optimise integrated health information technology solutions. Objective: This research aimed to provide a bibliometric overview of the role of interoperability and linkage in health data management and governance. Method: Data were acquired by entering selected search queries into Google Scholar, PubMed, and Web of Science databases and bibliometric data obtained were then imported to Endnote and checked for duplicates. The refined data were exported to Excel, where several levels of filtration were applied to obtain the final sample. These sample data were analysed using Microsoft Excel (Microsoft Corporation, Washington, USA), WORDSTAT (Provalis Research, Montreal, Canada) and VOSviewer software (Leiden University, Leiden, Netherlands). Results: The literature sample was retrieved from 3799 unique results and consisted of 63 articles, present in 45 different publications, both evaluated by two specific in-house global impact rankings. Through VOSviewer, three main clusters were identified: (i) e-health information stakeholder needs; (ii) e-health information quality assessment; and (iii) e-health information technological governance trends. A residual correlation between interoperability and linkage studies in the sample was also found. Conclusion: Assessing stakeholders' needs is crucial for establishing an efficient and effective health information system. Further and diversified research is needed to assess the integrated placement of interoperability and linkage in health information management and governance. Implications: This research has provided valuable managerial and theoretical contributions to optimise system interoperability and data linkage within health information research and information technology solutions.

Constructing a Clinical Patient Similarity Network of Gastric Cancer

OBJECTIVES

Clinical molecular genetic testing and molecular imaging dramatically increase the quantity of clinical data. Combined with the extensive application of electronic health records, a medical data ecosystem is forming, which calls for big-data-based medicine models. We tried to use big data analytics to search for similar patients in a cancer cohort, showing how to apply artificial intelligence (AI) algorithms to clinical data processing to obtain clinically significant results, with the ultimate goal of improving healthcare management.

METHODS

In order to overcome the weaknesses of most data processing algorithms that rely on expert labeling and annotation, we uniformly adopted one-hot encoding for all types of clinical data, calculating the Euclidean distance to measure patient similarity and subgrouping via an unsupervised learning model. Overall survival (OS) was investigated to assess the clinical validity and clinical relevance of the model.

RESULTS

We took gastric cancers (GCs) as an example to build a high-dimensional clinical patient similarity network (cPSN). When performing the survival analysis, we found that Cluster_2 had the longest survival rates, while Cluster_5 had the worst prognosis among all the subgroups. As patients in the same subgroup share some clinical characteristics, the clinical feature analysis found that Cluster_2 harbored more lower distal GCs than upper proximal GCs, shedding light on the debates.

CONCLUSION

Overall, we constructed a cancer-specific cPSN with excellent interpretability and clinical significance, which would recapitulate patient similarity in the real-world. The constructed cPSN model is scalable, generalizable, and performs well for various data types.

Evaluating compliance with the best practice guidelines for wrong-level surgery prevention in high-risk pediatric spine surgery

PURPOSE

In 2018, Best Practice Guidelines (BPGs) were published for preventing wrong-level surgery in pediatric spinal deformity, but successful implementation has not been established. The purpose of this study was to evaluate BPG compliance 5 years after publication. We hypothesized higher compliance among BPG authors and among surgeons with more experience, higher caseload, and awareness of the BPGs.

METHODS

We queried North American and European surgeons, authors and nonauthors, and members of pediatric spinal study groups on adherence to BPGs using an anonymous survey consisting of 18 Likert scale questions. Respondents provided years in practice, yearly caseload, and guideline awareness. Mean compliance scores (MCS) were developed by correlating Likert responses with MCS scores ("None of the time" = no compliance = MCS 0, "Sometimes" = weak to moderate = MCS 1, "Most of the time" = high = MCS 2, and "All the time" = perfect = MCS 3).

RESULTS

Of the 134 respondents, 81.5% reported high or perfect compliance. Average MCS for all guidelines was 2.4 ± 0.4. North American and European surgeons showed no compliance differences (2.4 vs. 2.3, p = 0.07). Authors and nonauthors showed significantly different compliance scores (2.8 vs 2.4, p < 0.001), as did surgeons with and without knowledge of the BPGs (2.5 vs 2.2, p < 0.001). BPG awareness and compliance showed a moderate positive correlation (r = 0.48, p < 0.001), with non-significant associations between compliance and both years in practice (r = 0.41, p = 0.64) and yearly caseload (r = 0.02, p = 0.87).

CONCLUSION

Surgeons reported high or perfect compliance 81.5% of the time with BPGs for preventing wrong-level surgery. Authorship and BPG awareness showed increased compliance. Location, study group membership, years in practice, and yearly caseload did not affect compliance.

LEVEL OF EVIDENCE

Level V-expert opinion.

Multidisciplinary user experience of a newly implemented electronic patient record in Ireland: An exploratory qualitative study

BACKGROUND

Implementation of an Electronic Patient Record (EPR) in a key milestone in the digital strategy of modern healthcare organisations. The implementation of EPR systems can be viewed as challenging and complex.

OBJECTIVE

The aim of the study was to investigate user perspectives and experiences of the implementation of an Electronic Medical Record in a major academic teaching hospital, with simultaneous 'go-live' across the whole hospital taking place.

METHODS

Focus groups and individual in-depth interviews were conducted with stakeholders and users (n = 105), approximately nine months post-EPR implementation. The study explored EPR users' perceptions using an extended theoretical framework of the DeLone and McLean Information Systems Success Model (2003), which measured information systems, system quality, information quality, service quality, use/perceived usefulness & user satisfaction and net benefits.

RESULTS

Staff engagement and satisfaction was high and the EPR is accepted as the new standard way of completing care. There was agreement that the EPR affords transparency, and greater accountability. There was some concern expressed regarding impact of the EPR on interprofessional and patient/provider interactions and communication. Physicians reported the inputting of social history through free text as an issue of concern and time consuming. The Big Bang approach with mandatory conversion was key to the successful adoption of EPR. There was consensus across professional and administrative respondents that there was no appetite to return to paper-based records.

CONCLUSION

The successful roll out of the EPR reflects the digital readiness of healthcare providers and organisations. The potential for unintended consequences on work process requires continual monitoring. A key future benefit of the EPR will be the capacity to reach a broader understanding and analysis of variation in processes and outcomes within healthcare organisations. It is clear that skills in data analytics will be needed to mine data successfully.

Distinguishing cardiac catheter ablation energy modalities by applying natural language processing to electronic health records

Aim: Catheter ablation is used to treat symptomatic atrial fibrillation (AF) and is performed using either cryoballoon (CB) or radiofrequency (RF) ablation. There is limited real world data of CB and RF in the US as healthcare codes are agnostic of energy modality. An alternative method is to analyze patients' electronic health records (EHRs) using Optum's EHR database. Objective: To determine the feasibility of using patients' EHRs with natural language processing (NLP) to distinguish CB versus RF ablation procedures. Data Source: Optum® de-identified EHR dataset, Optum® Cardiac Ablation NLP Table. Methods: This was a retrospective analysis of existing de-identified EHR data. Medical codes were used to create an ablation validation table. Frequency analysis was used to assess ablation procedures and their associated note terms. Two cohorts were created (1) index procedures, (2) multiple procedures. Possible note term combinations included (1) cryoablation (2) radiofrequency (3) ablation, or (4) both. Results: Of the 40,810 validated cardiac ablations, 3777 (9%) index ablation procedures had available and matching NLP note terms. Of these, 22% (n = 844) were classified as ablation, 27% (n = 1016) as cryoablation, 49% (n = 1855) as radiofrequency ablation, and 1.6% (n = 62) as both. In the multiple procedures analysis, 5691 (14%) procedures had matching note terms. 24% (n = 1362) were classified as ablation, 27% as cryoablation, 47% as radiofrequency ablation, and 2% as both. Conclusion: NLP has potential to evaluate the frequency of cardiac ablation by type, however, for this to be a reliable real-world data source, mandatory data entry by providers and standardized electronic health reporting must occur.

The relationship between electronic health records user interface features and data quality of patient clinical information: an integrative review

OBJECTIVES

Electronic health records (EHRs) user interfaces (UI) designed for data entry can potentially impact the quality of patient information captured in the EHRs. This review identified and synthesized the literature evidence about the relationship of UI features in EHRs on data quality (DQ).

MATERIALS AND METHODS

We performed an integrative review of research studies by conducting a structured search in 5 databases completed on October 10, 2022. We applied Whittemore & Knafl's methodology to identify literature, extract, and synthesize information, iteratively. We adapted Kmet et al appraisal tool for the quality assessment of the evidence. The research protocol was registered with PROSPERO (CRD42020203998).

RESULTS

Eleven studies met the inclusion criteria. The relationship between 1 or more UI features and 1 or more DQ indicators was examined. UI features were classified into 4 categories: 3 types of data capture aids, and other methods of DQ assessment at the UI. The Weiskopf et al measures were used to assess DQ: completeness (n = 10), correctness (n = 10), and currency (n = 3). UI features such as mandatory fields, templates, and contextual autocomplete improved completeness or correctness or both. Measures of currency were scarce.

DISCUSSION

The paucity of studies on UI features and DQ underscored the limited knowledge in this important area. The UI features examined had both positive and negative effects on DQ. Standardization of data entry and further development of automated algorithmic aids, including adaptive UIs, have great promise for improving DQ. Further research is essential to ensure data captured in our electronic systems are high quality and valid for use in clinical decision-making and other secondary analyses.

Impact of Electronic Health Records on Information Practices in Mental Health Contexts: Scoping Review

BACKGROUND

The adoption of electronic health records (EHRs) and electronic medical records (EMRs) has been slow in the mental health context, partly because of concerns regarding the collection of sensitive information, the standardization of mental health data, and the risk of negatively affecting therapeutic relationships. However, EHRs and EMRs are increasingly viewed as critical to improving information practices such as the documentation, use, and sharing of information and, more broadly, the quality of care provided.

OBJECTIVE

This paper aims to undertake a scoping review to explore the impact of EHRs on information practices in mental health contexts and also explore how sensitive information, data standardization, and therapeutic relationships are managed when using EHRs in mental health contexts.

METHODS

We considered a scoping review to be the most appropriate method for this review because of the relatively recent uptake of EHRs in mental health contexts. A comprehensive search of electronic databases was conducted with no date restrictions for articles that described the use of EHRs, EMRs, or associated systems in the mental health context. One of the authors reviewed all full texts, with 2 other authors each screening half of the full-text articles. The fourth author mediated the disagreements. Data regarding study characteristics were charted. A narrative and thematic synthesis approach was taken to analyze the included studies' results and address the research questions.

RESULTS

The final review included 40 articles. The included studies were highly heterogeneous with a variety of study designs, objectives, and settings. Several themes and subthemes were identified that explored the impact of EHRs on information practices in the mental health context. EHRs improved the amount of information documented compared with paper. However, mental health-related information was regularly missing from EHRs, especially sensitive information. EHRs introduced more standardized and formalized documentation practices that raised issues because of the focus on narrative information in the mental health context. EHRs were found to disrupt information workflows in the mental health context, especially when they did not include appropriate templates or care plans. Usability issues also contributed to workflow concerns. Managing the documentation of sensitive information in EHRs was problematic; clinicians sometimes watered down sensitive information or chose to keep it in separate records. Concerningly, the included studies rarely involved service user perspectives. Furthermore, many studies provided limited information on the functionality or technical specifications of the EHR being used.

CONCLUSIONS

We identified several areas in which work is needed to ensure that EHRs benefit clinicians and service users in the mental health context. As EHRs are increasingly considered critical for modern health systems, health care decision-makers should consider how EHRs can better reflect the complexity and sensitivity of information practices and workflows in the mental health context.

Diagnostic accuracy of ICD code versus discharge summary-based query for endocarditis cohort identification

Studies of infective endocarditis (IE) have relied on International Classification of Disease (ICD) codes to identify cases, a method vulnerable to misclassification. Clinical narrative data could offer greater accuracy and richness to cohort identification. We evaluated two algorithms: 1. a standard query of ICD-9/10 billing codes, with or without procedure codes for echocardiogram and 2. a text query of discharge summaries (DS) that selected on the term “endocarditis” in fields headed by “Discharge Diagnosis” or “Admission Diagnosis” or similar. Further coding extracted valve involved and organism responsible if present. All cases were chart reviewed using pre-specified criteria. Positive predictive value (PPV), sensitivity and specificity were calculated. The ICD-based query identified 612 individuals from July 2015 to July 2019 who had a hospital billing code for infective endocarditis; of these, 534 had an echocardiogram. The DS query identified 387 cases. PPV for the DS query was 84.5% (95% CI 80.6%, 87.8%) compared with 72.4% (95% CI 68.7%, 75.8%) for ICD only (P < .001) and 75.8% (95% CI 72.0%, 79.3%) for ICD + echo queries (P = .002). Sensitivity was 75.9% for DS query and 86.8% to 93.4% for ICD queries (P < .02 for these comparisons). Specificity was high for all queries >94%. The DS query also yielded valve data (prosthetic, tricuspid, aortic, etc) in 60% and microbiologic agent in 73% of identified cases with an accuracy of 94% and 90%, respectively when assessed by chart review. Compared with ICD-based queries, text-based queries of discharge summaries have the potential to improve precision of IE case ascertainment and extract key clinical variables.

Using computable knowledge mined from the literature to elucidate confounders for EHR-based pharmacovigilance

INTRODUCTION

Drug safety research asks causal questions but relies on observational data. Confounding bias threatens the reliability of studies using such data. The successful control of confounding requires knowledge of variables called confounders affecting both the exposure and outcome of interest. However, causal knowledge of dynamic biological systems is complex and challenging. Fortunately, computable knowledge mined from the literature may hold clues about confounders. In this paper, we tested the hypothesis that incorporating literature-derived confounders can improve causal inference from observational data.

METHODS

We introduce two methods (semantic vector-based and string-based confounder search) that query literature-derived information for confounder candidates to control, using SemMedDB, a database of computable knowledge mined from the biomedical literature. These methods search SemMedDB for confounders by applying semantic constraint search for indications treated by the drug (exposure) and that are also known to cause the adverse event (outcome). We then include the literature-derived confounder candidates in statistical and causal models derived from free-text clinical notes. For evaluation, we use a reference dataset widely used in drug safety containing labeled pairwise relationships between drugs and adverse events and attempt to rediscover these relationships from a corpus of 2.2 M NLP-processed free-text clinical notes. We employ standard adjustment and causal inference procedures to predict and estimate causal effects by informing the models with varying numbers of literature-derived confounders and instantiating the exposure, outcome, and confounder variables in the models with dichotomous EHR-derived data. Finally, we compare the results from applying these procedures with naive measures of association (χ2 and reporting odds ratio) and with each other.

RESULTS AND CONCLUSIONS

We found semantic vector-based search to be superior to string-based search at reducing confounding bias. However, the effect of including more rather than fewer literature-derived confounders was inconclusive. We recommend using targeted learning estimation methods that can address treatment-confounder feedback, where confounders also behave as intermediate variables, and engaging subject-matter experts to adjudicate the handling of problematic covariates.

STO: Stroke Ontology for Accelerating Translational Stroke Research

Introduction

Ontology-based annotation of evidence, using disease-specific ontologies, can accelerate analysis and interpretation of the knowledge domain of diseases. Although many domain-specific disease ontologies have been developed so far, in the area of cardiovascular diseases, there is a lack of ontological representation of the disease knowledge domain of stroke.

Methods

The stroke ontology (STO) was created on the basis of the ontology development life cycle and was built using Protégé ontology editor in the ontology web language format. The ontology was evaluated in terms of structural and functional features, expert evaluation, and competency questions.

Results

The stroke ontology covers a broad range of major biomedical and risk factor concepts. The majority of concepts are enriched by synonyms, definitions, and references. The ontology attempts to incorporate different users’ views on the stroke domain such as neuroscientists, molecular biologists, and clinicians. Evaluation of the ontology based on natural language processing showed a high precision (0.94), recall (0.80), and F-score (0.78) values, indicating that STO has an acceptable coverage of the stroke knowledge domain. Performance evaluation using competency questions designed by a clinician showed that the ontology can be used to answer expert questions in light of published evidence.

Conclusions

The stroke ontology is the first, multiple-view ontology in the domain of brain stroke that can be used as a tool for representation, formalization, and standardization of the heterogeneous data related to the stroke domain. Since this is a draft version of the ontology, the contribution of the stroke scientific community can help to improve the usability of the current version.

Use of the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) for Processing Free Text in Health Care: Systematic Scoping Review

Background

Interoperability and secondary use of data is a challenge in health care. Specifically, the reuse of clinical free text remains an unresolved problem. The Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) has become the universal language of health care and presents characteristics of a natural language. Its use to represent clinical free text could constitute a solution to improve interoperability.

Objective

Although the use of SNOMED and SNOMED CT has already been reviewed, its specific use in processing and representing unstructured data such as clinical free text has not. This review aims to better understand SNOMED CT's use for representing free text in medicine.

Methods

A scoping review was performed on the topic by searching MEDLINE, Embase, and Web of Science for publications featuring free-text processing and SNOMED CT. A recursive reference review was conducted to broaden the scope of research. The review covered the type of processed data, the targeted language, the goal of the terminology binding, the method used and, when appropriate, the specific software used.

Results

In total, 76 publications were selected for an extensive study. The language targeted by publications was 91% (n=69) English. The most frequent types of documents for which the terminology was used are complementary exam reports (n=18, 24%) and narrative notes (n=16, 21%). Mapping to SNOMED CT was the final goal of the research in 21% (n=16) of publications and a part of the final goal in 33% (n=25). The main objectives of mapping are information extraction (n=44, 39%), feature in a classification task (n=26, 23%), and data normalization (n=23, 20%). The method used was rule-based in 70% (n=53) of publications, hybrid in 11% (n=8), and machine learning in 5% (n=4). In total, 12 different software packages were used to map text to SNOMED CT concepts, the most frequent being Medtex, Mayo Clinic Vocabulary Server, and Medical Text Extraction Reasoning and Mapping System. Full terminology was used in 64% (n=49) of publications, whereas only a subset was used in 30% (n=23) of publications. Postcoordination was proposed in 17% (n=13) of publications, and only 5% (n=4) of publications specifically mentioned the use of the compositional grammar.

Conclusions

SNOMED CT has been largely used to represent free-text data, most frequently with rule-based approaches, in English. However, currently, there is no easy solution for mapping free text to this terminology and to perform automatic postcoordination. Most solutions conceive SNOMED CT as a simple terminology rather than as a compositional bag of ontologies. Since 2012, the number of publications on this subject per year has decreased. However, the need for formal semantic representation of free text in health care is high, and automatic encoding into a compositional ontology could be a solution.

Implementation of an Electronic Nursing Record for Nursing Documentation and Communication of Patient Care Information in a Tertiary Teaching Hospital

Despite the fact that implementing an electronic nursing record has become an everyday event for nurses, little is known about which type of documentation used in an electronic nursing record is better for nursing practice. The aim of this exploratory study was to identify the most suitable type of electronic nursing documentation that nurses used to record care and communicate with clinicians. Participants consisted of 118 nurses and 12 physicians. Researchers developed a self-report questionnaire of 17 items about electronic nursing record use for documentation and communication of patient care information. Data were analyzed using descriptive statistics to calculate frequencies and percentages. The χ2 test was used to identify differences in responses by demographic and clinical characteristics of participants. Bar charts were used to identify response patterns. Results showed that semistructured nursing documentation was the most preferred for care documentation and communication of patient information. Nurses did not always use the electronic nursing record to communicate patient care-related information. This study adds empirical knowledge about which type of documentation used in the electronic nursing record works well, what improvement is needed for better nursing practice, and whether the electronic nursing record has been used for communication.

Clinical Documentation as End-User Programming

As healthcare providers have transitioned from paper to electronic health records they have gained access to increasingly sophisticated documentation aids such as custom note templates. However, little is known about how providers use these aids. To address this gap, we examine how 48 ophthalmologists and their staff create and use content-importing phrases - a customizable and composable form of note template - to document office visits across two years. In this case study, we find 1) content-importing phrases were used to document the vast majority of visits (95%), 2) most content imported by these phrases was structured data imported by data-links rather than boilerplate text, and 3) providers primarily used phrases they had created while staff largely used phrases created by other people. We conclude by discussing how framing clinical documentation as end-user programming can inform the design of electronic health records and other documentation systems mixing data and narrative text.

Heart Failure Management Innovation Enabled by Electronic Health Records

Patients with congestive heart failure (CHF) require complex medical management across the continuum of care. Electronic health records (EHR) are currently used for traditional tasks of documentation, reviewing and managing test results, computerized order entry, and billing. Unfortunately many clinicians view EHR as merely digitized versions of paper charts, which create additional work and cognitive burden without improving quality or efficiency of care. In fact, EHR are revolutionizing the care of chronic diseases such as CHF. This review describes how appropriate use of technologies offered by EHR can help standardize CHF care, promote adherence to evidence-based guidelines, optimize workflow efficiency, improve performance metrics, and facilitate patient engagement. This review discusses a number of tools including documentation templates, telehealth and telemedicine, health information exchange, order sets, clinical decision support, registries, and analytics. Where available, evidence of their potential utility in management of CHF is presented. Together these EHR tools can also be used to enhance quality improvement, patient management, and clinical research as part of a learning health care system model. This review describes how existing EHR tools can support patients, cardiologists, and care teams to deliver consistent, high-quality, coordinated, patient-centered, and guideline-concordant care of CHF.

Can structured EHR data support clinical coding? A data mining approach

Structured data formats are gaining momentum in electronic health records and can be leveraged for decision support and research. Nevertheless, such structured data formats have not been explored for clinical coding, which is an essential process requiring significant manual workload in health organisations. This article explores the extent to which fully structured clinical data can support assignment of clinical codes to inpatient episodes, through a methodology that tackles high dimensionality issues, addresses the multi-label nature of coding and optimises model parameters. The methodology encompasses transformation of raw data to define a feature set, build a data matrix representation, and testing combinations of feature selection methods with machine learning models to predict code assignment. The methodology was tested with a real hospital dataset and showed varying predictive power across codes, while demonstrating the potential of leveraging structuring data to reduce workload and increase efficiency in clinical coding.

Ontology-based clinical information extraction from physician's free-text notes

Documenting clinical notes in electronic health records might affect physician's workflow. In this paper, an Ontology-based clinical information extraction system, OB-CIE, has been developed. OB-CIE system provides a method for extracting clinical concepts from physician's free-text notes and converts the unstructured clinical notes to structured information to be accessed in electronic health records. OB-CIE system can help physicians to document visit notes without changing their workflow. For recognizing named entities of clinical concepts, ontology concepts have been used to construct a dictionary of semantic categories, then, exact dictionary matching method has been used to match noun phrases to their semantic categories. A rule-based approach has been used to classify clinical sentences to their predefined categories. The system evaluation results have achieved an F-measure of 94.90% and 97.80% for concepts classification and sentences classification, respectively. The results have showed that OB-CIE system performed well on extracting clinical concepts compared with data mining techniques. The system can be used in another field by adapting its ontology and extraction rule set.

Current approaches to identify sections within clinical narratives from electronic health records: a systematic review

BACKGROUND

The identification of sections in narrative content of Electronic Health Records (EHR) has demonstrated to improve the performance of clinical extraction tasks; however, there is not yet a shared understanding of the concept and its existing methods. The objective is to report the results of a systematic review concerning approaches aimed at identifying sections in narrative content of EHR, using both automatic or semi-automatic methods.

METHODS

This review includes articles from the databases: SCOPUS, Web of Science and PubMed (from January 1994 to September 2018). The selection of studies was done using predefined eligibility criteria and applying the PRISMA recommendations. Search criteria were elaborated by using an iterative and collaborative keyword enrichment.

RESULTS

Following the eligibility criteria, 39 studies were selected for analysis. The section identification approaches proposed by these studies vary greatly depending on the kind of narrative, the type of section, and the application. We observed that 57% of them proposed formal methods for identifying sections and 43% adapted a previously created method. Seventy-eight percent were intended for English texts and 41% for discharge summaries. Studies that are able to identify explicit (with headings) and implicit sections correspond to 46%. Regarding the level of granularity, 54% of the studies are able to identify sections, but not subsections. From the technical point of view, the methods can be classified into rule-based methods (59%), machine learning methods (22%) and a combination of both (19%). Hybrid methods showed better results than those relying on pure machine learning approaches, but lower than rule-based methods; however, their scope was more ambitious than the latter ones. Despite all the promising performance results, very few studies reported tests under a formal setup. Almost all the studies relied on custom dictionaries; however, they used them in conjunction with a controlled terminology, most commonly the UMLSⓇ metathesaurus.

CONCLUSIONS

Identification of sections in EHR narratives is gaining popularity for improving clinical extraction projects. This study enabled the community working on clinical NLP to gain a formal analysis of this task, including the most successful ways to perform it.

Efficient Active Learning for Electronic Medical Record De-identification

Electronic medical records are often de-identified before disseminated for secondary uses. However, unstructured natural language records are challenging to de-identify while utilizing a considerable amount of expensive human annotation. In this investigation, we incorporate active learning into the de-identification workflow to reduce annotation requirements. We apply this approach to a real clinical trials dataset and a publicly available i2b2 dataset to illustrate that, when the machine learning de-identification system can actively request information to help create a better model from beyond the system (e.g., a knowledgeable human assistant), less training data will be needed to maintain or improve the performance of trained models in comparison to the typical passive learning framework. Specifically, with a batch size of 10 documents, it requires only 40 documents for an active learning approach to reach an F-measure of 0.9, while passive learning needs at least 25% more data for training a comparable model.

Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview

Health informatics is a vital technology that holds great promise in the healthcare setting. We describe two prominent health informatics tools relevant to emergency care, as well as the historical background and the current state of informatics. We also identify recent research findings and practice changes. The recent advances in machine learning and natural language processing (NLP) are a prominent development in health informatics overall and relevant in emergency medicine (EM). A basic comprehension of machine-learning algorithms is the key to understand the recent usage of artificial intelligence in healthcare. We are using NLP more in clinical use for documentation. NLP has started to be used in research to identify clinically important diseases and conditions. Health informatics has the potential to benefit both healthcare providers and patients. We cover two powerful tools from health informatics for EM clinicians and researchers by describing the previous successes and challenges and conclude with their implications to emergency care.

Electronic medical record implementation for a healthcare system caring for homeless people

Objective

Electronic medical record (EMR) implementation at centers caring for homeless people is constrained by limited resources and the increased disease burden of the patient population. Few informatics articles address this issue. This report describes Boston Health Care for the Homeless Program’s migration to new EMR software without loss of unique care elements and processes.

Materials and methods

Workflows for clinical and operational functions were analyzed and modeled, focusing particularly on resource constraints and comorbidities. Workflows were optimized, standardized, and validated before go-live by user groups who provided design input. Software tools were configured to support optimized workflows. Customization was minimal. Training used the optimized configuration in a live training environment allowing users to learn and use the software before go-live.

Results

Implementation was rapidly accomplished over 6 months. Productivity was reduced at most minimally over the initial 3 months. During the first full year, quality indicator levels were maintained. Keys to success were completing before go-live workflow analysis, workflow mapping, building of documentation templates, creation of screen shot guides, role-based phased training, and standardization of processes. Change management strategies were valuable. The early availability of a configured training environment was essential. With this methodology, the software tools were chosen and workflows optimized that addressed the challenges unique to caring for homeless people.

Conclusions

Successful implementation of an EMR to care for homeless people was achieved through detailed workflow analysis, optimizing and standardizing workflows, configuring software, and initiating training all well before go-live. This approach was particularly suitable for a homeless population.

"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.

Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media

Drug-drug interactions (DDIs) constitute an important concern in drug development and postmarketing pharmacovigilance. They are considered the cause of many adverse drug effects exposing patients to higher risks and increasing public health system costs. Methods to follow-up and discover possible DDIs causing harm to the population are a primary aim of drug safety researchers. Here, we review different methodologies and recent advances using data mining to detect DDIs with impact on patients. We focus on data mining of different pharmacovigilance sources, such as the US Food and Drug Administration Adverse Event Reporting System and electronic health records from medical institutions, as well as on the diverse data mining studies that use narrative text available in the scientific biomedical literature and social media. We pay attention to the strengths but also further explain challenges related to these methods. Data mining has important applications in the analysis of DDIs showing the impact of the interactions as a cause of adverse effects, extracting interactions to create knowledge data sets and gold standards and in the discovery of novel and dangerous DDIs.

What are they trying to do?: An analysis of Action Identities in using electronic documentation in an EHR

Documentation processes have changed substantially with EHR adoption. User satisfaction studies have focused on usability or cognitive analysis perspectives. Few studies have provided useful information to developers to improve designs. The purpose of this study is to report a 3-pronged approach to deepen understanding of the documentation process, with the intent to provide useful information for future design. This study was conducted in two phases, beginning with cognitive task interviews and observations, followed by post-observation interviews. Twenty-five constructs were identified across the phases, and we observed several patterns of note writing. Participants provided useful information to potentially inform future design. Our study illustrates how electronic documentation serves many clinical processes and is at the core of the medical record. Providers need multiple kinds of notes and ways to display notes. In order to meet provider goals, we must completely re-think the way electronic documentation is composed and displayed.

Increasing the Efficiency on Producing Radiology Reports for Breast Cancer Diagnosis by Means of Structured Reports

Background: Radiology reports are commonly written on free-text using voice recognition devices. Structured reports (SR) have a high potential but they are usually considered more difficult to fill-in so their adoption in clinical practice leads to a lower efficiency. However, some studies have demonstrated that in some cases, producing SRs may require shorter time than plain-text ones. This work focuses on the definition and demonstration of a methodology to evaluate the productivity of software tools for producing radiology reports. A set of SRs for breast cancer diagnosis based on BI-RADS have been developed using this method. An analysis of their efficiency with respect to free-text reports has been performed.

Material and Methods: The methodology proposed compares the Elapsed Time (ET) on a set of radiological reports. Free-text reports are produced with the speech recognition devices used in the clinical practice. Structured reports are generated using a web application generated with TRENCADIS framework. A team of six radiologists with three different levels of experience in the breast cancer diagnosis was recruited. These radiologists performed the evaluation, each one introducing 50 reports for mammography, 50 for ultrasound scan and 50 for MRI using both approaches. Also, the Relative Efficiency (REF) was computed for each report, dividing the ET of both methods. We applied the T-Student (T-S) test to compare the ETs and the ANOVA test to compare the REFs. Both tests were computed using the SPSS software.

Results: The study produced three DICOM- SR templates for Breast Cancer Diagnosis on mammography, ultrasound and MRI, using RADLEX terms based on BIRADs 5th edition. The T-S test on radiologists with high or intermediate profile, showed that the difference between the ET was only statistically significant for mammography and ultrasound. The ANOVA test performed grouping the REF by modalities, indicated that there were no significant differences between mammograms and ultrasound scans, but both have significant statistical differences with MRI. The ANOVA test of the REF for each modality, indicated that there were only significant differences in Mammography (ANOVA p = 0.024) and Ultrasound (ANOVA p = 0.008). The ANOVA test for each radiologist profile, indicated that there were significant differences on the high profile (ANOVA p = 0.028) and medium (ANOVA p=0.045).

Conclusions: In this work, we have defined and demonstrated a methodology to evaluate the productivity of software tools for producing radiology reports in Breast Cancer. We have evaluated that adopting Structured Reporting in mammography and ultrasound studies in breast cancer diagnosis improves the performance in producing reports.

Modeling Problem-oriented Clinical Notes

Objectives: To develop a model as a starting-point for developing a problem-oriented clinical notes application as a generic component of an Electronic Health Record (EHR).

Methods: We used the generic conceptualization of Weed’s problem-oriented medical record (POMR) to link progress notes to problems, and the Subjective, Objective, Assessment, Plan (SOAP) headings to classify elements of these notes. Health Level 7 (HL7) Version 3 and Unified Modeling Language (UML) were used for modeling. We looked especially at the role of Conditions and Concerns, and how to model these to document clinical reasoning.

Results: We developed a generic HL7-based model for progress notes. In this model the specific clinical note has a condition as its reason. An assertion can be made about a condition. Any condition, observation or procedure can be a concern that has to be tracked. Utmost important is the relationship between constituting parts of a progress note and specially between progress notes by linking a progress note to conditions that are part of an earlier progress note. From this model a comprehensive hierarchical condition tree can be built. Several views, such as chronological, SOAP and condition-oriented, are possible. The clinical notes application is used in daily clinical practice. The model meets explicit design criteria and clinical needs.

Conclusions: With the comprehensive HL7 standard it is possible to model and map progress notes using SOAP headings and POMR methodology. We have developed a generic, flexible and applicable paradigm by using acts for each assessment that refer to a condition (1), by separating conditions from concerns (2), and by an extensive use of the working list act (3).

End-user support for primary care electronic medical records: a qualitative case study of users' needs, expectations and realities

Support is considered an important factor for realizing the benefits of health information technology (HIT) but there is a dearth of research on the topic of support, especially in primary care. We conducted a qualitative multiple case study of 4 family health teams (FHTs) and one family health organization (FHO) in Ontario, Canada in an attempt to gain insight into users' expectations and needs, and the realities of end-user support for primary care electronic medical records (EMRs). Data were collected by semi-structured interviews, documents review, and observation of training sessions. The analysis highlights the important role of on-site information technology (IT) staff and super-users in liaising with various stakeholders to solve technical problems and providing hardware and functional ('how to') support; the local development of data support practices to ensure consistent documentation; and the gaps that exist in users' and support personnel's understanding of each other's work processes.

Generating Clinical Notes for Electronic Health Record Systems

Clinical notes summarize interactions that occur between patients and healthcare providers. With adoption of electronic health record (EHR) and computer-based documentation (CBD) systems, there is a growing emphasis on structuring clinical notes to support reusing data for subsequent tasks. However, clinical documentation remains one of the most challenging areas for EHR system development and adoption. The current manuscript describes the Vanderbilt experience with implementing clinical documentation with an EHR system. Based on their experience rolling out an EHR system that supports multiple methods for clinical documentation, the authors recommend that documentation method selection be made on the basis of clinical workflow, note content standards and usability considerations, rather than on a theoretical need for structured data.

A review of medical terminology standards and structured reporting

Much effort has been invested in standardizing medical terminology for representation of medical knowledge, storage in electronic medical records, retrieval, reuse for evidence-based decision making, and for efficient messaging between users. We only focus on those efforts related to the representation of clinical medical knowledge required for capturing diagnoses and findings from a wide range of general to specialty clinical perspectives (e.g., internists to pathologists). Standardized medical terminology and the usage of structured reporting have been shown to improve the usage of medical information in secondary activities, such as research, public health, and case studies. The impact of standardization and structured reporting is not limited to secondary activities; standardization has been shown to have a direct impact on patient healthcare.

Standardized Note Templates Improve Electronic Medical Record Documentation of Neurovascular Examinations for Pediatric Supracondylar Humeral Fractures

Background:

Optimization of the electronic medical record (EMR) is essential to support the clinician and to improve the quality and efficiency of patient care. The present report describes the development and implementation of a standardized template that is embedded in the EMR and is focused on a comprehensive physical examination during the evaluation of pediatric supracondylar humeral fractures. We compared the completeness of physical examinations as well as the timing of detection and documentation of neurovascular injuries before and after implementation of the template. We hypothesized that the use of a template would increase the completeness of examinations and would lead to earlier documentation of neurovascular injuries.

Methods:

A multidisciplinary quality-improvement task force was created to address neurovascular documentation practices for patients who underwent operative treatment of supracondylar humeral fractures. Following a series of formative and process evaluations, a standardized EMR template was implemented. Neurovascular examination documentation practices that were in use before (pre-template group, n = 224) and after (template group, n = 300) the implementation of the template were compared. Logistic regression analyses of the 2 groups were used to compare the likelihood of a complete neurovascular examination and the timing of neurovascular injury identification.

Results:

There was significant improvement in the documentation of the vascular (odds ratio [OR], 70.7; 95% confidence interval [CI], 39.5 to 126.6; p < 0.0001), motor (OR, 17.6; 95% CI, 9.5 to 32.7; p < 0.0001), and sensory (OR, 23.9; 95% CI, 12.9 to 44.4; p < 0.0001) examinations in the template group. Neurological injuries were more likely to be identified preoperatively in the template group compared with the pre-template group (OR, 6.8; 95% CI, 1.7 to 27.1; p = 0.0067).

Conclusions:

The incorporation of a standardized template in the EMR improved the completeness and timing of documentation of neurological injury. Standardized EMR templates developed by a clinically driven multidisciplinary task force have the potential to improve the quality of clinical documentation and to ease communication among providers.

Level of Evidence:

Level III. See Instructions for Authors for a complete description of levels of evidence.

Patient Privacy in the Era of Big Data

Privacy was defined as a fundamental human right in the Universal Declaration of Human Rights at the 1948 United Nations General Assembly. However, there is still no consensus on what constitutes privacy. In this review, we look at the evolution of privacy as a concept from the era of Hippocrates to the era of social media and big data. To appreciate the modern measures of patient privacy protection and correctly interpret the current regulatory framework in the United States, we need to analyze and understand the concepts of individually identifiable information, individually identifiable health information, protected health information, and de-identification. The Privacy Rule of the Health Insurance Portability and Accountability Act defines the regulatory framework and casts a balance between protective measures and access to health information for secondary (scientific) use. The rule defines the conditions when health information is protected by law and how protected health information can be de-identified for secondary use. With the advents of artificial intelligence and computational linguistics, computational text de-identification algorithms produce de-identified results nearly as well as those produced by human experts, but much faster, more consistently and basically for free. Modern clinical text de-identification systems now pave the road to big data and enable scientists to access de-identified clinical information while firmly protecting patient privacy. However, clinical text de-identification is not a perfect process. In order to maximize the protection of patient privacy and to free clinical and scientific information from the confines of electronic healthcare systems, all stakeholders, including patients, health institutions and institutional review boards, scientists and the scientific communities, as well as regulatory and law enforcement agencies must collaborate closely. On the one hand, public health laws and privacy regulations define rules and responsibilities such as requesting and granting only the amount of health information that is necessary for the scientific study. On the other hand, developers of de-identification systems provide guidelines to use different modes of operations to maximize the effectiveness of their tools and the success of de-identification. Institutions with clinical repositories need to follow these rules and guidelines closely to successfully protect patient privacy. To open the gates of big data to scientific communities, healthcare institutions need to be supported in their de-identification and data sharing efforts by the public, scientific communities, and local, state, and federal legislators and government agencies.

Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review

We followed a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to identify existing clinical natural language processing (NLP) systems that generate structured information from unstructured free text. Seven literature databases were searched with a query combining the concepts of natural language processing and structured data capture. Two reviewers screened all records for relevance during two screening phases, and information about clinical NLP systems was collected from the final set of papers. A total of 7149 records (after removing duplicates) were retrieved and screened, and 86 were determined to fit the review criteria. These papers contained information about 71 different clinical NLP systems, which were then analyzed. The NLP systems address a wide variety of important clinical and research tasks. Certain tasks are well addressed by the existing systems, while others remain as open challenges that only a small number of systems attempt, such as extraction of temporal information or normalization of concepts to standard terminologies. This review has identified many NLP systems capable of processing clinical free text and generating structured output, and the information collected and evaluated here will be important for prioritizing development of new approaches for clinical NLP.

Tool-supported Interactive Correction and Semantic Annotation of Narrative Clinical Reports

OBJECTIVES

Our main objective is to design a method of, and supporting software for, interactive correction and semantic annotation of narrative clinical reports, which would allow for their easier and less erroneous processing outside their original context: first, by physicians unfamiliar with the original language (and possibly also the source specialty), and second, by tools requiring structured information, such as decision-support systems. Our additional goal is to gain insights into the process of narrative report creation, including the errors and ambiguities arising therein, and also into the process of report annotation by clinical terms. Finally, we also aim to provide a dataset of ground-truth transformations (specific for Czech as the source language), set up by expert physicians, which can be reused in the future for subsequent analytical studies and for training automated transformation procedures.

METHODS

A three-phase preprocessing method has been developed to support secondary use of narrative clinical reports in electronic health record. Narrative clinical reports are narrative texts of healthcare documentation often stored in electronic health records. In the first phase a narrative clinical report is tokenized. In the second phase the tokenized clinical report is normalized. The normalized clinical report is easily readable for health professionals with the knowledge of the language used in the narrative clinical report. In the third phase the normalized clinical report is enriched with extracted structured information. The final result of the third phase is a semi-structured normalized clinical report where the extracted clinical terms are matched to codebook terms. Software tools for interactive correction, expansion and semantic annotation of narrative clinical reports has been developed and the three-phase preprocessing method validated in the cardiology area.

RESULTS

The three-phase preprocessing method was validated on 49 anonymous Czech narrative clinical reports in the field of cardiology. Descriptive statistics from the database of accomplished transformations has been calculated. Two cardiologists participated in the annotation phase. The first cardiologist annotated 1500 clinical terms found in 49 narrative clinical reports to codebook terms using the classification systems ICD 10, SNOMED CT, LOINC and LEKY. The second cardiologist validated annotations of the first cardiologist. The correct clinical terms and the codebook terms have been stored in a database.

CONCLUSIONS

We extracted structured information from Czech narrative clinical reports by the proposed three-phase preprocessing method and linked it to electronic health records. The software tool, although generic, is tailored for Czech as the specific language of electronic health record pool under study. This will provide a potential etalon for porting this approach to dozens of other less-spoken languages. Structured information can support medical decision making, quality assurance tasks and further medical research.

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

The Epic electronic health record (EHR) platform supports structured data entry systems (SDES), which allow developers, with input from users, to create highly customized patient-record templates in order to maximize data completeness and to standardize structure. There are many potential advantages of using discrete data fields in the EHR to capture data for secondary analysis and epidemiological research, but direct data acquisition from clinicians remains one of the largest obstacles to leveraging the EHR for secondary use. Physician resistance to SDES is multifactorial. A 35-item questionnaire based on Unified Theory of Acceptance and Use of Technology, was used to measure attitudes, facilitation, and potential incentives for adopting SDES for clinical documentation among 25 pediatric specialty physicians and surgeons. Statistical analysis included chi-square for categorical data as well as independent sample t-tests and analysis of variance for continuous variables. Mean scores of the nine constructs demonstrated primarily positive physician attitudes toward SDES, while the surgeons were neutral. Those under 40 were more likely to respond that facilitating conditions for structured entry existed as compared to the two older age groups (p = .02). Pediatric surgeons were significantly less positive than specialty physicians about SDES effects on Performance (p = .01) and the effect of Social Influence (p = .02); but in more agreement that use of forms was voluntary (p = .02). Attitudinal differences likely reflect medical training, clinical practice workflows, and division specific practices. Identified resistance indicate efforts to increase SDES adoption should be discipline-targeted rather than a uniform approach.

Natural Language Processing-Enabled and Conventional Data Capture Methods for Input to Electronic Health Records: A Comparative Usability Study

Background

The process of documentation in electronic health records (EHRs) is known to be time consuming, inefficient, and cumbersome. The use of dictation coupled with manual transcription has become an increasingly common practice. In recent years, natural language processing (NLP)–enabled data capture has become a viable alternative for data entry. It enables the clinician to maintain control of the process and potentially reduce the documentation burden. The question remains how this NLP-enabled workflow will impact EHR usability and whether it can meet the structured data and other EHR requirements while enhancing the user’s experience.

Objective

The objective of this study is evaluate the comparative effectiveness of an NLP-enabled data capture method using dictation and data extraction from transcribed documents (NLP Entry) in terms of documentation time, documentation quality, and usability versus standard EHR keyboard-and-mouse data entry.

Methods

This formative study investigated the results of using 4 combinations of NLP Entry and Standard Entry methods (“protocols”) of EHR data capture. We compared a novel dictation-based protocol using MediSapien NLP (NLP-NLP) for structured data capture against a standard structured data capture protocol (Standard-Standard) as well as 2 novel hybrid protocols (NLP-Standard and Standard-NLP). The 31 participants included neurologists, cardiologists, and nephrologists. Participants generated 4 consultation or admission notes using 4 documentation protocols. We recorded the time on task, documentation quality (using the Physician Documentation Quality Instrument, PDQI-9), and usability of the documentation processes.

Results

A total of 118 notes were documented across the 3 subject areas. The NLP-NLP protocol required a median of 5.2 minutes per cardiology note, 7.3 minutes per nephrology note, and 8.5 minutes per neurology note compared with 16.9, 20.7, and 21.2 minutes, respectively, using the Standard-Standard protocol and 13.8, 21.3, and 18.7 minutes using the Standard-NLP protocol (1 of 2 hybrid methods). Using 8 out of 9 characteristics measured by the PDQI-9 instrument, the NLP-NLP protocol received a median quality score sum of 24.5; the Standard-Standard protocol received a median sum of 29; and the Standard-NLP protocol received a median sum of 29.5. The mean total score of the usability measure was 36.7 when the participants used the NLP-NLP protocol compared with 30.3 when they used the Standard-Standard protocol.

Conclusions

In this study, the feasibility of an approach to EHR data capture involving the application of NLP to transcribed dictation was demonstrated. This novel dictation-based approach has the potential to reduce the time required for documentation and improve usability while maintaining documentation quality. Future research will evaluate the NLP-based EHR data capture approach in a clinical setting. It is reasonable to assert that EHRs will increasingly use NLP-enabled data entry tools such as MediSapien NLP because they hold promise for enhancing the documentation process and end-user experience.

I've got 99 problems but a phone ain't one: Electronic and mobile health in low and middle income countries

Mobile technology is very prevalent in Kenya-mobile phone penetration is at 88% and mobile data subscriptions form 99% of all internet subscriptions. While there is great potential for such ubiquitous technology to revolutionise access and quality of healthcare in low-resource settings, there have been few successes at scale. Implementations of electronic health (e-Health) and mobile health (m-Health) technologies in countries like Kenya are yet to tackle human resource constraints or the political, ethical and financial considerations of such technologies. We outline recent innovations that could improve access and quality while considering the costs of healthcare. One is an attempt to create a scalable clinical decision support system by engaging a global network of specialist doctors and reversing some of the damaging effects of medical brain drain. The other efficiently extracts digital information from paper-based records using low-cost and locally produced tools such as rubber stamps to improve adherence to clinical practice guidelines. By bringing down the costs of remote consultations and clinical audit, respectively, these projects offer the potential for clinics in resource-limited settings to deliver high-quality care. This paper makes a case for continued and increased investment in social enterprises that bridge academia, public and private sectors to deliver sustainable and scalable e-Health and m-Health solutions.

A secure and efficiently searchable health information architecture

Patient-centric repositories of health records are an important component of health information infrastructure. However, patient information in a single repository is potentially vulnerable to loss of the entire dataset from a single unauthorized intrusion. A new health record storage architecture, the personal grid, eliminates this risk by separately storing and encrypting each person's record. The tradeoff for this improved security is that a personal grid repository must be sequentially searched since each record must be individually accessed and decrypted. To allow reasonable search times for large numbers of records, parallel processing with hundreds (or even thousands) of on-demand virtual servers (now available in cloud computing environments) is used. Estimated search times for a 10 million record personal grid using 500 servers vary from 7 to 33min depending on the complexity of the query. Since extremely rapid searching is not a critical requirement of health information infrastructure, the personal grid may provide a practical and useful alternative architecture that eliminates the large-scale security vulnerabilities of traditional databases by sacrificing unnecessary searching speed.

The lexeme hypotheses: Their use to generate highly grammatical and completely computerized medical records

INTRODUCTION

Medical records often contain free text created by harried clinicians. Free text often contains errors which make it an unsuitable target for computerized data extraction. The cost of healthcare can be reduced by creating medical records that are fully computerized at their inception. We examine hypotheses that enable us to construct such records.

METHODS

We regard the text of the medical record as being an ordered collection of meaningful fragments. The intellectual content (or "lexeme") of each text fragment in the record is considered separately from the language that used to express it. We further consider that each lexeme exists as a combination of a lexeme query (defining the issue being addressed) and a lexeme response to that query. The medical record can then be perceived as a stream of these responses. The responses can be expressed in any style or language, including computer code. Examining medical records in this light gives rise to a number of observations and hypotheses.

OBSERVATIONS AND HYPOTHESES

The physical location and nature of the medical episode (which we term "context") determines the general layout of the record. The order that lexeme-queries are addressed in within the record is highly consistent ("coherence"). Issues are only addressed if they are logically called-for by the context or by a previously-selected lexeme response ("predicance"), and only to a needed depth of detail ("level"). We hypothesize that all of the lexeme queries required to write any clinical notes can be stored in a large database ("lexicon") in coherence order, wherein each lexeme query is associated with its own collection of lexeme responses. We hypothesize that the issue a note-writer will need to address next is identifiable purely by using the rules of coherence, level and predicance.

TESTING THE HYPOTHESES AND THEIR UTILITY

We have tested these hypotheses with a computer program which repeatedly offers the user a menu of lexeme responses with associated text. On selection, the program issues the text fragment, and its corresponding computer code, to output files. The program then uses coherence, predicance and level to navigate to the next appropriate lexeme query for presentation to the user. The net result is that the user creates a grammatically correct and completely computerized note at the time of its inception. The value of this approach and its practical implementation to create medical records are discussed. In our work so far, the hypotheses appear not to be false, but further testing is needed using a larger lexicon to establish their robustness in actual clinical practice.

Shared Longitudinal Health Records for Clinical and Population Health

The ability of a health information exchange to consolidate information, collected in multiple, disparate information systems, into a single, person-centric health record can provide a comprehensive and longitudinal representation of an individual’s medical history. Shared, longitudinal health records can be leveraged to enhance the delivery of individual clinical care and provide opportunities to improve health outcomes at the population level. This chapter will describe the clinical benefits imparted by the shared health record (SHR) component of the OpenHIE infrastructure. It will also characterize the potential population health benefits of the aggregate level data contained and distributed by the Health Management Information System component of OpenHIE. The chapter will further discuss the implementation of these systems.

By the end of the chapter, the reader should be able to:●

Identify and describe the differences among an electronic medical record, electronic health record, and a shared heath record.

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Explain the role of a shared health record in a health information exchange.

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List and describe the components of a shared health record.

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Discuss the role and benefits of a health management information system within a health information exchange.

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Define a population health indicator.

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Identify and describe application domains for a health management information system.

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Define a database management system.

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Compare the implications of implementing a shared health record using an electronic health record system versus a database management system.

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Discuss emerging trends likely to shape the evolution of shared health records and health management information systems.

An Assessment of Family History Information Captured in an Electronic Health Record

Family history is considered a core element of clinical care. In this study we assessed the quality of family history data captured in an established commercial electronic health record (EHR) at a large academic medical center. Because the EHR had no centralized location to store family history information, it was collected as part of clinical notes in structured or free-text format. We analyzed differences between 10,000 free-text and 9,121 structured family history observations. Each observation was classified according to disease presence/absence and family member affected (e.g., father, mother, etc.). The structured notes did not collect a complete family history as defined by standards endorsed by the U.S. Agency for Healthcare Research and Quality; the free-text notes contained more information than the structured notes, but still not enough to be considered "complete." Several barriers remain for collecting complete, useful family history data in electronic health records.

Challenges Associated With Using Large Data Sets for Quality Assessment and Research in Clinical Settings

The rapidly expanding use of electronic records in health-care settings is generating unprecedented quantities of data available for clinical, epidemiological, and cost-effectiveness research. Several challenges are associated with using these data for clinical research, including issues surrounding access and information security, poor data quality, inconsistency of data within and across institutions, and a paucity of staff with expertise to manage and manipulate large clinical data sets. In this article, we describe our experience with assembling a data-mart and conducting clinical research using electronic data from four facilities within a single hospital network in New York City. We culled data from several electronic sources, including the institution's admission-discharge-transfer system, cost accounting system, electronic health record, clinical data warehouse, and departmental records. The final data-mart contained information for more than 760,000 discharges occurring from 2006 through 2012. Using categories identified by the National Institutes of Health Big Data to Knowledge initiative as a framework, we outlined challenges encountered during the development and use of a domain-specific data-mart and recommend approaches to overcome these challenges.

A comparison of mental state examination documentation by junior clinicians in electronic health records before and after the introduction of a semi-structured assessment template (OPCRIT+)

OBJECTIVES

The mental state examination (MSE) provides crucial information for healthcare professionals in the assessment and treatment of psychiatric patients as well as potentially providing valuable data for mental health researchers accessing electronic health records (EHRs). We wished to establish if improvements could be achieved in the documenting of MSEs by junior doctors within a large United Kingdom mental health trust following the introduction of an EHR based semi-structured MSE assessment template (OPCRIT+).

METHODS

First, three consultant psychiatrists using a modified version of the Physician Documentation Quality Instrument-9 (PDQI-9) blindly rated fifty MSEs written using OPCRIT+ and fifty normal MSEs written with no template. Second, we conducted an audit to compare the frequency with which individual components of the MSE were documented in the normal MSEs compared with the OPCRIT+MSEs.

RESULTS

PDQI-9 ratings indicated that the OPCRIT+MSEs were more 'Thorough', 'Organized', 'Useful' and 'Comprehensible' as well as being of an overall higher quality than the normal MSEs. The audit identified that the normal MSEs contained fewer mentions of the individual components of 'Thought content', 'Anxiety' and 'Cognition & Insight'.

CONCLUSIONS

These results indicate that a semi-structured assessment template significantly improves the quality of MSE recording by junior doctors within EHRs. Future work should focus on whether such improvements translate into better patient outcomes and have the ability to improve the quality of information available on EHRs to researchers.