Generating Clinical Notes for Electronic Health Record Systems

Designing a minimum data set of laboratory data for the electronic summary sheet of pediatric ward in Iran: A cross-sectional study

Background and Aim

Iranian hospitals are provided with hospital information systems (HISs) from different vendors, which make it hardly possible to summarize laboratory data in an consistent manner. Therefore, there is a need to design a minimum data set of laboratory data that will define standard criteria and reduce potential medical errors. The purpose of this study was to design a minimum data set (MDS) of laboratory data for an electronic summary sheet to be used in the pediatric ward of Iranian hospitals.

Methods

This study consists of three phases. In the first phase, out of 3997 medical records from the pediatric ward, 604 summary sheets were chosen as sample. The laboratory data of these sheets were examined and the recorded tests were categorized. In the second phase, based on the types of diagnosis we developed a list of tests. Then we asked the physicians of the ward to select which ones should be documented for each patient's diagnosis. In the third phase, the tests that were reported in 21%-80% of the records, and were verified by the same percentage of physicians, were evaluated by the experts' panel.

Results

In the first phase, 10,224 laboratory data were extracted. Of these, 144 data elements reported in more than 80% of the records, and more than 80% of experts approved them to be included in the MDS for patients' summary sheet. After data elements were investigated in the experts' panel, 292 items were chosen for the final list of the data set.

Conclusions

This MDS was designed such that, if implemented in hospital information systems, it could automatically enable registering data in the summary sheet when patient's diagnosis is registered.

Assessing the Contribution of Scanned Outside Documents to the Completeness of Real-World Data Abstraction

PURPOSE

Electronic health record (EHR) data are widely used in precision medicine, quality improvement, disease surveillance, and population health management. However, a significant amount of EHR data are stored in unstructured formats including scanned documents external to the treatment facility presenting an informatics challenge for secondary use. Studies are needed to characterize the clinical information uniquely available in scanned outside documents (SODs) to understand to what extent the availability of such information affects the use of these real-world data for cancer research.

MATERIALS AND METHODS

Two independent EHR data abstractions capturing 30 variables commonly used in oncology research were conducted for 125 patients treated for advanced non-small-cell lung cancer at a comprehensive cancer center, with and without consideration of SODs. Completeness and concordance were compared between the two abstractions, overall, and by patient groups and variable types.

RESULTS

The overall completeness of the data with SODs was 77.6% as compared with 54.3% for the abstraction without SODs. The differences in completeness were driven by data related to biomarker tests, which were more likely to be uniquely available in SODs. Such data were prone to missingness among patients who were diagnosed externally.

CONCLUSION

There were no major differences in completeness between the two abstractions by demographics, diagnosis, disease progression, performance status, or oral therapy use. However, biomarker data were more likely to be uniquely contained in the SODs. Our findings may help cancer centers prioritize the types of SOD data being abstracted for research or other secondary purposes.

Self‐reported documentation of goals and outcomes of nutrition care – A cross‐sectional survey study of Scandinavian dietitians

BACKGROUND

The documentation of goals and outcomes of nutrition care in Electronic Health Records is insufficient making further exploration of this of particular interest. Identifying common features in documentation practice among Scandinavian dietitians might provide information that can support improvement in this area.

AIMS

To explore the associations between clinical dietitians' self-reported documentation of patients' goals and outcomes and demographic factors, self-reported implementation of the systematic framework the Nutrition Care Process 4th step (NCP) and its associated terminology, and factors associated with the workplace.

METHODS

Data from a cross-sectional study based on a previously tested web-based survey (INIS) disseminated in 2017 to dietitians in Scandinavia (n = 494) was used. Respondents were recruited through e-mail lists, e-newsletters and social media groups for dietitians. Associations between countries regarding the reported documentation of goals and outcomes, implementation levels of the NCP 4th step, demographic information and factors associated with the workplace were measured through Chi-square test. Associations between dependent- and independent variables were measured through logistic regression analysis.

RESULTS

Clinically practicing dietitians (n = 347) working in Scandinavia, Sweden (n = 249), Norway (n = 60), Denmark (n = 38), who had completed dietetic education participated. The reported documentation of goals and outcomes from nutrition intervention was highly associated with the reported implementation of NCP 4th step terminology (OR = 5.26; p = 0.009, OR = 3.56; p = 0.003), support from the workplace (OR = 4.0, p < 0.001, OR = 8.89, p < 0.001) and area of practice (OR = 2.02, p = 0.017). Years since completed dietetic training and educational level did not have any significant associations with documentation practice regarding goals and outcomes.

CONCLUSION

Findings highlight strong associations between the implementation of the NCP 4th step terminology and the documentation of goals and outcomes. Strategies to support dietitians in using standardized terminology and the development of tools for comprehensive documentation of evaluation of goals and outcome are required.

Evaluation and comparison of errors on nursing notes created by online and offline speech recognition technology and handwritten: an interventional study

BACKGROUND

Despite the rapid expansion of electronic health records, the use of computer mouse and keyboard, challenges the data entry into these systems. Speech recognition software is one of the substitutes for the mouse and keyboard. The objective of this study was to evaluate the use of online and offline speech recognition software on spelling errors in nursing reports and to compare them with errors in handwritten reports.

METHODS

For this study, online and offline speech recognition software were selected and customized based on unrecognized terms by these softwares. Two groups of 35 nurses provided the admission notes of hospitalized patients upon their arrival using three data entry methods (using the handwritten method or two types of speech recognition software). After at least a month, they created the same reports using the other methods. The number of spelling errors in each method was determined. These errors were compared between the paper method and the two electronic methods before and after the correction of errors.

RESULTS

The lowest accuracy was related to online software with 96.4% and accuracy. On the average per report, the online method 6.76, and the offline method 4.56 generated more errors than the paper method. After correcting the errors by the participants, the number of errors in the online reports decreased by 94.75% and the number of errors in the offline reports decreased by 97.20%. The highest number of reports with errors was related to reports created by online software.

CONCLUSION

Although two software had relatively high accuracy, they created more errors than the paper method that can be lowered by optimizing and upgrading these softwares. The results showed that error correction by users significantly reduced the documentation errors caused by the software.

Extraction of Information Related to Drug Safety Surveillance From Electronic Health Record Notes: Joint Modeling of Entities and Relations Using Knowledge-Aware Neural Attentive Models

BACKGROUND

An adverse drug event (ADE) is commonly defined as "an injury resulting from medical intervention related to a drug." Providing information related to ADEs and alerting caregivers at the point of care can reduce the risk of prescription and diagnostic errors and improve health outcomes. ADEs captured in structured data in electronic health records (EHRs) as either coded problems or allergies are often incomplete, leading to underreporting. Therefore, it is important to develop capabilities to process unstructured EHR data in the form of clinical notes, which contain a richer documentation of a patient's ADE. Several natural language processing (NLP) systems have been proposed to automatically extract information related to ADEs. However, the results from these systems showed that significant improvement is still required for the automatic extraction of ADEs from clinical notes.

OBJECTIVE

This study aims to improve the automatic extraction of ADEs and related information such as drugs, their attributes, and reason for administration from the clinical notes of patients.

METHODS

This research was conducted using discharge summaries from the Medical Information Mart for Intensive Care III (MIMIC-III) database obtained through the 2018 National NLP Clinical Challenges (n2c2) annotated with drugs, drug attributes (ie, strength, form, frequency, route, dosage, duration), ADEs, reasons, and relations between drugs and other entities. We developed a deep learning-based system for extracting these drug-centric concepts and relations simultaneously using a joint method enhanced with contextualized embeddings, a position-attention mechanism, and knowledge representations. The joint method generated different sentence representations for each drug, which were then used to extract related concepts and relations simultaneously. Contextualized representations trained on the MIMIC-III database were used to capture context-sensitive meanings of words. The position-attention mechanism amplified the benefits of the joint method by generating sentence representations that capture long-distance relations. Knowledge representations were obtained from graph embeddings created using the US Food and Drug Administration Adverse Event Reporting System database to improve relation extraction, especially when contextual clues were insufficient.

RESULTS

Our system achieved new state-of-the-art results on the n2c2 data set, with significant improvements in recognizing crucial drug-reason (F1=0.650 versus F1=0.579) and drug-ADE (F1=0.490 versus F1=0.476) relations.

CONCLUSIONS

This study presents a system for extracting drug-centric concepts and relations that outperformed current state-of-the-art results and shows that contextualized embeddings, position-attention mechanisms, and knowledge graph embeddings effectively improve deep learning-based concepts and relation extraction. This study demonstrates the potential for deep learning-based methods to help extract real-world evidence from unstructured patient data for drug safety surveillance.

A qualitative investigation into clinical documentation: why do clinicians document the way they do?

BACKGROUND

Clinical documentation is a fundamental component of patient care. The transition from paper based to electronic medical records/electronic health records has highlighted a number of issues associated with documentation practices including duplication. Developing new ways to document the care provided to patients and in turn, persuading clinicians to accept a change, must be supported by evidence that a change is required. In Australia, there has been a limited number of studies exploring the clinical documentation practices and beliefs of clinicians.

OBJECTIVE

To gain an in-depth understanding of clinician documentation practices.

METHOD

A qualitative design using semi-structured interviews with clinicians (allied health professionals, doctors (physicians) and nurses) working in a tertiary-level hospital in South-East Queensland, Australia.

RESULTS

Several themes emerged from the data: environmental factors, including departmental policy and systemic issues, and personal factors, including verification, clinical reasoning and experience influencing documentation practices.

CONCLUSION

Our study identified that the documentation practices of clinicians are complex, being driven by both environmental and systemic factors and personal factors. This in turn leads to duplication and some redundancy. The documentation burden of duplication could be reduced by changes in policy, supported by multidisciplinary documentation procedures and electronic systems aligned with clinician workflows, while retaining some flexible documentation practices. The documentation practices of individuals, when considered from the perspective of enhancing quality care, are considered legitimate and therefore will continue to form part of the health (medical) record regardless of the format.

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.

Words prediction based on N-gram model for free-text entry in electronic health records

The process of documentation is one of the most important parts of electronic health records (EHR). It is time-consuming, and up until now, available documentation procedures have not been able to overcome this type of EHR limitations. Thus, entering information into EHR still has remained a challenge. In this study, by applying the trigram language model, we presented a method to predict the next words while typing free texts. It is hypothesized that using this system may save typing time of free text. The words prediction model introduced in this research was trained and tested on the free texts regarding to colonoscopy, transesophageal echocardiogram, and anterior-cervical-decompression. Required time of typing for each of the above-mentioned reports calculated and compared with manual typing of the same words. It is revealed that 33.36% reduction in typing time and 73.53% reduction in keystroke. The designed system reduced the time of typing free text which might be an approach for EHRs improvement in terms of documentation.

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.

Root Cause Analysis of Oncology Adverse Events in the Veterans Health Administration

PURPOSE

Oncology providers are leaders in patient safety. Despite their efforts, oncology-related medical errors still occur, sometimes resulting in patient injury or death. The Veterans Health Administration (VHA) National Center of Patient Safety used data obtained from root cause analysis (RCA) to determine how and why these adverse events occurred in the VHA, and how to prevent future reoccurrence. This study details the types of oncology adverse events reported in VHA hospitals and their root causes and suggests actions for prevention and improvement.

METHODS

We searched the National Center for Patient Safety adverse event reporting database for RCA related to oncology care from October 1, 2013, to September 8, 2017, to identify event types, root causes, severity of outcomes, care processes, and suggested actions. Two independent reviewers coded these variables, and inter-rater agreement was calculated by κ statistic. Variables were evaluated using descriptive statistics.

RESULTS

We identified 48 RCA reports that specifically involved an oncology provider. Event types included care delays (39.5% [n = 19]), issues with chemotherapy (25% [n = 12]) and radiation (12.5% [n = 6]), other (12.5% [n = 6]), and suicide (10.5% [n = 5]). Of the 48 events, 27.1% (n = 13) resulted in death, 4.2% (n = 2) in severe harm, 18.8% (n = 9) in temporary harm, 20.8% (n = 10) in minimal harm, and 2.1% (n = 1) in no harm. The majority of root causes identified a need to improve care processes and policies, interdisciplinary communication, and care coordination.

CONCLUSION

This analysis highlights an opportunity to implement system-wide changes to prevent similar events from reoccurring. These actions include comprehensive cancer clinics, usability testing of medical equipment, and standardization of processes and policies. Additional studies are necessary to assess oncologic adverse events across specialties.

Structured Documentation of Home Ventilator Settings in Children: A Quality Improvement Project

INTRODUCTION

In children requiring long-term mechanical ventilation (LTMV), insufficient admission charting can lead to adverse events. Our purpose in this study was to create and evaluate a structured documentation tool of home LTMV settings to improve communication, documentation, and patient safety.

METHOD

This study used a pretest-posttest survey of pulmonary unit nurses' satisfaction with the tool and perceptions of patient safety, chart reviews of documentation compliance, and reports of education session attendance. Mann-Whitney U and Fisher exact tests, category analyses, and descriptive statistics were applied.

RESULTS

Nurses' reports of positive communication of LTMV settings increased from 54.5% to 100% (p = .002), overall satisfaction with associated documentation increased (p < .001), and witnessed related adverse events decreased from 50% to 18.75%. Nurse compliance for education attendance and documentation was 97.4% and 97.3%, respectively.

DISCUSSION

Structured admission charting of LTMV settings should be continued and yielded improvements in pulmonary unit nurses' perceptions of communication, patient safety, and documentation compliance.

Preprocessing structured clinical data for predictive modeling and decision support. A roadmap to tackle the challenges

BACKGROUND

EHR systems have high potential to improve healthcare delivery and management. Although structured EHR data generates information in machine-readable formats, their use for decision support still poses technical challenges for researchers due to the need to preprocess and convert data into a matrix format. During our research, we observed that clinical informatics literature does not provide guidance for researchers on how to build this matrix while avoiding potential pitfalls.

OBJECTIVES

This article aims to provide researchers a roadmap of the main technical challenges of preprocessing structured EHR data and possible strategies to overcome them.

METHODS

Along standard data processing stages - extracting database entries, defining features, processing data, assessing feature values and integrating data elements, within an EDPAI framework -, we identified the main challenges faced by researchers and reflect on how to address those challenges based on lessons learned from our research experience and on best practices from related literature. We highlight the main potential sources of error, present strategies to approach those challenges and discuss implications of these strategies.

RESULTS

Following the EDPAI framework, researchers face five key challenges: (1) gathering and integrating data, (2) identifying and handling different feature types, (3) combining features to handle redundancy and granularity, (4) addressing data missingness, and (5) handling multiple feature values. Strategies to address these challenges include: cross-checking identifiers for robust data retrieval and integration; applying clinical knowledge in identifying feature types, in addressing redundancy and granularity, and in accommodating multiple feature values; and investigating missing patterns adequately.

CONCLUSIONS

This article contributes to literature by providing a roadmap to inform structured EHR data preprocessing. It may advise researchers on potential pitfalls and implications of methodological decisions in handling structured data, so as to avoid biases and help realize the benefits of the secondary use of EHR data.

Electronic health records improve clinical note quality

BACKGROUND AND OBJECTIVE

The clinical note documents the clinician's information collection, problem assessment, clinical management, and its used for administrative purposes. Electronic health records (EHRs) are being implemented in clinical practices throughout the USA yet it is not known whether they improve the quality of clinical notes. The goal in this study was to determine if EHRs improve the quality of outpatient clinical notes.

MATERIALS AND METHODS

A five and a half year longitudinal retrospective multicenter quantitative study comparing the quality of handwritten and electronic outpatient clinical visit notes for 100 patients with type 2 diabetes at three time points: 6 months prior to the introduction of the EHR (before-EHR), 6 months after the introduction of the EHR (after-EHR), and 5 years after the introduction of the EHR (5-year-EHR). QNOTE, a validated quantitative instrument, was used to assess the quality of outpatient clinical notes. Its scores can range from a low of 0 to a high of 100. Sixteen primary care physicians with active practices used QNOTE to determine the quality of the 300 patient notes.

RESULTS

The before-EHR, after-EHR, and 5-year-EHR grand mean scores (SD) were 52.0 (18.4), 61.2 (16.3), and 80.4 (8.9), respectively, and the change in scores for before-EHR to after-EHR and before-EHR to 5-year-EHR were 18% (p<0.0001) and 55% (p<0.0001), respectively. All the element and grand mean quality scores significantly improved over the 5-year time interval.

CONCLUSIONS

The EHR significantly improved the overall quality of the outpatient clinical note and the quality of all its elements, including the core and non-core elements. To our knowledge, this is the first study to demonstrate that the EHR significantly improves the quality of clinical notes.

QNOTE: an instrument for measuring the quality of EHR clinical notes

BACKGROUND AND OBJECTIVE

The outpatient clinical note documents the clinician's information collection, problem assessment, and patient management, yet there is currently no validated instrument to measure the quality of the electronic clinical note. This study evaluated the validity of the QNOTE instrument, which assesses 12 elements in the clinical note, for measuring the quality of clinical notes. It also compared its performance with a global instrument that assesses the clinical note as a whole.

MATERIALS AND METHODS

Retrospective multicenter blinded study of the clinical notes of 100 outpatients with type 2 diabetes mellitus who had been seen in clinic on at least three occasions. The 300 notes were rated by eight general internal medicine and eight family medicine practicing physicians. The QNOTE instrument scored the quality of the note as the sum of a set of 12 note element scores, and its inter-rater agreement was measured by the intraclass correlation coefficient. The Global instrument scored the note in its entirety, and its inter-rater agreement was measured by the Fleiss κ.

RESULTS

The overall QNOTE inter-rater agreement was 0.82 (CI 0.80 to 0.84), and its note quality score was 65 (CI 64 to 66). The Global inter-rater agreement was 0.24 (CI 0.19 to 0.29), and its note quality score was 52 (CI 49 to 55). The QNOTE quality scores were consistent, and the overall QNOTE score was significantly higher than the overall Global score (p=0.04).

CONCLUSIONS

We found the QNOTE to be a valid instrument for evaluating the quality of electronic clinical notes, and its performance was superior to that of the Global instrument.

Prevalence of heart failure signs and symptoms in a large primary care population identified through the use of text and data mining of the electronic health record

BACKGROUND

The electronic health record (EHR) contains a tremendous amount of data that if appropriately detected can lead to earlier identification of disease states such as heart failure (HF). Using a novel text and data analytic tool we explored the longitudinal EHR of over 50,000 primary care patients to identify the documentation of the signs and symptoms of HF in the years preceding its diagnosis.

METHODS AND RESULTS

Retrospective analysis consisted of 4,644 incident HF cases and 45,981 group-matched control subjects. Documentation of Framingham HF signs and symptoms within encounter notes were carried out with the use of a previously validated natural language processing procedure. A total of 892,805 affirmed criteria were documented over an average observation period of 3.4 years. Among eventual HF cases, 85% had ≥1 criterion within 1 year before their HF diagnosis, as did 55% of control subjects. Substantial variability in the prevalence of individual signs and symptoms were found in both case and control subjects.

CONCLUSIONS

HF signs and symptoms are frequently documented in a primary care population as identified through automated text and data mining of EHRs. Their frequent identification demonstrates the rich data available within EHRs that will allow for future work on automated criterion identification to help develop predictive models for HF.

Relationship between documentation method and quality of chronic disease visit notes

OBJECTIVE

To assses the relationship between methods of documenting visit notes and note quality for primary care providers (PCPs) and specialists, and to determine the factors that contribute to higher quality notes for two chronic diseases.

METHODS

Retrospective chart review of visit notes at two academic medical centers. Two physicians rated the subjective quality of content areas of the note (vital signs, medications, lifestyle, labs, symptoms, assessment & plan), overall quality, and completed the 9 item Physician Documentation Quality Instrument (PDQI-9). We evaluated quality ratings in relation to the primary method of documentation (templates, free-form or dictation) for both PCPs and specialists. A one factor analysis of variance test was used to examine differences in mean quality scores among the methods.

RESULTS

A total of 112 physicians, 71 primary care physicians (PCP) and 41 specialists, wrote 240 notes. For specialists, templated notes had the highest overall quality scores (p≤0.001) while for PCPs, there was no statistically significant difference in overall quality score. For PCPs, free form received higher quality ratings on vital signs (p = 0.01), labs (p = 0.002), and lifestyle (p = 0.002) than other methods; templated notes had a higher rating on medications (p≤0.001). For specialists, templated notes received higher ratings on vital signs, labs, lifestyle and medications (p = 0.001).

DISCUSSION

There was no significant difference in subjective quality of visit notes written using free-form documentation, dictation or templates for PCPs. The subjective quality rating of templated notes was higher than that of dictated notes for specialists.

CONCLUSION

As there is wide variation in physician documentation methods, and no significant difference in note quality between methods, recommending one approach for all physicians may not deliver optimal results.

A qualitative analysis evaluating the purposes and practices of clinical documentation

OBJECTIVES

An important challenge for biomedical informatics researchers is determining the best approach for healthcare providers to use when generating clinical notes in settings where electronic health record (EHR) systems are used. The goal of this qualitative study was to explore healthcare providers' and administrators' perceptions about the purpose of clinical documentation and their own documentation practices.

METHODS

We conducted seven focus groups with a total of 46 subjects composed of healthcare providers and administrators to collect knowledge, perceptions and beliefs about documentation from those who generate and review notes, respectively. Data were analyzed using inductive analysis to probe and classify impressions collected from focus group subjects.

RESULTS

We observed that both healthcare providers and administrators believe that documentation serves five primary domains: clinical, administrative, legal, research, education. These purposes are tied closely to the nature of the clinical note as a document shared by multiple stakeholders, which can be a source of tension for all parties who must use the note. Most providers reported using a combination of methods to complete their notes in a timely fashion without compromising patient care. While all administrators reported relying on computer-based documentation tools to review notes, they expressed a desire for a more efficient method of extracting relevant data.

CONCLUSIONS

Although clinical documentation has utility, and is valued highly by its users, the development and successful adoption of a clinical documentation tool largely depends on its ability to be smoothly integrated into the provider's busy workflow, while allowing the provider to generate a note that communicates effectively and efficiently with multiple stakeholders.

Secondary use of clinical data: the Vanderbilt approach

The last decade has seen an exponential growth in the quantity of clinical data collected nationwide, triggering an increase in opportunities to reuse the data for biomedical research. The Vanderbilt research data warehouse framework consists of identified and de-identified clinical data repositories, fee-for-service custom services, and tools built atop the data layer to assist researchers across the enterprise. Providing resources dedicated to research initiatives benefits not only the research community, but also clinicians, patients and institutional leadership. This work provides a summary of our approach in the secondary use of clinical data for research domain, including a description of key components and a list of lessons learned, designed to assist others assembling similar services and infrastructure.

Association of Medical Directors of Information Systems consensus on inpatient electronic health record documentation

In 2013, electronic documentation of clinical care stands at a crossroads. The benefits of creating digital notes are at risk of being overwhelmed by the inclusion of easily importable detail. Providers are the primary authors of encounters with patients. We must document clearly our understanding of patients and our communication with them and our colleagues. We want to document efficiently to meet without exceeding documentation guidelines. We copy and paste documentation, because it not only simplifies the documentation process generally, but also supports meeting coding and regulatory requirements specifically. Since the primary goal of our profession is to spend as much time as possible listening to, understanding and helping patients, clinicians need information technology to make electronic documentation easier, not harder. At the same time, there should be reasonable restrictions on the use of copy and paste to limit the growing challenge of 'note bloat'. We must find the right balance between ease of use and thoughtless documentation. The guiding principles in this document may be used to launch an interdisciplinary dialogue that promotes useful and necessary documentation that best facilitates efficient information capture and effective display.

Chapter 13: Mining electronic health records in the genomics era

Abstract: The combination of improved genomic analysis methods, decreasing genotyping costs, and increasing computing resources has led to an explosion of clinical genomic knowledge in the last decade. Similarly, healthcare systems are increasingly adopting robust electronic health record (EHR) systems that not only can improve health care, but also contain a vast repository of disease and treatment data that could be mined for genomic research. Indeed, institutions are creating EHR-linked DNA biobanks to enable genomic and pharmacogenomic research, using EHR data for phenotypic information. However, EHRs are designed primarily for clinical care, not research, so reuse of clinical EHR data for research purposes can be challenging. Difficulties in use of EHR data include: data availability, missing data, incorrect data, and vast quantities of unstructured narrative text data. Structured information includes billing codes, most laboratory reports, and other variables such as physiologic measurements and demographic information. Significant information, however, remains locked within EHR narrative text documents, including clinical notes and certain categories of test results, such as pathology and radiology reports. For relatively rare observations, combinations of simple free-text searches and billing codes may prove adequate when followed by manual chart review. However, to extract the large cohorts necessary for genome-wide association studies, natural language processing methods to process narrative text data may be needed. Combinations of structured and unstructured textual data can be mined to generate high-validity collections of cases and controls for a given condition. Once high-quality cases and controls are identified, EHR-derived cases can be used for genomic discovery and validation. Since EHR data includes a broad sampling of clinically-relevant phenotypic information, it may enable multiple genomic investigations upon a single set of genotyped individuals. This chapter reviews several examples of phenotype extraction and their application to genetic research, demonstrating a viable future for genomic discovery using EHR-linked data.

Quality of outpatient clinical notes: a stakeholder definition derived through qualitative research

BACKGROUND

There are no empirically-grounded criteria or tools to define or benchmark the quality of outpatient clinical documentation. Outpatient clinical notes document care, communicate treatment plans and support patient safety, medical education, medico-legal investigations and reimbursement. Accurately describing and assessing quality of clinical documentation is a necessary improvement in an increasingly team-based healthcare delivery system. In this paper we describe the quality of outpatient clinical notes from the perspective of multiple stakeholders.

METHODS

Using purposeful sampling for maximum diversity, we conducted focus groups and individual interviews with clinicians, nursing and ancillary staff, patients, and healthcare administrators at six federal health care facilities between 2009 and 2011. All sessions were audio-recorded, transcribed and qualitatively analyzed using open, axial and selective coding.

RESULTS

The 163 participants included 61 clinicians, 52 nurse/ancillary staff, 31 patients and 19 administrative staff. Three organizing themes emerged: 1) characteristics of quality in clinical notes, 2) desired elements within the clinical notes and 3) system supports to improve the quality of clinical notes. We identified 11 codes to describe characteristics of clinical notes, 20 codes to describe desired elements in quality clinical notes and 11 codes to describe clinical system elements that support quality when writing clinical notes. While there was substantial overlap between the aspects of quality described by the four stakeholder groups, only clinicians and administrators identified ease of translation into billing codes as an important characteristic of a quality note. Only patients rated prioritization of their medical problems as an aspect of quality. Nurses included care and education delivered to the patient, information added by the patient, interdisciplinary information, and infection alerts as important content.

CONCLUSIONS

Perspectives of these four stakeholder groups provide a comprehensive description of quality in outpatient clinical documentation. The resulting description of characteristics and content necessary for quality notes provides a research-based foundation for assessing the quality of clinical documentation in outpatient health care settings.

Development of a relational database to capture and merge clinical history with the quantitative results of radionuclide renography

Our objective was to design and implement a clinical history database capable of linking to our database of quantitative results from (99m)Tc-mercaptoacetyltriglycine (MAG3) renal scans and export a data summary for physicians or our software decision support system.

METHODS

For database development, we used a commercial program. Additional software was developed in Interactive Data Language. MAG3 studies were processed using an in-house enhancement of a commercial program. The relational database has 3 parts: a list of all renal scans (the RENAL database), a set of patients with quantitative processing results (the Q2 database), and a subset of patients from Q2 containing clinical data manually transcribed from the hospital information system (the CLINICAL database). To test interobserver variability, a second physician transcriber reviewed 50 randomly selected patients in the hospital information system and tabulated 2 clinical data items: hydronephrosis and presence of a current stent. The CLINICAL database was developed in stages and contains 342 fields comprising demographic information, clinical history, and findings from up to 11 radiologic procedures. A scripted algorithm is used to reliably match records present in both Q2 and CLINICAL. An Interactive Data Language program then combines data from the 2 databases into an XML (extensible markup language) file for use by the decision support system. A text file is constructed and saved for review by physicians.

RESULTS

RENAL contains 2,222 records, Q2 contains 456 records, and CLINICAL contains 152 records. The interobserver variability testing found a 95% match between the 2 observers for presence or absence of ureteral stent (κ = 0.52), a 75% match for hydronephrosis based on narrative summaries of hospitalizations and clinical visits (κ = 0.41), and a 92% match for hydronephrosis based on the imaging report (κ = 0.84).

CONCLUSION

We have developed a relational database system to integrate the quantitative results of MAG3 image processing with clinical records obtained from the hospital information system. We also have developed a methodology for formatting clinical history for review by physicians and export to a decision support system. We identified several pitfalls, including the fact that important textual information extracted from the hospital information system by knowledgeable transcribers can show substantial interobserver variation, particularly when record retrieval is based on the narrative clinical records.

The future state of clinical data capture and documentation: a report from AMIA's 2011 Policy Meeting

Much of what is currently documented in the electronic health record is in response toincreasingly complex and prescriptive medicolegal, reimbursement, and regulatory requirements. These requirements often result in redundant data capture and cumbersome documentation processes. AMIA's 2011 Health Policy Meeting examined key issues in this arena and envisioned changes to help move toward an ideal future state of clinical data capture and documentation. The consensus of the meeting was that, in the move to a technology-enabled healthcare environment, the main purpose of documentation should be to support patient care and improved outcomes for individuals and populations and that documentation for other purposes should be generated as a byproduct of care delivery. This paper summarizes meeting deliberations, and highlights policy recommendations and research priorities. The authors recommend development of a national strategy to review and amend public policies to better support technology-enabled data capture and documentation practices.

Rapid implementation of inpatient electronic physician documentation at an academic hospital

Electronic physician documentation is an essential element of a complete electronic medical record (EMR). At Lucile Packard Children's Hospital, a teaching hospital affiliated with Stanford University, we implemented an inpatient electronic documentation system for physicians over a 12-month period. Using an EMR-based free-text editor coupled with automated import of system data elements, we were able to achieve voluntary, widespread adoption of the electronic documentation process. When given the choice between electronic versus dictated report creation, the vast majority of users preferred the electronic method. In addition to increasing the legibility and accessibility of clinical notes, we also decreased the volume of dictated notes and scanning of handwritten notes, which provides the opportunity for cost savings to the institution.

Data from clinical notes: a perspective on the tension between structure and flexible documentation

Clinical documentation is central to patient care. The success of electronic health record system adoption may depend on how well such systems support clinical documentation. A major goal of integrating clinical documentation into electronic heath record systems is to generate reusable data. As a result, there has been an emphasis on deploying computer-based documentation systems that prioritize direct structured documentation. Research has demonstrated that healthcare providers value different factors when writing clinical notes, such as narrative expressivity, amenability to the existing workflow, and usability. The authors explore the tension between expressivity and structured clinical documentation, review methods for obtaining reusable data from clinical notes, and recommend that healthcare providers be able to choose how to document patient care based on workflow and note content needs. When reusable data are needed from notes, providers can use structured documentation or rely on post-hoc text processing to produce structured data, as appropriate.

Use of Headings and Classifications by Physicians in Medical Narratives of EHRs: An evaluation study in a Finnish hospital

OBJECTIVE

The purpose of this study was to describe and evaluate patient care documentation by hospital physicians in EHRs and especially the use of national headings and classifications in these documentations.

MATERIAL AND METHODS

The initial material consisted of a random sample of 3,481 medical narratives documented in EHRs during the period 2004-2005 in one department of a Finnish central hospital. The final material comprised a subset of 1,974 medical records with a focus on consultation requests and consultation responses by two specialist groups from 871 patients. This electronic documentation was analyzed using deductive content analyses and descriptive statistics.

RESULTS

The physicians documented patient care in EHRs principally as narrative text. The medical narratives recorded by specialists were structured with headings in less than half of the patient cases. Consultation responses in general were more often structured with headings than consultation requests. The use of classifications was otherwise insignificant, but diagnoses were documented as ICD 10 codes in over 50% of consultation responses by both medical specialties.

CONCLUSION

There is an obvious need to improve the structuring of narrative text with national headings and classifications. According to the findings of this study, reason for care, patient history, health status, follow-up care plan and diagnosis are meaningful headings in physicians' documentation. The existing list of headings needs to be analyzed within a consistent unified terminology system as a basis for further development. Adhering to headings and classifications in EHR documentation enables patient data to be shared and aggregated. The secondary use of data is expected to improve care management and quality of care.