Physicians' perceptions about a semantically integrated display for chart review: A Multi-Specialty survey.

OBJECTIVE: To assess physicians' perceptions about integrated displays for chart review based on a formal representation of patients' care context. METHODS: We iteratively designed a conceptual prototype of an integrated patient summary and conducted an online survey with a multi-specialty panel of outpatient physicians from a large health system to collect their perceptions of the usefulness of our prototype. Survey questions were responded with a 7-point Likert scale and include two open-ended questions for comments on challenges and suggestions related to electronic health record (EHR) navigation, with which a thematic analysis was performed. RESULTS: Forty-nine physicians completed the survey. The usefulness of our integrated display was rated slightly positive, and respondents did not consider it confusing. Challenges related to EHR navigation frequently reported by physicians included the need to navigate between multiple functionalities and to manually search for relevant data. The most common suggestions were related to facilitating integration of data from multiple parts of the record to facilitate data visualization and comprehension. CONCLUSION: Physicians' rating of usefulness was slightly positive, and several insights to improve EHR navigation were derived from their comments. More effective EHR navigation may be achieved through facilitating integration of data from multiple parts of the record to simplify data retrieval and synthesis.

Physicians' perceptions about narrative note sections format and content: A multi-specialty survey.

OBJECTIVE: To assess physicians' perceptions about narrative note sections format and content commonly reported in visit notes to inform future research and EHR development. METHODS: We conducted two online surveys with a multi-specialty panel of outpatient physicians from a large health system to collect their perceptions of the usefulness of three narrative formats and the relevance of content reported in the note sections history of present illness (HPI) and assessment and plan (AP). Survey questions were responded with a 7-point Likert scale and include two open-ended questions for comments on challenges and suggestions related to electronic clinical documentation. RESULTS: Eighty-eight physicians completed the surveys. The most preferred format for HPI was story (i.e., coherent paragraph), followed by list without categories (i.e., non-categorized sentences) and list with categories (i.e., categorized sentences). The most preferred format for AP was list with categories, followed by story and list without categories. The most relevant type of content in HPI was temporal information and finding/condition. The most relevant type of content reported in AP was intervention and reasons and justifications. Challenges frequently mentioned include suboptimal note creation interfaces and bloated notes, and the most common suggestions for improvements are related to note entry facilitators and organizational improvements. CONCLUSION: Physicians' input is extremely valuable to inform improvements to EHRs. More effective clinical documentation systems should include less intrusive, more intuitive and automated user interfaces for note creation, smarter autopoluation functionality and linkage between note content and data from other parts of the record.

Formal representation of patients' care context data: the path to improving the electronic health record.

OBJECTIVE: To develop a collection of concept-relationship-concept tuples to formally represent patients' care context data to inform electronic health record (EHR) development. MATERIALS AND METHODS: We reviewed semantic relationships reported in the literature and developed a manual annotation schema. We used the initial schema to annotate sentences extracted from narrative note sections of cardiology, urology, and ear, nose, and throat (ENT) notes. We audio recorded ENT visits and annotated their parsed transcripts. We combined the results of each annotation into a consolidated set of concept-relationship-concept tuples. We then compared the tuples used within and across the multiple data sources. RESULTS: We annotated a total of 626 sentences. Starting with 8 relationships from the literature, we annotated 182 sentences from 8 inpatient consult notes (initial set of tuples = 43). Next, we annotated 232 sentences from 10 outpatient visit notes (enhanced set of tuples = 75). Then, we annotated 212 sentences from transcripts of 5 outpatient visits (final set of tuples = 82). The tuples from the visit transcripts covered 103 (74%) concepts documented in the notes of their respective visits. There were 20 (24%) tuples used across all data sources, 10 (12%) used only in inpatient notes, 15 (18%) used only in visit notes, and 7 (9%) used only in the visit transcripts. CONCLUSIONS: We produced a robust set of 82 tuples useful to represent patients' care context data. We propose several applications of our tuples to improve EHR navigation, data entry, learning health systems, and decision support.

The anatomy of clinical documentation: an assessment and classification of narrative note sections format and content.

Introduction. We systematically analyzed the most commonly used narrative note formats and content found in primary and specialty care visit notes to inform future research and electronic health record (EHR) development. Methods. We extracted data from the history of present illness (HPI) and impression and plan (IP) sections of 80 primary and specialty care visit notes. Two authors iteratively classified the format of the sections and compared the size of each section and the overall note size between primary and specialty care notes. We then annotated the content of these sections to develop a taxonomy of types of data communicated in the narrative note sections. Results. Both HPI and IP were significantly longer in primary care when compared to specialty care notes (HPI: n = 187 words, SD[130] vs. n = 119 words, SD [53]; p = 0.004 / IP: n = 270 words, SD [145] vs. n = 170 words, SD [101]; p < 0.001). Although we did not find a significant difference in the overall note size between the two groups, the proportion of HPI and IP content in relation to the total note size was significantly higher in primary care notes (40%, SD [13] vs. 28%, SD [11]; p < 0.001). We identified five combinations of format of HPI + IP sections respectively: (A) story + list with categories; (B) story + story; (C) list without categories + list with categories; (D) list with categories + list with categories; and (E) list with categories + story. HPI and IP content was significantly smaller in combination C compared to combination A (-172 words, [95% Conf. -326, -17.89]; p = 0.02). We identified seven taxa representing 45 different types of data: finding/condition documented (n = 14), intervention documented (n = 9), general descriptions and definitions (n = 7), temporal information (n = 6), reasons and justifications (n = 4), participants and settings (n = 4), and clinical documentation (n = 1). Conclusion. We identified commonly used narrative note section formats and developed a taxonomy of narrative note content to help researchers to tailor their efforts and design more efficient clinical documentation systems.

Using clinical reasoning ontologies to make smarter clinical decision support systems: a systematic review and data synthesis.

OBJECTIVE: The study sought to describe the literature describing clinical reasoning ontology (CRO)-based clinical decision support systems (CDSSs) and identify and classify the medical knowledge and reasoning concepts and their properties within these ontologies to guide future research. METHODS: MEDLINE, Scopus, and Google Scholar were searched through January 30, 2019, for studies describing CRO-based CDSSs. Articles that explored the development or application of CROs or terminology were selected. Eligible articles were assessed for quality features of both CDSSs and CROs to determine the current practices. We then compiled concepts and properties used within the articles. RESULTS: We included 38 CRO-based CDSSs for the analysis. Diversity of the purpose and scope of their ontologies was seen, with a variety of knowledge sources were used for ontology development. We found 126 unique medical knowledge concepts, 38 unique reasoning concepts, and 240 unique properties (137 relationships and 103 attributes). Although there is a great diversity among the terms used across CROs, there is a significant overlap based on their descriptions. Only 5 studies described high quality assessment. CONCLUSION: We identified current practices used in CRO development and provided lists of medical knowledge concepts, reasoning concepts, and properties (relationships and attributes) used by CRO-based CDSSs. CRO developers reason that the inclusion of concepts used by clinicians' during medical decision making has the potential to improve CDSS performance. However, at present, few CROs have been used for CDSSs, and high-quality studies describing CROs are sparse. Further research is required in developing high-quality CDSSs based on CROs.

Clinical decision support alert malfunctions: analysis and empirically derived taxonomy.

Objective: To develop an empirically derived taxonomy of clinical decision support (CDS) alert malfunctions. Materials and Methods: We identified CDS alert malfunctions using a mix of qualitative and quantitative methods: (1) site visits with interviews of chief medical informatics officers, CDS developers, clinical leaders, and CDS end users; (2) surveys of chief medical informatics officers; (3) analysis of CDS firing rates; and (4) analysis of CDS overrides. We used a multi-round, manual, iterative card sort to develop a multi-axial, empirically derived taxonomy of CDS malfunctions. Results: We analyzed 68 CDS alert malfunction cases from 14 sites across the United States with diverse electronic health record systems. Four primary axes emerged: the cause of the malfunction, its mode of discovery, when it began, and how it affected rule firing. Build errors, conceptualization errors, and the introduction of new concepts or terms were the most frequent causes. User reports were the predominant mode of discovery. Many malfunctions within our database caused rules to fire for patients for whom they should not have (false positives), but the reverse (false negatives) was also common. Discussion: Across organizations and electronic health record systems, similar malfunction patterns recurred. Challenges included updates to code sets and values, software issues at the time of system upgrades, difficulties with migration of CDS content between computing environments, and the challenge of correctly conceptualizing and building CDS. Conclusion: CDS alert malfunctions are frequent. The empirically derived taxonomy formalizes the common recurring issues that cause these malfunctions, helping CDS developers anticipate and prevent CDS malfunctions before they occur or detect and resolve them expediently.