The clinician in the Driver’s Seat: Part 1 – A drag/drop user-composable electronic health record platform

Senathirajah, Yalini; Kaufman, David R.; Bakken, Suzanne

doi:10.1016/j.jbi.2014.09.002

Cited by 21 publications

(16 citation statements)

References 34 publications

(37 reference statements)

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“…Recent advances in usability have led to the development of frameworks, new methods, and robust assessment tools that can be used to more precisely delineate the source of the problems associated with an interface [41,42]. In addition, novel approaches to design have provided new EHR approaches that better support flexibility and expressivity [43]. We anticipate that NLP-enabled data entry tools will form an important part of the solution space and will serve to enhance the user’s experience.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2016

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BackgroundThe 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.ObjectiveThe 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.MethodsThis 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.ResultsA 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.ConclusionsIn 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 ...

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Section: Discussionmentioning

confidence: 99%

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

et al. 2016

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“…We have made a start, reporting on various other aspects of user studies including efficiency and timesaving [4, 5]. Larger-scale studies of groups of users can also elucidate patterns of use, evolutionary development, and clusters of communication.…”

Section: Future Workmentioning

confidence: 99%

“…The purpose of this paper is to describe clinician users’ intelligent uses of space in a user-composable EHR platform (MedWISE)[2, 7] during completion of real patient case studies in a laboratory setting.…”

Section: Introductionmentioning

confidence: 99%

“…The ability to gather and view elements together on one screen layout means that the user need not remember them between screens. Further, a greater proportion of required elements are external during the individual user’s diagnostic reasoning process, facilitating distributed cognition [18–20]. …”

Section: Introductionmentioning

confidence: 99%

“…External materials, e.g., RSS feeds from the medical literature, can also be included. The appendix and [18] have explanatory screenshots and detailed descriptions of the full set of features and functions.…”

Section: Introductionmentioning

confidence: 99%

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The clinician in the driver’s seat: Part 2 – Intelligent uses of space in a drag/drop user-composable electronic health record

Senathirajah

Kaufman

Bakken

2014

Journal of Biomedical Informatics

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User-composable approaches provide clinicians with the control to design and assemble information elements on screen via drag/drop. They hold considerable promise for enhancing the electronic-health-records (EHRs) user experience. We previously described this novel approach to EHR design and our illustrative system, MedWISE. The purpose of this paper is to describe clinician users’ intelligent uses of space during completion of real patient case studies in a laboratory setting using MedWISE. Thirteen clinicians at a quaternary academic medical center used the system to review four real patient cases. We analyzed clinician utterances, behaviors, screen layouts (i.e., interface designs), and their perceptions associated with completing patient case studies. Clinicians effectively used the system to review all cases. Two coding schemata pertaining to human-computer interaction and diagnostic reasoning were used to analyze the data. Users adopted three main interaction strategies: rapidly gathering items on screen and reviewing (‘opportunistic selection’ approach); creating highly structured screens (‘structured’ approach); and interacting with small groups of items in sequence as their case review progressed (‘dynamic stage’ approach). They also used spatial arrangement in ways predicted by theory and research on workplace spatial arrangement. This includes assignment of screen regions for particular purposes (24% of spatial codes), juxtaposition to facilitate calculation or other cognitive tasks (‘epistemic action’), and grouping elements with common meanings or relevance to the diagnostic facets of the case (20.3%). A left-to-right progression of orienting materials, data, and action items or reflection space was a commonly observed pattern. Widget selection was based on user assessment of what information was useful or relevant. We developed and tested an illustrative system that gives clinicians greater control of the EHR, and demonstrated its feasibility for case review by typical clinicians. Producing the simplifying inventions, such as user-composable platforms that shift control to the user, may serve to promote productive EHR use and enhance its value as an instrument of patient care.

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Patient-Centered Care Systems

Bakken

Dykes

Rossetti

et al. 2021

Biomedical Informatics

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The clinician in the Driver’s Seat: Part 1 – A drag/drop user-composable electronic health record platform

Cited by 21 publications

References 34 publications

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

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

The clinician in the driver’s seat: Part 2 – Intelligent uses of space in a drag/drop user-composable electronic health record

Patient-Centered Care Systems

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