Order Entry Protocols Are an Amenable Target for Workflow Automation

Tudor, James; Klochko, Chad; Patel, Milind; Siegal, Daniel

doi:10.1016/j.jacr.2018.02.003

Cited by 10 publications

(5 citation statements)

References 2 publications

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“…Reasons for not achieving those goals include selection of processes that are not suitable for automation, such as those listed in Table 3 , selection of technology not suitable for the workflow, a lack of suitable available technology, or use of available technology that is improperly applied. In addition, insufficient understanding and response to organizational aspects of implementation can lead to unmet automation goals, 9 such as stakeholder concerns about losing the ability to interact with or access decision-making authority 22 62 63 64 65 66 67 and worries related to the impact on quality, safety, privacy, and security. 48 68 69 70 71 72 73 74 …”

Section: Workflow Automation Approaches Across Industriesmentioning

confidence: 99%

“…In particular, the decision to automate a particular workflow and to what degree is based on several factors, summarized in Table 3 . 63 75 76 In contrast, the literature revealed characteristics associated with workflows that are more difficult to automate, summarized in Table 3 . 8 12 18 27 39 54 62 77 78 79 Careful selection with these characteristics in mind can help to prevent some of the documented problems that automation can cause 59 across industries, including health care.…”

Section: Workflow Automation Approaches Across Industriesmentioning

confidence: 99%

“…Identifying workflows and determining suitability for and degree of automation are typically based on analysis of three aspects. 63 75 80 The first relates to people and the degree of human intervention required. The second aspect relates to process—the characteristics of the tasks within a workflow and repetition of workflows with clear roles and responsibilities.…”

Section: Workflow Automation Approaches Across Industriesmentioning

confidence: 99%

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Identifying Opportunities for Workflow Automation in Health Care: Lessons Learned from Other Industries

2021

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Background Workflow automation, which involves identifying sequences of tasks that can be streamlined by using technology and modern computing, offers opportunities to address the United States health care system's challenges with quality, safety, and efficiency. Other industries have successfully implemented workflow automation to address these concerns, and lessons learned from those experiences may inform its application in health care. Objective Our aim was to identify and synthesize (1) current approaches in workflow automation across industries, (2) opportunities for applying workflow automation in health care, and (3) considerations for designing and implementing workflow automation that may be relevant to health care. Methods We conducted a targeted review of peer-reviewed and gray literature on automation approaches. We identified relevant databases and terms to conduct the searches across sources and reviewed abstracts to identify 123 relevant articles across 11 disciplines. Results Workflow automation is used across industries such as finance, manufacturing, and travel to increase efficiency, productivity, and quality. We found automation ranged from low to full automation, and this variation was associated with task and technology characteristics. The level of automation is linked to how well a task is defined, whether a task is repetitive, the degree of human intervention and decision-making required, and the sophistication of available technology. We found that identifying automation goals and assessing whether those goals were reached was critical, and ongoing monitoring and improvement would help to ensure successful automation. Conclusion Use of workflow automation in other industries can inform automating health care workflows by considering the critical role of people, process, and technology in design, testing, implementation, use, and ongoing monitoring of automated workflows. Insights gained from other industries will inform an interdisciplinary effort by the Office of the National Coordinator for Health Information Technology to outline priorities for advancing health care workflow automation.

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Section: Workflow Automation Approaches Across Industriesmentioning

confidence: 99%

Section: Workflow Automation Approaches Across Industriesmentioning

confidence: 99%

Section: Workflow Automation Approaches Across Industriesmentioning

confidence: 99%

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Identifying Opportunities for Workflow Automation in Health Care: Lessons Learned from Other Industries

2021

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“…Although protocoling is recognized as an amendable target for workflow automation through the application of AI [15], and efforts are undertaken to automate protocoling (see Section "Literature Review"), published results have several limitations that diminish workflow benefits. These limitations are outlined, analyzed, and quantified in this work.…”

Section: Introductionmentioning

confidence: 99%

Automated Protocoling for MRI Exams—Challenges and Solutions

et al. 2022

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Automated protocoling for MRI examinations is an amendable target for workflow automation with artificial intelligence. However, there are still challenges to overcome for a successful and robust approach. These challenges are outlined and analyzed in this work. Through a literature review, we analyzed limitations of currently published approaches for automated protocoling. Then, we assessed these limitations quantitatively based on data from a private radiology practice. For this, we assessed the information content provided by the clinical indication by computing the overlap coefficients for the sets of ICD-10-coded admitting diagnoses of different MRI protocols. Additionally, we assessed the heterogeneity of protocol trees from three different MRI scanners based on the overlap coefficient, on MRI protocol and sequence level. Additionally, we applied sequence name standardization to demonstrate its effect on the heterogeneity assessment, i.e., the overlap coefficient, of different protocol trees. The overlap coefficient for the set of ICD-10-coded admitting diagnoses for different protocols ranges from 0.14 to 0.56 for brain/head MRI exams and 0.04 to 0.57 for spine exams. The overlap coefficient across the set of sequences used at two different scanners increases when applying sequence name standardization (from 0.81/0.86 to 0.93). Automated protocoling for MRI examinations has the potential to reduce the workload for radiologists. However, an automated protocoling approach cannot be solely based on admitting diagnosis as it does not provide sufficient information. Moreover, sequence name standardization increases the overlap coefficient across the set of sequences used at different scanners and therefore facilitates transfer learning.

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“…Furthermore, the overall change rate for radiologists and residents for the most commonly ordered CT and MRI studies is similarly very low. These factors have led experts to conclude that order entry protocol selection is amenable for automation [17].…”

Section: Introductionmentioning

confidence: 99%

Deep-learning based Tools for Automated Protocol Definition of Advanced Diagnostic Imaging Exams

Nencka¹,

Sherafati²,

Goebel³

et al. 2021

Preprint

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This study evaluates the effectiveness and impact of automated order-based protocol assignment for magnetic resonance imaging (MRI) exams using natural language processing (NLP) and deep learning (DL).Methods: NLP tools were applied to retrospectively process orders from over 116,000 MRI exams with 200 unique sub-specialized protocols ("Local" protocol class). Separate DL models were trained on 70% of the processed data for "Local" protocols as well as 93 American College of Radiology ("ACR") protocols and 48 "General" protocols. The DL Models were assessed in an "autoprotocoling (AP)" inference mode which returns the top recommendation and in a "clinical decision support (CDS)" inference mode which returns up to 10 protocols for radiologist review. The accuracy of each protocol recommendation was computed and analyzed based on the difference between the normalized output score of the corresponding neural net for the top two recommendations.Results: The top predicted protocol in AP mode was correct for 82.8%, 73.8%, and 69.3% of the test cases for "General", "ACR", and "Local" protocol classes, respectively. Higher levels of accuracy over 96% were obtained for all protocol classes in CDS mode. However, at current validation performance levels, the proposed models offer modest, positive, financial impact on large-scale imaging networks.Conclusions: DL-based protocol automation is feasible and can be tuned to route substantial fractions of exams for auto-protocoling, with higher accuracy with more general protocols. Economic analyses of the tested algorithms indicate that improved algorithm performance is required to yield a practical exam auto-protocoling tool for sub-specialized imaging exams.

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Order Entry Protocols Are an Amenable Target for Workflow Automation

Cited by 10 publications

References 2 publications

Identifying Opportunities for Workflow Automation in Health Care: Lessons Learned from Other Industries

Identifying Opportunities for Workflow Automation in Health Care: Lessons Learned from Other Industries

Automated Protocoling for MRI Exams—Challenges and Solutions

Deep-learning based Tools for Automated Protocol Definition of Advanced Diagnostic Imaging Exams

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