Difference between revisions of "Journal:Informatics-driven quality improvement in the modern histology lab"
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|journal = ''JAMIA Open'' | |journal = ''JAMIA Open'' | ||
|authors = Seifert, Robert P.; Casler, Vektra; Al Qaysi, Nada; Gothi, Shaileshbhai R.; Williams, Leah; Christensen, Patricia R.; Flax, Sherri; Chamala, Srikar | |authors = Seifert, Robert P.; Casler, Vektra; Al Qaysi, Nada; Gothi, Shaileshbhai R.; Williams, Leah; Christensen, Patricia R.; Flax, Sherri; Chamala, Srikar | ||
|affiliations = University of Florida | |affiliations = University of Florida, UF Health Medical Laboratories | ||
|contact = schamala at ufl dot edu | |contact = schamala at ufl dot edu | ||
|editors = | |editors = | ||
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==Introduction== | ==Introduction== | ||
The microscopic examination of hematoxylin and eosin (H&E) stained slides has been the cornerstone of [[Anatomical pathology|anatomic pathology]] diagnosis for over a century. Virtually every surgical specimen is processed, embedded in paraffin, cut, mounted on a slide, and stained in a [[histology]] [[laboratory]]. Anatomic pathology laboratory processes contain a multitude of technical and manual [[workflow]] steps which can be further complicated by being located offsite. Automated instruments, particularly tissue processors and stainers, have accelerated histology lab work. However, important manual tasks such as specimen grossing, tissue cassetting, embedding, cutting, mounting, and tissue examination under a microscope for [[quality control]] remain largely unautomated. If these processes are not closely monitored, the quality of patient care suffers. | |||
Most processes in a modern histology lab are captured by an integrated [[laboratory information system]] (LIS). [[Data visualization]] is a powerful tool which can illustrate workflow trends using charts, diagrams, and tables.<ref name="KFWhatIs">{{cite web |url=https://www.klipfolio.com/resources/articles/what-is-data-visualization |title=What is Data Visualization? |work=Klipfolio |accessdate=19 November 2020}}</ref><ref name="FewEenie04">{{cite web |url=http://www.perceptualedge.com/articles/ie/the_right_graph.pdf |format=PDF |title=Eenie, Meenie, Minie, Moe: Selecting the Right Graph for Your Message |author=Few, S. |work=Perceptual Edge |date=18 September 2004 |accessdate=19 November 2020}}</ref><ref name="HalwaniAReal16">{{cite journal |title=A real-time dashboard for managing pathology processes |journal=Journal of Pathology Informatics |author=Halwani, F.; Li, W.C.; Banerjee, D. et al. |volume=7 |at=24 |year=2016 |doi=10.4103/2153-3539.181768 |pmid=27217974 |pmc=PMC4872478}}</ref> The use of data visualization on captured histology data elements from the LIS grants the ability to monitor nearly all steps of histology lab operation. Careful upstream capture of workflow steps and their corresponding data elements in the LIS can help build robust data visualizations, leading to rapid identification of processes and their improvement.<ref name="HalwaniAReal16" /> This improves the accuracy and timely reporting of the final pathology diagnosis, which directly affects patient care and compensation. | |||
Our institution, the University of Florida, serves a large, roughly 1000-bed tertiary care [[hospital]] with multiple surgical subspecialties available. Diagnoses range from mundane to extraordinarily rare. Our histology laboratory also acts as an outreach lab for a number of regional clinics and is geographically separated from our main hospital and anatomic pathology offices. Our pathology department uses Beaker (Epic Systems Corporation, Verona, WI, USA) as our LIS. Beaker, as with other LIS platforms, makes use of 1D and 2D bar code reading for patient and [[Sample (material)|specimen]] identification. Labs can use these to track specimen movement in exquisite detail and can be leveraged to evaluate anatomic pathology lab performance. We found such tracking to be essential given our lab’s geographical separation from the hospital and pathologists. | |||
To our knowledge, few authors have explored data visualization and anatomic pathology workflows. Standout work from the University of Iowaref name="BlauImplem17">{{cite journal |title=Implementation of Epic Beaker Anatomic Pathology at an Academic Medical Center |journal=Journal of Pathology Informatics |author=Blau, J.L.; Wilford, J.D.; Dane, S.K. et al. |volume=8 |at=47 |year=2017 |doi=10.4103/jpi.jpi_31_17 |pmid=29387505 |pmc=PMC5760958}}</ref> and Duke University<ref name="AlbertUps19">{{cite journal |title=Ups and downs of bringing in Beaker AP LIS |journal=CAP Today |author=Albert, C. |issue=August 2019 |year=2019 |url=https://www.captodayonline.com/ups-and-downs-of-bringing-in-beaker-ap-lis/}}</ref> provided our institution with guidance during our Beaker implementation. However, those authors did not fully explore the potential of data visualization for quality improvement. Using a case-oriented format, this report demonstrates data visualization techniques, as applied to anatomic pathology workflows and may serve as a guide for others. | |||
==Encountered challenges and tailored informatics solutions== | |||
===Histotechnologist status board=== | |||
Revision as of 22:35, 2 May 2021
| Full article title | Informatics driven quality improvement in the modern histology lab |
|---|---|
| Journal | JAMIA Open |
| Author(s) | Seifert, Robert P.; Casler, Vektra; Al Qaysi, Nada; Gothi, Shaileshbhai R.; Williams, Leah; Christensen, Patricia R.; Flax, Sherri; Chamala, Srikar |
| Author affiliation(s) | University of Florida, UF Health Medical Laboratories |
| Primary contact | schamala at ufl dot edu |
| Year published | 2020 |
| Volume and issue | 3 |
| Page(s) | 4 |
| DOI | 10.1093/jamiaopen/ooaa066 |
| ISSN | 2574-2531 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://academic.oup.com/jamiaopen/article/3/4/530/6012900 |
| Download | https://academic.oup.com/jamiaopen/article-pdf/3/4/530/36625866/ooaa066.pdf (PDF) |
 |
This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed. |
Abstract
Laboratory information systems (LISs) and data visualization techniques have untapped potential in anatomic pathology laboratories. The pre-built functionalities of an LIS do not address all the needs of a modern histology laboratory. For instance, “go live” is not the end of LIS customization, only the beginning. After closely evaluating various histology lab workflows, we implemented several custom data analytics dashboards and additional LIS functionalities to monitor and address weaknesses. Herein, we present our experience with LIS and data-tracking solutions that improved trainee education, slide logistics, staffing and instrumentation lobbying, and task tracking. The latter was addressed through the creation of a novel “status board” akin to those seen in inpatient wards. These use-cases can benefit other histology laboratories.
Lay summary: Histopathology is the branch of medicine that involves the gross and microscopic examination of sampled tissue to help reach a diagnosis. Histology lab workflows incorporate a multitude of automated and manual steps that require close monitoring. Data visualization is a powerful tool that can illustrate workflow trends using a graphic representation of data. This manuscript describes data visualization and data analysis techniques leveraged to address frequent pitfalls in histology laboratory workflows. This led to multiple improvements in patient safety and quality of care. Our use-case solutions, presented here, can be adapted by other histology laboratories to overcome common laboratory challenges.
Keywords: informatics, laboratory information system, quality improvement, pathology, workflow
Introduction
The microscopic examination of hematoxylin and eosin (H&E) stained slides has been the cornerstone of anatomic pathology diagnosis for over a century. Virtually every surgical specimen is processed, embedded in paraffin, cut, mounted on a slide, and stained in a histology laboratory. Anatomic pathology laboratory processes contain a multitude of technical and manual workflow steps which can be further complicated by being located offsite. Automated instruments, particularly tissue processors and stainers, have accelerated histology lab work. However, important manual tasks such as specimen grossing, tissue cassetting, embedding, cutting, mounting, and tissue examination under a microscope for quality control remain largely unautomated. If these processes are not closely monitored, the quality of patient care suffers.
Most processes in a modern histology lab are captured by an integrated laboratory information system (LIS). Data visualization is a powerful tool which can illustrate workflow trends using charts, diagrams, and tables.[1][2][3] The use of data visualization on captured histology data elements from the LIS grants the ability to monitor nearly all steps of histology lab operation. Careful upstream capture of workflow steps and their corresponding data elements in the LIS can help build robust data visualizations, leading to rapid identification of processes and their improvement.[3] This improves the accuracy and timely reporting of the final pathology diagnosis, which directly affects patient care and compensation.
Our institution, the University of Florida, serves a large, roughly 1000-bed tertiary care hospital with multiple surgical subspecialties available. Diagnoses range from mundane to extraordinarily rare. Our histology laboratory also acts as an outreach lab for a number of regional clinics and is geographically separated from our main hospital and anatomic pathology offices. Our pathology department uses Beaker (Epic Systems Corporation, Verona, WI, USA) as our LIS. Beaker, as with other LIS platforms, makes use of 1D and 2D bar code reading for patient and specimen identification. Labs can use these to track specimen movement in exquisite detail and can be leveraged to evaluate anatomic pathology lab performance. We found such tracking to be essential given our lab’s geographical separation from the hospital and pathologists.
To our knowledge, few authors have explored data visualization and anatomic pathology workflows. Standout work from the University of Iowaref name="BlauImplem17">Blau, J.L.; Wilford, J.D.; Dane, S.K. et al. (2017). "Implementation of Epic Beaker Anatomic Pathology at an Academic Medical Center". Journal of Pathology Informatics 8: 47. doi:10.4103/jpi.jpi_31_17. PMC PMC5760958. PMID 29387505. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5760958. </ref> and Duke University[4] provided our institution with guidance during our Beaker implementation. However, those authors did not fully explore the potential of data visualization for quality improvement. Using a case-oriented format, this report demonstrates data visualization techniques, as applied to anatomic pathology workflows and may serve as a guide for others.
Encountered challenges and tailored informatics solutions
Histotechnologist status board
References
- ↑ "What is Data Visualization?". Klipfolio. https://www.klipfolio.com/resources/articles/what-is-data-visualization. Retrieved 19 November 2020.
- ↑ Few, S. (18 September 2004). "Eenie, Meenie, Minie, Moe: Selecting the Right Graph for Your Message" (PDF). Perceptual Edge. http://www.perceptualedge.com/articles/ie/the_right_graph.pdf. Retrieved 19 November 2020.
- ↑ 3.0 3.1 Halwani, F.; Li, W.C.; Banerjee, D. et al. (2016). "A real-time dashboard for managing pathology processes". Journal of Pathology Informatics 7: 24. doi:10.4103/2153-3539.181768. PMC PMC4872478. PMID 27217974. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4872478.
- ↑ Albert, C. (2019). "Ups and downs of bringing in Beaker AP LIS". CAP Today (August 2019). https://www.captodayonline.com/ups-and-downs-of-bringing-in-beaker-ap-lis/.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation and updates to spelling and grammar. In some cases important information was missing from the references, and that information was added.
