|
1
|
Evans H, Hero E, Minhas F, Wahab N, Dodd K, Sahota H, Ganguly R, Robinson A, Neerudu M, Blessing E, Borkar P, Snead D. Standardized Clinical Annotation of Digital Histopathology Slides at the Point of Diagnosis. Mod Pathol 2023; 36:100297. [PMID: 37544362 DOI: 10.1016/j.modpat.2023.100297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/26/2023] [Accepted: 07/25/2023] [Indexed: 08/08/2023]
Abstract
As digital pathology replaces conventional glass slide microscopy as a means of reporting cellular pathology samples, the annotation of digital pathology whole slide images is rapidly becoming part of a pathologist's regular practice. Currently, there is no recognizable organization of these annotations, and as a result, pathologists adopt an arbitrary approach to defining regions of interest, leading to irregularity and inconsistency and limiting the downstream efficient use of this valuable effort. In this study, we propose a Standardized Annotation Reporting Style for digital whole slide images. We formed a list of 167 commonly annotated entities (under 12 specialty subcategories) based on review of Royal College of Pathologists and College of American Pathologists documents, feedback from reporting pathologists in our NHS department, and experience in developing annotation dictionaries for PathLAKE research projects. Each entity was assigned a suitable annotation shape, SNOMED CT (SNOMED International) code, and unique color. Additionally, as an example of how the approach could be expanded to specific tumor types, all lung tumors in the fifth World Health Organization of thoracic tumors 2021 were included. The proposed standardization of annotations increases their utility, making them identifiable at low power and searchable across and between cases. This would aid pathologists reporting and reviewing cases and enable annotations to be used for research. This structured approach could serve as the basis for an industry standard and be easily adopted to ensure maximum functionality and efficiency in the use of annotations made during routine clinical examination of digital slides.
Collapse
Affiliation(s)
- Harriet Evans
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, United Kingdom; Warwick Medical School, University of Warwick, Coventry, United Kingdom.
| | - Emily Hero
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, United Kingdom; Histopathology Department, University Hospitals of Leicester NHS Trust, Leicester, United Kingdom
| | - Fayyaz Minhas
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Noorul Wahab
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Katherine Dodd
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, United Kingdom
| | - Harvir Sahota
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, United Kingdom; Department of Psychiatry, Coventry and Warwickshire Partnership Trust, Coventry, United Kingdom
| | - Ratnadeep Ganguly
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, United Kingdom
| | - Andrew Robinson
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, United Kingdom
| | - Manjuvani Neerudu
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, United Kingdom
| | - Elaine Blessing
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, United Kingdom
| | - Pallavi Borkar
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, United Kingdom
| | - David Snead
- Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, United Kingdom; Warwick Medical School, University of Warwick, Coventry, United Kingdom
| |
Collapse
|
|
2
|
Abstract
Laboratory clinical decision support (CDS) typically relies on data from the electronic health record (EHR). The implementation of a sustainable, effective laboratory CDS program requires a commitment to standardization and harmonization of key EHR data elements that are the foundation of laboratory CDS. The direct use of artificial intelligence algorithms in CDS programs will be limited unless key elements of the EHR are structured. The identification, curation, maintenance, and preprocessing steps necessary to implement robust laboratory-based algorithms must account for the heterogeneity of data present in a typical EHR.
Collapse
|
|
3
|
The International Collaboration on Cancer Reporting (ICCR): 10 Years Progress in the Development of Cancer Pathology Datasets. Int J Gynecol Pathol 2022; 41:S3-S7. [DOI: 10.1097/pgp.0000000000000899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
|
4
|
Cassim N, Ramdin N, Moodly S, Glencross DK. Cost of running a full-service receiving office at a centralised testing laboratory in South Africa. Afr J Lab Med 2022; 11:1504. [PMID: 35937761 PMCID: PMC9350462 DOI: 10.4102/ajlm.v11i1.1504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 04/19/2022] [Indexed: 11/04/2022] Open
Abstract
Background The National Health Laboratory Service operates a platform of 226 laboratories across South Africa, ranging from highly sophisticated central academic hospitals to distant rural hospitals. The core function of the National Health Laboratory Service is to provide cost-effective and efficient health laboratory services in the public healthcare sector. Objective This study aimed to assess the comprehensive cost of running a full-service receiving office (RO) at the Charlotte Maxeke Johannesburg Academic Hospital (CMJAH) laboratory. Methods Top-down costing was conducted, with the cost per registration as the main outcome of interest. The annual equivalent costs (AEC) for the following categories were determined: registration materials, collection materials, staffing, laboratory equipment, building and electricity, and other operating costs. Data for the period from 01 April 2019 to 31 March 2020 were included in the analyses. Results The AEC was $1 657 483.00 United States dollars (USD) and the cost per registration was $0.766 USD. Staff contributed 59.9% of the total cost per registration, while collection materials contributed 21.4%. The RO core staff (data clerks) contributed 50.8% of the total staffing costs, while messengers and drivers contributed 31.2%. The introduction of order entry at the CMJAH and other primary healthcare facilities reduced the total AEC by 20%. A single order entry application would serve both the CMJAH and primary healthcare facilities - hence we would prefer to not refer to order entries. Conclusion Providing a comprehensive RO service costs approximately $1.00 USD per registration. The implementation of order entry at the CMJAH would reduce AECs substantially and improve efficiency.
Collapse
Affiliation(s)
- Naseem Cassim
- Department of Molecular Medicine and Haematology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- National Health Laboratory Service, Johannesburg, South Africa
| | - Neeshan Ramdin
- Department of Molecular Medicine and Haematology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Sadhaseevan Moodly
- Department of Molecular Medicine and Haematology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- National Health Laboratory Service, Johannesburg, South Africa
| | - Deborah K. Glencross
- Department of Molecular Medicine and Haematology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- National Health Laboratory Service, Johannesburg, South Africa
| |
Collapse
|
|
5
|
Thierauf JC, Farahani AA, Indave BI, Bard AZ, White VA, Smith CR, Marble H, Hyrcza MD, Chan JKC, Bishop J, Shi Q, Ely K, Agaimy A, Martinez-Lage M, Nose V, Rivera M, Nardi V, Dias-Santagata D, Garg S, Sadow P, Le LP, Faquin W, Ritterhouse LL, Cree IA, Iafrate AJ, Lennerz JK. Diagnostic Value of MAML2 Rearrangements in Mucoepidermoid Carcinoma. Int J Mol Sci 2022; 23:4322. [PMID: 35457138 PMCID: PMC9026998 DOI: 10.3390/ijms23084322] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/10/2022] [Accepted: 04/11/2022] [Indexed: 02/04/2023] Open
Abstract
Mucoepidermoid carcinoma (MEC) is often seen in salivary glands and can harbor MAML2 translocations (MAML2+). The translocation status has diagnostic utility as an objective confirmation of the MEC diagnosis, for example, when distinction from the more aggressive adenosquamous carcinoma (ASC) is not straightforward. To assess the diagnostic relevance of MAML2, we examined our 5-year experience in prospective testing of 8106 solid tumors using RNA-seq panel testing in combinations with a two-round Delphi-based scenario survey. The prevalence of MAML2+ across all tumors was 0.28% (n = 23/8106) and the majority of MAML2+ cases were found in head and neck tumors (78.3%), where the overall prevalence was 5.9% (n = 18/307). The sensitivity of MAML2 for MEC was 60% and most cases (80%) were submitted for diagnostic confirmation; in 24% of cases, the MAML2 results changed the working diagnosis. An independent survey of 15 experts showed relative importance indexes of 0.8 and 0.65 for "confirmatory MAML2 testing" in suspected MEC and ASC, respectively. Real-world evidence confirmed that the added value of MAML2 is a composite of an imperfect confirmation test for MEC and a highly specific exclusion tool for the diagnosis of ASC. Real-world evidence can help move a rare molecular-genetic biomarker from an emerging tool to the clinic.
Collapse
Affiliation(s)
- Julia C. Thierauf
- Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (J.C.T.); (A.A.F.); (A.Z.B.); (H.M.); (M.R.); (V.N.); (D.D.-S.); (S.G.); (L.P.L.); (L.L.R.); (A.J.I.)
- Department of Otorhinolaryngology, Head and Neck Surgery, Heidelberg University Hospital and Research Group Molecular Mechanisms of Head and Neck Tumors, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Alex A. Farahani
- Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (J.C.T.); (A.A.F.); (A.Z.B.); (H.M.); (M.R.); (V.N.); (D.D.-S.); (S.G.); (L.P.L.); (L.L.R.); (A.J.I.)
| | - B. Iciar Indave
- International Agency for Research on Cancer (IARC), World Health Organization, 69372 Lyon, France; (B.I.I.); (V.A.W.); (I.A.C.)
| | - Adam Z. Bard
- Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (J.C.T.); (A.A.F.); (A.Z.B.); (H.M.); (M.R.); (V.N.); (D.D.-S.); (S.G.); (L.P.L.); (L.L.R.); (A.J.I.)
| | - Valerie A. White
- International Agency for Research on Cancer (IARC), World Health Organization, 69372 Lyon, France; (B.I.I.); (V.A.W.); (I.A.C.)
| | - Cameron R. Smith
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (C.R.S.); (M.M.-L.); (V.N.); (P.S.); (W.F.)
| | - Hetal Marble
- Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (J.C.T.); (A.A.F.); (A.Z.B.); (H.M.); (M.R.); (V.N.); (D.D.-S.); (S.G.); (L.P.L.); (L.L.R.); (A.J.I.)
| | - Martin D. Hyrcza
- Department of Pathology and Laboratory Medicine, University of Calgary, Calgary, AB 2500, Canada;
| | - John K. C. Chan
- Department of Pathology, Queen Elizabeth Hospital, Kowloon, Hong Kong, China;
| | - Justin Bishop
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA;
| | - Qiuying Shi
- Department of Pathology, Emory University Hospital, Atlanta, GA 30322, USA;
| | - Kim Ely
- Department of Pathology, Vanderbilt University Medical Center, Nashville, TN 37232, USA;
| | - Abbas Agaimy
- Institute of Pathology, Friedrich Alexander University Erlangen-Nürnberg, University Hospital, 91054 Erlangen, Germany;
| | - Maria Martinez-Lage
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (C.R.S.); (M.M.-L.); (V.N.); (P.S.); (W.F.)
| | - Vania Nose
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (C.R.S.); (M.M.-L.); (V.N.); (P.S.); (W.F.)
| | - Miguel Rivera
- Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (J.C.T.); (A.A.F.); (A.Z.B.); (H.M.); (M.R.); (V.N.); (D.D.-S.); (S.G.); (L.P.L.); (L.L.R.); (A.J.I.)
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (C.R.S.); (M.M.-L.); (V.N.); (P.S.); (W.F.)
| | - Valentina Nardi
- Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (J.C.T.); (A.A.F.); (A.Z.B.); (H.M.); (M.R.); (V.N.); (D.D.-S.); (S.G.); (L.P.L.); (L.L.R.); (A.J.I.)
| | - Dora Dias-Santagata
- Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (J.C.T.); (A.A.F.); (A.Z.B.); (H.M.); (M.R.); (V.N.); (D.D.-S.); (S.G.); (L.P.L.); (L.L.R.); (A.J.I.)
| | - Salil Garg
- Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (J.C.T.); (A.A.F.); (A.Z.B.); (H.M.); (M.R.); (V.N.); (D.D.-S.); (S.G.); (L.P.L.); (L.L.R.); (A.J.I.)
| | - Peter Sadow
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (C.R.S.); (M.M.-L.); (V.N.); (P.S.); (W.F.)
| | - Long P. Le
- Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (J.C.T.); (A.A.F.); (A.Z.B.); (H.M.); (M.R.); (V.N.); (D.D.-S.); (S.G.); (L.P.L.); (L.L.R.); (A.J.I.)
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (C.R.S.); (M.M.-L.); (V.N.); (P.S.); (W.F.)
| | - William Faquin
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (C.R.S.); (M.M.-L.); (V.N.); (P.S.); (W.F.)
| | - Lauren L. Ritterhouse
- Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (J.C.T.); (A.A.F.); (A.Z.B.); (H.M.); (M.R.); (V.N.); (D.D.-S.); (S.G.); (L.P.L.); (L.L.R.); (A.J.I.)
| | - Ian A. Cree
- International Agency for Research on Cancer (IARC), World Health Organization, 69372 Lyon, France; (B.I.I.); (V.A.W.); (I.A.C.)
| | - A. John Iafrate
- Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (J.C.T.); (A.A.F.); (A.Z.B.); (H.M.); (M.R.); (V.N.); (D.D.-S.); (S.G.); (L.P.L.); (L.L.R.); (A.J.I.)
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (C.R.S.); (M.M.-L.); (V.N.); (P.S.); (W.F.)
| | - Jochen K. Lennerz
- Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (J.C.T.); (A.A.F.); (A.Z.B.); (H.M.); (M.R.); (V.N.); (D.D.-S.); (S.G.); (L.P.L.); (L.L.R.); (A.J.I.)
| |
Collapse
|
|
6
|
Silva MC, Eugénio P, Faria D, Pesquita C. Ontologies and Knowledge Graphs in Oncology Research. Cancers (Basel) 2022; 14:cancers14081906. [PMID: 35454813 PMCID: PMC9029532 DOI: 10.3390/cancers14081906] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 03/25/2022] [Accepted: 04/07/2022] [Indexed: 11/16/2022] Open
Abstract
The complexity of cancer research stems from leaning on several biomedical disciplines for relevant sources of data, many of which are complex in their own right. A holistic view of cancer—which is critical for precision medicine approaches—hinges on integrating a variety of heterogeneous data sources under a cohesive knowledge model, a role which biomedical ontologies can fill. This study reviews the application of ontologies and knowledge graphs in cancer research. In total, our review encompasses 141 published works, which we categorized under 14 hierarchical categories according to their usage of ontologies and knowledge graphs. We also review the most commonly used ontologies and newly developed ones. Our review highlights the growing traction of ontologies in biomedical research in general, and cancer research in particular. Ontologies enable data accessibility, interoperability and integration, support data analysis, facilitate data interpretation and data mining, and more recently, with the emergence of the knowledge graph paradigm, support the application of Artificial Intelligence methods to unlock new knowledge from a holistic view of the available large volumes of heterogeneous data.
Collapse
|
|
7
|
Hwang JE, Park HA, Shin SY. Mapping the Korean National Health Checkup Questionnaire to Standard Terminologies. Healthc Inform Res 2021; 27:287-297. [PMID: 34788909 PMCID: PMC8654331 DOI: 10.4258/hir.2021.27.4.287] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 08/11/2021] [Indexed: 12/02/2022] Open
Abstract
Objectives An increasing emphasis has been placed on the integration of clinical data and patient-generated health data (PGHD), which are generated outside of hospitals. This study explored the possibility of using standard terminologies to represent PGHD for data integration. Methods We chose the 2020 general health checkup questionnaire of the Korean Health Screening Program as a resource. We divided every component of the questionnaire into entities and values, which were mapped to standard terminologies—Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) version 2020-07-31 and Logical Observation Identifiers Names and Codes (LOINC) version 2.68. Results Eighty-nine items were derived from the 17 questions of the 2020 health examination questionnaire, of which 76 (85.4%) were mapped to standard terms. Fifty-two items were mapped to SNOMED CT and 24 items were mapped to LOINC. Among the items mapped to SNOMED CT, 35 were mapped to pre-coordinated expressions and 17 to post-coordinated expressions. Forty items had one-to-one relationships, and 17 items had one-to-many relationships. Conclusions We achieved a high mapping rate (85.4%) by using both SNOMED CT and LOINC. However, we noticed some issues while mapping the Korean general health checkup questionnaire (i.e., lack of explanations, vague questions, and overly narrow concepts). In particular, items combining two or more concepts into a single item were not appropriate for mapping using standard terminologies. Although it is not the case that all items need to be expressed in standard terminology, essential items should be presented in a way suitable for mapping to standard terminology by revising the questionnaire in the future.
Collapse
Affiliation(s)
- Ji Eun Hwang
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea.,Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea
| | - Hyeoun-Ae Park
- College of Nursing, Seoul National University, Seoul, Korea
| | - Soo-Yong Shin
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea.,Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea.,Center for Research Resource Standardization, Samsung Medical Center, Seoul, Korea
| |
Collapse
|
|
8
|
Srigley JR, Judge M, Helliwell T, Birdsong GG, Ellis DW. The International Collaboration on Cancer Reporting (ICCR): a decade of progress towards global pathology standardisation and data interoperability. Histopathology 2021; 79:897-901. [PMID: 34783048 DOI: 10.1111/his.14431] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 06/13/2021] [Indexed: 12/01/2022]
Affiliation(s)
- John R Srigley
- Trillium Health Partners and Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Meagan Judge
- International Collaboration on Cancer Reporting, Sydney, Australia
| | - Tim Helliwell
- Department of Cellular Pathology, University of Liverpool, Liverpool, UK
| | - George G Birdsong
- Emory University School of Medicine at Grady Hospital, Atlanta, GA, USA
| | | |
Collapse
|
|
9
|
Abstract
Lack of interoperability is one of the greatest challenges facing healthcare informatics. Recent interoperability efforts have focused primarily on data transmission and generally ignore data capture standardization. Structured Data Capture (SDC) is an open-source technical framework that enables the capture and exchange of standardized and structured data in interoperable data entry forms (DEFs) at the point of care. Some of SDC’s primary use cases concern complex oncology data such as anatomic pathology, biomarkers, and clinical oncology data collection and reporting. Its interoperability goals are the preservation of semantic, contextual, and structural integrity of the captured data throughout the data’s lifespan. SDC documents are written in eXtensible Markup Language (XML) and are therefore computer readable, yet technology agnostic—SDC can be implemented by any EHR vendor or registry. Any SDC-capable system can render an SDC XML file into a DEF, receive and parse an SDC transmission, and regenerate the original SDC form as a DEF or synoptic report with the response data intact. SDC is therefore able to facilitate interoperable data capture and exchange for patient care, clinical trials, cancer surveillance and public health needs, clinical research, and computable care guidelines. The usability of SDC-captured oncology data is enhanced when the SDC data elements are mapped to standard terminologies. For example, an SDC map to Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) enables aggregation of SDC data with other related data sets and permits advanced queries and groupings on the basis of SNOMED CT concept attributes and description logic. SDC supports terminology maps using separate map files or as terminology codes embedded in an SDC document.
Collapse
Affiliation(s)
- Alexander K Goel
- Cancer Protocols and Data Standards, College of American Pathologists, Northfield, IL
| | - Walter Scott Campbell
- Department of Pathology/Microbiology, University of Nebraska Medical Center, Omaha, NE
| | - Richard Moldwin
- Cancer Protocols and Data Standards, College of American Pathologists, Northfield, IL
| |
Collapse
|
|
10
|
Yeh CY, Peng SJ, Yang HC, Islam M, Poly TN, Hsu CY, Huff SM, Chen HC, Lin MC. Logical Observation Identifiers Names and Codes (LOINC ®) Applied to Microbiology: A National Laboratory Mapping Experience in Taiwan. Diagnostics (Basel) 2021; 11:diagnostics11091564. [PMID: 34573905 PMCID: PMC8464801 DOI: 10.3390/diagnostics11091564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 08/13/2021] [Accepted: 08/26/2021] [Indexed: 11/21/2022] Open
Abstract
Background and Objective: Logical Observation Identifiers Names and Codes (LOINC) is a universal standard for identifying laboratory tests and clinical observations. It facilitates a smooth information exchange between hospitals, locally and internationally. Although it offers immense benefits for patient care, LOINC coding is complex, resource-intensive, and requires substantial domain expertise. Our objective was to provide training and evaluate the performance of LOINC mapping of 20 pathogens from 53 hospitals participating in the National Notifiable Disease Surveillance System (NNDSS). Methods: Complete mapping codes for 20 pathogens (nine bacteria and 11 viruses) were requested from all participating hospitals to review between January 2014 and December 2016. Participating hospitals mapped those pathogens to LOINC terminology, utilizing the Regenstrief LOINC mapping assistant (RELMA) and reported to the NNDSS, beginning in January 2014. The mapping problems were identified by expert panels that classified frequently asked questionnaires (FAQs) into seven LOINC categories. Finally, proper and meaningful suggestions were provided based on the error pattern in the FAQs. A general meeting was organized if the error pattern proved to be difficult to resolve. If the experts did not conclude the local issue’s error pattern, a request was sent to the LOINC committee for resolution. Results: A total of 53 hospitals participated in our study. Of these, 26 (49.05%) used homegrown and 27 (50.95%) used outsourced LOINC mapping. Hospitals who participated in 2015 had a greater improvement in LOINC mapping than those of 2016 (26.5% vs. 3.9%). Most FAQs were related to notification principles (47%), LOINC system (42%), and LOINC property (26%) in 2014, 2015, and 2016, respectively. Conclusions: The findings of our study show that multiple stage approaches improved LOINC mapping by up to 26.5%.
Collapse
Affiliation(s)
- Chih-Yang Yeh
- Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; (C.-Y.Y.); (H.C.Y.); (M.I.); (T.N.P.)
| | - Syu-Jyun Peng
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan;
| | - Hsuan Chia Yang
- Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; (C.-Y.Y.); (H.C.Y.); (M.I.); (T.N.P.)
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 11031, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
| | - Mohaimenul Islam
- Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; (C.-Y.Y.); (H.C.Y.); (M.I.); (T.N.P.)
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 11031, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
| | - Tahmina Nasrin Poly
- Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; (C.-Y.Y.); (H.C.Y.); (M.I.); (T.N.P.)
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 11031, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
| | - Chien-Yeh Hsu
- Department of Information Management, National Taipei University of Nursing and Health Science, Taipei 11219, Taiwan;
- Master Program in Global Health and Development, Taipei Medical University, Taipei 11031, Taiwan
| | - Stanley M. Huff
- Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT 84132, USA;
- Department of Biomedical Informatics, Intermountain Healthcare, Murray, UT 84107, USA
| | - Huan-Chieh Chen
- Department of Neurosurgery, Taipei Medical University-Wan Fang Hospital, Taipei 116, Taiwan;
- Taipei Neuroscience Institute, Taipei Medical University, Taipei 11031, Taiwan
| | - Ming-Chin Lin
- Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; (C.-Y.Y.); (H.C.Y.); (M.I.); (T.N.P.)
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan;
- Taipei Neuroscience Institute, Taipei Medical University, Taipei 11031, Taiwan
- Department of Neurosurgery, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
- Correspondence:
| |
Collapse
|
|
11
|
Torous VF, Simpson RW, Balani JP, Baras AS, Berman MA, Birdsong GG, Giannico GA, Paner GP, Pettus JR, Sessions Z, Sirintrapun SJ, Srigley JR, Spencer S. College of American Pathologists Cancer Protocols: From Optimizing Cancer Patient Care to Facilitating Interoperable Reporting and Downstream Data Use. JCO Clin Cancer Inform 2021; 5:47-55. [PMID: 33439728 PMCID: PMC8140812 DOI: 10.1200/cci.20.00104] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
The College of American Pathologists Cancer Protocols have offered guidance to pathologists for standard cancer pathology reporting for more than 35 years. The adoption of computer readable versions of these protocols by electronic health record and laboratory information system (LIS) vendors has provided a mechanism for pathologists to report within their LIS workflow, in addition to enabling standardized structured data capture and reporting to downstream consumers of these data such as the cancer surveillance community. This paper reviews the history of the Cancer Protocols and electronic Cancer Checklists, outlines the current use of these critically important cancer case reporting tools, and examines future directions, including plans to help improve the integration of the Cancer Protocols into clinical, public health, research, and other workflows.
Collapse
Affiliation(s)
| | | | - Jyoti P Balani
- University of Texas Southwestern Medical Center, Dallas, TX
| | | | - Michael A Berman
- Jefferson Hospital, Allegheny Health Network, Jefferson Hills, PA
| | | | | | | | | | | | | | | | | |
Collapse
|
|
12
|
Şık AS, Aydınoğlu AU, Aydın Son Y. Assessing the readiness of Turkish health information systems for integrating genetic/genomic patient data: System architecture and available terminologies, legislative, and protection of personal data. Health Policy 2020; 125:203-212. [PMID: 33342546 DOI: 10.1016/j.healthpol.2020.12.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 11/29/2020] [Accepted: 12/05/2020] [Indexed: 02/08/2023]
Abstract
Advances in genetic/genomic research and translational studies drive the progress on molecular diagnosis, personalised treatment, and monitoring. Healthcare professionals and governments are encouraged to set administrative regulations and implement structured and interoperable representation to utilise the genetic/genomic data, which will support precision medicine approaches through Health Information Systems (HIS). Clear regulations and careful legislation are also crucial for the security and privacy of genetic/genomic test data. In this article, we present a review of the National Health Information System of Turkey (NHIS-T) about interoperable health data representation for genetic tests. We discuss the content of rules and regulations related to genetic/genomic testing and structured data representation in Turkey. A brief comparison of the Turkish "Law on the Protection of Personal Data" (LPPD) in genetic/genomic data privacy with its counterparts is presented. The final discussion about the shortcomings of Turkey is transferable to health information systems worldwide. Constructing a national reference database and IT infrastructure to enable data integration and exchange between genomic data, metadata, and health records will improve genetics studies' utility and outcomes. The critical success factors behind integration are establishing broadly accepted terminologies and government guidance. The governments should set clear a transparent policy defining the legal and ethical framework, workforce training, clinical decision-support tools, public engagement, and education concurrently.
Collapse
Affiliation(s)
- Ayhan Serkan Şık
- Department of Medical Informatics, Middle East Technical University, METU Informatics Institute, Universiteler Mahallesi, Dumlupinar Bulvari, No:1, 06800, Ankara, Turkey; Department of Management Information Systems, Ankara Medipol University, Faculty of Economics, Administrative and Social Sciences, Haci Bayram Mahallesi, Talatpasa Bulvari, No:2, Ankara, Turkey.
| | - Arsev Umur Aydınoğlu
- Department of Science and Technology Policy Studies, Middle East Technical University, Universiteler Mahallesi, Dumlupinar Bulvari, No:1, MM Building 3rd Floor No: 320, 06800, Ankara, Turkey.
| | - Yeşim Aydın Son
- Department of Medical Informatics, Middle East Technical University, METU Informatics Institute, Universiteler Mahallesi, Dumlupinar Bulvari, No:1, 06800, Ankara, Turkey.
| |
Collapse
|
|
13
|
Giannaris PS, Al-Taie Z, Kovalenko M, Thanintorn N, Kholod O, Innokenteva Y, Coberly E, Frazier S, Laziuk K, Popescu M, Shyu CR, Xu D, Hammer RD, Shin D. Artificial Intelligence-Driven Structurization of Diagnostic Information in Free-Text Pathology Reports. J Pathol Inform 2020; 11:4. [PMID: 32166042 PMCID: PMC7045509 DOI: 10.4103/jpi.jpi_30_19] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 12/18/2019] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Free-text sections of pathology reports contain the most important information from a diagnostic standpoint. However, this information is largely underutilized for computer-based analytics. The vast majority of NLP-based methods lack a capacity to accurately extract complex diagnostic entities and relationships among them as well as to provide an adequate knowledge representation for downstream data-mining applications. METHODS In this paper, we introduce a novel informatics pipeline that extends open information extraction (openIE) techniques with artificial intelligence (AI) based modeling to extract and transform complex diagnostic entities and relationships among them into Knowledge Graphs (KGs) of relational triples (RTs). RESULTS Evaluation studies have demonstrated that the pipeline's output significantly differs from a random process. The semantic similarity with original reports is high (Mean Weighted Overlap of 0.83). The precision and recall of extracted RTs based on experts' assessment were 0.925 and 0.841 respectively (P <0.0001). Inter-rater agreement was significant at 93.6% and inter-rated reliability was 81.8%. CONCLUSION The results demonstrated important properties of the pipeline such as high accuracy, minimality and adequate knowledge representation. Therefore, we conclude that the pipeline can be used in various downstream data-mining applications to assist diagnostic medicine.
Collapse
Affiliation(s)
- Pericles S. Giannaris
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Zainab Al-Taie
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Computer Science, College of Science for Women, University of Baghdad, Baghdad, Iraq
| | - Mikhail Kovalenko
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Nattapon Thanintorn
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Olha Kholod
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Yulia Innokenteva
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
| | - Emily Coberly
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Shellaine Frazier
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Katsiarina Laziuk
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Mihail Popescu
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Electrical Engineering and Computer Science, College of Engineering, University of Missouri, Columbia, MO 65211, United States
- Department of Health Management and Informatics, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Chi-Ren Shyu
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Electrical Engineering and Computer Science, College of Engineering, University of Missouri, Columbia, MO 65211, United States
| | - Dong Xu
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Electrical Engineering and Computer Science, College of Engineering, University of Missouri, Columbia, MO 65211, United States
| | - Richard D. Hammer
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Dmitriy Shin
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
- Department of Electrical Engineering and Computer Science, College of Engineering, University of Missouri, Columbia, MO 65211, United States
| |
Collapse
|
|
14
|
Stram M, Seheult J, Sinard JH, Campbell WS, Carter AB, de Baca ME, Quinn AM, Luu HS. A Survey of LOINC Code Selection Practices Among Participants of the College of American Pathologists Coagulation (CGL) and Cardiac Markers (CRT) Proficiency Testing Programs. Arch Pathol Lab Med 2019; 144:586-596. [DOI: 10.5858/arpa.2019-0276-oa] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Context.—
Biomedical terminologies such as Logical Observation Identifiers, Names, and Codes (LOINC) were developed to enable interoperability of health care data between disparate health information systems to improve patient outcomes, public health, and research activities.
Objective.—
To ascertain the utilization rate and accuracy of LOINC terminology mapping to 10 commonly ordered tests by participants of the College of American Pathologists (CAP) Proficiency Testing program.
Design.—
Questionnaires were sent to 1916 US and Canadian laboratories participating in the 2018 CAP coagulation (CGL) and/or cardiac markers (CRT) surveys requesting information on practice setting, instrument(s) and test method(s), and LOINC code selection and usage in the laboratory and electronic health records.
Results.—
Ninety of 1916 CGL and/or CRT participants (4.7%) responded to the questionnaire. Of the 275 LOINC codes reported, 54 (19.6%) were incorrect: 2 codes (5934-2 and 12345-1) (0.7%) did not exist in the LOINC database and the highest error rates were observed in the property (27 of 275, 9.8%), system (27 of 275, 9.8%), and component (22 of 275, 8.0%) LOINC axes. Errors in LOINC code selection included selection of the incorrect component (eg, activated clotting time instead of activated partial thromboplastin time); selection of panels that can never be used to obtain an individual analyte (eg, prothrombin time panel instead of international normalized ratio); and selection of an incorrect specimen type.
Conclusions.—
These findings of real-world LOINC code implementation across a spectrum of laboratory settings should raise concern about the reliability and utility of using LOINC for clinical research or to aggregate data.
Collapse
Affiliation(s)
- Michelle Stram
- From the Department of Forensic Medicine, New York University School of Medicine, New York (Dr Stram); the Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (Dr Seheult); the Department of Pathology, Yale University School of Medicine, New Haven, Connecticut (Dr Sinard); the Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha (Dr
| | - Jansen Seheult
- From the Department of Forensic Medicine, New York University School of Medicine, New York (Dr Stram); the Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (Dr Seheult); the Department of Pathology, Yale University School of Medicine, New Haven, Connecticut (Dr Sinard); the Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha (Dr
| | - John H. Sinard
- From the Department of Forensic Medicine, New York University School of Medicine, New York (Dr Stram); the Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (Dr Seheult); the Department of Pathology, Yale University School of Medicine, New Haven, Connecticut (Dr Sinard); the Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha (Dr
| | - W. Scott Campbell
- From the Department of Forensic Medicine, New York University School of Medicine, New York (Dr Stram); the Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (Dr Seheult); the Department of Pathology, Yale University School of Medicine, New Haven, Connecticut (Dr Sinard); the Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha (Dr
| | - Alexis B. Carter
- From the Department of Forensic Medicine, New York University School of Medicine, New York (Dr Stram); the Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (Dr Seheult); the Department of Pathology, Yale University School of Medicine, New Haven, Connecticut (Dr Sinard); the Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha (Dr
| | - Monica E. de Baca
- From the Department of Forensic Medicine, New York University School of Medicine, New York (Dr Stram); the Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (Dr Seheult); the Department of Pathology, Yale University School of Medicine, New Haven, Connecticut (Dr Sinard); the Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha (Dr
| | - Andrew M. Quinn
- From the Department of Forensic Medicine, New York University School of Medicine, New York (Dr Stram); the Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (Dr Seheult); the Department of Pathology, Yale University School of Medicine, New Haven, Connecticut (Dr Sinard); the Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha (Dr
| | - Hung S. Luu
- From the Department of Forensic Medicine, New York University School of Medicine, New York (Dr Stram); the Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (Dr Seheult); the Department of Pathology, Yale University School of Medicine, New Haven, Connecticut (Dr Sinard); the Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha (Dr
| | | |
Collapse
|
|
15
|
Lyudovyk O, Weng C. SNOMEDtxt: Natural Language Generation from SNOMED Ontology. Stud Health Technol Inform 2019; 264:1263-1267. [PMID: 31438128 PMCID: PMC6852688 DOI: 10.3233/shti190429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
SNOMED Clinical Terms (SNOMED CT) defines over 70,000 diseases, including many rare ones. Meanwhile, descriptions of rare conditions are missing from online educational resources. SNOMEDtxt converts ontological concept definitions and relations contained in SNOMED CT into narrative disease descriptions using Natural Language Generation techniques. Generated text is evaluated using both computational methods and clinician and lay user feedback. User evaluations indicate that lay people prefer generated text to the original SNOMED content, find it more informative, and understand it significantly better. This method promises to improve access to clinical knowledge for patients and the medical community and to assist in ontology auditing through natural language descriptions.
Collapse
Affiliation(s)
- Olga Lyudovyk
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| |
Collapse
|
|
16
|
Stram M, Gigliotti T, Hartman D, Pitkus A, Huff SM, Riben M, Henricks WH, Farahani N, Pantanowitz L. Logical Observation Identifiers Names and Codes for Laboratorians. Arch Pathol Lab Med 2019; 144:229-239. [PMID: 31219342 DOI: 10.5858/arpa.2018-0477-ra] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
CONTEXT.— The Logical Observation Identifiers Names and Codes (LOINC) system is supposed to facilitate interoperability, and it is the federally required code for exchanging laboratory data. OBJECTIVE.— To provide an overview of LOINC, emerging issues related to its use, and areas relevant to the pathology laboratory, including the subtleties of test code selection and importance of mapping the correct codes to local test menus. DATA SOURCES.— This review is based on peer-reviewed literature, federal regulations, working group reports, the LOINC database (version 2.65), experience using LOINC in the laboratory at several large health care systems, and insight from laboratory information system vendors. CONCLUSIONS.— The current LOINC database contains more than 55 000 numeric codes specific for laboratory tests. Each record in the LOINC database includes 6 major axes/parts for the unique specification of each individual observation or measurement. Assigning LOINC codes to a laboratory's test menu should be a defined process. In some cases, LOINC can aid in distinguishing laboratory data among different information systems, whereby such benefits are not achievable by relying on the laboratory test name alone. Criticisms of LOINC include the complexity and resource-intensive process of selecting the most correct code for each laboratory test, the real-world experience that these codes are not uniformly assigned across laboratories, and that 2 tests that may have the same appropriately assigned LOINC code may not necessarily have equivalency to permit interoperability of their result data. The coding system's limitations, which subsequently reduce the potential utility of LOINC, are poorly understood outside of the laboratory.
Collapse
Affiliation(s)
- Michelle Stram
- From the Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (Drs Stram, Hartman, and Pantanowitz); the Information Services Division, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania (Mr Gigliotti); Laboratory Informaticist & Laboratory LOINC Committee member, Buffalo Grove, Illinois (Dr Pitkus); the Healthcare Transformation Lab, Department of Pathology, University of Utah, Murray (Dr Huff); the Department of Pathology, MD Anderson Cancer Center, Houston, Texas (Dr Riben); the Center for Pathology Informatics, Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, Ohio (Dr Henricks); and 3Scan, San Francisco, California (Dr Farahani)
| | - Tony Gigliotti
- From the Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (Drs Stram, Hartman, and Pantanowitz); the Information Services Division, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania (Mr Gigliotti); Laboratory Informaticist & Laboratory LOINC Committee member, Buffalo Grove, Illinois (Dr Pitkus); the Healthcare Transformation Lab, Department of Pathology, University of Utah, Murray (Dr Huff); the Department of Pathology, MD Anderson Cancer Center, Houston, Texas (Dr Riben); the Center for Pathology Informatics, Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, Ohio (Dr Henricks); and 3Scan, San Francisco, California (Dr Farahani)
| | - Douglas Hartman
- From the Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (Drs Stram, Hartman, and Pantanowitz); the Information Services Division, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania (Mr Gigliotti); Laboratory Informaticist & Laboratory LOINC Committee member, Buffalo Grove, Illinois (Dr Pitkus); the Healthcare Transformation Lab, Department of Pathology, University of Utah, Murray (Dr Huff); the Department of Pathology, MD Anderson Cancer Center, Houston, Texas (Dr Riben); the Center for Pathology Informatics, Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, Ohio (Dr Henricks); and 3Scan, San Francisco, California (Dr Farahani)
| | - Andrea Pitkus
- From the Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (Drs Stram, Hartman, and Pantanowitz); the Information Services Division, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania (Mr Gigliotti); Laboratory Informaticist & Laboratory LOINC Committee member, Buffalo Grove, Illinois (Dr Pitkus); the Healthcare Transformation Lab, Department of Pathology, University of Utah, Murray (Dr Huff); the Department of Pathology, MD Anderson Cancer Center, Houston, Texas (Dr Riben); the Center for Pathology Informatics, Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, Ohio (Dr Henricks); and 3Scan, San Francisco, California (Dr Farahani)
| | - Stanley M Huff
- From the Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (Drs Stram, Hartman, and Pantanowitz); the Information Services Division, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania (Mr Gigliotti); Laboratory Informaticist & Laboratory LOINC Committee member, Buffalo Grove, Illinois (Dr Pitkus); the Healthcare Transformation Lab, Department of Pathology, University of Utah, Murray (Dr Huff); the Department of Pathology, MD Anderson Cancer Center, Houston, Texas (Dr Riben); the Center for Pathology Informatics, Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, Ohio (Dr Henricks); and 3Scan, San Francisco, California (Dr Farahani)
| | - Michael Riben
- From the Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (Drs Stram, Hartman, and Pantanowitz); the Information Services Division, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania (Mr Gigliotti); Laboratory Informaticist & Laboratory LOINC Committee member, Buffalo Grove, Illinois (Dr Pitkus); the Healthcare Transformation Lab, Department of Pathology, University of Utah, Murray (Dr Huff); the Department of Pathology, MD Anderson Cancer Center, Houston, Texas (Dr Riben); the Center for Pathology Informatics, Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, Ohio (Dr Henricks); and 3Scan, San Francisco, California (Dr Farahani)
| | - Walter H Henricks
- From the Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (Drs Stram, Hartman, and Pantanowitz); the Information Services Division, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania (Mr Gigliotti); Laboratory Informaticist & Laboratory LOINC Committee member, Buffalo Grove, Illinois (Dr Pitkus); the Healthcare Transformation Lab, Department of Pathology, University of Utah, Murray (Dr Huff); the Department of Pathology, MD Anderson Cancer Center, Houston, Texas (Dr Riben); the Center for Pathology Informatics, Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, Ohio (Dr Henricks); and 3Scan, San Francisco, California (Dr Farahani)
| | - Navid Farahani
- From the Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (Drs Stram, Hartman, and Pantanowitz); the Information Services Division, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania (Mr Gigliotti); Laboratory Informaticist & Laboratory LOINC Committee member, Buffalo Grove, Illinois (Dr Pitkus); the Healthcare Transformation Lab, Department of Pathology, University of Utah, Murray (Dr Huff); the Department of Pathology, MD Anderson Cancer Center, Houston, Texas (Dr Riben); the Center for Pathology Informatics, Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, Ohio (Dr Henricks); and 3Scan, San Francisco, California (Dr Farahani)
| | - Liron Pantanowitz
- From the Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (Drs Stram, Hartman, and Pantanowitz); the Information Services Division, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania (Mr Gigliotti); Laboratory Informaticist & Laboratory LOINC Committee member, Buffalo Grove, Illinois (Dr Pitkus); the Healthcare Transformation Lab, Department of Pathology, University of Utah, Murray (Dr Huff); the Department of Pathology, MD Anderson Cancer Center, Houston, Texas (Dr Riben); the Center for Pathology Informatics, Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, Ohio (Dr Henricks); and 3Scan, San Francisco, California (Dr Farahani)
| |
Collapse
|
|
17
|
Campbell WS, Carter AB, Cushman-Vokoun AM, Greiner TC, Dash RC, Routbort M, de Baca ME, Campbell JR. A Model Information Management Plan for Molecular Pathology Sequence Data Using Standards: From Sequencer to Electronic Health Record. J Mol Diagn 2019; 21:408-417. [PMID: 30797065 DOI: 10.1016/j.jmoldx.2018.12.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 11/10/2018] [Accepted: 12/04/2018] [Indexed: 10/27/2022] Open
Abstract
Incorporating genetic variant data into the electronic health record (EHR) in discrete computable fashion has vexed the informatics community for years. Genetic sequence test results are typically communicated by the molecular laboratory and stored in the EHR as textual documents. Although text documents are useful for human readability and initial use, they are not conducive for data retrieval and reuse. As a result, clinicians often struggle to find historical gene sequence results on a series of oncology patients within the EHR that might influence the care of the current patient. Second, identification of patients with specific mutation results in the EHR who are now eligible for new and/or changing therapy is not easily accomplished. Third, the molecular laboratory is challenged to monitor its sequencing processes for nonrandom process variation and other quality metrics. A novel approach to address each of these issues is presented and demonstrated. The authors use standard Health Level 7 laboratory result message formats in conjunction with international standards, Systematized Nomenclature of Medicine Clinical Terms and Human Genome Variant Society nomenclature, to represent, communicate, and store discrete gene sequence data within the EHR in a scalable fashion. This information management plan enables the support of the clinician at the point of care, enhances population management, and facilitates audits for maintaining laboratory quality.
Collapse
Affiliation(s)
- Walter S Campbell
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, Nebraska.
| | - Alexis B Carter
- Department of Pathology, Children's Healthcare of Atlanta, Atlanta, Georgia
| | - Allison M Cushman-Vokoun
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, Nebraska
| | - Timothy C Greiner
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, Nebraska
| | - Rajesh C Dash
- Department of Pathology, Duke University Health System, Durham, North Carolina
| | - Mark Routbort
- Department of Hematopathology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | | | - James R Campbell
- Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Nebraska
| |
Collapse
|
|
18
|
Gansel X, Mary M, van Belkum A. Semantic data interoperability, digital medicine, and e-health in infectious disease management: a review. Eur J Clin Microbiol Infect Dis 2019; 38:1023-1034. [PMID: 30771124 DOI: 10.1007/s10096-019-03501-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 01/30/2019] [Indexed: 12/31/2022]
Abstract
Disease management requires the use of mixed languages when discussing etiology, diagnosis, treatment, and follow-up. All phases require data management, and, in the optimal case, such data are interdisciplinary and uniform and clear to all those involved. Such semantic data interoperability is one of the technical building blocks that support emerging digital medicine, e-health, and P4-medicine (predictive, preventive, personalized, and participatory). In a world where infectious diseases are on a trend to become hard-to-treat threats due to antimicrobial resistance, semantic data interoperability is part of the toolbox to fight more efficiently against those threats. In this review, we will introduce semantic data interoperability, summarize its added value, and analyze the technical foundation supporting the standardized healthcare system interoperability that will allow moving forward to e-health. We will also review current usage of those foundational standards and advocate for their uptake by all infectious disease-related actors.
Collapse
Affiliation(s)
- Xavier Gansel
- bioMérieux, Centre C. Mérieux, 5 rue de Berges, 38000, Grenoble, France.
| | - Melissa Mary
- bioMérieux, 3 route de Port Michaud, 38390, La Balme Les Grottes, France.,LITIS EA 4108, Université de Rouen Normandie, Place Emile Blondel, 76821, Mont Saint Aignan, France
| | - Alex van Belkum
- bioMérieux, 3 route de Port Michaud, 38390, La Balme Les Grottes, France
| |
Collapse
|