Serotonin reduction in post-acute sequelae of viral infection

Cell · 2023 · 352 citations

• Biology
• Virology
• Immunology

Real-world integration of genomic data into the electronic health record: the PennChart Genomics Initiative

Genetics in Medicine · 2020 · 58 citations

• Computer Science
• Political Science
• Data science

Technologies in genomic medicine have rapidly evolved and transformed the ability to deliver precision medicine in nearly every field of health care. As genomic medicine has advanced, the electronic health record (EHR) has simultaneously been adopted into routine practice. A recent Points to Consider Statement by the American College of Medical Genetics and Genomics (ACMG) provides a framework for the optimal integration of genomic data into the EHR.1.Grebe T.A. et al.The interface of genomic information with the electronic health record: a points to consider statement of the American College of Medical Genetics and Genomics (ACMG).10.1038/s41436-020-0841-2Genet. Med. 2020; 22: 1431-1436Google Scholar The PennChart Genomics Initiative (PGI) at the University of Pennsylvania is a multidisciplinary collaborative effort including Penn Medicine clinicians, researchers, pathologists, legal staff, and information services with input and efforts from Epic Systems Corporation (Wisconsin) and Ambry Genetics Corporation (California), a commercial genetic testing laboratory. We describe our efforts to operationalize the ACMG guidelines in the “real world” to optimize our EHR (PennChart) for the delivery of precision medicine (Supplemental Fig. 1).INTEGRATION OF UNSTRUCTURED GENETIC DATA INTO THE EHRWe have taken a two-staged approach to integrating germline and somatic genetic data into the EHR. Currently, most genetic results are reported in unstructured PDF documents. We established common procedures across all Penn Medicine’s clinical genetics services for genetic testing reports, labeling them with a common naming convention and scanning them into a specific Genetic Results document type, which we created specifically for genetic testing results. This document filters both into our Lab (standard results) and PennChart Precision Medicine Tabs. We created the latter tab as a centralized location in the EHR to enable easy access to all genetic data, ensuring that it is not overlooked amid all the other testing that happens over a patient’s lifetime. This approach has standardized the real-time integration of unstructured genetic data into the EHR. Further, it has facilitated our efforts to import legacy data, as we began scanning all genetics documents with the common naming convention several years before implementing the Precision Medicine Tab.INTEGRATION OF DISCRETE GENETIC DATA INTO THE EHRAlthough the ACMG recommends that genetic results be incorporated into patient records, at minimum, as scanned PDF files or images, it is preferable to store them in discrete, computable format to enable electronic searching, clinical decision support (CDS), and secondary use for research and operations.2.Marsolo K. Spooner S.A. Clinical genomics in the world of the electronic health record.10.1038/gim.2013.88Genet. Med. 2013; 15: 786-791Google Scholar,3.Warner J.L. Jain S.K. Levy M.A. Integrating cancer genomic data into electronic health records.10.1186/s13073-016-0371-3Genome Med. 2016; 8Google Scholar The second stage of our efforts therefore aimed to integrate structured genetic data into the EHR. The PGI has leveraged Epic’s Genomics Module to record discrete genetic variant information in Human Genome Variation Society (HGVS) nomenclature along with the notation of significance (e.g., TP53 c.743G>A [p.Arg248Gln], pathogenic); transcript, genome build, chromosome, and genomic location are also included. Pharmacogenetic results are entered as diplotypes based on PharmVar star allele definitions (e.g., DPYD *1/*2A). Content experts throughout Penn Medicine collaborated to develop standard operating procedures (SOPs) to ensure institutional consistency for both manual and automated entry of discrete results into the Genomics Module. To date, these SOPs have been developed for autosomal dominant and pharmacogenetic variants with plans to expand to other result types over time, such as cytogenetics and autosomal recessive alleles. Manual entry of discrete genetic data into the Genomics Module is currently completed by genetics providers, who spend less than five minutes per variant.Interfacing directly with genetic testing laboratories is essential to move from manual to automated entry of discrete results into the EHR. The PGI partnered with Ambry Genetics to implement computerized order entry so providers can place genetic test orders directly into PennChart for import into the Ambry portal. The return of results has followed a phased approach using a Health Level 7 (HL7) interface, first with PDF reports and then with discrete results, which are automatically imported into the Genomics Module, linked to their associated PDF documents, and accompanied by direct provider notification. We are working on expanding our HL7 capacity with other commercial genetic testing laboratories.The ACMG also recommends that updated genetic test results be clearly linked to the original report in the EHR, as the interpretation of results may evolve and result in reclassifications over time.2.Marsolo K. Spooner S.A. Clinical genomics in the world of the electronic health record.10.1038/gim.2013.88Genet. Med. 2013; 15: 786-791Google Scholar The PGI’s partnership with Ambry Genetics enables the automatic import of results both at the time of initial testing and as variant reclassification occurs. If an update occurs, a notification is sent to the ordering provider and care team for review of the amended report.LINKAGE OF GENETIC DATA TO CLINICAL DECISION SUPPORTOne of the greatest potential benefits of integrating genetic data into the EHR is the ability to link results to CDS.2.Marsolo K. Spooner S.A. Clinical genomics in the world of the electronic health record.10.1038/gim.2013.88Genet. Med. 2013; 15: 786-791Google Scholar,4.Hoffman M.A. The genome-enabled electronic medical record.1:CAS:528:DC%2BD28Xht1KrsbzP10.1016/j.jbi.2006.02.010J. Biomed. Informatics. 2007; 40: 44-46Google Scholar Not only should providers be able to retrieve external educational content to learn more about a patient’s genetic findings, but they should also receive automated recommendations at the point of care to facilitate clinical decision-making.5.Kawamoto K. Houlihan C.A. Balas E.A. Lobach D.F. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success.10.1136/bmj.38398.500764.8FBMJ. 2005; 330: 765Google Scholar,6.Overby C.L. et al.Opportunities for genomic clinical decision support interventions.10.1038/gim.2013.128Genet. Med. 2013; 15: 817-823Google Scholar The ACMG highlights that EHR vendors may not be fully equipped to build CDS systems in isolation due to the complex and dynamic nature of genomic medicine. The multidisciplinary nature of the PGI addresses this challenge by fostering collaboration between clinical, pathology, and information technology (IT) experts to build CDS tools that are seamlessly implemented into routine care.The PGI has leveraged Epic’s Genomic Indicators, driven by variants that are pathogenic/likely pathogenic or medically actionable, which are tags added to a patient’s record to indicate potential disease risk or drug sensitivity based on his/her genetic testing results. Genomic indicators are displayed on the Snapshot Tab (chart front page) and facilitate clinical decision-making by triggering automated recommendations directly in the EHR targeted to both providers and patients. By using triggered genomic indicators, we prevent variants of uncertain significance from being misinterpreted by nongenetics providers as disease associated. Our initial use cases for CDS provide guideline-concordant recommendations on colonoscopy timing intervals for patients with Lynch syndrome and fluoropyrimidine dose adjustments in patients with dihydropyrimidine dehydrogenase deficiency identified on DPYD gene testing.7.Natioanl Comprehensive Clinical Network, Clinical Practice Guidelines in Oncology. Genetic/Familial High-Risk Assesment: Colorectal, V 1.2020 - July 21, 2020. www.nccn.org/professionals/physician_gls/pdf/genetics_colon.pdf.Google Scholar,8.Amstutz U. et al.Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for dihydropyrimidine dehydrogenase genotype and fluoropyrimidine TO GENETIC ACMG recommends that genetic data in the EHR be to patients in and the time of patients are that they receive their genetic results in with on their clinical and that may patients receive their results in As we have developed a to these data, as by patients are on their results, which ordering genetics providers them to nongenetics providers and patient portal. This electronic also features the Genetic a centralized location patients may their results, along with educational information in for genetic and pharmacogenetic our in the Genetic Results document to enable centralized document in the Precision Medicine Tab to patient it also provides the to genetic results from other EHR able to genetic data to be by the at our to provide the to that data for such as in health information use Genetic results from also are as a document the EHR so that they are only to genetics providers and be to external as of the patient’s PGI has in integrating genomic data into the EHR for the of patient care. To date, documents have been into the Precision Medicine including over legacy and discrete results from Ambry We our to to and efforts by with clinical, pathology, and legal throughout Penn our have not been The of genomic medicine educational to ensure that our multidisciplinary team has the to patient care. as we expand our discrete to other have the to with our EHR the PGI has from support and from Penn Medicine that may not be at As we are to our decision and other with the genomic medicine to efforts to optimize the integration of genomic data into the PGI SOPs autosomal dominant and pharmacogenetic variants for other types of genetic CDS systems for genetic test results, on genetic testing and risk with genetic testing laboratories for computerized order entry and discrete result with other to enable of genetic data for patients care at and and as genetic data more both and between We also to working with the genomic medicine to develop for such as genomic data variant reclassifications from external genetic results from and ensuring access to genomic medicine for all patients. The EHR is a for the delivery of precision we are that the efforts of the PGI and other be in patient care and the of genomic medicine over the of research from the University of is an of Ambry The other is by of Health and are by the for and are by is by is by the Technologies in genomic medicine have rapidly evolved and transformed the ability to deliver precision medicine in nearly every field of health care. As genomic medicine has advanced, the electronic health record (EHR) has simultaneously been adopted into routine practice. A recent Points to Consider Statement by the American College of Medical Genetics and Genomics (ACMG) provides a framework for the optimal integration of genomic data into the EHR.1.Grebe T.A. et al.The interface of genomic information with the electronic health record: a points to consider statement of the American College of Medical Genetics and Genomics (ACMG).10.1038/s41436-020-0841-2Genet. Med. 2020; 22: 1431-1436Google Scholar The PennChart Genomics Initiative (PGI) at the University of Pennsylvania is a multidisciplinary collaborative effort including Penn Medicine clinicians, researchers, pathologists, legal staff, and information services with input and efforts from Epic Systems Corporation (Wisconsin) and Ambry Genetics Corporation (California), a commercial genetic testing laboratory. We describe our efforts to operationalize the ACMG guidelines in the “real world” to optimize our EHR (PennChart) for the delivery of precision medicine (Supplemental Fig. OF UNSTRUCTURED GENETIC DATA INTO THE EHRWe have taken a two-staged approach to integrating germline and somatic genetic data into the EHR. Currently, most genetic results are reported in unstructured PDF documents. We established common procedures across all Penn Medicine’s clinical genetics services for genetic testing reports, labeling them with a common naming convention and scanning them into a specific Genetic Results document type, which we created specifically for genetic testing results. This document filters both into our Lab (standard results) and PennChart Precision Medicine Tabs. We created the latter tab as a centralized location in the EHR to enable easy access to all genetic data, ensuring that it is not overlooked amid all the other testing that happens over a patient’s lifetime. This approach has standardized the real-time integration of unstructured genetic data into the EHR. Further, it has facilitated our efforts to import legacy data, as we began scanning all genetics documents with the common naming convention several years before implementing the Precision Medicine We have taken a two-staged approach to integrating germline and somatic genetic data into the EHR. Currently, most genetic results are reported in unstructured PDF documents. We established common procedures across all Penn Medicine’s clinical genetics services for genetic testing reports, labeling them with a common naming convention and scanning them into a specific Genetic Results document type, which we created specifically for genetic testing results. This document filters both into our Lab (standard results) and PennChart Precision Medicine Tabs. We created the latter tab as a centralized location in the EHR to enable easy access to all genetic data, ensuring that it is not overlooked amid all the other testing that happens over a patient’s lifetime. This approach has standardized the real-time integration of unstructured genetic data into the EHR. Further, it has facilitated our efforts to import legacy data, as we began scanning all genetics documents with the common naming convention several years before implementing the Precision Medicine OF DISCRETE GENETIC DATA INTO THE EHRAlthough the ACMG recommends that genetic results be incorporated into patient records, at minimum, as scanned PDF files or images, it is preferable to store them in discrete, computable format to enable electronic searching, clinical decision support (CDS), and secondary use for research and operations.2.Marsolo K. Spooner S.A. Clinical genomics in the world of the electronic health record.10.1038/gim.2013.88Genet. Med. 2013; 15: 786-791Google Scholar,3.Warner J.L. Jain S.K. Levy M.A. Integrating cancer genomic data into electronic health records.10.1186/s13073-016-0371-3Genome Med. 2016; 8Google Scholar The second stage of our efforts therefore aimed to integrate structured genetic data into the EHR. The PGI has leveraged Epic’s Genomics Module to record discrete genetic variant information in Human Genome Variation Society (HGVS) nomenclature along with the notation of significance (e.g., TP53 c.743G>A [p.Arg248Gln], pathogenic); transcript, genome build, chromosome, and genomic location are also included. Pharmacogenetic results are entered as diplotypes based on PharmVar star allele definitions (e.g., DPYD *1/*2A). Content experts throughout Penn Medicine collaborated to develop standard operating procedures (SOPs) to ensure institutional consistency for both manual and automated entry of discrete results into the Genomics Module. To date, these SOPs have been developed for autosomal dominant and pharmacogenetic variants with plans to expand to other result types over time, such as cytogenetics and autosomal recessive alleles. Manual entry of discrete genetic data into the Genomics Module is currently completed by genetics providers, who spend less than five minutes per variant.Interfacing directly with genetic testing laboratories is essential to move from manual to automated entry of discrete results into the EHR. The PGI partnered with Ambry Genetics to implement computerized order entry so providers can place genetic test orders directly into PennChart for import into the Ambry portal. The return of results has followed a phased approach using a Health Level 7 (HL7) interface, first with PDF reports and then with discrete results, which are automatically imported into the Genomics Module, linked to their associated PDF documents, and accompanied by direct provider notification. We are working on expanding our HL7 capacity with other commercial genetic testing laboratories.The ACMG also recommends that updated genetic test results be clearly linked to the original report in the EHR, as the interpretation of results may evolve and result in reclassifications over time.2.Marsolo K. Spooner S.A. Clinical genomics in the world of the electronic health record.10.1038/gim.2013.88Genet. Med. 2013; 15: 786-791Google Scholar The PGI’s partnership with Ambry Genetics enables the automatic import of results both at the time of initial testing and as variant reclassification occurs. If an update occurs, a notification is sent to the ordering provider and care team for review of the amended the ACMG recommends that genetic results be incorporated into patient records, at minimum, as scanned PDF files or images, it is preferable to store them in discrete, computable format to enable electronic searching, clinical decision support (CDS), and secondary use for research and operations.2.Marsolo K. Spooner S.A. Clinical genomics in the world of the electronic health record.10.1038/gim.2013.88Genet. Med. 2013; 15: 786-791Google Scholar,3.Warner J.L. Jain S.K. Levy M.A. Integrating cancer genomic data into electronic health records.10.1186/s13073-016-0371-3Genome Med. 2016; 8Google Scholar The second stage of our efforts therefore aimed to integrate structured genetic data into the EHR. The PGI has leveraged Epic’s Genomics Module to record discrete genetic variant information in Human Genome Variation Society (HGVS) nomenclature along with the notation of significance (e.g., TP53 c.743G>A [p.Arg248Gln], pathogenic); transcript, genome build, chromosome, and genomic location are also included. Pharmacogenetic results are entered as diplotypes based on PharmVar star allele definitions (e.g., DPYD *1/*2A). Content experts throughout Penn Medicine collaborated to develop standard operating procedures (SOPs) to ensure institutional consistency for both manual and automated entry of discrete results into the Genomics Module. To date, these SOPs have been developed for autosomal dominant and pharmacogenetic variants with plans to expand to other result types over time, such as cytogenetics and autosomal recessive alleles. Manual entry of discrete genetic data into the Genomics Module is currently completed by genetics providers, who spend less than five minutes per directly with genetic testing laboratories is essential to move from manual to automated entry of discrete results into the EHR. The PGI partnered with Ambry Genetics to implement computerized order entry so providers can place genetic test orders directly into PennChart for import into the Ambry portal. The return of results has followed a phased approach using a Health Level 7 (HL7) interface, first with PDF reports and then with discrete results, which are automatically imported into the Genomics Module, linked to their associated PDF documents, and accompanied by direct provider notification. We are working on expanding our HL7 capacity with other commercial genetic testing The ACMG also recommends that updated genetic test results be clearly linked to the original report in the EHR, as the interpretation of results may evolve and result in reclassifications over time.2.Marsolo K. Spooner S.A. Clinical genomics in the world of the electronic health record.10.1038/gim.2013.88Genet. Med. 2013; 15: 786-791Google Scholar The PGI’s partnership with Ambry Genetics enables the automatic import of results both at the time of initial testing and as variant reclassification occurs. If an update occurs, a notification is sent to the ordering provider and care team for review of the amended OF GENETIC DATA TO CLINICAL DECISION SUPPORTOne of the greatest potential benefits of integrating genetic data into the EHR is the ability to link results to CDS.2.Marsolo K. Spooner S.A. Clinical genomics in the world of the electronic health record.10.1038/gim.2013.88Genet. Med. 2013; 15: 786-791Google Scholar,4.Hoffman M.A. The genome-enabled electronic medical record.1:CAS:528:DC%2BD28Xht1KrsbzP10.1016/j.jbi.2006.02.010J. Biomed. Informatics. 2007; 40: 44-46Google Scholar Not only should providers be able to retrieve external educational content to learn more about a patient’s genetic findings, but they should also receive automated recommendations at the point of care to facilitate clinical decision-making.5.Kawamoto K. Houlihan C.A. Balas E.A. Lobach D.F. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success.10.1136/bmj.38398.500764.8FBMJ. 2005; 330: 765Google Scholar,6.Overby C.L. et al.Opportunities for genomic clinical decision support interventions.10.1038/gim.2013.128Genet. Med. 2013; 15: 817-823Google Scholar The ACMG highlights that EHR vendors may not be fully equipped to build CDS systems in isolation due to the complex and dynamic nature of genomic medicine. The multidisciplinary nature of the PGI addresses this challenge by fostering collaboration between clinical, pathology, and information technology (IT) experts to build CDS tools that are seamlessly implemented into routine care.The PGI has leveraged Epic’s Genomic Indicators, driven by variants that are pathogenic/likely pathogenic or medically actionable, which are tags added to a patient’s record to indicate potential disease risk or drug sensitivity based on his/her genetic testing results. Genomic indicators are displayed on the Snapshot Tab (chart front page) and facilitate clinical decision-making by triggering automated recommendations directly in the EHR targeted to both providers and patients. By using triggered genomic indicators, we prevent variants of uncertain significance from being misinterpreted by nongenetics providers as disease associated. Our initial use cases for CDS provide guideline-concordant recommendations on colonoscopy timing intervals for patients with Lynch syndrome and fluoropyrimidine dose adjustments in patients with dihydropyrimidine dehydrogenase deficiency identified on DPYD gene testing.7.Natioanl Comprehensive Clinical Network, Clinical Practice Guidelines in Oncology. Genetic/Familial High-Risk Assesment: Colorectal, V 1.2020 - July 21, 2020. www.nccn.org/professionals/physician_gls/pdf/genetics_colon.pdf.Google Scholar,8.Amstutz U. et al.Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for dihydropyrimidine dehydrogenase genotype and fluoropyrimidine Scholar of the greatest potential benefits of integrating genetic data into the EHR is the ability to link results to CDS.2.Marsolo K. Spooner S.A. Clinical genomics in the world of the electronic health record.10.1038/gim.2013.88Genet. Med. 2013; 15: 786-791Google Scholar,4.Hoffman M.A. The genome-enabled electronic medical record.1:CAS:528:DC%2BD28Xht1KrsbzP10.1016/j.jbi.2006.02.010J. Biomed. Informatics. 2007; 40: 44-46Google Scholar Not only should providers be able to retrieve external educational content to learn more about a patient’s genetic findings, but they should also receive automated recommendations at the point of care to facilitate clinical decision-making.5.Kawamoto K. Houlihan C.A. Balas E.A. Lobach D.F. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success.10.1136/bmj.38398.500764.8FBMJ. 2005; 330: 765Google Scholar,6.Overby C.L. et al.Opportunities for genomic clinical decision support interventions.10.1038/gim.2013.128Genet. Med. 2013; 15: 817-823Google Scholar The ACMG highlights that EHR vendors may not be fully equipped to build CDS systems in isolation due to the complex and dynamic nature of genomic medicine. The multidisciplinary nature of the PGI addresses this challenge by fostering collaboration between clinical, pathology, and information technology (IT) experts to build CDS tools that are seamlessly implemented into routine care. The PGI has leveraged Epic’s Genomic Indicators, driven by variants that are pathogenic/likely pathogenic or medically actionable, which are tags added to a patient’s record to indicate potential disease risk or drug sensitivity based on his/her genetic testing results. Genomic indicators are displayed on the Snapshot Tab (chart front page) and facilitate clinical decision-making by triggering automated recommendations directly in the EHR targeted to both providers and patients. By using triggered genomic indicators, we prevent variants of uncertain significance from being misinterpreted by nongenetics providers as disease associated. Our initial use cases for CDS provide guideline-concordant recommendations on colonoscopy timing intervals for patients with Lynch syndrome and fluoropyrimidine dose adjustments in patients with dihydropyrimidine dehydrogenase deficiency identified on DPYD gene testing.7.Natioanl Comprehensive Clinical Network, Clinical Practice Guidelines in Oncology. Genetic/Familial High-Risk Assesment: Colorectal, V 1.2020 - July 21, 2020. www.nccn.org/professionals/physician_gls/pdf/genetics_colon.pdf.Google Scholar,8.Amstutz U. et al.Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for dihydropyrimidine dehydrogenase genotype and fluoropyrimidine Scholar TO GENETIC ACMG recommends that genetic data in the EHR be to patients in and the time of patients are that they receive their genetic results in with on their clinical and that may patients receive their results in As we have developed a to these data, as by patients are on their results, which ordering genetics providers them to nongenetics providers and patient portal. This electronic also features the Genetic a centralized location patients may their results, along with educational information in for genetic and pharmacogenetic results. The ACMG recommends that genetic data in the EHR be to patients in and the time of patients are that they receive their genetic results in with on their clinical and that may patients receive their results in As we have developed a to these data, as by patients are on their results, which ordering genetics providers them to nongenetics providers and patient portal. This electronic also features the Genetic a centralized location patients may their results, along with educational information in for genetic and pharmacogenetic results. our in the Genetic Results document to enable centralized document in the Precision Medicine Tab to patient it also provides the to genetic results from other EHR able to genetic data to be by the at our to provide the to that data for such as in health information use Genetic results from also are as a document the EHR so that they are only to genetics providers and be to external as of the patient’s our in the Genetic Results document to enable centralized document in the Precision Medicine Tab to patient it also provides the to genetic results from other EHR able to genetic data to be by the at our to provide the to that data for such as in health information use Genetic results from also are as a document the EHR so that they are only to genetics providers and be to external as of the patient’s PGI has in integrating genomic data into the EHR for the of patient care. To date, documents have been into the Precision Medicine including over legacy and discrete results from Ambry We our to to and efforts by with clinical, pathology, and legal throughout Penn our have not been The of genomic medicine educational to ensure that our multidisciplinary team has the to patient care. as we expand our discrete to other have the to with our EHR the PGI has from support and from Penn Medicine that may not be at As we are to our decision and other with the genomic medicine to efforts to optimize the integration of genomic data into the PGI SOPs autosomal dominant and pharmacogenetic variants for other types of genetic CDS systems for genetic test results, on genetic testing and risk with genetic testing laboratories for computerized order entry and discrete result with other to enable of genetic data for patients care at and and as genetic data more both and between We also to working with the genomic medicine to develop for such as genomic data variant reclassifications from external genetic results from and ensuring access to genomic medicine for all patients. The EHR is a for the delivery of precision we are that the efforts of the PGI and other be in patient care and the of genomic medicine over The PGI has in integrating genomic data into the EHR for the of patient care. To date, documents have been into the Precision Medicine including over legacy and discrete results from Ambry We our to to and efforts by with clinical, pathology, and legal throughout Penn our have not been The of genomic medicine educational to ensure that our multidisciplinary team has the to patient care. as we expand our discrete to other have the to with our EHR the PGI has from support and from Penn Medicine that may not be at As we are to our decision and other with the genomic medicine to efforts to optimize the integration of genomic data into the EHR.

Social Media Engagement at Academic Conferences: Report of the Association of Pathology Chairs 2018 and 2019 Annual Meeting Social Media Committee

Academic Pathology · 2020 · 14 citations

• Political Science
• Computer Science
• Public relations

The use of social media at academic conferences is expanding, and platforms such as Twitter are used to share meeting content with the world. Pathology conferences are no exception, and recently, pathology organizations have promoted social media as a way to enhance meeting exposure. A social media committee was formed ad hoc to implement strategies to enhance social media involvement and coverage at the 2018 and 2019 annual meetings of the Association of Pathology Chairs. This organized approach resulted in an 11-fold increase in social media engagement compared to the year prior to committee formation (2017). In this article, the social media committee reviews the strategies that were employed and the resultant outcome data. In addition, we categorize tweets by topic to identify the topics of greatest interest to meeting participants, and we discuss the differences between Twitter and other social media platforms. Lastly, we review the existing literature on this topic from 23 medical specialties and health care fields.