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The American Gastroenterological Association (AGA) is proud to announce the 71 recipients selected to receive research funding through its annual AGA Research Foundation Awards Program. The program serves as a catalyst for discovery and career growth among the most promising researchers in gastroenterology and hepatology.

“This year’s recipients are determined to make an impact on digestive health care through their research,” said Michael Camilleri, MD, AGAF, chair, AGA Research Foundation. “We are honored to support these talented individuals at a critical stage in their careers and research projects. We look forward to seeing their great accomplishments.”

AGA Institute

Research Scholar Awards

AGA Research Scholar Award

Alexander Nguyen, MD, PhD, The Regent of the University of California, Los Angeles

Jeffrey W. Patterson-Fortin, MD, PhD, Dana-Farber Cancer Institute, Boston, Massachusetts

Sean Spencer, MD, PhD, Stanford Medicine, California

Ken Y. Hui, MD, PhD, Johns Hopkins University School of Medicine, Baltimore, Maryland

AGA-Gastric Cancer Foundation Ben Feinstein Memorial Research Scholar Award in Gastric Cancer

Martina Molgora, PhD, Washington University School of Medicine, St. Louis, Missouri

AGA-Takeda Pharmaceuticals Research Scholar Award in Inflammatory Bowel Disease

Brooke R. Druliner, PhD, Mayo Clinic, Rochester, Minnesota
 

Specialty Awards

AGA-Caroline Craig Augustyn & Damian Augustyn Award in Digestive Cancer

Simon Schwörer, PhD, University of Chicago, Illinois

AGA-R. Robert & Sally Funderburg Research Award in Gastric Cancer

Bryson W. Katona, MD, PhD, University of Pennsylvania Perelman School of Medicine, Philadelphia

AGA-Amgen Fellowship-to-Faculty Transition Award

Cynthia Hsu, MD, PhD, University of California, San Diego

AGA-Bristol Myers Squibb Fellowship-to-Faculty Transition Award

Siyan Cao, MD, PhD, Washington University in St. Louis

Amit Ringel, MD, Brigham and Women’s Hospital, Boston, Massachusetts

Pilot Awards

AGA Pilot Research Award In Digestive Disease Health Disparities

Sharad Wadhwani, MD, MPH, University of California, San Francisco

AGA Pilot Research Award in Health Disparities

Enrique Soto Pérez de Celis, MD, PhD, MS, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán

AGA Pilot Research Award

Diana L. Snyder, MD, Mayo Clinic, Rochester, Minnesota

Michael Li, MD, MPH, University of California, San Francisco

Patricia Bloom, MD, University of Michigan, Ann Arbor

Edward Barnes, MD, MPH, University of North Carolina School of Medicine, Chapel Hill

AGA-Amgen Pilot Research Award In Digestive Disease Health Disparities

Laura Targownik, MD, MSHS, University of Toronto/Mount Sinai Hospital, Toronto, ON

Undergraduate Research Awards

AGA-Aman Armaan Ahmed Family Summer Undergraduate Research Award

Gwyneth Garramone, Loyola Marymount University, Los Angeles, California

Ella McLaren, University of California, San Diego

Nathan Moy, University of Southern California, Los Angeles

Hussein Elfayoumy, Johns Hopkins University, Baltimore, Maryland

Isabelle Garcia-Fischer, Tufts University, Medford, Massachusetts

Lidia Appell, University of New Mexico, Albuquerque

Katherine Burkman, Duke University, Durham, North Carolina

Alexa Boylan, Spelman College, Atlanta, Georgia

AGA-Dr. Harvey Young Education and Development Foundation’s Young Guts Scholar Program

Lucy Zhao, Massachusetts Institute of Technology Koch Institute for Integrative Cancer Research, Cambridge

Andrew Tran, Duke University, Durham, North Carolina

Sohaib Hassan, Rutgers University – Verzi Lab, New Brunswick, New Jersey

Varun Ponnusamy, University of Michigan Medical School, Ann Arbor

Daniella Montalvo, University of Miami, Coral Gables, Florida

Sara Chough, Columbia University Irving Medical Center, New York, New York

Abstract Awards

Fellow Abstract Awards

David Flores Marin, MD, Beth Israel Deaconess Medical Center, Boston, Massachusetts

Jesse Platt, MD, PhD, Massachusetts General Hospital, Boston

Devika Gandhi, MD, Loma Linda University, California

Amanda Krause, MD, University of California, San Diego

Cynthia Tsay, MD, Mphil, Johns Hopkins Hospital, Baltimore, Maryland

Suha Abushamma, MD, Cleveland Clinic Foundation, Ohio

Md Obaidul Islam, PhD, University of Miami, Coral Gables, Florida

Sakteesh Gurunathan, MD, New York University School of Medicine, New York

Aaron Yeoh, MD, Stanford Hospital & Clinics, California

Yang Xiao, PhD, Mayo Clinic, Rochester, Minnesota

Jacques Gonzales, PhD, MS, Michigan State University, East Lansing

Kai Wang, MD, PhD, Harvard T.H. Chan School of Public Health, Cambridge, Massachusetts

Hoyeol Kim, PhD, Cedars Sinai Medical Center, New York, New York

Babajide Ojo, PhD, MS, Stanford University, California

AGA Fellow Abstract of the Year Award

Stefania Tocci, PhD, MS, University of Massachusetts, Cambridge

Student Abstract Awards

Pritha Chatterjee, MS, University of California, Riverside

Ela Contreras Panta, Vanderbilt University, Nashville, Tennessee

Mihir Shah, MD, MBBS, John H. Stroger Hospital of Cook County, Chicago, Illinois

Yuhan Fu, DO, Metrohealth Medical Center, Cleveland, Ohio

Raissa Nana Sede Mbakop, MD, Piedmont Athens Regional Medical Center, Athens, Georgia

Eleazar Montalvan-Sanchez, MD, Indiana University School of Medicine, Bloomington

Sarang Gupta, MD, St. Michael’s Hospital, Toronto, Ontario

Daniel Kim, Harvard Medical School, Cambridge, Massachusetts

Hannah Hrncir, Emory University, Decatur, Georgia

Zarwa Saqib, McMaster University, Hamilton, Ontario

Ying Zhu, MD, PhD, University of Michigan, Ann Arbor

Lizeth Cifuentes, MD, University of Pittsburgh Medical Center, Pennsylvania

Sharvani Dhandibhotla, MBBS, MS, Massachusetts General Hospital, Boston

Lauren Lynch, Baylor College of Medicine, Houston, Texas

AGA Student Abstract of The Year Award

Gabrielle Waclawik, MD, MPH, University of Wisconsin, Madison

AGA Abstract Award for Health Disparities Research

Soyoun Min, PhD, Lerner Research Institute (fellow), Cleveland, Ohio

Xiaobei Zhang, PhD , David Geffen School of Medicine at University of California, Los Angeles (fellow)

Matthew Zhao, David Geffen School of Medicine at University of California, Los Angeles (student)

Hannah Fiske, MD, Brown University/Rhode Island Hospital (student), Providence

AGA-APFED Abstract Award in Eosinophilic GI Diseases

Matthew Buendia, MD, Vanderbilt University Medical Center – Monroe Carell Jr. Children’s Hospital, Nashville, Tennessee

Alexandra L. Strauss, MD, University of Pennsylvania Health System, Philadelphia

Mira Yang, Northwestern Feinberg School of Medicine, Chicago, Illinois

AGA-Moti L. & Kamla Rustgi International Travel Award

Aviv Pudipeddi, MBBS, Concord Repatriation General Hospital, Sydney, Australia

Dianqin Sun, MBBS, Mmed, Erasmus University Medical Center, Rotterdam, Netherlands

The American Gastroenterological Association (AGA) is proud to announce the 71 recipients selected to receive research funding through its annual AGA Research Foundation Awards Program. The program serves as a catalyst for discovery and career growth among the most promising researchers in gastroenterology and hepatology.

“This year’s recipients are determined to make an impact on digestive health care through their research,” said Michael Camilleri, MD, AGAF, chair, AGA Research Foundation. “We are honored to support these talented individuals at a critical stage in their careers and research projects. We look forward to seeing their great accomplishments.”

AGA Institute

Research Scholar Awards

AGA Research Scholar Award

Alexander Nguyen, MD, PhD, The Regent of the University of California, Los Angeles

Jeffrey W. Patterson-Fortin, MD, PhD, Dana-Farber Cancer Institute, Boston, Massachusetts

Sean Spencer, MD, PhD, Stanford Medicine, California

Ken Y. Hui, MD, PhD, Johns Hopkins University School of Medicine, Baltimore, Maryland

AGA-Gastric Cancer Foundation Ben Feinstein Memorial Research Scholar Award in Gastric Cancer

Martina Molgora, PhD, Washington University School of Medicine, St. Louis, Missouri

AGA-Takeda Pharmaceuticals Research Scholar Award in Inflammatory Bowel Disease

Brooke R. Druliner, PhD, Mayo Clinic, Rochester, Minnesota
 

Specialty Awards

AGA-Caroline Craig Augustyn & Damian Augustyn Award in Digestive Cancer

Simon Schwörer, PhD, University of Chicago, Illinois

AGA-R. Robert & Sally Funderburg Research Award in Gastric Cancer

Bryson W. Katona, MD, PhD, University of Pennsylvania Perelman School of Medicine, Philadelphia

AGA-Amgen Fellowship-to-Faculty Transition Award

Cynthia Hsu, MD, PhD, University of California, San Diego

AGA-Bristol Myers Squibb Fellowship-to-Faculty Transition Award

Siyan Cao, MD, PhD, Washington University in St. Louis

Amit Ringel, MD, Brigham and Women’s Hospital, Boston, Massachusetts

Pilot Awards

AGA Pilot Research Award In Digestive Disease Health Disparities

Sharad Wadhwani, MD, MPH, University of California, San Francisco

AGA Pilot Research Award in Health Disparities

Enrique Soto Pérez de Celis, MD, PhD, MS, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán

AGA Pilot Research Award

Diana L. Snyder, MD, Mayo Clinic, Rochester, Minnesota

Michael Li, MD, MPH, University of California, San Francisco

Patricia Bloom, MD, University of Michigan, Ann Arbor

Edward Barnes, MD, MPH, University of North Carolina School of Medicine, Chapel Hill

AGA-Amgen Pilot Research Award In Digestive Disease Health Disparities

Laura Targownik, MD, MSHS, University of Toronto/Mount Sinai Hospital, Toronto, ON

Undergraduate Research Awards

AGA-Aman Armaan Ahmed Family Summer Undergraduate Research Award

Gwyneth Garramone, Loyola Marymount University, Los Angeles, California

Ella McLaren, University of California, San Diego

Nathan Moy, University of Southern California, Los Angeles

Hussein Elfayoumy, Johns Hopkins University, Baltimore, Maryland

Isabelle Garcia-Fischer, Tufts University, Medford, Massachusetts

Lidia Appell, University of New Mexico, Albuquerque

Katherine Burkman, Duke University, Durham, North Carolina

Alexa Boylan, Spelman College, Atlanta, Georgia

AGA-Dr. Harvey Young Education and Development Foundation’s Young Guts Scholar Program

Lucy Zhao, Massachusetts Institute of Technology Koch Institute for Integrative Cancer Research, Cambridge

Andrew Tran, Duke University, Durham, North Carolina

Sohaib Hassan, Rutgers University – Verzi Lab, New Brunswick, New Jersey

Varun Ponnusamy, University of Michigan Medical School, Ann Arbor

Daniella Montalvo, University of Miami, Coral Gables, Florida

Sara Chough, Columbia University Irving Medical Center, New York, New York

Abstract Awards

Fellow Abstract Awards

David Flores Marin, MD, Beth Israel Deaconess Medical Center, Boston, Massachusetts

Jesse Platt, MD, PhD, Massachusetts General Hospital, Boston

Devika Gandhi, MD, Loma Linda University, California

Amanda Krause, MD, University of California, San Diego

Cynthia Tsay, MD, Mphil, Johns Hopkins Hospital, Baltimore, Maryland

Suha Abushamma, MD, Cleveland Clinic Foundation, Ohio

Md Obaidul Islam, PhD, University of Miami, Coral Gables, Florida

Sakteesh Gurunathan, MD, New York University School of Medicine, New York

Aaron Yeoh, MD, Stanford Hospital & Clinics, California

Yang Xiao, PhD, Mayo Clinic, Rochester, Minnesota

Jacques Gonzales, PhD, MS, Michigan State University, East Lansing

Kai Wang, MD, PhD, Harvard T.H. Chan School of Public Health, Cambridge, Massachusetts

Hoyeol Kim, PhD, Cedars Sinai Medical Center, New York, New York

Babajide Ojo, PhD, MS, Stanford University, California

AGA Fellow Abstract of the Year Award

Stefania Tocci, PhD, MS, University of Massachusetts, Cambridge

Student Abstract Awards

Pritha Chatterjee, MS, University of California, Riverside

Ela Contreras Panta, Vanderbilt University, Nashville, Tennessee

Mihir Shah, MD, MBBS, John H. Stroger Hospital of Cook County, Chicago, Illinois

Yuhan Fu, DO, Metrohealth Medical Center, Cleveland, Ohio

Raissa Nana Sede Mbakop, MD, Piedmont Athens Regional Medical Center, Athens, Georgia

Eleazar Montalvan-Sanchez, MD, Indiana University School of Medicine, Bloomington

Sarang Gupta, MD, St. Michael’s Hospital, Toronto, Ontario

Daniel Kim, Harvard Medical School, Cambridge, Massachusetts

Hannah Hrncir, Emory University, Decatur, Georgia

Zarwa Saqib, McMaster University, Hamilton, Ontario

Ying Zhu, MD, PhD, University of Michigan, Ann Arbor

Lizeth Cifuentes, MD, University of Pittsburgh Medical Center, Pennsylvania

Sharvani Dhandibhotla, MBBS, MS, Massachusetts General Hospital, Boston

Lauren Lynch, Baylor College of Medicine, Houston, Texas

AGA Student Abstract of The Year Award

Gabrielle Waclawik, MD, MPH, University of Wisconsin, Madison

AGA Abstract Award for Health Disparities Research

Soyoun Min, PhD, Lerner Research Institute (fellow), Cleveland, Ohio

Xiaobei Zhang, PhD , David Geffen School of Medicine at University of California, Los Angeles (fellow)

Matthew Zhao, David Geffen School of Medicine at University of California, Los Angeles (student)

Hannah Fiske, MD, Brown University/Rhode Island Hospital (student), Providence

AGA-APFED Abstract Award in Eosinophilic GI Diseases

Matthew Buendia, MD, Vanderbilt University Medical Center – Monroe Carell Jr. Children’s Hospital, Nashville, Tennessee

Alexandra L. Strauss, MD, University of Pennsylvania Health System, Philadelphia

Mira Yang, Northwestern Feinberg School of Medicine, Chicago, Illinois

AGA-Moti L. & Kamla Rustgi International Travel Award

Aviv Pudipeddi, MBBS, Concord Repatriation General Hospital, Sydney, Australia

Dianqin Sun, MBBS, Mmed, Erasmus University Medical Center, Rotterdam, Netherlands

The American Gastroenterological Association (AGA) is proud to announce the 71 recipients selected to receive research funding through its annual AGA Research Foundation Awards Program. The program serves as a catalyst for discovery and career growth among the most promising researchers in gastroenterology and hepatology.

“This year’s recipients are determined to make an impact on digestive health care through their research,” said Michael Camilleri, MD, AGAF, chair, AGA Research Foundation. “We are honored to support these talented individuals at a critical stage in their careers and research projects. We look forward to seeing their great accomplishments.”

AGA Institute

Research Scholar Awards

AGA Research Scholar Award

Alexander Nguyen, MD, PhD, The Regent of the University of California, Los Angeles

Jeffrey W. Patterson-Fortin, MD, PhD, Dana-Farber Cancer Institute, Boston, Massachusetts

Sean Spencer, MD, PhD, Stanford Medicine, California

Ken Y. Hui, MD, PhD, Johns Hopkins University School of Medicine, Baltimore, Maryland

AGA-Gastric Cancer Foundation Ben Feinstein Memorial Research Scholar Award in Gastric Cancer

Martina Molgora, PhD, Washington University School of Medicine, St. Louis, Missouri

AGA-Takeda Pharmaceuticals Research Scholar Award in Inflammatory Bowel Disease

Brooke R. Druliner, PhD, Mayo Clinic, Rochester, Minnesota
 

Specialty Awards

AGA-Caroline Craig Augustyn & Damian Augustyn Award in Digestive Cancer

Simon Schwörer, PhD, University of Chicago, Illinois

AGA-R. Robert & Sally Funderburg Research Award in Gastric Cancer

Bryson W. Katona, MD, PhD, University of Pennsylvania Perelman School of Medicine, Philadelphia

AGA-Amgen Fellowship-to-Faculty Transition Award

Cynthia Hsu, MD, PhD, University of California, San Diego

AGA-Bristol Myers Squibb Fellowship-to-Faculty Transition Award

Siyan Cao, MD, PhD, Washington University in St. Louis

Amit Ringel, MD, Brigham and Women’s Hospital, Boston, Massachusetts

Pilot Awards

AGA Pilot Research Award In Digestive Disease Health Disparities

Sharad Wadhwani, MD, MPH, University of California, San Francisco

AGA Pilot Research Award in Health Disparities

Enrique Soto Pérez de Celis, MD, PhD, MS, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán

AGA Pilot Research Award

Diana L. Snyder, MD, Mayo Clinic, Rochester, Minnesota

Michael Li, MD, MPH, University of California, San Francisco

Patricia Bloom, MD, University of Michigan, Ann Arbor

Edward Barnes, MD, MPH, University of North Carolina School of Medicine, Chapel Hill

AGA-Amgen Pilot Research Award In Digestive Disease Health Disparities

Laura Targownik, MD, MSHS, University of Toronto/Mount Sinai Hospital, Toronto, ON

Undergraduate Research Awards

AGA-Aman Armaan Ahmed Family Summer Undergraduate Research Award

Gwyneth Garramone, Loyola Marymount University, Los Angeles, California

Ella McLaren, University of California, San Diego

Nathan Moy, University of Southern California, Los Angeles

Hussein Elfayoumy, Johns Hopkins University, Baltimore, Maryland

Isabelle Garcia-Fischer, Tufts University, Medford, Massachusetts

Lidia Appell, University of New Mexico, Albuquerque

Katherine Burkman, Duke University, Durham, North Carolina

Alexa Boylan, Spelman College, Atlanta, Georgia

AGA-Dr. Harvey Young Education and Development Foundation’s Young Guts Scholar Program

Lucy Zhao, Massachusetts Institute of Technology Koch Institute for Integrative Cancer Research, Cambridge

Andrew Tran, Duke University, Durham, North Carolina

Sohaib Hassan, Rutgers University – Verzi Lab, New Brunswick, New Jersey

Varun Ponnusamy, University of Michigan Medical School, Ann Arbor

Daniella Montalvo, University of Miami, Coral Gables, Florida

Sara Chough, Columbia University Irving Medical Center, New York, New York

Abstract Awards

Fellow Abstract Awards

David Flores Marin, MD, Beth Israel Deaconess Medical Center, Boston, Massachusetts

Jesse Platt, MD, PhD, Massachusetts General Hospital, Boston

Devika Gandhi, MD, Loma Linda University, California

Amanda Krause, MD, University of California, San Diego

Cynthia Tsay, MD, Mphil, Johns Hopkins Hospital, Baltimore, Maryland

Suha Abushamma, MD, Cleveland Clinic Foundation, Ohio

Md Obaidul Islam, PhD, University of Miami, Coral Gables, Florida

Sakteesh Gurunathan, MD, New York University School of Medicine, New York

Aaron Yeoh, MD, Stanford Hospital & Clinics, California

Yang Xiao, PhD, Mayo Clinic, Rochester, Minnesota

Jacques Gonzales, PhD, MS, Michigan State University, East Lansing

Kai Wang, MD, PhD, Harvard T.H. Chan School of Public Health, Cambridge, Massachusetts

Hoyeol Kim, PhD, Cedars Sinai Medical Center, New York, New York

Babajide Ojo, PhD, MS, Stanford University, California

AGA Fellow Abstract of the Year Award

Stefania Tocci, PhD, MS, University of Massachusetts, Cambridge

Student Abstract Awards

Pritha Chatterjee, MS, University of California, Riverside

Ela Contreras Panta, Vanderbilt University, Nashville, Tennessee

Mihir Shah, MD, MBBS, John H. Stroger Hospital of Cook County, Chicago, Illinois

Yuhan Fu, DO, Metrohealth Medical Center, Cleveland, Ohio

Raissa Nana Sede Mbakop, MD, Piedmont Athens Regional Medical Center, Athens, Georgia

Eleazar Montalvan-Sanchez, MD, Indiana University School of Medicine, Bloomington

Sarang Gupta, MD, St. Michael’s Hospital, Toronto, Ontario

Daniel Kim, Harvard Medical School, Cambridge, Massachusetts

Hannah Hrncir, Emory University, Decatur, Georgia

Zarwa Saqib, McMaster University, Hamilton, Ontario

Ying Zhu, MD, PhD, University of Michigan, Ann Arbor

Lizeth Cifuentes, MD, University of Pittsburgh Medical Center, Pennsylvania

Sharvani Dhandibhotla, MBBS, MS, Massachusetts General Hospital, Boston

Lauren Lynch, Baylor College of Medicine, Houston, Texas

AGA Student Abstract of The Year Award

Gabrielle Waclawik, MD, MPH, University of Wisconsin, Madison

AGA Abstract Award for Health Disparities Research

Soyoun Min, PhD, Lerner Research Institute (fellow), Cleveland, Ohio

Xiaobei Zhang, PhD , David Geffen School of Medicine at University of California, Los Angeles (fellow)

Matthew Zhao, David Geffen School of Medicine at University of California, Los Angeles (student)

Hannah Fiske, MD, Brown University/Rhode Island Hospital (student), Providence

AGA-APFED Abstract Award in Eosinophilic GI Diseases

Matthew Buendia, MD, Vanderbilt University Medical Center – Monroe Carell Jr. Children’s Hospital, Nashville, Tennessee

Alexandra L. Strauss, MD, University of Pennsylvania Health System, Philadelphia

Mira Yang, Northwestern Feinberg School of Medicine, Chicago, Illinois

AGA-Moti L. & Kamla Rustgi International Travel Award

Aviv Pudipeddi, MBBS, Concord Repatriation General Hospital, Sydney, Australia

Dianqin Sun, MBBS, Mmed, Erasmus University Medical Center, Rotterdam, Netherlands

The AGA Government Affairs Committee and staff recently published in Gastroenterology the results from an AGA membership survey on policy priorities and how members can contribute to AGA advocacy efforts.¹ Here, we present key highlights from the survey findings and share opportunities for members to engage in GI advocacy.

AGA advocacy has contributed to significant recent successes that include lowering the average-risk of colorectal cancer screening age from 50 to 45 years, phasing out cost-sharing burdens associated with polypectomy at screening colonoscopy, encouraging federal support to focus on GI cancer disparities, ensuring coverage for telehealth services, expanding colonoscopy coverage after positive noninvasive colorectal cancer screening tests, and mitigating scheduled cuts in Medicare reimbursement for GI services.

Despite these important successes, the GI community faces significant challenges that include persisting GI health disparities; declines in reimbursement and increased prior authorization burdens for GI procedures and clinic visits, limited research funding to address the burden of GI disease, climate change, provider burnout, and increasing administrative burdens (such as insurance prior authorizations and step therapy policies.

The AGA sought to better understand policy priorities of the GI community by disseminating a 34-question policy priority survey to AGA members in December 2022. A total of 251 members responded to the survey with career stage and primary practice setting varying among respondents (Figure 1). The AGA vetted and selected 10 health policy issues of highest interest with 95% of survey respondents agreeing these 10 selected topics covered the top priority issues impacting gastroenterology (Figure 2).

From these 10 policy issues, members were asked to identify the top 5 issues that AGA advocacy efforts should address.

The issues most frequently identified included reducing administrative burdens and patient delays in care because of increased prior authorizations (78%), ensuring fair reimbursement for GI providers (68%), reducing insurance-initiated switching of patient treatments for nonmedical reasons (58%), maintaining coverage of video and telephone evaluation and management visits (55%), and reducing delays in clinical care resulting from step therapy protocols (53%).

Other important issues included ensuring patients with pre-existing conditions have access to essential benefits and quality specialty care (43%); protecting providers from medical licensing restrictions and liability to deliver care across state lines (35%); addressing Medicare Quality Payment Program reporting requirements and lack of specialty advanced payment models (27%); increasing funding for GI health disparities (24%); and, increasing federal research funding to ensure greater opportunities for diverse early career investigators (20%).
 

Most problematic burdens

Survey respondents identified insurer prior authorization and step therapy burdens as especially problematic. 93% of respondents described the impact of prior authorization on their practices as “significantly burdensome” (61%) or “somewhat burdensome” (32%).

About 95% noted that prior authorization restrictions have impacted patient access to clinically appropriate treatments and patient clinical outcomes “significantly” (56%) or “somewhat” (39%) negatively. 84% described the burdens associated with prior authorization policies as having increased “significantly” (60%) or “somewhat” (24%) over the last 5 years.

AGA’s advocacy work

About 76% of respondents were interested in learning more about the AGA’s advocacy work. We presented some of the various opportunities and resources for members to engage with and contribute to AGA advocacy efforts (see pie chart). Based on the tremendous efforts and dedication of AGA staff, some of these opportunities include educational modules on AGA University, DDW programming, the AGA Washington Insider monthly policy newsletter, preformatted communications available through the AGA Advocacy Action Center, participation in AGA Advocacy Days or the AGA Congressional Advocates Program, service on the AGA Government Affairs Committee, and/or contributing to the AGA Political Action Committee.

Overall, the survey respondents illustrate the diversity and enthusiasm of AGA membership. Importantly, 95% of AGA members responding to the survey agreed these 10 selected policy issues are inclusive of the current top priority issues of the GI community. Amidst an ever-shifting health care landscape, we – the AGA community – must remain vigilant and adaptable to best address expected and unexpected changes and challenges to our patients and colleagues. In this respect, we should encourage constructive communication and dialogue between AGA membership, leadership, other issue stakeholders, government representatives and entities, and payers.

Amit Patel, MD, is a gastroenterologist and associate professor of medicine at Duke University and the Durham Veterans Affairs Medical Center, both in Durham, N.C. He serves on the editorial review board of Gastroenterology. Rotonya McCants Carr, MD, is the Cyrus E. Rubin Chair and division head of gastroenterology at the University of Washington, Seattle. Both Dr. Patel and Dr. Carr serve on the AGA Government Affairs Committee. The contents of this article do not represent the views of the Department of Veterans Affairs.

Reference

Patel A et al. Gastroenterology. 2023 May;164[6]:847-50.

The AGA Government Affairs Committee and staff recently published in Gastroenterology the results from an AGA membership survey on policy priorities and how members can contribute to AGA advocacy efforts.¹ Here, we present key highlights from the survey findings and share opportunities for members to engage in GI advocacy.

AGA advocacy has contributed to significant recent successes that include lowering the average-risk of colorectal cancer screening age from 50 to 45 years, phasing out cost-sharing burdens associated with polypectomy at screening colonoscopy, encouraging federal support to focus on GI cancer disparities, ensuring coverage for telehealth services, expanding colonoscopy coverage after positive noninvasive colorectal cancer screening tests, and mitigating scheduled cuts in Medicare reimbursement for GI services.

Despite these important successes, the GI community faces significant challenges that include persisting GI health disparities; declines in reimbursement and increased prior authorization burdens for GI procedures and clinic visits, limited research funding to address the burden of GI disease, climate change, provider burnout, and increasing administrative burdens (such as insurance prior authorizations and step therapy policies.

The AGA sought to better understand policy priorities of the GI community by disseminating a 34-question policy priority survey to AGA members in December 2022. A total of 251 members responded to the survey with career stage and primary practice setting varying among respondents (Figure 1). The AGA vetted and selected 10 health policy issues of highest interest with 95% of survey respondents agreeing these 10 selected topics covered the top priority issues impacting gastroenterology (Figure 2).

From these 10 policy issues, members were asked to identify the top 5 issues that AGA advocacy efforts should address.

The issues most frequently identified included reducing administrative burdens and patient delays in care because of increased prior authorizations (78%), ensuring fair reimbursement for GI providers (68%), reducing insurance-initiated switching of patient treatments for nonmedical reasons (58%), maintaining coverage of video and telephone evaluation and management visits (55%), and reducing delays in clinical care resulting from step therapy protocols (53%).

Other important issues included ensuring patients with pre-existing conditions have access to essential benefits and quality specialty care (43%); protecting providers from medical licensing restrictions and liability to deliver care across state lines (35%); addressing Medicare Quality Payment Program reporting requirements and lack of specialty advanced payment models (27%); increasing funding for GI health disparities (24%); and, increasing federal research funding to ensure greater opportunities for diverse early career investigators (20%).
 

Most problematic burdens

Survey respondents identified insurer prior authorization and step therapy burdens as especially problematic. 93% of respondents described the impact of prior authorization on their practices as “significantly burdensome” (61%) or “somewhat burdensome” (32%).

About 95% noted that prior authorization restrictions have impacted patient access to clinically appropriate treatments and patient clinical outcomes “significantly” (56%) or “somewhat” (39%) negatively. 84% described the burdens associated with prior authorization policies as having increased “significantly” (60%) or “somewhat” (24%) over the last 5 years.

AGA’s advocacy work

About 76% of respondents were interested in learning more about the AGA’s advocacy work. We presented some of the various opportunities and resources for members to engage with and contribute to AGA advocacy efforts (see pie chart). Based on the tremendous efforts and dedication of AGA staff, some of these opportunities include educational modules on AGA University, DDW programming, the AGA Washington Insider monthly policy newsletter, preformatted communications available through the AGA Advocacy Action Center, participation in AGA Advocacy Days or the AGA Congressional Advocates Program, service on the AGA Government Affairs Committee, and/or contributing to the AGA Political Action Committee.

Overall, the survey respondents illustrate the diversity and enthusiasm of AGA membership. Importantly, 95% of AGA members responding to the survey agreed these 10 selected policy issues are inclusive of the current top priority issues of the GI community. Amidst an ever-shifting health care landscape, we – the AGA community – must remain vigilant and adaptable to best address expected and unexpected changes and challenges to our patients and colleagues. In this respect, we should encourage constructive communication and dialogue between AGA membership, leadership, other issue stakeholders, government representatives and entities, and payers.

Amit Patel, MD, is a gastroenterologist and associate professor of medicine at Duke University and the Durham Veterans Affairs Medical Center, both in Durham, N.C. He serves on the editorial review board of Gastroenterology. Rotonya McCants Carr, MD, is the Cyrus E. Rubin Chair and division head of gastroenterology at the University of Washington, Seattle. Both Dr. Patel and Dr. Carr serve on the AGA Government Affairs Committee. The contents of this article do not represent the views of the Department of Veterans Affairs.

Reference

Patel A et al. Gastroenterology. 2023 May;164[6]:847-50.

The AGA Government Affairs Committee and staff recently published in Gastroenterology the results from an AGA membership survey on policy priorities and how members can contribute to AGA advocacy efforts.¹ Here, we present key highlights from the survey findings and share opportunities for members to engage in GI advocacy.

AGA advocacy has contributed to significant recent successes that include lowering the average-risk of colorectal cancer screening age from 50 to 45 years, phasing out cost-sharing burdens associated with polypectomy at screening colonoscopy, encouraging federal support to focus on GI cancer disparities, ensuring coverage for telehealth services, expanding colonoscopy coverage after positive noninvasive colorectal cancer screening tests, and mitigating scheduled cuts in Medicare reimbursement for GI services.

Despite these important successes, the GI community faces significant challenges that include persisting GI health disparities; declines in reimbursement and increased prior authorization burdens for GI procedures and clinic visits, limited research funding to address the burden of GI disease, climate change, provider burnout, and increasing administrative burdens (such as insurance prior authorizations and step therapy policies.

The AGA sought to better understand policy priorities of the GI community by disseminating a 34-question policy priority survey to AGA members in December 2022. A total of 251 members responded to the survey with career stage and primary practice setting varying among respondents (Figure 1). The AGA vetted and selected 10 health policy issues of highest interest with 95% of survey respondents agreeing these 10 selected topics covered the top priority issues impacting gastroenterology (Figure 2).

From these 10 policy issues, members were asked to identify the top 5 issues that AGA advocacy efforts should address.

The issues most frequently identified included reducing administrative burdens and patient delays in care because of increased prior authorizations (78%), ensuring fair reimbursement for GI providers (68%), reducing insurance-initiated switching of patient treatments for nonmedical reasons (58%), maintaining coverage of video and telephone evaluation and management visits (55%), and reducing delays in clinical care resulting from step therapy protocols (53%).

Other important issues included ensuring patients with pre-existing conditions have access to essential benefits and quality specialty care (43%); protecting providers from medical licensing restrictions and liability to deliver care across state lines (35%); addressing Medicare Quality Payment Program reporting requirements and lack of specialty advanced payment models (27%); increasing funding for GI health disparities (24%); and, increasing federal research funding to ensure greater opportunities for diverse early career investigators (20%).
 

Most problematic burdens

Survey respondents identified insurer prior authorization and step therapy burdens as especially problematic. 93% of respondents described the impact of prior authorization on their practices as “significantly burdensome” (61%) or “somewhat burdensome” (32%).

About 95% noted that prior authorization restrictions have impacted patient access to clinically appropriate treatments and patient clinical outcomes “significantly” (56%) or “somewhat” (39%) negatively. 84% described the burdens associated with prior authorization policies as having increased “significantly” (60%) or “somewhat” (24%) over the last 5 years.

AGA’s advocacy work

About 76% of respondents were interested in learning more about the AGA’s advocacy work. We presented some of the various opportunities and resources for members to engage with and contribute to AGA advocacy efforts (see pie chart). Based on the tremendous efforts and dedication of AGA staff, some of these opportunities include educational modules on AGA University, DDW programming, the AGA Washington Insider monthly policy newsletter, preformatted communications available through the AGA Advocacy Action Center, participation in AGA Advocacy Days or the AGA Congressional Advocates Program, service on the AGA Government Affairs Committee, and/or contributing to the AGA Political Action Committee.

Overall, the survey respondents illustrate the diversity and enthusiasm of AGA membership. Importantly, 95% of AGA members responding to the survey agreed these 10 selected policy issues are inclusive of the current top priority issues of the GI community. Amidst an ever-shifting health care landscape, we – the AGA community – must remain vigilant and adaptable to best address expected and unexpected changes and challenges to our patients and colleagues. In this respect, we should encourage constructive communication and dialogue between AGA membership, leadership, other issue stakeholders, government representatives and entities, and payers.

Amit Patel, MD, is a gastroenterologist and associate professor of medicine at Duke University and the Durham Veterans Affairs Medical Center, both in Durham, N.C. He serves on the editorial review board of Gastroenterology. Rotonya McCants Carr, MD, is the Cyrus E. Rubin Chair and division head of gastroenterology at the University of Washington, Seattle. Both Dr. Patel and Dr. Carr serve on the AGA Government Affairs Committee. The contents of this article do not represent the views of the Department of Veterans Affairs.

Reference

Patel A et al. Gastroenterology. 2023 May;164[6]:847-50.

“Artificial intelligence (AI) in healthcare refers to the use of machine learning (ML), deep learning, natural language processing, and computer vision to process and analyze large amounts of health care data.”

The preceding line is a direct quote from ChatGPT when prompted with the question “What is AI in health care?” (OpenAI, 2022). AI has rapidly infiltrated our lives. From using facial recognition software to unlock our cellphones to scrolling through targeted media suggested by streaming services, our daily existence is interwoven with algorithms. With the recent introduction of GPT-3 (the model that powers ChatGPT) in late 2022 and its even more capable successor, GPT-4, in March 2023, AI will continue to dominate our everyday environment in even more complex and meaningful ways.

CHEST

For sleep medicine, the initial applications of AI in this field have been innovative and promising. To date, AI has been leveraged to explore sleep staging, respiratory event scoring, characterization of insomnia, prediction of circadian timing from gene expression, endotyping, and phenotyping of obstructive sleep apnea (OSA) (Bandyopadhyay A, et al. Sleep Breath. 2023;27[1]:39). Pépin and colleagues (JAMA Netw Open. 2020;3[1]:e1919657) combined ML with mandibular movement to diagnose OSA with a reasonable agreement to polysomnography as a novel home-based alternative for diagnosis. AI has also been used to predict adherence to positive airway pressure (PAP) therapy in OSA (Scioscia G, et al. Inform Health Soc Care. 2022;47[3]:274) and as a digital intervention tool accessed via a smartphone app for people with insomnia (Philip P, et al, J Med Internet Res. 2020;22[12]:e24268). The data-rich field of sleep medicine is primed for further advancements through AI, albeit with a few hurdles and regulations to overcome before becoming mainstream.
 

Future promise

Sleep medicine is uniquely positioned to develop robust AI algorithms because of its vast data trove. Using AI, scientists can efficiently analyze the raw data from polysomnography, consumer sleep technology (CST), and nightly remote monitoring (from PAP devices) to substantially improve comprehension and management of sleep disorders.

AI can redefine OSA through analysis of the big data available, rather than solely relying on the apnea-hypopnea index. In addition, novel variables such as facial structure; snoring index; temperature trends; and sleep environment, position, and timing using a camera-based contactless technology may be incorporated to enhance the diagnostic accuracy for OSA or better describe sleep quality. AI algorithms can also be embedded into the electronic health record (EHR) to facilitate screening for sleep disorders using patient characteristics, thus accelerating the recognition and evaluation of possible sleep disorders.

CHEST

New ways of collecting data may deliver deeper insights into sleep health, as well. CST such as wearables, nearables, and phone applications are improving with each iteration, resulting in more data about sleep for millions of people over thousands of nights.

AI can help achieve precision medicine by integrating multimodal data to establish endotypes and phenotypes of various sleep disorders. Delineating endotypes and phenotypes allows for personalized treatment recommendations, which may improve patient adherence and health outcomes.

Treatment personalization can also be achieved through AI by predicting compliance to various therapies and responses, as well as by discovering alternative forms of delivery to accomplish desired health outcomes. For example, to predict PAP compliance, we can record a patient encounter and use natural language processing to analyze their opinion of their treatment, extracting relevant keywords and combining such processing with other available data, such as environmental factors, sleep schedule, medical history, and other information extracted from the EHR. As another example, AI can determine the optimal time for cancer therapy by predicting a patient’s circadian timing (Hesse J, et al. Cancers (Basel). 2020;12[11]:3103). Circadian timing of drug delivery may be relevant in other specialties including cardiovascular disease, endocrine disorders, and psychiatric conditions due to its associations with sleep. Integration of the various “-omics” (eg, proteomics, genomics, and transcriptomics) with physiologic, behavioral, and environmental data can offer opportunities for drug discovery and possible prediction of sleep disorders and sleep-related morbidity. Although generative pretrained transformers are currently used to predict text (ie, ChatGPT), it is theoretically possible to also apply this technique to identify patients at risk for future sleep disorders from an earlier age.
 

Challenges to an AI renaissance

Despite making strides in numerous specialties such as radiology, ophthalmology, pathology, oncology, and dermatology, AI has not yet gained mainstream usage. Why isn’t AI as ubiquitous and heavily entrenched in health care as it is in other industries? According to the National Academy of Medicine’s AI in Healthcare: The Hope, The Hype, The Promise, The Peril, there are several realities to address before we fully embrace the AI revolution (Matheny M, et al. 2019).

First, AI algorithms should be trained on quality data that are representative of the population. Interoperability between health care systems and standardization across platforms is required to access large volumes of quality data. The current framework for data gathering is limited due to regulations, patient privacy concerns, and organizational preferences. The challenges to data acquisition and standardization of information will continue to snarl progress unless there are legislative remedies.

Furthermore, datasets should be diverse enough to avoid introducing bias into the AI algorithm. If the dataset is limited and health inequities (eg, societal bias and social determinants of health) are excluded from the training set, then the outcome will perpetuate further explicit and implicit biases.

The Food and Drug Administration (FDA) reviews and authorizes AI/ML-enabled devices. Its current regulatory structure treats AI as a static process and does not allow for exercise of its intrinsic ability to continuously learn from additional data, thereby preventing it from becoming more accurate and evolving with the population over time. A more flexible approach is needed.

Lastly, recent advanced AI algorithms including deep learning and neural network methodology function like a “black box.” The models are not explainable or transparent. Without clear comprehension of its methods, acceptance in clinical practice will be guarded and further risk of inherent biases may ensue.
 

A path forward

But these challenges, like any, can be overcome. Research in the area of differential privacy and the adoption of recent data-sharing standards (eg, HL7 FHIR) can facilitate access to training data (Saripalle R, et al. J Biomed Inform. 2019;94:103188). Regulators are also open to incorporating feedback from the AI research community and industry in favor of innovation in this frenetic domain. The FDA developed the AI/ML Software as a Medical Device Action Plan in response to stakeholder feedback for oversight (FDA, 2021). Specifically, the “Good Machine Learning Practice” will be developed to describe AI/ML best practices (eg, data management, training, interpretability, evaluation, and documentation) to guide product development and standardization.

Sleep medicine has significantly progressed over the last several decades. Rather than maintain the status quo, AI can help fill the existing knowledge gaps, augment clinical practice, and streamline operations by analyzing and processing data at a volume and efficiency beyond human capacity. Fallibility is inevitable in machines and humans; however, like humans, machines can improve with continued training and exposure.

We asked ChatGPT about the future of AI in sleep medicine. It states that AI could have a “significant impact” on sleep disorders diagnosis, treatment, prevention, and sleep tracking and monitoring. Only time will tell if its claims are accurate.

Dr. Tan is Clinical Associate Professor with the Division of Sleep Medicine at the Stanford University School of Medicine. Dr. Bhargava is Clinical Professor with the Division of Pediatric Pulmonary, Asthma, and Sleep Medicine at the Stanford University School of Medicine.

“Artificial intelligence (AI) in healthcare refers to the use of machine learning (ML), deep learning, natural language processing, and computer vision to process and analyze large amounts of health care data.”

The preceding line is a direct quote from ChatGPT when prompted with the question “What is AI in health care?” (OpenAI, 2022). AI has rapidly infiltrated our lives. From using facial recognition software to unlock our cellphones to scrolling through targeted media suggested by streaming services, our daily existence is interwoven with algorithms. With the recent introduction of GPT-3 (the model that powers ChatGPT) in late 2022 and its even more capable successor, GPT-4, in March 2023, AI will continue to dominate our everyday environment in even more complex and meaningful ways.

CHEST

For sleep medicine, the initial applications of AI in this field have been innovative and promising. To date, AI has been leveraged to explore sleep staging, respiratory event scoring, characterization of insomnia, prediction of circadian timing from gene expression, endotyping, and phenotyping of obstructive sleep apnea (OSA) (Bandyopadhyay A, et al. Sleep Breath. 2023;27[1]:39). Pépin and colleagues (JAMA Netw Open. 2020;3[1]:e1919657) combined ML with mandibular movement to diagnose OSA with a reasonable agreement to polysomnography as a novel home-based alternative for diagnosis. AI has also been used to predict adherence to positive airway pressure (PAP) therapy in OSA (Scioscia G, et al. Inform Health Soc Care. 2022;47[3]:274) and as a digital intervention tool accessed via a smartphone app for people with insomnia (Philip P, et al, J Med Internet Res. 2020;22[12]:e24268). The data-rich field of sleep medicine is primed for further advancements through AI, albeit with a few hurdles and regulations to overcome before becoming mainstream.
 

Future promise

Sleep medicine is uniquely positioned to develop robust AI algorithms because of its vast data trove. Using AI, scientists can efficiently analyze the raw data from polysomnography, consumer sleep technology (CST), and nightly remote monitoring (from PAP devices) to substantially improve comprehension and management of sleep disorders.

AI can redefine OSA through analysis of the big data available, rather than solely relying on the apnea-hypopnea index. In addition, novel variables such as facial structure; snoring index; temperature trends; and sleep environment, position, and timing using a camera-based contactless technology may be incorporated to enhance the diagnostic accuracy for OSA or better describe sleep quality. AI algorithms can also be embedded into the electronic health record (EHR) to facilitate screening for sleep disorders using patient characteristics, thus accelerating the recognition and evaluation of possible sleep disorders.

CHEST

New ways of collecting data may deliver deeper insights into sleep health, as well. CST such as wearables, nearables, and phone applications are improving with each iteration, resulting in more data about sleep for millions of people over thousands of nights.

AI can help achieve precision medicine by integrating multimodal data to establish endotypes and phenotypes of various sleep disorders. Delineating endotypes and phenotypes allows for personalized treatment recommendations, which may improve patient adherence and health outcomes.

Treatment personalization can also be achieved through AI by predicting compliance to various therapies and responses, as well as by discovering alternative forms of delivery to accomplish desired health outcomes. For example, to predict PAP compliance, we can record a patient encounter and use natural language processing to analyze their opinion of their treatment, extracting relevant keywords and combining such processing with other available data, such as environmental factors, sleep schedule, medical history, and other information extracted from the EHR. As another example, AI can determine the optimal time for cancer therapy by predicting a patient’s circadian timing (Hesse J, et al. Cancers (Basel). 2020;12[11]:3103). Circadian timing of drug delivery may be relevant in other specialties including cardiovascular disease, endocrine disorders, and psychiatric conditions due to its associations with sleep. Integration of the various “-omics” (eg, proteomics, genomics, and transcriptomics) with physiologic, behavioral, and environmental data can offer opportunities for drug discovery and possible prediction of sleep disorders and sleep-related morbidity. Although generative pretrained transformers are currently used to predict text (ie, ChatGPT), it is theoretically possible to also apply this technique to identify patients at risk for future sleep disorders from an earlier age.
 

Challenges to an AI renaissance

Despite making strides in numerous specialties such as radiology, ophthalmology, pathology, oncology, and dermatology, AI has not yet gained mainstream usage. Why isn’t AI as ubiquitous and heavily entrenched in health care as it is in other industries? According to the National Academy of Medicine’s AI in Healthcare: The Hope, The Hype, The Promise, The Peril, there are several realities to address before we fully embrace the AI revolution (Matheny M, et al. 2019).

First, AI algorithms should be trained on quality data that are representative of the population. Interoperability between health care systems and standardization across platforms is required to access large volumes of quality data. The current framework for data gathering is limited due to regulations, patient privacy concerns, and organizational preferences. The challenges to data acquisition and standardization of information will continue to snarl progress unless there are legislative remedies.

Furthermore, datasets should be diverse enough to avoid introducing bias into the AI algorithm. If the dataset is limited and health inequities (eg, societal bias and social determinants of health) are excluded from the training set, then the outcome will perpetuate further explicit and implicit biases.

The Food and Drug Administration (FDA) reviews and authorizes AI/ML-enabled devices. Its current regulatory structure treats AI as a static process and does not allow for exercise of its intrinsic ability to continuously learn from additional data, thereby preventing it from becoming more accurate and evolving with the population over time. A more flexible approach is needed.

Lastly, recent advanced AI algorithms including deep learning and neural network methodology function like a “black box.” The models are not explainable or transparent. Without clear comprehension of its methods, acceptance in clinical practice will be guarded and further risk of inherent biases may ensue.
 

A path forward

But these challenges, like any, can be overcome. Research in the area of differential privacy and the adoption of recent data-sharing standards (eg, HL7 FHIR) can facilitate access to training data (Saripalle R, et al. J Biomed Inform. 2019;94:103188). Regulators are also open to incorporating feedback from the AI research community and industry in favor of innovation in this frenetic domain. The FDA developed the AI/ML Software as a Medical Device Action Plan in response to stakeholder feedback for oversight (FDA, 2021). Specifically, the “Good Machine Learning Practice” will be developed to describe AI/ML best practices (eg, data management, training, interpretability, evaluation, and documentation) to guide product development and standardization.

Sleep medicine has significantly progressed over the last several decades. Rather than maintain the status quo, AI can help fill the existing knowledge gaps, augment clinical practice, and streamline operations by analyzing and processing data at a volume and efficiency beyond human capacity. Fallibility is inevitable in machines and humans; however, like humans, machines can improve with continued training and exposure.

We asked ChatGPT about the future of AI in sleep medicine. It states that AI could have a “significant impact” on sleep disorders diagnosis, treatment, prevention, and sleep tracking and monitoring. Only time will tell if its claims are accurate.

Dr. Tan is Clinical Associate Professor with the Division of Sleep Medicine at the Stanford University School of Medicine. Dr. Bhargava is Clinical Professor with the Division of Pediatric Pulmonary, Asthma, and Sleep Medicine at the Stanford University School of Medicine.

“Artificial intelligence (AI) in healthcare refers to the use of machine learning (ML), deep learning, natural language processing, and computer vision to process and analyze large amounts of health care data.”

The preceding line is a direct quote from ChatGPT when prompted with the question “What is AI in health care?” (OpenAI, 2022). AI has rapidly infiltrated our lives. From using facial recognition software to unlock our cellphones to scrolling through targeted media suggested by streaming services, our daily existence is interwoven with algorithms. With the recent introduction of GPT-3 (the model that powers ChatGPT) in late 2022 and its even more capable successor, GPT-4, in March 2023, AI will continue to dominate our everyday environment in even more complex and meaningful ways.

CHEST

For sleep medicine, the initial applications of AI in this field have been innovative and promising. To date, AI has been leveraged to explore sleep staging, respiratory event scoring, characterization of insomnia, prediction of circadian timing from gene expression, endotyping, and phenotyping of obstructive sleep apnea (OSA) (Bandyopadhyay A, et al. Sleep Breath. 2023;27[1]:39). Pépin and colleagues (JAMA Netw Open. 2020;3[1]:e1919657) combined ML with mandibular movement to diagnose OSA with a reasonable agreement to polysomnography as a novel home-based alternative for diagnosis. AI has also been used to predict adherence to positive airway pressure (PAP) therapy in OSA (Scioscia G, et al. Inform Health Soc Care. 2022;47[3]:274) and as a digital intervention tool accessed via a smartphone app for people with insomnia (Philip P, et al, J Med Internet Res. 2020;22[12]:e24268). The data-rich field of sleep medicine is primed for further advancements through AI, albeit with a few hurdles and regulations to overcome before becoming mainstream.
 

Future promise

Sleep medicine is uniquely positioned to develop robust AI algorithms because of its vast data trove. Using AI, scientists can efficiently analyze the raw data from polysomnography, consumer sleep technology (CST), and nightly remote monitoring (from PAP devices) to substantially improve comprehension and management of sleep disorders.

AI can redefine OSA through analysis of the big data available, rather than solely relying on the apnea-hypopnea index. In addition, novel variables such as facial structure; snoring index; temperature trends; and sleep environment, position, and timing using a camera-based contactless technology may be incorporated to enhance the diagnostic accuracy for OSA or better describe sleep quality. AI algorithms can also be embedded into the electronic health record (EHR) to facilitate screening for sleep disorders using patient characteristics, thus accelerating the recognition and evaluation of possible sleep disorders.

CHEST

New ways of collecting data may deliver deeper insights into sleep health, as well. CST such as wearables, nearables, and phone applications are improving with each iteration, resulting in more data about sleep for millions of people over thousands of nights.

AI can help achieve precision medicine by integrating multimodal data to establish endotypes and phenotypes of various sleep disorders. Delineating endotypes and phenotypes allows for personalized treatment recommendations, which may improve patient adherence and health outcomes.

Treatment personalization can also be achieved through AI by predicting compliance to various therapies and responses, as well as by discovering alternative forms of delivery to accomplish desired health outcomes. For example, to predict PAP compliance, we can record a patient encounter and use natural language processing to analyze their opinion of their treatment, extracting relevant keywords and combining such processing with other available data, such as environmental factors, sleep schedule, medical history, and other information extracted from the EHR. As another example, AI can determine the optimal time for cancer therapy by predicting a patient’s circadian timing (Hesse J, et al. Cancers (Basel). 2020;12[11]:3103). Circadian timing of drug delivery may be relevant in other specialties including cardiovascular disease, endocrine disorders, and psychiatric conditions due to its associations with sleep. Integration of the various “-omics” (eg, proteomics, genomics, and transcriptomics) with physiologic, behavioral, and environmental data can offer opportunities for drug discovery and possible prediction of sleep disorders and sleep-related morbidity. Although generative pretrained transformers are currently used to predict text (ie, ChatGPT), it is theoretically possible to also apply this technique to identify patients at risk for future sleep disorders from an earlier age.
 

Challenges to an AI renaissance

Despite making strides in numerous specialties such as radiology, ophthalmology, pathology, oncology, and dermatology, AI has not yet gained mainstream usage. Why isn’t AI as ubiquitous and heavily entrenched in health care as it is in other industries? According to the National Academy of Medicine’s AI in Healthcare: The Hope, The Hype, The Promise, The Peril, there are several realities to address before we fully embrace the AI revolution (Matheny M, et al. 2019).

First, AI algorithms should be trained on quality data that are representative of the population. Interoperability between health care systems and standardization across platforms is required to access large volumes of quality data. The current framework for data gathering is limited due to regulations, patient privacy concerns, and organizational preferences. The challenges to data acquisition and standardization of information will continue to snarl progress unless there are legislative remedies.

Furthermore, datasets should be diverse enough to avoid introducing bias into the AI algorithm. If the dataset is limited and health inequities (eg, societal bias and social determinants of health) are excluded from the training set, then the outcome will perpetuate further explicit and implicit biases.

The Food and Drug Administration (FDA) reviews and authorizes AI/ML-enabled devices. Its current regulatory structure treats AI as a static process and does not allow for exercise of its intrinsic ability to continuously learn from additional data, thereby preventing it from becoming more accurate and evolving with the population over time. A more flexible approach is needed.

Lastly, recent advanced AI algorithms including deep learning and neural network methodology function like a “black box.” The models are not explainable or transparent. Without clear comprehension of its methods, acceptance in clinical practice will be guarded and further risk of inherent biases may ensue.
 

A path forward

But these challenges, like any, can be overcome. Research in the area of differential privacy and the adoption of recent data-sharing standards (eg, HL7 FHIR) can facilitate access to training data (Saripalle R, et al. J Biomed Inform. 2019;94:103188). Regulators are also open to incorporating feedback from the AI research community and industry in favor of innovation in this frenetic domain. The FDA developed the AI/ML Software as a Medical Device Action Plan in response to stakeholder feedback for oversight (FDA, 2021). Specifically, the “Good Machine Learning Practice” will be developed to describe AI/ML best practices (eg, data management, training, interpretability, evaluation, and documentation) to guide product development and standardization.

Sleep medicine has significantly progressed over the last several decades. Rather than maintain the status quo, AI can help fill the existing knowledge gaps, augment clinical practice, and streamline operations by analyzing and processing data at a volume and efficiency beyond human capacity. Fallibility is inevitable in machines and humans; however, like humans, machines can improve with continued training and exposure.

We asked ChatGPT about the future of AI in sleep medicine. It states that AI could have a “significant impact” on sleep disorders diagnosis, treatment, prevention, and sleep tracking and monitoring. Only time will tell if its claims are accurate.

Dr. Tan is Clinical Associate Professor with the Division of Sleep Medicine at the Stanford University School of Medicine. Dr. Bhargava is Clinical Professor with the Division of Pediatric Pulmonary, Asthma, and Sleep Medicine at the Stanford University School of Medicine.

The American Gastroenterological Association announced the association’s venture capital fund, GI Opportunity Fund 1, invested in EvoEndo,, a medical device company developing platforms for unsedated transnasal endoscopy (TNE).

“AGA is proud to support EvoEndo® and its innovative technology that has the potential to improve care, save time, resources, and cost for hospitals and the GI community at large,” said Michael L. Kochman, MD, AGAF, MASGE, Wilmott Family Professor of Medicine and Surgery, Center for Endoscopic Innovation, Research and Training, gastroenterology division, University of Pennsylvania Health System; fund manager and adviser, AGA GI Opportunity Fund.

The EvoEndo® Single-Use Endoscopy System received FDA 510(k) clearance in February 2022. The EvoEndo System includes a sterile, single-use, flexible gastroscope designed for unsedated transnasal upper endoscopy and a small portable video controller. The EvoEndo Comfort Kit (not part of the cleared EvoEndo System) includes virtual reality (VR) goggles for patient distraction during the unsedated transnasal endoscopy procedure. Unsedated TNE can be used to evaluate and diagnose a wide range of upper GI conditions that may require frequent monitoring, including eosinophilic esophagitis (EoE), dysphagia, celiac disease, gastroesophageal reflux disease, Barrett’s esophagus, malabsorption, and abdominal pain.

“We are grateful for the support of the AGA, which is a testament to our ongoing commitment to improving GI outcomes with our technology,” said Jonathan T. Hartmann, CEO at EvoEndo. “The AGA has always been at the forefront of improving GI care. Our team could not be more excited that they have recognized EvoEndo, and we look forward to continuing to expand adoption of our technology to the GI community, its physicians, and their patients.”

TNE enabled by EvoEndo’s Single-Use Endoscopy System allows hospitals to move endoscopy procedures from an ambulatory procedural suite to an office-based environment and allows the “traditional” sedation procedure rooms to be used for more complex, therapeutic cases.

“Expanding our fund’s portfolio to include technologies that can transform the pediatric GI landscape is particularly exciting for Varia Ventures,” said Andrea Vossler, cofounder and managing director at Varia Ventures. “EvoEndo® has made significant progress in the TNE category, and we are excited for what’s to come in the future.”

The EvoEndo® Model LE Gastroscope is intended for the visualization of the upper digestive tract in adults and pediatric patients, specifically for the observation, diagnosis, and endoscopic treatment of the esophagus, stomach, and duodenal bulb in patients over the age of five. The gastroscope is a sterile, single-use device and can be inserted orally or transnasally. The EvoEndo® Controller is intended for use with an EvoEndo® Endoscope for endoscopic diagnosis, treatment, and video observation. The EvoEndo System is only intended for use by medical professionals. Physicians and other medical providers interested in learning more about EvoEndo’s TNE system or scheduling demonstrations and training can contact the company here.

The American Gastroenterological Association announced the association’s venture capital fund, GI Opportunity Fund 1, invested in EvoEndo,, a medical device company developing platforms for unsedated transnasal endoscopy (TNE).

“AGA is proud to support EvoEndo® and its innovative technology that has the potential to improve care, save time, resources, and cost for hospitals and the GI community at large,” said Michael L. Kochman, MD, AGAF, MASGE, Wilmott Family Professor of Medicine and Surgery, Center for Endoscopic Innovation, Research and Training, gastroenterology division, University of Pennsylvania Health System; fund manager and adviser, AGA GI Opportunity Fund.

The EvoEndo® Single-Use Endoscopy System received FDA 510(k) clearance in February 2022. The EvoEndo System includes a sterile, single-use, flexible gastroscope designed for unsedated transnasal upper endoscopy and a small portable video controller. The EvoEndo Comfort Kit (not part of the cleared EvoEndo System) includes virtual reality (VR) goggles for patient distraction during the unsedated transnasal endoscopy procedure. Unsedated TNE can be used to evaluate and diagnose a wide range of upper GI conditions that may require frequent monitoring, including eosinophilic esophagitis (EoE), dysphagia, celiac disease, gastroesophageal reflux disease, Barrett’s esophagus, malabsorption, and abdominal pain.

“We are grateful for the support of the AGA, which is a testament to our ongoing commitment to improving GI outcomes with our technology,” said Jonathan T. Hartmann, CEO at EvoEndo. “The AGA has always been at the forefront of improving GI care. Our team could not be more excited that they have recognized EvoEndo, and we look forward to continuing to expand adoption of our technology to the GI community, its physicians, and their patients.”

TNE enabled by EvoEndo’s Single-Use Endoscopy System allows hospitals to move endoscopy procedures from an ambulatory procedural suite to an office-based environment and allows the “traditional” sedation procedure rooms to be used for more complex, therapeutic cases.

“Expanding our fund’s portfolio to include technologies that can transform the pediatric GI landscape is particularly exciting for Varia Ventures,” said Andrea Vossler, cofounder and managing director at Varia Ventures. “EvoEndo® has made significant progress in the TNE category, and we are excited for what’s to come in the future.”

The EvoEndo® Model LE Gastroscope is intended for the visualization of the upper digestive tract in adults and pediatric patients, specifically for the observation, diagnosis, and endoscopic treatment of the esophagus, stomach, and duodenal bulb in patients over the age of five. The gastroscope is a sterile, single-use device and can be inserted orally or transnasally. The EvoEndo® Controller is intended for use with an EvoEndo® Endoscope for endoscopic diagnosis, treatment, and video observation. The EvoEndo System is only intended for use by medical professionals. Physicians and other medical providers interested in learning more about EvoEndo’s TNE system or scheduling demonstrations and training can contact the company here.

The American Gastroenterological Association announced the association’s venture capital fund, GI Opportunity Fund 1, invested in EvoEndo,, a medical device company developing platforms for unsedated transnasal endoscopy (TNE).

“AGA is proud to support EvoEndo® and its innovative technology that has the potential to improve care, save time, resources, and cost for hospitals and the GI community at large,” said Michael L. Kochman, MD, AGAF, MASGE, Wilmott Family Professor of Medicine and Surgery, Center for Endoscopic Innovation, Research and Training, gastroenterology division, University of Pennsylvania Health System; fund manager and adviser, AGA GI Opportunity Fund.

The EvoEndo® Single-Use Endoscopy System received FDA 510(k) clearance in February 2022. The EvoEndo System includes a sterile, single-use, flexible gastroscope designed for unsedated transnasal upper endoscopy and a small portable video controller. The EvoEndo Comfort Kit (not part of the cleared EvoEndo System) includes virtual reality (VR) goggles for patient distraction during the unsedated transnasal endoscopy procedure. Unsedated TNE can be used to evaluate and diagnose a wide range of upper GI conditions that may require frequent monitoring, including eosinophilic esophagitis (EoE), dysphagia, celiac disease, gastroesophageal reflux disease, Barrett’s esophagus, malabsorption, and abdominal pain.

“We are grateful for the support of the AGA, which is a testament to our ongoing commitment to improving GI outcomes with our technology,” said Jonathan T. Hartmann, CEO at EvoEndo. “The AGA has always been at the forefront of improving GI care. Our team could not be more excited that they have recognized EvoEndo, and we look forward to continuing to expand adoption of our technology to the GI community, its physicians, and their patients.”

TNE enabled by EvoEndo’s Single-Use Endoscopy System allows hospitals to move endoscopy procedures from an ambulatory procedural suite to an office-based environment and allows the “traditional” sedation procedure rooms to be used for more complex, therapeutic cases.

“Expanding our fund’s portfolio to include technologies that can transform the pediatric GI landscape is particularly exciting for Varia Ventures,” said Andrea Vossler, cofounder and managing director at Varia Ventures. “EvoEndo® has made significant progress in the TNE category, and we are excited for what’s to come in the future.”

The EvoEndo® Model LE Gastroscope is intended for the visualization of the upper digestive tract in adults and pediatric patients, specifically for the observation, diagnosis, and endoscopic treatment of the esophagus, stomach, and duodenal bulb in patients over the age of five. The gastroscope is a sterile, single-use device and can be inserted orally or transnasally. The EvoEndo® Controller is intended for use with an EvoEndo® Endoscope for endoscopic diagnosis, treatment, and video observation. The EvoEndo System is only intended for use by medical professionals. Physicians and other medical providers interested in learning more about EvoEndo’s TNE system or scheduling demonstrations and training can contact the company here.

Thoracic Oncology and Chest Procedures Network

Lung Cancer Section

The mortality benefit associated with lung cancer screening (LCS) using low dose CT (LDCT) relies, in large part, on adherence rates to annual screening of ≥90%. However, the first 1 million “real world” patients screened in the US had very low (22%) annual adherence (Silvestri, et al. Chest. 2023;S0012-3692[23]00175-7). Refining how we estimate future lung cancer risk is an important opportunity for personalized medicine to bolster adherence to follow-up after initial LDCT.

Researchers at MIT developed Sybil, a deep learning algorithm using radiomics on LDCT for LCS to accurately predict 6-year lung cancer risk (Mikhael, et al. J Clin Oncol. 2023;JCO2201345). The model was developed, trained, and tested in a total of 14,185 National Lung Screening Trial (NLST) participants including all cancer diagnoses. Within these data, Sybil’s accuracy in predicting 1-year lung cancer risk had AUC 0.92 (95% CI, 0.88-0.95) and at 6 years, AUC 0.75 (95% CI, 0.72-0.78).

The model was validated in two large independent LCS datasets, one in the US and one in Taiwan, where an LDCT can be obtained regardless of a personal smoking history. The cancer prevalence in these datasets was 3.4% and 0.9%, respectively. Reassuringly, Sybil’s performance was similar to the NLST data and was maintained in relevant subgroups such as sex, age and smoking history. Furthermore, Sybil reduced the false positive rate in the NLST to 8% at baseline scan, compared with 14% for Lung-RADS 1.0. Sybil’s algorithm, unlike others, has been made publicly available and hopefully will spur further validation and prospective study.

Robert Smyth, MD

Member-at-Large

Thoracic Oncology and Chest Procedures Network

Lung Cancer Section

The mortality benefit associated with lung cancer screening (LCS) using low dose CT (LDCT) relies, in large part, on adherence rates to annual screening of ≥90%. However, the first 1 million “real world” patients screened in the US had very low (22%) annual adherence (Silvestri, et al. Chest. 2023;S0012-3692[23]00175-7). Refining how we estimate future lung cancer risk is an important opportunity for personalized medicine to bolster adherence to follow-up after initial LDCT.

Researchers at MIT developed Sybil, a deep learning algorithm using radiomics on LDCT for LCS to accurately predict 6-year lung cancer risk (Mikhael, et al. J Clin Oncol. 2023;JCO2201345). The model was developed, trained, and tested in a total of 14,185 National Lung Screening Trial (NLST) participants including all cancer diagnoses. Within these data, Sybil’s accuracy in predicting 1-year lung cancer risk had AUC 0.92 (95% CI, 0.88-0.95) and at 6 years, AUC 0.75 (95% CI, 0.72-0.78).

The model was validated in two large independent LCS datasets, one in the US and one in Taiwan, where an LDCT can be obtained regardless of a personal smoking history. The cancer prevalence in these datasets was 3.4% and 0.9%, respectively. Reassuringly, Sybil’s performance was similar to the NLST data and was maintained in relevant subgroups such as sex, age and smoking history. Furthermore, Sybil reduced the false positive rate in the NLST to 8% at baseline scan, compared with 14% for Lung-RADS 1.0. Sybil’s algorithm, unlike others, has been made publicly available and hopefully will spur further validation and prospective study.

Robert Smyth, MD

Member-at-Large

Thoracic Oncology and Chest Procedures Network

Lung Cancer Section

The mortality benefit associated with lung cancer screening (LCS) using low dose CT (LDCT) relies, in large part, on adherence rates to annual screening of ≥90%. However, the first 1 million “real world” patients screened in the US had very low (22%) annual adherence (Silvestri, et al. Chest. 2023;S0012-3692[23]00175-7). Refining how we estimate future lung cancer risk is an important opportunity for personalized medicine to bolster adherence to follow-up after initial LDCT.

Researchers at MIT developed Sybil, a deep learning algorithm using radiomics on LDCT for LCS to accurately predict 6-year lung cancer risk (Mikhael, et al. J Clin Oncol. 2023;JCO2201345). The model was developed, trained, and tested in a total of 14,185 National Lung Screening Trial (NLST) participants including all cancer diagnoses. Within these data, Sybil’s accuracy in predicting 1-year lung cancer risk had AUC 0.92 (95% CI, 0.88-0.95) and at 6 years, AUC 0.75 (95% CI, 0.72-0.78).

The model was validated in two large independent LCS datasets, one in the US and one in Taiwan, where an LDCT can be obtained regardless of a personal smoking history. The cancer prevalence in these datasets was 3.4% and 0.9%, respectively. Reassuringly, Sybil’s performance was similar to the NLST data and was maintained in relevant subgroups such as sex, age and smoking history. Furthermore, Sybil reduced the false positive rate in the NLST to 8% at baseline scan, compared with 14% for Lung-RADS 1.0. Sybil’s algorithm, unlike others, has been made publicly available and hopefully will spur further validation and prospective study.

Robert Smyth, MD

Member-at-Large

Sleep Medicine Network

Respiratory-related Sleep Disorders Section

Home sleep apnea test: Peripheral arterial tonometry

OSA is associated with serious health consequences and increased health care utilization (Kapur V, et al. Sleep. 1999:22[6]:749).

Polysomnography (PSG) is the gold standard for diagnosis, but is expensive, cumbersome, and inconsistently accessible. Home sleep apnea test (HSAT) devices provide a cost effective, convenient method to diagnose OSA and are non-inferior to PSG when considering treatment outcomes in uncomplicated adults with suggestive symptoms (Kapur VK, et al. J Clin Sleep Med. 2017;13[3]:479; Skomro RP, et al. Chest. 2010;138[2]:257).

Utilization of HSAT devices has increased in recent years, partly due to the COVID-19 pandemic and limitations in insurance reimbursement for PSG as the initial diagnostic test. But while there are benefits to home testing with respect to convenience and increased access, we must take the clinical context into account.

Peripheral arterial tonometry (PAT) is a commonly used HSAT technology, which measures peripheral arterial vascular tone using plethysmography at the fingertip. It has a sensitivity of 80% and specificity of 83% for detecting OSA in patients without significant comorbidities and high pretest probability of OSA compared to PSG (Ward KL, et al. J Clin Sleep Med. 2015;11[4]:433). But PAT has also been criticized for lacking diagnostic accuracy, particularly when including patients with mild OSA in analysis (Ichikawa M, et al. J Sleep Res. 2022;31[6]:e13682).

HSAT devices using PAT technology have been studied in patients with atrial fibrillation (Tauman R, et al. Nat Sci Sleep. 2020;12:1115), adolescents (Choi JH, et al. J Clin Sleep Med. 2018;14[10]:1741), and pregnant women (O’Brien LM, et al. J Clin Sleep Med. 2012;8[3]:287), and to assess OSA treatment adequacy with varying sensitivity and specificity. Study in special populations may allow for increased access to testing with the benefit of increased recognition of a generally underdiagnosed disorder. But it’s important to use HSAT alongside awareness of its limitations and it should not replace good clinical judgment when making treatment decisions.

Dimple Tejwani, MD

Member-at-Large

Kara Dupuy-McCauley, MD

Member-at-Large

Sleep Medicine Network

Respiratory-related Sleep Disorders Section

Home sleep apnea test: Peripheral arterial tonometry

OSA is associated with serious health consequences and increased health care utilization (Kapur V, et al. Sleep. 1999:22[6]:749).

Polysomnography (PSG) is the gold standard for diagnosis, but is expensive, cumbersome, and inconsistently accessible. Home sleep apnea test (HSAT) devices provide a cost effective, convenient method to diagnose OSA and are non-inferior to PSG when considering treatment outcomes in uncomplicated adults with suggestive symptoms (Kapur VK, et al. J Clin Sleep Med. 2017;13[3]:479; Skomro RP, et al. Chest. 2010;138[2]:257).

Utilization of HSAT devices has increased in recent years, partly due to the COVID-19 pandemic and limitations in insurance reimbursement for PSG as the initial diagnostic test. But while there are benefits to home testing with respect to convenience and increased access, we must take the clinical context into account.

Peripheral arterial tonometry (PAT) is a commonly used HSAT technology, which measures peripheral arterial vascular tone using plethysmography at the fingertip. It has a sensitivity of 80% and specificity of 83% for detecting OSA in patients without significant comorbidities and high pretest probability of OSA compared to PSG (Ward KL, et al. J Clin Sleep Med. 2015;11[4]:433). But PAT has also been criticized for lacking diagnostic accuracy, particularly when including patients with mild OSA in analysis (Ichikawa M, et al. J Sleep Res. 2022;31[6]:e13682).

HSAT devices using PAT technology have been studied in patients with atrial fibrillation (Tauman R, et al. Nat Sci Sleep. 2020;12:1115), adolescents (Choi JH, et al. J Clin Sleep Med. 2018;14[10]:1741), and pregnant women (O’Brien LM, et al. J Clin Sleep Med. 2012;8[3]:287), and to assess OSA treatment adequacy with varying sensitivity and specificity. Study in special populations may allow for increased access to testing with the benefit of increased recognition of a generally underdiagnosed disorder. But it’s important to use HSAT alongside awareness of its limitations and it should not replace good clinical judgment when making treatment decisions.

Dimple Tejwani, MD

Member-at-Large

Kara Dupuy-McCauley, MD

Member-at-Large

Sleep Medicine Network

Respiratory-related Sleep Disorders Section

Home sleep apnea test: Peripheral arterial tonometry

OSA is associated with serious health consequences and increased health care utilization (Kapur V, et al. Sleep. 1999:22[6]:749).

Polysomnography (PSG) is the gold standard for diagnosis, but is expensive, cumbersome, and inconsistently accessible. Home sleep apnea test (HSAT) devices provide a cost effective, convenient method to diagnose OSA and are non-inferior to PSG when considering treatment outcomes in uncomplicated adults with suggestive symptoms (Kapur VK, et al. J Clin Sleep Med. 2017;13[3]:479; Skomro RP, et al. Chest. 2010;138[2]:257).

Utilization of HSAT devices has increased in recent years, partly due to the COVID-19 pandemic and limitations in insurance reimbursement for PSG as the initial diagnostic test. But while there are benefits to home testing with respect to convenience and increased access, we must take the clinical context into account.

Peripheral arterial tonometry (PAT) is a commonly used HSAT technology, which measures peripheral arterial vascular tone using plethysmography at the fingertip. It has a sensitivity of 80% and specificity of 83% for detecting OSA in patients without significant comorbidities and high pretest probability of OSA compared to PSG (Ward KL, et al. J Clin Sleep Med. 2015;11[4]:433). But PAT has also been criticized for lacking diagnostic accuracy, particularly when including patients with mild OSA in analysis (Ichikawa M, et al. J Sleep Res. 2022;31[6]:e13682).

HSAT devices using PAT technology have been studied in patients with atrial fibrillation (Tauman R, et al. Nat Sci Sleep. 2020;12:1115), adolescents (Choi JH, et al. J Clin Sleep Med. 2018;14[10]:1741), and pregnant women (O’Brien LM, et al. J Clin Sleep Med. 2012;8[3]:287), and to assess OSA treatment adequacy with varying sensitivity and specificity. Study in special populations may allow for increased access to testing with the benefit of increased recognition of a generally underdiagnosed disorder. But it’s important to use HSAT alongside awareness of its limitations and it should not replace good clinical judgment when making treatment decisions.

Dimple Tejwani, MD

Member-at-Large

Kara Dupuy-McCauley, MD

Member-at-Large

Diffuse Lung Disease and Transplant Network

Pulmonary Physiology and Rehabilitation Section

Pulmonary rehabilitation (PR) is an essential component of the management of chronic pulmonary disease. Interest in alternate PR delivery methods has grown in recent years. The official workshop report of the American Thoracic Society (Holland AE, et al. Ann Am Thorac Soc. 2021;18[5]:e12) identified 13 essential components of PR in response to new program models. They encompass patient assessment, program content, method of delivery, and quality assurance, and serve as a guide for successful implementation of emerging programs.

A recent study reported significant improvement in COPD Assessment Test (CAT) scores after PR in both in-person (n=383) and virtual programs (n=171). Similar improvements were found in health outcomes, attendance, and dropout rate (Huynh VC, et al. Chest. 2023;163[3]:529). Another concurrent 3-year prospective study enrolled COPD patients in standard PR (n=89) or community based tele-PR (n=177) at seven tele-sites and one standard site (Alwakeel AJ, et al. Ann Am Thorac Soc. 2022;19[1]:39).

This study established the accessibility, feasibility, and safety of a community based tele-PR program and noted no differences between groups in 6-minute walk test or CAT score improvement. On follow-up, only tele-PR participants had persistent improvements of CAT scores beyond 1 month after completion.

Ongoing challenges with tele-PR include standardization of programs and of initial clinical evaluations that determine eligibility for them. Patients on home oxygen and those with exercise desaturation are often excluded, but they have the most potential for improvement. Studies are needed to determine the characteristics of patients who would benefit most from non-traditional models of PR.

Fatima Zeba, MD

Fellow-in-Training

Rania Abdallah, MD

Member-at-Large

Malik Khurram Khan, MD

Member-at-Large

Diffuse Lung Disease and Transplant Network

Pulmonary Physiology and Rehabilitation Section

Pulmonary rehabilitation (PR) is an essential component of the management of chronic pulmonary disease. Interest in alternate PR delivery methods has grown in recent years. The official workshop report of the American Thoracic Society (Holland AE, et al. Ann Am Thorac Soc. 2021;18[5]:e12) identified 13 essential components of PR in response to new program models. They encompass patient assessment, program content, method of delivery, and quality assurance, and serve as a guide for successful implementation of emerging programs.

A recent study reported significant improvement in COPD Assessment Test (CAT) scores after PR in both in-person (n=383) and virtual programs (n=171). Similar improvements were found in health outcomes, attendance, and dropout rate (Huynh VC, et al. Chest. 2023;163[3]:529). Another concurrent 3-year prospective study enrolled COPD patients in standard PR (n=89) or community based tele-PR (n=177) at seven tele-sites and one standard site (Alwakeel AJ, et al. Ann Am Thorac Soc. 2022;19[1]:39).

This study established the accessibility, feasibility, and safety of a community based tele-PR program and noted no differences between groups in 6-minute walk test or CAT score improvement. On follow-up, only tele-PR participants had persistent improvements of CAT scores beyond 1 month after completion.

Ongoing challenges with tele-PR include standardization of programs and of initial clinical evaluations that determine eligibility for them. Patients on home oxygen and those with exercise desaturation are often excluded, but they have the most potential for improvement. Studies are needed to determine the characteristics of patients who would benefit most from non-traditional models of PR.

Fatima Zeba, MD

Fellow-in-Training

Rania Abdallah, MD

Member-at-Large

Malik Khurram Khan, MD

Member-at-Large

Diffuse Lung Disease and Transplant Network

Pulmonary Physiology and Rehabilitation Section

Pulmonary rehabilitation (PR) is an essential component of the management of chronic pulmonary disease. Interest in alternate PR delivery methods has grown in recent years. The official workshop report of the American Thoracic Society (Holland AE, et al. Ann Am Thorac Soc. 2021;18[5]:e12) identified 13 essential components of PR in response to new program models. They encompass patient assessment, program content, method of delivery, and quality assurance, and serve as a guide for successful implementation of emerging programs.

A recent study reported significant improvement in COPD Assessment Test (CAT) scores after PR in both in-person (n=383) and virtual programs (n=171). Similar improvements were found in health outcomes, attendance, and dropout rate (Huynh VC, et al. Chest. 2023;163[3]:529). Another concurrent 3-year prospective study enrolled COPD patients in standard PR (n=89) or community based tele-PR (n=177) at seven tele-sites and one standard site (Alwakeel AJ, et al. Ann Am Thorac Soc. 2022;19[1]:39).

This study established the accessibility, feasibility, and safety of a community based tele-PR program and noted no differences between groups in 6-minute walk test or CAT score improvement. On follow-up, only tele-PR participants had persistent improvements of CAT scores beyond 1 month after completion.

Ongoing challenges with tele-PR include standardization of programs and of initial clinical evaluations that determine eligibility for them. Patients on home oxygen and those with exercise desaturation are often excluded, but they have the most potential for improvement. Studies are needed to determine the characteristics of patients who would benefit most from non-traditional models of PR.

Fatima Zeba, MD

Fellow-in-Training

Rania Abdallah, MD

Member-at-Large

Malik Khurram Khan, MD

Member-at-Large

Diffuse Lung Disease and Lung Transplant Network

Lung Transplant Section

In March 2023, the Composite Allocation Score (CAS) will replace the Lung Allocation Score (LAS) for matching donor lungs to transplant candidates in the United States. The LAS was implemented in 2005 to improve lung organ utilization. Its score was determined by two main factors: (1) risk of 1-year waitlist mortality and (2) likelihood of 1-year post-transplant survival, with the first factor having twice the weight. However, LAS did not account for candidate biology attributes, such as pediatric age, blood type, allosensitization, or height. Long-term survival outcomes under LAS may be reduced, given the greater emphasis on waitlist mortality. Candidates were also subjected to strict geographical distributions within a 250-nautical-mile radius, which frequently resulted in those with lower LAS obtaining a transplant. CAS differs from the LAS in that it assigns an allocation score in a continuous distribution based on the following factors: medical urgency, expected survival benefit following transplant, pediatric age, blood type, HLA antibody sensitization, candidate height, and geographical proximity to the donor organ. Each factor has a specific weight, and because donor factors contribute to CAS, a candidate’s score changes with each donor-recipient match run. Continuous distribution removes hard geographical boundaries and aims for more equitable organ allocation. To understand how allocation might change with CAS, Valapour and colleagues created various CAS scenarios using data from individuals on the national transplant waiting list (Am J Transplant. 2022;22[12]:2971).

They found that waitlist deaths decreased by 36%-47%. This effect was greatest in scenarios where there was less weight on placement efficiency (ie, geography) and more weight on post-transplant outcomes. Transplant system equity also improved in their simulation models. It will be exciting to see how candidate and recipient outcomes are affected once CAS is implemented.

Gloria Li, MD
Member-at-Large

Keith Wille, MD, MSPH
Member-at-Large

Reference

1. United Network for Organ Sharing. www.unos.org.

Diffuse Lung Disease and Lung Transplant Network

Lung Transplant Section

In March 2023, the Composite Allocation Score (CAS) will replace the Lung Allocation Score (LAS) for matching donor lungs to transplant candidates in the United States. The LAS was implemented in 2005 to improve lung organ utilization. Its score was determined by two main factors: (1) risk of 1-year waitlist mortality and (2) likelihood of 1-year post-transplant survival, with the first factor having twice the weight. However, LAS did not account for candidate biology attributes, such as pediatric age, blood type, allosensitization, or height. Long-term survival outcomes under LAS may be reduced, given the greater emphasis on waitlist mortality. Candidates were also subjected to strict geographical distributions within a 250-nautical-mile radius, which frequently resulted in those with lower LAS obtaining a transplant. CAS differs from the LAS in that it assigns an allocation score in a continuous distribution based on the following factors: medical urgency, expected survival benefit following transplant, pediatric age, blood type, HLA antibody sensitization, candidate height, and geographical proximity to the donor organ. Each factor has a specific weight, and because donor factors contribute to CAS, a candidate’s score changes with each donor-recipient match run. Continuous distribution removes hard geographical boundaries and aims for more equitable organ allocation. To understand how allocation might change with CAS, Valapour and colleagues created various CAS scenarios using data from individuals on the national transplant waiting list (Am J Transplant. 2022;22[12]:2971).

They found that waitlist deaths decreased by 36%-47%. This effect was greatest in scenarios where there was less weight on placement efficiency (ie, geography) and more weight on post-transplant outcomes. Transplant system equity also improved in their simulation models. It will be exciting to see how candidate and recipient outcomes are affected once CAS is implemented.

Gloria Li, MD
Member-at-Large

Keith Wille, MD, MSPH
Member-at-Large

Reference

1. United Network for Organ Sharing. www.unos.org.

Diffuse Lung Disease and Lung Transplant Network

Lung Transplant Section

In March 2023, the Composite Allocation Score (CAS) will replace the Lung Allocation Score (LAS) for matching donor lungs to transplant candidates in the United States. The LAS was implemented in 2005 to improve lung organ utilization. Its score was determined by two main factors: (1) risk of 1-year waitlist mortality and (2) likelihood of 1-year post-transplant survival, with the first factor having twice the weight. However, LAS did not account for candidate biology attributes, such as pediatric age, blood type, allosensitization, or height. Long-term survival outcomes under LAS may be reduced, given the greater emphasis on waitlist mortality. Candidates were also subjected to strict geographical distributions within a 250-nautical-mile radius, which frequently resulted in those with lower LAS obtaining a transplant. CAS differs from the LAS in that it assigns an allocation score in a continuous distribution based on the following factors: medical urgency, expected survival benefit following transplant, pediatric age, blood type, HLA antibody sensitization, candidate height, and geographical proximity to the donor organ. Each factor has a specific weight, and because donor factors contribute to CAS, a candidate’s score changes with each donor-recipient match run. Continuous distribution removes hard geographical boundaries and aims for more equitable organ allocation. To understand how allocation might change with CAS, Valapour and colleagues created various CAS scenarios using data from individuals on the national transplant waiting list (Am J Transplant. 2022;22[12]:2971).

They found that waitlist deaths decreased by 36%-47%. This effect was greatest in scenarios where there was less weight on placement efficiency (ie, geography) and more weight on post-transplant outcomes. Transplant system equity also improved in their simulation models. It will be exciting to see how candidate and recipient outcomes are affected once CAS is implemented.

Gloria Li, MD
Member-at-Large

Keith Wille, MD, MSPH
Member-at-Large

Reference

1. United Network for Organ Sharing. www.unos.org.

Critical Care Network

Sepsis/Shock Section

Sepsis remains the commonest diagnosis for hospital stays in the United States and the top hospital readmission diagnosis, with aggregate costs of $23.7 billion in 2013 (https://datatools.ahrq.gov/hcup-fast-stats; Kim H, et al. Front Public Health. 2022;10:882715; Torio C, Moore B. 2016. HCUP Statistical Brief #204).

Since 2013, the Hospital Readmissions Reduction Program (HRRP) adopted pneumonia as a readmission measure, and in 2016, this measure included sepsis patients with pneumonia and aspiration pneumonia. For 2023, the Centers for Medicare and Medicaid Services (CMS) suppressed pneumonia as a readmission measure due to COVID-19’s significant impact (www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program). Though sepsis is not a direct readmission measure, it could be one in the future. Studies found higher long-term mortality for patients with sepsis readmitted for recurrent sepsis (Pandolfi F, et al. Crit Care. 2022;26[1]:371; McNamara JF, et al. Int J Infect Dis. 2022;114:34).

A systematic review showed independent risk factors predictive of sepsis readmission: older age, male gender, African American and Asian ethnicities, higher baseline comorbidities, and discharge to a facility. In contrast, sepsis-specific risk factors were extended-spectrum beta-lactamase gram-negative bacterial infections, increased hospital length of stay during initial admission, and increased illness severity (Shankar-Hari M, et al. Intensive Care Med. 2020;46[4]:619; Amrollahi F, et al. J Am Med Inform Assoc. 2022;29[7]:1263; Gadre SK, et al. Chest. 2019;155[3]:483).

McNamara and colleagues found that patients with gram-negative bloodstream infections had higher readmission rates for sepsis during a 4-year follow-up and had a lower 5-year survival rates Int J Infect Dis. 2022;114:34). Hospitals can prevent readmissions by strengthening antimicrobial stewardship programs to ensure appropriate and adequate treatment of initial infections. Other predictive risk factors for readmission are lower socioeconomic status (Shankar-Hari M, et al. Intensive Care Med. 2020;46[4]:619), lack of health insurance, and delays seeking medical care due to lack of transportation (Amrollahi F, et al. J Am Med Inform Assoc. 2022;29[7]:1263).

Sepsis readmissions can be mitigated by predictive analytics, better access to health care, establishing post-discharge clinic follow-ups, transportation arrangements, and telemedicine. More research is needed to evaluate sepsis readmission prevention.

Shu Xian Lee, MD

Fellow-in-Training

Deepa Gotur, MD, FCCP

Member-at-Large

Critical Care Network

Sepsis/Shock Section

Sepsis remains the commonest diagnosis for hospital stays in the United States and the top hospital readmission diagnosis, with aggregate costs of $23.7 billion in 2013 (https://datatools.ahrq.gov/hcup-fast-stats; Kim H, et al. Front Public Health. 2022;10:882715; Torio C, Moore B. 2016. HCUP Statistical Brief #204).

Since 2013, the Hospital Readmissions Reduction Program (HRRP) adopted pneumonia as a readmission measure, and in 2016, this measure included sepsis patients with pneumonia and aspiration pneumonia. For 2023, the Centers for Medicare and Medicaid Services (CMS) suppressed pneumonia as a readmission measure due to COVID-19’s significant impact (www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program). Though sepsis is not a direct readmission measure, it could be one in the future. Studies found higher long-term mortality for patients with sepsis readmitted for recurrent sepsis (Pandolfi F, et al. Crit Care. 2022;26[1]:371; McNamara JF, et al. Int J Infect Dis. 2022;114:34).

A systematic review showed independent risk factors predictive of sepsis readmission: older age, male gender, African American and Asian ethnicities, higher baseline comorbidities, and discharge to a facility. In contrast, sepsis-specific risk factors were extended-spectrum beta-lactamase gram-negative bacterial infections, increased hospital length of stay during initial admission, and increased illness severity (Shankar-Hari M, et al. Intensive Care Med. 2020;46[4]:619; Amrollahi F, et al. J Am Med Inform Assoc. 2022;29[7]:1263; Gadre SK, et al. Chest. 2019;155[3]:483).

McNamara and colleagues found that patients with gram-negative bloodstream infections had higher readmission rates for sepsis during a 4-year follow-up and had a lower 5-year survival rates Int J Infect Dis. 2022;114:34). Hospitals can prevent readmissions by strengthening antimicrobial stewardship programs to ensure appropriate and adequate treatment of initial infections. Other predictive risk factors for readmission are lower socioeconomic status (Shankar-Hari M, et al. Intensive Care Med. 2020;46[4]:619), lack of health insurance, and delays seeking medical care due to lack of transportation (Amrollahi F, et al. J Am Med Inform Assoc. 2022;29[7]:1263).

Sepsis readmissions can be mitigated by predictive analytics, better access to health care, establishing post-discharge clinic follow-ups, transportation arrangements, and telemedicine. More research is needed to evaluate sepsis readmission prevention.

Shu Xian Lee, MD

Fellow-in-Training

Deepa Gotur, MD, FCCP

Member-at-Large

Critical Care Network

Sepsis/Shock Section

Sepsis remains the commonest diagnosis for hospital stays in the United States and the top hospital readmission diagnosis, with aggregate costs of $23.7 billion in 2013 (https://datatools.ahrq.gov/hcup-fast-stats; Kim H, et al. Front Public Health. 2022;10:882715; Torio C, Moore B. 2016. HCUP Statistical Brief #204).

Since 2013, the Hospital Readmissions Reduction Program (HRRP) adopted pneumonia as a readmission measure, and in 2016, this measure included sepsis patients with pneumonia and aspiration pneumonia. For 2023, the Centers for Medicare and Medicaid Services (CMS) suppressed pneumonia as a readmission measure due to COVID-19’s significant impact (www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program). Though sepsis is not a direct readmission measure, it could be one in the future. Studies found higher long-term mortality for patients with sepsis readmitted for recurrent sepsis (Pandolfi F, et al. Crit Care. 2022;26[1]:371; McNamara JF, et al. Int J Infect Dis. 2022;114:34).

A systematic review showed independent risk factors predictive of sepsis readmission: older age, male gender, African American and Asian ethnicities, higher baseline comorbidities, and discharge to a facility. In contrast, sepsis-specific risk factors were extended-spectrum beta-lactamase gram-negative bacterial infections, increased hospital length of stay during initial admission, and increased illness severity (Shankar-Hari M, et al. Intensive Care Med. 2020;46[4]:619; Amrollahi F, et al. J Am Med Inform Assoc. 2022;29[7]:1263; Gadre SK, et al. Chest. 2019;155[3]:483).

McNamara and colleagues found that patients with gram-negative bloodstream infections had higher readmission rates for sepsis during a 4-year follow-up and had a lower 5-year survival rates Int J Infect Dis. 2022;114:34). Hospitals can prevent readmissions by strengthening antimicrobial stewardship programs to ensure appropriate and adequate treatment of initial infections. Other predictive risk factors for readmission are lower socioeconomic status (Shankar-Hari M, et al. Intensive Care Med. 2020;46[4]:619), lack of health insurance, and delays seeking medical care due to lack of transportation (Amrollahi F, et al. J Am Med Inform Assoc. 2022;29[7]:1263).

Sepsis readmissions can be mitigated by predictive analytics, better access to health care, establishing post-discharge clinic follow-ups, transportation arrangements, and telemedicine. More research is needed to evaluate sepsis readmission prevention.

Shu Xian Lee, MD

Fellow-in-Training

Deepa Gotur, MD, FCCP

Member-at-Large

The American Board of Internal Medicine’s (ABIM) Longitudinal Knowledge Assessment (LKA®) has entered its second year of availability, and was launched in January 2023 for the disciplines of pulmonary disease and critical care medicine, as well as infectious disease. Tens of thousands of physicians nationwide are taking advantage of this option for a flexible assessment that also incorporates more learning opportunities. If you are due for an ABIM assessment in 2023 in pulmonary disease or critical care medicine, the deadline to enroll in LKA is June 30, 2023.

Many diplomates—including myself—are taking advantage of the flexibility offered by the LKA to maintain certification in one or more specialties. Others are using it to regain certifications that they allowed to lapse. Both scenarios offer a lower-stakes and less time-intensive route to maintaining or recertifying that also promotes relevant and timely learning in a given discipline. Remember that you can still choose to take the traditional 10-year Maintenance of Certification (MOC) exam in any discipline if you feel that works better for you than the LKA.

CHEST

Detailed information about the LKA and how it works, as well as a walkthrough video and FAQs, are available on ABIM’s website. Following are some suggestions based on the experience of physicians who are currently enrolled in the LKA.
 

Take it one day at a time

With 30 questions released each quarter, the LKA is designed to be manageable and work with your schedule. You could take one question a day or every few days over the course of the quarter or you can choose to do all 30 in one sitting—whatever works for you. Each correct answer also earns you 0.2 MOC points, meaning that over time, you could potentially achieve all of your required MOC points through the LKA alone.

Don’t forget your time bank

Every question has a 4-minute time limit, but if you need more time to think through a question or look up a resource, you can draw from a 30-minute extra time bank that renews each year. On average, physicians answer most questions in less than 2 minutes.

Use resources

The LKA is essentially “open book,” meaning you can use any resource to help with a question except for another physician. Some physicians cite online sites or hard copy medical references as reliable resources, and CHEST offers additional resources that can be helpful, as well.

Set up your work area for success

Many physicians report using two screens or two devices while taking the LKA—one with the LKA platform open to answer questions and one for looking up resources. Questions involving viewing of media will prompt you when a larger screen may be helpful.

Consider the cost savings

The LKA is included in your annual MOC fee for each certificate you maintain at no additional cost. If you use the LKA to meet your MOC assessment requirement, you don’t need to take the traditional 10-year MOC exam or pay an additional exam fee.

Gauge areas of strength and weakness

Most questions on the LKA will give you rationale and feedback after you’ve answered, allowing you to brush up on knowledge gaps. In addition, you’ll receive interim quarterly score reports starting after your fifth quarter of participation showing your current score relative to the passing standard, including areas where you might need to focus more study.

Regain lapsed certification

The LKA is a simple and lower-stakes way to regain certification in a specialty that has lapsed, though it should be noted that you must complete your 5-year LKA cycle and achieve a passing score for the certificate to become active again. In the meantime, you can use the LKA to refresh your knowledge of current information in that specialty.

Ask about disability accommodations

ABIM offers some accommodations for the LKA in compliance with Title III of the Americans with Disabilities Act (ADA) for individuals with documented disabilities who demonstrate a need for accommodation. Physicians requesting special testing accommodations under the ADA can submit a request on ABIM’s website.

If you’re due for an assessment in 2023, and you haven’t looked into the LKA yet, now is the time: the second quarter closes on June 30, 2023, and you will not be able to enroll after that date. Sign in to your ABIM Physician Portal to see if you are eligible and visit ABIM.org/LKA to learn more.

The American Board of Internal Medicine’s (ABIM) Longitudinal Knowledge Assessment (LKA®) has entered its second year of availability, and was launched in January 2023 for the disciplines of pulmonary disease and critical care medicine, as well as infectious disease. Tens of thousands of physicians nationwide are taking advantage of this option for a flexible assessment that also incorporates more learning opportunities. If you are due for an ABIM assessment in 2023 in pulmonary disease or critical care medicine, the deadline to enroll in LKA is June 30, 2023.

Many diplomates—including myself—are taking advantage of the flexibility offered by the LKA to maintain certification in one or more specialties. Others are using it to regain certifications that they allowed to lapse. Both scenarios offer a lower-stakes and less time-intensive route to maintaining or recertifying that also promotes relevant and timely learning in a given discipline. Remember that you can still choose to take the traditional 10-year Maintenance of Certification (MOC) exam in any discipline if you feel that works better for you than the LKA.

CHEST

Detailed information about the LKA and how it works, as well as a walkthrough video and FAQs, are available on ABIM’s website. Following are some suggestions based on the experience of physicians who are currently enrolled in the LKA.
 

Take it one day at a time

With 30 questions released each quarter, the LKA is designed to be manageable and work with your schedule. You could take one question a day or every few days over the course of the quarter or you can choose to do all 30 in one sitting—whatever works for you. Each correct answer also earns you 0.2 MOC points, meaning that over time, you could potentially achieve all of your required MOC points through the LKA alone.

Don’t forget your time bank

Every question has a 4-minute time limit, but if you need more time to think through a question or look up a resource, you can draw from a 30-minute extra time bank that renews each year. On average, physicians answer most questions in less than 2 minutes.

Use resources

The LKA is essentially “open book,” meaning you can use any resource to help with a question except for another physician. Some physicians cite online sites or hard copy medical references as reliable resources, and CHEST offers additional resources that can be helpful, as well.

Set up your work area for success

Many physicians report using two screens or two devices while taking the LKA—one with the LKA platform open to answer questions and one for looking up resources. Questions involving viewing of media will prompt you when a larger screen may be helpful.

Consider the cost savings

The LKA is included in your annual MOC fee for each certificate you maintain at no additional cost. If you use the LKA to meet your MOC assessment requirement, you don’t need to take the traditional 10-year MOC exam or pay an additional exam fee.

Gauge areas of strength and weakness

Most questions on the LKA will give you rationale and feedback after you’ve answered, allowing you to brush up on knowledge gaps. In addition, you’ll receive interim quarterly score reports starting after your fifth quarter of participation showing your current score relative to the passing standard, including areas where you might need to focus more study.

Regain lapsed certification

The LKA is a simple and lower-stakes way to regain certification in a specialty that has lapsed, though it should be noted that you must complete your 5-year LKA cycle and achieve a passing score for the certificate to become active again. In the meantime, you can use the LKA to refresh your knowledge of current information in that specialty.

Ask about disability accommodations

ABIM offers some accommodations for the LKA in compliance with Title III of the Americans with Disabilities Act (ADA) for individuals with documented disabilities who demonstrate a need for accommodation. Physicians requesting special testing accommodations under the ADA can submit a request on ABIM’s website.

If you’re due for an assessment in 2023, and you haven’t looked into the LKA yet, now is the time: the second quarter closes on June 30, 2023, and you will not be able to enroll after that date. Sign in to your ABIM Physician Portal to see if you are eligible and visit ABIM.org/LKA to learn more.

The American Board of Internal Medicine’s (ABIM) Longitudinal Knowledge Assessment (LKA®) has entered its second year of availability, and was launched in January 2023 for the disciplines of pulmonary disease and critical care medicine, as well as infectious disease. Tens of thousands of physicians nationwide are taking advantage of this option for a flexible assessment that also incorporates more learning opportunities. If you are due for an ABIM assessment in 2023 in pulmonary disease or critical care medicine, the deadline to enroll in LKA is June 30, 2023.

Many diplomates—including myself—are taking advantage of the flexibility offered by the LKA to maintain certification in one or more specialties. Others are using it to regain certifications that they allowed to lapse. Both scenarios offer a lower-stakes and less time-intensive route to maintaining or recertifying that also promotes relevant and timely learning in a given discipline. Remember that you can still choose to take the traditional 10-year Maintenance of Certification (MOC) exam in any discipline if you feel that works better for you than the LKA.

CHEST

Detailed information about the LKA and how it works, as well as a walkthrough video and FAQs, are available on ABIM’s website. Following are some suggestions based on the experience of physicians who are currently enrolled in the LKA.
 

Take it one day at a time

With 30 questions released each quarter, the LKA is designed to be manageable and work with your schedule. You could take one question a day or every few days over the course of the quarter or you can choose to do all 30 in one sitting—whatever works for you. Each correct answer also earns you 0.2 MOC points, meaning that over time, you could potentially achieve all of your required MOC points through the LKA alone.

Don’t forget your time bank

Every question has a 4-minute time limit, but if you need more time to think through a question or look up a resource, you can draw from a 30-minute extra time bank that renews each year. On average, physicians answer most questions in less than 2 minutes.

Use resources

The LKA is essentially “open book,” meaning you can use any resource to help with a question except for another physician. Some physicians cite online sites or hard copy medical references as reliable resources, and CHEST offers additional resources that can be helpful, as well.

Set up your work area for success

Many physicians report using two screens or two devices while taking the LKA—one with the LKA platform open to answer questions and one for looking up resources. Questions involving viewing of media will prompt you when a larger screen may be helpful.

Consider the cost savings

The LKA is included in your annual MOC fee for each certificate you maintain at no additional cost. If you use the LKA to meet your MOC assessment requirement, you don’t need to take the traditional 10-year MOC exam or pay an additional exam fee.

Gauge areas of strength and weakness

Most questions on the LKA will give you rationale and feedback after you’ve answered, allowing you to brush up on knowledge gaps. In addition, you’ll receive interim quarterly score reports starting after your fifth quarter of participation showing your current score relative to the passing standard, including areas where you might need to focus more study.

Regain lapsed certification

The LKA is a simple and lower-stakes way to regain certification in a specialty that has lapsed, though it should be noted that you must complete your 5-year LKA cycle and achieve a passing score for the certificate to become active again. In the meantime, you can use the LKA to refresh your knowledge of current information in that specialty.

Ask about disability accommodations

ABIM offers some accommodations for the LKA in compliance with Title III of the Americans with Disabilities Act (ADA) for individuals with documented disabilities who demonstrate a need for accommodation. Physicians requesting special testing accommodations under the ADA can submit a request on ABIM’s website.

If you’re due for an assessment in 2023, and you haven’t looked into the LKA yet, now is the time: the second quarter closes on June 30, 2023, and you will not be able to enroll after that date. Sign in to your ABIM Physician Portal to see if you are eligible and visit ABIM.org/LKA to learn more.