Novitas Article on Bioengineered Coverage Q4206

 https://www.cms.gov/medicare-coverage-database/view/article.aspx?articleId=54117

Billing and Coding: Application of Bioengineered Skin Substitutes to Lower Extremity Chronic Non-Healing Wounds

A54117

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Contractor Information

Contractor Name	Contract Type	Contract Number	Jurisdiction	States
Novitas Solutions, Inc.	A and B MAC	04111 - MAC A	J - H	Colorado
Novitas Solutions, Inc.	A and B MAC	04112 - MAC B	J - H	Colorado
Novitas Solutions, Inc.	A and B MAC	04211 - MAC A	J - H	New Mexico
Novitas Solutions, Inc.	A and B MAC	04212 - MAC B	J - H	New Mexico
Novitas Solutions, Inc.	A and B MAC	04311 - MAC A	J - H	Oklahoma
Novitas Solutions, Inc.	A and B MAC	04312 - MAC B	J - H	Oklahoma
Novitas Solutions, Inc.	A and B MAC	04411 - MAC A	J - H	Texas
Novitas Solutions, Inc.	A and B MAC	04412 - MAC B	J - H	Texas
Novitas Solutions, Inc.	A and B MAC	04911 - MAC A	J - H	Colorado New Mexico Oklahoma Texas
Novitas Solutions, Inc.	A and B MAC	07101 - MAC A	J - H	Arkansas
Novitas Solutions, Inc.	A and B MAC	07102 - MAC B	J - H	Arkansas
Novitas Solutions, Inc.	A and B MAC	07201 - MAC A	J - H	Louisiana
Novitas Solutions, Inc.	A and B MAC	07202 - MAC B	J - H	Louisiana
Novitas Solutions, Inc.	A and B MAC	07301 - MAC A	J - H	Mississippi
Novitas Solutions, Inc.	A and B MAC	07302 - MAC B	J - H	Mississippi
Novitas Solutions, Inc.	A and B MAC	12101 - MAC A	J - L	Delaware
Novitas Solutions, Inc.	A and B MAC	12102 - MAC B	J - L	Delaware
Novitas Solutions, Inc.	A and B MAC	12201 - MAC A	J - L	District of Columbia
Novitas Solutions, Inc.	A and B MAC	12202 - MAC B	J - L	District of Columbia
Novitas Solutions, Inc.	A and B MAC	12301 - MAC A	J - L	Maryland
Novitas Solutions, Inc.	A and B MAC	12302 - MAC B	J - L	Maryland
Novitas Solutions, Inc.	A and B MAC	12401 - MAC A	J - L	New Jersey
Novitas Solutions, Inc.	A and B MAC	12402 - MAC B	J - L	New Jersey
Novitas Solutions, Inc.	A and B MAC	12501 - MAC A	J - L	Pennsylvania
Novitas Solutions, Inc.	A and B MAC	12502 - MAC B	J - L	Pennsylvania
Novitas Solutions, Inc.	A and B MAC	12901 - MAC A	J - L	Delaware District of Columbia Maryland New Jersey Pennsylvania

Article Information

General Information

Article ID
A54117

Article Title
Billing and Coding: Application of Bioengineered Skin Substitutes to Lower Extremity Chronic Non-Healing Wounds

Article Type
Billing and Coding

Original Effective Date
10/01/2015

Revision Effective Date
08/13/2020

Revision Ending Date
N/A

Retirement Date
N/A

AMA CPT / ADA CDT / AHA NUBC Copyright Statement

CPT codes, descriptions and other data only are copyright 2021 American Medical Association. All Rights Reserved. Applicable FARS/HHSARS apply.

Fee schedules, relative value units, conversion factors and/or related components are not assigned by the AMA, are not part of CPT, and the AMA is not recommending their use. The AMA does not directly or indirectly practice medicine or dispense medical services. The AMA assumes no liability for data contained or not contained herein.

Current Dental Terminology © 2021 American Dental Association. All rights reserved.

Copyright © 2013 - 2021, the American Hospital Association, Chicago, Illinois. Reproduced by CMS with permission. No portion of the American Hospital Association (AHA) copyrighted materials contained within this publication may be copied without the express written consent of the AHA. AHA copyrighted materials including the UB-04 codes and descriptions may not be removed, copied, or utilized within any software, product, service, solution or derivative work without the written consent of the AHA. If an entity wishes to utilize any AHA materials, please contact the AHA at 312-893-6816. Making copies or utilizing the content of the UB-04 Manual, including the codes and/or descriptions, for internal purposes, resale and/or to be used in any product or publication; creating any modified or derivative work of the UB-04 Manual and/or codes and descriptions; and/or making any commercial use of UB-04 Manual or any portion thereof, including the codes and/or descriptions, is only authorized with an express license from the American Hospital Association. To license the electronic data file of UB-04 Data Specifications, contact Tim Carlson at (312) 893-6816. You may also contact us at ub04@aha.org.

CMS National Coverage Policy

Social Security Act (Title XVIII) Standard References:

• Title XVIII of the Social Security Act, Section 1833(e) states that no payment shall be made to any provider of services or other person under this part unless there has been furnished such information as may be necessary in order to determine the amounts due such provider or other person under this part for the period with respect to which the amounts are being paid or for any prior period.

Article Guidance

Article Text

This Billing and Coding Article provides billing and coding guidance for Local Coverage Determination (LCD) L35041, Application of Bioengineered Skin Substitutes to Lower Extremity Chronic Non-Healing Wounds. Please refer to the LCD for reasonable and necessary requirements.

The addition of Skin Substitutes, Cellular or Tissue Based Products (CTPs) to certain wounds may afford a healing advantage over dressings and conservative treatments when these options appear insufficient to affect complete healing.

The individual products will continue to be identified with a Level II Healthcare Common Procedure Coding System (HCPCS) supply code from the section of the manual entitled “Skin Substitutes”.

The Current Procedural Terminology (CPT) application CPT codes 15271-15278 intended for the use of skin substitutes is entitled “Skin Substitute Grafts”. The skin replacement surgery Skin Substitute Grafts application guidelines in the current CPT codebook provide an overview of the types of procedures performed, measurement of the wound surface area, and reporting of skin closure, biological dressing, and supply of the skin substitute graft material.

These procedures are not to be reported for application of non-graft wound dressings or for a biologic implant for soft tissue reinforcement.

Coding Guidance:

Notice: It is not appropriate to bill Medicare for services that are not covered (as described by the entire LCD) as if they are covered. When billing for non-covered services, use the appropriate modifier.

Per the Current Procedural Terminology (CPT) definition, skin substitute grafts include non-autologous skin (dermal or epidermal, cellular and acellular) grafts (e.g., homograft, allograft), non-human skin substitute grafts (i.e., xenograft), and biological products that form a sheet scaffolding for skin growth. Skin substitute graft codes are not to be reported for application of non-graft wound dressings (e.g., gel, powder, ointment, foam, liquid) or injected skin substitutes.

Non-graft wound dressings or injected skin substitue codes are not used with skin replacement surgery application codes and are considered incorrect coding. Such products are bundled into other standard management procedures if medically necessary and not separately payable.

Claims reporting skin substitute grafts must contain the presence of an appropriate application CPT code.

If the service for the application code is denied, the service for the skin substitute will also be denied.

Effective 01/01/2017, per CR 9603, when billing for Part B drugs and biologicals (except those provided under Competitive Acquisition Program [CAP] for Part B drugs and biologicals), the use of the JW modifier to identify unused drugs or biologicals from single use vials or single use packages that are appropriately discarded is required. The discarded amount shall be billed on a separate claim line using the JW modifier. Providers are required to document the discarded drug or biological in the patient’s medical record.

Novitas expects that where multiple sizes of a specific product are available, the size that best fits the wound with the least amount of wastage will be utilized.

When a portion of a drug/biological is discarded, the medical record must clearly document the amount administered and the amount wasted. The documentation must include the date, time, amount of medication wasted, and the reason for the wastage.

In situations where a portion of a single use package must be discarded, payment will be made for the portion discarded along with the amount applied up to the amount of the product on the package label. Medical record documentation must clearly indicate the information noted above.

Note: The unused portion must actually be discarded and may not be used for another patient.

Documentation Requirements

1. All documentation must be maintained in the patient's medical record and made available to the contractor upon request.
2. Every page of the record must be legible and include appropriate patient identification information (e.g., complete name, dates of service[s]).  The documentation must include the legible signature of the physician or non-physician practitioner responsible for and providing the care to the patient.
3. The submitted medical record must support the use of the selected ICD-10-CM code(s). The submitted CPT/HCPCS code must describe the service performed.

Coding Information

CPT/HCPCS Codes

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Group 1

 (22 Codes)

Group 1 Paragraph

Note: Providers are reminded to refer to the long descriptors of the CPT codes in their CPT book.

The following CPT/HCPCS codes outlined in this Billing and Coding Article will not have diagnosis code limitations applied at this time.

Group 1 Codes

Code	Description
15002	Wound prep trk/arm/leg
15003	Wound prep addl 100 cm
15004	Wound prep f/n/hf/g
15005	Wnd prep f/n/hf/g addl cm
15040	Harvest cultured skin graft
15050	Skin pinch graft
15271	Skin sub graft trnk/arm/leg
15272	Skin sub graft t/a/l add-on
15273	Skin sub grft t/arm/lg child
15274	Skn sub grft t/a/l child add
15275	Skin sub graft face/nk/hf/g
15276	Skin sub graft f/n/hf/g addl
15277	Skn sub grft f/n/hf/g child
15278	Skn sub grft f/n/hf/g ch add
C5271	Low cost skin substitute app
C5272	Low cost skin substitute app
C5273	Low cost skin substitute app
C5274	Low cost skin substitute app
C5275	Low cost skin substitute app
C5276	Low cost skin substitute app
C5277	Low cost skin substitute app
C5278	Low cost skin substitute app

CPT/HCPCS Modifiers

N/A

ICD-10-CM Codes that Support Medical Necessity

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Group 1

 (1 Code)

Group 1 Paragraph

It is the provider’s responsibility to select codes carried out to the highest level of specificity and selected from the ICD-10-CM code book appropriate to the year in which the service is rendered for the claim(s) submitted.

Group 1 Codes

Code	Description
XX000	Not Applicable

ICD-10-CM Codes that DO NOT Support Medical Necessity

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Group 1

 (1 Code)

Group 1 Paragraph

N/A

Group 1 Codes

Code	Description
XX000	Not Applicable

Additional ICD-10 Information

N/A

Bill Type Codes

Contractors may specify Bill Types to help providers identify those Bill Types typically used to report this service. Absence of a Bill Type does not guarantee that the article does not apply to that Bill Type. Complete absence of all Bill Types indicates that coverage is not influenced by Bill Type and the article should be assumed to apply equally to all claims.

Code	Description
999x	Not Applicable

Revenue Codes

Contractors may specify Revenue Codes to help providers identify those Revenue Codes typically used to report this service. In most instances Revenue Codes are purely advisory. Unless specified in the article, services reported under other Revenue Codes are equally subject to this coverage determination. Complete absence of all Revenue Codes indicates that coverage is not influenced by Revenue Code and the article should be assumed to apply equally to all Revenue Codes.

Code	Description
99999	Not Applicable

Other Coding Information

N/A

Revision History Information

Revision History Date	Revision History Number	Revision History Explanation
08/13/2020	R21	Article revised and published on 08/13/2020. Based on review of this billing and coding article, the “Coding Guidance” section was updated to include proper coding information in regards to skin replacement surgery application codes and non-graft wound dressings (e.g., gel, powder, ointment, foam, liquid) or injected skin substitutes.
07/01/2020	R20	Article revised and published on 06/25/2020 effective for dates of service on and after 07/01/2020 to remove the parenthetical note related to examples of procedures not to be reported for application of non-graft wound dressings. Group 2 paragraph and codes have been deleted as Q codes representing skin substitutes, are covered when administered and consistent with the related LCD and billed with application codes. A note was added to the text to indicate HCPCS codes Q4177 and Q4206 are exceptions and do not require an application code. HCPCS codes Q4177 and Q4206 are retroactively covered for all dates of service when not billed with application codes 15271-15278.
04/30/2020	R19	Article revised and published on 04/30/2020 effective for dates of service on and after 01/01/2020. The following CPT/HCPCS code has been added to group 2: Q4170.
03/12/2020	R18	Article revised and published on 03/12/2020 effective for dates of service on and after 10/01/2019. The following HCPCS code has been added to group 2: Q4226. Standard language and format changes have been made throughout the article.
02/13/2020	R17	Article revised and published in response to provider inquiries. Healthcare Common Procedure Coding System (HCPCS) code Q4197 and Q4184 were added to the article on 02/13/2020 effective for dates of services on and after 10/21/2019.
01/01/2020	R16	Article revised and published on 01/16/2020 effective for dates of service on and after 01/01/2020 to reflect the annual CPT/HCPCS code updates. The following CPT/HCPCS code(s) have been added to the CPT/HCPCS code Group 2 in the article: Q4208, Q4209, Q4210, Q4211, Q4214, Q4216, Q4217, Q4218, Q4219, Q4220, Q4221 and Q4222. For the following CPT/HCPCS code(s) either the short description and/or the long description has been changed. Depending on which description is used in this article, there may not be any change in how the code displays in the document: Q4122 and Q4165.
10/01/2019	R15	Article revised and published on 10/31/2019 in response to the October 2019 Quarterly Healthcare Common Procedure Coding System (HCPCS) Drug/Biological Code Changes. The following HCPCS have undergone a code descriptor change: Q4165 and Q4122.
09/26/2019	R14	Article revised and published on 09/26/2019. In addition to the changes made in Revision History Number 13 below, due to system changes, the order of the Coding Section has been revised and new sections for CPT/HCPCS Modifiers and Other Coding Information have been made.
09/26/2019	R13	Article revised and published on 09/26/2019 efective for dates of service on and after 02/04/2019 to add codes Q4183, Q4187, Q4188 and Q4203 to Group 2 CPT/HCPCS codes.
03/21/2019	R12	Article revised and published on 03/21/2019 All codes from L35041, Application of Bioengineered Skin Substitutes to Lower Extremity Chronic Non-Healing Wounds, have been placed in this article per CMS Change Request 10901. Billing instruction for HCPCS code Q4172 has been removed due to code deleted with 2019 HCPCS Update. Article title has been changed to clarify that the Article is providing billing and coding information.
01/01/2019	R11	Article revised and published on 02/14/2019 effective for dates of service on and after 01/01/2019 to reflect the annual CPT/HCPCS code updates. The following CPT/HCPCS code(s) have been deleted and therefore removed from the Article: Q4131 and Q4172. The following CPT/HCPCS code(s) have been added to Group 2 Codes: Q4186, Q4190, Q4195 and Q4196. For the following CPT/HCPCS code(s) either the short description and/or the long description was changed. Depending on which description is used in this Article, there may not be any change in how the code displays in the document: Q4133 and Q4137.
09/17/2018	R10	Article revised and published on 11/08/2018 effective for dates of service on and after 09/17/2018 to add the following HCPCS code to CPT/HCPCS Code Group 2: Q4180.
07/26/2018	R9	Article revised and published on 07/26/2018 to add HCPCS code Q4178 to CPT/HCPCS Code Group 2 effective for dates of service on and after 04/09/2018.
04/12/2018	R8	Article revised and published on 04/12/2018 to revise statement that an appropriate application CPT code is necessary when billing a skin substitute Q code.
01/01/2018	R7	Article revised and published on 01/25/2018 effective for dates of service on and after 01/01/2018 to reflect the annual CPT/HCPCS code updates. For the following CPT/HCPCS codes either the short description and/or the long description was changed: Q4132, Q4133, Q4148, Q4156, Q4158, Q4163. Depending on which description is used in this article there may not be any change in how the codes display in the document.
05/05/2017	R6	Article revised and published 07/13/2017 effective for dates of service on and after 05/05/2017 to add the following CPT/HCPCS code to Group 2: Q4169. Revision history from 05/11/2017 should reflect that the article (not LCD) was revised.
01/01/2017	R5	LCD revised and published on 05/11/2017 effective for dates of service on and after 01/01/2017 to add the following CPT/HCPCS codes to Group 2: Q4173 and Q4175.
01/01/2017	R4	Article revised and published on 01/12/2017 effective for dates of service on and after 01/01/2017 to reflect the annual CPT/HCPCS code updates. The following CPT/HCPCS codes: C9349, Q4119, Q4120, and Q4129 have been deleted and therefore removed from group 2 of the Article. The following CPT/HCPCS codes: Q4166 and Q4172 have been added to group 2 of the Article. References to HCPCS code C9349 in the Coding Guidance section have been revised to HCPCS code Q4172. For the following CPT/HCPCS codes either the short description and/or the long description was changed. Depending on which description is used in this LCD, there may not be any change in how the code displays in the document: Q4105 and Q4131. Coding Guidance added regarding use of JW modifier.
04/18/2016	R3	Article revised and published on 07/14/2016 effective for dates of service on and after 04/18/2016 to add HCPCS code Q4128 to the Group 2 codes.
01/01/2016	R2	Article revised and published on 01/28/2016 effective for dates of service on and after 01/01/2016 to reflect the annual CPT/HCPCS code updates. The following CPT/HCPCS codes have been added to Group 2: Q4161, Q4163, Q4164, and Q4165. For the following CPT/HCPCS code, either the short description and/or the long description was changed. Depending on which description is used in this LCD, there may not be any change in how the code displays in the document: Q4153.
10/01/2015	R1	Article revised and published on 08/13/2015 to add HCPCS codes Q4146 and Q4147. The HCPCS code descriptor for C9349 has changed in response to the 2015 HCPCS Quarter 3 update.

Associated Documents

Related Local Coverage Documents
LCDs
L35041 - Application of Bioengineered Skin Substitutes to Lower Extremity Chronic Non-Healing Wounds 

Related National Coverage Documents
N/A

Statutory Requirements URLs
N/A

Rules and Regulations URLs
N/A

CMS Manual Explanations URLs
N/A

Other URLs
N/A

Public Versions

Updated On	Effective Dates	Status
08/06/2020	08/13/2020 - N/A	Currently in Effect	You are here
Some older versions have been archived. Please visit the MCD Archive Site  to retrieve them.

WPS Article on Stem Cell Recoupment

 https://www.wpsgha.com/wps/portal/mac/site/policies/news-and-updates/claim-denials-manupulated-amniotic-placental-tissue-injections/ 

WPS MAC

Claim Denials for Manipulated Amniotic 

and/or Placental Tissue Biologics for Injections

LAST UPDATED MAR 14 2022

← Back to the previous page

FB link  

Jurisdictions: J8A, J5A, J8B, J5B

Editor’s note: We updated this article on February 24, 2022, to add “drug” to the last sentence of the first paragraph, and to change “We refer you to” to “Please refer to” in the third paragraph.

Manipulated amniotic and/or placental tissue biologics for injections to treat illness are experimental exosome biologic products that have not been proven to be safe and effective for any medical use. All claims for dates of service on or after December 6, 2019, shall be denied. Under Section 1862(a)(1)(E) of the Social Security Act, per the Food and Drug Administration (FDA), these products may only be provided within approved investigational new drug (IND) trials.

Many patients seeking cures and remedies may be misled by information about products that are illegally marketed, have not been shown to be safe or effective, and, in some cases, present potential, significant safety concerns that put patients at risk. 

Please refer to the FDA’s Tissue Reference Group (TRG) or the FDA’s Office of Combination Products to obtain written feedback regarding how the product is appropriately regulated. 

You may find additional information from the FDA at the following:

Public Safety Notification on Exosome Products

Consumer Alert on Regenerative Medicine Products Including Stem Cells and Exosomes

Important Patient and Consumer Information About Regenerative Medicine Therapies

Medicare Provider Information:
  
Medicare payment of a service requires for it to be compliant with all applicable regulation. Providers at enrollment agree to abide by the Medicare laws, regulations, and program instructions and at enrollment they certify that they understand that payment of a claim by Medicare is conditioned upon the claim and the underlying transaction complying with such laws. 

The CMS Internet-Only Manual, Publication 100-02, Medicare Benefit Policy Manual, Chapter 15 , Section 50.4.1 states:
 
The A/B MAC (A), (B), or (HHH), or DME MAC will deny coverage for drugs and biologicals, which have not received final marketing approval by the FDA unless it receives instructions from CMS to the contrary.

If there is reason to question whether the FDA has approved a drug or biological for marketing, the MAC must obtain satisfactory evidence of FDA’s approval. Acceptable evidence includes:

• A copy of the FDA’s letter to the drug’s manufacturer approving the new drug application (NDA);
• A listing of the drug or biological in the FDA’s “Approved Drug Products” or “FDA Drug and Device Product Approvals”;
• A copy of the manufacturer’s package insert approved by the FDA as part of the labeling of the drug, containing its recommended uses and dosage, as well as possible adverse reactions and recommended precautions in using it; or
• Information from the FDA’s Web site.

Any claims processed and paid for dates of service on or after Dec. 6, 2019, will be adjusted and payment will be recouped. Impacted providers will receive an overpayment demand letter identifying the amount of the overpayment.

Tags: Overpayments,News,J8A,J5A,Policies,J8B,J5B,Claims

Some Key Dates for MolDx MRD coverage

 

July 2018

Guardant G360 Final (first LBX coverage)

http://www.discoveriesinhealthpolicy.com/2018/07/moldx-posts-final-lcd-for-guardant-g360.html

January 2019: MOLDX covers CLONOSEQ in ARTICLE

http://www.discoveriesinhealthpolicy.com/2019/01/very-brief-blog-moldx-covers-adaptive.html

##

September 2020

Release SIGNATERA FINAL

ALSO release MASTER LCD FOUNDATION

http://www.discoveriesinhealthpolicy.com/2020/09/you-can-understand-it-moldx-and-moldx.html

November 2021 MRD LCD is finalized in foundational form (both relapse and therapy monitoring, both solid cancers and heme cancers)

http://www.discoveriesinhealthpolicy.com/2021/11/dramatic-day-for-precision-oncology.html

First Draft Innovation Article

 This is only a first draft article, but welcome feedback.  It's 1800 words for a trade journal called Inside Precision Medicine.

####

Medicare Policy: How It Can Boost or Block Innovation

 

 

Bruce Quinn MD
Bruce Quinn Associates

Los Angeles

 

Dr. Bruce Quinn, a pathologist-MBA, is a full time consultant for companies bringing innovative new technologies towards Medicare coverage.   He holds an MD-PhD from Stanford University and an MBA from the Kellogg program at Northwestern University. After working in academic medicine, he’s been a strategy consultant for Accenture, two health policy firms in Washington DC, and for his own consulting practice based in Los Angeles and San Francisco.  

 

 

 

In every decade, healthcare changes a lot, but we still have enormous needs for continual innovation toward unmet needs, more effective healthcare, better coordinated services, and cost-effectiveness.  In particular, this is true in the field of precision medicine, where genomic and proteomic sciences have made huge strides in recent years.   Bringing these technological advances into healthcare devices and services is challenging, exciting, difficult, and very important.

 

Most frequently, the policy focus for healthcare innovations is on the FDA.   This usually leads to a perpetual ying-and-yang swing between concerns that the FDA is too strict, and keeping innovations from patients, or too lenient, and letting unvalidated drugs or devices slip through the cracks and into the marketplace.  The tension is captured in the title of a 2011 Congressional hearing on the topic – “Impact of Medical Device Regulation on Jobs and Patients,” (FN1) which was stimulated in part by an insightful study of device innovation by Josh Makower MD (FN2), who now leads the Byers Center for Biodesign at Stanford University. 

 

Less discussed is the critical role that Medicare policies have on device and diagnostics innovation.  Sure, Medicare pays for healthcare technologies, and makes coverage decisions which, like FDA decisions, may be viewed as too lax by some and too burdensome by others.   The role of CMS policies in accelerating valuable innovation is much more important than is usually appreciated, and spans a range of national and even local Medicare policy rules that can trip up the unwary.  

 

Let’s look more closely at some of these policy rules, praise the good ones, and suggest changes for rules that are dysfunctional.

 

National Coverage Decisions – The Value of Open-Ended Promises

 

Medicare makes national coverage determinations (NCDs) via a nine-month national comment process, led by a small number of physicians and other professional staff in the agency’s Coverage and Analysis Group. 

 

These professionals have written several recent national coverage decisions with important and innovative ramifications.   In 2017, CMS announced it would cover tumor genome diagnostics with next generation sequencing for on-label indications if they were FDA-approved.   One clear intention was to encourage developers to enter the FDA review pathway because of the guaranteed Medicare coverage at the end of it.   In a 2019 extension, CMS added coverage for hereditary cancer tests if they were similarly FDA cleared or approved.  

 

In a third decision, CMS created an innovative coverage category, promising to cover any liquid biopsy tests that screen for colorectal cancer (in place of stool tests or colonoscopy) as long as they were 74% sensitive (catching 3 cancers in 4) and 90% specific (having about 10% false positives).   CMS ratified this coverage position even though no such tests had yet been approved by the FDA.  

 

Together, these decisions in three major areas of genomics provide further coverage for several categories of important tests, each predicated on FDA review and approval, but providing first-day coverage from Medicare when that happens.   Interestingly, all three of these innovative decisions, which had no precedent in precision medicine, occurred during the Trump administration.

 

Local Coverage Decisions – Foundational LCDs Define Coverage Rules for Future Tests

 

Similar innovations have been rolled out in the last several years at the local level as well,  where Medicare contractors work.   Only about 60% of Medicare beneficiaries are still in the slowly shrinking fee-for-service program, but its policies also apply to patients in Medicare Advantage and Medicare Direct Contracting programs (now called REACH ACOs).  

 

Some 80% of genomic tests in the Medicare program are covered by contractors in the special MolDx program, which has a dedicated staff of molecular pathologists in charge of making uniform coverage decisions for about 30 states.  Since 2018, the vision of the MolDx program has been to broad umbrella coverage statements, called “foundational local coverage determinations,” that define areas of coverage as use cases for genomic tests. 

 

The most dramatic is an LCD finalized in November, 2021, which provides coverage for minimal residual disease monitoring for two use cases – tumor relapse after curative surgery, and treatment monitoring.   To achieve coverage, new tests just need to meet or exceed performance criteria laid out in the LCDs, and pass a close inspection or technological assessment by MolDx professionals.   The index test was the Natera Signatera test when used to manage colorectal cancer, but the LCD is open-ended enough to allow coverage of future tests in lung cancer, breast cancer, and other conditions. 

 

It’s Not All Good News – The Demise of “MCIT”

 

In September 2020, CMS announced a major new regulation called “MCIT” – Medicare Coverage for Innovative Technology.   After favorable public comments, CMS finalized the regulation in January 2021.  The theme was simple: if a device received the FDA Breakthrough Device Designation – a process established in the 21^st Century Cures bill – and goes on to successfully complete its clinical trials and FDA review, CMS would guarantee four years of provisional coverage for on-label applications.   Unfortunately, under new leadership, CMS put this policy on hold in Spring 2021 and canceled it by the end of the year.  Fortunately, as of Spring 2022, CMS is holding town halls on other ways it might help innovation.  

 

MCIT was smarter than its detractors realized, as later noted by policymakers like Scott Gottlieb MD and Joe Grogan, a physician and an attorney, respectively, who worked at high levels in the Trump administration.   The goal of MCIT was to boost investment in products that were highly promising and important, as defined by others – by Congress and the FDA.   The goal was to reduce the “valley of death” between early trials and full funding for clinical development, which is caused by two kinds of risk – science risk (will it succeed and pass FDA) and payor risk (will it meet uncertain payor criteria or suffer slow payor timelines).  MCIT nixed the payor risk.  

 

I think objections to MCIT were overestimated.  For example, if a product had too much science risk, it wouldn’t pass FDA muster, and might not even try for a final review.   If the product was so-so, it’s unlikely there would have been much clinical adoption, so CMS payments would have been few.   In short, some of the problems proposed by opponents were self-correcting.  In a simple model, using reasonable timelines, success rates, and discount rates, I found that MCIT (coupled with earned Breakthrough Status), raised the value of a Phase I product idea by 2.5X (such as from $15M to $36M).  In contrast, another CMS policy trick, Coverage with Evidence Development, destroyed value and slashed the product value by half, from $15M to only $8M.   Where the rubber meets the road is when you add a new factor, how much the clinical trials will cost.  If they cost $10M, and the product value under CED is $8M, you won’t invest, and the product dies.  But under MCIT, the exact same product, with the same odds of success, is worth $36M, so the $10M in development funds will quickly be invested.   (See how the math works in an online article, FN3).

 

 

Two Unnecessary Headaches:  Glacially Slow Local Decisions and Counterproductive Bundling Policies

 

There are anti-innovative policies at CMS that could be corrected fairly easily.   For one thing, the timeline for local coverage determinations – LCDs – has gotten far too slow, with proposals waiting in limbo for a year or more, and the timeline for executing a new LCD taking a year from first publication to finalization.   This comes largely from a CMS decision that even expansions in coverage – such as adding a new covered service to a relatively minor LCD – should go through a lengthy public comment cycle in advance.   In a quick Medicare fix, Congress could add a few words to the statute and solve this recently-created problem.   The slow timelines for LCD modifications has led some contractors to issue LCDs of maddening vagueness, such as covering microbiology genomic panels when “the test is timely and will improve care.”  That’s too vague to provide a safe harbor for test utilization, and a lightning rod for disputes in recoupment cases where for-profit reviewers, called recovery audit contractors, will take the provider’s money today and essentially dare him to try and get it back in court years from now.

 

In the hospital outpatient setting, where much specialty care like cancer care is delivered, CMS has been overzealous in applying bundling rules to lab tests.  Beginning in 2014, CMS has bundled nearly all lab tests to patient visits and procedures, such as hospital biopsies.   CMS makes an exception for tests of human DNA and RNA – but where does that leave advances in proteomics, metabolomics, molecular pathogens, and tests that rely on artificial intelligence?    Those are all bundled to the hospital visit, prostate biopsy, or other procedure.  Soon we’ll have accurate proteomic tests for Alzheimer’s disease, but if they are paired to a hospital neurology clinic visit or spinal tap, the value of the test will be “bundled” inside the $100 office visit or the procedure payment.   This discourages innovation in these areas, and innovators learn to navigate away from these areas where rules are toxic, but unmet clinical need is high.   

 

Awareness Will Support Change

 

For one thing, we need to bring some of these findings out of the dark basement of the reimbursement policy world, and into the daylight.  When CMS makes smart decisions that encourage innovation and improvement, at reasonable or even lower costs, we should celebrate it and look for more opportunities like that.  When CMS does stuff that’s just dysfunctional and counterproductive, we shouldn’t let them get away with it, and we should provide constructive feedback for fixes.    Also in the prior admininistrative, CMS established a new department, a “Technology Coding and Pricing Group,” designed to be a forum for hearing about problems and taking steps forward.   We should all recognize how important that kind of department and leadership is at CMS, and support them wherever we can.

 

 

 

 

 

 

 

 

 

FN1

Subcommittee on Health, House Committee on Energy and Commerce, February 17, 2011.  https://www.govinfo.gov/content/pkg/CHRG-112hhrg66467/pdf/CHRG-112hhrg66467.pdf

 

FN2

 

Makower, J., Meer, A., & Denend, L.  (2010)  FDA Impact on U.S. Medical Technology Innovation.  44pp.  https://cdn.ymaws.com/www.medicaldevices.org/resource/resmgr/docs/FDA_impact_on_US_med_tech_in.pdf  

 

 

FN3

Quinn, B.  (2022)   Revisiting MCIT with Numbers.  MCIT Could Triple the Value of Medtech Investments.  Discoveries in Health Policy (blog).  http://www.discoveriesinhealthpolicy.com/2022/02/revisiting-mcit-with-numbers-mcit-could.html  

 

 

Copy of WSJ October 2, 2009 Avastin/Lucentis Article (Code Q2024)

 https://www.wsj.com/articles/BL-HEB-23731

How Cutting Payments for a Drug Could Cost Medicare More

By Jacob Goldstein

Oct. 2, 2009 9:28 am ET

 

Medicare just started reimbursing doctors less for very small amounts of the cancer drug Avastin. Oddly enough, that could mean Medicare will start spending lots more money on the eye drug Lucentis. Here's why.

Lucentis and Avastin are very similar molecules. A few years back, before Lucentis was on the market, eye doctors realized that they could inject Avastin in patients' eyes to treat macular degeneration, a condition that can lead to significant loss of vision and occurs mostly in the elderly.

Avastin costs tens of thousands of dollars to treat cancer patients, but the tiny dose doctors inject into patients' eyes costs a very small fraction of that --the specialty pharmacy chain The Apothecary Shops repackages Avastin for use in the eye and sells it to doctors for $27 per dose, John Musil, the company's CEO, told the Health Blog.

This week, Medicare cut its reimbursement for the dose of Avastin commonly used in the eye to about $7. Previously, when there was no specific billing code for tiny doses of Avastin, doctors could get reimbursed about $50 for using the drug in the eye, Philip Rosenfeld, an eye doctor at the University of Miami, told the Health Blog.

For a dose of Lucentis, Medicare reimburses doctors $2,039. Doctors pay just under $2,000 for a dose of Lucentis, Rosenfeld said.

That means that eye doctors will now lose a bit of money when they use Avastin to treat patients' eyes, and make a bit of money when they use Lucentis. Avastin hasn't been approved for use in the eye, but it's legal for doctors to use it in that way. Many eye doctors use both Avastin and Lucentis, depending on patient preference, Rosenfeld said.

Rosenfeld, like many docs, believes both drugs work equally well to treat macular degeneration; the NIH is currently sponsoring a head-to-head trial. But the new payment structure could push eye docs away from Avastin and toward the far more expensive Lucentis.

"Doctors will do what's in the best interest of the patients," Rosenfeld said. "Given that both drugs are equal, it's not surprising that they'll do what's in their financial interest."

That could mean a hit for Medicare, as well as for Medicare patients who are on the hook for co-pays.

Both Lucentis and Avastin were developed by Genentech, now owned by Roche. U.S. sales of Lucentis were over $550 million in the first half of this year, Roche reported. Because the drug is primarily used by the elderly, much of that is paid for by Medicare.

Until now, there was no specific Medicare billing code for small doses of Avastin. A Medicare spokeswoman said the new reimbursement level is based on a formula that is determined by law, and doesn't take into account the costs of repackaging the drug.

Photo: iStockphoto

111 Titles in AI and JACR, Loosely Classified

Informally, I classed each paper in the broad (and fuzzy) categories of (1) radiology business & finance, (2) implementation and operations, (3) performance and clinical reports, (4) AI and people, such as "How Residents View AI,"), and (5) FDA or basic developmental & science.    This yielded:

(1) #22 titles classed as "business,"

(2) #40 titles classed as "implementation,"

(3) #14 titles classed as performance & clinical reports,

(4) #27 titles classed as focusing on "AI and People,"

(5) #8 titles directly citing to FDA or basic science.

These are fuzzy classifications but still allow some general grouping of topics.

111 Titles on AI/ML from JACR (February 2022)

PubMed search for journal JACR, and keyword "intelligence," 111 titles screened by hand and provided below.  NBIB format machine file in cloud here.

Main blog here:

http://www.discoveriesinhealthpolicy.com/2022/03/digital-pathology-triangulating-path-by.html

Trivedi, H. (2022). "The Business of Artificial Intelligence in Radiology Has Little to Do With Radiologists." J Am Coll Radiol.

Alkasab, T. K. and B. C. Bizzo (2022). "Response to Drs Sammer, Sher, and Seghers' Letter on "Lessons Learned From the Front Lines of Artificial Intelligence Implementation"." J Am Coll Radiol.

Banja, J. D., et al. (2022). "When Artificial Intelligence Models Surpass Physician Performance: Medical Malpractice Liability in an Era of Advanced Artificial Intelligence." J Am Coll Radiol.

Anderson, A. W., et al. (2022). "Independent External Validation of Artificial Intelligence Algorithms for Automated Interpretation of Screening Mammography: A Systematic Review." J Am Coll Radiol 19(2 Pt A): 259-273.

Dreyer, K. J., et al. (2022). "Real-World Surveillance of FDA-Cleared Artificial Intelligence Models: Rationale and Logistics." J Am Coll Radiol 19(2 Pt A): 274-277.

Sammer, M. B. K., et al. (2022). "Re: "Lessons Learned From the Front Lines of Artificial Intelligence Implementation"." J Am Coll Radiol.

Benjamin, M., et al. (2021). "Accelerating Development and Clinical Deployment of Diagnostic Imaging Artificial Intelligence." J Am Coll Radiol 18(11): 1514-1516.

Alkasab, T. K. and B. C. Bizzo (2021). "Lessons Learned From the Front Lines of Artificial Intelligence Implementation." J Am Coll Radiol 18(11): 1474-1475.

Bizzo, B. C., et al. (2021). "Data Management in Artificial Intelligence-Assisted Radiology Reporting." J Am Coll Radiol 18(11): 1485-1488.

Tartar, M., et al. (2021). "Artificial Intelligence Support for Mammography: In-Practice Clinical Experience." J Am Coll Radiol 18(11): 1510-1513.

Gish, D. S., et al. (2021). "Retrospective Evaluation of Artificial Intelligence Leveraging Free-Text Imaging Order Entry to Facilitate Federally Required Clinical Decision Support." J Am Coll Radiol 18(11): 1476-1484.

Allen, B., et al. (2021). "Evaluation and Real-World Performance Monitoring of Artificial Intelligence Models in Clinical Practice: Try It, Buy It, Check It." J Am Coll Radiol 18(11): 1489-1496.

Jain, R. (2021). "Introducing Artificial Intelligence Applications at Our Community Hospital: A Contrarian Approach." J Am Coll Radiol 18(11): 1506-1509.

Pierce, J. D., et al. (2021). "Seamless Integration of Artificial Intelligence Into the Clinical Environment: Our Experience With a Novel Pneumothorax Detection Artificial Intelligence Algorithm." J Am Coll Radiol 18(11): 1497-1505.

Strand, F., et al. (2021). "A Call for Controlled Validation Data Sets: Promoting the Safe Introduction of Artificial Intelligence in Breast Imaging." J Am Coll Radiol 18(11): 1564-1565.

Weisberg, E. M., et al. (2021). "Using Artificial Intelligence to Interpret CT Scans: Getting Closer to Standard of Care." J Am Coll Radiol 18(11): 1569-1571.

Puri, P. and S. Jha (2021). "Artificial Intelligence, Automation, and Medical Education: Lessons From Economic History." J Am Coll Radiol 18(9): 1345-1347.

Allen, B., et al. (2021). "2020 ACR Data Science Institute Artificial Intelligence Survey." J Am Coll Radiol 18(8): 1153-1159.

Voter, A. F., et al. (2021). "Diagnostic Accuracy and Failure Mode Analysis of a Deep Learning Algorithm for the Detection of Intracranial Hemorrhage." J Am Coll Radiol 18(8): 1143-1152.

Burdorf, B. T. (2021). "A Prospective Applicant's Outlook on Radiology in Light of Artificial Intelligence." J Am Coll Radiol 18(7): 893.

Chonde, D. B., et al. (2021). "RadTranslate: An Artificial Intelligence-Powered Intervention for Urgent Imaging to Enhance Care Equity for Patients With Limited English Proficiency During the COVID-19 Pandemic." J Am Coll Radiol 18(7): 1000-1008.

Wildman-Tobriner, B., et al. (2021). "Missed Incidental Pulmonary Embolism: Harnessing Artificial Intelligence to Assess Prevalence and Improve Quality Improvement Opportunities." J Am Coll Radiol 18(7): 992-999.

Weisberg, E. M., et al. (2021). "Man Versus Machine? Radiologists and Artificial Intelligence Work Better Together." J Am Coll Radiol 18(6): 887-889.

Adams, S. J., et al. (2021). "Development and Cost Analysis of a Lung Nodule Management Strategy Combining Artificial Intelligence and Lung-RADS for Baseline Lung Cancer Screening." J Am Coll Radiol 18(5): 741-751.

Tejani, A. S. (2021). "Identifying and Addressing Barriers to an Artificial Intelligence Curriculum." J Am Coll Radiol 18(4): 605-607.

Purkayastha, S., et al. (2021). "Failures Hiding in Success for Artificial Intelligence in Radiology." J Am Coll Radiol 18(3 Pt B): 517-519.

Larson, D. B., et al. (2021). "Regulatory Frameworks for Development and Evaluation of Artificial Intelligence-Based Diagnostic Imaging Algorithms: Summary and Recommendations." J Am Coll Radiol 18(3 Pt A): 413-424.

Rajiah, P. and P. Bhargava (2021). "Leadership Lessons From Equity Theory: The Interplay Between Radiologist Compensation and Motivation." J Am Coll Radiol 18(1 Pt B): 211-213.

Kotsenas, A. L., et al. (2021). "Rethinking Patient Consent in the Era of Artificial Intelligence and Big Data." J Am Coll Radiol 18(1 Pt B): 180-184.

Thrall, J. H., et al. (2021). "Rethinking the Approach to Artificial Intelligence for Medical Image Analysis: The Case for Precision Diagnosis." J Am Coll Radiol 18(1 Pt B): 174-179.

Ongena, Y. P., et al. (2021). "Artificial Intelligence in Screening Mammography: A Population Survey of Women's Preferences." J Am Coll Radiol 18(1 Pt A): 79-86.

Smith, J., et al. (2021). "The Age of Artificial Intelligence: Does "Why" Still Matter?" J Am Coll Radiol 18(1 Pt A): 87-89.

Slanetz, P. J., et al. (2020). "Artificial Intelligence and Machine Learning in Radiology Education Is Ready for Prime Time." J Am Coll Radiol 17(12): 1705-1707.

Lui, Y. W., et al. (2020). "How to Implement AI in the Clinical Enterprise: Opportunities and Lessons Learned." J Am Coll Radiol 17(11): 1394-1397.

Bhatia, N., et al. (2020). "Artificial Intelligence in Quality Improvement: Reviewing Uses of Artificial Intelligence in Noninterpretative Processes from Clinical Decision Support to Education and Feedback." J Am Coll Radiol 17(11): 1382-1387.

Tariq, A., et al. (2020). "Current Clinical Applications of Artificial Intelligence in Radiology and Their Best Supporting Evidence." J Am Coll Radiol 17(11): 1371-1381.

Kapoor, N., et al. (2020). "Workflow Applications of Artificial Intelligence in Radiology and an Overview of Available Tools." J Am Coll Radiol 17(11): 1363-1370.

Filice, R. W., et al. (2020). "Evaluating Artificial Intelligence Systems to Guide Purchasing Decisions." J Am Coll Radiol 17(11): 1405-1409.

Kottler, N. (2020). "Artificial Intelligence: A Private Practice Perspective." J Am Coll Radiol 17(11): 1398-1404.

Simpson, S. A. and T. S. Cook (2020). "Artificial Intelligence and the Trainee Experience in Radiology." J Am Coll Radiol 17(11): 1388-1393.

Goehler, A., et al. (2020). "Three-Dimensional Neural Network to Automatically Assess Liver Tumor Burden Change on Consecutive Liver MRIs." J Am Coll Radiol 17(11): 1475-1484.

Fishman, E. K., et al. (2020). "Mapping Your Career in the Era of Artificial Intelligence: It's Up to You, Not Google." J Am Coll Radiol 17(11): 1537-1538.

Fleishon, H. B. and C. Wald (2020). "Patient Safety: Considerations for Artificial Intelligence Implementation in Radiology." J Am Coll Radiol 17(10): 1192-1193.

Enzmann, D. R., et al. (2020). "Radiology's Information Architecture Could Migrate to One Emulating That of Smartphones." J Am Coll Radiol 17(10): 1299-1306.

Chu, L. C., et al. (2020). "The Potential Dangers of Artificial Intelligence for Radiology and Radiologists." J Am Coll Radiol 17(10): 1309-1311.

Woloshyn, O., et al. (2020). "Found in Translation: Unpacking the Artificial Intelligence Revolution That Has Already Arrived." J Am Coll Radiol 17(10): 1307-1308.

Lotan, E., et al. (2020). "Medical Imaging and Privacy in the Era of Artificial Intelligence: Myth, Fallacy, and the Future." J Am Coll Radiol 17(9): 1159-1162.

Adams, S. J., et al. (2020). "Patient Perspectives and Priorities Regarding Artificial Intelligence in Radiology: Opportunities for Patient-Centered Radiology." J Am Coll Radiol 17(8): 1034-1036.

Montaque, T., et al. (2020). "The Future of Digital Communication: Improved Messaging Context, Artificial Intelligence, and Your Privacy." J Am Coll Radiol 17(6): 821-823.

Kambadakone, A. (2020). "Artificial Intelligence and CT Image Reconstruction: Potential of a New Era in Radiation Dose Reduction." J Am Coll Radiol 17(5): 649-651.

Sigler, R., et al. (2020). "The Importance of Data Analytics and Business Intelligence for Radiologists." J Am Coll Radiol 17(4): 511-514.

Allen, B., et al. (2020). "Integrating Artificial Intelligence Into Radiologic Practice: A Look to the Future." J Am Coll Radiol 17(2): 280-283.

Fernandez, C., et al. (2020). "Feasibility and Impact of Emotional Intelligence Evaluation in Radiation Oncology Residency Interviews." J Am Coll Radiol 17(2): 289-292.

Valtchinov, V. I., et al. (2020). "Comparing Artificial Intelligence Approaches to Retrieve Clinical Reports Documenting Implantable Devices Posing MRI Safety Risks." J Am Coll Radiol 17(2): 272-279.

Mayo, R. C., et al. (2020). "Financing Artificial Intelligence in Medical Imaging: Show Me the Money." J Am Coll Radiol 17(1 Pt B): 175-177.

Browning, T., et al. (2020). "Special Considerations for Integrating Artificial Intelligence Solutions in Urban Safety-Net Hospitals." J Am Coll Radiol 17(1 Pt B): 171-174.

Alexander, A., et al. (2020). "An Intelligent Future for Medical Imaging: A Market Outlook on Artificial Intelligence for Medical Imaging." J Am Coll Radiol 17(1 Pt B): 165-170.

Geis, J. R., et al. (2019). "Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement." J Am Coll Radiol 16(11): 1516-1521.

Burdorf, B. (2019). "A Medical Student's Outlook on Radiology in Light of Artificial Intelligence." J Am Coll Radiol 16(11): 1514-1515.

Allen, B., Jr. (2019). "Machine Learning With Deep Neural Nets Artificially Augmenting My Intelligence in a Narrow but Occasionally Superhuman Kind of Way." J Am Coll Radiol 16(10): 1480-1481.

Kohli, M., et al. (2019). "Bending the Artificial Intelligence Curve for Radiology: Informatics Tools From ACR and RSNA." J Am Coll Radiol 16(10): 1464-1470.

Haan, M., et al. (2019). "A Qualitative Study to Understand Patient Perspective on the Use of Artificial Intelligence in Radiology." J Am Coll Radiol 16(10): 1416-1419.

Golding, L. P. and G. N. Nicola (2019). "A Business Case for Artificial Intelligence Tools: The Currency of Improved Quality and Reduced Cost." J Am Coll Radiol 16(9 Pt B): 1357-1361.

Bizzo, B. C., et al. (2019). "Artificial Intelligence and Clinical Decision Support for Radiologists and Referring Providers." J Am Coll Radiol 16(9 Pt B): 1351-1356.

Pisano, E. D. and L. R. Garnett (2019). "Big Data and Radiology Research." J Am Coll Radiol 16(9 Pt B): 1347-1350.

Akkus, Z., et al. (2019). "A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence-Powered Ultrasound for Improving Clinical Workflow." J Am Coll Radiol 16(9 Pt B): 1318-1328.

Rubin, D. L. (2019). "Artificial Intelligence in Imaging: The Radiologist's Role." J Am Coll Radiol 16(9 Pt B): 1309-1317.

Filice, R. W. (2019). "Radiology-Pathology Correlation to Facilitate Peer Learning: An Overview Including Recent Artificial Intelligence Methods." J Am Coll Radiol 16(9 Pt B): 1279-1285.

Pillai, M., et al. (2019). "Using Artificial Intelligence to Improve the Quality and Safety of Radiation Therapy." J Am Coll Radiol 16(9 Pt B): 1267-1272.

Vey, B. L., et al. (2019). "The Role of Generative Adversarial Networks in Radiation Reduction and Artifact Correction in Medical Imaging." J Am Coll Radiol 16(9 Pt B): 1273-1278.

Makeeva, V., et al. (2019). "The Application of Machine Learning to Quality Improvement Through the Lens of the Radiology Value Network." J Am Coll Radiol 16(9 Pt B): 1254-1258.

Martín Noguerol, T., et al. (2019). "Strengths, Weaknesses, Opportunities, and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology." J Am Coll Radiol 16(9 Pt B): 1239-1247.

Pfeifer, C. M. (2019). "Limitations of Ascribing Autonomy, Purpose, and Mastery as Primary Physician Motivators." J Am Coll Radiol 16(9 Pt A): 1130-1131.

Larson, D. B. and G. W. Boland (2019). "Imaging Quality Control in the Era of Artificial Intelligence." J Am Coll Radiol 16(9 Pt B): 1259-1266.

Luh, J. Y., et al. (2019). "Clinical Documentation and Patient Care Using Artificial Intelligence in Radiation Oncology." J Am Coll Radiol 16(9 Pt B): 1343-1346.

Filice, R. W. (2019). "Deep-Learning Language-Modeling Approach for Automated, Personalized, and Iterative Radiology-Pathology Correlation." J Am Coll Radiol 16(9 Pt B): 1286-1291.

Allen, B., Jr., et al. (2019). "A Road Map for Translational Research on Artificial Intelligence in Medical Imaging: From the 2018 National Institutes of Health/RSNA/ACR/The Academy Workshop." J Am Coll Radiol 16(9 Pt A): 1179-1189.

Mazurowski, M. A. (2019). "Artificial Intelligence May Cause a Significant Disruption to the Radiology Workforce." J Am Coll Radiol 16(8): 1077-1082.

Allen, B., et al. (2019). "Democratizing AI." J Am Coll Radiol 16(7): 961-963.

Feng, Q. X., et al. (2019). "An Intelligent Clinical Decision Support System for Preoperative Prediction of Lymph Node Metastasis in Gastric Cancer." J Am Coll Radiol 16(7): 952-960.

Harrington, S. G. and M. K. Johnson (2019). "The FDA and Artificial Intelligence in Radiology: Defining New Boundaries." J Am Coll Radiol 16(5): 743-744.

Allen, B. and K. Dreyer (2019). "The Role of the ACR Data Science Institute in Advancing Health Equity in Radiology." J Am Coll Radiol 16(4 Pt B): 644-648.

McIntosh-Clarke, D. R., et al. (2019). "Incentivizing Physician Diversity in Radiology." J Am Coll Radiol 16(4 Pt B): 624-630.

Wang, S. S., et al. (2019). "The Resilient Radiologist: You Will Still Feel the Burn." J Am Coll Radiol 16(4 Pt A): 523-525.

Alexander, A., et al. (2019). "Scanning the Future of Medical Imaging." J Am Coll Radiol 16(4 Pt A): 501-507.

Sensakovic, W. F. and M. Mahesh (2019). "Role of the Medical Physicist in the Health Care Artificial Intelligence Revolution." J Am Coll Radiol 16(3): 393-394.

Allen, B. (2019). "The Role of the FDA in Ensuring the Safety and Efficacy of Artificial Intelligence Software and Devices." J Am Coll Radiol 16(2): 208-210.

Powell, K., et al. (2019). "What Health Care Can Learn From Self-Driving Vehicles." J Am Coll Radiol 16(2): 261-263.

Ghosh, A. (2019). "Artificial Intelligence Using Open Source BI-RADS Data Exemplifying Potential Future Use." J Am Coll Radiol 16(1): 64-72.

Allen, B. (2018). "How Structured Use Cases Can Drive the Adoption of Artificial Intelligence Tools in Clinical Practice." J Am Coll Radiol 15(12): 1758-1760.

Collado-Mesa, F., et al. (2018). "The Role of Artificial Intelligence in Diagnostic Radiology: A Survey at a Single Radiology Residency Training Program." J Am Coll Radiol 15(12): 1753-1757.

Allen, B. and K. Dreyer (2018). "The Artificial Intelligence Ecosystem for the Radiological Sciences: Ideas to Clinical Practice." J Am Coll Radiol 15(10): 1455-1457.

Schoppe, K. (2018). "Artificial Intelligence: Who Pays and How?" J Am Coll Radiol 15(9): 1240-1242.

Kohli, M. and R. Geis (2018). "Ethics, Artificial Intelligence, and Radiology." J Am Coll Radiol 15(9): 1317-1319.

Nguyen, G. K. and A. S. Shetty (2018). "Artificial Intelligence and Machine Learning: Opportunities for Radiologists in Training." J Am Coll Radiol 15(9): 1320-1321.

Sana, M. (2018). "Machine Learning and Artificial Intelligence in Radiology." J Am Coll Radiol 15(8): 1139-1142.

Schier, R. (2018). "Artificial Intelligence and the Practice of Radiology: An Alternative View." J Am Coll Radiol 15(7): 1004-1007.

Yi, P. H., et al. (2018). "Artificial Intelligence and Radiology: Collaboration Is Key." J Am Coll Radiol 15(5): 781-783.

Dreyer, K. and B. Allen (2018). "Artificial Intelligence in Health Care: Brave New World or Golden Opportunity?" J Am Coll Radiol 15(4): 655-657.

Brink, J. A. (2018). "Artificial Intelligence for Operations: The Untold Story." J Am Coll Radiol 15(3 Pt A): 375-377.

Thrall, J. H., et al. (2018). "Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success." J Am Coll Radiol 15(3 Pt B): 504-508.

Carlos, R. C., et al. (2018). "Data Science: Big Data, Machine Learning, and Artificial Intelligence." J Am Coll Radiol 15(3 Pt B): 497-498.

Syeda-Mahmood, T. (2018). "Role of Big Data and Machine Learning in Diagnostic Decision Support in Radiology." J Am Coll Radiol 15(3 Pt B): 569-576.

Balthazar, P., et al. (2018). "Protecting Your Patients' Interests in the Era of Big Data, Artificial Intelligence, and Predictive Analytics." J Am Coll Radiol 15(3 Pt B): 580-586.

Jha, S. and E. J. Topol (2018). "Information and Artificial Intelligence." J Am Coll Radiol 15(3 Pt B): 509-511.

McGinty, G. B. and B. Allen, Jr. (2018). "The ACR Data Science Institute and AI Advisory Group: Harnessing the Power of Artificial Intelligence to Improve Patient Care." J Am Coll Radiol 15(3 Pt B): 577-579.

Giger, M. L. (2018). "Machine Learning in Medical Imaging." J Am Coll Radiol 15(3 Pt B): 512-520.

King, B. F., Jr. (2018). "Artificial Intelligence and Radiology: What Will the Future Hold?" J Am Coll Radiol 15(3 Pt B): 501-503.

Kirk, I. R., et al. (2018). "The Triumph of the Machines." J Am Coll Radiol 15(3 Pt B): 587-588.

Lakhani, P., et al. (2018). "Machine Learning in Radiology: Applications Beyond Image Interpretation." J Am Coll Radiol 15(2): 350-359.

Recht, M. and R. N. Bryan (2017). "Artificial Intelligence: Threat or Boon to Radiologists?" J Am Coll Radiol 14(11): 1476-1480.