Tuesday, January 16, 2024

Chat GPT and Excel mailing list

 I have some mailing lists I maintain by pulling up the old email of 30 names and "REPLY TO ALL" with a new monthly email mailing.

I want to convert to a system in Excel.   The excel will have names and emails down Column A and B.

A                    B

Joe Man        joe@man.com

If I can get in this format, I can use an Excel command to generate concatenated emails that my email will easily recognize.

Joe@man.com; Tom@man.com; Mary@man.com 

####

Myt problem: How do I get from a big block of names and brackets and emails, which I've cut and pasted from my email outbox, to the A, B format I want in Excel.   (I will maintain, add, delete, from the Column A and B system).

####

My problem framed for CHAT:

I have a set of names and <emails> in the following semicolon delimited format.  It is currently in WORD.  I would like to create an excel spreadsheet that show the name in column A and the email - without brackets - in Column B.   Can  you helpe me produce that?  Of course, I could do it very very slowly and manually, but I prefer not to.   ##### Joe Smith <joe@smith.com>; Mary Jones <mary@jones.com>; Bob Ryan <bob@ryan.com>; etc.

###

Adjusted Method

Step 1: Prepare the Data in Word

Replace the Opening < with a Tab Character:


Use Find and Replace (Ctrl + H) in Word.

In the Find box, enter <.

In the Replace box, enter a tab character ^t. This will replace the opening angle bracket with a tab, effectively separating the name from the email address.

Replace the Closing > and Semicolon ; with a Newline Character:


Again, use Find and Replace.

Find >; and replace it with a newline character ^p. This will place each name-email pair on a new line.

After these replacements, each line in your Word document should consist of a name, a tab, and then the email address, without any angle brackets.

John Smith  [tab]  john@smith.com


Step 2: Copy Data to Excel

Select and copy the modified data in Word (Ctrl + C).

Open Excel.

Paste the data into Excel (Ctrl + V).

The names should now be in column A, and the email addresses, without angle brackets, should be in column B.



This method ensures that both opening and closing angle brackets are removed before the data is copied to Excel, simplifying the process and eliminating the need for further manipulation in Excel.

####

I know from another Chat exchange, that if I can get the emails into columns in Excel, I can "concatenate" them into a long list of emails separated by semicolons that I can paste into a COPY: box in my email.


TEXTJOIN("; ", TRUE, A1:A3) 

Note that in our example above, I would use array B1:Bn since the emails are sitting in column B.






NABLA 10 Points

10 points on NABLA from a Forbes and a Techcrunch article.

## ChatGPT4

Here are the ten key points from the two articles about Nabla, an AI startup in healthcare records:


  1. Nabla's Foundation and Funding: Nabla, a Paris-based health tech startup, has raised $24 million in Series B funding, valuing the company at around $180 million. This funding will help shift their focus towards open-source AI models, moving away from reliance on OpenAI's models like GPT-4.
  2. Initial Use of GPT Models: Initially, Nabla explored the use of OpenAI's GPT-3 and later GPT-4 models in healthcare, particularly for structuring, analyzing, and summarizing large volumes of data to aid doctors in administrative tasks.
  3. Shift to Open Source Models: Nabla decided to transition from proprietary models like GPT-4 to open source models, including Facebook’s LLaMA 2 and others from startups like Mistral. This shift was motivated by a desire for more control and adaptability in the healthcare sector.
  4. Product Development and Applications: Nabla developed a tool called 'Copilot' that helps doctors by recording conversations with patients and automatically generating clinical notes required for medical documentation and billing.
  5. User Base and Contract Wins: As of March 2023, Nabla's medical note-taking software had over 20,000 users, including primary care doctors, psychologists, psychiatrists, and various medical specialties. Notably, they secured a contract with the Permanente Medical Group, involving 10,000 doctors.
  6. Competitive Landscape: Nabla operates in a competitive market with other companies like Abridge and major players like Microsoft, which acquired Nuance Communications. However, Nabla's unique approach and focus on open-source models give it a competitive edge.
  7. Sales and Marketing Strategy: Nabla adopts a tech company-like sales strategy, offering a freemium model for its software and focusing on grassroots adoption by physicians. This approach contrasts with typical healthcare marketing strategies.
  8. Future Development Plans: Nabla plans to launch services for clinics to generate codes needed for billing insurance companies and to expand its language support for patient exams.
  9. Data Privacy and Security: Nabla places a strong emphasis on data privacy. Transcripts and summaries are stored locally on the doctor's computer, and Nabla does not retain customer data for retraining its models, except in cases where explicit consent is given.
  10. Nabla's Vision for AI in Healthcare: While Nabla recognizes the potential of AI in healthcare, it remains cautious about fully automating healthcare services. The company sees its role as assisting doctors rather than replacing them, with a focus on enhancing diagnostic processes and easing administrative burdens.

Friday, January 12, 2024

Sidebar: AI on Kolodziev and Klein

 Sidebar to main article.

Which of these points, or other points, would make the most difference in a year or two in the practice of a  typical small group oncology practice caring for its patients primarily in the outpatient setting?   

ChatGPT

In the context of a small group oncology practice, particularly in the outpatient setting, the most impactful uses of AI and large language models in the short term (1-2 years) would likely be:


  • Improved Data Analysis: AI can quickly analyze patient data to identify trends and support personalized treatment decisions, which is vital in a small practice where resources may be limited.

  • Streamlined Administrative Tasks: AI can automate administrative tasks, like documentation and billing, freeing up time for patient care.

  • Enhanced Decision Support: AI can provide real-time decision support based on the latest research and treatment protocols, assisting oncologists in staying current with the best practices.

  • Patient Monitoring and Engagement: AI-enabled tools can help in monitoring patients' health remotely and maintaining engagement, crucial for outpatient care.


These applications have the potential to significantly improve patient care efficiency and outcomes in a relatively short time frame.


User

How can these forward-looking efforts over 1-2 years avoid the past flops and failed promises of computers and data in oncology care delivery in the clinic?

ChatGPT

To avoid past flops and unfulfilled promises of computers and data in oncology care delivery, future efforts should:


  • Focus on Practical Implementation: Ensure AI tools are user-friendly and integrate seamlessly into existing clinical workflows.

  • Validate and Test: Rigorously test AI systems in real-world settings to validate their effectiveness and reliability.

  • Customize for Oncology Needs: Tailor AI solutions to the specific needs and challenges of oncology practices, rather than adopting generic tools.

  • Ensure Data Security and Privacy: Prioritize patient data security and privacy in any AI implementation.

  • Continuous Learning and Adaptation: Implement AI systems that can learn and adapt continuously, improving their functionality over time.

  • Collaborate with Clinicians: Involve clinicians in the development process to ensure the tools meet their needs and gain their trust and acceptance.

  • Patient-Centered Design: Design AI tools with a focus on enhancing patient care and experience, rather than just administrative efficiency.

###
AI Pic (Intended to capture timeline concept)



Sunday, January 7, 2024

AI and Language Learning by leading YT creators

 Ollie Richards

Dec 18 2023

This Changes Everything

[Chatbots that you dialog with in the language]  He is investing in these but they are at "beta" level

https://www.youtube.com/watch?v=8idRUe-SlNA

###

Paul at LANGFOCUS

Dec 15 2023

Will AI Kill Language Learning

https://www.youtube.com/watch?v=CYwG8LmjmI8

Includes changing YOU into a nother language and how this can become real time soon

###

Dave Huxtable   Dec 30 2023

The future of language learning, AI's game changing impact

Includes changing YOU into another language, and more

https://www.youtube.com/watch?v=1-uvPi1RSJQ

###

Dr Alex Young   Old June 25 2023

How to learn any language for free fast using chat gpt

https://www.youtube.com/watch?v=gPErX8EnIiU

Jerry Registre

Best tools for AI language learning

https://www.youtube.com/watch?v=kXChJfHZrjU



MY ANGLEs

Intermediate grammar and nuance tutor

Translate things into German that I need to read anyway





FDA finalizes minimal risk rules for IRBs

 Not sure how this varies from existing minimal risk waivers for irb, which go back to 1990s.   Maybe just expanding them to fda domain where they were oddly missing till now

##

https://www.jdsupra.com/legalnews/fda-issues-final-rule-permitting-irb-9116244/

https://www.govinfo.gov/content/pkg/FR-2017-07-25/pdf/2017-15539.pdf

https://www.fdanews.com/ext/resources/files/2017/07/07-24-17-IRB-Waiver.pdf?1519353957


Thursday, January 4, 2024

How is HR 5378 Price Transparency, different from existing CMS price transparency rules about 3 years ago?

 Chat GPT 4



User

Please use the internet.  How is the recently passed in the House bill, Lower Costs, More Transparency Act (H.R. 5378), different from price transparency regulations already passed by CMS (HHS) a few years ago?

ChatGPT

Based on a quick search, here's what I found.


The recently passed Lower Costs, More Transparency Act (H.R. 5378) in the House differs from previous price transparency regulations implemented by CMS (Centers for Medicare & Medicaid Services) in several key aspects:


Comprehensive Price Transparency: The act aims to increase price transparency throughout the entire healthcare system, not just limited to specific areas. It empowers patients and employers to make informed decisions about healthcare by providing timely and accurate information about the cost of care, treatment, and services.


Extension to Various Healthcare Entities: The act requires hospitals, insurance companies, labs, imaging providers, and ambulatory surgical centers to publicly list the prices they charge patients. This builds upon the Trump administration's price transparency rules, which primarily focused on hospitals.


Disclosure of Drug Rebates and Discounts: A significant addition in the act is the requirement for health insurers and pharmacy benefit managers (PBMs) to disclose negotiated drug rebates and discounts, thereby revealing the true costs of prescription drugs.


Impact on Prescription Drug Costs: The act includes provisions to lower out-of-pocket costs for seniors who receive medication at hospital-owned outpatient facilities or doctor's offices, expands access to more affordable generic drugs, and equips employers with necessary drug price information for better negotiations.


Support for Healthcare Infrastructure: The act also includes measures to support patients, health care workers, community health centers, and hospitals. It does this by funding community health centers, supporting training programs for new doctors, preserving Medicaid funding for hospitals serving uninsured and low-income patients, and extending funding for diabetes research.


In contrast, the CMS price transparency regulations primarily focused on hospitals, requiring them to publish their standard charges, including the rates negotiated with insurers and the discounted price a hospital is willing to accept directly from a patient if paid in cash. The Lower Costs, More Transparency Act expands on these regulations by encompassing a broader range of healthcare providers and services, including prescription drug costs, and placing a greater emphasis on empowering patients and employers with detailed cost information.


The aim of the Lower Costs, More Transparency Act is to bring a higher level of transparency and accountability to the healthcare system, enabling patients to have more control and understanding of their healthcare expenses.


##
https://www.beckershospitalreview.com/finance/the-lower-costs-more-transparency-act-9-things-to-know.html

##

PDF 231 pp

https://d1dth6e84htgma.cloudfront.net/Final_TRANSP_FLR_PKGE_02_xml_a8c7850f5a.pdf


AI SUMMARY OF HR5378

The "Lower Costs More Transparency Act" (H.R. 5378) is a comprehensive legislative proposal aimed at increasing transparency in healthcare pricing and reducing healthcare costs for patients. This detailed summary provides an overview of its key components:

Improving Healthcare Transparency:

Hospital Price Transparency: Hospitals are required to publicly disclose their standard charges for items and services, including discounted cash prices, payer-specific negotiated charges, and de-identified minimum and maximum negotiated charges.

Transparency in Other Sectors: Similar transparency requirements are extended to clinical diagnostic laboratory tests, imaging, ambulatory surgical centers, health coverage, pharmacy benefits, and more.

Reducing Healthcare Costs for Patients:

Generic Drug Applications: The Act aims to increase transparency in generic drug applications.

Medicaid and Medicare Reforms: It addresses spread pricing in Medicaid and modifies payment structures in Medicare, particularly for services furnished off-campus.

Supporting Patients, Health Care Workers, Community Health Centers, and Hospitals:

The Act provides extensions and support for community health centers, National Health Service Corps, special diabetes programs, and addresses Medicaid disproportionate share hospital payment reductions.

Increasing Access to Quality Health Data and Lowering Hidden Fees:

Health Data Access: It focuses on increasing plan fiduciaries' access to health data.

Disclosure of Hidden Fees: The Act mandates the disclosure of hidden fees and prescription drug price information.

Enforcement and Compliance:

It establishes a framework for monitoring compliance and enforcing the transparency requirements, including civil monetary penalties for non-compliance.

Consumer-Friendly Approach:

The Act emphasizes the importance of presenting information in a consumer-friendly format, ensuring that patients can easily understand and compare healthcare prices.

Accessibility:

Measures are included to ensure that the disclosed information is accessible to individuals with limited English proficiency, possibly through interpretation services or translations.

Implementation and Funding:

The Secretary of Health and Human Services is tasked with implementing the Act's provisions, with specific focus on the accessibility of charge and information submission by hospitals.

This Act represents a significant effort towards increasing transparency in healthcare pricing, potentially leading to more informed healthcare decisions and potentially lower healthcare costs for patients.

###

LUGPA SAYS "YAY"

https://www.lugpa.org/the-lower-costs--more-transparency-act

JD SUPRA Summary (similar to AI)

https://www.jdsupra.com/legalnews/u-s-house-of-representatives-passes-5860741/

####

To be or not to be compliant? Hospitals' initial strategic

responses to the federal price transparency rule

https://pubmed.ncbi.nlm.nih.gov/37930618/

Jessica N. Mittler PhD1

###
Part D, JAMA aritlce, about 1/2024, on wide wide price range of generic drugs in part d.  Hernandez 330:2390.


##
GOOGLE SEARCH

hospital compliance with price transparency

https://www.google.com/search?sca_esv=596939601&rlz=1C1CHBF_enUS923US923&sxsrf=ACQVn0_ObOJltBlzeoKbNKE-B18kyychWQ:1704820238153&q=hospital+compliance+with+price+transparency&tbm=nws&source=lnms&sa=X&ved=2ahUKEwjnu82l5tCDAxVJJkQIHWKyCCUQ0pQJegQIDRAB&biw=1455&bih=665&dpr=1.1





##

Notes on AI Pathology Pricing at CMS

Here are some quickly collated notes AI Pathology pricing at CMS.

###

In May 2023, the Prelude multi-slide AI test, 0295U, won ADLT status, and the price rose to $5435, almost triple the $1897 price that had been set earlier by gapfill by MolDx for it.

https://www.discoveriesinhealthpolicy.com/2023/05/nerd-corner-ihcalgorithm-test-gets-adlt.html   

The ADLT status also flipped its payment status for hospital outpatient specimens to "payable", as I describe in the blog linked above.

###

Another slide-AI code, 0108U, is priced as an ADLT at $4950, and this caused the price of a code crosswalked to it (HalioDx 0261U), to also double to $4950 passively.

###

In summer and fall 2023, CMS had the chance to price several new PLA codes for slides plus AI, and CMS chose a much cheaper crosswalk, at only about $700.   I'll give the blog link next and then quote it.

https://www.discoveriesinhealthpolicy.com/2023/11/cms-posts-final-prices-for-new-lab.html

3 Slide-AI Codes All $600

There were 3 codes for AI-enhanced slide-based imaging, 0376U (Artera AI Prostate), 0414U (former X084U, LungOI Imagene), 0418U (former X088U, PreciseDX Breast Bx).  Requested prices were as high as the $2500 range (Artera request).   

The expert panel, the CMS proposed  price, and the CMS final price were all crosswalk to 0220U (PreciseDx Breast) at about $700.    (I've noted earlier that if you views "slides + AI" as a category, prices have reached as high as $5435 for ADLT pricing of the Prelude DCISioniRT test 0295U) or $4950 (in the case of 0108U, 0261U).  

Labs in this $700 price group can appeal, meaning the price would be revisited next summer.


###

 Category III Digital Pathology Codes

I discussed some of the administrative problems with these codes in an August 2023 blog.

https://www.discoveriesinhealthpolicy.com/2023/08/novitas-publishes-article-on-t-codes.html

The codes are add on codes so they must be on the same claim same day same lab as the primary service.    The codes are unpriced, which usually makes payment very slow and difficult.   The codes are "bundled" in the CMS outpatient setting, where many biopsies originate.   (They are also bundled in the DRG inpatient setting).   Thus, there are many high barriers to use of these codes, some of which can't be overcome (inpatient bundling).


###

I covered a CAP TODAY article on digital pathology in December 2023.

https://www.discoveriesinhealthpolicy.com/2023/12/cap-today-continues-rising-coverage-of.html


I also quoted myself in brief, on problems with the Cat III dig path codes:

BQ: There are a couple problems with the Category III codes.  (1) They are structured as add-on (+) codes, so they must be on the same claim same day as the original surg path (or staining) codes.  (2) A huge proportion of relevant specimens originate in hospital outpatient (or inpatient) environments, where CMS has made the codes nonpayable (bundled or packaged).

###

In an AI experiment, in July 2023, I dumped 50 new abstracts on digital pathology into Chat GPT and got summaries and discussions about them.

https://www.discoveriesinhealthpolicy.com/2023/07/ai-corner-ai-consolidates-50-new.html