Archive for the ‘HealthCare IT’ Category
Protected: KOL on Competitive Dynamics: LPBI Group’s Founder’s Radar Screens on AI in Health, Life Sciences & BioPharma
Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Medicine - Application for Diagnosis, Artificial Intelligence in Medicine - Applications in Therapeutics, BioTechnology - Venture Creation, BioTechnology - Venture Creation, Venture Capital, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Drug Delivery Platform Technology, Drug Development Process, Drug Discovery Chemistry, drug repurposing, Genetics & Pharmaceutical, Global Market of Medical Devices Technology, Global Partnering & Biotech Investment, HealthCare IT, Personalized and Precision Medicine & Genomic Research, Pharmaceutical Discovery, Pharmaceutical Drug Discovery, Pharmaceutical Industry Competitive Intelligence, Pharmaceutical R&D Investment, Pharmacogenomics, Precision Cancer Medicine, Rapid automation of plasma protein pools on June 15, 2026|
AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class II: 48 e-Books: English Edition & Spanish Edition. 152,000 pages downloaded under pay-per-view
Posted in AI Models in Healthcare, Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Applications in Therapeutics, Deep Learning, HealthCare IT, Intellectual Property, Innovations, Commercialization, Investment in technological breakthrough, IP Development by LPBI Group Team, LPBI Group, e-Scientific Media, DFP, R&D-M3DP, R&D-Drug Discovery, US Patents: SOPs and Team Management, Machine Learning, Natural Language Processing (NLP) on November 22, 2025| Leave a Comment »
AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class II: 48 e-Books: English Edition & Spanish Edition. 152,000 pages downloaded under pay-per-view
Curator: Aviva Lev-Ari, PhD, RN
We had researched the topic of AI Initiatives in Big Pharma in the following article:
- Authentic Relevance of LPBI Group’s Portfolio of IP as Proprietary Training Data Corpus for AI Initiatives at Big Pharma
We are publishing a Series of Five articles that demonstrate the Authentic Relevance of Five of the Ten Digital IP Asset Classes in LPBI Group’s Portfolio of IP for AI Initiatives at Big Pharma.
-
For the Ten IP Asset Classes in LPBI Group’s Portfolio, See
https://pharmaceuticalintelligence.com/portfolio-of-ip-assets/
- PharmaceuticalIntelligence.com Journal – Projecting the Annual Rate of Article Views

• IP Asset Class V: 7,500 Biological Images in our Digital Art Media Gallery, as prior art. The Media Gallery resides in WordPress.com Cloud of LPBI Group’s Web site
Expert‑curated healthcare corpus mapped to a living ontology, already packaged for immediate model ingestion and suitable for safe pre-training, evals, fine‑tuning and inference. If healthcare domain data is on your roadmap, this is a rare, defensible asset.
- AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class I: PharmaceuticalIntelligence.com Journal, 2.5MM Views, 6,250 Scientific articles and Live Ontology
- AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class II: 48 e-Books: English Edition & Spanish Edition. 152,000 pages downloaded under pay-per-view
Article Conclusions by @grok:
Conclusions and ImplicationsLPBI’s e-books are “ready-to-ingest” for Big Pharma AI, enabling from efficiency gains to diagnostic breakthroughs. No prior comprehensive ML attempts highlight untapped value [by Big Pharma. However, we conducted in-house ML on two of the e-Books]; bilingual editions support global/equitable applications. Technical Implications: Powers multilingual small models for precision medicine. Business Implications: Fuels ROI on investments like Pfizer’s $500M push; licensing potential for partnerships. Unique Insight: In AI’s scale race, these assets provide a “rare moat” via curated human opus—unlike raw data, they embed clinical foresight for transformative inference. The article is promotional yet substantive, with dense Amazon links and calls to resources (e.g., BioMed e-Series page, IP portfolio). It builds on the prior Class I piece by shifting to long-form, creative text for deeper AI personalization.
- AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class III: 100 e-Proceedings and 50 Tweet Collections of Top Biotech and Medical Global Conferences, 2013-2025
- AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class V: 7,500 Biological Images in LPBI Group’s Digital Art Media Gallery, as prior art
- AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class X: +300 Audio Podcasts Library: Interviews with Scientific Leaders
In the series of five articles, as above, we are presenting the key AI Initiatives in Big Pharma as it was created by our prompt to @Grok on 11/18/2025:
- What are PFIZER’s AI INITIATIVES?
@Grok Response:
x.com/i/grok/share/0ol5VOJsEYs11baXq4xkzNb0h
| Generative AI tools that save scientists up to 16,000 hours annually in literature searches and data analysis. |
| Drug Discovery and Development Acceleration Pfizer uses AI, supercomputing, and ML to streamline R&D timelines |
| Clinical Trials and Regulatory Efficiency AI:
-Predictive Regulatory Tools -Decentralize Trials -inventory management |
| Disease Detection and Diagnostics:
– ATTR-CM Initiative – Rare diseases |
| Generative AI and Operational Tools:
– Charlie Platform – Scientific Data Cloud AWS powered ML on centralized data – Amazon’s SageMaker /Bedrock for Manufacturing efficiency – Global Health Grants: Pfizer Foundation’s AI Learning Lab for equitable access to care and tools for community care |
| Partnerships and Education
– Collaborations: IMI Big Picture for 3M – sample disease database – AI in Pharma AIPM Symposium: Drug discovery and Precision Medicine – Webinars of AI for biomedical data integration – Webinar on AI in Manufacturing |
| Strategic Focus:
– $500M R&D reinvestment by 2026 targets AI for Productivity – Part of $7.7B cost savings – Ethical AI, diverse DBs – Global biotech advances: China’s AI in CRISPR |
-
AI Initiatives in Big Pharma @Grok prompt & Proprietary Training Data and Inference by LPBI Group’s IP Asset Class II: 48 e-Books: English Edition & Spanish Edition. 152,000 pages downloaded under pay-per-view
The Left Column was written @Grok
The Right Column was written by Aviva Lev-Ari, PhD, RN
|
AI Initiative at Big Pharma i.e., Pfizer |
e-Books Domain-aware Editorials and Curations |
| Generative AI tools that save scientists up to 16,000 hours annually in literature searches and data analysis. | The electronic Table of Contents of every e-book is a CONCEPTUAL MASTER PIECE of one unique occurrence in Nature generated by the Editor, or the Editors that had
– Commissioned articles for the e-Book – Had selected articles from collections of Categories of Research created by domain knowledge experts – Had reviewed the TOTALITY of the Journal’s Ontology and found new concept to cover in the e-Book not originally planned – The vision of the Editor-in-Chief of the BioMed e-Series that reflects the BIG PICTURE of Patient care delivery. – UC, Berkeley PhD’83 – Knowledge student and Knowledge worker, 10/1970 to Present – Conceptual pioneer of 26 algorithms in Decision Science of Operations Management decision support tools – 2005 to Present in the Healthcare field. – 2005-2012: Clinical Nurse Manager in Post-acute SNF settings and Long-term Acute care Hospital Supervisor – had developed a unique view on Diagnosis, Therapeutics and Patient care delivery – The BioMed e-Series is the EPITOM of human CREATIVITY in Healthcare an OPUS MAGNUM created by collaboration of top Scientists, Physicians and MD/PhDs – The 48 e-Books Published by LPBI Group – represent the ONLY one Publisher on Amazon.com with +151,000 pages downloaded since the 1st e-book published and Pay-per-View was launched by Amazon.com in 2016. |
| Drug Discovery and Development Acceleration Pfizer uses AI, supercomputing, and ML to streamline R&D timelines | Two volumes on the BioMed e-Series were subjected to Medical Text Analysis with ML, Natural Language Processing (NLP).
– Cancer, Volume 1 (In English part of the Spanish Edition, Series C) – Genomics, Volume 2 (In English part of the Spanish Edition, Series B) – GPT capabilities are warranted to attempt to subject to ML every book of the MUTUALLY EXCLUSIVE 48 URLs provided by Amazon.com to LPBI Group, the Publisher. – 5 URLs for 5 Bundles in The English Edition: – Series A,B,C,D,E – English Edition – All books in each series – 5 Corpuses for domain-aware Small Language Model in English – All books in each series – 5 Corpuses for domain-aware Small Language Model in Spanish – 5 URLs for 5 Bundles in The Spanish Edition: – Series A,B,C,D,E –Spanish Edition
|
| Clinical Trials and Regulatory Efficiency AI:
-Predictive Regulatory Tools -Decentralize Trials -inventory management |
|
| Disease Detection and Diagnostics:
– ATTR-CM Initiative – Rare diseases |
– No one had attempted ML on every book, only two books were analyzed by ML.
– No one had attempted ML on all the Volumes in any of the 5 Series. – No one had attempted ML on all the 48 books – WHEN that will be done – a REVOLUTION on Disease Detection and Diagnostics will be seen for the first time
|
| Generative AI and Operational Tools:
– Charlie Platform – Scientific Data Cloud AWS powered ML on centralized data – Amazon’s SageMaker/Bedrock for Manufacturing efficiency – Global Health Grants: Pfizer Foundation’s AI Learning Lab for equitable access to care and tools for community care |
Add the content of all the Books to Charlie Platform |
| Partnerships and Education
Collaborations: IMI Big Picture for 3M – sample disease database
AI in Pharma AIPM Symposium: Drug discovery and Precision Medicine
Webinars of AI for biomedical data integration
Webinard on Ai in Manufacturing |
e-Books are the SOURCE for Education
– Offer the books as Partnership sustenance |
| Strategic Focus:
– $500M R&D reinvestment by 2026 targets AI for Productivity – Part of $7.7B cost savings – Ethical AI, diverse DBs – Global biotech advances: China’s AI in CRISPR |
URLs for the English-language Edition by e-Series:
Series A: Cardiovascular Diseases ($515) https://www.amazon.com/gp/product/B07P981RCS?ref_=dbs_p_mng_rwt_ser_shvlr&storeType=ebooks Series B: Frontiers in Genomics ($200) https://www.amazon.com/gp/product/B0BSDPG2RX?ref_=dbs_p_pwh_rwt_anx_b_lnk&storeType=ebooks Series C: Cancer & Oncology ($175) https://www.amazon.com/gp/product/B0BSDWVB3H?ref_=dbs_p_mng_rwt_ser_shvlr&storeType=ebooks Series D: Immunology ($325) https://www.amazon.com/gp/product/B08VVWTNR4?ref_=dbs_p_pwh_rwt_anx_b_lnk&storeType=ebooks Series E: Patient-Centered Medicine ($274) https://www.amazon.com/gp/product/B0BSDW2K6C?ref_=dbs_p_mng_rwt_ser_shvlr&storeType=ebooks
|
CONCLUSIONS: The Voice of Dr. Stephen J. Williams PhD
Overview: This is the second installment in a five-article series on LPBI Group’s digital IP assets for AI in pharma. It focuses on IP Asset Class II—48 e-books (bilingual English/Spanish editions)—as a proprietary, expert-curated textual corpus for training and inference in healthcare AI models. Drawing from a November 18, 2025, Grok prompt on Pfizer’s AI efforts, the article maps e-book content to pharma applications, highlighting untapped ML/NLP potential for small language models. Unlike Class I (journal articles), this emphasizes long-form editorial creativity and bilingual scalability, positioning the assets as a “defensible moat” for Big Pharma’s AI acceleration.Main Thesis and Key Arguments
- Core Idea: LPBI’s e-books, with 152,000 pay-per-view downloads (largest for any single Amazon e-publisher since 2016), offer domain-specific, human-curated content (e.g., conceptual tables of contents as “masterpieces” reflecting patient care visions) that outperforms generic data in AI training. This enables precise inference for drug discovery, diagnostics, and efficiency, fostering human-AI synergy.
- Value Proposition: The BioMed e-Series (5 series: A-E, each bundled as a corpus) totals 48 volumes from collaborations with top scientists/MD/PhDs. Editor-in-Chief’s expertise (UC Berkeley PhD ’83, decision science algorithms, clinical nursing) infuses “big-picture” insights. Valued for multilingual models; only two volumes (Cancer Vol. 1, Genomics Vol. 2) have seen ML analysis—full application could “revolutionize” disease detection.
- Broader Context: Part of LPBI’s 10 IP classes; five (I, II, III, V, X) are AI-ready via living ontology. Contrasts with open-source data by emphasizing ethical, diverse, creative inputs for foundation models.
AI Initiatives in Big Pharma (Focus on Pfizer)Reuses the Grok prompt on Pfizer’s AI, with key highlights (verbatim from article’s table):
|
Initiative Category
|
Description
|
|---|---|
|
Generative AI Tools
|
Saves up to 16,000 hours annually in literature searches/data analysis.
|
|
Drug Discovery Acceleration
|
AI, supercomputing, ML to streamline R&D timelines.
|
|
Clinical Trials & Regulatory Efficiency
|
Predictive tools, decentralized trials, inventory management.
|
|
Disease Detection & Diagnostics
|
ATTR-CM Initiative, rare diseases focus.
|
|
Generative AI & Operational Tools
|
Charlie Platform; AWS-powered Scientific Data Cloud; SageMaker/Bedrock for manufacturing; Pfizer Foundation’s AI Learning Lab for equitable care.
|
|
Partnerships & Education
|
IMI Big Picture (3M sample disease database); AIPM Symposium (drug discovery/precision medicine); Webinars on AI for biomedical integration and manufacturing.
|
|
Strategic Focus
|
$500M R&D reinvestment by 2026 for AI productivity; part of $7.7B cost savings; ethical AI with diverse DBs; global advances (e.g., China’s CRISPR AI).
|
Mapping to LPBI’s Proprietary DataA core table aligns Pfizer initiatives with e-book alignments, showcasing ingestion for AI enhancement:
|
Pfizer AI Initiative
|
e-Books Alignment
|
|---|---|
|
Generative AI Tools (16,000 hours saved)
|
Electronic TOCs as conceptual masterpieces: Editor commissions/selections/ontology reviews reflect big-picture patient care (UC Berkeley PhD ’83, decision science pioneer, clinical experience); BioMed e-Series as opus magnum of human creativity; 48 e-books with 152,000+ downloads since 2016.
|
|
Drug Discovery Acceleration
|
ML/NLP applied to Cancer Vol. 1 (Series C) and Genomics Vol. 2 (Series B); Extend GPT to all 48 books via 5 English bundles (Series A-E) and 5 Spanish bundles as corpuses for domain-aware small language models.
|
|
Clinical Trials & Regulatory Efficiency
|
(No specific mapping provided.)
|
|
Disease Detection & Diagnostics (ATTR-CM, rare diseases)
|
Untapped: No prior ML on all books/volumes/series; Full analysis promises revolution in detection/diagnostics.
|
|
Generative AI & Operational Tools (Charlie, AWS, etc.)
|
Ingest all book content into Charlie Platform for centralized ML.
|
|
Partnerships & Education (IMI, AIPM, webinars)
|
e-Books as education source; Offer for partnership sustenance.
|
|
Strategic Focus ($500M reinvestment, ethics)
|
Bundles enable diverse, ethical DBs; URLs for English Series: A (Cardiovascular, $515): [link]; B (Genomics, $200): [link]; C (Cancer, $175): [link]; D (Immunology, $325): [link]; E (Patient-Centered, $274): [link]. Spanish equivalents implied.
|
Methodologies and Frameworks
- AI Training Pipeline: Package e-books (textual corpus) for pre-training (ontology-mapped ingestion), fine-tuning (e.g., ML on series bundles), and inference (e.g., diagnostics queries). Bilingual support for global models.
- ML/NLP Demo: Applied to two volumes; scale to 48 books via GPT for small language models (5 corpuses/language).
- Productivity Model: Amplifies Pfizer’s savings (16,000 hours, $7.7B total) by injecting expert creativity; ethical focus on diverse, compliant data for equity (e.g., AI Learning Lab).
- Insights: Quotes editor’s vision as “epitome of human creativity”; predicts full ML rollout as first-of-its-kind revolution, differing from Class I’s article-based ontology by focusing on editorial depth.
Conclusions and Implications
1 web page
Bridging the Gender Gap in Healthcare: Unlocking Biopharma’s Potential in Women’s Health
Posted in AI Models in Healthcare, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Digital HealthCare – biotech & internet joint ventures, Electronic Health Record, Genetics & Pharmaceutical, Genomics Pharmacy, Glycobiology: Biopharmaceutical Production, Health Care System by Country, Health Economics and Outcomes Research, Health Law & Patient Safety, Health Law Policy, Healthcare costs and reimbursement, HealthCare IT, Healthcare Reform, Human Immune System in Health and in Disease, Mobile Healthcare, Personal Health Applications: Tech Innovations serves HealhCare, Pharmaceutical Analytics, Pharmaceutical Discovery, Pharmaceutical Drug Discovery, Pharmaceutical Industry Competitive Intelligence, Pharmaceutical R&D Informatics, Pharmaceutical R&D Investment, Pharmacogenomics, Population Health Management, Population Health Management, Genetics & Pharmaceutical, Population Health Management, Nutrition and Phytochemistry, Technology Transfer: Biotech and Pharmaceutical, Transformative Technologies in Healthcare, Voices of Patients and Healthcare Providers, Wearable Tech + Digital Health, Women Health, tagged advance therapy, Biopharmaceutical, gap, gender, Targeted therapy, translational therapy, womens' health on February 4, 2025| Leave a Comment »
Curator: Dr. Sudipta Saha, Ph.D.
Nearly half of the global population—and 80 percent of patients in therapeutic areas such as immunology—are women. Yet, treatments are frequently developed without tailored insights for female patients, often ignoring critical biological differences such as hormonal impacts, genetic factors, and cellular sex. Historically, women’s health has been narrowly defined through the lens of reproductive organs, while for non-reproductive conditions, women were treated as “small men.” This lack of focus on sex-specific biology has contributed to significant gaps in healthcare.
A recent analysis found that women spend 25 percent more of their lives in poor health compared with men due to the absence of sex-based treatments. Addressing this disparity could not only improve women’s quality of life but also unlock over $1 trillion in annual global GDP by 2040.
Four key factors contribute to the women’s health gap: limited understanding of sex-based biological differences, healthcare systems designed around male physiology, incomplete data that underestimates women’s disease burden, and chronic underfunding of female-focused research. For instance, despite women representing 78 percent of U.S. rheumatoid arthritis patients, only 7 percent of related NIH funding in 2019 targeted female-specific studies.
However, change is happening. Companies have demonstrated how targeted R&D can drive better outcomes for women. These therapies achieved expanded FDA approvals after clinical trials revealed their unique benefits for female patients. Similarly, addressing sex-based treatment gaps in asthma, atrial fibrillation, and tuberculosis could prevent millions of disability-adjusted life years.
By closing the women’s health gap, biopharma companies can drive innovation, improve therapeutic outcomes, and build high-growth markets while addressing long-standing inequities. This untapped opportunity holds the potential to transform global health outcomes for women and create a more equitable future.
References
Genomic data can predict miscarriage and IVF failure
Posted in Advanced Computing Platform, Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Application for Diagnosis, Artificial Intelligence in Medicine - Applications in Therapeutics, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biological Engineering, Biological Networks, Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Cell Level, Computational Biology/Systems and Bioinformatics, Computational Histopathology, Deep Learning in Pathology, Developmental biology, Diagnostics and Lab Tests, Digital HealthCare – biotech & internet joint ventures, Disease Biology, Genomic Testing: Methodology for Diagnosis, Genomics Pharmacy, HealthCare IT, Healthcare Reform, Machine Learning, MicroEngineering Cell-Tissue & Systems, Placenta, Preimplantation Genetic Diagnosis and Reproductive Genomics, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, Reproductive Biology & Bio Instrumentation, therapeutics, Tissue Engineering, Translational Research, Women Health, tagged aneuploidy, Diagnosis, Genomic data, IVF, Miscarriage, treatment on June 15, 2022| Leave a Comment »
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
Infertility is a major reproductive health issue that affects about 12% of women of reproductive age in the United States. Aneuploidy in eggs accounts for a significant proportion of early miscarriage and in vitro fertilization failure. Recent studies have shown that genetic variants in several genes affect chromosome segregation fidelity and predispose women to a higher incidence of egg aneuploidy. However, the exact genetic causes of aneuploid egg production remain unclear, making it difficult to diagnose infertility based on individual genetic variants in mother’s genome. Although, age is a predictive factor for aneuploidy, it is not a highly accurate gauge because aneuploidy rates within individuals of the same age can vary dramatically.
Researchers described a technique combining genomic sequencing with machine-learning methods to predict the possibility a woman will undergo a miscarriage because of egg aneuploidy—a term describing a human egg with an abnormal number of chromosomes. The scientists were able to examine genetic samples of patients using a technique called “whole exome sequencing,” which allowed researchers to home in on the protein coding sections of the vast human genome. Then they created software using machine learning, an aspect of artificial intelligence in which programs can learn and make predictions without following specific instructions. To do so, the researchers developed algorithms and statistical models that analyzed and drew inferences from patterns in the genetic data.
As a result, the scientists were able to create a specific risk score based on a woman’s genome. The scientists also identified three genes—MCM5, FGGY and DDX60L—that when mutated and are highly associated with a risk of producing eggs with aneuploidy. So, the report demonstrated that sequencing data can be mined to predict patients’ aneuploidy risk thus improving clinical diagnosis. The candidate genes and pathways that were identified in the present study are promising targets for future aneuploidy studies. Identifying genetic variations with more predictive power will serve women and their treating clinicians with better information.
References:
https://pubmed.ncbi.nlm.nih.gov/35347416/
https://pubmed.ncbi.nlm.nih.gov/31552087/
https://pubmed.ncbi.nlm.nih.gov/33193747/
Digital Therapeutics In Healthcare: The Market Perspectives
Posted in AI Models in Healthcare, Health Care System by Country, HealthCare IT, Prescription Drugs Costs, Technology Capital Expenses, United States on February 7, 2022| Leave a Comment »
Digital Therapeutics In Healthcare: The Market Perspectives
Reporter: Aviva Lev-Ari, PhD, RN
based on e-mail from
CB Insights <healthcare.insights@cbinsights.com> Thu, 20 Jan 2022 15:03:47 +0000
State Of Digital Health 2021 report
Breaking records and taking names
At the end of Q3’21, the digital health sector was on track to collect nearly $53B by the end of the year.
Fast-forward through Q4’21. Digital health startups surpassed that and pulled in a record-breaking $57.2B (https://cbinsights.us1.list-manage.com/track/click?u=0c60818e26ecdbe423a10ad2f&id=3fa5150475&e=d62ca003c7) in funding in 2021, up 79% from 2020.
The US digital health market continued to dominate, reaching a record $37.9B (https://cbinsights.us1.list-manage.com/track/click?u=0c60818e26ecdbe423a10ad2f&id=4e867a5607&e=d62ca003c7) in 2021.
In Q4’21 alone, the region collected $10.8B — more than Asia, the second-largest global market, recorded in all of 2021.
Within digital health, we saw 2 sectors take off in 2021:
Digital therapeutics (DTx) and
Mental health tech
Along with 127% funding growth compared to 2020, the DTx market (https://cbinsights.us1.list-manage.com/track/click?u=0c60818e26ecdbe423a10ad2f&id=ca977c4de1&e=d62ca003c7) saw significant momentum, including increased business relationships (https://cbinsights.us1.list-manage.com/track/click?u=0c60818e26ecdbe423a10ad2f&id=98081124d2&e=d62ca003c7) , expanding clinical evidence, and new regulatory approvals.
Want to go deeper into DTx?
Clients can download our DTx market map
Mental health tech also had a show-stopping year, with funding up 139% YoY (https://cbinsights.us1.list-manage.com/track/click?u=0c60818e26ecdbe423a10ad2f&id=334bfe3143&e=d62ca003c7) to reach $5.5B.
But we are still in early innings. With 68% of 2021 deals being early-stage, there is room for growth in the mental health tech space.
Want to go deeper? Clients can download our mental health tech market map here (https://cbinsights.us1.list-manage.com/track/click?u=0c60818e26ecdbe423a10ad2f&id=57dacdc7a2&e=d62ca003c7) .
Here are a few more notable trends covered in the report:
* What happened in healthcare IT (page 131 (https://cbinsights.us1.list-manage.com/track/click?u=0c60818e26ecdbe423a10ad2f&id=e60dc58576&e=d62ca003c7) )? Will this continue in 2022?
* In Q4, US digital health funding (page 146 (https://cbinsights.us1.list-manage.com/track/click?u=0c60818e26ecdbe423a10ad2f&id=97b46b9d4e&e=d62ca003c7) ) hit an all-time high, even though deals sank to a low for the year (it’s typical to see fewer deals in Q4 due to the holidays).
* While M&A exits (pages 65-66 (https://cbinsights.us1.list-manage.com/track/click?u=0c60818e26ecdbe423a10ad2f&id=dd55fc09b0&e=d62ca003c7) ) soared in 2021 on the whole, they dipped meaningfully in Q4 (this has not been historically seasonal).
If you want to see how your favorite sector performed, check out the “Collection Spotlights” section, which begins on page 83 (https://cbinsights.us1.list-manage.com/track/click?u=0c60818e26ecdbe423a10ad2f&id=360bcc7859&e=d62ca003c7) .
Preliminary survey results
Earlier this month we surveyed our audience for 2022 healthcare predictions. We plan to unveil the responses in the next newsletter.
Until then, I wanted to share the results of our bonus question, ”When will you wake up and not think about Covid?”
Nearly half (44%) of our audience picked “After 2023,” which is a big change from June 2021, when just 18% of you selected that option.
Stay healthy,
Marc Albanese (https://cbinsights.us1.list-manage.com/track/click?u=0c60818e26ecdbe423a10ad2f&id=a6f2990166&e=d62ca003c7)
P.S. Thank you to Amanda (https://cbinsights.us1.list-manage.com/track/click?u=0c60818e26ecdbe423a10ad2f&id=d4b485d71f&e=d62ca003c7) and Nicole (https://cbinsights.us1.list-manage.com/track/click?u=0c60818e26ecdbe423a10ad2f&id=117de54447&e=d62ca003c7) for your help compiling the newsletter this week!
P.P.S. Are you looking for the best place to write data-driven healthcare research? We are hiring for a senior analyst on our healthcare team (https://cbinsights.us1.list-manage.com/track/click?u=0c60818e26ecdbe423a10ad2f&id=4d78da1730&e=d62ca003c7) .
CB INSIGHTS HITS
Healthcare Research: Startups, Trends, Tech Solutions & More. (https://cbinsights.us1.list-manage.com/track/click?u=0c60818e26ecdbe423a10ad2f&id=951a587727&e=d62ca003c7)
Here’s a sample of our digital health and healthcare research on the sector’s investment and funding trends, business models, tech solutions, and more. Read it here (https://cbinsights.us1.list-manage.com/track/click?u=0c60818e26ecdbe423a10ad2f&id=82c24532c1&e=d62ca003c7) .
[Client Research] Tech Market Map Report —
Digital Therapeutics In Healthcare.
This report highlights 150 digital therapeutics companies that are addressing 15 distinct therapeutic areas, from smoking cessation to anxiety and depression. Read it here (https://cbinsights.us1.list-manage.com/track/click?u=0c60818e26ecdbe423a10ad2f&id=ebb1bd80f3&e=d62ca003c7) .
[Client Research] Why Healthcare Providers Are Prioritizing Digital Patient Payments. (https://cbinsights.us1.list-manage.com/track/click?u=0c60818e26ecdbe423a10ad2f&id=f5511b9a5b&e=d62ca003c7)
Digital patient payments have garnered high market momentum and widespread industry leader activity in the revenue cycle management space — making it an industry worth prioritizing. Read it here (https://cbinsights.us1.list-manage.com/track/click?u=0c60818e26ecdbe423a10ad2f&id=7f5edfb70f&e=d62ca003c7) .
[Client Research] Tech Market Map Report — Clinical Trials In Healthcare. (https://cbinsights.us1.list-manage.com/track/click?u=0c60818e26ecdbe423a10ad2f&id=983995d94d&e=d62ca003c7) This report looks at the clinical trials tech companies serving pharmaceutical companies and CROs. Read it here (https://cbinsights.us1.list-manage.com/track/click?u=0c60818e26ecdbe423a10ad2f&id=e0c17d0cec&e=d62ca003c7) .
SOURCE
based on e-mail from
CB Insights <healthcare.insights@cbinsights.com> Thu, 20 Jan 2022 15:03:47 +0000
IBM has reached an agreement to sell its Watson Health data and analytics business to the private-equity firm Francisco Partners
Posted in AI Models in Healthcare, Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, HealthCare IT, United States on January 26, 2022| Leave a Comment »
IBM has reached an agreement to sell its Watson Health data and analytics business to the private-equity firm Francisco Partners
Reporter: Aviva Lev-Ari, PhD, RN
UPDATED on 2/5/2022
UPDATED on 1/31/2022
AI Hot in Healthcare Despite IBM’s Watson Health Pullout
28/01/2022
- Big pharma companies are snapping up collaborations with firms using AI to speed up drug discovery, with one of the latest being Sanofi’s pact with Exscientia.
- Tech giants are placing big bets on digital health analysis firms, such as Oracle’s €25.42B ($28.3B) takeover of Cerner in the US.
- There’s also a steady flow of financing going to startups taking new directions with AI and bioinformatics, with the latest example being a €20M Series A round by SeqOne Genomics in France.
“IBM Watson uses a philosophy that is diametrically opposed to SeqOne’s,” said Jean-Marc Holder, CSO of SeqOne. “[IBM Watson seems] to rely on analysis of large amounts of relatively unstructured data and bet on the volume of data delivering the right result. By opposition, SeqOne strongly believes that data must be curated and structured in order to deliver good results in genomics.”
UPDATED on 1/31/2022
Key M&A in Health IT include:
Last month,
- IBM arch-rival Oracle announced a $28 billion takeover of electronic health record company Cerner, while 2021 also saw
- Microsoft’s $19.7 billion play for AI specialist Nuance and a
- $17 billion takeover of Athenahealth by investment groups Bain Capital and Hellman & Friedman.
IBM sells off large parts of Watson Health business
January 24, 2022
Francisco Partners is picking up a range of databases and analytics tools – including
- Health Insights,
- MarketScan,
- Clinical Development,
- Social Programme Management,
- Micromedex and
- other imaging and radiology tools, for an undisclosed sum estimated to be in the region of $1 billion.
IBM said the sell-off is tagged as “a clear next step” as it focuses on its platform-based hybrid cloud and artificial intelligence strategy, but it’s no secret that Watson Health has failed to live up to its early promise.
The sale also marks a retreat from healthcare for the tech giant, which is remarkable given that it once said it viewed health as second only to financial services market as a market opportunity.
IBM said it “remains committed to Watson, our broader AI business, and to the clients and partners we support in healthcare IT.”
The company reportedly invested billions of dollars in Watson, but according to a Wall Street Journal report last year, the health business – which provided cloud-based access to the supercomputer and a range of analytics services – has struggled to build market share and reach profitability.
An investigation by Stat meanwhile suggested that Watson Health’s early push into cancer for example was affected by a premature launch, interoperability challenges and over-reliance on human input to generate results.
For its part, IBM has said that the Watson for Oncology product has been improving year-on-year as the AI crunches more and more data.
That is backed up by a meta analysis of its performance published last year in Nature found that the treatment recommendations delivered by the tool were largely in line with human doctors for several cancer types.
However, the study also found that there was less consistency in more advanced cancers, and the authors noted the system “still needs further improvement.”
Watson Health offers a range of other services of course, including
- tools for genomic analysis and
- running clinical trials that have found favour with a number of pharma companies.
- Francisco said in a statement that it offers “a market leading team [that] provides its customers with mission critical products and outstanding service.”
The deal is expected to close in the second quarter, with the current management of Watson Health retaining “similar roles” in the new standalone company, according to the investment company.
IBM’s step back from health comes as tech rivals are still piling into the sector.
SOURCE
https://pharmaphorum.com/news/ibm-sells-off-large-parts-of-watson-health-business/
@pharma_BI is asking: What will be the future of WATSON Health?
@AVIVA1950 says on 1/26/2022:
Aviva believes plausible scenarios will be that Francisco Partners will:
A. Invest in Watson Health – Like New Mountains Capital (NMC) did with Cytel
B. Acquire several other complementary businesses – Like New Mountains Capital (NMC) did with Cytel
C. Hold and grow – Like New Mountains Capital (NMC) is doing with Cytel since 2018.
D. Sell it in 7 years to @Illumina or @Nvidia or Google’s Parent @AlphaBet
1/21/2022
IBM said Friday it will sell the core data assets of its Watson Health division to a San Francisco-based private equity firm, marking the staggering collapse of its ambitious artificial intelligence effort that failed to live up to its promises to transform everything from drug discovery to cancer care.
https://www.statnews.com/2022/01/21/ibm-watson-health-sale-equity/
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Patients with type 2 diabetes may soon receive artificial pancreas and a smartphone app assistance
Posted in Artificial Intelligence in Health Care - Tools & Innovations, Artificial Pancreas for Type 2 Diabetes, Artificial Pancreas for Type1 Diabetes, Diabetes Mellitus, HealthCare IT, Medical Devices R&D and Inventions, Mobile Healthcare, tagged artificial pancreas, Blood sugar, dialysis, Hypoglycemia, kidney disease, Medical device, type 2 diabetes on August 13, 2021| Leave a Comment »
Patients with type 2 diabetes may soon receive artificial pancreas and a smartphone app assistance
Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc
In a brief, randomized crossover investigation, adults with type 2 diabetes and end-stage renal disease who needed dialysis benefited from an artificial pancreas. Tests conducted by the University of Cambridge and Inselspital, University Hospital of Bern, Switzerland, reveal that now the device can help patients safely and effectively monitor their blood sugar levels and reduce the risk of low blood sugar levels.
Diabetes is the most prevalent cause of kidney failure, accounting for just under one-third (30%) of all cases. As the number of people living with type 2 diabetes rises, so does the number of people who require dialysis or a kidney transplant. Kidney failure raises the risk of hypoglycemia and hyperglycemia, or unusually low or high blood sugar levels, which can lead to problems ranging from dizziness to falls and even coma.
Diabetes management in adults with renal failure is difficult for both the patients and the healthcare practitioners. Many components of their therapy, including blood sugar level targets and medications, are poorly understood. Because most oral diabetes drugs are not indicated for these patients, insulin injections are the most often utilized diabetic therapy-yet establishing optimum insulin dose regimes is difficult.
A team from the University of Cambridge and Cambridge University Hospitals NHS Foundation Trust earlier developed an artificial pancreas with the goal of replacing insulin injections for type 1 diabetic patients. The team, collaborating with experts at Bern University Hospital and the University of Bern in Switzerland, demonstrated that the device may be used to help patients with type 2 diabetes and renal failure in a study published on 4 August 2021 in Nature Medicine.
The study’s lead author, Dr Charlotte Boughton of the Wellcome Trust-MRC Institute of Metabolic Science at the University of Cambridge, stated:
Patients living with type 2 diabetes and kidney failure are a particularly vulnerable group and managing their condition-trying to prevent potentially dangerous highs or lows of blood sugar levels – can be a challenge. There’s a real unmet need for new approaches to help them manage their condition safely and effectively.
The Device
The artificial pancreas is a compact, portable medical device that uses digital technology to automate insulin delivery to perform the role of a healthy pancreas in managing blood glucose levels. The system is worn on the outside of the body and consists of three functional components:
- a glucose sensor
- a computer algorithm for calculating the insulin dose
- an insulin pump
The artificial pancreas directed insulin delivery on a Dana Diabecare RS pump using a Dexcom G6 transmitter linked to the Cambridge adaptive model predictive control algorithm, automatically administering faster-acting insulin aspart (Fiasp). The CamDiab CamAPS HX closed-loop app on an unlocked Android phone was used to manage the closed loop system, with a goal glucose of 126 mg/dL. The program calculated an insulin infusion rate based on the data from the G6 sensor every 8 to 12 minutes, which was then wirelessly routed to the insulin pump, with data automatically uploaded to the Diasend/Glooko data management platform.
The Case Study
Between October 2019 and November 2020, the team recruited 26 dialysis patients. Thirteen patients were randomly assigned to get the artificial pancreas first, followed by 13 patients who received normal insulin therapy initially. The researchers compared how long patients spent as outpatients in the target blood sugar range (5.6 to 10.0mmol/L) throughout a 20-day period.
Patients who used the artificial pancreas spent 53 % in the target range on average, compared to 38% who utilized the control treatment. When compared to the control therapy, this translated to approximately 3.5 more hours per day spent in the target range.
The artificial pancreas resulted in reduced mean blood sugar levels (10.1 vs. 11.6 mmol/L). The artificial pancreas cut the amount of time patients spent with potentially dangerously low blood sugar levels, known as ‘hypos.’
The artificial pancreas’ efficacy improved significantly over the research period as the algorithm evolved, and the time spent in the target blood sugar range climbed from 36% on day one to over 60% by the twentieth day. This conclusion emphasizes the need of employing an adaptive algorithm that can adapt to an individual’s fluctuating insulin requirements over time.
When asked if they would recommend the artificial pancreas to others, everyone who responded indicated they would. Nine out of ten (92%) said they spent less time controlling their diabetes with the artificial pancreas than they did during the control period, and a comparable amount (87%) said they were less concerned about their blood sugar levels when using it.
Other advantages of the artificial pancreas mentioned by study participants included fewer finger-prick blood sugar tests, less time spent managing their diabetes, resulting in more personal time and independence, and increased peace of mind and reassurance. One disadvantage was the pain of wearing the insulin pump and carrying the smartphone.
Professor Roman Hovorka, a senior author from the Wellcome Trust-MRC Institute of Metabolic Science, mentioned:
Not only did the artificial pancreas increase the amount of time patients spent within the target range for the blood sugar levels, but it also gave the users peace of mind. They were able to spend less time having to focus on managing their condition and worrying about the blood sugar levels, and more time getting on with their lives.
The team is currently testing the artificial pancreas in outpatient settings in persons with type 2 diabetes who do not require dialysis, as well as in difficult medical scenarios such as perioperative care.
“The artificial pancreas has the potential to become a fundamental part of integrated personalized care for people with complicated medical needs,” said Dr Lia Bally, who co-led the study in Bern.
The authors stated that the study’s shortcomings included a small sample size due to “Brexit-related study funding concerns and the COVID-19 epidemic.”
Boughton concluded:
We would like other clinicians to be aware that automated insulin delivery systems may be a safe and effective treatment option for people with type 2 diabetes and kidney failure in the future.
Main Source:
Other Related Articles published in this Open Access Online Scientific Journal include the following:
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Developing Machine Learning Models for Prediction of Onset of Type-2 Diabetes
Posted in Academic Publishing, Advanced Computing Platform, AI Models in Healthcare, Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Application for Diagnosis, Artificial Intelligence in Medicine - Applications in Therapeutics, Big Data, Big Data & Analytics, BioIT: BioInformatics, BioTechnology - Venture Creation, BioTechnology - Venture Creation, Venture Capital, Blockchain Transactions System, Centers for Medicare & Medicaid Services, Computational Biology/Systems and Bioinformatics, Curation, Data Science, Health Care System by Country, HealthCare IT, Innovations, Intelligent Information Systems, Machine Learning, Machine learning in predicting type 2 diabetes, Scientific Publishing, Technology Advance Assessment of, tagged AI in Healthcare, Artificial intelligence, Big data, databases, deep learning, Machine Learning, Machine Learning models, Medical history, Population-wide approach, type 2 diabetes on May 29, 2021| Leave a Comment »
Developing Machine Learning Models for Prediction of Onset of Type-2 Diabetes
Reporter: Amandeep Kaur, B.Sc., M.Sc.
A recent study reports the development of an advanced AI algorithm which predicts up to five years in advance the starting of type 2 diabetes by utilizing regularly collected medical data. Researchers described their AI model as notable and distinctive based on the specific design which perform assessments at the population level.
Image source: https://healthinformatics.uic.edu/blog/machine-learning-in-healthcare/
The first author Mathieu Ravaut, M.Sc. of the University of Toronto and other team members stated that “The main purpose of our model was to inform population health planning and management for the prevention of diabetes that incorporates health equity. It was not our goal for this model to be applied in the context of individual patient care.”
Research group collected data from 2006 to 2016 of approximately 2.1 million patients treated at the same healthcare system in Ontario, Canada. Even though the patients were belonged to the same area, the authors highlighted that Ontario encompasses a diverse and large population.
The newly developed algorithm was instructed with data of approximately 1.6 million patients, validated with data of about 243,000 patients and evaluated with more than 236,000 patient’s data. The data used to improve the algorithm included the medical history of each patient from previous two years- prescriptions, medications, lab tests and demographic information.
When predicting the onset of type 2 diabetes within five years, the algorithm model reached a test area under the ROC curve of 80.26.
The authors reported that “Our model showed consistent calibration across sex, immigration status, racial/ethnic and material deprivation, and a low to moderate number of events in the health care history of the patient. The cohort was representative of the whole population of Ontario, which is itself among the most diverse in the world. The model was well calibrated, and its discrimination, although with a slightly different end goal, was competitive with results reported in the literature for other machine learning–based studies that used more granular clinical data from electronic medical records without any modifications to the original test set distribution.”
This model could potentially improve the healthcare system of countries equipped with thorough administrative databases and aim towards specific cohorts that may encounter the faulty outcomes.
Research group stated that “Because our machine learning model included social determinants of health that are known to contribute to diabetes risk, our population-wide approach to risk assessment may represent a tool for addressing health disparities.”
Sources:
Reference:
Ravaut M, Harish V, Sadeghi H, et al. Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes. JAMA Netw Open. 2021;4(5):e2111315. doi:10.1001/jamanetworkopen.2021.11315 https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2780137
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COVID-related financial losses at Mass General Brigham
Posted in Accountable Care Organizations, COVID-19, Economic Impact of Coronavirus Pandemic, Health Care System by Country, Health Economics and Outcomes Research, Health Law & Patient Safety, Healthcare costs and reimbursement, HealthCare IT, Healthcare Reform, Hospital-based Medical Innovations, Population Health Management, Population Health Management, Genetics & Pharmaceutical, SARS-CoV-2, Seasonality & Environmental Factors in Resurgence, Serology tests for coronavirus antibodies, Technology Capital Expenses, Treatment Protocols for COVID-19, Uninsured and Underinsured, United States, Virus Infective Acute Respiratory Syndrome: SARS-CoV on March 28, 2021| Leave a Comment »
COVID-related financial losses at Mass General Brigham
Reporter: Aviva Lev-Ari, PhD, RN
Based on
Mass General Brigham reports COVID-related financial losses not as bad as expected
By Priyanka Dayal McCluskey Globe Staff,Updated December 11, 2020, 3:02 p.m.
START QUOTE
The state’s largest hospital system on Friday reported the worst financial loss in its history while fighting the COVID-19 pandemic — but still ended the fiscal year in better shape than expected.
Mass General Brigham, formerly known as Partners HealthCare, lost $351 million on operations in the fiscal year that ended Sept. 30. In 2019, the system recorded a gain of $382 million.
The loss, however, is not as great as projected, thanks in part to an infusion of federal aid and patients returning to hospitals in large numbers after the first COVID surge receded.
“2020 is like no other year,” said Peter Markell, chief financial officer at Mass General Brigham, which includes Massachusetts General Hospital, Brigham and Women’s Hospital, and several community hospitals. “At the end of the day, we came out of this better than we thought we might.”
Total revenue for the year remained relatively stable at about $14 billion.
When the pandemic first hit Massachusetts in March, hospitals across the state suddenly experienced sharp drops in revenue because they canceled so much non-COVID care to respond to the crisis at hand. They also faced new costs related to COVID, including the personal protective equipment needed to keep health care workers safe from infection.
Federal aid helped to make up much of the losses, including $546 million in grant money that went to Mass General Brigham. The nonprofit health system also slashed capital expenses in half, by about $550 million, and temporarily froze employee wages and cut their retirement benefits.
Among the unusual new costs for Mass General Brigham this year was the expense of building a field hospital, Boston Hope, at the Boston Convention and Exhibition Center. The project cost $15 million to $20 million, Markell said, and Mass General Brigham is working to recoup those costs from government agencies.
The second surge of COVID, now underway, could hit hospitals’ bottom lines again, though Markell expects a smaller impact this time. One reason is because hospitals are trying to treat most of the patients who need care for conditions other than COVID even while treating growing numbers of COVID patients. In the spring, hospitals canceled vastly more appointments and procedures in anticipation of the first wave of COVID.
Mass General Brigham hospitals were treating more than 300 COVID patients on Friday, among the more than 1,600 hospitalized across the state.
Steve Walsh, president of the Massachusetts Health & Hospital Association, said hospitals across the state will need more federal aid as they continue battling COVID into the new year.
“The financial toll of COVID-19 has been felt by every hospital and health care organization in the Commonwealth,” he said. “Those challenges will continue during 2021.”
Priyanka Dayal McCluskey can be reached at priyanka.mccluskey@globe.com. Follow her on Twitter @priyanka_dayal.
END QUOTE
SOURCE
Integration of Mass General Hospital and Brigham Women’s Hospital was accelerated by the COVID-19 pandemic
Reporter: Aviva Lev-Ari, PhD, RN
BASED on
At Mass General Brigham, a sweeping effort to unify hospitals and shed old rivalries
Executives say greater cooperation is necessary to stay relevant in a dynamic and competitive health care industry. But the aggressive push to integrate is stirring tensions and sowing discontent among doctors and hospital leaders.
By Priyanka Dayal McCluskey and Larry Edelman Globe Staff and Globe Columnist,Updated March 27, 2021, 6:15 p.m.125
START QUOTE
The work of integration was accelerated by the COVID-19 pandemic. As patients flooded hospitals last spring, Mass General Brigham — not each of its individual hospitals — set pandemic policies, from what kind of personal protective equipment health care providers should wear, to which visitors were allowed inside hospitals, to how employees would be paid if they were out sick with the virus.
During the winter surge of COVID, Mass General Brigham officials closely tracked beds across their system and transferred patients daily from one hospital to another to ensure that no one facility became overwhelmed.
And, in the early months of the pandemic, the company dropped the name Partners, which meant little to patients, and unveiled a new brand to reflect the strength of its greatest assets, MGH and the Brigham.
Officials at the nonprofit health system have instructeddepartment heads across their hospitals to coordinate better, so, for example, if a patient needs surgery at the Brigham but is facing a long wait, they can refer that patient to another site within Mass General Brigham.
Some executives want patients, eventually, to be able to go online and book appointments at any Mass General Brigham facility, as easily as they make reservations for dinner or a hotel.
Walls described it like this: “How do we put things together that make things better and easier for patients, and leave alone things that are better where they are?
“We’re not going to push things together that don’t fit together,” he said.
And yet the aggressive pursuit of “systemness,” as executives call it, is taking a toll. Physicians and hospital leaders are struggling with the loss of control over their institutions and worried that the new era of top-down management threatens to homogenize a group of hospitals with different cultures and identities.
Veteran physicians and leaders have been surprised and upset by the power shift that is stripping them of the ability to make key decisions and unhappy with abrupt changes they feel are occurring with little discussion. Most are uncomfortable sharing their concerns publicly.
“If you’re not on the train, you’re getting run over by the train,” said one former Mass General Brigham executive who requested anonymity in orderto speak openly. “It’s not an environment to invite debate.”
Amid the restructuring, senior executives are departing in droves. They include the CEO of the MGH physicians group, Dr. Timothy Ferris; Brigham and Women’s president Dr. Elizabeth Nabel; chief financial officer of the system, Peter Markell; Cooley Dickinson Hospital president Joanne Marqusee; and president of Spaulding Rehabilitation Network, David Storto.
Some also fear the internal discord could hinder Mass General Brigham’s ability to attract talented leaders.
Top executives acknowledge there is angst — “Change is hard,” Klibanski said — but are pushing ahead.
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