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Tuesday, November 19, 2024

Remote Care Today Turnkey Solutions


In-home Virtual Care has become a major focus for Medicare and with good reason. The results for patients, physicians, hospitals, and home health care agencies have been more than remarkable.

They have been astounding.


REMOTE CARE & YOU,

THE PATIENT


Outside of their offices, physicians don't know what is happening with their patients. That's why Remote Care Management is becoming the "go-to" program for seniors across the country. Remote care allows your physician to monitor your health continuously, so your medical care is always tailored to your needs. And it's covered by Medicare. (Copay may apply.)


With 24 remote care management CPT codes, CMS will pay physicians to provide remote patient monitoring, or a physician may contract with a qualified provider, to provide these services on behalf of the physician.


​Regular communication to ensure:            
Mental & Physical Wellness
Vitals Collected & Transmitted Regularly     
Medication Adherence                          
Healthy Habits /Exercise Adherence

Following the Plan of Care  

CCM - Chronic Care Management
RPM - Remote Physiologic/Patient Monitoring
RTM - Remote Therapeutic Monitoring
BHI - Behavioral Health Integration
      Plus: TCM, PCM, PIN, CHI, SDOH, CGT

Pulmonary Rehabilitation (Three Months, In-Home)
Cardiac Rehabilitation (Three Months, In-Home)
CereSkills


The Benefits for Physicians

No upfront expense - No equipment cost
Reduce staffing workload
New staff for your practice at no cost
Happier and more efficient practice
Healthier and happier patients
New net revenue for your practice​  (CMS approved with appropriate CPT codes)

Telemedicine Specific Codes

99421-99423: Online digital evaluation and management services.
99441-99443: Telephone evaluation and management services.
Mental Health Services
90832-90837: Psychotherapy codes that can be used for telehealth.
90791: Psychiatric diagnostic evaluation.
Other Services
99457: Remote physiological monitoring treatment management services.
99458: Additional remote monitoring services.



The Fountain of Youth: Radical extension of the human lifespan, science fiction or reality?


Tales of sacred, restorative waters existed well before the birth of Spanish conquistador Juan Ponce de León around 1474. Alexander the Great, for example, was said to have come across a healing “river of paradise” in the fourth century B.C., and similar legends cropped up in such disparate locations as the Canary Islands, Japan, Polynesia, and England. During the Middle Ages, some Europeans even believed in the mythical king Prester John, whose kingdom allegedly contained a fountain of youth and a river of gold. “You could trace that up until today,” said Ryan K. Smith, a history professor at Virginia Commonwealth University. “People are still touting miracle cures and miracle waters.”

Spanish sources asserted that the Taino Indians of the Caribbean also spoke of a magic fountain and rejuvenating river that existed somewhere north of Cuba. These rumors conceivably reached the ears of Ponce de León, who is thought to have accompanied Christopher Columbus on his second voyage to the New World in 1493. After helping to brutally crush a Taino rebellion on Hispaniola in 1504, Ponce de León was granted a provincial governorship and hundreds of acres of land, where he used forced Indian labor to raise crops and livestock. In 1508 he received royal permission to colonize San Juan Bautista (now Puerto Rico). He became the island’s first governor a year later but was soon pushed out in a power struggle with Christopher Columbus’ son Diego.

Eight years later, Ponce de León returned to Florida’s southwestern coast in an attempt to establish a colony, but he was mortally wounded by an Indian arrow. Just before leaving, he sent letters to his new king, Charles V, and to the future Pope Adrian VI. Once again, the explorer made no mention of the Fountain of Youth, focusing instead on his desire to settle the land, spread Christianity, and discover whether Florida was an island or a peninsula. No log of either voyage has survived, and no archaeological footprint has ever been uncovered.

Nonetheless, historians began linking Ponce de León with the Fountain of Youth not long after his death. In 1535 Gonzalo Fernández de Oviedo y Valdés accused Ponce de León of seeking the fountain to cure his sexual impotence. “He was being discredited [as] an idiot and weakling,” Smith explained. “This is machismo culture in Spain at the height of the Counter-Reformation.” The accusation is almost certainly untrue, Smith added, since Ponce de León fathered several children and was under 40 years old at the time of his first expedition.

Hernando de Escalante Fontaneda, who lived with Indians in Florida for many years after surviving a shipwreck, also derided Ponce de León in his 1575 memoir, saying it was a cause for merriment that he sought out the Fountain of Youth. One of the next authors to weigh in was Antonio de Herrera y Tordesillas, the Spanish king’s chief historian of the Indies. In 1601 he penned a detailed and widely read account of Ponce de León’s first voyage. Although Herrera only referred to the Fountain of Youth in passing, writing that it turned “old men to boys,” he helped solidify it in the public’s imagination. “They are really more entertainment than attempts to write a true history,” Francis said of these works.

The Fountain of Youth legend was now alive and well.

In 2024 The Fountain of Youth is promoted in supplements, and the science of genetics, DNA, antioxidants, hormones, and preventive medicine. It has been enhancing with life saving surgeries on the heart and the avoidance of pollution and toxic elements.






















Radical extension of the human lifespan, science fiction or reality?

AI in healthcare: Latest updates on generative AI, ChatGPT, more | Modern Healthcare


Tracking the latest in AI, ChatGPT


Organizations use AI to solve some of their most fundamental challenges, from health systems and payers to vendors. The release of OpenAI’s generative AI-enabled chatbot ChatGPT has opened healthcare organizations to a world of possibilities. Check back for the latest in healthcare AI. 

The companies said that GE HealthCare signed an AI-centered partnership with radiology provider RadNet on Monday. The two companies will work together to develop AI solutions that can address clinical challenges in imaging. Its initial work within this collaboration will focus on women receiving breast care through AI-enabled mammography systems. The Chicago-based digital health company is looking to stake its claim as radiology emerges as a key area poised to benefit from AI. RadNet, which operates nearly 400 imaging centers, saw a 20% jump in its share price on Monday after the deal was announced.

Patients want small talk from AI doctors

Patients don’t mind an artificial intelligence doctor as long as they’re willing to engage in small talk, according to a study from researchers at Penn State. Researchers asked 382 online participants to interact with a medical chatbot over two visits spaced about two weeks apart. They found that the more social information an AI doctor recalls about patients, the higher the patients’ satisfaction, but only if they were offered privacy control. The AI doctor used a pre-compiled script to chat with patients about topics related to diet, fitness, lifestyle, sleep, and mental health.


Where health systems are heading with AI

Health system executives are cautious about the hype of AI. They are trying to understand the risks, opportunities, and processes needed to adopt the technology. Here’s what executives at seven healthcare organizations said about where they stand with AI today.   


Microsoft partners with Medline for AI tool

Technology giant Microsoft announced Wednesday it planned to build an AI tool with medical supply chain company Medline. The companies said the tool, dubbed Mpower, will aim to ease inventory management workflows and give users recommendations they can choose to implement. The tool will be built on Microsoft’s 365 suite of applications. Last Thursday, Microsoft said it was adding new AI tools for healthcare customers in partnership with electronic health record vendor Epic Systems. 


GE Healthcare to lead generative health AI consortium

GE Healthcare said it is taking a leadership role in Synthia, a consortium that will evaluate synthetic data generation methods for their use in the development of AI in healthcare. Synthetic data is artificially generated to replicate real patient data. Synthetic data may potentially be used to overcome challenges such as the scarcity of real datasets, biased or non-generalizable training data, and privacy concerns. GE will be joined by investment Gates Ventures and big pharmaceutical companies Novo Nordisk and Pfizer. The Chicago-based digital health company is looking to stake its claim as radiology emerges as a key area poised to benefit from AI.

Community Health Systems to bring in AI chatbots for call centers

Community Health Systems said Monday it has signed a deal to bring chatbots from artificial intelligence startup Denim Health to work in the health system’s call centers. The Franklin, Tennessee-based hospital chain will use Denim’s AI chatbots in its call center to serve around 1,000 CHS-affiliated primary care providers and handle more than 25,000 inbound calls daily. The health system said it has been working with Denim Health since late 2023 to develop the technology and incorporate conversational AI into its call centers. A CHS spokesperson said staffing would not be affected by this move. 


Abridge launches AI research effort with Epic, CMS

AI vendor Abridge is launching a clinical research collaborative dedicated to studying the impact of ambient AI across five key focus areas: clinician experience, patient experience, healthcare costs, outcomes, and health equity. Dr. Jackie Gerhart, chief medical officer at EHR vendor Epic will be a part of Abridge’s research collaborative along with leaders from Yale New Haven Health System, Stanford School of Medicine, University of California San Francisco, The University of Chicago Pritzker School of Medicine and the Centers for Medicare and Medicaid Services. Ambient AI documentation technology takes a recording of a doctor-patient conversation and turns it into usable clinical notes in the electronic health record. Abridge, which is partnering with Epic for the EHR company’s Workshop program, is one of the leading vendors in the space


California governor signs AI bills targeting providers, insurers

California Gov. Gavin Newsom (D) has signed several artificial intelligence-related bills into law, including two specifically focused on healthcare. Read more. 


AI in healthcare: Latest updates on generative AI, ChatGPT, more | Modern Healthcare

Monday, November 18, 2024

A.I. Chatbots Defeated Doctors at Diagnosing Illness


A small study found ChatGPT outdid human physicians when assessing medical case histories, even when those doctors were using a chatbot.

Gina Kolata

By Gina Kolata

Nov. 17, 2024

Dr. Adam Rodman, an expert in internal medicine at Beth Israel Deaconess Medical Center in Boston, confidently expected that chatbots built to use artificial intelligence would help doctors diagnose illnesses.


He was wrong.

Instead, in a study Dr. Rodman helped design, doctors who were given ChatGPT-4 along with conventional resources did only slightly better than doctors who did not have access to the bot. And, to the researchers’ surprise, ChatGPT alone outperformed the doctors.

“I was shocked,” Dr. Rodman said.

The chatbot, from the company OpenAI, scored an average of 90 percent when diagnosing a medical condition from a case report and explaining its reasoning. Doctors randomly assigned to use the chatbot got an average score of 76 percent. Those randomly assigned not to use it had an average score of 74 percent.

The study showed more than just the chatbot’s superior performance.

It unveiled doctors’ sometimes unwavering belief in a diagnosis they made, even when a chatbot potentially suggests a better one.

The study illustrated that while doctors are being exposed to the tools of artificial intelligence for their work, few know how to exploit the abilities of chatbots. As a result, they failed to take advantage of A.I. systems’ ability to solve complex diagnostic problems and offer explanations for their diagnoses.

A.I. systems should be “doctor extenders,” Dr. Rodman said, offering valuable second opinions on diagnoses.

But it looks as if there is a way to go before that potential is realized.

Case History, Case Future

The experiment involved 50 doctors, a mix of residents and attending physicians recruited through a few large American hospital systems, and was published last month in the journal JAMA Network Open.

The test subjects were given six case histories and were graded on their ability to suggest diagnoses and explain why they favored or ruled them out. Their grades also included getting the final diagnosis right.


The graders were medical experts who saw only the participants’ answers, without knowing whether they were from a doctor with ChatGPT, a doctor without it, or from ChatGPT by itself.

The case histories used in the study were based on real patients and are part of a set of 105 cases that have been used by researchers since the 1990s. The cases intentionally have never been published so that medical students and others could be tested on them without any foreknowledge. That also meant that ChatGPT could not have been trained on them.


But, to illustrate what the study involved, the investigators published one of the six cases the doctors were tested on, along with answers to the test questions on that case from a doctor who scored high and from one whose score was low.

That test case involved a 76-year-old patient with severe pain in his low back, buttocks and calves when he walked. The pain started a few days after he had been treated with balloon angioplasty to widen a coronary artery. He had been treated with the blood thinner heparin for 48 hours after the procedure.

The man complained that he felt feverish and tired. His cardiologist had done lab studies that indicated a new onset of anemia and a buildup of nitrogen and other kidney waste products in his blood. The man had had bypass surgery for heart disease a decade earlier.

The case vignette continued to include details of the man’s physical exam and then provided his lab test results.

The correct diagnosis was cholesterol embolism — a condition in which shards of cholesterol break off from plaque in arteries and block blood vessels.

Participants were asked for three possible diagnoses, with supporting evidence for each. They also were asked to provide, for each possible diagnosis, findings that do not support it or that were expected but not present.

The participants also were asked to provide a final diagnosis. Then they were to name up to three additional steps they would take in their diagnostic process.

Like the diagnosis for the published case, the diagnoses for the other five cases in the study were not easy to figure out. But neither were they so rare as to be almost unheard-of. Yet the doctors on average did worse than the chatbot.

What, the researchers asked, was going on?

The answer seems to hinge on questions of how doctors settle on a diagnosis, and how they use a tool like artificial intelligence.

The Physician in the Machine

How, then, do doctors diagnose patients?

The problem, said Dr. Andrew Lea, a historian of medicine at Brigham and Women’s Hospital who was not involved with the study, is that “we really don’t know how doctors think.”

In describing how they came up with a diagnosis, doctors would say, “intuition,” or, “based on my experience,” Dr. Lea said.

That sort of vagueness has challenged researchers for decades as they tried to make computer programs that can think like a doctor.

The quest began almost 70 years ago.

“Ever since there were computers, there were people trying to use them to make diagnoses,” Dr. Lea said.

One of the most ambitious attempts began in the 1970s at the University of Pittsburgh. Computer scientists there recruited Dr. Jack Myers, chairman of the medical school’s department of internal medicine who was known as a master diagnostician. He had a photographic memory and spent 20 hours a week in the medical library, trying to learn everything that was known in medicine.

Dr. Myers was given medical details of cases and explained his reasoning as he pondered diagnoses. Computer scientists converted his logic chains into code. The resulting program, called INTERNIST-1, included over 500 diseases and about 3,500 symptoms of disease.

To test it, researchers gave it cases from the New England Journal of Medicine. “The computer did really well,” Dr. Rodman said. Its performance “was probably better than a human could do,” he added.

But INTERNIST-1 never took off. It was difficult to use, requiring more than an hour to give it the information needed to make a diagnosis. And, its creators noted, “the present form of the program is not sufficiently reliable for clinical applications.”

Research continued. By the mid-1990s there were about a half dozen computer programs that tried to make medical diagnoses. None came into widespread use.

“It’s not just that it has to be user-friendly, but doctors had to trust it,” Dr. Rodman said.

And with the uncertainty about how doctors think, experts began to ask whether they should care. How important is it to try to design computer programs to make diagnoses the same way humans do?

“There were arguments over how much a computer program should mimic human reasoning,” Dr. Lea said. “Why don’t we play to the strength of the computer?”

The computer may not be able to give a clear explanation of its decision pathway, but does that matter if it gets the diagnosis right?

The conversation changed with the advent of large language models like ChatGPT. They make no explicit attempt to replicate a doctor’s thinking; their diagnostic abilities come from their ability to predict language.

“The chat interface is the killer app,” said Dr. Jonathan H. Chen, a physician and computer scientist at Stanford who was an author of the new study.

“We can pop a whole case into the computer,” he said. “Before a couple of years ago, computers did not understand language.”

However many doctors may not be exploiting its potential.Operator Error

After his initial shock at the results of the new study, Dr. Rodman decided to probe a little deeper into the data and look at the actual logs of messages between the doctors and ChatGPT. The doctors must have seen the chatbot’s diagnoses and reasoning, so why didn’t those using the chatbot do better?

It turns out that the doctors often were not persuaded by the chatbot when it pointed out something that was at odds with their diagnoses. Instead, they tended to be wedded to their own idea of the correct diagnosis.

“They didn’t listen to A.I. when A.I. told them things they didn’t agree with,” Dr. Rodman said.

That makes sense, said Laura Zwaan, who studies clinical reasoning and diagnostic error at Erasmus Medical Center in Rotterdam and was not involved in the study.

“People generally are overconfident when they think they are right,” she said.

But there was another issue: Many of the doctors did not know how to use a chatbot to its fullest extent.

Dr. Chen said he noticed that when he peered into the doctors’ chat logs, “they were treating it like a search engine for directed questions: ‘Is cirrhosis a risk factor for cancer? What are possible diagnoses for eye pain?  “It was only a fraction of the doctors who realized they could literally copy-paste the entire case history into the chatbot and just ask it to give a comprehensive answer to the entire question,” Dr. Chen added.

“Only a fraction of doctors actually saw the surprisingly smart and comprehensive answers the chatbot was capable of producing.”


Wednesday, November 13, 2024

8 Top Pharma Trends In The Digital Health and AI Era - The Medical Futurist

One area of. AI adoption is in PHARMA. The reasons are simple



1. Large corporations have significant resources.
2. Intense competition.
3. Regulatory effects from CMS, and FDA

Amid the digital health era, the pharmaceutical industry has been experiencing a rapid evolution. Thanks to the new paradigms that novel healthcare delivery approaches and digital health technologies have brought about, pharma companies need to adopt new strategies and consider new investment opportunities to stay relevant in the ever-evolving healthcare landscape.

In this article, we cover 8 major trends for pharma companies to consider in the digital health era. We rank them based on their return on investment (ROI) and return on vision (ROV). The latter is particularly relevant in this era considering its rapid rate of evolution. 

To better appreciate the importance of ROV, we can consider the Apollo program. While it was focused on space exploration, the endeavour has resulted in a multitude of spin-off products across various fields. In the healthcare sector, those spin-offs range from medical imaging to innovations in dental care. 

As such, current investments can have wide-ranging applications in the future. From AI in drug development to digital therapeutics, a host of new technologies and trends hold promising ROI and ROV for pharma companies.

We share a collection of 8 major ones below, ordered from the most practical and promising to the least

The niche is rich in  applications

1. Artificial Intelligence for drug research and development

The process of drug research and development has traditionally been a time-consuming and labor-intensive one. This has involved considerable trial-and-error research before a drug can proceed to further developmental stages. This process can be made more time- and cost-efficient with the assistance of artificial intelligence (AI).

AI models, such as those developed by Benevolent AI, can analyze significant amounts of datasets from scientific literature, clinical records, and chemical databases in a more time-efficient manner than humans can. From this information, they can precisely identify targets and how potential drugs will interact with them.

Companies like Schrödinger and Google DeepMind have used AI for drug formulation. Their software predicts the behavior of drug candidates and assesses their safety and effectiveness.

2. New reimbursement models

Pharma companies can tap into the new healthcare experience that patients can have in the digital health era to offer more than just medication. By combining medication and technology packages, they can offer more enticing reimbursement models for both payers and providers.

There have been several examples of such innovative models in the past that combine pharmaceuticals with technology. GSK has worked with Propeller Health on smart inhalers. Partners Healthcare Center and Japanese drug maker Daichii-Sankyo teamed up to bring a connected wearable for patients with atrial fibrillation.

Digital tools have been shown to improve health outcomes while minimizing financial costs. With such offerings, pharma companies can make their products stand out while being beneficial for both patients and insurance providers.

3. Large language models for improved workflow and customer service

Large language models (LLMs) have been popularised by tools such as ChatGPT and Google Gemini. Beyond the hype, the technology is a practical trend in the pharma industry.  LLMs can boost a company’s efficiency by optimizing internal operations and customer service.

Roche’s internal LLM tool, Roche GPT, assists the pharma company’s team in optimizing repetitive tasks and sharing knowledge. The tool further supports their business by automating structured data extraction about therapies and patients from scientific articles and clinical test results. Pfizer has also deployed a similar tool to help with its marketing efforts.

LLMs could further be used to improve customer service. With an LLM-powered chatbot, patients can get answers to their queries such as medication side effects in their native language

4. Automation in the supply chain

The pharma industry’s supply chain stands to gain a lot by embracing automation in its midst. For example, by integrating AI, drug shortages can be averted. By analyzing data from various sources, AI software can forecast potential disruptions and suggest adequate measures to ensure a steady supply of essential medication.


Automation in the supply chain does not only involve AI software but robotics is also part of the picture. Denso Robotics’ robots are capable of automating tasks in the manufacturing process. Exoskeletons are another example of robotics assistance. While not fully automating tasks, they augment manual factory worker’s ability to carry heavy loads and work in uncomfortable positions. In the future, we can even expect automated drone deliveries to be carried out within manufacturing sites and beyond.

5. Digital therapeutics 

Using software as treatment might have sounded like a science fiction concept a decade or so ago, but this prospect is very real and promising with the advent of digital therapeutics (DTx). DTx can be described as evidence-based software applications designed to prevent, manage, or treat medical conditions.

The accessibility, privacy, and minimal side effects that DTx provides have enticed pharma companies to invest in this trend. Pfizer has teamed with Sidekick Health to launch a DTx solution for atopic dermatitis. Eli Lilly also partnered with Sidekick Health to develop apps to support breast cancer treatment. 

Other companies like RelieVRx or HelloBetter integrate cognitive behavioral therapy principles in their apps to ease chronic pain. We share more promising DTx examples in a dedicated article.

6. in silico clinical trials

in silico clinical trials promise to enable the conduction of experiments wholly via computer simulation, without the need for animal or human testing. By running drug trials on computer simulations of organs, this approach can be both time and cost-effective while circumventing the side effects on live participants. 


While this promise has yet to be fully realized, progress has been made towards it. The Wyss Institute has developed organs-on-a-chip to emulate the complex structures and functions of living human organs. Their technology has been leveraged by Emulate Inc. for efficient drug development. The mathematical model of human physiology created by HumMod has been used in several research projects. Further envisioning a future trending towards in silico trials, the Virtual Physiological Human Institute has been set up to encourage the effective adoption of in silico medical research.

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8 Top Pharma Trends In The Digital Health and AI Era - The Medical Futurist