Category Archives: Artificial Intelligence

Modern finance team makeovers: Controllers

Posted on by • Posted in Artificial Intelligence, Chief Accounting Officer (CAO), Controller, Controllership Function, Digital Transformation, Finance Department, Financial Close, Generally Accepted Accounting Principles (GAAP), Sarbanes Oxley Act (SOX), Talent Imperative, Talent Management, Talent Pipelines, Trusted Advisor • Leave a comment

https://www.cfodive.com/news/controllers-unsung-finance-heroes/704643

As finance departments undergo seismic tech-driven changes, controllers are poised to play a crucial role as the CFOs’ right hand.

Today’s finance chiefs are making strategic decisions and driving digital transformation, but to execute their changing roles successfully, they need to be supported by an equally resilient, adaptive team.

New technologies, ways of working and shifting business needs are impacting the day-to-day roles not just of the CFO, but of other crucial financial executives as “at the highest level, the entire finance organization is [undergoing] a seismic shift in ways that they haven’t seen ever,” said Sanjay Sehgal, advisory head of markets for Big Four accounting firm KPMG.

Taking a look at the evolving new responsibilities that controllers — as well as other staff in finance departments  — must embrace will be crucial for finance chiefs who must build modern finance teams capable of tackling the upcoming challenges of 2024.

Trusted advisor

The controller “is really becoming and has become the trusted advisor to the CFO,” Sehgal said in an interview.

As with many jobs, the role can vary depending on the company. But generally controllers oversee their company’s daily accounting operations, along with payroll and the accounts payable and receivable departments, according to human resource consulting firm Robert Half. It can also entail preparing internal and external records, handling the firm’s general ledger and taxes as well as reconciling accounts, coordinating audits and managing budgets. 

Already, the importance of the controller position is reflected in compensation trends: the role ranks among the most well-paid members of the finance team, with corporate controllers in the 75th percentile — meaning they take home salaries greater than three-quarter of financial professionals — in compensation earning annual average salaries around $210,750, according to data from human resource consulting firm Robert Half.

Controllers rank among top paid financial professionals

Starting salaries for corporate accounting executives in the 75th percentile

Central to the role too is the responsibility controllers take for their company’s close activities, ensuring the business is “producing information in a controlled fashion, to report to the street and to the Securities and Exchange Commission for a public company,” said Kevin McBride, corporate controller and chief accounting officer for software-as-a-service company ServiceNow.

In his capacity as controller for the Santa Clara, California-based SaaS company, McBride oversees global payroll, accounts payable, travel, collections, and credit, he said in an interview. The role of controller and chief accounting officer can also have some overlap, but don’t need to be combined; a CAO can be another name for a principal accounting officer as required under the Sarbanes-Oxley Act, for example, McBride said. A CAO typically focuses on more broad corporate governance, therefore, while a controller’s focus is more narrowly on processes such as the close and ensuring financial statements are compliant with GAAP.

Controllership is “really getting to the numbers and the descriptors and the story behind financial performance and ensuring that process is well-controlled,” McBride said. Joining ServiceNow in November 2021, he previously logged a 21-year tenure at tech giant Intel, where he served in a variety of key financial roles including as its vice president of finance and corporate controller as well as its global accounting and financial services controller. He also spent time at the Financial Accounting Standards Board as an industry fellow before joining Intel.

Opening a path to the touchless close

In recent years, however, controllers have also found themselves branching out from a pure numbers function as part of the ongoing “seismic shift” taking place in the whole of finance — driven partly by the advent of generative AI, machine learning, cloud technologies and other digital tools which have captivated the attention of finance leaders in recent months, Sehgal said.

New technologies such as GenAI could fundamentally change how controllers operate and the purpose of the role — for example, “I can see a future where we have a touchless close process,” Sehgal said.

This would mean the entire financial close process would no longer need routine manual intervention by such people as the controller, according to a 2022 report by Gartner which noted 55% of finance executives were targeting a touchless close by 2025.

Finance teams could inch closer to making such a process a reality in 2024 as companies continue to experiment with the applications of generative AI, something that could rapidly shift where today’s controllers are directing their time and focus.

The new technologies that have filtered into accounting over the past few decades have enabled their own improvements in quality, efficiency and cost, McBride said, allowing business leaders to get the information they need to run the business at a lower cost. When it comes to the controllership, “it also gives us capacity to invest in other ways to help drive business impact,” he said.

However, it’s also important to remember that technology is “nothing new in accounting,” McBride — who started his career working on paper spreadsheets — said and that in “each one of these technology introductions, there’s the hype and then there’s the reality,” he said. Generative AI and the promise it brings remains in its early stages, he said.

As automation seeps into finance, technology opens up more time by removing routine tasks, in turn enabling the controller and the CFO to deepen their relationship. “With the CFO, we’re spending more time talking about strategic matters and how to best position not just the controllership but finance,” McBride said.

The evolution of the relationship comes as CFOs are likewise pivoting to a role more focused on driving strategy and controllers are finding themselves responsible for processes that may previously have been under the remit of the finance chief.

“As the CFO elevates himself or herself, I think the controller plays a bigger role in the organization,” Sehgal said.

Finance chiefs are serving more and more often as the “right hand” of the CEO and spending less time poring over day-to-day numbers, said Claire Bramley, CFO of San Diego, California-based AI cloud analytics and data platform Teradata. The controller and the CFO work closely together to drive an effective, innovative and forward-looking finance function, but that focus on day-to-day operations is what separates the two positions, Bramley said in an interview.

As a finance chief, “you need to make sure that you’ve got the processes in place, you understand what’s going on,” she said. However, the finance chief is now spending more time figuring out how to drive things forward at the company, she said.

Adding free cash flow forecasts 

Bramley pointed to something like free cash flow as an example: because she’s now spending more time conducting strategy transformation work on part of Teradata, she’s now relying on her controller to take on free cash flow management forecasting, she said.

Controllers, critically, still serve as “the owners of the financial data, from a protocols perspective, from a reporting perspective, and the CFO and the executive teams depend on that,” Sehgal said. Indeed, taking responsibility for the numbers is still the core of the controller’s role, McBride agreed.

However, controllers are not immune to the job creep plaguing the financial function amid a lack of qualified accounting talent, emerging technologies and new business needs. As the CFO’s role evolves into a more strategic position, the rest of finance could potentially be pulled along in their wake.  

“It’s very easy for a controller to be kind of put off to one side … and not be pulled into, I’ll say some of the business and strategic decisions,” Bramley said. “But if you decide as a controller that you want to be more involved in that, I think many companies give you the opportunity to build your business acumen, to build your business relationships and to be able to be an important part of managing the business.”

For example, the controller today has a huge opportunity to take point on digital transformation at a business — the controller organization tends to be the biggest team in the finance function, “so if they can drive [digital transformation], and they can be leading edge, then the rest of finance can adopt that moving forward,” Bramley said.

This can also provide a pathway to controllers to the CFO seat — Bramley spent two years serving as the global controller for HP, where she logged a 14-year tenure before making the jump to Teradata.

“The modern-day controller who is involved in strategic decision making, who is helping add business value, who is having an impact from a technology standpoint, I think, is an obvious candidate for a CFO,” she said.

Healthcare CFOs explore M&A, automation and service line cuts in 2024

Posted on by • Posted in Artificial Intelligence, Artificial Intelligence Generative, CFO, Cost Management, Cost Structure, Cybersecurity, Data Breaches, Days Cash on Hand, Financial Performance, Financial Pressure, Financial Reserves, Financial Stewardship, Financial Strength, Financial Viability, Financially Distressed, Market Consolidation, Market Trends, Mergers & Acquisitions, Operating Income, Operating Margin, Operating Margin Drivers, Operating Metrics, Operating Performance, Service Line Profitability, Service Line Rationalization • Leave a comment
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Companies grappling with liquidity concerns are looking to cut costs and streamline operations, according to a new survey.

Dive Brief:

  • Over three-quarters of healthcare chief financial officers expect to see profitability increases in 2024, according to a recent survey from advisory firm BDO USA. However, to become profitable, many organizations say they will have to reduce investments in underperforming service lines, or pursue mergers and acquisitions.
  • More than 40% of respondents said they will decrease investments in primary care and behavioral health services in 2024, citing disruptions from retail players. They will shift funds to home care, ambulatory services and telehealth that provide higher returns, according to the report.
  • Nearly three-quarters of healthcare CFOs plan to pursue some type of M&A deal in the year ahead, despite possible regulatory threats.

Dive Insight:

Though inflationary pressures have eased since the height of the COVID-19 pandemic, healthcare CFOs remain cognizant of managing costs amid liquidity concerns, according to the report.

The firm polled 100 healthcare CFOs serving hospitals, medical groups, outpatient services, academic centers and home health providers with revenues from $250 million to $3 billion or more in October 2023.

Just over a third of organizations surveyed carried more than 60 days of cash on hand. In comparison, a recent analysis from KFF found that financially strong health systems carried at least 150 days of cash on hand in 2022.

Liquidity is a concern for CFOs given high rates of bond and loan covenant violations over the past year. More than half of organizations violated such agreements in 2023, while 41% are concerned they will in 2024, according to the report. 

To remain solvent, 44% of CFOs expect to have more strategic conversations about their economic resiliency in 2024, exploring external partnerships, options for service line adjustments and investments in workforce and technology optimization.

The majority of CFOs surveyed are interested in pursuing external partnerships, despite increased regulatory roadblocks, including recent merger guidance that increased oversight into nontraditional tie-ups. Last week, the FTC filed its first healthcare suit of the year to block the acquisition of two North Carolina-based Community Health Systems hospitals by Novant Health, warning the deal could reduce competition in the region.

Healthcare CFOs explore tie-ups in 2024

Types of deals that CFOs are exploring, as of Oct. 2023.

https://datawrapper.dwcdn.net/aiFBJ/1

Most organizations are interested in exploring sales, according to the report. Financially struggling organizations are among the most likely to consider deals. Nearly one in three organizations that violated their bond or loan covenants in 2023 are planning a carve-out or divestiture this year. Organizations with less than 30 days of cash on hand are also likely to consider carve-outs.

Organizations will also turn to automation to cut costs. Ninety-eight percent of organizations surveyed had piloted generative AI tools in a bid to alleviate resource and cost constraints, according to the consultancy. 

“Healthcare leaders believe AI will be essential to helping clinicians operate at the top of their licenses, focusing their time on patient care and interaction over administrative or repetitive tasks,” authors wrote. Nearly one in three CFOs plan to leverage automation and AI in the next 12 months.

However, CFOs are keeping an eye on the risks. As more data flows through their organizations, they are increasingly concerned about cybersecurity. More than half of executives surveyed said data breaches are a bigger risk in 2024 compared to 2023.

Sam Altman’s wild year offers 3 critical lessons for healthcare leaders in 2024

Posted on by • Posted in Artificial Intelligence, Artificial Intelligence Generative, Board, Board Purpose, Leadership Commitment, Leadership Vision, Mission • Leave a comment

https://www.linkedin.com/pulse/sam-altmans-wild-year-offers-3-critical-lessons-2024-pearl-m-d–sj1kc/?trackingId=G7JzFhoHSvuK7BRMyo4gcQ%3D%3D

What a wild end to the year it was for Sam Altman, CEO of OpenAI.

In the span of five white-knuckle days in November, the head of Silicon Valley’s most advanced generative AI company was fired by his board of directors, replaced by not one but two different candidates, hired to lead Microsoft’s AI-research efforts and, finally, rehired back into his CEO position at OpenAI with a new board.

A couple weeks later, TIME selected him “CEO of the Year.” Altman’s saga is more than a tale of tech-industry intrigue. His story provides three valuable lessons for not only aspiring and current healthcare leaders, but also everyone who works with and depends on them.

1. Agree On The Goal, Define It, Then Pursue It Tirelessly

OpenAI’s governance structure presented a unique case: a not-for-profit board, whose stated mission was to protect humanity, found itself overseeing an enterprise valued at more than $80 billion. Predictably, this setup invited conflict, as the company’s humanitarian mission began to clash with the commercial realities of a lucrative, for-profit entity.

But there’s little evidence the bruhaha resulted from Altman’s financial interests. According to IRS filings, the CEO’s salary was only $58,333 at the time of his firing, and he reportedly owns no stock.

While Altman clearly knows the company needs to raise money to fund the creation of ever-more-powerful AI tools, his primary goal doesn’t appear to revolve around maximizing shareholder value or his own wealth.

In fact, I believe Altman and the now-disbanded board shared a common mission: to save humanity. The problem was that the parties were 180 degrees apart when it came to defining how exactly to protect humanity.

Altman’s path to saving humanity involved racing forward as fast as possible. As CEO, he understands generative AI’s potential to radically enhance productivity and alleviate threats like world hunger and climate change.

By contrast, the board feared that breakneck AI development could spiral out of control, posing a threat to human existence. Rather than perceiving AGI (artificial general intelligence) as a savior, much of the board worried that a self-learning system might harm humanity.

This dichotomy pitted a CEO intent on changing the world against a board intent to progress at a cautious, incremental pace.

For Healthcare Leaders: Like OpenAI, American healthcare leaders share a common goal. Be they doctors, insurers or government health agencies, all tout the importance of “value-based care” (VBC), which in general terms, constitutes a financial and care-delivery model based on paying healthcare professionals for the quality of clinical outcomes they achieve rather than the quantity of services they provide. But despite agreeing on the target, leaders differ on what it means and how best to accomplish it. Some think of VBC as “pay for performance,” whereby doctors are paid small incentives based on metrics around prevention and patient satisfaction. These programs fail because clinicians ignore the metrics without incentives and total health suffers.

Other leaders believe VBC means paying insurers a set, annual, upfront fee to provide healthcare to a population of patients. This, too, fails since the insurers turn around and pay doctors and hospitals on a fee-for-service basis, and implement restrictive prior authorization requirements to keep costs down.

Instead of making minor financial tweaks that keep falling short of the goal, leaders who want to transform American medicine must play to win. This will require them to move quickly and completely away from fee-for-service payments (which rewards volume of care) to capitation at the delivery-system level (rewarding superior results by prepaying doctors and hospitals directly without insurers playing the part of middlemen).

Like OpenAI’s former board members, today’s healthcare leaders are playing “not to lose.” They avoid making big changes because they fear the backlash of risk-averse providers. But anything less than all in won’t make a dent given the magnitude of problems. To be effective, leaders must make hard decisions, accept the risks and be confident that once the changes are in place, no one will want to go back to the old ways of doing things.

2. Hire Visionary Leaders Who Inspire Boldly

Many tech-industry commentators have drawn comparisons between Altman and Steve Jobs. Both leaders possess(ed) the rare ability to foresee a better future and turn their visions into reality. And both demonstrate(d) passion for exceeding people’s wants and expectations—not for their own benefit but because they believe in a greater mission and purpose.

Altman and Jobs are what I call visionary leaders, who push their organizations and people to accomplish remarkable outcomes few could’ve believed possible. These types of leaders always challenge conservative boards.

When the OpenAI board realized it’s hard to constrain a CEO like Sam Altman, they fired him.

On day one of that decision, the board might have assumed their action would protect humanity and, therefore, earn the approval of OpenAI’s employees. But the story took a sharp turn when nearly all the company’s 770 workers signed a letter to the board in support of Altman, threatening to quit unless (a) their visionary leader was brought back immediately and (b) the board resigned.

Five days after the battle began, the board was facing a rebellion and had little choice but to back down.

For Healthcare Leaders: The American healthcare system is struggling. Half of Americans say they can’t afford their out-of-pocket expenses, which max out at $16,000 for an insured family. American health is languishing with average life expectancy virtually unchanged since the start of this century. Maternal and infant mortality rates in the U.S. are double what they are in other wealthy nations. And inside medicine, burnout runs rampant. Last year, 71,000 physicians left the profession.

Visionary leadership, often sidelined in favor of the status quo, is crucial for transformative change. In healthcare, boards typically prioritize hiring CEOs with the ability to consolidate market control and achieve positive financial results rather than the ability to drive excellence in clinical outcomes. The consequence for both the providers and recipients of care proves painful.

Like OpenAI’s employees, healthcare professionals want leaders who are genuine, who have the courage to abandon bureaucratic safety in favor of innovative solutions, and who can ignite their passion for medicine. For a growing number of clinicians, the practice of medicine has become a job, not a mission. Without that spark, the future of medicine will remain bleak.

3. Embrace Transformative Technology

OpenAI’s board simultaneously promoted and feared ChatGPT’s potential. In this era of advanced technology, the dilemma of embracing versus restraining innovation is increasingly common.

The board could have shut down OpenAI or done everything in its power to advance the AI. It couldn’t, however, do both. When organizations in highly competitive industries try to strike a safe “balance,” choosing the less-contentious middle ground, they almost always fail to accomplish their goals.

For Healthcare Leaders: Despite being data-driven scientists, healthcare professionals often hesitate to embrace information technologies. Having been burned by tools like electronic healthcare records, which were designed to maximize revenue and not to facilitate medical care, their skepticism is understandable.

But generative AI is different because it has the potential to simultaneously increase quality, accessibility and affordability. This is where technology and skilled leadership must combine forces. It’s not enough for leaders to embrace generative AI. They must also inspire clinicians to apply it in ways that promote collaboration and achieve day-to-day operational efficiency and effectiveness. Without both, any other operational improvements will be incremental and clinical advances minimal at best.

If the boards of directors and other similar decision-making bodies in healthcare want their organizations to lead the process of change, they’ll need to select and support leaders with the vision, courage, and skill to take radical and risky leaps forward. If not, as OpenAI’s narrative demonstrates, they and their organizations will become insignificant and be left behind.

So Much to Worry About

Posted on by • Posted in Artificial Intelligence, GitHub, Investment, Leadership, Leadership Vision, Microsoft, OpenAI, OpenAI Technology • Leave a comment
https://www.kaufmanhall.com/insights/thoughts-ken-kaufman/healthcare-artificial-intelligence?utm_source=mkto&utm_campaign=ken-weekly-blog&utm_medium=em&utm_term=231220&mkt_tok=NjU0LUNOWS0yMjQAAAGQJreF1GXoKFWdYimi6XOvhFZZTn_ERh70xFUHj4GJyFthUXLTraGlsUWkUCnffGsGNP4nPrGDejbg9zBmwKYfc_9ZQHg8R5hu9-Kg9Fp1H5sA

If you are a hospital executive—and if you are reading this, you probably are—then you have no shortage of worries. The worry list is long:

  • Trying to control expenses.
  • Dealing with declining revenue, especially when considered on an after-inflation basis.
  • Struggling with ongoing staffing issues that have no immediate solutions.
  • Solving the longstanding problem of patient access to appointments and service.

And the list could go on and on.

But maybe the biggest concern is one that is not on many worry lists: the remarkable development of artificial intelligence (AI) and how AI is relentlessly pushing into business practice generally and into healthcare more specifically.

While the long-term worry is how your hospital will carefully and properly adopt AI inside the business and clinical parts of your organization, the more immediate and short-term worry is whether you, as an executive, understand AI in a way that you can be ultimately useful to your organization.

Full disclosure: I can’t help here much. For me, AI is a pretty big black box. But when I confront this kind of business problem I start reading and learning. One of the most useful AI articles I have come across is “The Optimists: The Full Story of Microsoft’s Relationship with OpenAI,” which was published in the December 11, 2023, issue of The New Yorker magazine. The article was written by Charles Duhigg, a former winner of the Pulitzer Prize.

I am hoping that for your own professional development you will read the Duhigg article, but just in case, here are the highlights:

  • Microsoft has reportedly invested $13 billion in the for-profit arm of OpenAI.
  • Using OpenAI technology, Microsoft has built a series of AI assistants into Word, Outlook, and PowerPoint. These AI assistants are now known as Office Copilots.
  • Knowledgeable commentators say these Microsoft applications are only moderately sophisticated but, honestly, they seem rather remarkable to me. Here are some of Duhiggs’s examples of requests Office Copilot users can make:
    • “Tell me the pros and cons of each plan described on that video call.”
    • “What’s the most profitable product in these twenty spreadsheets?”
  • How about writing projects? Duhigg notes that the Office Copilot can:
    • Create a financial narrative of the past decade based on a company’s last ten executive summaries.
    • Turn a memo into a PowerPoint.
    • Compile a to-do list for Teams video attendees, in multiple languages, after listening in on a meeting.
  • Later in the article Duhigg details the functionality of the Word Copilot:
    • “You can ask it to reduce a five-page document to ten bullet points…[o]r…it can take the ten bullet points and transform them into a five-page document.”
    • It can write a memo based on previous emails you have written.
    • “You can ask, ‘Did I forget to include anything that usually appears in a contract like this?,’ and the Copilot will review your previous contracts.”

Duhigg reports that Microsoft previously acquired a company called GitHub. GitHub is “a website where users shared code and collaborated on software.” Microsoft operates GitHub as an independent division. GitHub has been a very big success and is used by software engineers and, in a short period of time, has grown to over 100 million users.

OpenAI created an artificial intelligence tool that autocompletes software code. Despite reservations at Microsoft, GitHub President Nat Friedman decided to release the GitHub Copilot autocomplete tool. The result has been $100 million in revenue to GitHub in less than a year. 

At the end of the article, Duhigg notes that these early AI business applications are both “impressive and banal.” Banal because they don’t yet live up to the sci-fi predictions for AI and its long-term impact on society.

Honestly, I don’t see it that way. This OpenAI/Microsoft collaboration is only scratching the surface and its potential uses are already endless, waiting to be invented by 100s of millions of users all over the world, including in healthcare. From my seat, the sky is the limit here. Almost anything seems possible.

I hope this summary of Mr. Duhigg’s exceptional article proves useful and advances your awareness of AI’s aggressive and rapid move into day-to-day business—here, through many of the Microsoft productivity programs that every one of us uses every day. In any case, I recommend that you read Duhigg’s entire article. It is most certainly worth your time.

ChatGPT will reduce clinician burnout, if doctors embrace it

Posted on by • Posted in Artificial Intelligence, Artificial Intelligence Generative, ChatGPT, Chronic Care Management, Chronic Conditions, Chronic Diseases, Consumer Culture, Medical Culture, Patient Empowerment, Physician Burnout • Leave a comment
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Clinician burnout is a major problem. However, as I pointed out in a previous newsletter post, it is not a distinctly American problem.

A recent report from the Commonwealth Fund compared the satisfaction of primary care physicians in 10 high-income nations. Surprisingly, U.S. doctors ranked in the middle, reporting higher satisfaction rates than their counterparts in the U.K., Germany, Canada, Australia and New Zealand.

A Surprising Insight About Burnout

In self-reported surveys, American doctors link their dissatisfaction to problems unique to the U.S. healthcare system: excessive bureaucratic tasks, clunky computer systems and for-profit health insurance. These problems need to be solved, but to reduce clinician burnout we also need to address another factor that negatively impacts doctors around the globe.

Though national healthcare systems may vary greatly in their structure and financing, clinicians in wealthy nations all struggle to meet the ever-growing demand for medical services. And that’s due to the mounting prevalence and complications of chronic disease.

At the heart of the burnout crisis lies a fundamental imbalance between the volume and complexity of patient health problems (demand) and the amount of time that clinicians have to care for them (supply). This article offers a way to reverse both the surge in chronic illnesses and the ongoing clinician burnout crisis.

Supply vs. Demand: Reframing Burnout

When demand for healthcare exceeds doctors’ capacity to provide it, one might assume the easiest solution is to increase the supply of clinicians. But that outcome remains unlikely so long as the cost increases of U.S. medicine continue to outpace Americans’ ability to afford care.

Whenever healthcare costs exceed available funds, policymakers and healthcare commentators look to rationing. The Oregon Medicaid experiment of the 1990s offers a profound reminder of why this approach fails. Starting in 1989, a government taskforce brought patients and providers together to rank medical services by necessity. The plan was to provide only as many as funding would allow. When the plan rolled out, public backlash forced the state to retreat. They expanded the total services covered, driving costs back up without any improvement in health or any relief for clinicians.

Consumer Culture Can Drive Medical Culture

Ultimately, to reduce burnout, we will have to find a way to decrease clinical demand without raising costs or rationing care.

The best—and perhaps only viable—solution is to embrace technologies that empower patients with the ability to better manage their own medical problems.

American consumers today expect and demanded greater control over their lives and daily decisions. Time and again, technology has made this possible.

Take stock trading, for example. Once the sole domain of professional brokers and financial advisors, today’s online trading platforms give individual investors direct access to the market and a wealth of information to make prudent financial decisions. Likewise, technology transformed the travel industry. Sites like Airbnb and Expedia empowered consumers to book accommodations, flights and travel experiences directly, bypassing traditional travel agents.

Technology will soon democratize medical expertise, as well, giving patients unprecedented access to healthcare tools and knowledge. Within the next five to 10 years, as ChatGPT and other generative AI applications become significantly more powerful and reliable, patients will gain the ability to self-diagnose, understand their diseases and make informed clinical decisions.

Today, clinicians are justifiably skeptical of outsized AI promises. But as technology proves itself worthy, clinicians who embrace and promote patient empowerment will not only improve medical outcomes, but also increase their own professional satisfaction.

Here’s how it can happen:

Empowering Patients With Generative AI

In the United States, health systems (i.e., large hospitals and medical groups) that heavily prioritize preventive medicine and chronic-disease management are home to healthier patients and more satisfied clinicians.

In these settings, patients are 30% to 50% less likely to die from heart attack, stroke and colon cancer than patients in the rest of the nation. That’s because their healthcare organizations provide effective chronic-disease prevention programs and assist patients in managing their diabetes, hypertension, obesity and asthma. As a result, patients experience fewer complications like heart attacks, strokes, and cancer.

Most primary care physicians, however, don’t have the time to accomplish this by themselves. According to one study, physicians would need to work 26.7 hours per day to provide all the recommended preventive, chronic and acute care to a typical panel of 2,500 adult patients.

GenAI technologies like ChatGPT can help lessen the load. Soon, they’ll be able to offer patients more than just general advice about their chronic illnesses. They will give personalized health guidance. By connecting to electronic health records (EHR)—even when those systems are spread across different doctors’ offices—GenAI will be able to analyze a patient’s specific health data to provide tailored prevention recommendations. It will be able to remind patients when they need a health screening, and help schedule it, and even sort out transportation. That’s not something Google or any other online platform can currently do.

Moreover, with new tools (like doctor-designed plugins expected in future ChatGPT updates) and data from fitness trackers and home health monitors, GenAI will be capable of not just displaying patient health data, but also interpreting it in the context of each person’s health history and treatment plans. These tools will be able to provide daily updates to patients with chronic conditions, telling them how they’re doing based on their doctor’s plan.

When the patient’s health data show they’re on the right track, there won’t be a need for an office visit, saving time for everyone. But if something seems off—say, blood pressure readings remain excessively high after the start of anti-hypertensive drugs—clinicians will be able to quickly adjust medications, often without the patient needing to come in. And when in-person visits are necessary, GenAI will summarize patient health information so the doctor can quickly understand and act, rather than starting from scratch.

ChatGPT is already helping people make better lifestyle choices, suggesting diets tailored to individual health needs, complete with shopping lists and recipes. It also offers personalized exercise routines and advice on mental well-being.

Another way generative AI can help is by diagnosing and treating common, non-life-threatening medical problems (e.g., musculoskeletal, allergic or viral issues). ChatGPT and Med-PaLM 2 have already demonstrated the capability in diagnosing a range of clinical issues as effectively and safely as most clinicians. Looking ahead, GenAI’s will offer even greater diagnostic accuracy. When symptoms are worrisome, GenAI will alert patients, speeding up definitive treatment. Its ability to thoroughly analyze symptoms and ask detailed questions without the time pressure doctors feel today will eradicate many of our nation’s 400,000 annual deaths from misdiagnosis.

The outcomes—fewer chronic diseases, fewer heart attacks and strokes and more medical problems solved without an office visit—will decrease demand, giving doctors more time with the patients they see. As a result, clinicians will leave the office feeling more fulfilled and less exhausted at the end of the day.

The goal of enhanced technology use isn’t to eliminate doctors. It’s to give them the time they desperately need in their daily practice, without further increasing already unaffordable medical costs. And rather than eroding the physician-patient bond, the AI-empowered patient will strengthen it, since clinicians will have the time to dive deeper into complex issues when people come to the office.

A More Empowered Patient Is Key To Reducing Burnout

AI startups are working hard to create tools that assist physicians with all sorts of tasks: EHR data entry, organizing office duties and submitting prior authorization requests to insurance companies.

These function will help clinicians in the short run. But any tool that fails to solve the imbalance between supply (of clinician time) and demand (for medical services), will be nothing more than a temporary fix.

Our nation is caught in a vicious cycle of rising healthcare demand, leading to more patient visits per day per doctor, producing higher rates of burnout, poorer clinical outcomes and ever-higher demand. By empowering patients with GenAI, we can start a virtuous cycle in which technology reduces the strain on doctors, allowing them to spend more time with patients who need it most. This will lead to better health outcomes, less burnout for clinicians and further decreases in overall healthcare demand.

Physicians and medical societies have the opportunity to take the lead. They’ll have to educate the public on how to use this technology effectively, assist in connecting it to existing data sources and ensure that the recommendations it makes are reliable and safe. The time to start this process is now.

UnitedHealth Group AI algorithm cutting off patient care

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This week, Stat published a scathing investigation into the way UnitedHealth Group subsidiary NaviHealth uses an algorithm, nH Predict, to deny Medicare Advantage (MA) patients access to rehabilitation services and long-term care. United set a target to keep rehab stays within one percent of nH Predict’s projection for the year.

Interviews with former case managers and access to internal documents reveal that NaviHealth employees faced disciplinary action and even termination if they approved care that strayed from these algorithmic recommendations.

UnitedHealthcare, the nation’s largest insurer, is now subject to a class-action lawsuit filed this week over these practices. But NaviHealth’s impact extends beyond just United beneficiaries, as other insurers, covering around 15M MA enrollees, also use its services.

The Gist: This article provides a stark example of what can happen when an artificial intelligence (AI) algorithm is used not to complement, but to replace, clinical judgment. 

While profit incentives in US healthcare are nothing new, what’s pernicious about an algorithm like nH Predict is how it replaces individual patients, whose needs vary, with a theoretical “average patient”, whose health and life needs can be easily predicted by the handful of data points available to the insurer. 

When patients fail to recover along expected timelines—that are imperfectly calculated by incomplete datasets—they’re the ones who suffer.

How generative AI will change the doctor-patient relationship

Posted on by • Posted in Artificial Intelligence, Artificial Intelligence Generative, ChatGPT, ChatGPT-4, Knowledge, Physician Patient Relationship, Second Opinions, Self Diagnosis • Leave a comment

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After decades of “doctor knows best,” the traditional physician-patient relationship is on the verge of a monumental shift. Generative AI tools like OpenAI’s ChatGPT, Google’s Bard and Microsoft’s Bing are poised to give people significantly more power and control—not just over their personal lives and professional tasks, but over their own medical health, as well.

As these tools become exponentially smarter, safer and more reliable (an estimated 32 times more powerful in the next five years), everyday Americans will gain access to unparalleled medical expertise—doled out in easily understandable terms, at any time, from any place.

Already, Google’s Med-PaLM 2 has scored an expert-level 86.5% on the U.S. medical license exam while other AI tools have matched the skill and accuracy of average doctors in diagnosing complex medical diseases.

Soon, AI tools will be able to give patients detailed information about their specific medical problems by integrating with health monitors and electronic medical records (such EHR projects are already underway at Oracle/Cerner and Epic). In time, people will be able to self-diagnose and manage their own diseases as accurately and competently as today’s clinicians.

This newfound expertise will shake the very foundation of clinical practice.

Although public health experts have long touted the concept of clinicians and patients working together through shared decision-making, this rarely happens in practice. Generative AI will alter that reality.

Building on part one of this article, which explained why generative AI constitutes a quantum leap ahead of all the tech that came before it, part two provides a blueprint for strengthening the doctor-patient alliance in the era of generative AI.

Patients Today: Sick And Confused

To understand how generative AI will impact the practice of medicine, it’s best to look closer at the current doctor-patient dynamic.

The relationship has undergone significant evolution. In the past century, patients and doctors held close, enduring relationships, built on trust and a deep understanding of the patient’s individual needs. These bonds were characterized by a strong sense of personal connection, as doctors had the time to listen to their patients’ concerns and provided not only medical treatment but also emotional support.

Today, the doctor-patient relationship remains vitally important, but it has undergone several meaningful changes. While medical advancements have greatly expanded the possibilities for diagnosis and treatment, the relationship itself has suffered from less trust and a more transactional focus. The average visit lasts just 15 minutes, barely enough time to address the patient’s current medical concerns. The doctor’s computer and electronic healthcare record systems sit, quite literally, between doctors and patients. The result is that patients feel rushed and find their medical care increasingly impersonal. Modern healthcare is characterized by time constraints, administrative burdens and a focus on efficiency. This can lead to a sense of impersonality and decreased communication between doctors and patients.

But throughout these changes, one thing has remained constant. The doctor-patient relationship, which dates back more than five millennia, has always existed on an uneven playing field, with patients forced to rely almost entirely on doctors to understand their diseases and what to do about them.

Though patients can and do access the internet for a list of possible diagnoses and treatment options, that’s not the same as possessing medical expertise. In fact, sorting through dozens of online sources—often with conflicting, inaccurate, outdated and self-serving information—proves more confusing than clarifying. Nowhere can web-surfers find personalized and credible advice based on their age, medical history, genetic makeup, current medications and laboratory results.

What’s needed now is modern doctor-patient relationship, one that is strong enough to meet the demands of medicine today and restore the vital, personal and emotional connections of the past.    

Patients Tomorrow: Self-Diagnosing And Confident

In the future, generative AI will alter the doctor-patient dynamic by leveling the playing field.

Already, consumer AI tools can equip users with not just knowledge, but expertise. They allow the average person to create artistic masterpieces, produce hit songs and write code with unimagined sophistication. Next generations will offer a similar ability for patients, even those without a background in science or medicine.

Like a digitized second opinion, generative AI will shrink the knowledge gap between doctors and patients in ways that search engines can’t. By accessing millions of medical texts, peer-reviewed journals and scientific articles, ChatGPT will deliver accurate and unbiased medical expertise in layman’s language. And unlike internet sources, generative AI tools don’t have built-in financial incentives or advertising models that might skew responses.

To help patients and doctors navigate the upcoming era of generative AI, here’s a model for the future of medical practice based on proven approaches in education:  

Introducing The ‘Flipped Healthcare’ Model

The “flipped classroom” can be traced back nearly four decades, but it became popularized in the United States in the early 2000s through the Khan Academy in Northern California.

Students begin the learning process by watching videos and engaging with interactive tools online rather than sitting through traditional lectures. This pre-class preparation (or “homework in advance”) allows people to learn at their own pace. Moreover, it enhances classroom discussions, letting teachers and students dive much deeper into topics than they ever could before. Indeed, students spend time in class applying knowledge and collaborating to solve problems—not merely listening and taking notes.  

The introduction of generative AI opens the door to a similar approach in healthcare. Here’s how that might work in practice:

  1. Pre-Consultation Learning: Before visiting a doctor, patients would use generative AI tools to understand their symptoms or medical conditions. This foundational knowledge would accelerate the diagnostic process and enhance patient understanding. Even in the absence of advanced diagnostic testing (X-rays or bloodwork), this pre-consultation phase allows the patient to understand the questions their clinicians will ask and the steps they will take.
  2. In-Depth Human Interactions: With the patient’s knowledge base already established, consultations will dive deep into proactive health strategies and/or long-term chronic-disease management solutions, rather than having to start at square one. This approach maximizes the time patients and clinicians spend together. It also addresses the reality that at least 50% of patients leave the doctor’s office unsure of what they’ve been told.
  3. Home Monitoring: For the 60% of American patients living with chronic diseases, generative AI combined with wearable monitors will provide real-time feedback, thereby optimizing clinical outcomes. These patients, instead of going in for periodic visits (every three to six months), will obtain daily medical analysis and insights. And in cases where generative AI spots trouble (e.g., health data deviates from the doctor’s expectations), the provider will be able to update medications immediately. And when the patient is doing well, physicians can cancel follow-up visits, eliminating wasted time for all.
  4. Hospital At Home: Inpatient (hospital) care accounts for 30% of all healthcare costs. By continuously monitoring patients with medical problems like mild pneumonia and controllable bacterial infections, generative AI (combined with home monitoring devices and telemedicine access) would allow individuals to be treated in the comfort of their home, safely and more affordably than today.
  5. Lifestyle Medicine: Generative AI would support preventive health measures and lifestyle changes, reducing the overall demand for in-person clinical care. Studies confirm that focusing on diet, exercise and recommended screenings can reduce the deadliest complications of chronic disease (heart attack, stroke, cancer) by 30% or more. Decreasing the need for intensive procedures is the best way to make healthcare affordable and address the projected shortage of doctors and nurses in the future.

The Future: Collaborative Care For Superior Outcomes

The U.S. healthcare model often leaves patients feeling frustrated and overwhelmed. Meanwhile, time constraints placed on doctors lead to rushed consultations and misdiagnoses, which cause an estimated 800,000 deaths and disabilities annually.

The “flipped” approach, inspired by the Khan Academy, leverages the patient expertise that generative AI will create. Following this model will free up clinician time to make the most of every visit. Implementing this blueprint will require improvements in AI technology and an evolution of medical culture, but it offers the opportunity to make the doctor-patient relationship more collaborative and create empowered patients who will improve their health.

Talk with educators at the Khan Academy, and they will tell you how their innovative model results in better-educated students. They’ll also tell you how much more satisfied teachers and students are compared to those working in the traditional educational system. The same can be true for American medicine.

The AI-empowered patient is coming. Are doctors ready?

Posted on by • Posted in Artificial Intelligence, Artificial Intelligence Generative, ChatGPT, ChatGPT-4, Disruption, Disruptive Technology, Google’s Med-PaLM 2, Innovation, Physician • Leave a comment

https://www.linkedin.com/pulse/ai-empowered-patient-coming-doctors-ready-robert-pearl-m-d-/

Artificial intelligence (AI) has long been heralded as an emerging force in medicine. Since the early 2000s, promises of a technological transformation in healthcare have echoed through the halls of hospitals and at medical meetings.

But despite 20-plus years of hype, AI’s impact on medical practice and America’s health remains negligible (with minor exceptions in areas like radiological imaging and predictive analytics).

As such, it’s understandable that physicians and healthcare administrators are skeptical about the benefits that generative AI tools like ChatGPT will provide.

They shouldn’t be. This next generation of AI is unlike any technology that has come before. 

The launch of ChatGPT in late 2022 marked the dawn of a new era. This “large language model” developed by OpenAI first gained notoriety by helping users write better emails and term papers. Within months, a host of generative AI products sprang up from Google, Microsoft and Amazon and others. These tools are quickly becoming more than mere writing assistants.

In time, they will radically change healthcare, empower patients and redefine the doctor-patient relationship. To make sense of this bold vision for the future, this two-part article explores:

  1. The massive differences between generative AI and prior artificial intelligences
  2. How, for the first time in history, a technological innovation will democratize not just knowledge, but also clinical expertise, making medical prowess no longer the sole domain of healthcare professionals.

To understand why this time is different, it’s helpful to compare the limited power of the two earliest generations of AI against the near-limitless potential of the latest version.

Generation 1: Rules-Based Systems And The Dawn Of AI In Healthcare

The latter half of the 20th century ushered in the first generation of artificial intelligence, known as rule-based AI.

Programmed by computer engineers, this type of AI relies on a series of human-generated instructions (rules), enabling the technology to solve basic problems.

In many ways, the rule-based approach resembles a traditional medical-school pedagogy where medical students are taught hundreds of “algorithms” that help them translate a patient’s symptoms into a diagnosis.

These decision-making algorithms resemble a tree, beginning with a trunk (the patient’s chief complaint) and branching out from there. For example, if a patient complains of a severe cough, the doctor first assesses whether fever is present. If yes, the doctor moves to one set of questions and, if not, to a different set. Assuming the patient has been febrile (with fever), the next question is whether the patient’s sputum is normal or discolored. And once again, this leads to the next subdivision. Ultimately each end branch contains only a single diagnosis, which can range from bacterial, fungal or viral pneumonia to cancer, heart failure or a dozen other pulmonary diseases.

This first generation of AI could rapidly process data, sorting quickly through the entire branching tree. And in circumstances where the algorithm could accurately account for all possible outcomes, rule-based AI proved more efficient than doctors.

But patient problems are rarely so easy to analyze and categorize. Often, it’s difficult to separate one set of diseases from another at each branch point. As a result, this earliest form of AI wasn’t as accurate as doctors who combined medical science with their own intuition and experience. And because of its limitations, rule-based AI was rarely used in clinical practice.

Generation 2: Narrow AI And The Rise Of Specialized Systems

As the 21st century dawned, the second era of AI began. The introduction of neural networks, mimicking the human brain’s structure, paved the way for deep learning.

Narrow AI functioned very differently than its predecessors. Rather than researchers providing pre-defined rules, the second-gen system feasted on massive data sets, using them to discern patterns that the human mind, alone, could not.

In one example, researchers gave a narrow AI system thousands of mammograms, half showing malignant cancer and half benign. The model was able to quickly identify dozens of differences in the shape, density and shade of the radiological images, assigning impact factors to each that reflected the probability of malignancy. Importantly, this kind of AI wasn’t relying on heuristics (a few rules of thumb) the way humans do, but instead subtle variations between the malignant and normal exams that neither the radiologists nor software designers knew existed.

In contrast to rule-based AI, these narrow AI tools proved superior to the doctor’s intuition in terms of diagnostic accuracy. Still, narrow AI showed serious limitations. For one, each application is task specific. Meaning, a system trained to read mammograms can’t interpret brain scans or chest X-rays.

But the biggest limitation of narrow AI is that the system is only as good as the data it’s trained on. A glaring example of that weakness emerged when United Healthcare relied on narrow AI to identify its sickest patients and give them additional healthcare services.

In filtering through the data, researchers later discovered the AI had made a fatal assumption. Patients who received less medical care were categorized as healthier than patients who received more. In doing so, the AI failed to recognize that less treatment is not always the result of better health. This can also be the result of implicit human bias.

Indeed, when researchers went back and reviewed the outcomes, they found Black patients were being significantly undertreated and were, therefore, underrepresented in the group selected for additional medical services.

Media headlines proclaimed, “Healthcare algorithm has racial bias,” but it wasn’t the algorithm that had discriminated against Black patients. It was the result of physicians providing Black patients with insufficient and inequitable treatment. In other words, the problem was the humans, not narrow AI.

Generation 3: The Future Is Generative

Throughout history, humankind has produced a few innovations (printing press, internet, iPhone) that transformed society by democratizing knowledge—making information easier to access for everyone, not just the wealthy elite.

Now, generative AI is poised to go one step further, giving every individual access to not only knowledge but, more importantly, expertise as well.

Already, the latest AI tools allow users to create a stunning work of art in the style of Rembrandt without ever having taken a painting class. With large language models, people can record a hit song, even if they’ve never played a musical instrument. Individuals can write computer code, producing sophisticated websites and apps, despite never having enrolled in an IT course.

Future generations of generative AI will do the same in medicine, allowing people who never attended medical school to diagnose diseases and create a treatment plan as well as any clinician.

Already, one generative AI tool (Google’s Med-PaLM 2) passed the physician licensing exam with an expert level score. Another generative AI toolset responded to patient questions with advice that bested doctors in both accuracy and empathy. These tools can now write medical notes that are indistinguishable from the entries that physicians create and match residents’ ability to make complex diagnoses on difficult cases.

Granted, current versions require physician oversight and are nowhere close to replacing doctors. But at their present rate of exponential growth, these applications are expected to become at least 30 times more powerful in the next five years. As a result, they will soon empower patients in ways that were unimaginable even a year ago.

Unlike their predecessors, these models are pre-trained on datasets that encompass the near-totality of publicly available information—pulling from medical textbooks, journal articles, open-source platforms and the internet. In the not-distant future, these tools will be securely connected to electronic health records in hospitals, as well as to patient monitoring devices in the home. As generative AI feeds on this wealth of data, its clinical acumen will skyrocket.

Within the next five to 10 years, medical expertise will no longer be the sole domain of trained clinicians. Future generations of ChatGPT and its peers will put medical expertise in the hands of all Americans, radically altering the relationship between doctors and patients.

Whether physicians embrace this development or resist is uncertain. What is clear is the opportunity for improvement in American medicine. Today, an estimated 400,000 people die annually from misdiagnoses, 250,000 from medical errors, and 1.7 million from mostly preventable chronic diseases and their complications.

In the next article, I’ll offer a blueprint for Americans as they grapple to redefine the doctor-patient relationship in the context of generative AI. To reverse the healthcare failures of today, the future of medicine will have to belong to the empowered patient and the tech-savvy physician. The combination will prove vastly superior to either alone.

Epic expanding its lead on EHR competitors

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Two large nonprofit health systems made headlines earlier this month announcing that they plan to transition, enterprise-wide, from Oracle Cerner to Epic for their electronic health record (EHR) system.

Using data from KLAS Research, the graphic below shows how Epic has emerged in recent years as the leader in the hospital EHR market. From 2016 to 2022, Epic increased its acute care hospital market share from 26 percent to 36 percent, while its main rival, Oracle Cerner, held flat at 25 percent.

Moreover, Epic is gaining popularity among larger health systems, while Oracle Cerner lost almost 5K beds in 2022, despite gaining 22 hospitals, as it trades large systems for smaller hospitals. 

Epic’s ability to consolidate multiple archives into a single, more functional platform has made it popular with physicians, whose feedback was cited by Intermountain as a key reason behind the system’s decision to switch. 

With three quarters of Americans having an Epic record, the company is leveraging its pole position in aggregating healthcare data as healthcare approaches the cusp of a generative AI boom, recently announcing an expanded partnership with Microsoft focused on integrating AI tools into its EHR system.