Readers Write: How Healthcare is Using AI to Address the Staffing Shortage

How Healthcare is Using AI to Address the Staffing Shortage
By Ed Ricks

Ed Ricks, MHA is managing director of healthcare consulting for Baker Tilly US of Chicago, IL.

One of the greatest challenges facing healthcare providers in 2023 is staffing shortages, particularly among medical doctors and nursing professionals. This stems from a variety of factors, including shifting demographics with an aging Baby Boomer population that is increasing the demand for medical care, a generation of nurses and doctors retiring, the education system not being equipped to create enough new medical professionals, and the demands of the profession that was exacerbated from the coronavirus pandemic.

With staffing shortages not going away any time soon, patients are continuing to experience adverse effecting, including a greater chance for errors, higher morbidity and mortality, employee burnout and dissatisfaction, and patient dissatisfaction, which create a spiraling expense to provider organizations.

Nonetheless, many experts predict that the influence of AI can help address the shortage, so I thought I would ask an AI engine to tell me a story of how AI could help. Here is the response from ChatGPT:

Once upon a time, in a world filled with endless technology, healthcare was facing a major problem. The nursing shortage was plaguing hospitals and nursing homes, leaving patients without proper care.

But then, a group of tech geniuses decided to put their minds together and come up with a solution. They created AI robots designed specifically for the healthcare industry. These robots were programmed with all the knowledge and skills necessary to be a nurse, and they could work 24/7 without taking breaks or getting tired.

At first, people were skeptical about the use of robots in the healthcare field. But as soon as the AI robots started working, everyone was blown away by their efficiency and care. They could do everything a human nurse could do, and they did it better! They never made a mistake, and they never complained about working overtime.

The nursing shortage was soon a thing of the past, and hospitals and nursing homes were able to provide the best possible care to their patients. Everyone was happy, except for the nurses who had been replaced by the robots. But they eventually found other jobs, and they were happy too.

And that, my friends, is the story of how AI robots fixed the nursing shortage in healthcare and made the world a better place. The end.

While this sounds cool, the reality is that proven methods already blend AI technology using computer vision and other input feeds with a repeatable change management framework. By doing so, providers can apply advanced analytics to determine ROI and the best impact opportunities that will lead to dramatic results for their patients and their margins.

Using this type of technology can help harmonize workflows, automate documentation tasks, and simplify communications to give nurses time back to spend with their patients. This leads to reducing friction from the act of providing care. When this type of automation is coupled with a remote nursing and care strategy across an organization with appropriate change management, the results can be far greater.

When combining the use of a proven technology platform with proven outcome driven change management strategies, health systems can see a dramatic impact on reductions in staffing costs, nursing turnover and associated costs, and patient falls. They can also see improved patient outcomes and increased staff and patient satisfaction.

We don’t have to wait for the day that we really have the sentient robots running around delivering full care in health systems. Let’s use the available processes and AI tools now to help providers make a difference on their transformational journey.

Readers Write: I Know You’re Not Thinking About Blocking Patient Data

I Know You’re Not Thinking About Blocking Patient Data
By Troy Bannister

Troy Bannister, MS is co-founder and CEO of Particle Health of New York, NY.

If you’ve worked in healthcare for 10+ years, this is probably the one hundredth time you’ll read an article about patients being unable to access their own medical data. Here’s why I think that trend might be coming to an end soon.

During healthcare’s slow digital transformation, care providers moved from fax machines to 1:1 digital pipelines to manual portal scraping tools, just to get the charts of their patients. In many instances, these processes are occurring in what are negatively referred to as data silos.

Technology solutions for improving healthcare data accessibility have evolved over the years. The legal requirements for complying with data accessibility have followed closely behind. Most clinicians are connected to nationwide interoperability networks through an EHR, theoretically giving them at least some ability to exchange patient records. Now, the US government has made it clear that their patients have the right to access their medical records in those same technically feasible ways.

Enter the Anti-Information Blocking Rule, the culmination of government efforts to legislate clinical data accessibility. Just like it sounds, its goal is to eliminate practices that interfere with the access, exchange, and use of electronic health information.

Whereas HIPAA has long made it clear that individuals can request their own records in paper format, information blocking rules theoretically give patients access to the same advanced Health Information Networks that care providers use to digitally exchange data.

The Office of the National Coordinator for Health IT (ONC), which sets the rules for information blocking, is working particularly hard to prevent cases where an individual is arbitrarily blocked from access to their own personal health information.

Despite ONC’s intentions, repercussions for information blocking were nonexistent. For that reason, healthcare organizations have continued to drag their feet when it comes to allowing patient data exports.

I’m here to spread the news that information blocking is coming to an end. As of October 2022, the HHS Office of the Inspector General began collecting reports of information blocking on its new online portal, with a mandate to issue fines of up to $1 million per violation. It is no longer in question that healthcare organizations will be held accountable unless they improve patients’ access to their own data.

Information blocking regulations don’t have any health IT loopholes. Providers can’t implement patient-only release delays, turn information into an unreadable format, or seek other ways to water down the rule’s provisions. Generally speaking, EHR vendors, Health Information Networks, and provider organizations must release the entire HIPAA-established designated records set of electronic health information (EHI) to a patient upon request.

In other words, if information exchange is doable for doctors, it’s doable for patients too. Information must be in the manner a patient initially requested. That could entail release over the same efficient digital channels, including APIs, that clinicians have access to.

The lack of access to clinical data at scale remains a core problem for US healthcare that can be linked to many of our system’s inefficiencies. These issues have a fundamental impact on healthcare costs, care quality, and ultimately on patient outcomes. Stakeholders cannot continue to wait for healthcare organizations to make their data-sharing preparations. Information blocking is no longer an acceptable policy, and yet we see it every day.

Ahead of the October 2022 information sharing deadline, there were concerted efforts from several healthcare organizations urging HHS to postpone enforcement, and not for the first time. However, regulators felt there was too much at stake to keep patients and digital health pioneers waiting any longer. Information blocking rules have survived multiple presidential administrations, and there’s bipartisan agreement that they are here to stay.

In reality, not every health system is at the same level of readiness when it comes to anti-information blocking compliance. Patient access to EHI has been a complicated task for many healthcare institutions, especially those that are not prepared to share patient data electronically. Many organizations are scrambling to find their own tools to support such efforts.

Fortunately, the technical challenges of anti-information blocking compliance are addressed by the 21st Century Cures Act, the law which led to information blocking rules in the first place.

The Cures Act aims to increase innovation by fostering an ecosystem that supports the development of data-driven applications. The rule also calls on the healthcare industry to adopt standardized APIs, which will help allow individuals access structured EHI using smartphone applications securely and easily. From new health tools to transferring records between providers, information blocking rules will help patients to take greater ownership of their care.

“In 2023, the vast majority of docs and hospitals will have FHIR APIs live,” Steven Posnack, the Deputy National Coordinator of ONC, predicted last month. With the ONC incentivizing this change, digital health organizations should urgently consider a single, comprehensive API that enables a seamless data sharing experience for institutions of all shapes and sizes. Integrating a pre-built API lets providers retrieve medical records in just a few clicks, eliminating reliance on faxes, portals, and other tired forms of data exchange.

For large organizations that support value-based care models, being able to work with patient health records at scale lets providers proactively address patient needs. This is especially critical when it comes to evaluating patients with chronic disease and comorbidities, leading to reduced readmissions. For example, on an ongoing basis, an API can help kidney care organizations that track glomerular filtration rate (GFR), endocrinologists who chart A1c values, and cancer care institutions that research case histories. In these instances, the ability to easily query patient health records allows healthcare providers to more efficiently and effectively care for their patient populations.

Moving ahead, digital organizations can look forward to obtaining data quickly and easily, powering tools that deliver more effective care. But it’s not just the data itself that matters; it’s how you get it. Leveraging advanced technology like a single API helps healthcare organizations and their patients tap into their health history when they need it.

Readers Write: For Safety’s Sake, Healthcare Must Address Its Patient Matching Problem

For Safety’s Sake, Healthcare Must Address Its Patient Matching Problem
By Gregg Church

Gregg Church is president of 4medica of Marina del Rey, CA.

Imagine if you log into your bank’s website to conduct a transaction, only to find that the bank can’t match your identity to your account. Not only are you unable to complete your transaction, you can’t even access your own information.

You rightfully would complain, and the bank almost certainly would move quickly to resolve the issue because, (a) it’s clearly unacceptable, and (b) the bank wants to continue benefiting from providing you with financial services.

Yet patient identification matching problems still proliferate in healthcare and are a major contributor to data integrity issues within electronic health records (EHRs). Average duplication rates among health organizations in America still range between 10% and 20%, with some duplication rates reaching as high as 30%.

At the 20% rate, this means one in five patients are at risk of having their medical records either duplicated or overlaid (when one patient’s data is placed in another patient’s medical file). It’s hard to imagine a bank or its customers tolerating a 20% error rate in customer records.

Wrong medical records accessed by providers and care team members could easily result in a wrong patient diagnosis, wrong medication prescribed, wrong lab test performed, wrong procedure or surgery conducted, and perhaps death due to a misdiagnosed condition. The problem is real, and the number of duplicate patient records is expanding as more clinical data is being moved digitally and shared across networks by hospitals, health systems, health information exchanges, labs and clinics.

What can healthcare organizations do to reduce patient identification matching problems and improve overall health data quality? Here are three things that could help healthcare organizations improve patient matching.

Standardize how data is collected at the point of care

Patient identification problems most commonly originate at registration. Busy staffers may make mistakes when entering data manually, or a lack of identifying information prompts the intake worker to create a new patient record, which can become a duplicate. Bad data can remain in an EHR for years, unbeknownst to clinicians or patients.

However, even if a healthcare organization is able to standardize data collection internally, other organizations with which they exchange data may have different processes, coding and data collection standards. The inevitable result for all stakeholders is low-quality data. Intelligent technologies such as artificial intelligence (AI), machine learning (ML) and referential matching can be used to identify and correct errors in patient data.

Patient ID to verify correct patient to medical record

Provider organizations lack a simple way to accurately identify patients. They are forced to rely on a combination of driver licenses, home addresses, Social Security numbers, phone numbers, and other non-medical identifiers. But what happens when a patient changes addresses or phone numbers? That’s when you see frustrated frontline staffers defaulting to creating a new (and duplicate) record for the patient.

A unified, single patient identifier would help resolve this problem. Unfortunately, a HIPAA proposal calling for the creation of a unique patient identifier (UPI), has been stalled by lack of funding. Until there is national patient ID, healthcare organizations must rely on technology to improve patient matching and mitigate related safety issues.

Data governance standards

Data governance is a framework for healthcare organizations to capture, process, normalize, use, store, and dispose of patient data. By consistently applying best practices to data, healthcare organizations can help ensure the accuracy of records in the EHRs and clinical systems is never comprised.

Effective data governance benefits healthcare organizations and patients in several ways. It improves the patient experience, leads to better clinical outcomes, and reduces healthcare costs through increased efficiency and better resource utilization. Finally, data governance can increase the value of a healthcare organization’s data because the governance process has improved its quality. This makes the data more attractive to prospective buyers such as pharmaceuticals and health policy researchers.

Accurately matching patients to their medical records is a daunting challenge to healthcare organizations as the volume of patient data – and the number of sources – continues to explode. Collecting and organizing patient data in a more standardized way will enable providers, labs, and other stakeholders to better serve patients while lowering costs and increasing the value of their data.

Readers Write: Social Care Help Shouldn’t Come at Social Cost: Why Dignity and Ease Should Be at the Heart of Modernizing America’s Safety Net

Social Care Help Shouldn’t Come at Social Cost: Why Dignity and Ease Should Be at the Heart of Modernizing America’s Safety Net
By Jaffer Traish

Jaffer Traish is COO of Findhelp of Austin, TX.

The Safety Net

Social safety nets are different in each country. Some focus on poverty alleviation, economic mobility, or disaster relief. The World Bank has estimated that safety net programs have helped 36% of the poorest in the world escape extreme poverty.

In the US, we have seen federal administrations strengthen and weaken safety net funding over decades. The history of social safety nets in the US has been shaped by voluntarism, the notion that the voluntary actions and agreements undertaken by private charity and industry are preferable to state-mandated social welfare programs.

Nonetheless, the US has tens of millions of vulnerable people looking for services. 

This is a breakdown of healthcare specific needs after analyzing 10% of searches from a population of 16 million Americans. 

COVID-19 placed a spotlight on the vast array of needs. Our public benefit data mirrors what we heard and saw of the struggle for individuals and families.

Policy Winds

With the increasing awareness of social disparities and impact on health, we now see many major policy changes:

• ACO REACH Model. This model began on January 1 and includes focus areas on equity, access, and community health. There are reimbursement calculations and withholdings related to the area deprivation index (API) and SDoH quality measures.
• Joint Commission. New rules in effect January 1 include identifying a system leader who is accountable to address disparities in the patient population, as well as social needs screening and the sharing of community resources.
• NCQA. They released new HEDIS measures, including the SNS-E measure related to assistance for plan members needing food, housing, and transportation.
• CMS guidance letter. CMS issued a guidance letter on January 4 to state Medicaid Directors related to ILOS (In Lieu of Services) for Medicaid Managed Care, which relates to the Cal-AIM California waiver and many future waivers. The guidance strongly emphasizes the importance of measuring utilization and impact of non-traditional services such as social supports.

Private Company Technology is Accelerating Modernization

With any emerging market, we’ll see some companies taking advantage for short-term profit, multi-million dollar software costs, and a story of hype that fades post contract signature.  We will see others that aim to maximize funding going to communities, to capacity building, and to community health worker staffing. Government and private sector buyers must be well educated to avoid the expensive shiny object that doesn’t deliver.

The good news is that healthcare leaders have long had a vision for what’s possible, including Judy Faulkner and Epic, where social services digitization is found in her original business plan.

SDoH is in the spotlight, with 80 bills proposed in Congress and $90 million of requested funding by states related to social care. Requests fall into several categories,including Medicaid waivers and federal match money.

We have the opportunity to build private-public partnerships that align on key principles. Education of key stakeholders is essential.

Influence 

People go to helpers to get help. Those helpers might be a librarian, a pastor, or a neighbor. Those helpers may also be care coordinators, social workers, and hospital discharge planners.

State government can heavily influence the funding for services. Medicaid directors, Health and Human Services secretaries, commissioners, and deputies control how money is spent through Medicaid waivers, MCO contracting, non-profit capacity grants, and more. There are tough decisions to make, and there is incredible respect for people in these roles who are lobbied heavily by industry.

A large influence on these decisions is improving overall health and driving down the cost of healthcare. We all know over the last two decades of electronic health record implementations that technology alone is not the answer. Technology enables us to work more efficiently and more collaboratively, though it doesn’t solve governance, community engagement, or equitable service delivery.

State agencies want to understand the needs of their populations, where people are going to receive help, the services delivered, and when possible, the correlations with healthcare cost and clinical outcomes.

Some vendors promise a panacea of results via a top-down monopoly that goes something like this:

• Mandate use of a specific, single technology system.
• Force communities to use one system.
• Force non-profits into a contract.
• Force a per-user license model so the vendor makes more money with every user whether they adopt or not.
• Force a one-time consent so the vendor owns data sharing.
• Restrict federal dollars being used outside of this system.

This sounds like a good way to make a vendor rich and to skirt consumer privacy, interoperability, and non-profit autonomy.

The Choice

Procure one technology system with a hand in the face of private sector procurement or empower the community and health systems to choose the tools that make the most sense for them. Require that they report the data using standards and even certifications as the Office of National Coordinator (ONC) has supported for years.

Dignity and Ease

At the heart of this work is privacy. Imagine that you signed a single consent form for your healthcare provider to put your information into a system to facilitate a referral to a local food pantry or domestic violence shelter. But with that one action, you’ve granted more than 120 non-profit organizations the ability to access all of the personal financial, social, and medical data you reported. This is happening today because social care privacy standards haven’t kept pace with healthcare. People expect that their sensitive information will only be visible to the organizations and people they choose. Too many are blindly forced into one-time, all-in consent models in exchange for getting the help they so desperately need.

Healthcare, government, and non-profit leaders must improve safeguarding personal information by building a consumer-directed privacy approach to social care technology. Last year, the State of New Hampshire adopted a first-in-the-nation privacy protection law that established a policy framework to prioritize a person’s right to informed consent when seeking social services.

The Future of the Safety Net

• Imagine a future where applying for state benefits is a dignified, fast, digital process, not a paper process with a custom system.
• Imagine a future where a person in need can log in to a patient portal and see covered benefits, whether value-added, supplemental, community bank funded, or other non-profit led.
• Imagine a future where payers can see member needs and where their member has received help (with permission) anywhere in the country, and (with permission), intervene to prevent costly chronic or other clinical adverse events.
• Imagine a future where social workers, discharge planners, and other helpers simply use their EHR or care management system to make referrals and orders without worrying about which vendor is powering the SDoH network, a true API network model.

This is all possible. We should not be asking governments to pick winners and then learning too late about the risks to privacy and dignity taking place behind a curtain.

With network effects, community engagement, and trust building, the outcomes that we all want to see are possible. We can enact policies that do not create data silos or create monopolies with false promise. The right interoperability policies will create innovation in health and human services that are ubiquitous in every other sector.

Readers Write: What Health IT Companies Can Expect in 2023

What Health IT Companies Can Expect in 2023
By Jodi Amendola

Jodi Amendola is founder and CEO of Amendola Communications of Scottsdale, AZ.

My crystal ball says tighter budgets, LinkedIn, and more targeted, integrated campaigns.

The beginning of a new year provides an irresistible temptation to make predictions. Unlike the ancient Romans, we don’t look to the flight patterns of birds to foretell the future, but base our predictions on what we see in the industry and the economy, what we hear from clients, technological developments, etc. Here are my predictions for what to expect this year in healthcare/health IT.

A tumultuous economy

Economists have been arguing for months about whether the US is headed toward a recession and, if so, how severe it will be. I don’t pretend to know the answer, but I do expect 2023 to be challenging for the healthcare and health IT industry given the challenges faced by providers and payers. Hospitals are still recovering from the COVID-19 pandemic and coping with rising expenses, ongoing staffing shortages, and continuing capacity constraints. In response, health systems have restricted services, laid off employees, closed departments, and even shut down entire hospitals.

However, systems are also investing in new digital technologies that allow them to operate more efficiently and expand care models, such as remote and virtual care, in the face of these challenges. The vendors who sell these products and others that support the digitization of healthcare are ramping up marketing and PR efforts to position themselves as the solutions to help systems clear these barriers.

To best reach their target audiences, what I am seeing for healthcare and health IT companies are budget considerations, such as whether to invest heavily in trade show marketing or if those dollars should be reallocated to more targeted account-based marketing (ABM), digital marketing, or integrated marketing programs.

Twitter down, LinkedIn up

I think 2023 will be a critical year for Twitter as an advertising vehicle and, quite possibly, as a viable social media platform. The uncertainty over Elon Musk’s takeover, the departure of so many staff, and Musk’s controversial decision to largely stop moderating tweets and to welcome back accounts that had been banned for misleading or false content has made brands understandably wary of being associated with Twitter.

According to a recent Media Matters report, half of Twitter’s top 100 advertisers have stopped promoting on the platform, brands which have accounted for nearly $2 billion in advertising since 2020. Some publicly announced their break with Twitter, while others have quietly stepped away. And Musk’s myopic claim that companies who choose not to advertise on Twitter are somehow engaging in censorship or violating free speech principles is not the way to woo them back. Brands have every right to avoid unnecessary and unwelcome controversy when choosing where to advertise.

By contrast, we’re seeing heightened interest among clients in LinkedIn as a marketing and advertising platform. The oldest of the social media platforms, LinkedIn has evolved from a glorified jobs board to a place where companies — including healthcare providers and payers — research vendors, network, and promote themselves.

LinkedIn content is becoming richer and more interesting as well. Yes, there are still plenty of photos of people sitting in hotel ballrooms captioned: “Excited to be attending the annual Widget Trade Show in Walla Walla,”, but creative brands are using it to tell stories and connect with target audiences on a deeper level. To encourage this, LinkedIn is adding content-friendly features, such as Creator Mode, Auto Embed, and image templates.

As LinkedIn becomes more important and versatile, brands would be wise to re-evaluate their approach to the platform with an eye toward expanding their content to engage target audiences beyond what they’ve traditionally posted.

Marketing and PR integration

It’s long been a good idea to integrate PR and marketing, but at many health IT companies, they are still separate silos. With tight budgets for both likely in 2023, it’s never been more important that they work closely together to achieve shared goals, measured using agreed upon performance metrics.

Comprehensive, integrated marketing programs that include webinars, events, digital marketing, and account-based marketing, in addition to media relations, social media, and thought leadership activities, will deliver better returns than separate, disjointed campaigns and help rise above the noise.

That also supports another 2023 trend: companies focusing more on strategic messaging and marketing to reach specific prospects. In a tough economy, it makes sense for businesses to focus on satisfying their most important accounts and landing the whales that could make the difference in a difficult year. Making the best use of tight resources and budgets through integrated marketing and PR campaigns can make all the difference.

Marketing resolutions

January is a great time for making resolutions, as well as predictions. In anticipation of what is likely to happen this year, here’s what healthcare and health IT companies should do:

• Look for new ideas and partnerships that work within their budgets while continuing to deliver great ROI.
• Integrate marketing and PR. Synchronizing efforts delivers a greater punch than pursuing separate tracks. If 2023 does prove to be a difficult year economically, it’s even more important to deliver a strong, coordinated message.
• Take creative risks. One of the great things about marketing and PR is that it’s not static; there’s always a new medium, platform or strategy to explore. This is going to be a good year to go exploring.

Whatever your resolutions, I hope you achieve them. Here’s to a happy and fulfilling new year.

Readers Write: Netflix and Reed Hastings: Ghost of Christmas Past

Netflix and Reed Hastings: Ghost of Christmas Past
By Chuck Dickens

As the countdown to Christmas 2022 ticks away, Reed Hastings sits alone in the dim basement of his parents’ house, lost in the immersive world of video games. But the monotony of his day job at Blockbuster weighs heavy on his mind.

Every day, he dutifully rewinds VHS tapes and updates spreadsheets, tracking the $5 fines for customers who neglect to rewind their rentals. It’s a tedious task, but it’s a necessary one. After all, late fees and rewind fines are the company’s second-largest source of profit, surpassed only by the seemingly endless stream of “Die Hard” rentals that pour in every holiday season.

But just as Reed finishes his fifth cup of coffee, something strange happens. A shimmering light appears out of nowhere, coalescing into a humanoid form that seems to float effortlessly in the air. For a moment, Reed is startled, but then he recognizes the ghostly figure as the Ghost of Christmas Past, as depicted in countless retellings of “A Christmas Carol.” With a jolt, he’s suddenly transported back to 1997, reliving the excitement of a disruptive new idea that once seemed destined for greatness.

After cashing in on the sale of his software company, Reed was on the hunt for his next big opportunity. He wanted something innovative and disruptive, and he had his sights set on the movie rental industry.

In 1997, movie rentals were a major form of entertainment in the United States, with most employers offering them as a employment benefit and the government eventually extending the perk to everyone over the age of 65. As a result, movie theaters dwindled in number, and the rental market was dominated by a few large players such as Blockbuster and Hollywood Movies.

But after paying a hefty late fee to Blockbuster for “Apollo 13,” Reed began talking to his friend Marc Randolph about the frustrating experiences they and their friends and family had had with the rental giants. The local store had limited titles, and the popular ones were often unavailable. Even though movie rentals were offered as a benefit, the co-pay was still substantial, and if customers wanted to drive to a different location to find a specific movie, they had to pay extra out of pocket.

Determined to bring a better rental experience to customers, Reed and Marc came up with the idea for Netflix. The company that would offer DVDs by mail for a low, fixed monthly fee, with no late penalties, a vast selection of movies to choose from, and fast turnaround time. They were confident that their service would revolutionize the industry and put an end to the frustrations of traditional rental models. They were so convinced of the superiority of their service that they invested a large part of their own money in addition to VC funds to get the company off the ground.

But by next Christmas, as Reed and Marc struggled to scale up their business and delved deeper into the movie rental market, they discovered a number of strange quirks and injustices.

The compensation paid to movie industry professionals was set by a committee (Relative Video Unit Update Committee – RUC) that was largely composed of people involved in a genre called “film noir,” who claimed that these films were the most expensive to produce and should therefore be paid the highest rates. This left other genres such as romantic comedies, which were popular with customers but low on the payment scale, struggling to find funding and talent.

Another example was that every time a movie was rented, Blockbuster used a special code to designate the genre of the movie and other details. These codes eventually determined who got paid how much for the rental. Not only did the American Movie Association (AMA) controlled who got paid, but they charged everyone a licensing fees to use the code set itself in a classic case of double-dipping.

Additionally, the distributors were owned by Blockbuster and Hollywood Movies. They negotiated with movie studios and employers to determine which movies would be made available and at what rental price. Since everyone got a percentage of the rental price, lowering the rental price wasn’t in anyone’s business interest collectively. Further, government was not allowed to negotiate late fees and penalties, as it was prohibited by law (American Movie Association and American Hollywood Association had strong lobbying arms).

From the very beginning, Netflix faced an uphill battle in convincing consumers to sign up for its flat fee subscription model. Many people received movie rental benefits through their employer and weren’t willing to pay for Netflix out of pocket. And while the company’s vast library and lack of late fees were appealing, people were hesitant to trust a new company with such a crucial part of their entertainment.

Netflix tried to appeal to employers, offering to provide subscriptions as a benefit to employees, but benefits managers were resistant due to long-term contracts with established rental companies like Blockbuster and Hollywood Movies. As for other charges, such as late fees and facility fees — to compensate them for higher operational expenses of a physical location – Blockbuster and Hollywood Movies had arranged it so that those charges were paid directly by the employer and people never perceived those being charged to them, even though indirectly, it was all coming out from their paychecks and taxes.

Undeterred, Netflix approached Blockbuster and Hollywood Movie (because everyone said that that’s where the money was) with the idea of using their advanced technology to lower the cost and improve the availability of movie rentals. But they were met with laughter and derision, as the traditional companies saw higher costs as a way to increase revenue.

Despite this, Reed and Marc remained convinced that technology could be a game-changer for their company. So when their engineering team came up with the idea of streaming movies directly into living rooms all over the country, they were thrilled. However, they quickly realized that employers and the government wouldn’t pay for these streaming movies. Regulations prohibited them from streaming across state lines, requiring them to set up streaming centers in each state and significantly increasing their costs.

But even with these setbacks, Reed and Marc were undeterred. They saw the potential for incorporating AI into their streaming service to create an even more attractive offering. As Christmas Eve 2007 approached, they had signed up a few thousand direct subscribers, mostly in affluent communities, and a few progressive employers were conducting pilots with their service. Despite the challenges they faced, they remained convinced that they were on the cusp of something big.

Despite its advanced technology and AI, Netflix struggled to overcome the stranglehold of monopolies and regulations in the movie rental industry. For over two decades, the company barely made a profit and continued to hemorrhage money.

But in the winter of 2020, everything changed. A global pandemic swept the world, forcing people to stay at home and closing down stores like Blockbuster and Hollywood Movies. In response to widespread discontent, the government allowed nationwide streaming of movies and set up a system to pay for it. Suddenly, Netflix was a household name, valued at billions of dollars despite still not turning a profit.

However, the success of the streaming service sparked a wave of competition, including from Blockbuster and Hollywood Movies, which created their own streaming video service with the help of their legacy IT vendor. As the pandemic waned and the traditional players saw their core business model threatened, they worked to regain the upper hand. They pressured the government to reinstate state-level restrictions on streaming and encouraged the movie industry to charge a “streaming fee” for on-demand movies.

By Christmas 2021, Netflix was deep in debt and Reed and Marc were forced to liquidate the company to avoid personal bankruptcy. They both moved back in with their parents and took jobs at Blockbuster and Hollywood Movies, which were thriving again thanks to government loans during the pandemic.

As he headed off to work at Blockbuster, Reed couldn’t help but wonder why the movie rental industry couldn’t use technology to improve customer service and reduce prices like other industries such as healthcare. In fact, at every innovation forum, people kept asking him, “Why can’t movie rental business be innovative like healthcare and be agile at adoption of technology?”

“Because healthcare is not insanely regulated and doesn’t have government and private monopolies to distort the market and incentives like the movie rental business,” he muttered to himself, recalling the Ghost of Christmas Past’s explanation. And with that, he set off for another day at the grindstone.

Readers Write: It’s Time to Make Price Transparency Data Useful

It’s Time to Make Price Transparency Data Useful
By Lewis Parker

Lewis “Lew” Parker, MSIS, MBA is SVP of engineering and CTO of Arrive Health of Denver, CO.

The price transparency movement in healthcare is growing, supported by factors including consumer demand and rules from CMS requiring health plans and hospitals to post pricing information for covered items and services.

It’s good that organizations are complying with these regulations by uploading detailed machine-readable documents to their websites. However, it will only be great when this overwhelming amount of information is digested, personalized, and presented in a way that enables decision-making for all parties involved, at the right time.

These moments include when providers are engaging with patients and making care decisions, when care team members are helping patients manage affordability barriers, and when patients are trying to find lower-cost care options.

How do we get to this future state, when data drives behavior change in a way that impacts affordability, accessibility, and outcomes? Here are three things that must happen to make price transparency data useful.

• Translate the data. The US healthcare system is highly complex, and multiple data sources are used when completing a medication order: electronic health records (EHRs), drug databases (First Databank, Medispan, etc.), and insurance plan design and patient accumulators (plans, PBMs, and payers). There are rarely consistent fields, and even standards such as those provided by NCPDP may not go far enough to create a seamless prescribing experience. This is why the first step to price transparency must be translation.
• Make it simple. New tools must be simple and integrated into existing workflows if they are going to be adopted, especially considering growing provider burnout worries. Neither consumers nor providers are accustomed to pharmacy benefit jargon. Real-time cost and coverage tools must go beyond unifying and normalizing content from disparate sources by translating that information into messages that are meaningful to the appropriate audience. For example, providers, care teams, and patients all want to see different pricing information. The provider may want to see lower-cost clinically equivalent alternatives, the care team considers options that avoid a prior authorization (PA), and the patient wants to know how much they will owe at the pharmacy counter. Price transparency solutions need to account for this and deliver useful and actionable information to each individual.
• Build insights. Price transparency data will become its most powerful the more it is understood. Analytics tools can identify where opportunities exist to maximize price transparency data in clinical decision-making. Robust reporting and user-friendly dashboards show which providers are engaging the most (or least) with cost and coverage data and which are ultimately making decisions — like switching to a lower-cost medication or selecting a clinically relevant option that doesn’t require prior authorization — based on that data. Insights can also highlight which medication classes have available competitive options, and where there are barriers to medication switches.

Achieving price transparency in healthcare is challenging but possible. With all types of costs increasing, not just healthcare costs, now is the time to go beyond the bare minimum and invest in tools that will make price transparency data usable and impactful for all stakeholders involved.

Readers Write: It’s Time to Level Up Value-Based Care by Integrating Real-Time Patient Insights into Workflows

It’s Time to Level Up Value-Based Care by Integrating Real-Time Patient Insights into Workflows
By Rob Cohen

Rob Cohen, MS, MBA is CEO of Bamboo Health of Louisville, KY.

The push for transformation in healthcare is ushering in a new era of care that focuses on delivering better patient outcomes, at better costs, and with better experiences to drive value-based care (VBC). In pursuit of this vision, governing bodies such as the Centers for Medicare and Medicaid Services (CMS) and the Department of Health and Human Services (HHS) are working to implement changes to improve the quality of care nationwide. While this work is a step in the right direction, it has major implications for healthcare providers on the frontlines of care delivery.

For example, CMS has set the expectation that all Medicare beneficiaries must be covered by VBC models by 2030, meaning that the need to meet quality measures will only increase in the coming years. This is concurrently happening when many of the adjustments that afforded health plans some flexibility in CMS Star Ratings measures during the COVID-19 public health emergency (PHE) are about to expire. As such, health plans and provider organizations are seeing a significant impact. Findings released from CMS this October highlighted that the number of Medicare Advantage plans with drug coverage with a Star Rating of four or more in 2023 is down 68% compared to 2022.

To combat these challenges, real-time patient data is one of the most critical tools available that can help improve quality measures in alignment with VBC goals. However, today, patient health data is often disjointed, delayed, and overwhelming for providers to manually sift through. Without patient information seamlessly flowing with patients as they go from one point of care to the next, healthcare professionals often lack an easy way to surface treatment gaps and provide context to the care they previously received. This fragmentation and data overload add friction for healthcare providers, which causes clinical and administrative burdens, and ultimately, leads to missed opportunities to positively impact patient care.

Digital healthcare technology solutions can address this disconnect by shifting the focus from simply sharing as much raw data as possible towards providing health plans and providers with real-time insights directly in their clinical workflows during high-impact moments. In turn, this helps providers ensure patients are receiving the right care interventions, at the right time, for the right outcome.

Accomplishing this requires technology that enables quick, fluid insights shared among providers, and between health plans and providers, to ensure the identification and solving of gaps in patient care. By offering healthcare providers actionable insights within their clinical workflows, they can more easily pinpoint patients who could benefit from follow up and transitions of care support.

This is especially true during high-impact moments in a patient’s care journey such as medication checks care transitions, post-treatment follow-ups and screenings, where complete insights can make timely follow-up care and achieving associated quality measures more attainable. During these critical moments, enhanced care collaboration efforts and improved real-time intelligence sharing help to bring care gaps to light, as well as support quality measures that have been built into VBC models such as CMS Star Ratings and HEDIS measures.

From there, providers can make informed care decisions and implement corresponding workflows for timely, effective post-discharge transitions and follow-up treatment to increase member engagement. Furthermore, the increased adoption of and adherence to evidence-based guidelines and technologies for care gap closure can help healthcare organizations lessen information barriers and misaligned workflows between providers and health plans to increase revenue and alleviate clinical and administrative burdens. 

As we look to the future of healthcare IT, technologies that identify and solve care gaps will position providers for the most success. By helping health plans and clinicians more effectively collaborate and engage patients during the high-impact moments that matter, these technologies can enable more-informed care. In turn, this improves patient outcomes, lowers costs and drives the industry towards a greater adoption of VBC.

Readers Write: How CMS Can Build a National Directory of Healthcare Providers

How CMS Can Build a National Directory of Healthcare Providers
By Justin Sims

Justin Sims is president and chief operating officer of CareMesh of Reston, VA.

Four weeks ago, CMS issued a Request For Information (RFI) to collect feedback on whether it should build a national directory of healthcare providers and services. They highlighted the problems the lack of quality provider information causes for consumers and the industry and asked for feedback on solving those problems.

But doesn’t CMS already have a provider directory?

NPPES is the closest thing that CMS has to a directory. It is used to issue ID numbers to healthcare professionals (NPIs) and covers almost all physicians (about a million) and many other healthcare workers (about five million). However, it suffers from infrequent update (the average age of an entry is 6.7 years old) and has gaps in the information it collects (it lists only 200k validated secure email addresses when there are well over a million).

There is also the Medicare Provider Enrollment, Chain, and Ownership System (PECOS). This is routinely updated by physicians every five years, so it is a little more current, but because it focuses on Medicare enrollment, it doesn’t cover all physicians and doesn’t collect the same information as NPPES.

Why hasn’t the problem already been fixed? In the words of Tom Hanks in “A League of Their Own,” if it wasn’t hard, everyone would do it.

There are several reasons that a single national provider directory has eluded us.

First, there’s scale. Maintaining information on a million of anything is hard.

Second, there’s the structure. Physicians often work for multiple organizations and keep a fluid list of physical locations with different contact information at each one.

Third, there’s content. Do you, for example, know your EIN? And how many physicians do you think know their Direct Address? (Not a lot!) Or their EHR end-points? (Even fewer.) Or can readily list the insurance carriers they accept at each organization and location? Not to mention that providers and their staff are busy.

If CMS is going to take this challenge on, and we hope that they do, we see four broad options:

Provider-Supplied Data

There are already regulations to encourage providers to submit information updates to NPPES and PECOS within 30 days. These rules have some teeth. For example, providers can be suspended from the Medicaid program if they don’t comply. As part of their strategy, CMS could certainly make it easier for providers and their staff to make updates and could increase penalties for those who don’t. But asking a million physicians and a further five million healthcare professionals to update their information manually will be a tough strategy to deliver success.

System-Supplied Data

In most cases, basic profile information about providers is maintained in the EHR. Another strategy that CMS might consider is to modify its Certification of Electronic Health Record Technology (CEHRT) standards and establish a process for EHRs to send directory information electronically using HL7 FHIR standards. While this would only cover EHR users, it would account for almost every prescriber in the country, and done right, it could reduce physician burden and result in continuously updated information, at least for some.

Combine Multiple Data Sources

While the EHR concept sounds promising, it would take some years to implement and a few more to iron out the wrinkles. Another approach that CMS might follow is to combine data from many sources. In addition to CMS data sources, there are many others, including state Medicaid agencies, medical licensing boards, Medicare Advantage plans, Medicaid MCOs, Qualified Health Plans, DirectTrust, and health system and provider group websites (many of which follow the schema.org standard), to name but a few.

By combining all of these sources and using statistical techniques to validate the data, CMS could create a more accurate picture of the provider than any single source alone. Minimally, it could use these techniques to identify where data quality issues may exist and then follow up with the provider.

Help Industry Solve the Problem

Finally, CMS could do more to help the industry solve the problem. Several companies, ours included, are already doing a combination of the above. But it would be much easier if CMS standardized its data (in NPPES and PECOS) and modified regulations to ensure that health plans, in particular, shared their information in a standardized electronic format.

For a problem as old as the US healthcare industry — states gained the right to regulate health and license doctors in the Bill of Rights in 1791 — we doubt that CMS will solve this overnight. But it is a challenge that most segments of the healthcare industry are cheering for, and one for which the ultimate solution will lie in a combination of the options described above.

Readers Write: Reversing RCM Brain Drain and Creating Revenue Cycle’s Digital Twin

Reversing RCM Brain Drain and Creating Revenue Cycle’s Digital Twin
By Jim Dumond

Jim Dumond, MS is senior product manager at VisiQuate of Santa Rosa, CA,

Across all industries, the need to retain knowledge of key processes and details has gained new emphasis as the labor supply has tightened and grown more expensive. In the revenue cycle space, health systems are competing against not only each other, but other industries to retain talent and ensure that their organizations run smoothly. The loss of seasoned RCM professionals is creating a knowledge gap or “brain drain,” which makes it harder for systems to keep their businesses moving, let alone do so efficiently.

As result, the question these organizations must answer is: how do we guard against this loss of RCM knowledge by having robust, prescriptive workflow systems in place that direct employees what to do, when to do it, and how to do it based on predictive analytics that mine data to suggest actions that successfully have solved the same issues in the past?

Health systems today are primarily reliant on their human “tribes” of users to pass key knowledge about specific payer processes, required details, and thousands of other minutiae. This has created a system where users inefficiently share that knowledge via occasional Zoom calls, PowerPoints or job aids, and often emails or hallway conversations (if they are back in the office) that don’t get recorded except in a single brain at a time. That verbal tradition of the health system is what is creating the impact that sites are seeing today as users leave for other systems or careers.

Why not create a centralized database of knowledge for all the activities that move an account through the revenue cycle from scheduling to a zero balance? We live in a proactive world. Amazon and Netflix use a recommendation engine to identify what we should buy or watch next. Why not utilize that same approach for the revenue cycle? Use all the available data and user history to provide specific next best steps help the user efficiently work the account.

Just like Waze takes real-time data from drivers, the recommendation engine could be further enhanced by crowdsourcing, gathering data from revenue cycle shops across the country and getting smarter every day.

A digital twin is a virtual representation of a machine, system, or other complex organism that exists in real life. Think of it like a simulated wind turbine in a computer program. You can run it through different kinds of environmental or mechanical break downs and make real-time design changes without costly real-world experiments.

In other words, digital twins are complete, virtual representations of all the actions and sequences of actions taken by a human agent performing a job. In the revenue cycle world, this means curating and combing through all the data signals that are created by a human worker, as well as signals that are coming from third-party systems like payer remits, to create a perfect representation of what the human is doing to a given encounter record.

Some might say that creating such system is unnecessary. After all, most systems have some form of bot automation. That should solve the problem just as well, right?

Automation and bots can be great for productivity, as once online they work endlessly and never skip a step. But bots have to be methodically crafted to perform specific sets of tasks in a specific order, and they require continual maintenance. Turnover contributes to the problem, when the employees who depart are the ones who developed the business rules for the bot.

The next step then is to start to combine intelligent process automation with the centralized, ever-learning, ever-adapting recommendation engine. That recommendation engine should continuously breadcrumb what a worker is doing and even allow workers to add new recommendations to a knowledge repository. That knowledge repository should be connected to incoming data signals so the engine can show the right knowledge to the right person at the right time for a given piece of work the staff member is doing.

Using the recommendation engine enables the system to visualize the end-to-end revenue cycle process, allowing organizations to see where those recommendations and changes lead to better performance or not. The digital twin provides the data and analytics to help revenue cycle leaders prioritize the right work for their users, determine process inefficiencies, help define where best to apply bots, and help those bots change over time. More efficient revenue cycle operations benefit the organization overall because its focus can be placed on the core mission of delivering exceptional patient care.

Readers Write: Lessons Learned from the COVID-19 Pandemic: How Data Sharing is Improving Chronic Disease Outcomes

Lessons Learned from the COVID-19 Pandemic: How Data Sharing is Improving Chronic Disease Outcomes
By Brett Furst

Brett Furst is president of HHS Technology Group of Fort Lauderdale, FL.

Although the worst of the COVID-19 pandemic is likely behind us, many Americans living with chronic disease will feel its effects for years to come. That’s because chronic diseases such as heart disease, diabetes, chronic kidney disease, and obesity increase the risk for severe and lasting illness from COVID-19.

According to the Centers for Disease Control and Prevention, this improved risk matters, because chronic diseases represent seven of the top 10 causes of death in the United States and six in 10 Americans live with at least one chronic condition, such as heart disease, stroke, cancer, kidney disease, or diabetes. Chronic diseases also are the leading drivers of the nation’s $3.8 trillion annual healthcare costs.  

Among many lessons learned since its start, COVID-19 highlighted the need for health equity, as some patient populations were affected more severely than others. For example, African Americans, Hispanics, and Native Americans have a disproportionate burden of chronic disease, COVID-19 infection, hospitalization, and mortality, primarily due to challenges associated with social determinants of health. Even among the general population, healthcare utilization dropped during the pandemic, with a decline in screenings and subsequent diagnoses for diseases such as cancer. Delayed screening and treatment for breast and colorectal cancers alone could result in almost 10,000 preventable deaths in the US, according to the CDC.

The lasting impact that COVID-19 has on individuals living with chronic disease, and the entire healthcare system, underscores the many lessons we can learn from the pandemic and the need for improved data sharing across all stakeholders. For example, researchers have yet to know the extent to which COVID-19 exacerbates chronic disease, causes chronic illness, or will be determined to be a chronic disease. Although long-term studies and longitudinal surveillance will help clarify these questions, much research remains.

The COVID-19 Research Database (RDB) is a leading industry example of how collaboration and improved access to patient ecosystems can accelerate innovation and understanding, improving immediate and future cost and quality outcomes. Several organizations were led by RDB to accelerate real-world pandemic research, knowledge of condition identification and treatment, and evidence-based healthcare policy.

With 85 billion HIPAA-compliant, patient-level records, RDB enables public health and policy researchers to access real-world data to understand better and combat the COVID-19 pandemic and future health-related events. The RDB provides a standard data schema that allows researchers to access linkable data sets — including claims, electronic health records, and consumer data — and has powered over 70 publications and presentations addressing the direct and indirect effects of the COVID-19 pandemic on population health.

Among the publications and studies resulting from the RDB is the publication of research in Nature Medicine examining the impact of COVID-19 infection on risk for neurological disorders and a separate study published in the Journal of Alzheimer’s Disease that showed a substantially higher risk for older adults in developing Alzheimer’s disease within a year of contracting COVID-19.

Accurate, comprehensive, real-world data represents the healthcare industry’s straightest path toward developing a deeper understanding of the connection between COVID-19, chronic disease, and population health. Data sharing and collaboration provide researchers, providers, and healthcare organizations with the keys to actionable insights, data-driven decision-making, and accelerated innovation related to critical issues like improving health equity and driving healthcare cost and quality outcomes across populations.

Readers Write: Applying AI to Improve Patient Care

Applying AI to Improve Patient Care
By Tomas Gogar

Tomas Gogar, MS is co-founder and CEO of Rossum of London, England.

Despite the technological advancements in healthcare over the past decade, the administration and quality of patient care has not kept pace. The industry is faced with the realization that if technological changes aren’t implemented at a foundational level, providers, payers, and patients won’t be able to realize the full value of the technology available to them.

The majority of medical institutions rely on electronic health records (EHR) to input, read, and upload critical documents related to patient care into online portals. The EHR concept, introduced in the 1960s, while valuable to the healthcare community, has yet to eliminate the need for manual paperwork. Paperwork is a huge drain and cost, taking time, energy, and precise attention to detail to ensure that all documents are properly scanned into the correct patient files.

Missing information can lead to delays in care, misdiagnosis, miscommunication around treatment plans, and the duplication of costly tests and procedures. Relying strictly on manual processes to manage such large amounts of information can be administratively crippling to a healthcare organization. The World Health Organization estimates that up to 50% of all medical documentation mistakes result from administrative errors.

By integrating intelligent document processing (IDP) into the systems, hospitals and healthcare institutions save time, reduce operational costs, and improve workflows. Introducing an IDP system into the EHR workflow means medical professionals across departments can easily scan and upload documentation into a secure SOC 2 and HIPAA compliant operating system. IDP efficiently captures, categorizes, extracts, and classifies data from documents, streamlining the workflow process and reducing the paperwork necessary for a patient file.

IDP also helps sustain HIPAA compliance, which can be challenging when dealing with thousands of physical documents stored in different formats and locations across a health system. Accounting for small margins for human error causes long input times and exhaustive efforts to safeguard physical documents containing patient information. With the implementation of IDP, this process eliminates any chance of human error in handling sensitive information and allows for patient data to be processed quickly, safely, and securely.

From a patient perspective, automating and streamlining document processing enables providers to get complete, accurate data straight into a patient’s hands via online portals. From the healthcare organization side, IDP can reduce document burnout that healthcare professionals are prone to experiencing.

For hospitals struggling with overhead operational costs, implementing IDP is a lucrative resource. By using IDP to process documents like prescription referrals, lab records, billing, and claims forms, manual data entry is drastically reduced, thereby reducing the need for resources associated with data entry into EHR and patient portals and enabling the healthcare organization to re-allocate them to more strategic tasks. In addition to labor costs, implementing IDP reduces costs associated with paper storage, security measures in place to store these documents, and any costs associated with administrative errors.

During a time when all our hospitals are critically understaffed and underfunded, ensuring that every worker is given the necessary tools and resources to adequately and efficiently perform their jobs is more crucial than ever.

Readers Write: Thinking Differently About OR Block Time

Thinking Differently About OR Block Time
By Michael Burke

Michael Burke, MBA is founder and CEO of Copient Health of Atlanta, GA.

The operating room is the hospital’s largest source of earnings, as well as the largest hospital cost category. Most OR time is allocated in advance to surgeons in chunks of time called blocks. Surgeons schedule cases into their allocated block time, such as Tuesdays from 7 a.m. to 3 p.m.

Block time often goes unfilled due to poor allocation decisions, case volume that can vary meaningfully from week to week, and surgeons neglecting to release block time when traveling or otherwise unavailable to use it. Often, OR time that sits empty can be filled with elective cases that have an average contribution margin of $2,000 per OR hour. Instead, the fixed costs from unused OR hours add up with no revenue to offset them.

Identifying block time that would otherwise go unfilled, getting it released, then refilling the time is something hospitals have attempted to do for quite a while. The process has been largely manual and has missed a meaningful portion of the opportunity, as evidenced by block utilization statistics.

New tools use machine learning to predict block time that is likely to go unfilled, along with mechanisms for seeking the release of the identified time and requesting the time. Finding more time, getting it released earlier, and getting it into the hands of those who can use it are all excellent reasons for adopting such a solution. Hospitals can make real gains with this approach. The core of the strategy is that any block time that would otherwise go unfilled should be filled with positive contribution margin cases whenever possible.

Surgeons are hesitant to release block time allocated to them, even if they don’t have cases to fill it. In most compensation scenarios, a surgeon has a financial incentive to hold on to any OR time allocated to them in the event that a case might come along later. Even if they are an equity holder in an ASC and benefit from facility earnings shared as dividends, they are still subject to a form of the prisoner’s dilemma. This affects their decision-making and can bias them against releasing allocated block time for which they don’t have cases to fill. Although some portion of unused time is collected from surgeons by proactive nudge reminders and the ad hoc efforts of the scheduling team, diverging incentives unnecessarily limit the amount of time that can be recaptured and repurposed.

In many ways, the math behind the predictions is the easy part. The difficulty lies in aligning incentives and driving changes in behavior. The structure of your incentives and your willingness to push will have as much or more impact on the success of an OR optimization effort as the predictive software you select. Maybe we should also consider taking lessons from other industries dealing with similar scarce resource challenges.

What if we thought of a hospital as an airline and an OR block day as a flight? Travelers or travel agents (schedulers) book seats on the plane (cases in the OR). However, from its predictive analytics, the airline knows that some seats will go unfilled, even if booked to capacity. The OR block appears to be booked to capacity in much the same way  since 100% of the block’s time is allocated to the block holder.

But we know the block holder won’t fill all the allocated time, just like the airline knows that without intervention, many more seats on the plane would go empty due to no-shows or missed connections. The airline uses predictive analytics to intentionally and confidently overbook the flight to account for this.

The hospital should consider a similar process because the block holder often won’t fill an entire block with cases. To be clear, you wouldn’t be overbooking, since the chunks of time into which you would book cases are empty and predicted to remain so. The math behind the predictions for an OR is different from that of an airline flight, but the analogy still applies. By adopting this strategy, hospitals could fill much more time in their OR blocks with a high degree of certainty that the block holder won’t need it. This approach bypasses the behavioral challenge of seeking permission from the block holder early enough for the unneeded time to be usable, resulting in more recaptured OR time and more contribution margin.

Readers Write: Five Lessons from the Five Years Since the EClinicalWorks Settlement

Five Lessons from the Five Years Since the EClinicalWorks Settlement
By Colette Matzzie, JD

Colette Matzzie, JD is an attorney and partner with Phillips & Cohen, LLP of Washington, DC.

The June 2017 announcement by the Department of Justice of a $155 million settlement with EClinicalWorks for alleged misrepresentation of the capabilities of its electronic medical record software heralded the start of a new area for health fraud enforcement. Both DOJ and the HHS – Office of Inspector General announced that investigations of alleged fraud involving electronic health records systems would be a top enforcement priority. Enforcement has continued at a steady clip, with DOJ bringing actions against six additional electronic health records vendors. There is every reason to think more will be forthcoming.

Most actions have been initiated by whistleblowers using the False Claims Act, but, at least two actions, including one resulting in a $145 million settlement, were initiated by the government.

Five lessons can be drawn from this period of robust enforcement.

DOJ and HHS-OIG have made good on their promise to investigate allegations of fraud in the development and implementation of electronic health records.

Since June 2017, five settlements and one additional intervention have been announced:

• February 2019 settlement with Greenway for $57.25 million.
• January 2020 settlement with Practice Fusion for $145 million.
• August 2020 settlement with Konica Minolta for $500,000.
• January 2021 settlement with Athenahealth for $18.25 million.
• April 2021 settlement with CareCloud for $3.8 million.
• March 2022 intervention in a pending qui tam against Modernizing Medicine.

The US Attorney in Vermont has led the way, but with US attorneys in Northern Georgia, Northern California, New Jersey, Southern Florida, and Massachusetts joining in. Five of the cases were initiated by whistleblowers. Three settlements (EClinicalWorks, Greenway, and Practice Fusion) required Corporate Integrity Agreements (CIAs) with OIG with ongoing federal oversight of software development, relationships with customers, and financial arrangements.

Financial relationships between electronic medical record companies and providers have been a major enforcement focus.

All but one settlement allege violations of the federal Anti-Kickback statute, which prohibits the payment of remuneration to induce referrals for items or services paid for by federal health programs. For example, DOJ alleged that CareCloud provided customers with credits, cash bonuses, and other payments to recommend the software and not to say anything negative. We can expect vigorous enforcement of the Anti-Kickback statute for health IT vendors where federal payments, whether under the Meaningful Use or Promoting Interoperability programs or otherwise, provide the necessary federal funding hook for allegations.

Kickbacks paid to EMR vendors by pharmaceutical companies and other third-party medical providers to influence clinical decisions are also ripe for enforcement.

Of major significance is the January 2020 resolution of criminal and civil charges with Practice Fusion for soliciting and receiving kickbacks from a major opioid company for utilizing its EMR to influence physician prescribing of opioid pain medication. Clinical decision support is an essential requirement for EMRs to deliver their promise of evidence-based clinical care. The Practice Fusion settlement brought scrutiny on EHRs leveraging their power to influence clinical decisions and extracting payments from pharmaceutical companies to implement CDS tools to increase prescribing of the sponsor’s drugs. This practice threatens to undermine the promise of EMRs to improve patient health in favor of profits for the EHR vendor.

Individual accountability has been an important feature of EMR enforcement actions.

DOJ’s interest in holding individuals accountable for corporate wrongdoing has peaked in the last five years and can be seen in a wide variety of industries. No less with EMR enforcement, DOJ has held accountable individuals for their participation in alleged misconduct involving EMR software. In EClinicalWorks, three of the company founders were held jointly and severally liable for payment of nearly $155 million, with three others responsible for smaller payments for their role. Health IT companies can expect continued scrutiny of the knowing decisions of individuals.

Future enforcement actions will include recovery of funds spent as part of the Merit-Based Incentive Payment System or MIPS.

Damages in the EClinicalWorks settlement recovered payments made under the Meaningful Use program. But recent settlements have also referenced recovery of payments under MIPS. There is every reason to think that DOJ will continue to seek recoupment from vendors of the portion of payments allocated for compliance with Promoting Interoperability requirements. Likewise, one should anticipate that DOJ and OIG will turn to enforcement of the Cures Act, including compliance with interoperability and information blocking mandates.

Readers Write: The Clinical Dilemma at the Tipping Point – How We All Can Drive Transformation in Healthcare

The Clinical Dilemma at the Tipping Point – How We All Can Drive Transformation in Healthcare
By Ted Ottenheimer

Ted Ottenheimer is VP of clinical data transformation for Ascom Americas of Morrisville, NC.

I have read countless articles regarding the shortage of staff in healthcare. I have experienced it myself in the pre-hospital EMS (emergency medical services) setting. So much of what I read provides a great depiction of the situation in which we find ourselves, yet few of them offer solutions. If you’re a nurse, administrator, leader, institution, or anyone interested in the healthcare field, I’m sharing my perspective here on how to be part of the change.

When I first left the military, I was looking for a nursing school to expand my career. The one I had intended to apply to, hosted right in the hospital, was closing its final year of the program. I was left to search for a college that I could attend in a traditional manner. As a single father, this posed a challenge, as I had to continue to maintain gainful employment.Hence, I entered the workforce. 

From time to time, I would look for that nursing opportunity. I finally found it two years ago in a program that runs on evenings and weekends with manageable clinical time during the standard work week. I wish I was 20 years younger, but I believe you are never too old to pursue your dreams.

Now is the time to invest in the programs necessary to continue turning out the critical staffing that the ever-changing healthcare industry is demanding – nursing aides, LPNs, RNs, RTs, and so on. What if these programs have more to offer for non-traditional students? What if programs are brought back into the health system? Partnerships between hospitals and higher learning can be successful, although I understand the prestige associated with being able to attend these opportunities for higher learning is a fierce battle of minds in which only the highest aptitude may attend. However, capacity continues to be an issue to provide the necessary staffing, and we need to think creatively to solve today’s challenges. 

To make a change requires a significant amount of effort and the ability to think outside the box. Let’s look at an example of a way that we made a change. The minimum provider level to staff an ambulance is an emergency medical technician – basic. There is an aide position that requires less training called an emergency medical responder. After years of work by some determined individuals, they were able to incorporate this into the local high school curriculum. The intention is to engage the students in assisting our local community. This is similar to having a CNA (certified nurse’s aide) program in high school or vocational / technical school. Both examples are great options to engage at an early age with hopes of pursuing a career in the healthcare field. It amazes me how many doctors and nurses I have spoken with whom have been trained in EMS, which drove them to continue in healthcare.

We see that there are policies in place for continuing education in nearly all of the health systems. Are you seeking out the employees with potential? I suspect with the current burnout rate it is difficult to think of continuing education. However, helping build one’s career is always rewarding in both directions. I will always remember those leaders who have taken the time for me and encouraged me to work towards improvement.

My last point is adopting technology. Clinical staff are caring for more patients than ever before. As the workload increases, the cognitive load grows as well. This situation will not diminish any time soon.  Technology can assist in capturing routine clerical entries, alerting clinicians to actionable patient events, provide collaboration tools, and clinical decision support that can reduce the burden on staff. It will reduce the negative outcomes we all worry about and want to avoid.  Engage the clinicians early in the process and watch them become the champions for you.  Take some time to see what is most important to them by reading our recent report, “Nursing Satisfaction: What Matters Most At Work.”

These are some simple concepts that can be the change that is so desperately needed in healthcare today.

Readers Write: Taking Clinical Natural Language Processing Mainstream for Effective Care Management

Taking Clinical Natural Language Processing Mainstream for Effective Care Management
By Kevin Agatstein

Kevin Agatstein is CEO of Kaid Health of Boston.

Across healthcare, clinical natural language processing continues to play an ever more influential role. Kreimeyer et al.’s “Natural language processing systems for capturing and standardizing unstructured clinical information” identified over 70 different CNLP systems in the literature, spanning multiple clinical domains. Unfortunately, few of these directly address the applicability of CNLP to care management. This lack of CNLP supporting care managers will and should change. Making this reality will require adapting the technology to the real-world needs of care management programs and the front-line clinicians who staff them. 

To fuel effective care management, actionable data is required across the entire workflow. Examples of actionable data include information that: 

• Identifies which patients require help.
• Stratifies patients for intervention.
• Summarizes the patient for the care manager.
• Determines the specific actions the patient needs.
• Uncovers the barriers to effective care.
• Measures intervention outcomes.

Claims data, lab data, health risk assessments, and motivational interviewing all meaningfully contribute to the above. While all of these are necessary, alas, they are not sufficient. For the care manager to meaningfully coordinate patient care, to accomplish the six steps listed above, they must have access to actual clinical data. They need the medical record. More precisely, they needs the nuggets of actionable insights buried in the massive EHR data set. Finally, they need it to be quickly digestible. Thus, CNLP can not only help, it is all but required.

This actionable data is almost all in the EHR; however, it can be hard to find. A patient’s medical record is often hundreds of pages of text, alongside hundreds of discrete data points (labs, medications, allergies, etc.) Within this morass of usually loosely organized data is the patient’s health history. While claims and labs can give some sense of the patient’s clinical experience, the chart has the diagnosed but not coded conditions, the written but not filled prescriptions, and more. It also has a plethora of exam findings, laboratory reports, radiologic data, and pathology findings that never get put into “structured” EHR fields.

Kharrazi et al., in “The Value of Unstructured Electronic Health Record Data in Geriatric Syndrome Case Identification,” found that the EHR text resulted in finding 1.5 times more patients with dementia than just reviewing the structured EHR data. That same ratio was 1.7 with decubitus ulcers, 2.9 for weight loss, and 3.2 for a history of falling.

Beyond traditional clinical data, the chart often contains insights into the patient’s family health history. It also has data on psychosocial barriers to care, limitations on activities of daily living, and other elements impacting the patient’s care journey. Just as crucial for care managers, the chart typically has data on the patient’s social determinants of health. While SDOH are almost never coded in claims, (and yes, there are ICD-10 SDOH codes), they are noted in charts. AI-powered healthcare data analysis and provider engagement platforms have found hundreds of SDOH in primary care, specialists, ED, and behavioral health charts. Kharrazi found similar results. For example, they found that it is 456 times more likely to find a patient with a “lack of social support” in the free text of the medical note than in the structured data.

For a care manager to do their job well, this data cannot be ignored.

More than just summarizing the patient’s health, the medical record can help translate the EHR text into a structured, actionable, trackable ambulatory care plan by summarizing the physician’s treatment plan noted for each encounter. Specifically, NLP can create a patient to-do list such as follow-up visits, getting testing or labs, addressing unhealthy behaviors, and more. These identified tasks can become the basis of a care management care plan or added to existing plans. As new data enters the chart, either as structured information or new medical notes, the to-do list can be updated. Tasks can be marked as completed, new tasks added, existing tasks amended, and more.

It’s important to remember that NLP algorithms do not digest a medical note the way a human does. Rather, they predict how a trained human would interpret the presented text. This is much more than finding key words. CNLP solutions also need to account for:

• Negation (“does not have cancer”).
• Family history (“the patient’s mother had an MI before age 55”).
• Uncertainty, (e.g., “initial lab findings mean early-stage chronic kidney disease possible, but additional testing is needed”).
• And more.

Making such determinations isn’t perfect, but making useful interpretations of clinical text has been proven possible. Moreover, CNLP does not fatigue as humans do. For example, Suh, et. al. found in “Identification of Preanesthetic History Elements by a Natural Language Processing Engine” that CNLP frequently identified salient clinical facts that a physician reviewer missed. 

Now, new data standards, notably FHIR, and regulatory mandates to share data combine to markedly simplify a CNLP deployment process. This, plus cloud and other emerging data exchange standards, mean CNLP go-lives can be measured in days, not months. By working with partners with rigid technological and workflow controls, extensive security training, and a culture of data security, the data can be processed safely as well.

For a real-world deployment, a care management CNLP solution should be intuitive to clinicians. It should be focused on the needs of care managers to anticipate the workflow. Care managers today deal with several different medical record and care management documentation systems. Effectively managing these variations, and the vagaries of existing workflows, comes only with experience. Most importantly, CNLP needs to add value for the user practically out of the box. They can, and they will.

Readers Write: Diagnostically Connected Data – The Key to EHR Clinical Usability

Diagnostically Connected Data: The Key to EHR Clinical Usability
By Dave Lareau

Dave Lareau is CEO of Medicomp Systems of Chantilly, VA.

Clinicians are among the most highly trained knowledge workers in any industry, yet the systems they use to care for patients often actually hinder their ability to deliver care. We hear anecdotes from patients about clinicians spending inordinate amounts of time trying to find information in their EHRs – only to often give up rather than search through other sections of the chart to find a lab result, view past encounter notes, or try to correlate medications with problems or the course of a condition.

EHRs require users to spend too much time searching for clinically relevant information for the patient they are treating and, once that information is located, to go through a series of disconnected processes to complete their work.

This situation will only get worse once the floodgates of healthcare data interoperability are opened. Then, it will be even more challenging for clinical users to find what they need.

Consider the bright side of this data-driven conundrum: The effects of the 21^st Century Cures Act and TEFCA will make it easier for HIT systems to send and receive information. Plus, emerging terminology standards and the use of common codes such as ICD-10, SNOMED, LOINC, RxNorm, CPT, DSM5, CTCAE, UNII, CVX, and others will provide a basis for what is often called “semantic interoperability.” And today, the performance of natural language processing is getting more consistent and reliable, providing a means to convert free-text notes that use those same terminologies and codes.

So, does that mean that more coded data is a good thing?

Not necessarily – that is, unless clinicians can readily locate the information they need to assess, evaluate, treat, and manage a given patient and their clinical problems. With the widespread adoption of risk-based reimbursement through Medicare Advantage and similar programs sharpening the focus on chronic condition management, it will be increasingly crucial for clinicians to see a diagnostically focused view for each patient along with their medical problems. They need instant access to this view, without searching through disparate sections of the EHR.

Semantic interoperability facilitated by standard terminologies and code sets is a great start – and is necessary for sharing clinical information between systems. It will also drive better analytics and population health insights. But it will not make it easier for clinicians to find the data they need for the patient at the point of care (whether that patient is in-office or on a screen.)

Most existing EHRs, and the terminologies and codes for semantic interoperability, are structured in distinct “domains.” In an EHR, this typically shows up as separate sections or tabs – problem list, medication list, laboratory orders and results, procedures, encounter notes, discharge summaries, etc. Problems, meanwhile, have ICD-10 and SNOMED codes, labs have CPT and LOINC codes, medications have RxNorm or NDC codes, and other domains use other code sets. These codes were designed for their specific domain. They were not designed to work together for the clinical user.

The key to usability is to link these to the problem list, so that the user can click on a problem and immediately view the related medications, labs, procedures, therapies, co-morbidities, and findings from encounter notes that all are related to the problem. This diagnostically filtered presentation could be viewed longitudinally and supported by millions of mappings from standard terminologies and code sets.

Such a unique diagnostic relevancy engine would provide both the semantic – and diagnostic – interoperability that enables clinicians to not only see what they need at the point of care, but also to harness the flood of interoperability-driven data that will soon complicate their work.