Tag Archives: doctor

It’s Time to Embrace Telemedicine

At hospitals and clinics across New Jersey, thousands of new doctors could soon be on call — literally.

In Trenton, lawmakers are considering two bills that would enable doctors and patients to skip the office visit and conduct appointments using video-conferencing tools like Skype.

They’re right to embrace this kind of technology. The increasing use of “telemedicine” promises to improve patients’ access to doctors and slash healthcare costs.

Virtual medicine makes it a lot easier — and cheaper — to see the doctor. By first consulting with a patient by video, doctors and nurses can determine whether a costly in-person trip to the emergency room or to the doctor’s office is necessary — or whether two aspirin and plenty of rest will do.

See Also: 5 Questions on Telemedicine Coverage

For patients who end up in the hospital, telemedicine can facilitate faster and cheaper convalescence.

Consider a patient recovering from heart surgery. His doctor may want to continuously monitor his blood pressure and pulse. Telemedicine can accomplish that remotely and automatically. That saves the patient the trip and the doctor the time measuring those vital signs.

Telemedicine can also save money. Take a program called Health Buddy, which asks patients daily, tailored questions about their health through a handheld device at home. After reviewing the answers, doctors know when and how to offer care. A study published in Health Affairs found that Health Buddy reduced Medicare spending by as much as 13% per patient.

Other programs offer patients hospital-level care inside their own homes. Doctors and nurses visit one to two times a day while other providers monitor vital signs remotely. Participating patients often require fewer tests and less time under observation, so these “hospital at home” programs can cut costs by 19% compared with conventional inpatient care.

Telemedicine can also alleviate the mental stress of being sick. Someone diagnosed with heart disease, for instance, may understandably worry about his prognosis. That can take a toll on his physical health and jeopardize his chances of recovery.

Healthcare providers can ease these concerns with remote counseling. One such telecounseling program helped cardiovascular disease patients deal with anxiety and depression through video sessions. Over six months, the program reduced hospital admissions by 38% compared with a control group, according to a report published by the American Journal of Managed Care.

Telemedicine can improve healthcare providers’ ability to communicate with one another, too. By connecting doctors with health workers in emergency rooms, for example, telemedicine can prevent 850,000 unnecessary transfers between ERs each year. The savings? More than $530 million.

There’s even evidence that telemedicine can offer care that’s superior to inpatient care. Take Teladoc, a videoconferencing technology that allows patients to consult with a doctor around the clock. According to one study, those who used Teladoc were less likely to need to see the doctor again for the same illness than patients who actually went to the doctor’s office.

Finally, telemedicine may also decrease wait times. American Well, for example, offers a mobile app that allows patients to send out a request for a doctor — much like one does for an Uber — and the first to respond does the consultation via videoconferencing. Over the last three years, the average wait time has been three minutes.

See Also: Questions to Ask on Telemedicine Risk

New Jersey’s lawmakers seem to be paying attention to all this research, particularly Sens. Joe Vitale, D-Middlesex, and Shirley Turner, D-Mercer, and Assembly representatives Pamela Lampitt, D-Burlington-Camden, and Daniel Benson, D-Mercer-Middlesex. One of Lampitt’s bills (A-2668) would establish parity for insurance coverage of telemedicine with conventional in-patient care. A bill sponsored by Vitale (S-291) would allow patients to seek telemedicine services from out-of-state doctors. This latter measure would also permit New Jersey’s Medicaid program to reimburse for telemedicine.

Thus far, the Garden State has been slow to adopt telemedicine. Insurers in many other states already cover it. The American Telemedicine Association recently gave New Jersey six Fs on crucial telemedicine issues, including allowing for the reimbursement of remote patient monitoring and videoconferencing.

State leaders now have the chance to raise those grades. Telemedicine controls costs and improves patients’ health. It’s time for New Jersey to take advantage.

An Open Letter on the Oklahoma Option

I’m the founder and CEO of WorkersCompensationOptions.com (WCO), a company dedicated to workers’ compensation (WC) and its legal alternatives. This letter is intended to quell the concerns of employees in our client companies—employees who may have been distressed by the recent (mostly negative) publicity from ProPublica and NPR regarding options to WC in Texas and Oklahoma.

In case you only saw one installment from the Insult to Injury series, I’ll provide a quick summary. In 2014, the project’s authors started to assimilate massive amounts of data from their research concerning each state’s (and the federal government’s) WC system. In March 2015, the authors began releasing articles with an indisputable premise: Collectively, these systems need improvement.

That commendable beginning eventually gave way, however, to a hypothesis that is supported neither by reality nor by the overwhelming quantity of data the authors provide. Their conclusion (that employers are in cahoots with insurers to pressure attorneys, anonymous doctors and legislators into discarding the lives of an unfortunate few for the sake of bolstering corporate profits) completely misses the mark in pinpointing why so many WC systems are broken beyond repair. In fact, attorneys and doctors put at least as much pressure on WC systems as insurers, and any attempt to depict the medical and legal communities as innocent bystanders in the WC feud is simply too naive to be taken seriously.[1] I do not doubt the authors’ sincerity in addressing a serious societal problem, but I also do not believe they are equipped to understand the problem they sought—however earnestly—to demystify for their readers. Worse yet, I fear they have positioned themselves in the WC space in a manner that is only likely to retard the implementation of practical solutions.

This letter is prompted by the article on Oct. 14, 2015, which painted an inaccurate—even an irresponsible—picture of both Texas nonsubscription (TXNS) and the Oklahoma option (OKO). As that article’s title (“Inside Corporate America’s Campaign to Ditch Workers’ Comp”) is lengthy, I’ll shorten it to CDWC going forward.

Texas Nonsubscribing Employees: What Can We Learn?

Texas is exceptional in the WC world because it has, for more than a century, offered employers a viable alternative to WC. Of approximately 380,000 employers in Texas, roughly two-thirds subscribe to a traditional WC system; the other third are nonsubscribers who develop their own models. That’s about 120,000 different systems, and there is plenty to be learned. We’ve seen various organically grown components develop from these disparate systems, many of which superficially resemble WC. Despite those similarities, however, industry experts understand how counterproductive it is to make unilateral comparisons between TXNS and WC.

The authors of CDWC didn’t get that memo.

Of all the various lessons learned from diverse TXNS models, one runs counter to conventional WC dogma: Employers can protect themselves while delivering superior care for employees at a fraction of the cost of WC. Eliminating the inflated costs associated with abusive practices that run rampant in WC is a critical component of that particular lesson.

Because the CDWC authors insist on judging TXNS through the lens of WC, TXNS looks to them like a system that would appeal to skinflint employers who simply do not care whether their employees get hurt. However, because employees of nonsubscribing companies can sue their employers for tort, the decision to opt out of WC is likely to be penny-wise and pound-foolish for employers who do not take measures to ensure the safety of employees. The CDWC authors’ failure to unpack the importance of tort negligence means many readers will come away from the article without understanding that a typical $50,000 payout in WC could easily be either $0 or $5 million in TXNS—depending on who is at fault for the accident. Even more disappointing is CDWC’s attempt, in a one-sentence paragraph, to gloss over one of WC’s most dangerous shortcomings: the extent to which the no-fault arrangement between employers and employees has removed incentives for safety in the workplace throughout the country.

If you are an employee of one of our Texas nonsubscribers, rest assured that your employer has every reason to minimize workplace accidents and to take very good care of you if an occupational injury occurs.

In a nutshell, your interests are aligned with your employer’s—another critical lesson we’ve learned from TXNS.

Oklahoma Option Employees: A Whack-a-Mole WC System Led You Here

ProPublica and NPR harp on a consistent theme throughout the Insult to Injury series: WC is broken. We at WCO agree, and Oklahoma may provide the single best example of how and why a state’s WC system becomes unsustainable.

The WC ecosystem is made up of five major communities: insurance, medical, legal, employer and employee. Abuse within the system by any of these communities leads to adjustments to the boundaries of the system. Throughout the Insult to Injury series, the authors go out of their way to sidestep the discussion of systemic abuse. They even attempt to dismiss fraud by citing a study that minimizes its role. Abuse and fraud in WC are, in some ways, analogous to speeding on the highway: Almost all drivers abuse the speed limit, but very few are issued citations. Similarly, the cases of clear-cut fraud in WC only reflect a small portion of the amount of abuse going on. But even if we allow the authors to exclude all instances of clear-cut fraud from the WC conversation, we are still left with rampant abuse driven by insidious systemic incentives.

For decades, abuses and inefficiencies within the WC system have led to each of the five communities touting the need for major reforms—at the others’ expense. Real reform threatens each community, which leads to stalemates in negotiations. Major upheaval has been avoided via the compromise of pushing and pulling the system’s boundaries, resulting in a decades-long game of whack-a-mole being played across the nation. If one voice cries, “Data shows an alarming trend in opioid abuse,” that mole gets swatted by requiring more medical credentials for prescribing pain killers. When another shrieks, “Overutilization is surging,” that mole is whacked through costly and time-consuming independent medical examinations. When someone else observes, “Our disability payouts are higher than neighboring jurisdictions,” that mole prompts us to lower disability payouts. Immediately, a fourth voice shouts, “Pharmaceutical abuses make up 8.4% of total costs,” and that mole persuades us to introduce drug formularies. But there isn’t even a moment of silence before another voice remarks, “Our analysis shows dismemberment payouts in this jurisdiction are lower than those of our neighboring jurisdiction.” That mole gets whacked by proposing legislation to increase dismemberment payouts—legislation that is dead on arrival.[2] At some point, we have to realize the moles are multiplying faster than we can whack them. (If my commentary doesn’t apply to other jurisdictions, I’m happy to restrict it to Oklahoma and Texas because writers can best serve their readers by acknowledging the limitations of their own expertise.)

Even if we concede that the changes detailed in the paragraph above aren’t necessarily bad (which I’m not conceding; I’m just trying to be polite and move the argument along), they demonstrate a persistent pattern of outcomes, inclusive of abuse, inherent in any hierarchical bureaucratic system. Regulators are busy reacting to entrenched abuses while market participants find new and exciting ways to game the system. This futile game of whack-a-mole is endless.

The Sooner State had a front row seat to witness what TXNS accomplished—both the good and the bad.[3] With that first-hand knowledge, the Oklahoma legislature has finally provided the state—and the country—with an opportunity to see whether real change can restore function to a malfunctioning system. While WC stakeholders assure us they are only a few more whacks-at-the-mole away from making WC hum, Oklahoma lawmakers have written a new chapter in the history of workplace accident legislation. The OKO is neither WC nor TXNS.

The brilliance of the OKO is that it doesn’t attempt to overhaul a broken WC system. The legislators effectively stepped away from that decades-old stalemate. Instead of an all-out overthrow, they left WC in place and created an option for employers who were willing to try something new—which is exactly how WC itself was introduced a century ago.

Because the OKO is substantially modeled on TXNS, it is easy to see why the CDWC writers conflated the two in their analysis. The errors in CDWC concerning ERISA’s applicability, employee benefits and appeals committee processes in Oklahoma are all presumably honest mistakes made by writers who, in their zeal to distinguish TXNS and the OKO from WC, failed to distinguish TXNS and the OKO from each other.

Nevertheless, it’s important for employees to understand that TXNS varies dramatically from one employer to another, and many of the rules concerning TXNS do not apply north of the Red River.

Although the CDWC authors misleadingly couple TXNS and the OKO with respect to ERISA’s applicability, ERISA plays no direct role in occupational accidents in the OKO.[4] We’ll be happy to get you a legal opinion on that, but for our purposes regarding CDWC, take my non-legal opinion as on the record. If others disagree, they should go on the record, as well. While ERISA has served employers and employees well in TXNS, its role in the OKO is only implied (if that). We are free to use it where we wish, as long as we are compliant at the state level.

Presumably tied to their ERISA misapplication, the CDWC authors assert that “benefits under opt-out plans are subject to income and payroll taxes.” Such tax advice is unusual from investigative journalists without citation, and I have asked the authors to share their source. Although the jury is still out on this tax issue, it is a point the CDWC authors must distort to substantiate their otherwise baffling claim that the workplace accident plans of OKO employers “almost universally have lower benefits.”[5] If any OKO plans really do offer benefits that aren’t at least as good as those provided by WC, they’re illegal. That’s how the legislators have written the law, and it’s what they’re dedicated to achieving for workers, regardless of obfuscations invoking TXNS, ERISA and unresolved tax implications.

The authors of CDWC also completely misrepresent appeals committees for at least a majority of OKO employers. The authors overlook a dramatic improvement to employee protection that the OKO makes to TXNS when they claim that appeals committees in Oklahoma work analogously to appeals committees in Texas: “Workers must accept whatever is offered or lose all benefits. If they wish to appeal, they can—to a committee set up by their employers.” That’s dead wrong. Executives at each of our OKO employers are fully aware that, in case of an employee appeal, the employer has nothing to do with the selection of the appeals committee panel members or the work they complete. The process is independent from the employer and extremely fair.[6] The CDWC authors would do well to read Section 211 of the law more carefully.

On the subject of benefit denials, I’ll share a single data point from our OKO book: To date, we have denied exactly one claim. This is a nascent system, so we must be very careful in drawing actuarial conclusions. Still, our company has led more employers from traditional WC into the new OKO than any other retailer, so we have a bit of credibility to offer on this subject. The point of the system isn’t to deny benefits to deserving employees but to ensure benefits are delivered more efficiently. The system is working.

The CDWC authors only provide one OKO case study, Rachel Jenkins. Strangely, they lump Jenkins in with four TXNS case studies. The Jenkins case is still being tried. We will withhold opinions—as we hope others would—until a more appropriate time.

As a reminder, while the OKO law is stronger today than ever, if it were to be deemed unconstitutional by the Oklahoma Supreme Court, we would have 90 days to get everyone back into traditional WC (per Section 213.B.4.).

Next: Vigilance and Diligence

My comments are mine and mine alone. I do not speak for any associations or lobbyists. I have no interest in debating those who inexplicably assume that any alternatives proposed to a failing system must stem from sinister motives. However, I encourage anyone (from prospective clients to employees of existing clients) with questions or concerns to call me.

Another option for learning more is to click here and watch a formal debate regarding the OKO. This footage was shot in September 2015. It features Michael Clingman arguing against the OKO while I, predictably, argue for it. One thing you can’t miss in that video is my desire to oust most attorneys from the scene. To help explain, I’ll adapt a quotation from John F. Kennedy (who was discussing taxation) to my own area of concern (the well-being of employees): “In short, it is a paradoxical truth that employee outcomes from increased WC protections are worse today, while economic results suffer, and the soundest way to create higher and better standards of living for employees is to eliminate these abused protections.” For philosopher kings, the theory of the OKO may not sound as good as the theory of WC, but when it comes to practical realities the results demand everyone’s attention.

To summarize my primary criticism of Insult to Injury, it simply hasn’t done enough. The story it tells is insufficient and smacks of partisanship and ideology, two biases that ProPublica’s journalists allegedly avoid. WC is substantially more complex than a corporation-out-to-exploit-its-workforce short story. Ignoring abuse in each of the communities in a five-sided WC debate demonstrates a lack of journalistic impartiality and a stunning deficiency of perception. Moreover, to my knowledge, ProPublica hasn’t crafted any relevant suggestions for legislation, simply leaving its readers with the vague and implicit notion that federal oversight is needed. If that is the goal of Insult to Injury—to provide one-sided, emotional yarns alongside a treasure trove of data, hoping it will all spur some federally elected officials to create real change at long last—then I suspect ProPublica will still be holding this subject up to the light of opprobrium upon the retirement of each of the series’ authors.

We do not aspire to win over the authors or even their followers. We will focus our energies each day on providing the best workplace accident programs for employers and employees alike. Our results should speak for themselves.

Finally, I am not an attorney, and nothing in this letter should be taken as legal advice.

Sincere regards,

Daryl Davis

Footnotes:

[1] With medical providers, overutilization is always a concern. Click here and watch the video from the 12-minute to the 15-minute mark for a detailed description of rampant WC abuse by surgeons who provide unnecessary and damaging back procedures. If the workers weren’t disabled prior to the surgeries, many were afterward. As for the legal community, simply view slide 73 of the NCCI’s 2013 Oklahoma Advisory Forum. WC disability payments, which is where attorneys get their cut, were 38% higher in Oklahoma than in neighboring states—not because jobs are 38% more dangerous in Oklahoma than in Kansas or Texas but because Oklahoma attorneys are 38% more effective at gaming the state’s WC system.

[2] Alabama SB 330—which was prompted by Insult to Injurynever got out of conference. From what I could gather, lengthy negotiations between several different interest groups led nowhere, with the Alabama Medical Association at the center of this particular stalemate. Not surprisingly, the two special sessions called by Alabama Gov. Bentley in 2015 were strictly focused on the state’s budgetary crisis; this bill was never discussed.

[3] The final Texas case study offered in CDWC deals with Billy Walker, who fell to his death while on the job. The upside to TXNS is his estate’s common law right to pursue a tort lawsuit against his employer. The employer could have been ordered to pay Walker’s estate a settlement in the millions, but the employer filed bankruptcy before any such judgment could be awarded, which is plainly an unacceptable outcome. This demonstrates a lack of surety—the single biggest problem in TXNS. OKO addresses this issue in various ways, most notably in Section 205 of Title 85A, which guarantees surety for injured workers.

[4] For the non-occupational components of your OKO program, ERISA does apply.

[5] Per Section 203.B. of the statute, compliant plans “shall provide for payment of the same forms of benefits included in the Administrative Workers’ Compensation Act for temporary total disability; temporary partial disability; permanent partial disability; vocational rehabilitation; permanent total disability; disfigurement; amputation or permanent total loss of use of a scheduled member; death; and medical benefits as a result of an occupational injury, on a no-fault basis, with the same statute of limitations, and with dollar, percentage and duration limits that are at least equal to or greater than the dollar, percentage and duration limits contained in Sections 45, 46 and 47 of this act.” (Emphasis mine.)

[6] Details of OKO appeals committee procedures are generally misunderstood—for now—by plaintiffs’ attorneys (and, apparently, investigative journalists). Attorneys frequently assume that, because the employer foots the bill, the employer controls the process. For a peek at how the appeals committee process really works for a majority of OKO employers, those curious should watch this video.

6 Technologies That Will Define 2016

Please join me for “Path to Transformation,” an event I am putting on May 10 and 11 at the Plug and Play accelerator in Silicon Valley in conjunction with Insurance Thought Leadership. The event will not only explore the sorts of technological breakthroughs I describe in this article but will explain how companies can test and absorb the technologies, in ways that then lead to startling (and highly profitable) innovation. My son and I have been teaching these events around the world, and I hope to see you in May. You can sign up here.

Over the past century, the price and performance of computing has been on an exponential curve. And, as futurist Ray Kurzweil observed, once any technology becomes an information technology, its development follows the same curve. So, we are seeing exponential advances in technologies such as sensors, networks, artificial intelligence and robotics. The convergence of these technologies is making amazing things possible.

Last year was the tipping point in the global adoption of the Internet, digital medical devices, blockchain, gene editing, drones and solar energy. This year will be the beginning of an even bigger revolution, one that will change the way we live, let us visit new worlds and lead us into a jobless future. However, with every good thing, there comes a bad; wonderful things will become possible, but with them we will create new problems for mankind.

Here are six of the technologies that will make the change happen.

1. Artificial intelligence

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There is merit to the criticism of AI—even though computers have beaten chess masters and Jeopardy players and have learned to talk to us and drive cars. AI such as Siri and Cortana is still imperfect and infuriating. Yes, those two systems crack jokes and tell us the weather, but they are nothing like the seductive digital assistant we saw in the movie “Her.” In the artificial-intelligence community, there is a common saying: “AI is whatever hasn’t been done yet.” People call this the “AI effect.” Skeptics discount the behavior of an artificial intelligence program by arguing that, rather than being real intelligence, it is just brute force computing and algorithms.

But this is about to change, to the point even the skeptics will say that AI has arrived. There have been major advances in “deep learning” neural networks, which learn by ingesting large amounts of data. IBM has taught its AI system, Watson, everything from cooking, to finance, to medicine and to Facebook. Google and Microsoft have made great strides in face recognition and human-like speech systems. AI-based face recognition, for example, has almost reached human capability. And IBM Watson can diagnose certain cancers better than any human doctor can.

With IBM Watson being made available to developers, Google open-sourcing its deep-learning AI software and Facebook releasing the designs of its specialized AI hardware, we can expect to see a broad variety of AI applications emerging because entrepreneurs all over the world are taking up the baton. AI will be wherever computers are, and it will seem human-like.

Fortunately, we don’t need to worry about superhuman AI yet; that is still a decade or two away.

2. Robots

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The 2015 DARPA Robotics Challenge required robots to navigate over an eight-task course that simulated a disaster zone. It was almost comical to see them moving at the speed of molasses, freezing up and falling over. Forget folding laundry and serving humans; these robots could hardly walk. While we heard some three years ago that Foxconn would replace a million workers with robots in its Chinese factories, it never did so.

Breakthroughs may, however, be at hand. To begin with, a new generation of robots is being introduced by companies—such as Switzerland’s ABB, Denmark’s Universal Robots, and Boston’s Rethink Robotics—robots dextrous enough to thread a needle and sensitive enough to work alongside humans. They can assemble circuits and pack boxes. We are at the cusp of the industrial-robot revolution.

Household robots are another matter. Household tasks may seem mundane, but they are incredibly difficult for machines to perform. Cleaning a room and folding laundry necessitate software algorithms that are more complex than those required to land a man on the moon. But there have been many breakthroughs of late, largely driven by AI, enabling robots to learn certain tasks by themselves and by teaching each other what they have learned. And with the open source robotic operating system (ROS), thousands of developers worldwide are getting close to perfecting the algorithms.

Don’t be surprised when robots start showing up in supermarkets and malls—and in our homes. Remember Rosie, the robotic housekeeper from the TV series “The Jetsons”?  I am expecting version No. 1 to begin shipping in the early 2020s.

3. Self-driving cars

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Once considered to be in the realm of science fiction, autonomous cars made big news in 2015. Google crossed the million-mile mark with its prototypes; Tesla began releasing functionality in its cars; and major car manufacturers announced their plans for robocars. These cars are coming, whether or not we are ready. And, just as the robots will, they will learn from each other—about the landscape of our roads and the bad habits of humans.

In the next year or two, we will see fully functional robocars being tested on our highways, and then they will take over our roads. Just as the horseless carriage threw horses off the roads, these cars will displace us humans. Because they won’t crash into each other as we humans do, the robocars won’t need the bumper bars or steel cages, so they will be more comfortable and lighter. Most will be electric. We also won’t have to worry about parking spots, because they will be able to drop us where we want to go to and pick us up when we are ready. We won’t even need to own our own cars, because transportation will be available on demand through our smartphones. Best of all, we won’t need speed limits, so distance will be less of a barrier—enabling us to leave the cities and suburbs.

4. Virtual reality and holodecks

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In March, Facebook announced the availability of its much-anticipated virtual reality headset, Oculus Rift. And Microsoft, Magic Leap and dozens of startups aren’t far behind with their new technologies. The early versions of these products will surely be expensive and clumsy and cause dizziness and other adverse reactions, but prices will fall, capabilities will increase and footprints will shrink as is the case with all exponential technologies. 2016 will mark the beginning of the virtual reality revolution.

Virtual reality will change how we learn and how we entertain ourselves. Our children’s education will become experiential, because they will be able to visit ancient Greece and journey within the human body. We will spend our lunchtimes touring far-off destinations and our evenings playing laser tag with friends who are thousands of miles away. And, rather than watching movies at IMAX theaters, we will be able to be part of the action, virtually in the back seat of every big-screen car chase.

5. Internet of Things

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Mark Zuckerberg recently announced plans to create his own artificially intelligent, voice-controlled butler to help run his life at home and at work. For this, he will need appliances that can talk to his digital butler: a connected home, office and car. These are all coming, as CES, the big consumer electronics tradeshow in Las Vegas, demonstrated. From showerheads that track how much water we’ve used, to toothbrushes that watch out for cavities, to refrigerators that order food that is running out, all these items are on their way.

Starting in 2016, everything will be be connected, including our homes and appliances, our cars, street lights and medical instruments. These will be sharing information with each other (perhaps even gossiping about us) and will introduce massive security risks as well as many efficiencies. We won’t have much choice because they will be standard features—just as are the cameras on our smart TVs that stare at us and the smartphones that listen to everything we say.

6. Space

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Rockets, satellites and spaceships were things that governments built. That is, until Elon Musk stepped into the ring in 2002 with his startup SpaceX. A decade later, he demonstrated the ability to dock a spacecraft with the International Space Station and return with cargo. A year later, he launched a commercial geostationary satellite. And then, in 2015, out of the blue, came another billionaire, Jeff Bezos, whose space company Blue Origin launched a rocket 100 kilometers into space and landed its booster within five feet of its launch pad. SpaceX achieved the feat a month later.

It took a space race in the 1960s between the U.S. and the USSR to even get man to the moon. For decades after this, little more happened, because there was no one for the U.S. to compete with. Now, thanks to technology costs falling so far that space exploration can be done for millions—rather than billions—of dollars and the raging egos of two billionaires, we will see the breakthroughs in space travel that we have been waiting for. Maybe there’ll be nothing beyond some rocket launches and a few competitive tweets between Musk and Bezos in 2016, but we will be closer to having colonies on Mars.

This surely is the most innovative period in human history, an era that will be remembered as the inflection point in exponential technologies that made the impossible possible.

The Worst Doctors From 2015

This list of worst doctors came to me via email, and I thought it was too good not to post. The origin of this is a Medscape article written by Lisa Pevtzow, Deborah Flapan, Fredy Perojo and Darbe Rotach. Please read the Medscape article in full. It’s a gem. The Medscape article shows pictures of these offenders.

Here is a summary of the worst doctors:

1) In July, Farid Fata, MD, was sentenced to 45 years in prison in Detroit for administering excessive or unnecessary chemotherapy to 543 patients. Some of them he deliberately misdiagnosed with cancer. In addition to enduring needless chemotherapy, the patients suffered anguish at the possibility of death. The massive criminal scheme netted at least $17 million from Medicare and private insurers.

2) Ophthalmologist David Ming Pon, MD, was found guilty in October of cheating Medicare by pretending to perform procedures on patients who did not need them. A federal jury convicted Dr. Pon on 20 counts of healthcare fraud. The scam netted Dr. Pon more than $7 million, according to the Department of Justice.

3) Joseph Mogan III, MD, was sentenced to about eight years in prison in March for operating two “pill mills” in suburban New Orleans. He gave out illegal prescriptions for narcotics and other controlled substances on a cash-and-carry basis. Dr. Mogan might have received a longer sentence had he not previously testified against a former New Orleans police officer who gave advice on how to operate under the radar of law enforcement. Prosecutors said the officer helped Dr. Mogan and his co-operator, Tiffany Miller, because Miller provided sexual favors and thousands of dollars in cash.

4) Dr. Aria Sabit pleaded guilty in a federal district court in Detroit in May to conspiring to receive kickbacks from a medical technology company. In 2010, Apex Medical Technologies, which distributes spinal surgery instruments, told the surgeon that, if he invested $5,000 in the company and used its hardware, he would share in the revenue. Ultimately, he received $439,000 from his investment. Dr. Sabit also pleaded guilty to stealing $11 million in insurance proceeds after billing Medicare, Medicaid and private insurers.

5) A Virginia jury awarded a patient $500,000 in June after an anesthesiologist made mocking and derogatory comments, which the patient accidentally recorded on a cellphone while he was sedated. The case inflamed the public after the Washington Post reported the story. The recording captured anesthesiologist Tiffany Ingham, MD, commenting on the patient’s penis and making fun of him. The surgical team also entered a fake diagnosis of hemorrhoids into his medical record.

6) A former researcher at Iowa State University was sentenced to 57 months in prison in July for systematically falsifying data to make an experimental HIV vaccine look effective. The researcher, Dong Pyou Han, PhD, was supposed to inject rabbits with a vaccine and test their sera for HIV antibodies. Dr. Han not only gave the head of the lab false test results about the vaccine, but he also injected the rabbits with human antibodies.

7) The Washington Medical Quality Assurance Commissions suspended the license of Arthur Zilberstein, MD, in June for sexting from the operating room. The commission said Dr. Zilberstein “compromised patient safety due to his preoccupation with sexual matters” during surgery. He was charged with exchanging sexually explicit texts during surgeries when he was the responsible anesthesiologist, improperly accessing medical-record imaging for sexual gratification and having sexual encounters in his office.

8) An Ohio cardiologist was convicted in September of billing Medicare and other insurers for $7.2 million in unnecessary tests and procedures. Dr. Harold Persaud put lives at risk by performing stent insertions, catheterizations, imaging tests and referrals for coronary artery bypass graft surgery that were not medically warranted, according to prosecutors.

Alas, such patient mistreatment and fraud is not that rare, as my readers.

Why Your Doctor Is Never on Time

Why is it that every time I go to a doctor, I am given an appointment for a precise time, and then every single time the doctor shows up at least 20 minutes late? Does the healthcare system hate me? Do doctors not want to fix the problem? Or are they just simply incompetent?

To dig deeper into the question, we at LeanTaaS dove into the operations of more than 50 healthcare providers this past year. We looked at resource utilization profiles at three different types of clinics – cancer infusion treatment, oncology and hematology – to understand the problem and how best to solve it.

The truth is that most healthcare providers have the patient’s interest at heart and are trying their level best. However, “optimal patient slotting” is a lot more complex than might appear on the surface – in fact, it is “googol-sized” in complexity. The good news is it’s a problem solvable with advanced data science; the sobering news is it MUST be solved if we are to handle the incoming onslaught of an increasing, aging patient population all carrying affordable insurance over the next 20 years.

The Doctor Will Be Right With You. NOT.

There are few things I take for granted in life, and waiting to see a doctor is one of them. The average wait time for a routine visit to a physician is 24 minutes. I am sure I am not the only one who has sat in a doctor’s waiting room thinking, “You said you would see me at 3:00 p.m. – why am I being called at 3:24? This happens every time; I bet you could have predicted it. So, why didn’t you just ask me to come at 3:24 instead?”

A Press Ganey study of 2.3 million patients at 10,000 sites nationwide found that a five-minute wait can drop patient satisfaction by 5%, a 10-minute wait by 10% and more than 10 minutes by 20%.

Source: http://www.pressganey.com/

 

That 24-minute stat is, in fact, not so bad compared with anyone who has had to get an infusion (chemo) treatment, visit a diabetes clinic, prepare for surgery or see just about any specialist. Those wait times can be hours.

Just visit any hospital or infusion center waiting room, and you will see the line of patients who have brought books, games and loved ones along to pass that agonizing wait time before the doctor sees them.

I spent the past year researching this problem and saw for myself just how overworked and harried nurses and doctors operating across the healthcare system are. I spoke to several nurses who have had days they were not able to take a single bathroom break. Clinics routinely keep a “missed meal metric” – how often nurses miss lunch breaks – and most of the ones I spoke to ring that bell loudly every day. I even heard of stories of nurses suing hospitals for having to go a whole day without breaks or meals.

The fact is that long patient wait times are terrible for hospitals, too. Long wait times are symptomatic of chronically inefficient “patient flow” through the system, and that has serious negative impact on the hospital’s economic bottom line and social responsibility:

  • Lower Access and Revenue: A natural corollary to long patient wait times is that the hospital sees fewer patients than it possibly could each day. The Medical Group Management Association found that the average utilization of operating rooms at large hospitals in 2013 was only 53%. Fewer patients served directly implies reduced access to care, lower revenues and higher unit costs.
  • Rising Labor Costs and Declining Nurse Satisfaction: Nurses are an expensive and scarce skill set. Because of the “peaks and valleys” caused by inefficient scheduling during the day, hospitals have to staff for the “peak” and simultaneously experience periods of low activity while still needing significant overtime hours from nurses.

Hospital leaders know this well. Every administrator I spoke to in my research – CEO / CAO / CNO – has some kind of transformation effort going on internally to improve patient flow – “lean” teams, 6-sigma teams, rules for how to schedule patients when they call into various clinics and so on. Leaders know that if patients could be scheduled perfectly and doctors could see them on time, the resulting “smoothing of patient flow” throughout the system would make their facilities, staff and the bottom line much better off.

The Real Reason

It’s not for a lack of motivation that the system is broken. It’s just a complex math problem.

The system is broken because hospitals are using a calculator, standard electronic health record (EHR) templates and a whiteboard to solve a math problem that needs a cluster of servers and data scientists to crunch.

To illustrate why scheduling is such a complex problem, let’s take the case of a mid-sized infusion (chemo) treatment center I studied during my research.

This infusion center has 33 chairs and sees approximately 70 patients a day. Infusion treatments come in different lengths (e.g., 1-2 hours, 3-4 hours and 5-plus hours long), and the typical daily mix of patients for these three types are 35 patients, 25 patients and 10 patients, respectively. The center schedules patients every 15 minutes starting at 8:00 a.m. with the last appointment offered at 5:30 p.m. So there are 39 possible starting times: 8:00 a.m., 8:15 a.m., 8:30 a.m., etc, ending at 5:30 p.m. The center can accommodate three simultaneous starts because of the nursing workload of getting a patient situated, the IV connected, etc. That makes a total of 39*3 = 117 potential “appointment start slots.”

That may not seem like a lot, but it results in 2.6 times 10 to the 61st power possible ways to schedule a typical, 70-patient day. (I’ll save you the math.) That’s 26 million million million million million million million million million million possibilities.

And that number is just the start. Now add in the reality of a hospital – some days nurse schedules are different from others, the pattern of demand for infusion services varies widely by day of week, doctors’ schedules are uneven across the week, special occurrences like clinical trials or changes in staff need to be considered and so on. You are looking at a problem that you can’t solve with simple heuristics and rules of thumb.

How Today’s “Patient-Centric” Scheduling Often Works – and Backfires

Very few hospitals I spoke to understand or consider this math. Rather, in trying to “make the patient happy,” most providers have been trained to use a “first come, first served” approach to booking appointments. Sometimes, providers use rules of thumb based on their knowledge of busy times of day or week, e.g., start long appointments in the morning and shorter ones later.

If hospitals were scheduling patients for one chair, one nurse and the same treatment type, some simple rules could work. But reality is a lot more complicated – the right schedule would need to consider varying treatment times across patients, include multiple treatment rooms/chairs, varying staff schedules, lab result availability and so on. Without sophisticated tools, there is an almost zero chance a scheduler can arrange appointments so treatment durations fall like Tetris blocks that align perfectly over the course of the day, and seamlessly absorb patients as they arrive, orchestrating doctor, nurse and room availability, while accounting for all the other constraints of the operation.

In effect, hospitals are scheduling “blind,” not taking into account the effect of appointments already scheduled before, during or soon after the slot being allotted on a first-come basis. Schedule currently is like adding traffic to rush hour and almost always results in a “triangle shaped utilization curve” – massive peaks in the middle of the day and low utilization on either side.

Typical utilization in an infusion treatment center with 63 chairs

 

Each of the 50 hospitals I spoke to identified precisely with this utilization curve. In fact, they identify with “the midday rush and slower mornings and evenings” so well that they have given them affectionate names – one called it their “Mount Everest,” another “Mount Rainier.”

From a cancer center’s standpoint, this chair utilization curve has several issues even beyond long patient wait times:

  • The center can only see a fraction of patients it could have with a “flatter” utilization curve.
  • Nurse scheduling has to be done for the peak, and the treatment center typically deals with lots of overtime issues.
  • Nurses find it hard to take lunch breaks because of the midday peak, while half the time the chairs are empty.
  • On any day, given the number of interdependent moving parts, a small perturbation to the system (e.g., a patient’s labs are late, another patient didn’t arrive on time) creates a domino effect, further exacerbating delays, not unlike a fender bender in rush hour traffic that delays everyone for hours.

In effect, when hospitals think they are scheduling in patient-centric ways, they are doing exactly the opposite.

They are promising patients what they cannot deliver – instead of giving the patient that 10:00 a.m. Wednesday appointment, an 11:40 a.m. appointment may have been much better for the patient and the whole system.

As we will see, the patient could have had a 70% shorter wait time, the hospital could have seen 20% more patients that week, every nurse could have taken a lunch break every day and a lot less (if any) overtime would have been required.

So How Do You Solve This “Googol-Sized Patient Slotting” Problem?

The solution lies in data science and mathematics, using inspiration from lean manufacturing practices pioneered by Toyota decades ago, such as push-pull models, production leveling, reducing waste and just-in-time production.

In mathematical terms, it means taking those 10^61 possibilities and imposing the right set of “constraints” – demand patterns, staffing schedules, desired breaks and whatever is unique to the hospital’s specific situation – to come up with a much tighter set of possible patient arrangements that solve for maximizing the utilization of hospital resources and therefore the number of patients seen.

In the case of the infusion center, the algorithm optimizes utilization of infusion chairs, making sure they are occupied uniformly for as much of the day as possible as opposed to the “peaks and valleys” in Figure 3. In essence, “rearranging the way the Tetris blocks (patients) come in” so they appear in the exact order they can be met by a nurse, prepped and readied for a doctor whose schedule has been incorporated into the algorithm.

The first step in doing this is mining the pattern of prior appointments to develop a realistic estimate of the volume and mix of appointment types for each day of the week.

The second step is imposing the real operational constraints in the clinic (e.g., the hours of operation, doctor and nurse schedules, the number of chairs, various “rules” that depend on clinic schedules, as well as patient-centric policies such as that treatments longer than four hours should be assigned to a bed and not a chair).

Finally, constraint-based optimization techniques can be applied to create an optimal pattern of “slots,” which reflect the number of “appointment starts” of each duration.

In the case of the infusion center, that means how many one-hour duration, three-hour duration and five-hour duration slots can be made available at each appointment time (i.e. 7:00 a,m., 7:15 a.m., 7:30 a.m. and so on).

Optimized shape of utilization curve for the same center as in Figure 1. 20% lower peak, much smoother utilization of resources, significant capacity freed

 

Doing this optimally results in moving the chair utilization graph from the “triangle that peaks somewhere between 11:00 a.m. and 2:00 p.m.” in Figure 3 to a “trapezoid that ramps up smoothly between 7:00 a.m. and 9:00 a.m., stays flat from 9:00 a.m. until 4:00 p.m. and then ramps down smoothly from 4:00 p.m. on” in Figure 4.

Coming up with realistic slots that keep patients moving smoothly throughout the day cuts patient waiting times drastically, reduces nurse overtime without eliminating breaks and keeps chair utilization as high as possible for as long as possible. Small perturbations in this system are more like a fender bender at midnight, a small annoyance that causes a few minutes of delay for a small number of people instead of holding up rush hour traffic for hours.

Smoothing Patient Flow – A Large Economic Opportunity

The above graphs are sanitized versions of real data from a cancer infusion treatment center at a real hospital that used these techniques to solve their flow problems. The results they achieved are staggering and point to the massive economic and social opportunity optimal patient flow presents.

Post implementation of a product called “LeanTaaS iQueue,” they now experience:

  • 25% higher patient volumes
  • 17% lower unit cost of service delivery
  • 31% decrease in median patient wait times
  • 50% lower nurse overtime
  • Significantly higher nurse satisfaction – no missed meals

Imagine applying this kind of performance improvement to every clinic, hospital and surgery suite in the country and the impact it will have on population health through increased patient access to the system.

The Problem Is Going to Get a Lot Worse Unless Providers Address It Now

This problem is going to get a lot worse for a simple reason – the demand for medical services has never been stronger, and it’s only going to increase. Just looking at the U.S. market:

  • Population Growth: By 2050, there will be more than 438 million Americans, up from 320 million in 2015.
  • Demographics: By 2030, more than 20% of the country is expected to be older than 65, up from 15% in 2015 – increasing the demand for chronic clinical therapies. In raw numbers, the Census Bureau estimates that by 2030, when the last round of Baby Boomers will hit retirement age, the number of Americans older than 65 will hit 71 million, up from 41 million in 2011, a 73% increase. When this happens, one in five Americans will be older than 65. Not surprisingly, by 2025, 49% of Americans will be affected by a chronic disease and need corresponding therapies.
Access to healthcare is a looming crisis – multiple drivers of significant demand growth

  • The Affordable Care Act: The Affordable Care Act will add 30 million Americans to the healthcare system by 2025. That means more demand for healthcare – more doctor visits, more hospital visits, more emergency emergency room visits and more need for resources (e.g., surgery rooms, MRI / CAT scans). Reimbursements will increasingly depend on outcomes and efficacy, quality of care and patient access. Unless providers become a lot more efficient in how they process and treat patients, they will need to spend billions in capital spending on new infrastructure – clinics, operating rooms, infusion centers and the like.
  • In an online poll conducted by the American College of Emergency Physicians (ACEP), 86% expect emergency visits to increase over the next three years. More than three-fourths (77%) say their ERs are not adequately prepared for significant increases.
  • The Commonwealth Fund, a New York-based fund that tracks healthcare performance, projects that primary care providers will see, on average, 1.34 additional office visits per week, accounting for a 3.8% increase in visits nationally. Hospital outpatient departments will see, on average, 1.2 to 11 additional visits per week, or an average increase of about 2.6% nationally.
  • It is estimated that the U.S. will face a shortage of 90,000 physicians and 500,000 nurses by 2030.

The Good News

Most healthcare providers are waking up to the fact that their operations need a data-driven, scientific overhaul much the same way as auto manufacturing, semiconductor manufacturing and all other asset-intensive, “flow”-based systems have experienced.

The good news is that there are tools, software and resources that can be used to bring about this transformation. Companies like LeanTaaS are at the forefront of this thinking and are applying complex data science algorithms to help hospitals solve these problems.

Hospitals that are serious about solving patient flow issues and the related problems now have access to the best computational minds and tools.

I see a world in which our healthcare system can see every patient on time without imposing hardship on care providers, disruption on current processes or increasing cost of services.

Here’s to that world!