If you are thinking that what’s next for the life insurance industry has something to do with the experience surrounding buying and owning life insurance products, think again.
Yes, the life insurance industry has a big opportunity to improve the customer experience. Companies are improving the complex and inauthentic language used in communications, improving engagement levels with consumers, reducing friction in the underwriting process and creating the ability to transact in an omni-channel way. We are even seeing new peer-to-peer models cropping up for other insurance lines, and it is just a matter of time before life insurance becomes a focus within them.
And, yes, the life insurance industry now at least has a handle on what needs to be done to improve the experience. Companies are putting significant effort into catching up to other categories. Some of the progress is coming from within the established carriers, and even more of it is coming from disruptors that are improving the model rapidly, giving established carriers new capability to buy instead of building.
So the industry is just a short time away from meeting the demands of today’s consumer. Bravo!
But the industry needs to get beyond improving today’s experience and focus on what’s next. And what is that?
Are you ready? Well, here it is: Death just isn’t what it used to be.
Social, scientific and technological advances have dramatically reduced the probability of death for those under the age of 55. This is the group of people whose untimely death would cause the greatest financial burden on families and businesses and is the group we depict as needing life insurance most.
Granted, many life insurance policies sold are issued on older people to implement tax strategies. However, the original intent of the insurance industry was to protect families and businesses from becoming destitute as a result of the loss of a breadwinner or key person.
What happens if that probability is significantly reduced? Do we continue to try and find more “death pool” needs. Or, do we find new needs that our unique skills and competencies can solve?
What’s next, in my opinion, lies in the latter. We can define our business more broadly. Are we in the business of “insuring” lives or “assuring” them? In other words, are we assuring that someone will live longer by avoiding or recovering from the things that are likely to cause death, such as drug use, cancer and suicide?
What does this question mean for what’s next in the insurance industry?
First, let’s examine avoidance. Could life insurers use technology and probability to help individuals and communities further reduce the likelihood of accidents? We need to go beyond driving and household safety tips and into true early warning systems or algorithms that can enable consumers to be proactive.
Could we better predict the likelihood of suicides or accidental drug overdoses? Could we help people understand the role of new, emerging risks such as “hackccidents”? (This is my term for an accident that is a result of human intervention into a computer system that may be controlling a car, a train, a plane or some other technology.)
Secondly, let’s examine recovery. Suppose someone has an incident, and death is now imminent. Could the life insurance industry guarantee access to the latest technology? Could it design investment futures (similar to investments in gold or pork belly futures) in the ability to get an organ transplant or expensive medicine or to be frozen until a cure arrives?
This may all sound far-fetched, but how far-fetched did the innovations of today sound just 10 years ago?
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
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.
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
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
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
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.
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.
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 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%.
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.
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).
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.
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.
The article says,“The declines follow the U.S. Preventive Services Task Force’s recommendations against routine testing for prostate cancer, first for men aged 75 and older in 2008, and then for men of all ages in 2012, on the grounds that the benefits likely don’t outweigh the harms.”
I repeat: The benefits of prostate screening likely don’t outweigh the risks.
In short, the diagnosis rate is down because, apparently, more doctors are following new guidelines on prostate screening. At last…at long last.
But there is a joker in the deck. Every wellness program I’ve looked at has not adopted the USPSTF’s prostate screening guidelines. (There may be some that have adopted the new recommendations, but I haven’t seen them.)
It’s worse. I asked a wellness vendor why the company was persisting in promoting prostate over-screening. His reply made my stomach churn. He said that, if his wellness company changed the guidelines, it would have to admit it was wrong in the first place. So it is keeping flawed recommendations to save face. I’d name the vendor, but I agreed to keep what he told me in confidence. Alas.
If you have a wellness program, and the vendor is not following that guideline on prostate screening, you need to give it a big nudge.
P.S. Years ago, I asked my primary care doctor to stop doing PSA tests on me.