Tag Archives: chemotherapy

On Air Traffic Control and Health Costs

Explosion of data volumes. Interoperability of systems. Large servers in the sky that can analyze enormous amounts of data, compute complex algorithms in real time and communicate in microseconds. Mobile communication through devices that patients, providers and staff all carry all the time.

What does this all mean for hospital operations?

Based on our work with dozens of hospitals and conversations with 100-plus others, we think the near future of hospital operations is quite exciting. Call it what you will—“Hospital 2.0,” “No Waiting Rooms,” “Hospital Operations Center”—the basic building blocks to enable the future of hospital operations are already here.

Today, two major shifts are putting pressure on hospitals to rethink how they deliver care: (a) increased demand for care from the Affordable Care Act and the growing number of people with chronic illnesses and (b) the move toward value-based care.

See Also: 5 Trends in Health IT

These shifts have big implications across the board but, most importantly, in operations. Hospitals are under constant pressure to do more with less. Every day, they face an operational paradox: Scarce resources are both overbooked and underutilized within the same day. This leads to several undesirable outcomes: long patient waiting times, overworked staff, millions of dollars of unnecessary operational costs and an insatiable appetite for expanding existing facilities or constructing entirely new ones. For specialty services like chemotherapy, it could take days or weeks for a new patient to be given a slot, yet the typical infusion chair is occupied less than 60% of the time between 7 a.m. and 7 p.m. The same is true of operating rooms; study after study shows that hospitals don’t utilize their resources optimally.

Historically, process improvement efforts in hospitals worked with small, historical snapshots of data from which the core operational issues were identified. From this, strategies were developed, implementation plans were executed and disciplines for continuous improvement were established. This was the best approach when all that was available were rear-view mirror data snapshots and Microsoft Excel as the analytic engine of choice. Today, there’s a lot more data to learn from. On average, health systems produce as much as two terabytes of data per patient every year. Combined with the explosion of smart devices, computational power in the cloud and the growing pervasiveness of data science and machine learning algorithms, an entirely different realm of operational optimization has suddenly become possible. It is similar to the realization that, decades ago, general surgeons did the best they could with the insight they gleaned from grainy X-ray images. Today, armed with high-resolution MRI/PET images and fiber-optic cameras, the same surgeons can execute surgeries an order of magnitude more complex than those they could have imagined being able to do when they were surgical residents a few decades ago.

Consider the following scenarios on how predictive analytics is already optimizing patient pathways within hospitals:
  • Hospitals are working on optimizing access to treatments such as chemotherapy. By looking at historical demand patterns and operational constraints, sophisticated forecasting algorithms can predict the daily volume and mix of patient volume and can orchestrate appointment slots so there are no “gaps” between treatments. This radically improves chair utilization, lowers patient waiting times and reduces the overall cost of operations. Doing this without sophisticated data science is hard — for example, just arranging the order in which 70 patients can be slotted for their treatments in a 35-chair infusion center is a number exceeding 10^100, as this analysis shows. Trying to solve this problem with pen, paper or Excel is a pointless exercise.
  • Operating rooms are key resources within the hospital. Study after study shows that the OR utilization at most large hospitals is, at best, 50-60%. In most hospitals, operating rooms are allocated to surgeons using “blocks.” (For simplicity, the blocks are often either half-day or full-day blocks.) Even the most prolific and productive surgeons often don’t fully utilize the blocks they are given, and the process for reallocating blocks on a monthly basis—or even for last-minute block swaps—is cumbersome and manual. Using data science and machine learning, hospitals can monitor utilization, identify pockets for improvement, automatically reallocate underutilized blocks and improve overall operating room utilization. A three to five point improvement in block utilization is worth $2 million per year for a surgical suite with just four operating rooms.
  • In-patient bed capacity is a constraining bottleneck in most hospitals, yet virtually every hospital solves this problem with an arithmetic-based “huddle” approach that reviews the patient census from the overnight stay in each unit, adds known incoming patients, subtracts known discharges and then decides if the unit is flirting with the limits of its available capacity. This cycle repeats itself, often several times a day, with a planning horizon of the day at hand. On the other hand, Google completes the search bar while we are typing because it has analyzed millions of search terms similar to the one you are entering, and it automatically presents the four or five highest probability queries you intend to submit. Imagine looking at each overnight patient, finding the 1,000 patients over the last two years who entered the hospital with a similar diagnostic or procedure code and then reviewing their “flight path” through the hospital (i.e., number of days spent in each of the units prior to discharge). Then, an aggregate probabilistic assessment of the likely occupancy of each unit could be developed. Not only would it provide a better answer for today, it would help anticipate the evolving unit capacity situation over the next five to seven days, thereby leading to smarter operational decisions on transfers, elective surgery rescheduling, etc.
  • A similar machine-learning approach can help orchestrate patient flows at clinics, labs, the pharmacy and any unit within the hospital network that struggles with the operational paradox of being overbooked and underutilized at the same time.

An interesting metaphor for the future of hospital operations is how airport operations, air traffic control and sophisticated scheduling have transformed air travel for passengers. They, too, have enormous complexity and the mission-critical requirement of passenger safety in the face of challenging external conditions.

Three direct parallels:

  • For a single flight to transport passengers safely from point A to point B, it requires the “above the wing” services (boarding, food, crew) and “below the wing” services (baggage, fuel, tire check, other inspections) to come together seamlessly. Similarly, to perform even a routine surgery, services like labs, pharmacy, the clinician, the surgeon and the supporting team all need to come together to be able to safely and successfully treat the patient.
  • Every day, at any busy airport, tens of thousands of passengers  navigate their personal journey across connecting flights while relying on “invisible supporting services” such as bag transfers and re-bookings in the case of delays, weather systems, etc. Similarly, on any given day in a busy hospital, thousands of patients navigate their personal journey across a continuum of care while relying on the supporting services of labs, pharmacy, etc. to be timely and accurate.
  • The volume of airline passengers has grown from a few thousand to a few million per day, and airports and airlines have been forced to do “more with less.” Similarly, the Affordable Care Act and a growing and aging population combined with the increased incidence of chronic disease will require hospitals to do “more with less.”

The aviation industry has diligently invested in the required technology, systems and processes to monitor, measure, collaborate and orchestrate. Similarly, hospitals are beginning to invest in the technology, systems and processes to maximize patient access at each “node” and to streamline the linkages across nodes.

Just as the advent of air traffic control and fine-grained scheduling transformed airports like JFK from handling only a few hundred flights each day in the 1960s to managing thousands of takeoffs and landings a day within the same airspace, modern technologies and predictive analytics will lead to the creation of a similar air-traffic-control capability for hospitals. Assets like the OR, inpatient beds, clinics, infusion chairs and MRI machines will be far better utilized throughout the day. Many more patients will be treated within the same facilities, and they will need to wait far less between the “legs of their flight” across the continuum of care.

This post was written by Mohan Giridharadas, the CEO of LeanTaaS. 

Radical Thought on End-of-Life Care

Pull up your drink and relax – you’re in for a deeper read. Thanks in advance for your eyes and time. Read on …

Here, I subscribe to the Elon Musk school of applying “first principles” to problem solving. I like to call this one my “Big W.” It has the potential to save tens to hundreds of billions of dollars annually, as well as provide solutions for other challenges we have outside of healthcare.

[Note: “Big W” comes from one of my favorite movies – if you know it, feel free to let me know. Chime back. Let’s just say a “Big W” is something so obvious many people simply pass right by. Often, value comes from finding and digging deeper.]

https://www.youtube.com/watch?v=r97Nv8N7-mI

OK, let’s get the scary numbers out of the way and frame the situation.

Numbers: According to the latest government statistics, private health insurers, Medicaid and Medicare collectively pay nearly $2.7 trillion annually to doctors, hospitals and other healthcare services. There is $53 trillion in Medicare and Medicaid unfunded liabilities. Mind you, expectations call for these numbers to rise considerably.

Players: On one side, we have services and products, including health insurers, doctors, medical services, hospitals and big pharma. On the other side, we have consumers, which are self- and fully insured companies, private pay citizens and individuals who receive state or government benefits.

Problems: Our healthcare system is for-profit, with publicly owned companies in different sectors, with shareholders, with funds held by current and future retirees and with massive numbers of employed individuals.

So, which companies willingly lower their charges to let customers keep more money to afford growing healthcare costs? Aetna, Merck, HCA? If they do lower their revenues, what happens to their stocks? Do people continue to hold? If not, how does that affect employment in the respective healthcare sectors?

The latest stats show that nearly 35% (78.6 million) of U.S. adults are obese. Obesity leads to heart disease, stroke, type 2 diabetes and certain types of cancer, which together are some of the leading causes of preventable death. About half of all adults – 117 million people – had one or more chronic health conditions. One in four adults had two or more chronic health conditions. Just think about the magnitude of this.

Do you honestly believe Americans are going to change their poor eating and exercise habits en masse and in a reasonably short term? Do you really expect the majority of obese individuals to take action to lose their excessive weight?

The reality is that we have a connected group of self-serving and self-centered individuals, businesses and political leaders, none of whom are willing to make sizable efforts to fix our healthcare system. Add in the massive marketing from food and beverage companies (sodas and alcohol), as well as fast-food restaurants that appeal to a majority of Americans who have little in the way of savings. That all spells a continuing downward spiral for our country’s healthcare costs and future affordability.

Enter the Big W

The Big W is the creation of what I call the “transition plan.” This starts with the revelation that a mere 6% of Medicare patients who die each year make up an astounding 27% to 30% of all Medicare costs. This accounts for nearly $200 billion in outgoing payments. According to statistics ​provided ​from the Center for American Progress fellow Ezekiel Emanuel and the latest CMS report on ​our 2014 ​national health expenditures (NHE), ​we add in another $250 billion for end-of-life care ​on those ​covered under Medicaid and private insur​ers. ​In all, that is nearly a half-trillion dollars per year, which plays, I believe, a very important role in our healthcare crisis.

The “transition plan” starts with an understanding that those insured individuals who will die in the next 12 months are, in a sad way, a monetary commodity for medical professionals, hospitals, big pharma and medical services/products companies.

Many of these individuals will die with little to no net worth. Since 1989, the proportion of those older than 75 with mortgage debt has quadrupled. Many seniors have large amounts of debt because of high medical bills, long-term care and dwindling retirement savings. In addition, credit card debt for seniors is larger and is rising faster than for the younger generations.

Let’s also not forget that many “last year” patients are people who are not senior citizens. Some come from the nearly 47 million Americans living in poverty or from the working, lower- to middle-income earners. While a portion of this population has life insurance, the industry reports that, of all U.S. adults, only 60% carry any level of coverage at all, and that our country is underinsured for life insurance by nearly $15 trillion.

We’ve extended life, and that is a noble task, but ask a chronically ill person or even an elderly person what they think about being kept alive for as long as possible. Those who are suffering recognize the importance of dying with dignity, instead of slowly wasting away through a myriad of medical appointments, drugs, therapies, surgeries and lab tests.

Here’s where the transition plan begins. It’s a system where health payers identify terminal patients or those who are highly likely to become terminal patients and willingly choose to forego payment for most related medical services. In return, they receive a guaranteed tax-free, single-windfall payment. The payment constitutes a large portion of what would have been paid out to the medical community.  

Take Charles Smith, a 68-year-old man who has been diagnosed with Stage 3B lung cancer. The average case has a 95% chance of death. Let’s estimate that between chemotherapy, radiation, lab tests, doctor visits, home health, costly medications, pulmonary therapy and several possible surgeries, a typical health payer can expect to reimburse between $345,000 and $375,000.

The health payer, ABC Insurance Co., receives the patient’s initial diagnosis on a medical claim. It’s flagged – the case is passed to the company’s medical management department. Once substantiated with medical records, the health payers’ actuaries set an estimated value of $350,000 on the case.

Now, the health payer gets in contact with the patient and presents the offer for the transition plan. The letter would state the following:

  • That, with the current diagnosis, the payer is offering the opportunity for the beneficiary to participate in the offer.
  • That the program is voluntary. If the beneficiary does not choose it, nothing will change with his current health coverage.
  • That, if the offer is accepted, the payer will send a one-time, tax-free, non-refundable payment to Mr. Charles Smith for $150,000.

Once the insurer makes the payment, the following would occur:

  • The health payer would no longer be responsible for payment for any treatment or services, directly or indirectly related to the chronic condition. All such conditions would be clearly identified in the Transition Plan Agreement.
  • The insured could continue to see medical providers and have them bill services to the health payer, so long as such conditions are separate from the main diagnosis and other listed conditions. However, payments for any future medical services billed, in keeping with company policy, must be determined to be medically necessary.
  • The insurer would continue to pay, on an as-needed and medically necessary basis, any palliative or “pain management” care associated with the main condition. This would not include hospitalization, therapy, home health or premium-brand medications.

What has just happened is a unique meeting of the minds between poor to middle-class dying Americans and the health insurance industry. In giving insureds the option to be financially compensated, health payers shift payments from the medical establishment back to individuals who are taking clear control of their lives.

In this specific case, corporate or private insureds receive back a large portion of the $200,000 cost savings in the form of reduced premiums, perhaps mandated by government to certain levels based on certain savings points. Naturally, for self-insured entities, the savings would flow back to the company or organization.

Imagine Mr. Smith having less than $5,000 of total savings and no insurance.  He is divorced, yet he has two children who are also in the lower income class.  While $150,000 of tax-free money is not millions, it could make a difference to that family and perhaps their ability to afford healthcare, a home or even college.

Without the transition plan, Mr. Smith might struggle to pay his remaining debts and have no money left over for funeral expenses. With the transition plan, he might take, or at least send, his two kids and their families on a trip around the world. Perhaps he would choose to contribute to several 529 plans for the education of his grandchildren.  He could also choose to give to charities, political groups or churches or even share with his most beloved friends.

The transition plan is the patient’s choice. If the patient is mentally unable to make a choice, she may default to the normal relationship with the health insurer, or the decision could shift to her immediate family or appointed surrogate. 

Public payers, private health insurers and corporations that pay for their own health benefits will now have the ability to help others make the transition and perhaps leave better legacies. Nothing puts a smile on someone’s face, especially in times of stress and depression, quite like when they give and get to see others enjoy their gifts.  Contrast this transaction with the same money going into medical and drug house pockets, leading to a continual raise of everyone’s plan premiums and a decrease in savings.

Who are the financial losers here? End-of-life medical services including chemotherapy, radiation, imaging, testing, surgeries, therapies, medical equipment sales and home health services.

The transition plan may not be the entire answer for our growing healthcare crisis and spiraling costs. Certainly, it is not going to be the choice for those who are dying and have plenty of savings to pass on to their loved ones or charity; nor will it be the choice for those who want to fight their diseases to the very end.

However, for a large majority of Americans who don’t have the assets to leave but recognize their value per historical healthcare payments to providers, the transition plan could be a useful measure. It would effectively allow them to stake a claim toward money normally ending up in the pockets of medical service professional  for cases not often resolving positively.

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!