With the healthcare landscape changing from fee-for-service to fee-for-value models, healthcare provider systems (hospitals, clinics, independent physician associations, etc.) are now, more than ever, under pressure to effectively manage the health and cost outcomes of their given populations. Under such models, providers are not only providing healthcare service to the patients, but they are also sharing in the financial risk and reward of patient costs. To effectively become a value-based organization, providers today are adopting a process broadly termed “population health.”
The “population health” process usually starts with identifying key segments of a population that face certain risks of adverse health outcomes and thereby high cost — a step known as “risk stratification.” Once risk is stratified, appropriate patient intervention programs are employed to improve: access to health, targeted encounters with providers and continuous monitoring of patient risk. This leads to lower emergency room visits, better clinical outcomes (such as properly managed blood glucose levels for diabetics) and lower financial cost.
There are many proven methods of risk-stratification to assign patients to low-, medium- or high-risk groups. For example, the adjusted clinical groups method examines patient diagnoses, and the elder risk assessment method assigns risk based on patient demographics. In today’s market, we observe many proprietary methods of risk stratification developed by various provider systems. The variables used in risk stratification can be classified into the following categories:
Clinical: Data from electronic medical records (EMRs), patient vitals, laboratory data, etc.
Administrative: Usually patient claims that track diagnosis and procedures already conducted
Socio-Economic: Patients’ social situations, family and friend support systems, language preference, community involvement, the degree of influence that out-of-pocket expenses could have on the patient’s well-being, etc.
Lifestyle: Health and activity tracking devices such as Fitbit, Apple Watch, etc., which carry critical daily lifestyle data about a patient
While the above categories play a large role in risk stratification, a new dimension known as “spatial access” can significantly lend leverage to the provider systems in affecting patient outcomes. For some patients, the overall risk may increase significantly because of their spatial, geographical and transportation access to medical and wellness resources. Spatial access refers to patients’ geographic proximity and ease of mobility to resources such as hospitals, primary care physician offices, primary and specialty care clinics and nurses. The geographic arrangement of patient and provider resources can play a significant role in healthcare utilization. For example, patients living in areas with fewer healthcare resources — regions often termed “doctor deserts” — have been linked with higher rates of preventable ER visits that are notorious for raising healthcare costs without necessarily improving healthcare outcomes. Using geographical and spatial analysis to supplement existing risk stratification techniques can help providers with an untapped method of assessing risk and generating better ROI in the long run.
To incorporate spatial access analysis into risk stratification, providers must:
Gather patients’ social network geographic information
Most EMR systems already contain patient address information, but they often lack information about the patients’ social network. The following types of data should be collected and refreshed on an annual basis:
Distance to closest primary care clinic, both straight-line and network-distance;
Distance to closest primary care provider, both straight-line and network-distance;
Spatial density of medical resources in a given area, especially primary care services;
Access to vehicle transportation, either on the patient’s own or through a family member; and
Proximity to public transportation.
Conduct “spatial access” risk stratification
Using a geographic information system (GIS), assign relative risk to each patient based on each of the components listed above, then create a composite risk based on all of the attributes.
Represent population risk stratification visually via mapping
Examine which areas of a provider’s service areas are prone to having individuals with high risk; look for clusters of high- or low-risk patients in doctor deserts. Viewing individual or aggregate risk through mapping would offer analysts and decision makers a comprehensive view of what types of risk are occurring in their service area.
Strategize how to implement interventions based on locations of high-risk patients
If clusters of high-risk patients exist in a certain area, begin to strategize about what kinds of interventions may alleviate the problem. Interventions may include the placement of new primary or specialty care clinics. Because creating clinics can be challenging, increased use of mobile provider teams can be an alternate solution. Lastly, a combination of telemedicine and mobile medicine should be assessed for the right mix of care for doctor deserts and lack of physical clinics.
Understanding the spatial context of patient demand vs. provider supply of healthcare service is an important component for accountable care organizations to conduct accurate risk stratification. Moreover, incorporating GIS into healthcare service analyses improves decision-making capabilities for evaluating, planning and implementing strategic initiatives. By taking advantage of the analytic capabilities of GIS and spatial access risk stratification, healthcare service providers are better equipped to more comprehensively understand their patient population and to thrive in this new value-based world.
In Part 1 and Part 2 of this series, David Toomey and I described a wildly successful collaboration with Virginia Mason Medical Center (VM) and a few Seattle employers.
During the the time of the VM collaboration, we invited major physician groups to meet with the employers. One of the most memorable meetings was with the CEO and chief medical officer (CMO) from a very well-regarded physician group in Seattle that has high fees but low performance.
As you would suspect, the employers were better prepared for this meeting than they had been for the meetings with VM. When the CEO and CMO talked about their strong emphasis on quality, the employers asked about quality monitoring and the process of care. Rather than acknowledging opportunities for further analysis and professing an openness to collaboration, the providers responded with confidence about their model of care.
Afterward, the employers expressed concerns about whether this premier provider could improve care and reduce costs. We posed a couple of questions: Are you saying you don’t want this provider in the network? Are you really ready to tell your leadership that this physician group, which many executives use, is not in the top tier?
The employers were aware of the dynamics with network configuration and the trouble that businesses have when a provider is dropped from the network and even a few employees complain. The employers responded that they wanted to have additional meetings with this group, because of its reputation.
After a couple of follow-up meetings, the employers recognized that this group was not committed to the process of care that they expected. They decided that the group should not be in the performance-based network. Importantly, the employers were now equipped to discuss their rationale with their leadership teams.
The CEO of the provider group felt respected, because of the time the employers spent with him, even though he did not like the outcome. He eventually acknowledged the group had work to do.
Employers make purchasing decisions with suppliers every day. For some reason, the healthcare procurement process involves the carriers and other vendors but often skips the actual suppliers of healthcare (except in a fairly small, but rapidly growing, number of major corporations).
The big question is: Why are more self-insured employers not engaging directly with providers?
In a broad network, there will be a bell curve around performance. Most employers say they want quality providers in their networks, but half the providers in their broad-based networks are below average. While everyone espouses “quality,” the variation in care is significant, and the medical ethics around treatment often drive that differential. Healthcare is big business. It is time to reward employees and channel them to primary care physicians and specialists who are truly committed to medically appropriate care.
A major reason why healthcare costs grow faster than general inflation is because most self-insured employers are simply not dealing with healthcare providers in the way we have described in this series of posts.
There is a myth out there that healthcare providers are unwilling to adopt new technology. It’s just not true. In the last few months, I have spoken to dozens of healthcare leaders at hospitals both small and large, and I am amazed at their willingness to understand and adopt technology.
Pretty much every hospital CEO, COO, CMIO or CIO I talk to believes two things:
With growing demand, rising costs and constrained supply, healthcare is facing a crisis unless providers figure out how to “do more with less.”
Technology is a key enabler. There is technology out there to help save more lives, deliver better care, reduce costs and achieve a healthier America. If a technology solution solves a real problem and has a clearly articulated return on investment (ROI), healthcare isn’t that different from any other industry, and the healthcare industry is willing to adopt that technology.
Given my conversations, here are the five biggest IT trends I see in healthcare:
1. Consumerization of the electronic health record (EHR). Love it or hate it, the EHR sits at the center of innovation. Since the passage of the HITECH Act in 2009—a $30 billion effort to transform healthcare delivery through the widespread use of EHRs—the “next generation” EHR is becoming a reality driven by three factors:
Providers feeling the pressure to find innovative ways to cut costs and bring more efficiency to healthcare delivery
The explosion of “machine-generated” healthcare data from mobile apps, wearables and sensors
The “operating terminal” shifting from a desktop to a smartphone/tablet, forcing providers to reimagine how patient care data is produced and consumed
The “next generation” EHR will be built around physicians’ workflows and will make it easier for them to produce and consume data. It will, of course, need to have proper controls in place to make sure data can only be accessed by the right people to ensure privacy and safety. I expect more organizations will adopt the “app store” model Kaiser pioneered so that developers can innovate on their open platform.
2. Interoperability— Lack of system interoperability has made it very hard for providers to adopt new technologies such as data mining, machine learning, image recognition, the Internet of Things and mobile. This is changing fast because:
3. Mobile— With more than 50% of patients using their smartphone to monitor health and more than 50% of physicians using (or wanting to use) their smartphone to monitor patient health, and with seamless data sharing on its way, the way care is delivered will truly change.
Telemedicine is showing significant gains in delivering primary care. We will continue to see more adoption of mobile-enabled services for ambulatory and specialty care in 2016 and beyond for three reasons:
Mobile provides “situational awareness” to all stakeholders so they can know what’s going on with a patient in an instant and can move the right resources quickly with the push of a button.
Mobile-enabled services radically reduce communication overhead, especially when you’re dealing with multiple situations at the same time with urgency and communication is key.
The services can significantly improve the patient experience and reduce operating costs. Studies have shown that remote monitoring and mobile post-discharge care can significantly reduce readmissions and unnecessary admissions.
The key hurdle here is regulatory compliance. For example, auto-dialing 9-1-1 if a phone detects a heart attack can be dangerous if not properly done. As with the EHR, mobile services have to be designed around physician workflows and must comply with regulations.
4. Big data— Healthcare has been slower than verticals such as retail to adopt big data technologies, mainly because the ROI has not been very clear to date. With more wins on both the clinical and operational sides, that’s clearly changing. Of all the technology capabilities, big data can have the greatest near-term impact on the clinical and operational sides for providers, and it will be one of the biggest trends in 2016 and beyond. Successful companies providing big data solutions will do three things right:
Clean up data as needed: There’s lots of data, but it’s not easy to access it, and isn’t not quite primed “or clean” for analysis. There’s only so much you can see, and you spend a lot of time cleansing before you can do any meaningful analysis.
Meaningful results: It’s not always hard to build predictive analytic models, but they have to translate to results that enable evidence-based decision-making.
Deliver ROI: There are a lot of products out there that produce 1% to 2% gains; that doesn’t necessarily justify the investment.
5. Internet of Things— While hospitals have been a bit slow in adopting IoT, three key trends will shape faster adoption:
Innovation in hardware components (smaller, faster CPUs at lower cost) will create cheaper, more advanced medical devices, such as a WiFi-enabled blood pressure monitor connected to the EHR for smoother patient care coordination.
General-purpose sensors are maturing and becoming more reliable for enterprise use.
Devices are becoming smart, but making them all work together is painful. It’s good to have bed sensors that talk to the nursing station, and they will become part of a top level “platform” within the hospital. More sensors also mean more data, and providers will create a “back-end platform” to collect, process and route it to the right place at the right time to can create “holistic” value propositions.
With increased regulatory and financial support, we’re on our way to making healthcare what it should be: smarter, cheaper and more effective. Providers want to do whatever it takes to cut costs and improve patient access and experience, so there are no real barriers.
With the recent terrorist attack in San Bernardino, CA, fresh on people’s minds, workplace violence has received major media coverage, but little to no attention is paid to deaths by suicide even though rates in the U.S. have gone up considerably in recent years. Suicides claim an average of 36,000 lives annually, and, while most people take their lives in or near home, suicide on the job is also increasing.
The Bureau of Labor Statistics reported that workplace suicides rose to 282 in 2013, the highest level since the numbers have been reported. In 2014, the suicide rate went down slightly to 271, but that is still the second highest level. The annual average number of suicides deaths that occurred at work during the time period 2003 – 2014 is 237, for a total of 2,848. Since 2007, the numbers have been above the average.
Source: U.S. Department of Labor, Bureau of Labor Statistics, Census of Fatal Occupational Injuries
The rise in suicide rates at work is even more significant given that overall homicides in the workplace have been steadily decreasing since the mid-’90s.
The obvious question is: Why is this startling rise in suicide rates at work occurring?
“The reasons for suicide are complex, no matter where they take place,” said Christine Montier, CMO of the American Foundation for Suicide Prevention. “Usually, many factors are at play.”
Many suicide prevention experts linked the increase in one way or another to the Great Recession. I believe the recession played a major role because it put a triple whammy on people. Housing, which has traditionally been the major investment and retirement source for Americans, was in the toilet. Foreclosures were at an all-time high. Companies were laying off people, and job prospects were slim.
I believe that many working people experienced daily stress about employment. Every day, they might be laid off. Many were severely overworked because they needed to pick up the slack caused by reductions in workforces. They faced continuous fear of taking time off for vacations or illness and had few options to leave because jobs elsewhere were scarce.
Put all these issues in the pot together, and some people could not see their way out of their dilemma except through suicide.
Researchers in a study published by the American Journal of Preventive Medicine suspect that suicides occur at work because the perpetrators wanted to protect family and friends from discovering their bodies.
In the midst of the fear of terrorist attacks and active shooting incidents, organizations are significantly challenged in how to deal with the spectrum of violence they may face. However, it is critical that organizations not shy away from addressing these issues and muster the resources to engage their employees.
Managers need clear guidelines on healthy approaches to manage and prevent violence in the multiple forms it can take. Two industries that have taken the issue of suicides at work head-on are construction and law enforcement.
What can management do?
Stop thinking and acting like “it couldn’t happen at your company.” Provide regular communications through the channels that are most effective in your company regarding the potential warning signs that employees or others are at risk of acting in a violent manner. See a list of the classic early warning signs of workplace violence here. Many of the signs are also telltale signs symptoms of depression and suicidal behavior.
Sally Spencer-Thomas, Psy.D., co-founder of Working Minds, a Colorado-based workplace-suicide-prevention organization, described a giveaway that’s more obvious than one might suspect: The employee will tell you.
When contemplating suicide, a person can be entirely consumed by the thought, she said. The problem may be coded in conversation—the individual may talk about death often, for instance.
As uncomfortable as it may seem, it’s important to bite the bullet and ask the awkward questions. “It is very hard to resist a human who is coming at you with compassion,” Spencer-Thomas observed. She suggests that HR professionals frame their questions in an understanding manner, giving the employee the opportunity to explain his or her condition. Statements such as, “I’ve noticed that …,” “It’s understandable given …,” and “I’m wondering if it’s true for you…” should be followed by a nonjudgmental statement.
Promote resources available to help employees
If your firm has an employee assistance program (EAP) or your healthcare provider offers counseling service, make sure that managers are trained about the program and skilled in how to make an effective employee referral. If your employee usage rates are below your industry average, you need to assess why and take action to increase usage. Talking to a professional counselor can make a big difference to a troubled employee.
If your firm does not offer an EAP, then identify community resources that can assist your employees and keep the list current.
HR should also have a strategy to deal with the devastating impact of a homicide or suicide at work.
I believe the time has come for executives to take a comprehensive approach to violence that occurs in the workplace and especially to bring mental health and suicide issues out of the closet into mainstream workplace conversations. We are past the point where organizations can think of suicide as a dirty little secret and hope it will go away. The time has come for meaningful action.
Don’t wait until something happens and people lose their lives. If you really mean that your employees are your most important asset, now is the time to step up.
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.