Is the Patient an Afterthought in Healthcare in America?

healthcare in america

A close examination of healthcare in America leads to the inevitable conclusion that patients are one of the least important players in the healthcare system. I’m the first to admit that this claim is both counterintuitive and provocative, but hear me out.  The evidence could not be clearer. This is particularly ironic because the healthcare field is staffed with professionals who were attracted to the field specifically to provide patient care. The problem does not lie with the people in the system; the problem lies in the system itself.


Healthcare in America is a Private Sector Function

Unlike every other developed country in the world, healthcare in America is treated as a profit-making operation. This is true for profit making institutions as well as the non-profit or not-for-profit healthcare organizations. Rather than talk about profit, these hospitals talk about a “surplus” that is required to see the hospital through lean times and fund the purchase of new equipment or grow the institution. This is the first in a series of blogs that will provide ample evidence of this remarkable claim. Stay tuned.

Turning a profit is baked into the very DNA of the American culture. It is part of what it means to be an American. Healthcare is no exception.

But unlike every other business in the country, in healthcare, there is remarkably little focus on the holistic welfare of patients. Every other business in the country – and most throughout the world – have customer service centers that are tasked with handling customer problems as they arise.  Customer satisfaction is paramount. At the end of every call I make to customer service departments, the agents always ask, “Is there anything else I can help you with?” That question rarely comes up in healthcare!

Let me give a few examples of the extent to which healthcare in America is a profit-making industry rather than a service to the community.


The Arbitrary Nature of “Master Charge” Lists

Hospitals develop what they call “master charge” lists. These are the prices they propose charging for patients admitted with various admitting diagnoses. In fact, these lists are just starting points for negotiations with insurance companies. During the negotiations, the insurance companies will negotiate deep discounts from these lists and the negotiators will be seen as heroes because they were able to win those discounts. But the negotiation is highly misleading because the “master charge” lists are created only for the purpose of negotiating with the insurance companies. Hospitals and health clinics don’t have any solid data about what it really costs to treat medical conditions because they don’t have cost accounting systems that allow them to develop those costs.  They make these lists up out of thin air.

Medicare and Medicaid don’t pay according to these lists.  They ignore them.  The federal government pays according to its own payment schedule.  Hospitals have the choice of charging the government in line with those government payment schedules or not taking Medicare and Medicaid patients. Most hospitals are willing to work with the government payment schedules.

The “master charge” lists vary considerably from one institution to another. This is true for institutions of comparable quality and in the same geography. Further, unlike restaurant menus, these price lists are rarely shown in advance. This makes comparative shopping impossible!

But even if the “master lists” were available, it wouldn’t make much difference in most cases. When a relative is screaming in pain and terrorized by her imminent death, her relatives are unlikely to show the same due diligence in selecting a healthcare provider that they would show, for example, in buying a new car.


With Healthcare in America, those Who Can Afford the Least Are Charged the Most

They only patients who get hit with the “master charge” prices are poor people who can’t afford to buy insurance in the first place. Relatives may take an ailing relative to the hospital in a moment of desperation and sign whatever pieces of paper are put before them. They may not realize they’ve signed legally binding financial commitments with no upper limit.

When the bill comes it could be in the five figures for something as simple as a paper cut. Anything halfway serious is liable to be in the six digits.  And the hospitals and clinics are serious about collecting on their bills. They retain a cadre of well-paid debt-collecting lawyers who are first-rate at what they do.

First, they take the sponsor’s savings accounts. Then they go after her retirement funds.  Those are easy to pick up.  Then they take her home. A sponsor who tries to declare bankruptcy discovers that healthcare bills – like education loans – are exempt from bankruptcy.  That means that no matter how little money she may have or how little she may earn, she can’t escape healthcare bills through bankruptcy.  It’s not even worth thinking about.

This aggressive bill collecting effort is a clear sign that the welfare of the healthcare institution, not the patient, is what is at stake.  I am not trying to argue that people should not pay their bills. But having a different set of rules for collecting healthcare debts than for collecting all other debts tells me there is a double standard.

This odd situation doesn’t mean that hospital administrators are acting in a malevolent way. It means they are acting in a way that our laws and customs endorse. Those administrators have a fiduciary responsibility to their Boards of Directors to collect all the money owed to them.  They would be negligent if they did not try collect every account as vigorously as possible.


Fee-for-Service is NOT Geared to Good Patient Care

For the last hundred years or so, general practitioners and specialists have charged on a fee-for-service basis.  That means exactly what it says: doctors provide services and bill someone (i.e., the patient, an insurance company, the government) for the service provided. There is no requirement that the service needs to be required in order to improve their patients’ medical condition.  None whatsoever. Often hospitals or clinicians carry out tests not because they contribute to their patients’ well-being, but because they protect the medical community in the event of a legal suit.

Typically, patients approach GPs with a complaint of some type. The GPs will refer the patients for a series of tests that they believe will contribute to the patients’ recovery. Often, they also refer their patients to specialists. The specialists may order even more (and often more expensive) tests than the previous tests.

In the end, it really doesn’t matter whether the patients improve or not – although there is a universal hope that the tests and procedures will lead to improvements. But, regardless of the outcome, the laboratories, medical practitioners, and hospitals all charge – and collect – for the work they did, not the results they deliver.

In no other industry will professionals, executives, mechanics, or salesmen get paid for their activities without respect to the achievement of their end goals. Healthcare is unique in this respect.

To put it more bluntly, the welfare of the patients is simply not a key factor in the operation and economics of the healthcare system. I believe that every individual in the system acts in good faith in contributing to the welfare of their patients within the protocols of their professions, their institutions, and the law. Each professional likely plays her own part as well as possible, but the system rarely assigns any one individual to look after the welfare of the patient in a holistic sense. This is a sign of a problem with the system of healthcare in America – not the administrators or medical staff.


We Have an “Illnesscare” System, NOT a Healthcare System

If we are completely honest, we need to acknowledge that, with the exception of public health (which is a marginal component of the overall healthcare system), our healthcare is not primarily concerned with promoting health.  There’s no money in it. The real money is in treating patients after they get sick, suffer from cancer, sink into a preventable chronic disease, or break a bone. That’s where the big money is.  Saving lives and working minor miracles is heroic. “Illnesscare” galvanizes everyone who witnesses it.

But promoting health by recommending improvements to diets, exercise programs, or cleaning up the environment really doesn’t carry the same WOW factor. It is routine and undramatic. But that is truly right at the heart of healthcare and as far removed from “illnesscare” as one can imagine.


Stay Tuned for More Revelations about How Healthcare in America Works

My claim in the first paragraph that patients are the least important part of the system of healthcare in America needs a lot more justification than I’ve given here. I urge you to read the entire series of upcoming blogs about how the healthcare system works (or doesn’t work).  You will learn that we have one of the most expensive and least effective systems in the world. You will learn that our government agencies mandated to protect our health often do exactly the opposite. (And these dynamics started long before Trump came on the scene.) You will learn that Americans are among the least healthy demographic on the planet – and that this poor health is driven by policies that are known to be counterproductive. It is not driven by callous healthcare staff.

Further, what you will read in this healthcare series is NOT a conspiracy theory or a secret. Far from it. In fact, everything I’ll talk about is well known and published in articles and books that anyone can read if they choose to. But, given the pressures of everyday life, people just don’t have the time, energy, and motivation to learn about the greatest threats to their health.



Read up on Hospital Readmission Rates as a sign of Poor Healthcare Delivery Here: Part 2  Part 1

Are 30-Day Readmissions Rates a Reliable Indicator for Poor Healthcare Delivery?  (Part 2 of 2)

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A look at poor healthcare delivery through hospital readmission rates.

Obamacare (Patient Protection and Affordable Care Act) has provisions that require the Centers for Medicare and Medicaid Services (CMS) to financially penalize hospitals that have unacceptably high 30-day readmission rates for Medicare and Medicaid patients. Institutions with high 30-day readmission rates in just a handful of situations will suffer financial penalties for ALL Medicare and Medicaid charges during the following fiscal year – not just the handful that are monitored.  Specifically, the CMS tracks 30-day readmission rates for[1]:

  • Heart failure
  • Heart attack
  • Pneumonia
  • Chronic lung problems (emphysema and bronchitis)
  • Elective knee and hip replacements

The penalties can be as much as 3% of all the Obamacare charges for the coming fiscal year. In most organizations, this can easily amount to millions of dollars. In larger institutions, this can amount to tens of millions of dollars.

The rationale behind this policy is that high 30-day readmission rates are a reliable sign of poor healthcare delivery. The idea is that if the hospitals had done a good job in the first place, patients wouldn’t need to come back so soon.

Part 2 of this blog argues that 30-day readmission rates are not a good metric for assessing the quality of care.  I fully recognize that my position is incompatible with the current wisdom, but I’ll give several reasons to support this position.  I suspect there are many others who feel the same way but haven’t argued their position.

The most striking issue that occurs to me is that 25% of all hospitals will automatically be classified as “losers” regardless of the reasons for their high readmission rates. This automatic and simple-minded categorization is grossly unfair.


Hospitals Are Only One Component in a Complex Healthcare Web

Hospitals are highly visible nodes in a complex web of healthcare delivery.  Other components include general practitioners, medical and surgical specialists, independent laboratories, the social welfare system, and family support among others. Unfortunately, it is not unusual for the elderly to have no family support. Everyone knows healthcare is a highly fragmented and fragile system. Failure in any component of this web can lead to readmissions. Nevertheless, Medicare and Medicaid (and perhaps society at large) hold hospitals solely accountable for readmissions.

Given the complexity of the healthcare web, it is highly unfair to single out hospitals as culprits when many of the factors affecting readmissions are beyond the control of hospitals.

Readmissions are a function of hospital care and discharge planning.  That is true.  But it is not the full story.  Another factor that impacts readmissions is the severity of the illnesses treated; those with severe illnesses are more likely to be readmitted. Hospitals can lower their readmission rates by declining to treat patients with severe illnesses.  I know that this is gaming the system, but it makes the metrics look good.

In some communities, elderly patients are discharged into the care of loving, stable, supportive families. In other communities, elderly patients go back to a bleak room in solitude. When they need help – even a ride to see their GP – there is no one to turn to. In other cases, elderly patients may live with their children.  But their children often have jobs and lives of their own. Although they are available to give help sometimes, they are simply not available to help all the time.

At discharge, hospitals routinely advise patients to schedule follow up appointments with their GPs. Patients promise to do so – but often don’t. In some cases, they don’t have GPs to call.  In other cases, they simply forget to make the appointments.  Sometimes they try to schedule an appointment but cannot get one for a month or more. Then there are the patients who simply don’t have access to transportation to get to their appointments.

Discharge staff generally give extensive instructions to patients about their medications, diet, exercise, etc. But it is not unusual for patients to fail to understand these instructions. Or they understand but they don’t have the money to buy the medications. Or they have the money for their medicines but they forget to take them.

There are any number of points of failure and many of them are beyond the hospital control – but hospitals take the hit for readmissions.


Race and Minority Status Are Correlated with Readmission Rates[2]

Blacks and Hispanics have higher rates or readmission to hospitals than whites. Many of these readmissions are avoidable. This means that hospitals serving Black and Hispanic populations are doomed to look bad on their readmission stats. There is no justice in this.

Why is race and ethnic background so important in determining readmissions?  Well, for one thing, the research shows they are less likely to schedule follow-up visits with their GPs or ongoing care givers. They are also less likely to even have GPs and, therefore, are more likely to rely on their local hospitals. Many new immigrants don’t have adequate proficiency in the English language to understand their discharge instructions or read and understand the written materials their hospitals give them. Unlike whites, they have no experience in taking the initiative to look after their own health; they often take the position that whatever happens to them is beyond their control. Some don’t trust Western medicine and discount what they are told.

These demographics suffer more anxiety and depression than whites. These mental health issues contribute to the likelihood of readmissions.

These demographics often have co-morbidities. In other words, they often have several problems at the same time.  If patients don’t bring their other problems to the attention of hospital staff – or if hospital staff fail to stumble across them – those problems can pop up after discharge and trigger other, but unrelated readmissions.

The factors listed here are not due to unsubstantiated biases but to solid research funded by the Centres for Medicare and Medicaid Services and conducted by the The Disparities Solutions Center, Mongan Institute for Health Policy, Massachusetts General Hospital. Yet, even with this solid research, well-known in the healthcare community, hospitals serving these disadvantaged populations are held responsible for readmission rates beyond their control.


Readmission Rates Vary by Geography and No One Knows Why[3]

In Part 1 of this blog, I showed a map of the readmission rates across the country. Now there are two interesting points about those maps.  The first is that the maps remain unchanged year after year. This means that the geographic-based dynamics are consistent year over year.


The other interesting point is that the underlying health profile across these geographic regions is essentially the same.  In other words, the factors that drive readmission rates are not tied to differences in the health of the general population on a regional basis.  There are other drivers, but those drivers are not well understood.


We Think We Know the Answers; Not Sure We Do

The experts are in general agreement about how to reduce readmission rates. Surprisingly, only very few of the hospitals that adopt the recommended practices actually see reductions in readmission rates!  This is counterintuitive.

The four generally recognized ways to reduce readmissions are:

  • Improve discharge management with follow-up
  • Patient coaching
  • Disease/health management
  • Telehealth services

Unfortunately, the evidence shows that these common-sense techniques do NOT generally lead to lower readmissions.  The research is consistent on this finding in both community hospitals as well as teaching and research hospitals. What the data for a study CMS conducted looking at changes in readmission rates during 2008 to 2010 showed is that reductions in readmission rates are slow and inconsistent.


Do You Like to Play Whack-A-Mole?

As a boy, I remember going to the country fairs in August and playing Whack-A-Mole. Some of you may know the game.  The game has a board with about a dozen holes cut into it. “Moles” would pop out of the woodwork at random times; I never knew when and where the next one would pop out. My job was to hit the mole on the head with a mallet.  I often missed.

In some respects, taking steps to reduce 30-day readmission rates reminds me of playing Whack-A-Mole – although it shouldn’t. It seems that even though we know what we should do to reduce readmission rates, doing the “right thing” rarely leads to the desired outcome. To the extent this is true, it suggests that we don’t understand the underlying problem or that we don’t know how to address the problem.


Here Are the Best Ways to Reduce Readmission Rates

The best way to reduce readmission rates is to only accept patients who are not very sick in the first place. These folks can be patched up fairly quickly and put back on the street with a much lower chance of being readmitted.

Another technique is to reduce the overall intensity of healthcare delivery.  One would think that intensive levels of healthcare would lead to healthier populations. That, in turn, would lead to lower rates of readmission. Not true.

A third technique is to change the regional practices of hospital site care.  In some areas, patients are more likely to go to a hospital for initial care rather than a local clinic or a GP. In those cases, readmission rates are higher. If we could discourage patients from using hospitals as their primary source of healthcare, we could reduce readmission rates.

We also need to change the financial incentives. Hospitals that are given the choice between leaving a bed empty and losing the revenue or readmitting a patient and increasing its readmission counts will rarely pass up the opportunity to earn a dollar today.

Experience also shows that taking steps to reduce readmissions in only one area (e.g., better discharge planning) has little impact. But if steps are taken in a number of mutually reinforcing areas, the hospital will see better results.


So, What Does It All Mean?

So, what’s the “take away” from all this?  Well, the first thing that occurs to me is that this is a very complex problem that we don’t seem to understand well in spite of the focus it has received.

Second, we should not hold hospitals accountable for outcomes they cannot control.  We need system-wide changes, not simply improved hospital procedures.

Third, even teaching and research hospitals – where we presumably find the best-of-the-best in healthcare – have not shown significant improvements in spite of their efforts.

Fourth, readmission rates vary geographically but change very little over time for any given geography.  That means there are forces at play we have not yet identified.

Fifth, racial and ethnic minorities have higher rates of hospital readmissions. These demographics have lower levels of trust in the “system,” take less personal responsibility for their health, have lower levels of health literacy, and suffer from higher rates of mental illness.

Sixth, 30 days is an arbitrary time frame.  It’s even possible that hospitals that focus on reducing 30-day readmissions will create unexpected negative consequences in other parts of the delivery system – although no research has substantiated this fear.


Read Part 1 HERE


[1] A Guide to Medicare’s Readmissions Penalties and Data,

[2] Guide to Preventing Readmissions Among Racially and Ethnically Diverse Medicare Beneficiaries,

[3] The Revolving Door: A Report on U.S. Hospital Readmissions,


Are 30-Day Readmissions Rates a Reliable Indicator for Poor Healthcare Delivery? (Part 1 of 2)

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A look at healthcare delivery quality through hospital readmission rates.

Obamacare (Patient Protection and Affordable Care Act) has provisions that require the Centers for Medicare and Medicaid Services (CMS) to financially penalize hospitals and clinics that have unacceptably high readmission rates for Medicare and Medicaid patients within 30 days. Institutions with high 30-day readmission rates in just a handful of situations will suffer financial penalties for ALL Medicare and Medicaid charges during the following fiscal year – not just the handful that are monitored.  Specifically, the CMS tracks 30-day readmission rates for[1]:


  • Heart failure
  • Heart attack
  • Pneumonia
  • Chronic lung problems (emphysema and bronchitis)
  • Elective knee and hip replacements


The penalties can be as much as 3% of all the Obamacare charges for the coming fiscal year. In most organizations, this can easily amount to millions of dollars. In larger institutions, this can amount to tens of millions of dollars.

The rationale behind this policy is that high 30-day readmission rates are a reliable sign of poor healthcare delivery. The idea is that if the hospitals had done a good job in the first place, patients wouldn’t need to come back so soon.

Ironically, I would say that this claim is both true and false. There are good reasons to treat 30-day readmission rates as a reliable surrogate for poor healthcare delivery.  But there are equally good reasons to treat this arbitrary metric as completely misleading.  We will explore both sides of this argument. Part 1 of this blog will argue that 30-day readmission rates are a reliable guide to the overall quality of the healthcare provided.  Part 2 of this blog will argue just the opposite: 30-day readmission rates are a bogus measure of the healthcare provided.


Medicare Readmissions Cost $17 Billion a Year

The most compelling argument in favor of using the 30-day readmission rates as a metric of quality comes directly from the Centers for Medicare and Medicaid Services (CMS). CMS claims that of the total $26 billion it pays annually for readmissions, $17 billion of that figure is for avoidable readmissions[2]. One in five elderly patients returns within 30 days of discharge. These are staggering numbers and, if true, are a strong indictment of the healthcare industry.

Further, this is the figure only for Medicare and Medicaid readmissions – a minority of all hospital admissions. Since there is no organization charged with tracking the costs of readmissions for those with private health insurance or no insurance at all, we will never know the full extent of avoidable readmissions for all patients.


Poor Communications at Discharge Is a Primary Driver of Readmissions

High readmission rates have been tracked to poor communications between hospitals and their discharged patients. Patients are often discharged with little explanation about the medications they are to take or the pain they will experience.  Post discharge pain is particularly severe for patients with hip and knee replacements.[3] Patients who expect the pain, know that it is normal, and know how to manage it are far less liable to return to the hospital than those who suffer pain and believe something has gone wrong.

There are other examples of poor communications that lead to rapid readmissions. Some patients who are admitted for chronic obstructive pulmonary disease have their condition treated and are discharged promptly. But the hospital personnel fail to tell some of those patients to stop smoking! They continue to smoke and return to the hospital promptly. Better communications at discharge about the need to stop smoking would make these readmissions unnecessary.

One patient suffered from type 2 diabetes for 14 years. She showed up at the hospital because her blood sugar was out of control. She got patched up and was back on the street again – but with no idea how to administer her insulin or manage her diet. Wham! She was back in the hospital again. This time the nurses and dietician showed her how to handle her insulin and how to change her diet. This was the first she had heard of these things in 14 years.  Strange but true.

Some research[4] indicates that 30-day readmissions could be reduced by 5% simply by improving communications with the patient prior to and at discharge while following a defined process of care protocol.  This is a cheap solution to an expensive problem.

If the solution is so obvious, why hasn’t it been widely adopted? Well, it really boils down to the way our healthcare system is organized. Each of the participants in the system does his or her job as they were trained to. If the system doesn’t focus on clear, thorough communications at discharge, it won’t happen. But that is changing.  Now that CMS is tracking readmission rates, financial penalties are applied regularly, and research uncovers the underlying reasons, the system is changing. Again, we need to point the finger at the hospital protocols, not the individual practitioners.


Poor Follow Up is a Big Problem, Too

Half the Medicare patients do not see their general practitioners or a specialist during the first two weeks after their discharge. We have no numbers for non-Medicare/Medicaid patients, but it is reasonable to assume that the story is somewhat similar.

This lack of follow up leaves patients who suffer problems – real or imagined – little recourse but to return to the hospital where they received their most recent care.  Most of them don’t know what else to do.

“Evidence Based Medicine” May Be Another Culprit

Medical and nursing training focuses on the technical aspects of healthcare. This training focuses on the “evidence-based” aspects of what works and what doesn’t. Since there have been few (perhaps no) studies of the importance of patient/clinician based interactions, patient communication hasn’t attracted the attention it should as an important factor in long-term healthcare.

But even if there have been no studies to validate the importance of those communications, common sense should have done the trick.  In any case, the culture is likely to change. Hospital staff will pay more attention to discharge communications in the future.


Race and Ethnic Background Are Major Factors in Readmissions

Race and ethnic background are important factors in determining readmissions. Blacks and Hispanics have higher rates of avoidable readmissions than whites.[5] There is a multitude of reasons for this:

  • Less likely to see a primary care provider or specialist
  • Less likely to have a primary care provider they visit regularly
  • Limited proficiency in English leads to poor follow up (less likely to take the medicines prescribed, less likely to understand the discharge instructions, etc.)
  • Poorer health literacy and, as a result, less likely to take personal responsibility for their health
  • Cultural beliefs and customs
  • Less likely to have adequate food, transportation, and social support to follow medical regimens
  • More likely to suffer anxiety, depression, and poor mental health
  • More likely to suffer from a host of medical problems that lead to readmission

Collectively, this means that it is costlier and more time consuming to deal with these patients. When hospital readmission rates were not measured, there was no financial incentive for hospitals to make special efforts to deal with these demographic groups. But now that these statistics are measured and reported publicly and there are financial penalties, we are likely to see hospitals take the steps necessary to minimize readmissions with this demographic.

This does not suggest that hospital administrators were negligent in the past. Rather, it suggests that they were responding to public evaluation and financial metrics that made sense at that time. Once we change the system, we change behaviors.


What Gets Measured, Gets Done

This is an old management bromide that applies directly to hospital readmissions. Until the CMS started focusing on hospital readmissions, the issue simply escaped notice. Since it was never an issue, it was never addressed. It was only when healthcare administrators found that their institutions were evaluated and financially penalized with this metric that they focused on it.  That is normal.

Measuring 30-day readmissions and penalizing the worst performing 25% brought a focus to healthcare quality that has been missing for the last three millennia.

The fee-for-service payment model that has been used in this country since day one has never brought light to bear on the quality of healthcare. We have always automatically assumed that all clinicians showed superb judgment and did all that can be done. This uncritical attitude never held anyone in the healthcare field accountable for actual results.

Now, here’s the important point: By pointing a spotlight on high readmission rates and putting penalties in place to penalize poor performers, the federal government believes it can change behaviors.  The rise of Accountable Care Organizations to address this issue is unlikely to have occurred without this sort of impetus. Further, there is evidence (The Revolving Door) that this new-found attention is, in fact, changing some behaviors at the community level. In other words, by measuring readmission rates, hospitals find that they can improve their performance on this metric.


Readmissions Are Determined by Where Patients Live

If patient demographics and healthcare delivery systems were homogeneous across the country, we would expect to find the same rate of readmissions uniformly everywhere.  That is not the case. Rather, we see a lot of “lumpiness.” In other words, the rates or readmissions to hospitals are determined to a surprising degree by where patients live.

The map below shows the intensity of readmission rates within hospital referral regions.

Although it would be convenient to tie these widely ranging readmission rates solely to quality of medical care, that would be a mistake.  There are other forces at play:

  • Patient health status
  • Discharge planning
  • Care coordination with primary care physicians and other community based resources
  • Quality and availability of ambulatory care services

Further, some places treat their hospitals as a routine site of care. In other words, it is normal for those in some areas to go to the hospital rather than doctors’ offices or community clinics.

Percent of patients readmitted within 30 days following medical discharge among hospital referral regions (2009)


Here is something else I find interesting. If you look at the readmission rates for any one of the five factors I listed immediately after the first paragraph above, you’ll find that the readmission rates for the other four factors are nearly the same for hospitals in the same geographic region.   This correlation suggests that there is some dynamic at play that is independent of the illnesses and chronic conditions in the region.

In other words, the patient is not at the hub of the healthcare system.


So, What Does It All Mean?

It requires some judgment to stand back, look at this disparate information, and draw conclusions.  In fact, different people are likely to draw different conclusions.

Nevertheless, I think it’s reasonable to say that 30-day readmission rates can be used, at a minimum, as a rough measure of quality of care. The rise of Accountable Care Organizations (which we will discuss later) and the fact that hospitals have been able to shift their position significantly on the readmissions scale suggests that improvements are possible if we develop the right metrics, measure all hospitals by the same yardstick, and provide rewards accordingly.


Read Part 2 Here


[1] A Guide to Medicare’s Readmissions Penalties and Data,

[2] The Revolving Door: A Report on U.S. Hospital Readmissions,


[3] Reducing Readmission Rates with Superior Pain Management, by Bobbie Gerhart, owner, BGerhart & Associates, LLC; former president, Miami Valley Hospital, Dayton, Ohio


[4] What Has the Biggest Impact on Hospital Readmission Rates, by Claire Senot and Aravind Chandrasekaran


[5] Guide to Preventing Readmissions among Racially and Ethnically Diverse Medicare Beneficiaries, Prepared by: The Disparities Solutions Center, Mongan Institute for Health Policy, Massachusetts General Hospital, Boston, MA

Digitally Transforming Healthcare Industry

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Big Data Has Changed the Practice of Healthcare Forever – and the Change is Just Beginning. Healthcare organizations – old and new – are investing heavily in Big Data applications.

Big Data projects process data measured in petabytes to deliver significant healthcare benefits. Only a small proportion of that data comes from traditional databases with well-structured data. Instead, almost all of the data comes from sources that are messy, inconsistent, and never intended for a computer to use. I’m talking about messy, unstructured patient records. Accessing this unstructured data and making sense of it gives health care professionals and leaders insights they would never have otherwise. They directly affect the way health care is delivered on a patient-by-patient basis.

I’ll give you four real-world examples the health care industry has already realized. We’ll take a quick look at Apixio, Fitbit, the center for Disease Control, and IBM’s Watson Health.


Medical research has always been conducted on randomized trials of small populations. No one tried to conduct massive healthcare research using all the data on all patients because the work would have been over whelming. Limiting the size of the data sets researchers used made their research manageable. Working with small sample sizes creates methodological flaws of its own. This is not to criticize those studies but to recognize the limitations of the research outcomes based on the limitations of what was feasible at the time those studies were conducted.

Apixio set out to change all that. Apixio developed mechanisms for conducting healthcare research based on studies of actual patient healthcare records. Their mechanisms leverage both Big Data and machine learning. Further, they work with ALL the patient healthcare records a facility has to offer – not just a randomized subset. As new patients are treated, Apixio collects data about the symptoms, diagnoses, treatment plans, and actual outcomes. By integrating these new cases into the mix, the company can quickly determine what works and what doesn’t. The difference between discovering the effectiveness of healthcare treatment programs based on limited clinical research studies and those based on analyses of the effectiveness of treatment programs based on reviews of ALL patients can be dramatic. I’m talking here about studying the treatment outcomes for all patients, not just a small number included in clinical research studies.

Only about 20% of the patient healthcare records reside in well-ordered databases. 80% of the data is messy, unstructured data. I’m talking about the GP’s notes, consultant’s notes, and forms prepared for Medicare reimbursement purposes. Working with unstructured data used to be problematical. Institutions had to hire and train “coders” who would read free form materials (handwritten notes, typed notes, etc.) and capture the meanings of those notes in a form suitable for computer processing. Apixio dealt with this issue quite differently. It used computer based algorithms to scan and interpret this data. The company found that its computer assisted techniques enable coders to process two to three more patient records per hour. Further, the coded data it created this way can be as much as 20% more accurate than the manual only approach.

This computer-assisted approach also finds gaps in the documentation. In one nine-month period, Apixio reviewed 25,000 patient records and found 5,000 records that either did not record a disease or didn’t label it correctly. Correcting the data can only improve diagnoses and treatment programs.

Apixio does far more than produce studies that physicians can use to inform their treatment plans. It takes the next step. It reviews the healthcare records of each patient and develops personalized treatment plans based on a combination of the data it has collected for that patient and the results of its analyses of practice-based clinical data. This enables physicians to only order the tests that are useful and avoid expensive but worthless procedures.

This pays off handsomely for insurance companies that treat patients who are enrolled in the Medicare Advantage Plans. Under these plans, Medicare pays a “capitated payment.” This is a payment paid to treat patients based on their expected healthcare costs. By tailoring the diagnostic tests and treatment programs by individual, the company is able to reduce its costs dramatically. Those savings drop directly to the bottom line.

It’s not just the insurance companies that benefit, though. Patients benefit as well. Patients are not required to undergo inconvenient or painful procedures that would provide no benefit.


Fitbit is the leader in the sale of wearable devices that track fitness metrics, although Apple is hot on its heels with its Apple Watch. Fitbit sold 11 million devices between its founding in 2007 and March 2014. These devices track fitness metrics such as activity, exercise, sleep, and calorie intake. The data collected daily can be synchronized with a cumulative database that allows users to track their progress over time.

The driving principle here is that people can improve their health and fitness if they can measure their activity, diet, and its outcomes over time. In other words, people need to be informed in order to make better fitness decisions. Fitbit provides users with progress reports presented in a preformatted dashboard. This dashboard tracks body fat percentage, body mass index (BMI), and weight among other metrics.

Patients can share their data with their physicians to give them an on-going record of their key healthcare parameters. This means that doctors are not forced to rely on the results of tests that they order on an infrequent basis. To be fair, however, not all physicians are open to treating the data their patients collect on their own to be as credible as that collected in a clinical setting.

Insurance companies are prepared to adjust their premiums based on the extent to which their policyholders look after themselves as measured by Fitbit. This means that policyholders are required to share their Fitbit or Apple Watch data with the company. John Hancock already offers discounts to those who wear Fitbit devices and the trend is likely to spread to other insurance companies.

The fastest growing sub-market for Fitbit is employers. Employers can then provide their employees with Fitbit devices to monitor their health and activity levels (with their permission).

The CDC and NIH

The Center for Disease Control (CDC) and the National Institutes of Health (NIH) are leaders is applying Big Data identifying epidemics, tracking the spread of those epidemics, and – in some cases – projecting how they are likely to spread.

The CDC is tracks the spread of public health threats including epidemics through analyses of social media such as Facebook posts.

The NIH launched a project in 2012 it calls Big Data to Knowledge or BD2K. This project encourages initiatives to improve healthcare innovation by applying data analytics. The NIH website says, “Overall, the focus of the BD2K program is to support the research and development of innovative and transforming approaches and tools to maximize and accelerate the integration of Big Data and data science into biomedical research.”

A couple years ago the CDC used Big Data to track the likely spread of the Ebola virus. It used BigMosaic. BigMosaic is a Big Data analytics program that the CDC coupled with HealthMap. HealthMap is a data base that maps census data and migration patterns. HealthMap shows where immigrants from various countries are likely to live – right down to the county or even the community level. When the CDC identifies countries where there is a public health problem – like the Ebola virus – it can link that census data showing the distribution of expat communities with airline schedules to determine how the disease is likely to spread in the US – or even other countries. This allows the CDC to track the spread of disease in near real time. In some cases, it could even project how diseases are likely to spread.

These Big Data applications merge data about weather patterns, climate data, and even the distribution of poultry and swine. These applications present this data in a graphic form that makes it easier for epidemiologists to visualize how diseases are spreading geographically. The benefit, of course, is that the CDC and the World Health Organization can deploy its scarce resources to the areas where they can do the most good. They can do that because Big Data provides the tools to chart the spread of diseases by international travellers.

The Center for Disease Control now uses Big Data linked with Social Media to forecast the spread of communicable diseases. Historically, CDC tracked how they observed the reported spread of diseases; forecasting how diseases will spread is a new ball game. The CDC ran competitions for research groups to develop Big Data models that accurately forecasted the spread of diseases. The CDC received proposals for 28 systems. The two most successful were both submitted by Carnegie Mellon’s Delphi research group. These models are not predetermined but, instead, leverage Machine Learning to develop tailored models to forecast the specific spread of each disease.

The model is by no means perfect. The CDC gave the Carnegie Mellon model a score of .451 where 1.000 would be a perfect model. The average score for all 28 models was .430. That means that the model the CDC will use is the best available and much better than nothing, but still has considerable room for improvement.

The Delphi group is studying the spread of the dengue fever. It has plans to study the spread of HIV, Ebola, and Zika.

IBM and Watson Health

IBM is particularly proud of Watson, its artificial intelligence system on steroids. Although Watson has produced some stunning results such as winning the TV game Jeopardy against the two best Jeopardy contestants, our interests today are in healthcare.

Watson is machine learning at its finest. In the healthcare field, its managers feed it an on-going stream of peer reviewed research papers from medical journals and pharmaceutical data. Given that Big Data knowledge base, Watson applies that knowledge to individual patient records to suggest the most effective treatment programs for cancer patients. Watson’s suggestions are personalized to each patient.

Watson’s handlers don’t program the software to deliver predetermined outcomes. Instead, they apply Big Data algorithms to enable Watson to learn for itself based on the research it reviews as well as the diagnoses, treatment programs, and observed outcomes for individual patients.

IBM is partnering with Apple, Johnson & Johnson, and Medtronic to build and deploy a cloud-based service to provide personalized, tailored guidance to hospitals, insurers, physicians, researchers and even individual patients. This IBM offering is based on Watson – its remarkably successful system that integrates Big Data with machine learning to enable personalized healthcare on a massive scale.

Until now, IBM has used Watson in leading edge medical centers including the University of Texas MD Anderson Cancer Center, the Cleveland Clinic, and the Memorial Sloan Kettering Cancer Center in New York. Given its successes to date, IBM is now ready to take its system mainstream and broad based.

How Medical Mobile Apps are Transforming Healthcare

mobile medical apps

Medical mobile apps are transforming the Healthcare Industry, promising to improve quality of healthcare while lowering costs.

In 2017, global medical healthcare apps were a $26 billion industry with a global average CAGR of 32.5%. The United States currently has the largest market for mobile medical apps. However, the Asia-Pacific region is showing the fastest growth rate in the world – with an estimated average CAGR of 70.8%. By 2022, the worldwide mobile medical app market is anticipated to reach a $102.43 billion.

As of 2017, mobile healthcare apps have been downloaded over 3.2 billion times – this marks a 25% increase since 2015. In the United States alone, there are over 500 million smartphone users with mobile health-related apps. The greatest growth in mobile medical apps has been in the management of chronic care – particularly diabetes, obesity, high blood pressure, cancer, and cardiac illnesses.

As the prevalence of chronic illnesses worldwide increases, so is the increase in medical apps created to help manage these chronic illnesses. Nearly half of all Americans, around 133 million individuals, currently live with a chronic illness. Per the Centers for Disease Control and Prevention, now seven of the top ten causes of death in the US are due either directly or partially to chronic illness.

Chronic illness is on the rise globally as well. According to the World Health Organization, as of 2017, over 79% of all deaths related to chronic illness occur in developing countries, and this rate is anticipated to continue to climb. Heart diseases and other cardiovascular illnesses will continue to be the major cause of mortality throughout the globe. Asia, in particular, is experiencing the greatest rise is cardiac disease and death due to heart-related complications.

The widespread availability of tablets and smartphones in healthcare today is what is helping spur the use of mobile healthcare apps by patients and providers alike. According to referralmd, over 80% of physicians in 2017 use their smartphone at the point of care – whether for patient services or for administrative reasons. The wide access to and use of smartphones by providers and patients alike has been the primary driver behind the increasing availability of mobile healthcare apps year-over-year.

How can mobile apps help? What kind of mobile apps do patients want? And which kind do physicians need?

The healthcare industry is filled with opportunities for digitally savvy companies and mobile app developers.

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