How Accepted Standard of Practice in Medicine Has Harmed Patients

How Accepted Standard of Practice in Medicine Has Harmed Patients

The US (and sometimes global) medical community has not infrequently accepted and disseminated medical practices that ended up harming, including killing, millions of patients. If you want to learn more than what is in this post, just Google “medical treatments that harmed more than helped”. Here are just a few that not only became accepted standard of practice, but doctors were unwilling to stop them when they were proven wrong. Some of these I’ve already described.

X-ray Therapy for Adolescent Acne

In the late 1800’s and early 1900’s doctors began treating acne with x-rays. There was very little knowledge or understanding of the long-term consequences of x-ray treatments. The patient’s skin looked better. That’s all doctors focused on. As it turned out, x-ray treatment for acne caused thyroid cancer, parotid (salivary gland) and parathyroid tumors. Some doctors were still using the treatment in the 1990’s even though a causal relationship was suspected and proven before 1960. Yes, a hundred years of using a treatment for a non-life-threatening condition whose side-effects were life-threatening, despite warnings.

The Low-fat, Low-cholesterol Diet for Cardiovascular Disease Prevention Leads to the Obesity and Diabetes Epidemics

In the mid-late 1970’s and really peaking in the 1980’s, the Cardiology Societies and literature fanatically promoted a low-fat (including a no-fat), low cholesterol diet for primary and secondary prevention of heart disease, mainly heart attacks. It turns out the research was funded by the Sugar Industry. This was revealed in a 2016 article published in JAMA Internal Medicine (Sugar Industry and Coronary Heart Disease ResearchA Historical Analysis of Internal Industry Documents; Cristin E. Kearns, DDS, MBA; Laura A. Schmidt, PhD, MSW, MPH; Stanton A. Glantz, PhD; JAMA Internal Medicine Published online September 12, 2016). This resulted in people switching from their current diet to as low a fat and cholesterol diet they could get. The food industry flooded the market with low or no fat food products, like Snackwells, that were predominantly carbohydrates (lots of sugar) and processed grains. Around this time, the average daily caloric intake increased from~2000 calories a day to ~2600 calories a day without a commensurate increase in average daily caloric expenditure. Some experts assert that the low/no fat diets fueled the increase in caloric intake, as carbohydrates don’t create long-term satiety (that’s why you get hypoglycemic and start looking for something to snack on at 10:00 AM when you eat a predominantly carbohydrate breakfast at 7-8:00 AM). You need a certain amount of fat and protein to suppress hunger and maintain an adequate blood sugar level.

So, the Sugar Industry started, guess what, making a lot of money. In the meantime, the obesity epidemic started. This has resulted the Diabetes Type 2 epidemic, which is killing people from complications of diabetes, including, guess what, HEART ATTACKS! And, for decades, there has been plenty of evidence that MOST HIGH CHOLESTEROL IS DUE TO GENETICS NOT DIET! Most of the high cholesterol in your blood is internally created. Diet accounts for less than 15% of the cholesterol in your blood. So, now, recent guidelines downplay diet, since it doesn’t contribute that much. Here’s a quote from the Cleveland Clinics website: “Research is beginning to show that your genetic makeup – not diet – is the driving force behind cholesterol levels. About 85 percent of the cholesterol in the circulation is manufactured by the body in the liver”. By the way, this has been known for at least two decades, so the “research is beginning to show” statement is a little disingenuous.

The result of all of this misinformation and Cardiology fanaticism is The Obesity and Diabetes Epidemics. These were foisted on the US and global population by the Cardiologists and flawed research. I remember when the Cardiologists bashed the Atkins Diet, which is low carbs, high fat/protein dieting. Now research is showing that’s better for weight reduction and maintenance than the low fat diet.

So, millions of people are dying due to fanatical promotion of a dangerous lifestyle by medical specialists. As a primary care physician I always was suspicious about the low fat diet. But, I had to recommend it because for 40 years the “guidelines” promoted it and I would get into trouble (and potentially get sued) for deviating from these guidelines (See my post about “Why Health Care Costs So Much – Part 3 – Overtreatment”: The science isn’t as scientific as it looks”(http://www.truthsabouthealthcare.com/costs/overtreatment/).

The Opioid Epidemic

Now we have the Opioid Epidemic. I’m not going to get into this one here as I have an entire post on this (See: “The Real Story Behind The Opioid Epidemic” (http://www.truthsabouthealthcare.com/general/the-real-story-behind-the-opioid-epidemic/)). All I’m going to say here is, the Opioid Epidemic was totally avoidable if the medical community did its due diligence instead of blindly following the American Pain Society (another specialty fanatically promoting unsubstantiated evidence) and the VA and The Joint Commission. I personally never got caught up in this because I never prescribed opioids for chronic pain; I used non-medication interventions for chronic pain.

So, now we have THREE epidemics caused by and maintained by the medical profession, fueled by the physician specialists who got carried away.

High Dose Chemotherapy and Bone Marrow Transplantation for Breast Cancer

I already posted about this one, too. A “standard of care” based on faulty, and even falsified, medical evidence and then fanatically promoted by Oncologists. See my post titled “Why Health Care Costs So Much – Part 3 – Overtreatment”  (http://www.truthsabouthealthcare.com/costs/overtreatment/) for a more detailed description of this one. Women dying of the treatment more than the disease itself.

Estrogen and Progesterone Therapy for Post-Menopause Women

Again, I already commented on this (See my post about “Why Health Care Costs So Much – Part 3 – Overtreatment” (http://www.truthsabouthealthcare.com/costs/overtreatment/ )). Major promotion of giving post-menopausal women Hormone Replacement Therapy (HRT) by Endocrinologists and OB/GYNs, which was said to prevent heart attacks, strokes, cancer, etc, when it actually had a detrimental effect on women’s health to the point where the mortality rate was higher in women treated with HRT. Now it is considered malpractice to treat most women with HRT.

Millions of women killed by a treatment fanatically promoted by physician specialists.

And I’m not even getting into Patient Safety, which is a process deficiency issue killing at least 250,000 people a year because neither health care institutions nor physicians see the incredibly urgent need to make health care systems and processes as reliable and error free as possible.

The bottom line is, be wary of anything being fanatically promoted by medical specialists, drug companies or hospital systems. Your life may depend on it. A good rule to live by is “everything in moderation”.

 

The “We Need More Doctors and Advance Practice Clinicians” Myth

Before you read this post/chapter of TruthsAboutHealthCare, you should read the post/chapter pasted below:

http://www.truthsabouthealthcare.com/wp-admin/post.php?post=64&action=edit

An excerpt a propo to this post/Chapter is this:

“On May 16, 2017, this article was published in JAMA (“Reassessing the Data on Whether a Physician Shortage Exists”; JAMA May 16, 2017, Volume 317, Number 19). Here is an excerpt:

“The United States currently has more than 900,000 active physicians. Of these, 441 735 are primary care physicians and 484,384 are specialists. Approximately 12% of primary care physicians work part time, leaving slightly more than 388,000 full-time primary care physicians. Of these, nearly 80 000 are pediatricians. According to recommendations from the Agency for Healthcare Research and Quality, the average physician panel size—the number of unique patients under the care of an individual physician—should be between 1500 and 2000. A recent Medical Group Management Association survey of primary care physicians found that the median panel size was 1906 and the average was 2184. Conservatively, if each of the 388,000 full-time primary care physicians cares for an average of 1500 patients, they could care for an estimated 583 million people. Today, there are 240 million adults in the United States. Even at the low panel size of 1500 patients, all adults could be cared for by 160,000 primary care physicians; at a panel size of 2000 patients, the United States would require an estimated 120,000 full time primary care physicians. Similarly, the 73 million US children younger than 18 years could be cared for by an estimated 49,000 pediatricians, assuming that each provides care for 1500patients, or by an estimated 36,500 pediatricians with panel sizes of 2000 patients. Add to these conservative calculations the care provided by the more than 50,000 part-time primary care physicians and there seems a significant surplus, rather than a shortage, of full-time primary care physicians.”

The thing is, you can find many articles and projections and you hear on the news all the time that the US needs more doctors and advanced practice clinicians (APC) (combined to be referred to as “Providers”).

I’m going to expand on why this dichotomy in thinking exists. The main reason being that the supporters of more Providers is they completely lack knowledge into what are the drivers of their conclusion that are not going to be solved by just adding more Providers.

Let’s just take Primary Care Providers (PCPs). Why can’t the current number of PCPs take care of 320,000,000 US people?

Problem #1: Inefficiency of the US health care system.

The US health care system is incredibly wasteful, as described in the post attached to this URL:

http://www.truthsabouthealthcare.com/wp-admin/post.php?post=280&action=edit

It’s been well established by efficiency gurus like Joseph Juran that the more inefficient a system or process is, the more people you need to run the system or process to offset the inefficiency. To illustrate this point, here is a fictitious analogy.

A burger joint opens. It is the only burger joint for 50 miles. The burgers are good and cost 50 cents each. The critical step in the delivery of burgers to the customer is “grill the burger”. The burger joint has one griddle in the joint and it can only grill four burgers at a time. When the burger joint first opens, there are few customers, everything goes well, people get their burgers in 2-3 minutes. Soon, the word is out that the burgers are really good, and more customers arrive. Due to the limited number of burgers a griddle employee can cook at one time, the wait time for a burger increases to 10-15 minutes. People start to complain. To assuage the customers, the burger joint owner tells the customers every 2 minutes that they will be getting their burger soon, assuming as long as people know they will eventually get their burger, customers will complain less.

The burger joint owner then installs another four-patty burger grill and hires another griddle staffer to man the griddle. Because of the cost of the griddle and the new employee, the burger joint owner has to increase the price of the burger by 20%. People grumble but anti up the new price as the wait time goes down to 5 minutes. Again, more and more customers frequent the joint and the wait time increases. The burger joint owner installs two more four-patty griddles and hires two more griddle staffers. The price goes up to $0.90, but the wait time is back down to 5 minutes. Customers are angry about the price increase but, they can’t drive 50 miles to another burger joint and so they simply pay the price.

This is how the US health care system works today. Add more people into an inefficient system, replicate that inefficient system in new clinics and hospitals and just up the price because health care is what, in economics, is called an “inelastic product/good”. In economics, this means you can increase the price and the demand will stay relatively the same because people really need it. Gasoline is a similar inelastic product/good. Health care administrators know this fact, so, rather than trying to fix the system, it is more lucrative to build more revenue centers and install the same inefficient processes. Besides, they can just continue their cost plus strategies. People will pay anyway. And besides, the people don’t know that there is a better way to do things. Easy decision for the bean counters.

The real solution to the burger joint wait time problem is to rip out the four-patty grill and install a 16-patty griddle manned by one staffer. The price increase would have been only a nickel and the wait time would have stayed at 2-3 minutes.

The inefficiencies in the health care system are incredibly numerous. There are six chapters about this at truthsabouthealthcare.com. Here’s a short list at the PCP level:

  1. Low performing clinic throughput systems (registration, rooming patients, the EMR, etc.)
  2. Provider-centric patient scheduling. For example, all appointments are in 30-minute intervals, all new patients (no matter the patient complaint) are scheduled for a 40-60 minute appointment, start and stop times inconvenient for patients, etc.)
  3. Days and times Providers are available are Provider centric and not in synch with peak demand times or patient preferred times and days.
  4. Time spent on inquiry that duplicates or triplicates already documented facts that are readily available.
  5. Rooms, like Treatment Rooms, that lie vacant >90% of the time, when they could be used as exam rooms.
  6. Exam rooms “saved” for patients who are undergoing testing, reducing the availability to move patients through the clinic. (in other words, a patient has a test ordered where they have to vacate the exam room they were in and the exam room remains empty while they are getting the test, instead of rooming a new patient in the exam room and having the patient consult with the Provider about the test in the Provider’s office.)
  7. Not utilizing Nursing staff to do tasks within the scope of their licensure, having Providers do those instead. Things like reviewing test results and notifying the patients with normal results or abnormal results after consulting the Provider about the care plan. 
  8. Not having pre-set-up “kits” for things like laceration repair or pelvic exams so staff don’t have to search and collate the necessary materials.
  9. Not having exam rooms set up to provide Providers with what they need with minimal movement. Like, instead of tongue depressors being in a drawer or jar, have a dispenser on the wall next to the oto/ophthalmoscope so that a Provider can reach one without moving from the exam table.
  10. Providers refuse to do add-on appointments for their own patients, creating a daily fixed number of patients they will see. This is why Walk-in and “minute-clinics” are doing so well.

I’m sure there are more, but I think you’re getting the point.

Problem #2: Distribution of PCPs.

While not as extreme as some would have you think, there is somewhat of a preference for PCPs to practice in urban areas. See figure below from AHRQ. (Reference:  https://www.ahrq.gov/research/findings/factsheets/primary/pcwork3/index.html)

At the same time there is a significant issue with distribution among states and among counties within states, especially the larger more rural states. Patient to PCP ratios, for example in a state, can range from 700:1 to 3000:1. There is obviously a PCP supply problem in the county with a 3000:1 ratio. For example, go to this URL to see the distribution by county for New Mexico: http://www.countyhealthrankings.org/app/new-mexico/2018/measure/factors/4/data

Problem #3: More Providers are part-time.

More than any other time in US history, physicians and APCs are working part time, and their colleagues decline seeing their patients when they are not in the clinics. Patients have to wait. For acute problems they go to Urgent Care. Their skewed access metrics contribute to the idea of Provider shortages.

Problem #4: Failure to successfully implement a team model that optimizes the skills, licensure and availability of each team member. There are pockets of these models, but only a few function well, and often the team members are stuck in the same set of issues outlined above in the inefficiency section.

Problem #5: Inability to “mass customize”.

Health care systems, including PCP offices, try to put all patients in the same “box”. The same time slots, the same rooming procedures, etc. no matter what the patient type or their problem or problems. It’s like everyone having to have the same smartphone with the same apps on it, or the same computer with the same RAM and disk space and features.

I’m sure this isn’t all of it, but you see what I mean. Instead of improving the systems that Providers and patients are stuck in, people will advocate for more Providers. We can’t sustain the increase in brick and mortar and people (like the burger joint griddles and staff). It’s time for a major shift in thinking and action.

Oops, I forgot, the people running the health care systems are incapable of mustering up that shift. Same ol’ same ol’ persists. The sacred cows are still munching. The leaders are clueless.

The State of Mediocrity in Medicine: Just OK is not OK

The State of Mediocrity in Medicine: Just OK is not OK

I’ve mentioned this problem before, but it manifests in more than one way in health care.

There are plenty of articles reporting that the Patient Safety Movement, which is now over 20 years old, has gained little movement in those 20+ years.

If you go to a surgeon and ask them what their surgical complication rates are, they quote the national averages and that their rates are similar to the national average.

Have you seen those commercials where everything is “OK”? One has a doctor walking in saying his privileges were, maybe, just reversed and the nurse says he is “OK”. Or the waitress at a sushi joint who says the sushi is “OK” and the chef got sick after eating the sushi but a replacement is “OK” (then the kid preparing the sushi says, “Hey, this fish is raw!”)

You see, being OK with being average or having average performance is being OK with being OK. Even in those commercials, being OK is not OK. Right? Don’t you want better than a “C” performance? From everything? Your car? Your smartphone? Your surround sound system? YOUR HEALTH CARE??? Just OK is not OK!

Try it sometime. Ask your health care practitioner what their performance scores are. Ask your PCP what his/her hypertension control or diabetes control rate is. What is that performance compared to national performances. More than likely s/he hasn’t a clue. Maybe if they actually read a practitioner performance profile they get from a health insurer they might know their own performance data, but, most practitioners throw them into the circular file (anyone under 30 know what that is?).

Here’s an example. I had to have a major abdominal surgery. I chose a surgeon I knew and whom I respect (she is a terrific surgeon!). At the pre-op visit, I asked her to go through the risks and complications of the surgery. She was very knowledgeable in the national averages, which I already knew. So, then, I asked her what her personal complication rates were and she said her hospital rates were the similar to the national averages. I said I was more interested in her personal rates. She said she didn’t know that. I told her that, since she was working in an integrated health care system (Physician Group + Hospitals + Health Plans) and that, especially for the system’s health plan members, they had all of the data necessary to calculate her own personal complication rates, she should know her personal rates. She said the surgeons had asked for that, but the system had never made that happen. The reason? The finance folks running the system didn’t perceive that that would help the health care system improve their bottom line (remember, it’s all about the money). First, it costs money to prepare those kinds of statistics. Second, people coming back in for treatment for complications adds to the bottom line….except for “capitated” patients (pre-paid monthly), of which this system has a significant number, but their thinking is still “fee-for-service” (the more you do the more you get paid). In actuality, being excellent in post-op complications would lower the cost per patient in their capitated population. But, of course, your expecting C-player MBA’s to understand this. And the doctors don’t even know how the system is getting paid.

So, let’s have a short lesson in math. To calculate the rate of something in a data set (for example, the cost of a house in a state) you have many data points.  Using the house example, not every house has the exact same sale price, so the values for the sales prices in dollars are variable. You’ll see periodic articles in your local newspapers about house prices. Data sets have what’s called a distribution of values (sales prices). Some houses may cost as little as $100,000 and some as much as $2,000,000. When you graph the number of houses against the price of the houses you get a “distribution curve” which usually comes out looking like a bell, so, they call them “bell curves”. There are two basic types of bell curves: a normal one and non-normal one. A normal curve looks just like the Liberty Bell: symmetrical. A non-normal curve has a tail on it either on the right or left side. In the case of houses, the tail is usually on the right, since houses over $1,000,000 are usually less prevalent than houses less than $1,000,000. For example, in most states or cities, most houses are in the $200,000 to $700,000 range.

Now, there is a statistical test for “Normalcy” called the Anderson-Darling test. If that test says your bell curve is normal, then it is OK to calculate and use the average of the data set. If the test says your data is not normal, you can’t use the average because the average will be skewed towards the tail of the curve. In the house example, the average house price will be artificially high because the prices skew (or drags) out to the right (higher prices). Therefore, you have to use the “median”, which is defined as the value for which 50% of the values are lower and 50% are higher than the stated value. In other words, it is the value separating the higher half from the lower half of a data sample.

If you notice, house price reports usually state three values: The range of prices (e.g., $100,000-$3,500,000), the average, and the median. Almost without exception, the median is lower than the average and is more indicative of your experience in buying a house.

Having worked for years in health care data, often, health care data to have a non-normal curve. As a matter of fact, Medicare, about 25 years ago, figured this out relative to the DRG length of hospital stays (in days). A DRG is a Diagnosis Related Group (like, Pneumonias), and Medicare pays a lump sum (like, $8,000) for a hospital stay for a DRG (of, say, Pneumonia). They have an “outlier” payment if the length of stay exceeds what they historically called the average length of stay (ALOS). (actually the ALOS is used extensively in the health care insurance industry when it shouldn’t be, it should be the median) Medicare, after about 10 years, realized that hospital lengths of stay, when distributed, did not create a normal distribution. The tail is to the right, like house prices. The ALOS was skewed to the right. For the past 20 years or so, Medicare uses the geometric mean, which, incidentally, is the wrong statistical methodology for that purpose (I won’t get into this here as the explanation is several paragraphs long). It should be the median. Thankfully and coincidentally, the geometric mean approximates the median.

I’ve never seen data presented for surgical complications that detail if the bell curve is normal or not. Health care folks love averages, without testing for normalcy. I don’t know whether a complication data set has a higher or lower tail or no tail at all, so it is hard to figure out what it means when a surgeon or the hospital says they are average. But, even if you use the median, it’s really not where you want your health care provider to be. It means 50% of the health care providers in that bandwidth are better than the person you are seeing. Unfortunately, no one knows who those better 50% are. So, your care is a crap-shoot!

Whether they use average or median, is that where you want your surgeon (or any health care professional) to be? Wouldn’t you want your health care in the hands of a practitioner who is above average or median? Me, I’d like someone who is an “A” not a “C”. To me that would be 3 standard deviations better than average or median. They don’t report practitioners that way.

This is similar to the use of an “index” where one divides their performance rate by the average comparator rate (the comparator rate is usually the average performance of a number of similar entities; for example, the average performance of 400 hospitals who are reporting their data into the hospital data set; and no one figures out if it really should be the median). An index value of 1.0 means the person or hospital is performing at the average of the data set. A higher index score means they are worse if having a lower raw rate is better. For example, post-op infections are bad, so the lower the score the better. If your infection rate is 5% and the comparator average rate is 4%, your index score is 1.25. 

The problem is, practitioners and health care administrators perceive an index of 1.0 or close to that, as being just fine, or “OK” when it is not. It is better to be as close to the best performing entity in the data set. In the above example, an index of 0.3 might be the best in the group.

Another one is scoring by “percentiles”. They like it when they can say they are at the 90thpercentile or better. Scoring by percentiles is the same as scoring school essays on a “curve”. You take the range of scores and take the best score, even if the actual best score is an F, and make that an “A”, then you adjust all of the other scores so at least 60% of the class passes. For example, if all patients with diabetes are supposed to get an annual eye exam, and only 50% get them, 50% becomes the 99thpercentile, 40% becomes the 75thpercentile, etc. People think they are doing “OK” if they are above the 75thpercentile because that is a “C+ and better”, when actually they have a failing performance. 

OK, enough about statistics, but the above points actually hamper health care performance improvement, which brings us back to why health care continues to be generally mediocre and health care folks are OK with being average.

The main reason is, the practitioner, hospital, skilled nursing facility, etc. is not the one who suffers from the complication or medical error. They do not feel the pain. They don’t get the post-op infection, get put on antibiotics, then get C.difficile (a bacteria that causes horrible diarrhea that can be fatal), etc. The patient is the one who suffers. They don’t suffer when they cut off the wrong leg. The patient suffers. And the practitioner feels OK about it because the patient just suffered a complication at the average rate; it was bound to happen, right?

So, they will sanguinely report that they are “average”. They don’t try to find out who is best. They don’t strive to be best. No one reports who is best!! (FYI, US News and World Report doesn’t really know, as their results are largely based on process metrics, not outcomes metrics). No one in health care tries to figure out what the best performer is doing and tries to beat that performance to be the best! Even if they do find out who is the best, they usually don’t have the ability or the will to do what it takes to make the improvement.

I was in health care for 45 years and there are very few places that try to figure out who is doing it best and how to be as good as or better than the best. You can call me jaded but I don’t see that happening in the near future, if ever, because it is hard to do, and it is easier to settle to be average. Plus, the people running health care don’t have the will or knowledge to make it happen.

Perpetual mediocrity. Get used to it. Everything is “OK”.