by Patrick Quanten MD
Modern life is ruled by statistics. Every aspect of our lives is put into figures to prove or disprove one thing or another. Policy decisions are based on these figures and the general idea is that life gets better as we are gaining more information about statistical risks and performance. Schools and hospitals are now presented in league tables which supposedly reflect their performance and as they are made public, it is believed that people are now in a position to clearly see which are the best and which are the worst ones as far as delivering the service is concerned.
In spite of the well-known rule that you can prove anything with statistics, we persevere in our efforts to base our future on them. A statement without statistics to back it up is not worth hearing; in fact, nobody will print it for fear of looking a fool.
League tables can make or break reputations and it is therefore essential to present the available data in the best possible light. Here, we will endeavour to review ways in which figures can be, and are, "massaged" in order to meet government standards or to improve ratings. These techniques are used everywhere and are no different from the way you can improve punctuality figures of the railway network. By adding 5 minutes to every train journey you will improve punctuality figures considerably without actually having to improve the service.
Manipulation of non-clinical performance targets
A House of Commons investigation in 2002 uncovered strategies to bring waiting times and numbers of patients waiting for treatment within national targets. Records were altered, patients were inappropriately suspended from waiting lists, and some hospitals did not report patients waiting longer than government targets. Though such techniques are readily exposed, one in 10 healthcare managers admitted to "fiddling figures" in a recent survey.
More intelligent managers inquire when patients intend to go on holiday and then offer an appointment during this period. Few patients cancel their holiday for medical reasons, preferring to postpone their appointment. Since the patients initiate these delays, their wait is no longer recorded. A related strategy offers patients non-existent appointments at impossibly short notice to attend; cancellation shifts them to the back of another list whose waiting times are not officially recorded. If you identify patients waiting longer than the permitted limit, you could arrange admission when their consultant is on holiday; then apologise profusely for the cancellation of their operation and offer a new date for surgery in the distant future.
If you are only required to record patient's waiting lists for treatment in hospital, you can reduce your list dramatically by ensuring that those patients on the list are treated as outpatients, wherever possible.
If you cannot place a patient on an unpublished waiting list, use the date you periodically update the waiting list, rather than the date of referral, as the starting point. This can knock several weeks off apparent waiting time. Variations include not placing patients on the waiting list until the month of their appointment or failing to reinstate previously suspended patients.
Giving advance warning of assessment allows managers time to ensure that systems are in place to meet targets. The Department of Health chooses one week each year to record waiting times in accident and emergency departments. Cancelling unnecessary operations and keeping extra beds open that week ensures your hospital meets the national target (90% of patients seen by a doctor within four hours of arrival) at least once a year. A BMA survey in 2003 found that 72% of accident and emergency departments introduced exceptional arrangements during the audit week, including hiring agency staff, introducing double shifts, and cancelling routine operations. This strategy proved highly effective at meeting government targets: during the audit week 85% of hospital trusts met the target, but the following week only 63% still met target waiting times.
Another way to shorten waiting times in accident and emergency departments is to refuse to book in ambulance patients until your clinical staff are ready to assess them. Although patients are on hospital premises, you choose when to "start the clock," and until then the patients officially remain under the care of paramedics (jeopardising their performance targets instead of yours).
Remember to "stop the clock" once you have transferred patients from trolley to bed since they have now been admitted (even if they remain in the department for the next two days). Once patients have seen a doctor, discharge them from the computer rather than wait for their transport to arrive and take them home. If your hospital is full, simply remove the wheels of a trolley to transform it into a bed, and erect a partition in the corridor to create an "observation ward."
Fraudulent reimbursement claims
The prospective payment system, in which healthier costs are paid prospectively, is based on a standard sum for well-defined medical conditions (the diagnosis-related group, DRG). This has created a golden opportunity to maximise profits without extra work. When classifying your patient's illness, always "up-code" into the highest treatment category possible. For example, never dismiss a greenstick fracture as a simple fracture, but inspect the x-ray for tiny shards of bone. That way you can upgrade your patient's break from a simple to a compound fracture and claim more money from the insurance company. "DRG creep" is a well-recognized means of boosting hospital income, as well as GP's income, by obtaining more reimbursement than would otherwise be due.
Another reason for upcoding your patients' illnesses is to manipulate reimbursement rules for your patients' benefit. A recent national survey of US doctors showed 39% had used such tactics, including exaggerating symptoms, changing billing diagnoses, or reporting signs or symptoms that patients did not have, to secure additional services felt to be clinically necessary. Medical fraud is estimated to account for 10% of total US spending on health care (some $120bn) in 2001.
Reducing mortality figures and clinical performance data
Mortality figures are particularly feared amongst hospitals and surgeons as above average figures could mean loss of reputation and job. Statistically, of course, half the figures will be above average as half of them will be below!
1. Upcoding of morbidities
"Coding creep" refers to the excessive or inappropriate coding of those risk factors that are required for calculating risk adjusted mortality. Calculated risk factors relate to additional illnesses that the patient may have, which will increase the risk of undergoing certain procedures. Higher risk patients allow for a higher mortality rating.
Between 1989 and 1991, the proportion of patients recorded pre-operatively as having chronic obstructive pulmonary disease increased from 6.9% to 17.4% (at one hospital this increased from 1.8% to 51.9%). If a major risk factor is recorded in a higher proportion of patients before surgery the unit's predicted mortality rate will increase, as will the likelihood that the unit's actual mortality falls within or below the expected range.
Clearly smokers have an increased risk of dying during surgery, so any patients who deny smoking when their history is taken should be questioned further. Perhaps they stopped recently, they might enjoy a cigarette on social occasions, or they may share a house or workplace with a smoker, in which case record them as being a smoker. Similarly, even a faint wheeze in any patient over 40 years old who has ever been exposed to cigarette smoke could signify early chronic obstructive pulmonary disease, and patients with this condition have a higher risk of dying. By placing as many patients as possible in a high-risk category, your figures for risk-adjusted mortality will be reduced.
2. Selection of risk adjustment procedure
When calculating risk adjusted mortality, you can enter a bewildering number of risk factors into a great variety of equations. Many such risk-adjustment formulas are available. Rankings of individual hospitals vary widely depending on how you adjust for disease severity. This can be done by choosing the best possible formula for your particular needs, showing your overall adjusted risk in the best possible light.
3. Transfer of patients
Looking to compare overall mortality in hospitals is an almost impossible task. Terminally ill patients can be discharged and as long as they do not die on the premises, they will not show up in these statistics.
Many hospital databases record only those deaths that occur in the hospital of operation, so deaths in continuing care facilities may be overlooked when calculating mortality. Conversely, if your hospital seems to have a particularly high mortality, perhaps it is admitting more terminally ill patients. Consider opening an off-site hospice in order to discharge the sickest patients to die there!
4. Change of operative class
The only major cardiac surgical procedure for which mortality data have been publicly reported in the United States is coronary artery bypass grafting (CABG). When confronted with a high-risk patient, or if things start going wrong during an operation, just convert the procedure to an operation, for which you do not need to report the data. Simply adding a few extra stitches can convert a conventional CABG to a CABG plus mitral valve repair. The apparent mortality in your CABG series falls, albeit at the expense of more deaths from the (unreported) combined procedure.
You could even invent an entirely new condition by means of computer-enhanced images and allocate your highest risk patients to that category (so called pixel-byte syndrome). This could be of particular interest to doctors who are approaching retirement but who have not yet been credited with an eponymous syndrome.
5. Refusing to operate
Despite reassurances that risk adjustment techniques do not penalise surgeons who operate on high-risk patients, an anonymous survey of all cardiac surgeons in New York state found that 62% had refused to operate on at least one high risk CABG patient, mainly because of fear of public reporting.
It is a well-known fact that certain hospitals refuse operations that others do perform, on the basis of "too high a risk". The person then dies of the condition rather then as a consequence of the procedure. One mortality list the surgeon is responsible for, the other he isn't.
6. Cream skimming
It is in the interests of health insurance plans to recruit only the most profitable patients. One US health insurance company recruited members at a dinner dance, realising that elderly people who are fit enough to dance are healthy. In the UK, all the major medical insurance companies penalise patients with existing conditions by either excluding them totally from the scheme or imposing higher contributions. In recent years many have capped their payments; others increase yearly premiums on the basis of what you have cost them the previous year (there is never a reduction in contribution payments).
Clinicians benefit too from pruning high-risk patients from their lists: for example, doctors who are high outliers can dramatically improve their profile simply by removing their three patients with the highest haemoglobin A1c levels. Selecting your patients very carefully can dramatically improve your performance rating.
7. Reporting risks
Always report absolute rather than relative risks. If your hospital's mortality figure is 6% and the average rate is 4%, you should point out that the absolute death rate is only 2% higher than average. If people insist on reporting your unit as having a 50% higher mortality than average, you can retort that the average is actually only 33% lower.
Other Statistical "Facts"
The total mortality rate of the population is, and always has been, 100%. Everybody dies. "Saving lives" in terms of averting death only makes real sense in acute certain death situations. The ones we refer to are easily identified when we are talking about real situations such as someone being trapped in a burning house or someone drowning. Once these people are over their ordeal they are essentially back to where they were before. When we make the same assumptions regarding disease and ill-health, the "certainty" becomes a bit more obscure. We now rely on an expert to tell us whether or not that person in that particular situation is in immediate danger of losing his/her life. The more the expert can convince us of the severity of the situation, the more prominent his own position becomes. In truth, medical care in diseases (not accidents) never "saves" a life; it only at best prolongs it, at worst shifts it into a different death category.
Mortality causes are determined by the medical doctor filling in the death certificate. We assume that that person knows exactly what the cause of death is. How the certificate is filled in, however, determines the official cause of death, as registered by the authority. As an example, a person with cancer may be registered as having died of bronchitis, or a heart attack, or kidney failure. As a consequence, the cancer death statistics will be reduced, and who cares about people dying from bronchitis! Although statistics recording mortality are more accurate than those recording morbidity (specific illnesses), they are still, and never can be, the total truth.
By definition, as the death certificate reveals, someone's death must be due to one reason, and one reason only. However, prolonged illness, as well as old age, ensures malfunctioning of most of the system's parts and it is the sum total of the impact of this deterioration that eventually will lead to the non-functional state. To put the blame on one is to be ignorant of the reality of Nature. People used to know what someone had died off. "Old age", "Just given up", "Broken heart", "Wanted to be reunited with deceased loved one", are all genuine reasons not used on death certificates.
"Survival" rates are used to assess the efficacy of treatments for terminal illnesses such as cancer. A 5-year survival from the date of diagnosis of cancer is recorded as a life saved. Even when that person dies of cancer a little while later the statistics will show him/her to have survived cancer. These statistics, used as a measure to show the population that the war on cancer is being won, are positively influenced by early diagnosis. As a result of this, although the number of people dying of cancer is increasing every year, the statistics show an increased survival rate, which is attributed to better treatment rather than to manipulating the figures.
Reasons for compulsory notification of diseases include assessment of the scale of the problem and providing the authorities with a tool to assess the impact of treatment. Diagnosing a particular disease is not an exact science, in spite of what the authorities may want us to believe. Skin rashes can appear for a great number of reasons and the doctors ultimate labelling will depend almost entirely on his/her assessment and beliefs. A child, vaccinated against measles, is not likely to be reported as having contracted measles because the doctor believes that the vaccination will protect the child from contracting the disease. In July 1955, in Los Angeles County, there were 273 cases of polio and 50 cases of aseptic meningitis. A year later there were just five cases of polio and 256 cases of aseptic meningitis (the symptoms of which are hard to tell apart).
"Misdiagnosis" is the most frequently claimed reason for inexplicable cancer recoveries. This proves that the medical profession acknowledges misdiagnosis as part of the health care system. Of course we appreciate that it only happens when the authorities are at a loss; on all other occasions they are spot on, immune to making mistakes.
The recording of medical mistakes is only done by the peer group. Only authoritative medical figures have the power, therefore the knowledge, to judge the work of their colleagues. The fact that medical history is nothing but a catalogue of appalling "mistakes" and "mistaken beliefs" is excused by the statement that "they did not know as much as we do now". Equally of course, we do not know as much as the medical authorities of the future! Yet they want us to believe everything they tell us today as the whole truth, just as their illustrious colleagues did all those years ago. Doctors are taught that mistakes are unacceptable. Medical mistakes are therefore viewed as a failure of character and any error equals negligence. We can see how a great deal of sweeping under the rug takes place since nobody is taught what to do when medical error does occur.
Statistics telling us that, on average, we live a lot longer these days than we used to, are just as floored as any other statistics in as much that they do not inform us of how the statistics are compiled. Measuring the length of life from the day of birth until the day of death certainly reveals on average the prospect of a longer life. However, if one looks at the statistics showing life expectancy from the age of 40 onwards, we see that there has been no significant change in the last century. How can this be true?
Due to improvements of hygiene and nutrition baby and infant death was almost eradicated in the latter part of the twentieth century. The effect on the longevity statistics was dramatic. These particular statistics are based on the average life span, which means that in order to achieve an average age of 50 for every death close to birth (age 0) another person has to live for 100 years. If infant deaths are drastically reduced and people very rarely die young, one can easily see that for every death at the age of 30 the next person only has to live for 70 years to reach the same average life expectancy of 50. So our average life span statistics do not accurately inform us of how long adults can expect to live, nor does it support the idea behind the proclamation that we live longer, which is that our health facilities, medical care and treatments are so much more effective.
It now turns out that our "improved" longevity figures relating to old history are no longer true either. Recently, historians have had to admit that the generally held belief that early man only had a life expectancy of about 35 to 40 years, has been based on floored scientific evidence. Bone density has been widely used to determine the age of a skeleton and it was generally found that the bone density of those early skeletons related to middle age men and women. However, we now know that the bone weakening, which occurs with age, used to be a lot slower than it is nowadays and consequently the modern charts used for determining the age of the bones seriously underestimated the real age. Scientists now estimate that the average life span of early man was somewhere between 80 and 100 years, which would mean that we have lost, rather than gained, years on the average human life.
Humans living much longer than the average life span have always intrigued scientists. Even today there are people living up to 120 and 130 years of age. There are two remarkable things about these people compared with the average 70 year old you and I know. One is that all of these very old people are healthy and still living a normal life; many of them even are still looking after themselves. And secondly, the great majority of them live in extreme conditions. We find them either tucked away in a corner of the jungle, untouched by modern society, or in harsh climates such as in Northern Russia. Very often, these people have a very restricted diet, nowhere near balanced by our modern standards. Many live in extremely polluted areas, have smoked all their lives and have a high alcohol consumption. Unable to explain their longevity and health our scientists tend to dismiss it as "genetic". As much as the constitutional factor plays a part in an individual's resistance we ought to be baffled by the way these people are seemingly unaffected by their extremely unhealthy lifestyle.
As much as the length of life of an individual is of value we surely should not separate it from the quality of life: a long life of misery is for most people less desirable than a short enjoyable one. However, authorities in our culture only seem interested in informing us about the length of the life we can look forward to, on average, not how healthy it will be. Figures relating to the number of people with severe illnesses, handicaps and serious life restrictions are not in the public domain. A small oversight in the interest of the general public?
- The total mortality rate is 100%: all people die. When statistics show that the mortality in a certain category has fallen, that only means that we now believe that more people have died from other causes.
- There are some statistics that the medical authorities do not want you to see. Counting real numbers is the closest statistical form we have to the truth.
¶ A definitive review and close reading of medical peer-review journals, and government health statistics shows that American medicine frequently causes more harm than good. The number of people having in-hospital, adverse drug reactions (ADR) to prescribed medicine is 2.2 million. Dr. Richard Besser, in 1995, said the number of unnecessary antibiotics prescribed annually for viral infections was 20 million. Dr. Besser, in 2003, now refers to tens of millions of unnecessary antibiotics. The number of unnecessary medical and surgical procedures performed annually is 7.5 million. The number of people exposed to unnecessary hospitalisation annually is 8.9 million. The total number of iatrogenic deaths (caused by the medical system) is 783,936. It is evident that the American medical system is the leading cause of death and injury in the United States. The 2001 heart disease annual death rate is 699,697; the annual cancer death rate, 553,251.
¶ Only about 5 to 20% of iatrogenic incidents are even recorded. And, our outpatient iatrogenic statistics only include drug-related events and not surgical cases, diagnostic errors, or therapeutic mishaps.
¶ Longer term sickness has more than trebled in the past 25 years and one in four men is on incapacity benefit in some areas. The report in the Economist is based on research from Sheffield Hallam University and the Government's own figures. A record 5.9 million are now on the sick list on any given day. Some 166 million sick days are taken each year - equivalent to 6.8 days for every employee and businesses are being hit by an £11 billion a year bill for sick leave. The number of people on short term incapacity benefit more than trebled between 1979 and 2002 and now stands at 2.7 million. That is more than the combined total of single parents and the unemployed who are claiming benefit. The number of long term incapacity claimants of working age - those who have been sick for more than a year - has shot up from half a million in 1980 to 2 million today.
- Numbers of people, whether recording illness or death, should always be related to the total population, if we want to know whether, in real terms, there are more or less. Similarly, the total number of deaths on our roads surely relates to the number of motor vehicles on the roads. As these numbers go up it is reasonable to assume that more deaths are going to occur.
Being told that you can expect to live to the age of 72, that you have 80% chance of having a successful medical intervention, that you increase your risk of an early death by 50% if you do not follow the given advice, are all figures provided to show how well the authority is doing. They do not contain any information relevant to you personally.
I want to make sure that my life is the best it can possibly be, not in comparison with my neighbour's, but in comparison with my own life, then I must learn to understand what I need to make it happen. What is required for me to achieve this may well be totally different from what the statistics tell me I need. Never forget that:
- there is no such thing as the average person
- there are far more people way outside the average than there are inside