Boston Globe: “The incessant din of beeping monitors can numb or distract hospital staff; the consequences can be deadly”

The February 13, 2011 Boston Globe has a disturbing report about how alarms can blend in with ambient background for healthcare workers. This really is where usability and quality assurance and medical informatics and medical IT all need to come together, no? But that would require money and training, and I can tell you as someone finishing a PhD focusing on medical cognitive science and medical informatics, these are in short supply. I have written previously about how a checklist evidently can lessen medical error, and sometimes there is (relatively) low-hanging fruit where a modest investment can yield impressive savings, but solving the problems written about here I think are more often not going to be cheap, quick, or easy. The devices themselves typically represent massive expense. What we would need is a holistic, integrated usability analysis of the sort that human-factors engineers perform for NASA or the cockpit of an aircraft. This isn’t cheap though.

“At Tobey Hospital in Wareham, nurses failed to heed a different type of warning on a September morning in 2008. An elderly man’s electrocardiogram displayed a “flat line’’ for more than two hours because the battery in his heart monitor had died. While nurses checked on him, no one changed the battery. The man suffered a heart attack and was found unresponsive and without a pulse.

These were just two of more than 200 hospital patients nation wide whose deaths between January 2005 and June 2010 were linked to problems with alarms on patient monitors that track heart function, breathing, and other vital signs, according to an investigation by The Boston Globe. As in these two instances, the problem typically wasn’t a broken device. In many cases it was because medical personnel didn’t react with urgency or didn’t notice the alarm.

They call it “alarm fatigue.’’ Monitors help save lives, by alerting doctors and nurses that a patient is — or soon could be — in trouble. But with the use of monitors rising, their beeps can become so relentless, and false alarms so numerous, that nurses become desensitized — sometimes leaving patients to die without anyone rushing to their bedside. On a 15-bed unit at Johns Hopkins Hospital in Baltimore, staff documented an average of 942 alarms per day — about 1 critical alarm every 90 seconds.

In some cases, busy nurses have not heard or ignored alarms warning of failing batteries or other problems not considered life-threatening. But even the highest-level crisis alarms, which are typically faster and higher-pitched, can go unheeded. At one undisclosed US hospital last year, manufacturer Philips Healthcare, based in Andover, found that one of its cardiac monitors blared at least 19 dangerous-arrhythmia alarms over nearly two hours but that staff, for unexplained reasons, temporarily silenced them at the central nursing station without “providing therapy warranted for this patient.’’ The patient died, according to Philips’s report to federal officials.

In other instances, staff have misprogrammed complicated monitors or forgotten to turn them on.

The Globe enlisted the ECRI Institute, a nonprofit health care research and consulting organization based in Pennsylvania, to help it analyze the Food and Drug Administration’s database of adverse events involving medical devices. The institute listed monitor alarms as the number-one health technology hazard for 2009. Its review found 216 deaths nationwide from 2005 to the middle of 2010 in which problems with monitor alarms occurred.

But ECRI, based on its work with hospitals, believes that the health care industry underreports these cases and that the number of deaths is far higher.”

An informatics problem: confusion over the meaning of the term “gene”

There is an issue I rarely if ever see addressed in the healthcare informatics world, but one that looms much larger in bioinformatics and pharmacogenomics: what is a gene, anyway? My students have been struggling with me forcing them to memorize  two definitions: the gene as a unit of heredity, recessive or dominant, but also gene as a DNA sequence that makes protein (as in this Wikipedia graphic):

A couple of years ago the New York Times ran an excellent piece about changing views of the gene. They went so far as to characterize the gene as having an “identity crisis”:

new large-scale studies of DNA are causing her and many of her colleagues to rethink the very nature of genes. They no longer conceive of a typical gene as a single chunk of DNA encoding a single protein. “It cannot work that way,” Dr. Prohaska said. There are simply too many exceptions to the conventional rules for genes.

It turns out, for example, that several different proteins may be produced from a single stretch of DNA. Most of the molecules produced from DNA may not even be proteins, but another chemical known as RNA. The familiar double helix of DNA no longer has a monopoly on heredity. Other molecules clinging to DNA can produce striking differences between two organisms with the same genes. And those molecules can be inherited along with DNA.

The gene, in other words, is in an identity crisis.

There was a reference to a fascinating paper: “Genomics Counfounds Gene Classification” by Gerstein and Seringhaus (2008). The upshot is that the classical view of the gene/DNA relationship, where sections of DNA that is transcribed by RNA and translated into a protein, doesn’t account for data about noncoding DNA,noncoding RNA, and alternative splicing:

This iterative one-gene, one-protein, one-function relationship paints a relatively straightforward picture of subcellular life. When describing the function of a given gene in a cell, biologists can conceive an individual protein as a single indivisible unit or node within the larger cellular network. In turn, when mapping genes across species using sequence similarity, they can assume a protein is either fully preserved in various organisms or entirely absent. Thus, related proteins in different organisms can easily be grouped together into consistent families, which can be given simple, unitary descriptions of their function. Thus, the extended dogma expands the central dogma to include regultion, function and conservation

Complex Reality

To the modern genomics scientist, the classical image of a gene and the ex- tended dogma associated with it are quaint. High-throughput experiments that simultaneously probe the activity of millions of bases in the genome deliver a far less tidy view. First, the process of creating an RNA transcript from a DNA region is more complex than once was imagined. Genes make up only a small fraction of the human genome. But RNA expression studies on human DNA suggest that a substantial amount of the genome outside the boundaries of known or predicted genes is transcribed.

In the quest to accurately describe biological systems, defining basic units is only part of the job. Scientists ultimately want to understand biological function. Function in the genetic sense initially was inferred from the phenotypic effects of genes. A person might have green or blue eyes and a gene related to this characteristic could then be assigned the “eye color” function. Phenotypic function of this sort is most directly shown by deleting or disrupting, or “knocking out,” a particular gene. Disrupting a gene in this way might cause an organism to develop cancer, to change color or to die early. Disabling the yeast mitochondrial gene FZO1, for instance, causes mutant strains to display slow growth and a petite phenotype. But a phenotypic effect doesn’t capture function on the molecular level. To really elucidate the importance of a gene, it’s vital to understand the detailed biochemistry of its products.

Figure 4. Multiple methods exist for capturing gene functions. In a simple hierarchy, at left, a gene is described in single relationships. One unit descends from one “parent”. Directed acyclic graphs (DAGs) capture more complexity. Above the hierarchy captures that FZO1 plays a role in the biogenesis of cellular parts but the DAG gives a wider view of the scope of those roles. (Data contributed by QuickGO: ebi.ac.uk/ego/)

“Genomics is a way to do science, not medicine”

Over the last ten years or so many of us have been following developments in pharmacogenomics and bioinformatics, wondering if the revolution was truly upon us. The completion of the Human Genome Project, the advances in gene sequencing chips, computational chemistry algorithms, and ever more sophisticated models of signaling pathways in cells, not to mention the impressive capital available to the biotech industry, all made it seem as if a new class of drugs based on genomic variations was on the way. Optimistic thinkers heralded the coming era of personal drugs tailored to individual genomic differences. Certainly the textbook from my 2009 Epidemiology class made it seem as if gene sequencing would play a progressively larger role in modeling variance in human disease outcomes, data that could be fed back into the pharmaceutical development process. A friend getting his PhD in neuroscience who had no wet-lab experience prior told me how easy it is to run the new automated PCR systems to amplify particular sequences of DNA. These and other developments had me convinced that advances in wet lab science, combined with computational modeling of how drugs interact with receptors and other cellular targets to change gene expression and signaling pathways, would quickly lead to a major new category of medical advances (say, by 2015 or 2020). It seemed the revolution truly was nigh…

As I wrote a few months back, the difficulties that personal genomics companies were having in staying solvent served to dampen optimism somewhat. But more significant than the perilous balance sheet of formerly hyped biotech firms is the accumulating change in the conventional wisdom, suggesting that gene sequencing may not lead to many valuable therapies anytime soon. Certainly the jury is still out on this. But mounting evidence suggests the low-hanging fruit has already been plucked in pharmaceutical design, with the easier molecular targets in the common diseases already identified, leaving the drug companies nervous about pouring billions more into r&d. Most of what I am reading suggests we should still expect great things from applying gene sequencing to pharmacology, but not a new class of breakthrough drugs, much less personalized medicine anytime soon (before, say, 2020 or 2030).

Last spring I went to a well-attended meeting of the Austin Forum called “Bio-tech: the Next Big Thing”, and it was like the Internet bonanza of 1999 all over again. Various scientists and boosters extolled the coming great wave of healthcare benefits resulting from genomic medicine and sundry bioengineering advances. I was teaching a class dealing with this material and thought some dissenting perspectives needed to be aired. At question time, I took the mike and pointed out how vanishingly few actual new drugs pharmacogenomics and bioinformatics etc. have delivered after many billions of private and public dollars spent, and thus should we not be cautious about big investments in risky projects? To his credit, UT Provost and pharmacologist Steven Leslie agreed with me and added a much-needed tone of sobriety to the otherwise exuberant mood (if anyone has a link to his answer, please fwd. as he is a man worth listening to).

The last few months have seen a certain backlash against the genomic medicine hype. Here is a nice summary from the eminently readable Nicholas Wade in the June 1 New York Times: “A Decade Later, Human Genome Project Yields Few New Cures”:

The pharmaceutical industry has spent billions of dollars to reap genomic secrets and is starting to bring several genome-guided drugs to market. While drug companies continue to pour huge amounts of money into genome research, it has become clear that the genetics of most diseases are more complex than anticipated and that it will take many more years before new treatments may be able to transform medicine.

“Genomics is a way to do science, not medicine,” said Harold Varmus, president of the Memorial Sloan-Kettering Cancer Center in New York, who in July will become the director of the National Cancer Institute.

The last decade has brought a flood of discoveries of disease-causing mutations in the human genome. But with most diseases, the findings have explained only a small part of the risk of getting the disease. And many of the genetic variants linked to diseases, some scientists have begun to fear, could be statistical illusions.

The Human Genome Project was started in 1989 with the goal of sequencing, or identifying, all three billion chemical units in the human genetic instruction set, finding the genetic roots of disease and then developing treatments. With the sequence in hand, the next step was to identify the genetic variants that increase the risk for common diseases like cancer and diabetes.

It was far too expensive at that time to think of sequencing patients’ whole genomes. So the National Institutes of Health embraced the idea for a clever shortcut, that of looking just at sites on the genome where many people have a variant DNA unit. But that shortcut appears to have been less than successful.

The theory behind the shortcut was that since the major diseases are common, so too would be the genetic variants that caused them. Natural selection keeps the human genome free of variants that damage health before children are grown, the theory held, but fails against variants that strike later in life, allowing them to become quite common. In 2002 the National Institutes of Health started a $138 million project called the HapMap to catalog the common variants in European, East Asian and African genomes.

With the catalog in hand, the second stage was to see if any of the variants were more common in the patients with a given disease than in healthy people. These studies required large numbers of patients and cost several million dollars apiece. Nearly 400 of them had been completed by 2009. The upshot is that hundreds of common genetic variants have now been statistically linked with various diseases.

But with most diseases, the common variants have turned out to explain just a fraction of the genetic risk. It now seems more likely that each common disease is mostly caused by large numbers of rare variants, ones too rare to have been cataloged by the HapMap.

Here are some excerpts from the December 2009 edition of the Economist: “Looming crisis in Human Genetics” by evolutionary psychologist Geoffrey Miller:

Human geneticists have reached a private crisis of conscience, and it will become public knowledge in 2010…

About five years ago, genetics researchers became excited about new methods for “genome-wide association studies” (GWAS). We already knew from twin, family and adoption studies that all human traits are heritable: genetic differences explain much of the variation between individuals. We knew the genes were there; we just had to find them….

In 2010, GWAS fever will reach its peak. Dozens of papers will report specific genes associated with almost every imaginable trait—intelligence, personality, religiosity, sexuality, longevity, economic risk-taking, consumer preferences, leisure interests and political attitudes. The data are already collected, with DNA samples from large populations already measured for these traits. It’s just a matter of doing the statistics and writing up the papers for Nature Genetics. …

GWAS researchers will, in public, continue trumpeting their successes to science journalists and Science magazine. They will reassure Big Pharma and the grant agencies that GWAS will identify the genes that explain most of the variation in heart disease, cancer, obesity, depression, schizophrenia, Alzheimer’s and ageing itself. …

In private, though, the more thoughtful GWAS researchers are troubled. They hold small, discreet conferences on the “missing heritability” problem: if all these human traits are heritable, why are GWAS studies failing so often? …

But the genes typically do not replicate across studies. Even when they do replicate, they never explain more than a tiny fraction of any interesting trait. In fact, classical Mendelian genetics based on family studies has identified far more disease-risk genes with larger effects than GWAS research has so far.

Why the failure? The missing heritability may reflect limitations of DNA-chip design: GWAS methods so far focus on relatively common genetic variants in regions of DNA that code for proteins. They under-sample rare variants and DNA regions translated into non-coding RNA, which seems to orchestrate most organic development in vertebrates. Or it may be that thousands of small mutations disrupt body and brain in different ways in different populations. At worst, each human trait may depend on hundreds of thousands of genetic variants that add up through gene-expression patterns of mind-numbing complexity.

Common Examples of Healthcare IT Difficulties

Nice site from the School of Information at Drexel U. Lots of detail about how different units of an organization get at cross-purposes when IT is deployed. WIll give the uninitiated a sense of the difference between healthcare informatics, which deals with people, knowledge, and technology, and healthcare information technology, which mostly deals with software and hardware:

http://www.ischool.drexel.edu/faculty/ssilverstein/failurecases/?loc=cases

“The informaticist’s credibility with administration had been tarnished by MIS. The informaticist’s concerns about the technical abilities of the MIS department to support equipment so closely involved in this critical patient care setting were also resented by administration. Regarding organizational changes recommended by the informaticist on clinical IT leadership, the chief medical officer seemed more concerned with the possible effects on IT personnel’s careers than with the effects of faulty computing on patient well being. The informaticist found such priorities simply stunning.

Meanwhile, the system proved more costly to support than MIS had predicted, requiring extensive development and customization (over and above the inflated costs of the fancy mounting accouterments), since it was immature, not entirely reliable, and user-unfriendly. One very valuable system feature, the severity scoring system, was never enabled. That feature might have allowed patients to be transferred out of the ICU earlier, saving a significant amount of money.

The system struggled with proving a return on investment, was nearly canceled after a year, and was given a “try-it-for-one-more-year-but-prove-the-ROI” reprieve only after a large degree of pleading and politicking by key personnel (including the informaticist). Its future became uncertain, plans to spread the technology to other ICU’s in the organization were canceled, and administration had been needlessly “turned off” to this type of technology”