Difference between revisions of "Dissertation Ideas"
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#*Governance via transparency
#*Governance via transparency
#*Anonymity tolerated (because expertise and authority de-emphasized)
#*Anonymity tolerated (because expertise and authority de-emphasized)
Latest revision as of 12:58, 7 May 2013
- The Gold Standard
- Article: Evidence-based medicine, clinical uncertainty, and learning to doctor. Timmermans
- The Body Multiple
- The Ontogeny of Information: Developmental Systems and Evolution - This is a reference from Dupre's book, talking about the role of DNA as "the master molecule", viewed as the "unmoved mover."
Conversation with Ellen:
- Take as the overall theme: Care of patients in particular provider-patient relationship (i.e. what we do and how we decide what to do). What does decision-making look like in this setting, with integration of personal knowledge and experience with patients and generalized data? How do introduction of technologies, including WGS and the informatics tools that are used to deliver those results, influence the way we navigate those influences?
- This theme is then the basis for addressing the more specific problem of dealing with genetic risk estimates, especially with respect to (1) determining when to tell them to patients and (2) determining which interventions to perform in response to them
- Since there is no synthesized model for medical decision-making, and EBM is clearly problematic here (due to no evidence, and due to conceptual problems with applying it in such a setting), a helpful technique will be to analogize from other practices in medicine, perhaps framing these as heuristics for dealing with uncertainty.
- I could do a chapter (or maybe a separate article) on genetic risk, taking my own risk for MI, and explore using different tools (the Mayo risk estimate tool, genetic test, family history alone, cholesterol markers alone, etc) to demonstrate that risk estimates vary, and that the formalization of risk in a specific number over-represents the certainty of such an event happening.
- Kuhn's definition of Disciplinary Matrix provides a way to frame what medicine is as a conceptual community as opposed to a practice (exemplars, values, etc), also Polanyi's "tacit knowledge"
- Clearly, practical reasoning (Aquinas and MacIntyre) is very important here, as well.
- Misunderstandings about risk derive from more fundamentals blindspots about role of mathematical models and values in science
- Scientific theories, framed as mathematical models, are what Berkeley call "mathematical hypotheses." The world does not exist as numbers of mathematical equations; mathematical models are derived from observation and gain support as they successfully represent, and sometimes predict, the way a particular natural event occurs.
- Possible example here would be an article about something in the natural world, such as modeling [the formation of the Milky Way].
- As Kuhn observes, paradigm shifts in science occur when paradigms, often based on specific mathematical models, are viewed as failing to respond to problems. This has traditionally assumed to mean that the model fails to predict or represent certain features of the field of study (such as the Ptolemaic models' increasing complexity as it required modifications to fit observed patterns in the solar system).
- There are a number of reasons that such a model may fail, however. A model might fail because it predicts a outcomes based on a certain set of variables. When a new variable comes to be recognized as having an effect on such systems, mathematical models that don't account for that variable could be viewed as failing. Possible examples include something like cervical cancer, when HPV came to be appreciated as the significant cause or stomach cancer + H. Pylori. Obesity would be a great example of the way that various models repeatedly fail as factors thought to be important fail to carry explanatory power: first endocrine, then psychological, then much later genetics.
- Ice cube melting is an example of a process that can be modeled quite successfully. This is because the important variables are assumedly all now known, and ice cubes can be represented quite effectively in models, since their characteristics are quite homogenous. (this is from Gorowitz and MacIntyre)
- However, more complex phenomenon like hurricanes cannot be modeled with complete accuracy. This is not, however, because the important variables are not known. On the contrary, the appreciation of physics related to air flow over warm water is quite well understood. In this way, the general features of hurricanes can be modeled effectively. But the reason that specific hurricanes cannot be modeled successfully is that inputs into such models in the real world are large in number, complex, and difficult to collect through available technologies. So the paths of hurricanes are represented as probability paths.
- Human risk for disease is similar. Many of the causes of many disease are well appreciated. The cause of CF has been known since ____. However, the course of health and wellness are extraordinarily complex such that in many cases the variables that are important in the development of a range of disease are not all known, even though some important inputs may be known. In addition, the histories of humans are even more complex than that of earthquakes, and infinitely more so than ice cubes (again, these examples are those of Gorowitz and MacIntyre). The ability to predict the onset of disease in an individual, then, varies on the basis of the comprehensive of available models in terms of the number of variables it can take account of, and also the inputs that are present in the history and ongoing life story of patients.
- Risk estimates for individuals emerge within this context of incomplete knowledge of biological systems and incomplete knowledge of particulars. This fact is demonstrated by the availability of co-existing models to predict disease. For example, MI risk estimate tools. The online Mayo Tool says one thing, 23inMe says another, etc. Each of these models is incomplete in the number of variables it is able to account for, and even the inputs it takes into account are snapshots, they can't account for the history of individuals, such as changes in cholesterol over time.
- A significant problem with risk estimates, then, is that they represent predictions for the future in mathematical terms in a way that explicitly communicates a probability (Your risk for having an MI is 20%), but lacks the ability to also communicate the success of the model, or, in other words, the probability of the probability. Disease risk predictions, like weather predictions, become confused with other scientific mathematical models. Even these models are "mathematical hypotheses," albeit ones that have succeeded in representing the pertinent systems accurately through a long history of experiments. But because that historical success comes to support a conflation between the natural reality and the mathematical modeling of that reality, a type of misunderstanding about the nature of scientific models becomes imported into disease risk models.
- Dupre's book provides an account of the different frames for thinking about genetics/genomics. There seems to be a potential harm in implementing genomic understandings into a world that still treats of DNA in the frame of classical genetics.
- Is evidence-based medicine a form of theoria or a form of phronesis? It appears to be an attempt for theoria to bridge the gap from the theoretical to the practical, using a skeletal account of phronesis that involves only deciding which evidence is most pertinent to a particular patient.
- While the problems with this account vis a vis phronesis are obvious, it's problems vis a vis theoria are even more important. EBM requires professional judgement on the part of the provider - Which evidence applies here? What are the implications of studies that don't quite fit? This is closely linked with another concern - that EBM does not generate desirable outcomes in all cases. Evidence generated in the ways currently used are based on relatively heterogeneous groups. The failure of EBM is based on two elements: the concern that available evidence isn't applicable, and the concern that available evidence is for heterogenous groups, and thus imperfectly predicts outcomes.
- Personalized Medicine has emerged from a deep anxiety about this shortcoming of EBM. Personalized Medicine is based on the commitment that (1) the application of evidence should be relatively straightforward (minimize professional judgement and, thus, contingency) and (2) the evidence applied should be for homogenous groups into which the patient fits perfectly. This is why the term "stratified" is sometimes used.
- The problem with Personalized Medicine, though, is that attempts to "fix" EBM but brings no new conceptual innovation to the situation. Personalized Medicine is, in fact, EBM repackaged. It could even be said that Personalized Medicine is a more fundamentalist version of EBM.
- One question, then, is whether PM effectively addresses the practical (i.e. translational) goals of medicine based on theoria, or are practical reasoning approaches more appropriate. Put more directly, can PM do something EBM can't, or does PM, like EBM, require practical reasoning to succeed?
- Personalized Medicine is a fundamentalist form of EBM.
- The anxiety about judgement in medicine inherent in PM (and to a lesser degree in EBM) drives a marginalization of judgement into the "art of medicine" - a marginalization that renders judgement as something for which an account cannot be given.
- But the goal of PM and EBM is to render all elements of medicine "measurable," in the strain of "metrics"
- However, a key part of judgement is "know how." We must know how to use evidence, physiology, observations of patients, patient preferences and self-reported history, patient perceptions of their own bodies and the causative role of different factors, and economic factors to decide how to act.
- A key element of this "know how" - how we learn and how we refine it - is feedback from the world. Our patients, colleagues, and spouses tell us in their words and actions when we have made mistakes - when our judgement went wrong. They also, of course, show us when we did well.
- However, the account of PM and EBM (but not, interestingly, physiology) treat this account of experience with individual patients as mere anecdote. Not only can we not learn from anecdote, but we emphatically should not learn from anecdote. The "metrics" are what matter. This account, therefore, not only robs us of our ability to provide an account of judgement, but drains us of our ability to improve our judgement.
- Joe here provides an Stout's account of know-how: learning to convert the rules of soccer into the practice of being a soccer ref. The application of the rules is at first mediated (I have to think about the rules and how they apply), but eventually they become automatic, and I know when I see an infraction. However, whether this type of know-how is mediated or immediate, it is still susceptible to feedback, from the coach who objects. When that happens, the ref needs to re-examine the judgement. This is from Chapter 12 of Tradition and Democracy.
- Reading about Carl Rogers in "A History of Pastoral Care"
- This text draws attention to the liberal underpinnings of Carl Rogers' counseling method - that it was a reaction against legalism, and that one of the critiques of this from within pastoral counseling circles was that communities of meaning provided a context for doing more than just moving forward a person's self-realization. ...the realization of selfhood occurred only within a community of language and commitment, which would provide guidance and direction" (306).
- Some of the stances on Personalized Medicine follow in these liberal footsteps - in reaction to open possibilities about ways that people might think about things, there is a reaction against paternalism toward nondirectiveness. Ravitsky and Wilfond come in here - they might derive meaning from it, so we should tell them.
- But this denies the possibilities of what a provider engaging in phronesis - through the interaction with the patient the provider should not be nondirective, as if telling someone everything with no care is somehow less directive than telling them nothing. Rather, phronesis allows the provider to determine what, in this setting, is the most helpful thing I can do for this patient - what can I do to help him or her attain human flourishing, which is inclusive of most forms of health. The provider is therefore directive, and the selection of data to reveal to the provider and the decisions about how to use the information is all based on this orientation.
This is from a grant I reviewed: One common approach to increase trustworthiness is to increase transparency (O'Neill 2006); However, information sharing per se does not necessarily increase transparency or trust. Provision of raw data with no interpretation can sometimes obfuscate rather than enlighten (O'Neill 2006; Archon Fung 2007) reducing a patient’s ability to make an informed decision. It can also shift responsibility to the patient explicitly or implicitly which, particularly in an acute care setting, may not be appropriate. Rather than focusing just on sharing informational content, O’Neil suggests that a more interactive model of communication (O'Neill 2009) is necessary to engender well-placed trust. The Royal Society’s recent report on ‘Open Science’ recommends ‘intelligent openness’ where shared information needs to be not only accessible but ‘understandable, assessable and useable’(Royal Society 2012). Relating these criteria to acute medicine might engender a clinically useful dialogue between clinician and patient. Ideally communication should foster an environment where patients are supported and equipped to ask appropriate questions in order to make their own decisions or to trust their doctors to make those decisions. Sharing of medical records is one possible way of achieving this.
- Distinction between epistemological and physical probability: We can assume that our risk for developing cancer (for example), based on all relevant effects at play in our bodies (prenatal, environmental, behavioral, dispositional) is a physical reality. That is, there is a "true" probability that we will develop cancer. This is never 100%, since there will still be purely stochastic effects (i.e. the chance mutation of a particular gene at a particular site).
- However, in our current state of knowledge, we can't know this "true" possibility. We are more interested in epistemological probability, that is, what can we know about our probability of developing cancer.
- The "belief" we may develop about our chances of developing cancer are based on all of the available data. Leibniz, with his background in jurisprudence, first introduced us to the idea that our epistemological probability is an imperfect approximation of our physical probability, but that we are forced, as we are in a court of law, to develop epistemological probabilities from the evidence we have available to us.
- In this way, there is a certain logic to obtaining more information about our risk for developing cancer using genetics - we could argue that failing to obtain this evidence is like failing to admit all relevant evidence in a court of law.
- The rationale for generating and revealing genetic information is, up to this point, entirely sound. In fact, when we add to the rationale that the information is already "known" in a computer somewhere, and all we need do is report it, then the justification for inputting that information into our formation of an epistemological probability for developing a disease becomes overwhelming.
- However sound this rationale may be in the abstract, when we place it into concrete situations, we begin to see how it may not lead to the more accurate "epistemological probability" that we seek. There are a number of reasons for this, and we should take each in turn:
- Genetic reductionism causes us to mistake the genetic among a particular population as the true physical probability. That is, it can cause us to ignore the significant contribution from a whole range of other factors in the combined pathophysiology of a condition.
- Insofar as we are able to be non-reductive in formulating a model that we believe represents the pathophysiological condition of a single human, we will be faced in many circumstances with combining probabilities from different sources, since as the number of factors we consider increase, the larger the total sample size we must pull from in considering the interaction among multiple factors. That, in order to generate a frequentist estimate of the risk for developing lung cancer in a particular patient, there are only to ways to eschew genetic reductionism:
- We can obtain a sample large enough to contain a significant number of individuals who are like our individual in terms of alleles at (for example) 10 genes, personal smoking behaviors, second hand smoking behaviors, family history of lung disease, occupational exposures, and exposure to environmental pollutants. We could then look at the risk of developing lung cancer among the population of study participants who are identical to our patient in all of these respects, and attribute that risk to our patient. The less reductive we are, the larger our sample size must be.
- Instead of looking only at patients who are identical to our patient, we could generate a statistical model that combines the various effects of each characteristic (risk of allele A on Gene A vs risk of allele B on Gene A) and extrapolates a risk for our patient who has a relatively unique set of characteristics. This approach not only make the probability we generate more conceptually opaque, it also renders our estimates more speculative. Is the combination of risks alleles in gene A and gene B additive, non-additive, or synergistic? What about gene A, gene B, and gene C? What about among smokers? Insofar as we understand the causative dimension of these effects we will be able to build better models. But it remains the case that our model is forever insufficient in comparison with the "true" physical probability. And we are forced to consider what the marginal benefit will be to our current model from incorporating additional genetic risk information. Notwithstanding the axiom that "more knowledge is good", when it comes to statistical models, this is not the case. In some cases, additional information may add no additional precision to our model. PRINCIPLE OF TOTAL EVIDENCE
- Failure to recognize distinction between epistemological and physical probabilities
- Regardless of whether we are reductive in thinking about the physical probability or nonreductive, NEEDS MORE HERE - PROBABILITY OF PROBABILITIES ("epistemic probabilities of statements about aleatory probabilities")
- Effectiveness at altering belief
- In our earlier discussion about statistical models, I observed that our frequentist estimation of risk is at risk for being "opaque". What does this mean? Leibniz was convinced that epistemological probability is objective. That is, even though we could not generate certainty by taking the available data (**ex datis**) into account, we could generate a conclusion that is "incomparably more probable" than the opposite, and that everyone, using this same data, could come to the same conclusion. Regardless of whether it is possible to generate a frequentist account of empirical probability that is theoretically objective, the field of psychology has demonstrated convincingly that assessments of risk are far more complex than frequentist accounts of the same. That is, the beliefs of actual persons about their risks for developing a disease are developed through a series of experiences, and this process of developing beliefs has its own set of rules and guidelines. For example, psychologist speak of "anchoring." That is, our beliefs develop as we encounter new information or experiences in our lives, but on the event of encountering new information, the new beliefs we develop are nearly always "anchored" to the belief we held before that experience. As a practical example, assume that I believe my risk for breast cancer to be very low, and I encounter a genetic result indicating that my risk might be high compared with the general population. My possible responses to this information are huge, but in general my beliefs will tend to be changed incrementally. That genetic result may be able to alter my perceived risk, but likely not as much as a frequentist account would consider reasonable.
- Anchoring is just one example of a tremendously complex set of effects that come into play when we speak of how we form beliefs about our expectations of the future, or what we might call our "perceived risk".
- Could discuss here "future self", change with age, importance attributed to effects (including genetic effects), anxiety, etc.
- Have a simplified schematic that shows how genetic information, taking genetic risk as a key case example, moves from from empirical evidence to "action". Something like this:
- Empirical research with large sample size
- Statistical modeling, stratifying risk estimates to be as specific to a particular patient as possible
- Reporting the risk estimate to a provider
- The provider reporting the risk estimate to the patient along with recommendations
- The patient decides which of the recommendations he or she will accept, some of which then lead to action on the provider's part, some of which lead to action on the patient's part
- The overall organization of this book will follow this schematic as it moves from step to step:
- Empirical data and statistical modeling generate probabilities in this way, and they will presumably be reported to the provider (introduction)
- The probabilities that are generated need to be clarified. Belief vs. frequentist definitions of probability, empirical vs. physical accounts of probability
- This clarification informs the way that patient and provider deliberate on what actions to take, which can be understood in terms of decision theory (need to understand decision theory in order to understand better where it fits here)
- Decision theory helps determine which decision maximizes utility, but it is not descriptive of how decisions are actually made. Aristotle's practical philosophy describes that in a way that is generic and, in that way, helpful to our aims
- When we think about the phronesis of the provider in this way, we can see that the aims (i.e. anticipated outcomes form using the data) should determine the policies for reporting and using the data. That is, our policies should take into account the way deliberations take place and actions are decided upon
- This specific implications are...
- Gadamer did for epistemology what Stout did for ethics: From an Aristotelian perspective, Gadamer framed the method for attaining knowledge as understanding. That is, as coming to the conclusions that we can within the context that we face specific problems. Gadamer's hermeneutics is a pragmatist epistemology.
Paper on EBM (and by extension to dissertation)
- Theorists have had trouble distinguishing between the utility of theoretical medicine and EBM. Some argue that EBM needs theoretical medicine so that hypotheses can be formed and to test the conclusions of trials with biological plausibility. On the other hand, EBM advocates argue that theoretical medicine can provide no empirical account for why something did or not work. The problem with all of this, of course, is that practitioners don't use one or the other. They pragmatically select from their various epistemological tools. In fact, they often utilize a complex combination: using theory to extend EBM to specific circumstances, using EBM to call theories into question.
- One of the proclaimed aims of EBM is decrease variation in treatment. More precisely, however, the aim of EBM is to reduce unwarranted variation in treatment.
- The model for scientifically-based medicine is that practitioners combine theory, evidence, experience, and a range of other commitments into an act of practical reasoning. The challenge of the practical reasoner, however, does end here. The practitioner must then discover whether that act produced the intended outcome. He or she has two methods to test this. First, he or she will seek feedback from patients and others: "How are things going?" This is an integral part of practical medicine. However, what this feedback cannot answer is whether some of the individual characteristics that providers take into account in their practical reasoning carry the weight they expect. They need feedback not only on whether a treatment worked, but whether the reasons attended to by the provider were meaningful to that outcome.
- Importantly, EBM provides a method for generating that type of feedback. But it is only a tool. For the most part, variations in the concrete situatedness from patient to patient are not comparable. It is only certain variations, albeit potentially important variations, that can be used to group patients meaningfully in a trial.
- Much of the debate over EBM is based around exactly how much of the important differences between patients can be accounted for in this way. As Stout would say, "Never mind. It doesn't matter." In the extraordinarily rare circumstances when a treatment works for every patient, providers would for the most part be irresponsible to use a different treatment. But they will still need to engage in practical reasoning to account for all of those other factors that are relevant: patient preference, economic considerations, adverse effects, drug interactions. And they will still use their practical reasoning to decide how and when to use that treatment.
- And when a treatment only works for some patients, or only works for some patients within a very specific group, relevant differences still remain unaccounted for. It is up to individual providers to negotiate that imperfection in particular circumstances. And through practical reasoning, they will test their conclusions against experience. In some cases, this feedback will lead to a reiteration of EBM. In other circumstances, no testable differences will emerge, and the muddling will continue.
- In the overall model, then, many factor serve as input to practical reasoning, and providers utilize a pragmatic weighing of those factors. While theorists may commit wholeheartedly to one epistemological model, providers have no such luxury. They will use those resources they have on hand. But experience, primarily in the form of feedback and evidence, will cycle around - telling the provider when their decision was wrong, and helping them get better the next time. (think of a schematic here).
- If a defense of EBM requires proving it as the one and final epistemological model for medical practice, then of course I have failed in this endeavor. But if a proper defense of EBM involves showing why it is indispensable to medicine, then perhaps I have made some progress in that respect.
Back to PM
- Personalized medicine is based on the unwarranted assumption that all relevant differences between patients can be accounted for using biomarkers. And Genomic Medicine, even more unlikely, is based on the assumption that much of this variation is genetic in nature.
- While my discussion above cannot rule out the possibility that in some circumstances the vast majority of variation can be accounted for by using these tools, it holds that even in such circumstances practical reasoning must still mediate the movement of that conclusion from theory to practice. This is because no epistemological account can be final in practice - the proof is in the pudding.
- Move sections on probabilities and Dupre's book to "The Worried Well" chapter - this will allow focus during the first half on practical reasoning.
- May still need to address probabilities in section on medical practical reasoning.
- Combine "Inactionability" and "Opportunity Costs" into one chapter on saying no and not ordering tests - focus will be on negotiating patient preferences and best interests of patients
- Proposed Digital Age Values
- Easy access to knowledge resources
- Participation in content generation
- Emphasis on discontinuity with previous stages (i.e. Web 2.0 implies all new version after Web 1.0; Information Revolution implies discontinuity with Industrial Revolution)
- Control of information about self (i.e. preferences regarding tracking and datasharing, emphasis on "your" information)
- No pay models (i.e. tolerating advertisements in return for services)
- Rhetoric of empowerment
- Emphasis on collaborative, amateur effort rather than individual, expert effort
- Governance via transparency
- Anonymity tolerated (because expertise and authority de-emphasized)
- Emphasis on superficial social connections rather than intimate relationships
- Dissertation Topic Relates to these Values:
- By showing what value expertise might have in a world where information is easy to access and focus is on collaborative effort
- By showing what value sustained, holistic, intimate relationships might provide over against brief, focused interactions
- Emphasizing continuity with more traditional values of medicine, but in light of modern technologies