Patent Publication Number: US-2016246930-A1

Title: Systems and methods for population tests of individualized treatments

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This patent application claims the benefit of Provisional Patent Application Ser. No. 61/649,104, titled “Systems and Methods for Population Tests of Individualized Treatments” filed on May 18, 2012, the contents of which are incorporated herein by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present disclosure is generally directed to technology for modeling scientific trials. 
     2. Description of the Related Art 
     The fields of medicine and advertising typically rely heavily on statistical hypothesis and inference testing to advance their knowledge. This is usually fraught with problems. 
     For example, many testing approaches involve statistical hypothesis testing with measures that are unclear and hard to interpret. Moreover, they operate under strong assumptions of finding an effective ingredient of a drug that can help treat a condition. This becomes problematic in testing complementary medicine treatments, which look at whole system effects and not a single drug approach. Moreover, complementary medicine practitioners might prescribe very different medicines to two people with the same condition. The test therefore, tends to be rejected by the practitioner, and its results are not usable to draw meaningful conclusions. Moreover, double-blind randomized trials can create ethical and practical difficulties for researchers. For instance, the insistence of attempting a known inferior method of treatment as a placebo would violate the caregiver&#39;s Hippocratic oath, even if the patient has agreed to the trial. Bigger practical difficulties arise when double-blind trials can simply not be designed for holistic medical treatments. For instance, if a holistic treatment involves lifestyle change that is customized for an individual, it is practically impossible to conceive of a way to fool a care provider into prescribing it in the garb of something else. These problems are unaddressed in conventional population trial design. 
     SUMMARY OF THE INVENTION 
     In accordance with one embodiment, there is provided a new method of testing that shifts the focus from testing a treatment on a randomized population to testing the logic that produces a customized treatment for a randomized population at an individual level. 
     The method involves the use of a model (e.g. Bayesian) to custom-fit systems of logic that produce treatments that are customized for patients. In medicine, this would allow the testing of customized medicine approaches as a whole over single medicine trials, and open the gateway to the testing of complementary medicine systems that tend to have a heavy focus on customized medicine. In conventional medicine, this would be the equivalent of bringing in structured systems thinking when designing trials. In advertising, this would allow the testing of a customized theory of change around market behavior. 
     In accordance with one embodiment, there is provided a method to custom-fit treatment logic by formulating the therapy and its application using an objective classification structure, using that structure to develop a prior probability distribution on effectiveness for each combination in the structure, collecting data against the structure from clinical observations and obtaining an updated posterior probability distribution to make future clinical decisions. 
     In accordance with one embodiment, a system and method includes receiving, by a computing device, therapeutic markers that are observed on a patient having a particular condition, using these markers to formulate therapy logic in the form of an objective classification structure that can be applied to the patient for customized treatment to produce outcomes; and providing, by the computing device, the outcomes as feedback to the therapy logic in post-trial learning. 
     The objective classification structure is modeled as k combinations of observed conditions, n combinations of treatments, and n*k assessments of a chance of a good outcome. The combinations of treatments should include options to not pursue any treatment involved in the mix. The therapy logic is encoded by assessing probability of a good outcome for each combination of therapy and conditions as specified in the objective classification structure. These assessments are made by the community of practitioners to produce a histogram for each combination of therapy and condition, which is then summarized as a continuous prior probability distribution on that combination&#39;s outcome to allow for probabilistic updating. In one embodiment, a consensus of knowledge in a professional field is determined when the prior probability distribution is narrow and a misunderstanding or disagreement of members of the professional field is determined when the prior probability distribution is broad. After encoding the prior probability distributions, the model is used to determine the best therapy for any member of the patient population based on the conditions that are met. The therapy offered to the patient may or may not be accepted. The choice of the patient and the subsequent outcome is noted and used to update the probability distribution on the outcome. The updated distribution is the posterior probability distribution and is used in subsequent trials as the new prior probability distribution. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings of which: 
         FIG. 1  is a block diagram describing research process steps, practictioner participation steps and their artifacts produced at the end of each research step in accordance with one embodiment; 
         FIG. 2  is a block diagram describing a method in accordance with one embodiment; 
         FIG. 3  is a block diagram describing treatment options in accordance with the example of  FIG. 2  in accordance with one embodiment; 
         FIGS. 4A-4H  depict a table in accordance with one embodiment; 
         FIG. 5  is a simplified block diagram of a computing device that can be used to implement various embodiments of the disclosed technology; 
         FIG. 6  depicts a block diagram of a shift of the focus from treatment to the logic that produces the treatment in accordance with one embodiment; 
         FIG. 7  is a block diagram showing a formulation consisting of k combinations of observed conditions, n combinations of treatments, and n*k assessments of the chance of a good outcome in accordance with one embodiment; 
         FIG. 8  is a block diagram of an embodiment in which 8 combinations of observed conditions are formulated, 8 combinations of treatments, and therefore, 64 assessments of the chance of a good outcome in accordance with one embodiment; 
         FIG. 9  depicts a histogram showing each member of a group having different assessments in accordance with one embodiment; 
         FIG. 10  depicts using the histogram mean and variance to obtain a prior probability distribution in accordance with one embodiment; 
         FIG. 11  shows a prior that looks like a uniform distribution in accordance with one embodiment; 
         FIG. 12  depicts a histogram that has two modes in accordance with one embodiment; and 
         FIG. 13  depicts a posterior distribution after updating the prior with the observed data of good outcomes in a sample size of 30 in accordance with one embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     In accordance with one embodiment, there is provided a new method of testing, involving a Bayesian model that is custom-fit to hypotheses in fields of knowledge where treatments are different for different people. In medicine, this would allow the testing of customized medicine approaches as a whole over single medicine trials, and open the gateway to the testing of complementary medicine systems that tend to have a heavy focus on customized medicine. In conventional medicine, this would be the equivalent of bringing in structured systems thinking when designing trials. In advertising, this would allow the testing of a customized theory of change around market behavior. 
     Many testing approaches involve statistical hypothesis testing with measures that are unclear and hard to interpret. Moreover, they involve a flaw in the design of the experiment—the test being undertaken does not often correspond to an actual theory of change that anyone believes in. For instance, when testing complementary medicine treatments, tests often focus exclusively on efficacy of medicines on conditions. However, complementary medicine practitioners might prescribe very different medicines to two people with the same condition. The test therefore, tends to be rejected by the practitioner, and its results are not usable to draw meaningful conclusions. Moreover, double-blind randomized trials are unethical owing to the need to lie to participants about the treatment they are receiving. 
     In accordance with one embodiment, there is provided a method that includes designing objective classification structure, collecting data and obtaining a posterior.  FIG. 1  is a block diagram of an embodiment describing research process steps  105 , practitioner participation steps  110 , and the artifacts produced  120  at the end of each research process step. The research process steps  105  are for designing an objective classification structure  125 , representing the practitioner community&#39;s current position on the effectiveness of therapies described in the objective classification structure  128 , and using clinical observations to update the community&#39;s position  130 , in one embodiment. The practitioner participation steps  110  feed into the research process steps  105  and rely on artifacts  120  produced by the research steps as indicated by the arrows. 
     Specifically, the practitioner participation steps  110  can include, for example, identify and synthesize treatments for conditions  135 , assess each treatment-condition combination  140 , use decision models to drive clinical decisions  145 , and use updated decision models to drive clinical decisions  150 . The artifacts produced  120  can include, for example, objective classification structure  155 , prior probability distributions  160 , and updated prior probability distributions  165 . 
     Complementary medicine treatments can be quite different for the same disease. Before designing a test, one embodiment includes first replicating the classification logic that determines the course of treatment in an objective manner, to the satisfaction of the community of practice. The artifact produced in this process is termed the “Objective Classification Structure” and is formalized below, where P̂, T̂ and Â are vectors: 
     T: {Treatments}, A: {Diagnostic Attributes}, 
     p̂(T̂, Â): Fraction of cures assessed by community of practice 
     In one embodiment, each P̂ is a vector that can be fit into a beta distribution, using the mean and the variance. This distribution can be treated as the community&#39;s prior. It will be narrow when there is a lot of agreement and broad when there is a lot of diversity of opinion. When forming the prior probability distribution in one embodiment, care is taken to note the background of those interviewed to verify that they can be considered “experts.” 
     In one embodiment, a data collection effort on the therapeutic logic under test would involve tapping clinics that are performing treatments, and noting each set of treatments performed for each set of attributes in one embodiment. The outcome measures are also noted. 
     The outcome measures are then used to perform a binomial update of the beta prior probability distribution of the corresponding therapy and condition combination in one embodiment. For narrow priors, a lot of evidence to the contrary will be needed to get a different posterior. The standard of evidence for broader priors will be lower. 
     The resulting posterior distributions will form the new prior probability distributions for subsequent trials and will be used to make subsequent treatment decisions. 
       FIG. 2  is a block diagram depicting a method  200  in accordance with one embodiment. For example,  FIG. 2  may demonstrate how this protocol can be used to test treatment of Osteo-arthritis in Ayurveda. 
     Phase 1: Design Objective Classification Structure. 
     The objective classification structure involves identifying the questions that need to be asked in designing an effective treatment, and the treatment pathways that correspond to the answers. 
       FIG. 3  is a block diagram depicting treatment options  300  in accordance with the example of  FIG. 2 . 
     The treatments depicted in  FIG. 3  are of the type of Guggul formulation, the type of Snehan and the type of Swedan. Guggul is a form of gum-raisin. Kaishore and Mahayograj are different formulations of Guggul. Snehan is application of oil externally. Janu Dhara is a flow of oil on the knee joint. Abhyanga is an oil massage over the joint. Swedan is fomentation of sweat. Patra Pottali Swedan refers to the use of medicated leaves that are packed in a cloth and exposed to heat, after which they are applied to the joint, causing sweating in the joint. Nadi Swedan involves steaming medicinal herbs (e.g. vacha) and applying that steam to the affected joint. 
     In one embodiment, a method of population testing involves administering a treatment indicated by the model based on the prior probability distributions, collecting data based on the treatment and administering updating the probability distribution with the outcome. If the patient does not wish to accept the treatment, then the patient&#39;s choice and subsequent outcome are recorded and used to update the corresponding treatment and condition combination. Instead of randomized trials, this approach utilizes the observation design approach and thereby avoids the ethical and practical pitfalls of double-blind trials. 
     Practitioners are asked to provide the probability of getting a good outcome for each treatment and condition combination in one embodiment. In one example, a good outcome is clearly defined as “all symptoms associated with osteo-arthritis have disappeared after 3 months of treatment.” 
       FIGS. 4A-4H  depict a spreadsheet  400  in accordance with one embodiment.  FIG. 7  shows a decision model that will be used to drive clinical decisions, in one embodiment. For each treatment and condition combination, practitioner doctors will assess the probability of a good outcome. These assessments are not just by one doctor, but by different doctors with an established level of experience (e.g., at least 10 years in the field) in one embodiment. The assessment data from doctors for each treatment condition combination is fit into a beta distribution, in one embodiment. The beta distributions describe where the community converges and diverges in its assessments. 
     In the example from  FIG. 2 , data will be collected from Ayurveda clinics participating in the trial. Patients coming for treatment of osteo-arthritis will be treated by the Ayurvedic doctors the way they normally would, and not in a double-blind manner. Participating doctors may also be encouraged to use a decision model ( FIGS. 7, 4A-4H  show one embodiment that uses the probability of a good outcome as the value measure) that uses the community&#39;s prior probability distribution to drive treatment choices. However, this is optional, and what is essential for research is that the doctor record the conditions noted, the treatments suggested to the patient, the actual treatment chosen by the patient and finally, the outcome. The doctor will also record the precise method of the treatment allowing for validation and comparability between different clinics. 
     For each data point of a good outcome that is obtained in the example, the prior probability distribution for the corresponding set of conditions and treatments will be updated (the beta parameters are incremented) in one embodiment. The result gives us a posterior distribution that may now be used for inference and decision-making.\ 
       FIG. 5  is a high level block diagram of a computing system which can be used to implement any of the computing devices described herein. The computing system of  FIG. 5  includes processor  80 , memory  82 , mass storage device  84 , peripherals  86 , output devices  88 , input devices  90 , portable storage  92 , and display system  94 . For purposes of simplicity, the components shown in  FIG. 5  are depicted as being connected via a single bus  96 . However, the components may be connected through one or more data transport means. In one alternative, processor  80  and memory  82  may be connected via a local microprocessor bus, and the mass storage device  84 , peripheral device  86 , portable storage  92  and display system  94  may be connected via one or more input/output buses. 
     Processor  80  may contain a single microprocessor, or may contain a plurality of microprocessors for configuring the computer system as a multiprocessor system. Memory  82  stores instructions and data for programming processor  80  to implement the technology described herein. In one embodiment, memory  82  may include banks of dynamic random access memory, high speed cache memory, flash memory, other nonvolatile memory, and/or other storage elements. Mass storage device  84 , which may be implemented with a magnetic disc drive or optical disc drive, is a nonvolatile storage device for storing data and code. In one embodiment, mass storage device  84  stores the system software that programs processor  80  to implement the technology described herein. Portable storage device  92  operates in conjunction with a portable nonvolatile storage medium, such as a floppy disc, CD-RW, flash memory card/drive, etc., to input and output data and code to and from the computing system of  FIG. 5 . In one embodiment, system software for implementing embodiments is stored on such a portable medium, and is input to the computer system via portable storage medium drive  92 . 
     Peripheral devices  86  may include any type of computer support device, such as an input/output interface, to add additional functionality to the computer system. For example, peripheral devices  86  may include one or more network interfaces for connecting the computer system to one or more networks, a modem, a router, a wireless communication device, etc. Input devices  90  provide a portion of a user interface, and may include a keyboard or pointing device (e.g. mouse, track ball, etc.). In order to display textual and graphical information, the computing system of  FIG. 5  will (optionally) have an output display system  94 , which may include a video card and monitor. Output devices  88  can include speakers, printers, network interfaces, etc. Device  100  may also contain communications connection(s)  112  that allow the device to communicate with other devices via a wired or wireless network. Examples of communications connections include network cards for LAN connections, wireless networking cards, modems, etc. The communication connection(s) can include hardware and/or software that enables communication using such protocols as DNS, TCP/IP, UDP/IP, and HTTP/HTTPS, among others. 
     The components depicted in the computing system of  FIG. 5  are those typically found in computing systems suitable for use with the technology described herein, and are intended to represent a broad category of such computer components that are well known in the art. Many different bus configurations, network platforms, operating systems can be used. The technology described herein is not limited to any particular computing system. 
     The technology described herein can be implemented using hardware, software, or a combination of both hardware and software. The software used is stored on one or more of the processor readable storage devices described above (e.g., memory  82 , mass storage  84  or portable storage  92 ) to program one or more of the processors to perform the functions described herein. The processor readable storage devices can include non-transitory, tangible computer readable media such as volatile and non-volatile media, removable and non-removable media. Tangible computer readable media may be implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of tangible computer readable media include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transitory, tangible medium which can be used to store the desired information and which can be accessed by a computer. In alternative embodiments, some or all of the software can be replaced by dedicated hardware including custom integrated circuits, gate arrays, FPGAs, PLDs, and special purpose computers. In one embodiment, software (stored on a storage device) implementing one or more embodiments is used to program one or more processors. The one or more processors can be in communication with one or more tangible computer readable media/storage devices, peripherals and/or communication interfaces. In alternative embodiments, some or all of the software can be replaced by dedicated hardware including custom integrated circuits, gate arrays, FPGAs, PLDs, and special purpose computers. 
     In one embodiment, the computing system is used for population testing of individualized treatments. In one embodiment, the computing system includes a formulation module configured to enable formulating of a therapy and its application using an objective classification structure, a distribution module for using the structure to develop a prior probability distribution on effectiveness for each combination in the structure, a data collection module for collecting data against the structure from clinical observations, and an updating module for obtaining an updated posterior probability distribution to make future clinical decisions. 
     A Continuous Updated Therapy (CUT) method shifts the focus of testing from therapy to logic that produces a custom-fit therapy. This logic should codify the analysis for the condition being treated and result in a therapeutic measure.  FIG. 6  depicts a block diagram  1700  of a shift of the focus from treatment to the logic that produces the treatment. In standard hypothesis tests, independently chosen subjects  1705  are subject to treatment  1710  (an independent variable) to produce outcomes (a dependent variable)  1720 . In CUT, independently-chosen subjects  1725  are subject to therapy logic  1730  (an independent variable) in an observational design context which produces customized treatment  1735  to produce outcomes  1740  (a dependent variable). These outcomes are provided as feedback to the therapy logic in post-trial learning  1745 . 
     CUT and Observational Design 
     The CUT method addresses the feasibility and ethical problems behind double-blind trials by incorporating observation design. Doctors are typically not asked to change their therapeutic approach. Instead, the recommendation of the doctor is recorded, along with the patient&#39;s actual choice, followed by the therapeutic outcome. 
     The steps in the CUT method are described below with the help of the Osteo-arthritis treatment example described above. 
     Phase 1: Formulation 
     Scope: Limited to Experienced Doctors 
     A small working group of experienced doctors (e.g., Ayurvedic doctors, or doctors whose treatments are formulated in a holistic manner after examining the current state of imbalance in the patient) will get together and formulate therapy logic for a particular condition (e.g. osteo-arthritis). This logic takes as its input therapeutic markers that are observed on the patient, and produces a custom-fit therapy as an output. An embodiment of the formulation phase has three deliverables:
         1. An agreement on the number of treatments and the number of uncertainties that drive those treatment decisions.   2. A clear and agreed-upon definition of a good outcome.   3. An agreement on the necessary qualification needed for the community of practitioners who will develop the prior according to this formulation.
 
The purpose of the working group is to check each other&#39;s filtering logic and come up with a standardized filter model that the broader (e.g., Ayurvedic) community will consider representative. The working group can aim to outline their collaboration in a paper focused on framing the problem. A good paper would include clear distinctions on the therapies and conditions, extant research on their combinations and a decision tree.  FIG. 7  is a block diagram  1800  showing a formulation consisting of k combinations of observed conditions  1805 , n combinations of treatments  1810 , and n*k assessments of the chance of a good outcome  1820 .
       

     As described above, suppose that a group has been obtained to ratify the filter logic for Arthritis treatment. That treatment is typically focused on three decision points: 
                                    Kaishore Guggul (KG) vs.   KG is a herbal formulation and MYG is a herbo-mineral       Maha Yogaraj Guggul (MYG)   formulation. Both tackle bone-degenerative conditions (or           sandhivata)       Janu Dhara vs. Snehan   Both are forms of external application of medicated oils. In the           Janu Dhara procedure, warm oil (mixture of Dhanvantar Tailam           and Muruvenna) is dropped in a regulated flow on the affected           joints. The Snehan procedure involves performing an Ayurvedic           massage on the affected part with the oil (Muruvenna and           Kottamchukkadi).       Patra Pottali vs. Nadi Swedan   Both are forms of external application of heat. In Patra Pottali,           leaves of certain herbs (dhatura, eranda, arka, nirgundi, shigru)           are wrapped in a cloth, heated and applied on the affected joint.           In Nadi Swedan, steam is given from a decoction of ten herbs           (dashamula).                    
Three uncertainties that may be associated with Osteo-arthritis are:
 
     
       
         
           
               
               
             
               
                   
               
             
            
               
                 Acidity or Hot 
                 Osteo-arthritis is primarily caused by vata (or air and 
               
               
                 Joints (Presence 
                 ether) affliction, according to Ayurveda. When pitta 
               
               
                 or Absence) 
                 dosha (fire element) is also associated with osteo- 
               
               
                   
                 arthritis, the joints may be warm to the touch, or the 
               
               
                   
                 patient may report heat in the joint, or there can be 
               
               
                   
                 systemic symptoms of pitta like acidity 
               
               
                 Tenderness 
                 A tactile test confirms the presence or absence of 
               
               
                 (Presence 
                 tenderness 
               
               
                 or Absence) 
               
               
                 Severe Swelling 
                 A visual test confirms the presence or absence of 
               
               
                 or No Swelling 
                 swelling 
               
               
                   
               
            
           
         
       
     
     The working group may also agree on a definition of a good outcome as “all symptoms of osteo-arthritis have disappeared after three months of treatment.” 
     In one embodiment, the working group formulates experience criteria to filter doctors who will be invited into the prior development group. For example, only doctors with two decades or more of experience may be invited. 
       FIG. 8  is a block diagram  1900  of an embodiment in which 8 combinations  1905  of observed conditions are formulated, 8 combinations of treatments  1910 , and therefore, 64 assessments  1920  of the chance of a good outcome. 
     Phase 2: Development of the Prior 
     In this phase, a larger prior probability development group composed of doctors at equivalent (and high) capability levels can be tapped and asked to assess the fraction (probability) of good outcomes for each combination of therapeutic choices and uncertainties. In one embodiment, these assessments are performed for each individual in the group, and the distribution of the fractions for a particular combination becomes a prior probability distribution on good outcomes for that combination (there may, however, be an entire spectrum of outcomes, each requiring an assessment). These distributions may play an important role in understanding the standard of (e.g., Ayurvedic) knowledge in the field. A narrow distribution may reveal consensus, while a broad distribution may reveal either misunderstanding or disagreement. Either way, a map of how closely leading Ayurvedic practitioners think would be obtained and our claims can be calibrated before any tests are conducted. The result of this phase may be a paper that illustrates where the (e.g., Ayurvedic) community stands on the therapeutic logic in the form of probability distributions for each combination. 
     To facilitate updating of these probability distributions when observations are made, the histogram may be converted to a continuous probability distribution. It may be noted that fitting the histogram onto a beta distribution might be the most prudent for two reasons. First, the beta is easy to fit due to its versatility, ranging from a uniform to a Gaussian distribution. Second, it is typically a trivial operation to update the beta distribution by assuming a binomial likelihood function on observations, which implies that every observation is “irrelevant” to other observations given our prior distribution on the fraction of good outcomes for a particular combination of therapy and conditions. Third, the update is further simplified by the fact that the beta distribution is a conjugate distribution, and the posterior distribution is also a beta, which can be obtained by simple addition of the observation counts to the prior distribution&#39;s parameters. Some may object to the use of the beta or any distribution from the normal family. Quantile-Parameterized Distributions (QPD) are designed to flexibly fit Cumulative Distribution Functions (CDFs) on quantile assessments. QPDs also have the advantage of being easy to feed into a Monte Carlo simulation. One could use a QPD or any other distribution for that matter that lends itself to a Monte Carlo simulation and combine it with a likelihood function (e.g. binomial) and do a Monte Carlo sampling of the posterior distribution. 
     In one embodiment, there are 2 6 =64 possible combinations of treatment choices and uncertainty outcomes in this particular formulation of the osteo-arthritis problem. A more real-world example would likely have more therapeutic choices, including the option to not apply a particular treatment. For each possible combination, the prior development group is asked to assess the probability of a good outcome. As shown in  FIG. 9 , each member of this group can have different assessments, and these differences are plotted as a histogram  2010 . A hypothetical histogram  2010  shows what a population of 200 doctors think on the chances of a good outcome for the therapeutic combination KG+JD+PP applied to patients with the condition A+T+SS. 
     Then, the beta distribution parameters can be calculated from the histogram mean (μ) and variance (σ 2 ) as follows: 
     
       
         
           
             
               α 
               = 
               
                 βμ 
                 
                   1 
                   - 
                   μ 
                 
               
             
             , 
             
               β 
               = 
               
                 
                   
                     μ 
                     3 
                   
                   - 
                   
                     2 
                      
                     
                       μ 
                       2 
                     
                   
                   + 
                   
                     μ 
                      
                     
                       ( 
                       
                         
                           σ 
                           2 
                         
                         + 
                         1 
                       
                       ) 
                     
                   
                   - 
                   
                     σ 
                     2 
                   
                 
                 
                   σ 
                   2 
                 
               
             
           
         
       
     
     As shown in  FIG. 10 , this can be used to obtain a prior probability distribution  2100 . In one embodiment, 64 priors representing possible combination of therapies and conditions are obtained. As shown in  FIG. 11 , prior  2200  looks like a uniform distribution. 
     Such a prior may result in a question as to why there is such a diversity of opinion in the practitioner community. This can result in an investigation as to whether the problem lies in framing this particular alternative combination or if respondent doctors have misunderstood the question. 
       FIG. 12  is a histogram  2300  that has two modes. Histograph  2300  looks like a bi-modal distribution and indicates that there are two opposing points of view. Perhaps there are two sub-schools of thought, and such a result yields a valuable opportunity to bring the two camps together and review why they have differing positions. For the purposes of going into the next phase, there may be two resolutions:
         i) The two camps find out that one of them was right, and the other changes their position, resulting in a unimodal distribution   ii) The two camps agree to disagree. In this case,
           a. we may deliberately take the uniform distribution. Or,   b. we may separate out the two modes into unimodal distributions, and hold two priors, and use both with the evidence that comes out of the next phase   
           iii) The two camps realize that there is an underlying factor they are treating differently, which should be included as a new uncertainty in the model.
 
Thus, the community has opposing points of view which need to be better understood and clarified.
       

     Phase 3: Conduction of the Test 
     The strategy of piggy-backing off the regular treatment of doctors without trying to influence it for the purposes of the study has another advantage—such an approach avoids the ethical pitfalls of needing to fool subjects with placebos. It also fundamentally assumes that the final decision-making power around the treatment is in the hands of the patient, who may decide to choose an alternative path than the one the doctor recommends. As described below, the outcome of the actual treatment delivered is of interest, regardless of whether the doctor chose it or the patient chose it. 
     Example: 
     Suppose that, out of 30 patients (n trial ) with A+T+SS who took the proposed treatment KG+JD+PP, 10 (r trial ) 1  had a good outcome. The beta distribution parameters are updated as follows: 
     
       
      
       r=α 
      
     
     
       
      
       n=α+β 
      
     
     
       
      
       r 
       updated 
       =r+r 
       trial  
      
     
     
       
      
       n 
       updated 
       =n+n 
       trial  
      
     
       α updated   =r   updated  
 
       β updated   =n   updated   −r   updated  
 
       FIG. 13  depicts the posterior distribution  2400  after updating the prior with the observed data of 10 good outcomes in a sample size of 30. The result shows that the posterior has shifted away to the left, contradicting the initial prior optimism. Its width is indicative of the quantity of data. Usually, the more data, the narrower the posterior distribution. However, the narrower the prior, the more the data is typically needed to cause a substantive shift.
 
Phase 4: Deployment into Decision-Making
 
     The updated distributions from the previous phase can then be used as the basis for decision systems that produce custom-fit decisions based on the condition of the patient. These could mark the emergence of decision-support systems that are quite different from previous expert system effort. Decision-support systems do not need to mimic the doctor—rather they apply a logical procedure that the doctor would agree with. In this phase, economic factors may also play a role. 
     Example: 
     Continuing with the hypothetical osteo-arthritis example, for a given combination of conditions, those alternatives that have the highest chance of a good outcome can be found. A more sophisticated model may be necessary if some treatment paths are marginally different in outcome probability but vastly different in cost or if some treatment paths have a chance of unwanted side effects. 
     The foregoing detailed description has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the claims to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. The described embodiments were chosen in order to best explain the principles of the claimed subject matter and its practical application to thereby enable others skilled in the art to best utilize it in various embodiments and with various modifications as are suited to the particular use contemplated. It is intended that the scope of the disclosure be defined by the claims appended hereto.