healthcare interoperability

2004 to 2017 Convergence of Big Data, Machine Learning, Semantic Web, Graph Analytics, High Performance Computing – All These and Yet Big Data Analytics Sucks

2004 – Tim Lee Berner

 

Semantic Web

OWL and RDF introduced to address Semantic Web and also Knowledge Representation. This really calls for BigData technology that was still not ready.

https://www.w3.org/2004/01/sws-pressrelease

 

2006 – Hadoop Apache Hadoop is an open source software framework for storage and large scale processing of data-sets on clusters of commodity hardware.

https://opensource.com/life/14/8/intro-apache-hadoop-big-data

 

2008

Scientific Method Obsolete for BigData

 The Data Deluge Makes the Scientific Method Obsolete

 

2008 – MapReduce

Large Data Processing – classification

Google created the framework for MapReduce – MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the paper.

•        https://research.google.com/archive/mapreduce.html

 

2009 – Machine Learning Emergence of BigData Machine Learning Framework and Libraries

 

2009 – Apache Mahout Apache Mahout – Machine Learning on BigData Introduced.  Apache Mahout is a linear algebra library that runs on top of any distributed engine that have bindings written.

https://www.ibm.com/developerworks/library/j-mahout/

Mahout ML is mostly restricted to set theory. Apache Mahout is a project of the Apache Software Foundation to produce free implementations of distributed or otherwise scalable machine learning algorithms focused primarily in the areas of collaborative filtering, clustering and classification.

 

 

2012 – Apache SPARK Apache SPARK Introduced to deal with Very Large Data and IN-Memorry Processing. It is an architecture for cluster computing – that increases the computing compared with slow MapReduce by 100 times and also better solves parallelization of the algorithm. Apache Spark is an open-source cluster-computing framework. Originally developed at the University of California, Berkeley’s AMPLab

https://en.wikipedia.org/wiki/Apache_Spark

 

Mahout vs Spark Difference between Mahout vs SPARK

https://www.linkedin.com/pulse/choosing-machine-learning-frameworks-apache-mahout-vs-debajani

 

2012 – GraphX GraphX is a distributed graph processing framework on top of Apache Spark. Because it is based on RDDs, which are immutable, graphs are immutable and thus GraphX is unsuitable for graphs that need to be updated, let alone in a transactional manner like a graph databasE. GraphX can be viewed as being the Spark in-memory version of Apache Giraph, which utilized Hadoop disk-based MapReduce.
2013 – DARPA PPAML https://www.darpa.mil/program/probabilistic-programming-for-advancing-machine-learning

 

Machine learning – the ability of computers to understand data, manage results and infer insights from uncertain information – is the force behind many recent revolutions in computing. Email spam filters, smartphone personal assistants and self-driving vehicles are all based on research advances in machine learning. Unfortunately, even as the demand for these capabilities is accelerating, every new application requires a Herculean effort. Teams of hard-to-find experts must build expensive, custom tools that are often painfully slow and can perform unpredictably against large, complex data sets.

The Probabilistic Programming for Advancing Machine Learning (PPAML) program aims to address these challenges. Probabilistic programming is a new programming paradigm for managing uncertain information.

Ingine Responded to DARPA’s RFQ with a detailed architecture based on Barry’s innovation in the algorithm that basically solves the above ask to some extent. Importantly it solve Probabilistic Ontology for  Knowledge Extraction from Uncertainty and Semantic Reasoning.

2017 – DARPA Graph Analytics https://graphchallenge.mit.edu/scenarios

 

In this era of big data, the rates at which these data sets grow continue to accelerate. The ability to manage and analyze the largest data sets is always severely taxed.  The most challenging of these data sets are those containing relational or network data. The HIVE challenge is envisioned to be an annual challenge that will advance the state of the art in graph analytics on extremely large data sets. The primary focus of the challenges will be on the expansion and acceleration of graph analytic algorithms through improvements to algorithms and their implementations, and especially importantly, through special purpose hardware such as distributed and grid computers, and GPUs. Potential approaches to accelerate graph analytic algorithms include such methods as massively parallel computation, improvements to memory utilization, more efficient communications, and optimized data processing units.

 

2013 Other Large Graph Analytics Reference An NSA Big Graph experiment

http://www.pdl.cmu.edu/SDI/2013/slides/big_graph_nsa_rd_2013_56002v1.pdf

2017 Data Science Dealing with Large Data Still Sucks

 

Despite emergence of Big Data, Machine Learning, Graphing Techniques and Semantic Web. The convergence is still far fleeting. Especially Semantic / Cognitive / Knowledge Extraction techniques are very poorly defined and there does not exists a framework approach to knowledge engineering leading into Machine Learning and automation in Knowledge Extraction, Representation, Learning and Reasoning. This is what  Q-UEL and HDN solves at the algorithmic level.
Advertisements

The BioIngine.com Platform Beta Release 1.0 on the Anvil

The BioIngine.com™ 

Ingine; Inc™, The BioIngine.com™, DiracIngine™, MARPLE™ are all Ingine Inc © and Trademark Protected; also The BioIngine.com is Patent Pending IP belonging to Ingine; Inc™.

Screen Shot 2016-09-01 at 8.32.18 PM

High Performance Cloud based Cognitive Computing Platform

The below figure depicts the healthcare analytics challenge as the order of complexity is scaled.

1. Introduction Beta Release 1.0

It is our pleasure to introduce startup venture Ingine; Inc that brings to market The BioIngine.com™Cognitive Computing Platform for the Healthcare market, delivering Medical Automated Reasoning Programming Language Environment (MARPLE) capability based on the mathematics borrowed from several disciplines and notably from late Prof Paul A M Dirac’s Quantum Mechanics.

The BioIngine.com™; is a High Performance Cloud Computing Platformdelivering HealthCare Large-Data Analytics capability derived from an ensemble of bio-statistical computations. The automated bio-statistical reasoning is a combination of “deterministic” and “probabilistic” methods employed against both structured and unstructured large data sets leading into Cognitive Reasoning.

The BioIngine.com™; delivers Medical Automated Reasoning based on a Medical Automated Programming Language Environment (MARPLE) capability, so better achieving 2nd order semantic interoperability1 in the Healthcare ecosystem. (Appendix Notes)

The BioIngine.com™ is a result of several years of efforts with Dr. Barry Robson; former Chief Scientific Officer, IBM Global Healthcare, Pharmaceutical and Life Science. His research has been in developing quantum math driven exchange and inference language achieving semantic interoperability, while also enabling Clinical Decision Support System, that is inherently Evidence Based Medicine (EBM). The solution, besides enabling EBM, also delivers knowledge graphs for Public Health surveys including those sought by epidemiologists. Based on Dr Robson’s experience in the biopharmaceutical industry and pioneering efforts in bioinformatics, this has the data mining driven potential to advance pathways planning from clinical to pharmacogenomics.

The BioIngine.com™; brings the machinery of Quantum Mechanics to Healthcare analytics; delivering a comprehensive data science experience that covers both Patient Health and Population Health (Epidemiology) analytics, driven by a range of bio-statistical methods from descriptive to inferential statistics, leading into evidence driven medical reasoning.

The BioIngine.com™; transforms the large clinical data sets generated by interoperability architectures, such as in Health Information Exchange (HIE) into “semantic lake” representing the Health ecosystem that is more amenable to bio-statistical reasoning and knowledge representation. This capability delivers evidence-based knowledge needed for Clinical Decision Support System, better achieving Clinical Efficacy by helping to reduce medical errors.

The BioIngine.com™; platform working against large clinical data sets or while residing within the large Patient Health Information Exchange (HIE) works in creating opportunity for Clinical Efficacy, while it also facilitates in the better achievement of “Efficiencies in the Healthcare Management” that Accountable Care Organization (ACO) seeks.

Our endeavors have resulted in the development of revolutionary Data Science to deliver Health Knowledge by Probabilistic Inference. The solution developed addresses critical areas in both scientific and technical, notably the healthcare interoperability challenges of delivering semantically relevant knowledge both at patient health (clinical) and public health level (Accountable Care Organization).

2. WhyThe BioIngine.com™?

The basic premise in engineering The BioIngine.com™ is in acknowledging the fact that in solving knowledge extraction from the large data sets (both structured and unstructured), one is confronted by very large data sets riddled by high-dimensionality and uncertainty.

Generally in solving insights from the large data sets the order in complexity is scaled as follows:-

A. Insights around :- “what” 

For large data sets, descriptive statistics are adequate to extract a “what” perspective. Descriptive statistics generally delivers statistical summary of the ecosystem and the probabilistic distribution.

B. Univariate Problem :- “what” 

Considering some simplicity in the variables relationships or is cumulative effects between the independent variables (causing) and the dependent variables (outcomes):-

a) Univariate regression (simple independent variables to dependent variables analysis)

b) Correlation Cluster – shows impact of set of variables or segment analysis.

           https://en.wikipedia.org/wiki/Correlation_clustering

[From above link:- In machine learningcorrelation clustering or cluster editing operates in a scenario where the relationships between the objects are known instead of the actual representations of the objects. For example, given a weighted graph G = (V,E), where the edge weight indicates whether two nodes are similar (positive edge weight) or different (negative edge weight), the task is to find a clustering that either maximizes agreements (sum of positive edge weights within a cluster plus the absolute value of the sum of negative edge weights between clusters) or minimizes disagreements (absolute value of the sum of negative edge weights within a cluster plus the sum of positive edge weights across clusters). Unlike other clustering algorithms this does not require choosing the number of clusters k in advance because the objective, to minimize the sum of weights of the cut edges, is independent of the number of clusters.]

C. Multivariate Analysis (Complexity increases) :- “what”

a) Multiple regression (considering multiple univariate to analyze the effect of the independent variables on the outcomes)

b) Multivariate regression – where multiple causes and multiple outcomes exists

All the above are still discussing the “what” aspect. When the complexity increases the notion of independent and dependent variables become non-deterministic, since it is difficult to establish given the interactions, potentially including cyclic paths of influence in a network of interactions, amongst the variables. A very simple example in just a simple case is that obesity causes diabetes, but the also converse is true, and we may also suspect that obesity causes type 2 diabetes cause obesity… In such situation what is best as “subject” and what is best as “object” becomes difficult to establish. Existing inference network methods typically assume that the world can be represented by a Directional Acyclic Graph, more like a tree, but the real world is more complex than that that: metabolism, neural pathways, road maps, subway maps, concept maps, are not unidirectional, and they are more interactive, with cyclic routes. Furthermore, discovering the “how” aspect becomes important in the diagnosis of the episodes and to establish correct pathways, while also extracting the severe cases (chronic cases which is a multivariate problem). Indeterminism also creates an ontology that can be probabilistic, not crisp.

Most ACO analytics addresses the above based on the PQRS clinical factors, which are all quantitative. Barely useful for advancing the ACO into solving performance driven or value driven outcomes most of which are qualitative.

D. Neural Net :- “what”

https://www.wolfram.com/language/11/neural-networks/?product=mathematica

The above discussed challenges of analyzing multivariate pushes us into techniques such as Neural Net; which is the next level to Multivariate Regression Statistical Approach…. where multiple regression models are feeding into the next level of clusters, again an array of multiple regression models.

The Neural Net method still remains inadequate in exposing “how” probably the human mind is organized in discerning the health ecosystem for diagnostic purposes, for which “how”, “why”, “when” etc becomes imperative to arrive at accurate diagnosis and target outcomes efficiently. Its learning is “smudged out”. A little more precisely put: it is hard to interrogate a Neural Net because it is far from easy to see what are the weights mixed up in different pooled contributions, or where they come from.

“So we enter Probabilistic Computations which is as such Combinatorial Explosion Problem”.

E. Hyperbolic Dirac Net (Inverse or Dual Bayesian technique): – “how”, “why”, “when” in addition to “what”.

Note:- Beta Release 1.0 only addresses HDN transformation and inference query against the structured data sets and Features A, B and E. However, as a non-packaged solution C and D features can still be explored.

Release 2.0 will deliver full A.I driven reasoning capability MARPLE working against both structured and unstructured data sets. Furthermore, it will be designed to be customized for EBM driven “Point Of Care” and “Care Planning” productized user experience.

The BioIngine.com™offers a comprehensive bio-statistical reasoning experience in the application of the data science as discussed above that blends descriptive and inferential statistical studies.

The BioIngine.com™; is a High Performance Cloud Computing Platformdelivering HealthCare Large-Data Analytics capability derived from an ensemble of bio-statistical computations. The automated bio-statistical reasoning is a combination of “deterministic” and “probabilistic” methods employed against both structured and unstructured large data sets leading into Cognitive Reasoning.

Given the challenge of analyzing against the large data sets both structured (EHR data) and unstructured data; the emerging Healthcare analytics are around above discussed methods D and E; Ingine Inc is unique in the Hyperbolic Dirac Net proposition.

Q-UEL Toolkit for Medical Decision Making :- Science of Uncertainty and Probabilities

Screen Shot 2016-08-24 at 11.07.49 AM

Quantum Universal Exchange Language

Emergent | Interoperability | Knowledge Mining | Blockchain

Q-UEL

  1. It is a toolkit / framework
  2. Is an Algorithmic Language for constructing Complex System
  3. Results into a Inferential Statistical mechanism suitable for a highly complex system – “Hyperbolic Dirac Net”
  4. Involves an approach that is based on the premise that a Highly Complex System driven by the human social structures continuously strives to achieve a higher order in the entropic journey by continuos discerning the knowledge hidden in the system that is in continuum.
  5. A System in Continuum seeking Higher and Higher Order is a Generative System.
  6. A Generative System; Brings System itself as a Method to achieve Transformation. Similar is the case for National Learning Health System.
  7. A Generative System; as such is based on Distributed Autonomous Agents / Organization; achieving Syndication driven by Self Regulation or Swarming behavior.
  8. Essentially Q-UEL as a toolkit / framework algorithmically addresses interoperability, knowledge mining and blockchain; while driving the Healthcare Eco-system into Generative Transformation achieving higher nd higher orders in the National Learning Health System.
  9. It has capabilities to facilitate medical workflow, continuity of care, medical knowledge extraction and representation from vast large sets of structured and unstructured data, automating bio-statistical reasoning leading into large data driven evidence based medicine, that further leads into clinical decision support system including knowledge management and Artificial Intelligence; and public health and epidemiological analysis.

http://www.himss.org/achieving-national-learning-health-system

GENERATIVE SYSTEM :-

https://ingine.wordpress.com/2013/01/09/generative-transformation-system-is-the-method/

A Large Chaotic System driven by Human Social Structures has two contending ways.

a. Natural Selection – Adaptive – Darwinian – Natural Selection – Survival Of Fittest – Dominance

b. Self Regulation – Generative – Innovation – Diversity – Cambrian Explosion – Unique Peculiarities – Co Existence – Emergent

Accountable Care Organization (ACO) driven by Affordability Care Act transforms the present Healthcare System that is adaptive (competitive) into generative (collaborative / co-ordinated) to achieve inclusive success and partake in the savings achieved. This is a generative systemic response contrasting the functional and competitive response of an adaptive system.

Natural selection seems to have resulted in functional transformation, where adaptive is the mode; does not account for diversity.

Self Regulation – seems like is a systemic outcome due to integrative influence (ecosystem), responding to the system constraints. Accounts for rich diversity.

The observer learns generatively from the system constraints for the type of reflexive response required (Refer – Generative Grammar – Immune System – http://www.ncbi.nlm.nih.gov/pmc/articles/PMC554270/pdf/emboj00269-0006.pdf)

From the above observation, should the theory in self regulation seem more correct and that adheres to laws of nature, in which generative learning occurs. Then, the assertion is “method” is offered by the system itself. System’s ontology has an implicate knowledge of the processes required for transformation (David Bohm – Implicate Order)

For very large complex system,

System itself is the method – impetus is the “constraint”.

In the video below, the ability for the cells to creatively create the script is discussed which makes the case for self regulated and generative complex system in addition to complex adaptive system.

 

Further Notes on Q-UEL / HDN :-

  1. That brings Quantum Mechanics (QM) machinery to Medical Science.
  2. Is derived from Dirac Notation that helped in defining the framework for describing the QM. The resulting framework or language is Q-UEL and it delivers a mechanism for inferential statistics – “Hyperbolic Dirac Net”
  3. Created from System Dynamics and Systems Thinking Perspective.
  4. It is Systemic in approach; where System is itself the Method.
  5. Engages probabilistic ontology and semantics.
  6. Creates a mathematical framework to advance Inferential Statistics to study highly chaotic complex system.
  7. Is an algorithmic approach that creates Semantic Architecture of the problem or phenomena under study.
  8. The algorithmic approach is a blend of linguistics semantics, artificial intelligence and systems theory.
  9. The algorithm creates the Semantic Architecture defined by Probabilistic Ontology :- representing the Ecosystem Knowledge distribution based on Graph Theory

To make a decision in any domain, first of all the knowledge compendium of the domain or the system knowledge is imperative.

System Riddled with Complexity is generally a Multivariate System, as such creating much uncertainty

A highly complex system being non-deterministic, requires probabilistic approaches to discern, study and model the system.

General Characteristics of Complex System Methods

  • Descriptive statistics are employed to study “WHAT” aspects of the System
  • Inferential Statistics are applied to study “HOW”, “WHEN”, “WHY” and “WHERE” probing both spatial and temporal aspects.
  • In a highly complex system; the causality becomes indeterminable; meaning the correlation or relationships between the independent and dependent variables are not obviously established. Also, they seem to interchange the position. This creates dilemma between :- subject vs object, causes vs outcomes.
  • Approaching a highly complex system, since the priori and posterior are not definable; inferential techniques where hypothesis are fixed before the beginning the study of the system become enviable technique.

Review of Inferential Techniques as the Complexity is Scaled

Step 1:- Simple System (turbulence level:-1)

Frequentist :- simplest classical or traditional statistics; employed treating data random with a steady state hypothesis – system is considered not uncertain (simple system). In Frequentist notions of statistics, probability is dealt as classical measures based only on the idea of counting and proportion. This technique is applied to probability to data, where the data sets are rather small.

Increase complexity: Larger data sets, multivariate, hypothesis model is not established, large variety of variables; each can combine (conditional and joint) in many different ways to produce the effect.

Step 2:- Complex System (turbulence level:-2)

Bayesian :- hypothesis is considered probabilistic, while data is held at steady state. In Bayesian notions of statistics, probability is of the hypothesis for a given sets of data that is fixed. That is, hypothesis is random and data is fixed. The knowledge extracted contains the more subjectivist notions of uncertainty, belief, reliability, or confidence often used in automated inference and decision support systems.

Additionally the hypothesis can be explored only in an acyclic fashion creating Directed Acyclic Graphs (DAG)

Increase the throttle on the complexity: Very large data sets, both structured and unstructured,  Hypothesis random, multiple Hypothesis possible, Anomalies can exist, There are hidden conditions, need arises to discover the “probabilistic ontology” as they represent the system and the behavior within.

Step 3: Highly Chaotic Complex System (turbulence level:-3)

Certainly DAG is now inadequate, since we need to check probabilities as correlations and also causations of the variables, and if they conform to a hypothesis producing pattern, meaning some ontology is discovered which describes the peculiar intrinsic behavior among a specific combinations of the variables to represent a hypothesis condition. And, there are many such possibilities within the system, hence  very chaotic and complex system.

Now the System itself seems probabilistic; regardless of the hypothesis and the data. This demands Multi-Lateral Cognitive approach

Telandic …. “Point – equilibrium – steady state – periodic (oscillatory) – quasiperiodic – Chaotic – and telandic (goal seeking behavior) are examples of behavior here placed in order of increasing complexity”

A Highly Complex System, demands a Dragon Slayer – Hyperbolic Dirac Net (HDN) driven Statistics (BI-directional Bayesian) for extracting the Knowledge from a Chaotic Uncertain System.

Hyperbolic Dirac Net (HDN) + Data Mining to Map Clinical Pathways (The Tacit Knowledge)

 

Bioingine.com employs algorithmic approach based on Hyperbolic Dirac Net that allows inference nets that are a general graph (GC), including cyclic paths, thus surpassing the limitation in the Bayes Net that is traditionally a Directed Acyclic Graph (DAG) by definition.

The Bioingine.com approach thus more fundamentally reflects the nature of probabilistic knowledge in the real world, which has the potential for taking account of the interaction between all things without limitation, and ironically this more explicitly makes use of Bayes rule far more than does a Bayes Net.

It also allows more elaborate relationships than mere conditional dependencies, as a probabilistic semantics analogous to natural human language but with a more detailed sense of probability. To identify the things and their relationships that are important and provide the required probabilities, the Bioingine.com scouts the large complex data of both structured and also information of unstructured textual character.

It treats initial raw extracted knowledge rather in the manner of potentially erroneous or ambiguous prior knowledge, and validated and curated knowledge as posterior knowledge, and enables the refinement of knowledge extracted from authoritative scientific texts into an intuitive canonical “deep structure” mental-algebraic form that the Bioingine.com can more readily manipulate.

Discussion on employing HDN to map Clinical Pathways (The Tacit Knowledge)

Screenshot 2016-01-05 21.04.17

In the below referenced articles on the employ of Bayesian Net to model Clinical Pathways as probabilistic inference net, replace Bayesian Net to achieve stress tested Hyperbolic Dirac Net (HDN) which is a non-acyclic Bayesian resolving both correlation and causation in both the direction; etymology –> outcomes and outcomes –> etymology

1. Elements of Q-UEL 

Q-UEL is based on the Dirac Notation and associated algebra The notation was introduced into later editions of Dirac’s book to facilitate understanding and use of quantum mechanics (QM) and it has been a standard notation in physics and theoretical chemistry since the 1940s

a) Dirac Notation

In the early days of quantum theory, P. A. M. (Paul Adrian Maurice) Dirac created a powerful and concise formalism for it which is now referred to as Dirac notation or bra-ket (bracket ) notation

<bra vector exprn* | operator exprn* | ket vector exprn*> 

[ exprn* is expression]

It  is an  algebra for observations and measurements, and probabilistic inference from  them

 QM is a system for representing observations and measurements, and drawing probabilistic inference from them.

In Dirac’s notation what is known is put in a ket, “|>” . So, for example, “|p >” expresses the fact that a particle has momentum p. It could also be more explicit: |p = 2> , the particle has momentum equal to 2; | x = 1.23 , the particle has position 1.23 |Ψ > represents a system in the state and is therefore called the state vector. 

The ket |> can also be interpreted as the initial state in some transition or event.

The bra <| represents the final state or the language in which you wish to express the content of the ket

Hyperbolic Dirac Net, has ket |> as row vector, and bra <| as column vector

b) hh = +1 Imaginary Number

QM is a system for representing observations and measurements, and drawing probabilistic inference from them. The Q in Q-UEL refers to QM, but a simple mathematical transformation of QM gives classical everyday behavior. Q-UEL inherits the machinery of QM by replacing the more familiar imaginary number i (such that ii = -1), responsible for QM as wave mechanics, by the hyperbolic imaginary number h (such that hh=+1). Hence our inference net in general is called the Hyperbolic Dirac Net (HDN)

In probability theory A, B, C, etc. represent things, states, events, observations, measurements, qualities etc. In this paper we mean medical factors, including demographic factors such as age and clinical factors such as systolic blood pressure value or history of diabetes.

They can also stand for expressions containing many factors, so note that by e.g.

P(A|B) we would usually mean that it also applies to, say, P(A, B | C, D, E). In text, P(A,B, C,…) with ellipsis ‘…’ means all combinatorial possibilities, P(A), P(B), P(A, C), P(B, D, H) etc.

2) Employing Q-UEL  preliminary inference net as the query can be created.

“Will my female patient age 50-59 taking diabetes medication and having a body mass index of 30-39 have very high cholesterol if the systolic BP is 130-139 mmHg and HDL is 50-59 mg/dL and non-HDL is 120-129 mg/dL?”.

This forms a preliminary inference net as the query, which may be refined and to which probabilities must be assigned

The real answers of interest here are not qualitative statements, but the final probabilities. The protocols involved map to what data miners often seem to see as two main options in mining, although we see them as the two ends of a continuum.

Method (A) may be recognized as Unsupervised (or unrestricted) data mining and post-filtering, and is the method mainly used here. In this approach

we (1) mine data (“observe”),(2) compute a very large number of the more significant probabilities and render them as tags and maintained as Knowledge Representative Store (KRS) or Semantic Lake (“evaluate”), (3) use a propose inference net as a query to search amongst the probabilities represented by those tags, but only looking for those relevant to complete the net and assign probabilities to it, assessing what is available, and seeing what can be substituted (“interpret”), and (4) compute the overall probability of the final inference net in order to make a decision (“decide”). Unsupervised data mining is preferred because it generates many tags for an SW-like approach, and may uncover new unexpected relationships that could be included in the net.

Method (B) uses supervised (or restricted) data mining and prefiltering. Data mining considers only what appears in the net. The down-stream user interested in inference always accesses the raw database, while in (A) he or she may never see it.

The advantage of (B) is that mining is far less computationally demanding both in terms of processing and memory. Useful to computing HDN for a specified Hypothesis.

The Popular Bayes Net BN Compared with our Hyperbolic Dirac Net HDN.

Each probabilities of any kind can also be manipulated for inference in a variety of ways, according to philosophy (which is a matter of concern ). The BN is probably the most popular method, perhaps because it does seem to be based on traditional, conservative, principles of probability. However, the BN is traditionally (and, strictly speaking, by definition) confined to a probability network that is a directed acyclic graph (DAG).

In general, reversibility, cyclic paths and feedback abound in the real world, and we need probabilistic knowledge networks that are general graphs, or even more diffuse fields of influence, not DAGs. In our response as the Hyperbolic Dirac Net (HDN), “Dirac” relates to use of Paul A. M. Dirac’s view of quantum mechanics (QM).

QM is not only a standard system for representing probabilistic observation and inference from it in physics, but also it manages and even promotes concepts like reversibility and cycles. The significance of “hyperbolic” is that it relates to a particular type of imaginary number rediscovered by Dirac. Dirac notation entities, Q-UEL tags, and the analogous building blocks of an HDN all have complex probabilities better described as probability amplitudes. This means that they have the form x + jy where x and y are real numbers and j is an imaginary number, though they can also be vectors or matrices with such forms as elements.

Q-UEL is seen as a Lorentz rotation i → h of QM as wave mechanics. The imaginary number involved is now no longer the familiar i such that ii = -1, but the hyperbolic imaginary number, called h in Q-UEL, such that hh = +1.

This renders the HDN to behave classically. A basic HDN is an h-complex BN.

Both BN and basic HDN may use Predictive Odds in which conditional probabilities (or the HDN’s comparable h-complex notions) are replaced by ratios of these.

Discussions on Employing Bayesian Net to Model Clinical Pathways (Replace BN by HDN to achieve Hyperbolic BN)

Development of a Clinical Pathways Analysis System with Adaptive Bayesian Nets and Data Mining Techniques 

D. KOPEC*, G. SHAGAS*, D. REINHARTH**, S. TAMANG

 

Pathway analysis of high-throughput biological data within a Bayesian network framework

Senol IsciCengizhan OzturkJon Jones and Hasan H. Otu

Are Standardized Clinical Pathways Stymying Drug Innovation?

HDN :- Need for Agile Clinical Pathways that do not impede Drug Innovation

Oncologists Say Clinical Pathways Are Too Confining

Creating fixed plans for treating common malignancies promises to make the work of nurses and other staff more predictable and practiced, increasing efficiency and reducing errors that could lead to poor outcomes and hospitalization. For payers, pathways also gave them another way to insert awareness of costs directly into the examining room.

“The way the pathways are constructed does promote consideration of value-driven practice, which is to say that the pathways vendors all take into account cost of care, but only after considering efficacy and toxicity,” said Michael Kolodziej, MD, national medical director of oncology solutions at Aetna, and a former medical director at US Oncology. “So there is an element here of reduction of cost of care, by trying to encourage physicians to consider the relative value of various treatment options. This has now become the mantra in oncology.”

Studies found that using pathways can indeed cut costs substantially without hurting outcomes.

Semantic Data Lake Delivering Tacit Knowledge – Evidence based Clinical Decision Support

Can the complexity be removed and tacit knowledge delivered from the plethora of the medical information available in the world.

” Let Doctors be Doctors”

Semantic Data Lake becomes the Book of Knowledge ascertained by correlation and causation resulting into Weighted Evidence

Characteristics of Bioingine.com Cognitive Computing Platform

  • Architecture style moves from Event driven into Semantics driven
  • Paradigm shift in defining system behavior – it is no more predicated and deterministic – Non Predicated Design
  • Design is “systemic” contrasting the technique such as objected oriented based design, development and assembling components
  • As such a system is better probabilistically studied.
  • Design is context driven, where the boundary diminishes between context and concept
  • System capability is probabilistically programmed by machine learning based on A.I, NLP and algorithms driven by ensemble of Math
  • Design based on Semantic mining and engineering takes precedence to complex event processing (CEP). CEP and Event Driven Architecture (EDA) are the part of the predicated system design. Business rules engine may be an overkill.
  • Ontology is created driven by both information and numbers theory

–Algebra – relationship amongst variables

–Calculus – rate of change in variable and its impact on the other

–Vector Space – study of states of the variables

Bioingine.com algorithm design driven by Probabilistic Ontology

  • Probabilistic Ontology characterizes the ecosystem’s behavior
  • Complex System’s semantic representation evolves generatively
  • System better represented by semantic multiples. Overcomes the barrier of Triple Store (RDF)
  • Human’s interact with the system employing knowledge inference technique
  • Inductive knowledge precedes knowledge by deduction

Bioingine.com is a Probabilistic Computing Machine

  • System’s behavior better modeled by the employ of probability, statistics and vector calculus (Statistics based on HDN an advancement to Bayes Net, where acyclic in DAG is overcome)
  • Generally the system is characterized by high dimensionality in its data set (variability) in addition to volume and velocity.
  • Most computing is in-memory 

BioIngine.com; is designed based on mathematics borrowed from several disciplines and notably from Paul A M Dirac’s quantum mechanics. The approach overcomes many of the inadequacies in the Bayes Net that is based on the directed acyclic graph (DAG). Like knowledge relationships in the real word, and as was required for quantum mechanics, our approaches are neither unidirectional nor do they avoid cycles.

Bioingine.com Features –

  • Bi-directional Bayesian Probability for knowledge Inference and Biostatistics (Hyperbolic complex).
  • Built upon medical ontology (in fact this is discovered by machine learning, AI techniques).
  • Can be both hypothesis and non-hypotheses driven.
  • Quantum probabilities transformed to classical integrating vector space, Bayesian knowledge inference, and Riemann zeta function to deal with sparse data and finally driven by overarching Hyperbolic Dirac Net.
  • Builds into web semantics employing NLP. (Integrates both System Dynamics and Systems Thinking).

Framework of Bioingine –Dirac-Ingine Algorithm Ensemble of Math 

Q-UEL & HDN (More Info click the link)

Part B – Healthcare Interoperability, Standards and Data Science (Resolving the Problem)

Srinidhi Boray | Ingine, Inc | Bioingine.com

Slide06

Introducing, Ingine, Inc. it is a startup in its incipient stages of developing BioIngine platform, which brings advancement in data science around Interoperability. Particularly with healthcare data mining and analytics dealing with medical knowledge extraction. Below are some of the lessons learned discussed while dealing with the healthcare transformation concerns, especially with the ONC’s Interoperability vision.

As an introduction, want to include the following passage from the book

The Engines of Hippocrates: From the Dawn of Medicine to Medical and Pharmaceutical Informatics

By Barry Robson, O. K. Baek

https://books.google.com/books?id=DVA0QouwC4YC&pg=PA8&lpg=PA8&dq=MEDICAL+FUTURE+SHOCK+barry+robson&source=bl&ots=Qv1cGRIY1L&sig=BgISEyThQS-8bXt-g7oIQ873cN4&hl=en&sa=X&ved=0CCQQ6AEwAWoVChMI0a2s-4zMyAIVSho-Ch216wrQ#v=onepage&q=MEDICAL%20FUTURE%20SHOCK%20barry%20robson&f=false

MEDICAL FUTURE SHOCK

Healthcare administration has often been viewed as one of the most conservative of institutions. This is not simply a matter of the inertia of any complex bureaucratic system. A serious body with an impressive history and profound responsibilities cannot risk unexpected disruptions to public service by changing with every fashionable new convenience, just for the sake of modernity. A strong motivation is needed to change a system on which lives depend and which, for all its faults, is still for the most part an improvement on anything that went before. However, this is also to be balanced against the obligation of healthcare, as an application of science and evolving human wisdom, to make appropriate use of the new findings and technologies available. This is doubly indicated when significant inefficiencies and accidents look as if they can be greatly relieved by upgrading the system. Sooner or later something has to give, and the pressure of many such accumulating factors can sometimes force a relatively entrenched system to change in a sudden way, just as geological pressures can precipitate an earthquake. An Executive Forum on Personalized Medicine organized by the American College of Surgeons in New York City in October 2002 similarly warned of the increasingly overwhelming accumulation of arguments demanding reform of the current healthcare system…if there is to be pain in making changes to an established system, then it makes sense to operate quickly, to incorporate all that needs to be incorporated and not spin out too much the phases of the transitions, and lay a basis for ultimately assimilating less painfully all that scientific vision can now foresee. But scientific vision is of course not known for its lack of imagination and courage, and is typically very far from conservative, still making an element of future shock inevitable in the healthcare industry.

  1. Complicated vs Complexity

A) Generally approaching to characterize a system, there are two views, complicated and complex. Complicated is with problems of system operations and population management, while complex problems are about multi-variability with an individual patient diagnosis.

Below link discusses providing better scenarios regarding complicated vs complexity

http://www.beckershospitalreview.com/healthcare-blog/healthcare-is-complex-and-we-aren-t-helping-by-making-it-more-complicated.html

https://www.bcgperspectives.com/content/articles/organization_design_human_resources_leading_complex_world_conversations_leaders_thriving_amid_uncertainty/

Generally, all management concerns around operations, payment models, healthcare ecosystem interactions, etc deal with delivering the systemic efficiencies. These are basically complicated problems residing in the system, which when resolved yield the hidden efficiencies.

All those that affect the delivery of the clinical efficacy have to deal with complex problem. Mostly owing to the high dimensionality (multi-variability) of the longitudinal patient data.

When both, complicated and complex concerns are addressed the Healthcare as an overarching complex system will begin to yield the desired performance driven outcomes.

B) Standards around Interoperability has generally dealt with following three levels of health information technology interoperability:

Ref:-http://www.himss.org/library/interoperability-standards/what-is-interoperability

From the above link:-

1) Foundational; 2) Structural; and 3) Semantic.

1 – “Foundational” interoperability allows data exchange from one information technology system to be received by another and does not require the ability for the receiving information technology system to interpret the data.

2 – “Structural” interoperability is an intermediate level that defines the structure or format of data exchange (i.e., the message format standards) where there is uniform movement of healthcare data from one system to another such that the clinical or operational purpose and meaning of the data is preserved and unaltered. Structural interoperability defines the syntax of the data exchange. It ensures that data exchanges between information technology systems can be interpreted at the data field level.

3 – “Semantic” interoperability provides interoperability at the highest level, which is the ability of two or more systems or elements to exchange information and to use the information that has been exchanged. Semantic interoperability takes advantage of both the structuring of the data exchange and the codification of the data including vocabulary so that the receiving information technology systems can interpret the data. This level of interoperability supports the electronic exchange of patient summary information among caregivers and other authorized parties via potentially disparate electronic health record (EHR) systems and other systems to improve quality, safety, efficiency, and efficacy of healthcare delivery.

The above levels of interoperability only deal with achieving semantic compatibility between systems in the data transacted from the large number of myriad systems (EHRs) while they converge into a heterogeneous architecture (HIE / IoT). This only deals with the complicated concerns within the system. They do not necessarily deal with the extraction and discernment of the knowledge hidden in the complex health ecosystem system. To achieve this for some simplicity sake, let us define need for a second order semantic interoperability that concerns with the data mining approaches required in the representation of the systemic medical knowledge. It is this medical knowledge; implicit, explicit and tacit that all together form evidence based medicine much desired to facilitate any clinical decision support system.

C) In the present efforts around Interoperability, which centers mostly around data standards (HL7v2, HLv3, FHIR, C-CDA, ICD-10, LOINC, SNOMED etc) and clinical quality measures (QRDA); only complicated concerns have been addressed and not necessarily the complex problems. This is the vexation in the quality measures reporting. While this has advanced the adoption of EHR by the hospitals, it is still far from it becoming an effective decision support tool for the physicians

It must be noted that in the MU2 criteria, it is suggested that besides achieving health information exchange pivotal to the creation of Accountable Care Organization (ACO), at the least five-health priority or critical health risk conditions must be addressed employing clinical decision support system. Deservedly created, this point creates a need for addressing clinical efficacy, in addition to achieving best possible system efficiencies leading to systemic performance driven outcomes. This means a much deeper perspective is required to be included in the Interoperability efforts to better drive data science around data mining that can help better engage physicians in the realization of the performance driven outcomes. Rather than allowing physicians to be encumbered by the reimbursement model driven EHRs. Also, although most EHR vendors employ C-CDA to frame the longitudinal patient view, they do not necessarily send all the data to the Health Information Exchange, this results into truncating the full view of the longitudinal patient records to the physicians.

D) Physician, Primary Care, Cost in Rendering and Shortage Physician workforce

http://www.ncbi.nlm.nih.gov/pubmed/20439861

When dealing with the primary care, it is desired that today’s physicians who are over-burdened, moving forward works as a team lead engaging variety of healthcare professionals, while also better enabling trained nurse practitioners. Furthermore, also rendering the work in a lesser-cost environment while moving away from higher cost environments such as hospitals and emergency care facilities. This also means moving away from service-based models into performance based payment models becomes imperative.

It must be noted that dealing with the way an organization generally works reassigning responsibilities both horizontally and vertically, has to do only with the complicated concerns of the system, not the complex problem. Again it must be emphasized that data mining related to evidence based medicine, which is in a way knowledge culled from the experiences of the cohorts within the health ecosystem, will play a vital role in improving the much desired clinical efficacy leading ultimately to better health outcomes. This begins to address the complex systemic problems, while also better engaging the physicians who find the mere data entry into the EHR cumbersome and intrusive; and not able to derive any clinical decision support from the integration of the System of systems (SoS).

  1. Correlation vs Causations

A) While we make a case for better enabling evidence based medicine (EBM) driven by data mining as a high priority in the interoperability scheme of things, we also would like to point out the need for creating thorough systematic review aided by automation which is vital to EBM. This also means dealing with Receiver-Operating Characteristic (ROC) Curves http://www.ncbi.nlm.nih.gov/pubmed/15222906

https://www.sciencebasedmedicine.org/evidence-in-medicine-correlation-and-causation/

From the above link:-

“”The consensus of expert opinion based upon systematic reviews can either result in a solid and confident unanimous opinion, a reliable opinion with serious minority objections, a genuine controversy with no objective resolution, or simply the conclusion that we currently lack sufficient evidence and do not know the answer.””

Also, another reference to:-

Reflections on the Nature and Future of Systematic Review in Healthcare. By:- Dr. Barry Robson

http://www.diracfoundation.com/?p=146

In the recent times Bayesian statistics has emerged as a gold standard to developing curated EBM (http://www.ncbi.nlm.nih.gov/pubmed/10383350) and; in this context we would like to draw attention that while correlation is important as discussed in the above linked article, which is developed from the consensus of the cohorts in the medical community, it is also important to ascertain the causation. This demands need for a holistic Bayesian statistics as proposed in the new algorithms, including those built on proven ideas in physics advancing the scope of the Bayesian Statistics as developed by Dr. Barry Robson. The approach and its impact on the Healthcare Interoperability and analytics are discussed in the link provided below.

http://www.ncbi.nlm.nih.gov/pubmed/26386548

From the above link: –

“”””Abstract

We extend Q-UEL, our universal exchange language for interoperability and

inference in healthcare and biomedicine, to the more traditional fields of public health surveys. These are the type associated with screening, epidemiological and cross-sectional studies, and cohort studies in some cases similar to clinical trials. “”There is the challenge that there is some degree of split between frequentist notions of probability as (a) classical measures based only on the idea of counting and proportion and on classical biostatistics as used in the above conservative disciplines, and (b) more subjectivist notions of uncertainty, belief, reliability, or confidence often used in automated inference and decision support systems. Samples in the above kind of public health survey are typically small compared with our earlier “Big Data” mining efforts. An issue addressed here is how much impact on decisions should sparse data have. “””””

B) Biostatistics, Algebra, Healthcare Analytics and Cognitive Computing

Another interesting aspect that emerges is the need for biostatistics and such many doctors with MD qualification are getting additionally qualified in Public Health Management, which also deals with Biostatistics. Dealing with population health one hand and clinical efficacy on the other, Interoperability via biostatistics has to deliver both views macro wrt systemic outcomes and at the micro level clinical efficacies. Developing such capabilities means much grander vision for Interoperability, as discussed in the OSEHRA, VA sponsored Open Source Efforts in making VistA available to the world market at a fraction cost. More discussion on the OSEHRA forum in the below link.

https://www.osehra.org/content/joint-dod-va-virtual-patient-record-vpr-iehr-enterprise-information-architecture-eia-0

From the above link:-

“”””Tom Munnecke – The Original Architect of VistA – This move to a higher level of abstraction is a bit like thinking of things in terms of algebra, instead of arithmetic. Algebra gives us computational abilities far beyond what we can do with arithmetic. Yet, those who are entrenched in grinding through arithmetic problems have a disdain for the abstract facilities of algebra.””””

Interesting point to note in the discussions on the above link, is that a case is being made for the role of data science (previously called Knowledge Engineering during last three decades) driving better new algorithms, including those built on proven ideas in physics in the Healthcare Interoperability. This helps in advancing the next generations of the EHR capabilities, eventually emerging as a medical science driven cognitive computing platform. The recommendation is in the employ of advances in the data science in moving the needle from developing a deterministic or a predicated System of systems (SoS) based on schemas such as FHIM (http://www.fhims.org), that proves design laborious and is outmoded, to harnessing the data locked in the heterogeneous system by the employ of advanced Bayesian statistics, new algorithms, including those built on proven ideas in physics and especially exploitation of the algebra. This approach delivered on a BigData architecture as a Cognitive Computing Platform with schema-less approaches has a huge benefit in terms of cost, business capability and time to market, delivering medical reasoning from the healthcare ecosystem as realized by the interoperability architectures.

Part A – Healthcare Interoperability Measures:- Cartesian Dilemma (Diagnosis)

Those in blue in the below content are reproduced from the referenced links.Slide06

Definition of Cartesian Dilemma; per Alexander Christopher

(what eyes sees and the mind sees are two different things)

Cartesian Dilemma

http://www.worldsystema.com/worldsystema/2011/10/christopher-alexander-templeto-1.html

From above link

“””””Alexander has been inexorably led to the revolutionary necessity of revising our basic picture of the universe to include a conception of the personal nature of order and our belonging to the world in which the wholeness of space and the extent to which it is alive is perceived as rooted in the plenum behind the visible universe, “the luminous ground” that holds us all. This form of extended objective truth will ultimately resolve our Cartesian dilemma by teaching us a new view of order and a new cosmology in which objective reality “out there” and a personal reality “in here” are thoroughly connected and the bifurcation of nature healed.””””””

“”To Rene Descartes the “Method” (1638) was a convenient mental trick but its success has left us with a mindset that conceives of the universe as a machine without any intrinsic value: the realms of human experience and of feeling are simply absent from the Cartesian world. Whilst inspiring generations of architects and many others from all walks of life concerned with the fate of the earth, Alexander’s ultimately life changing work has understandably provoked powerful opposition from those invested within the establishment of the old paradigm. Social disorder, mental illness, ecological degradation, these and many other problems are due to a misunderstanding of the structure of matter and the nature of the universe and, until quite recently, there has been no coherent way of explaining the order that we respond to and love in nature.””

———————————————————————-

Affordability Care Act and HITECH Act lead into EHR Incentive Program. Based on the EHR Incentive Program CMS has already payed out 24+ Billions of dollars to Eligible Participants. Has it or will it drive the envisioned Healthcare Interoperability still remains a big question. Specifically will it be possible to mine the millions of records and discover opportunity for improvement? Without emphasis on clinical decision support will it be possible to achieve efficacy in the healthcare delivery, while also advancing the opportunities for “pay for performance” outcomes?

To advance EHR adoption in the Healthcare Ecosystem CMS proposed formation of Accountable Care Organization

https://www.cms.gov/Newsroom/MediaReleaseDatabase/Fact-sheets/2011-Fact-sheets-items/2011-12-19.html

From the above link

“”The Pioneer ACO Model is designed for health care organizations and providers that are already experienced in coordinating care for patients across care settings. It will allow these provider groups to move more rapidly from a shared savings payment model to a population-based payment model on a track consistent with, but separate from, the Medicare Shared Services Program. And it is designed to work in coordination with private payers by aligning provider incentives, which will improve quality and health outcomes for patients across the ACO, and achieve cost savings for Medicare, employers and patients.””

Importantly CMS proposed roadmap for EHR Adoption based on Meaningful Use (MU) 3 Stages, in the hope of advancing interoperability in the healthcare ecosystem ultimately achieving performance driven model, where the payment models shifts from “pay for service” towards “pay for performance”. Looking at the Healthcare ecosystem, one must take note that achieving efficiency is in the healthcare management; while achieving efficacy is in the healthcare delivery.

You will see in the end of the discussion that somehow efforts of the EHR Incentive Program lays more emphasis on the helathcare efficiency without paying required attention to clinical efficacy. This leads to the systemic entropic discontinuity that can be described by the Boltzmann constant.

This results into missed Line of Sight, where the established “objective”s at the IT / EHR level do not deliver all the required the “business capabilities” or the output and hence the desired “transformative outcomes” are not realized.

https://en.wikipedia.org/wiki/Boltzmann%27s_entropy_formula

From the above link:-

“”In statistical mechanicsBoltzmann’s equation is a probability equation relating the entropy S of an ideal gas ( or consider healthcare ecosystem) to the quantity W, which is the number of microstates corresponding to a given macrostate.”””

Following are the EHR Adoption Meaningful Use Stages:-

MU Stage 1 :- Achieves electronic capture of the patient data (Data Capture and Sharing)

MU Stage 2 :- Achieves Health Information Exchanges (Advances co-ordinated clinical processes)

MU Stage 3:- Target Improved Outcomes ( achieved by moving the payment model from pay for service to pay for performance)

The eligible participants, physicians, hospitals and the ACOs have to demonstrate that they have met the MU criteria in stages. To demonstrate that they have met the requirements, first of all it is required to demonstrate that the data being captured adhere to a prescribed format. This is ascertained by MU attestation.

Additionally, the eligible participants are required to submit quality measures reports to CMS

https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/sharedsavingsprogram/Quality_Measures_Standards.html

From the above link

“””” Quality Measures and Performance Standards

Quality data reporting and collection support quality measurement, an important part of the Shared Savings Program. Before an ACO can share in any savings generated, it must demonstrate that it met the quality performance standard for that year. There are also interactions between ACO quality reporting and other CMS initiatives, particularly the Physician Quality Reporting System (PQRS) and meaningful use. The sections below provide resources related to the program’s 33 quality measures, which span four quality domains: Patient / Caregiver Experience, Care Coordination / Patient Safety, Preventive Health, and At-Risk Population. Of the 33 measures, 7 measures of patient / caregiver experience are collected via the CAHPS survey, 3 are calculated via claims, 1 is calculated from Medicare and Medicaid Electronic Health Record (EHR) Incentive Program data, and 22 are collected via the ACO Group Practice Reporting Option (GPRO) Web Interface.””””

https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment Instruments/QualityMeasures/index.htm/lredirect=/QUALITYMEASURES/

National Quality Forum (NQF) endorsed for CMS reports are :

  • The Hospital Inpatient Quality Reporting (IQR) Program,
  • The Hospital Outpatient Quality Reporting (OQR) Program,
  • The Physician Quality Reporting System (PQRS), and
  • Others as directed by CMS, such as long-term care settings and ambulatory care settings

The CMS quality reporting is based on the schematic derived from HL7, termed QRDA

https://www.cms.gov/regulations-and-guidance/legislation/ehrincentiveprograms/downloads/qrda_ep_hqr_guide_2015.pdf

From the above link

Overview of QRDA

“””The Health Level Seven International (HL7) QRDA is a standard document format for the exchange of electronic clinical quality measure (eCQM) data. QRDA reports contain data extracted from electronic health records (EHRs) and other information technology systems. QRDA reports are used for the exchange of eCQM data between systems for a variety of quality measurement and reporting initiatives, such as the Centers for Medicare & Medicaid Services (CMS) EHR Incentive Program: Meaningful Use Stage 2 (MU2).1

The Office of the National Coordinator for Health Information Technology (ONC) adopted QRDA as the standard to support both QRDA Category I (individual patient) and QRDA Category III (aggregate) data submission approaches for MU2 through final rulemaking in September 2012.2 CMS and ONC subsequently released an interim final rule in December 2012 that replaced the QRDA Category III standard adopted in the September 2012 final rule with an updated version of the standard.3 QRDA Category I and III implementation guides (IGs) are Draft Standards for Trial Use (DSTUs). DSTUs are issued at a point in the standards development life cycle when many, but not all, of the guiding requirements have been clarified. A DSTU is tested and then taken back through the HL7 ballot process to be formalized into an American National Standards Institute (ANSI)-accredited normative standard.

QRDA is a subset of CDA HL7 Standard; QRDA is a constraint on the HL7 Clinical Document Architecture (CDA), a document markup standard that specifies the structure and semantics of clinical documents for the purpose of exchange.4 To streamline implementations, QRDA makes use of CDA templates, which are business rules for representing clinical data consistently. Many QRDA templates are reused from the HL7 Consolidated CDA (C-CDA) standard5, which contains a library of commonly used templates that have been harmonized for MU2. Templates defined in the QRDA Category I and III IGs enable consistent representations of quality reporting data to streamline implementations and promote interoperability.”””

On the contrary we have Office Of National Coordinator (ONC) stipulate and regulate standards to achieve Healthcare Interoperability

ONC Roadmap Vision in the below link

https://www.healthit.gov/policy-researchers-implementers/interoperability

From above link:-

Sadly, although Evidence based is discussed, data mining and concerns around algorithm development is missing.

“””””””

Connecting Health and Care for the Nation: A Shared Nationwide Interoperability Roadmap version 1.0 (Roadmap) [PDF – 3.7 MB] supports the vision that ONC outlined in Connecting Health and Care for the Nation: A 10 Year Vision to Achieve An Interoperable Health IT Infrastructure [PDF – 607 KB]. The Roadmap, shaped by stakeholder input, lays out a clear path to catalyze the collaboration of stakeholders who are going to build and use the health IT infrastructure. The collaborative efforts of stakeholders is crucial to achieving the vision of a learning health system where individuals are at the center of their care; providers have a seamless ability to securely access and use health information from different sources; an individual’s health information is not limited to what is stored in electronic health records (EHRs), but includes information from many different sources and portrays a longitudinal picture of their health, not just episodes of care; and where public health agencies and researchers can rapidly learn, develop, and deliver cutting edge treatments.

“”””””””

http://www.healthit.gov/buzz-blog/from-the-onc-desk/oncinteroperability- roadmap-update/

There is no doubt that ONC aspires to achieve true Healthcare Interoperability, by bringing more clarity to the Health Information Exchange (HIE) as discussed in the below link.

Interoperability vs Health Information Exchange: Setting the Record Straight

ONC under its purview has Office of Standards and Technology, which drives the Interoperability Standards; and it acknowledges that there are numerous challenges in realizing the ONC roadmap; as discussed in the below link

Interoperability Standards – Shades of Gray

Also ONC specifies roadmap in achieving MU stages for physicians, hospitals and ACOs ( HIE)
Slide06https://www.healthit.gov/providers-professionals/ehrimplementation-steps/step-5-achieve-meaningful-use

Specifically for the Semantic Interoperability it recommends Consolidated – Clinical Document Architecture ( C-CDA).

https://www.healthit.gov/policy-researchers-implementers/consolidated-cda-overview

CDA helps in representing a comprehensive view of the patient; complete birth-to-death view – Longitudinal Record.

Also ONC Interoperability Specification Address the Following three levels (Not adequate to achieve EBM driven CDSS):-

There are three levels of health information technology interoperability:  1) Foundational; 2) Structural; and 3) Semantic.

1 – “Foundational” interoperability allows data exchange from one information technology system to be received by another and does not require the ability for the receiving information technology system to interpret the data.

2 – “Structural” interoperability is an intermediate level that defines the structure or format of data exchange (i.e., the message format standards) where there is uniform movement of healthcare data from one system to another such that the clinical or operational purpose and meaning of the data is preserved and unaltered. Structural interoperability defines the syntax of the data exchange. It ensures that data exchanges between information technology systems can be interpreted at the data field level.

3 – “Semantic” interoperability provides interoperability at the highest level, which is the ability of two or more systems or elements to exchange information and to use the information that has been exchanged. Semantic interoperability takes advantage of both the structuring of the data exchange and the codification of the data including vocabulary so that the receiving information technology systems can interpret the data. This level of interoperability supports the electronic exchange of patient summary information among caregivers and other authorized parties via potentially disparate electronic health record (EHR) systems and other systems to improve quality, safety, efficiency, and efficacy of healthcare delivery.

Desired or Recommended 2nd Order Semantic Interoperability

Probabilistic Ontology Driven Knowledge Engineering

Ref:- http://www.ncbi.nlm.nih.gov/pubmed/22269224

Chronically ill patients are complex health care cases that require the coordinated interaction of multiple professionals. A correct intervention of these sort of patients entails the accurate analysis of the conditions of each concrete patient and the adaptation of evidence-based standard intervention plans to these conditions. There are some other clinical circumstances such as wrong diagnoses, unobserved comorbidities, missing information, unobserved related diseases or prevention, whose detection depends on the capacities of deduction of the professionals involved.

< diagnosis > < procedures > < outcomes > [triple store]

Conclusion:-

From the above points it must be noted that QRDA and C-CDA achieves different things. Unfortunately, against MU attestation and quality reports that are filed by the eligible participants (physicians, hospitals and ACOs) based on QRDA (especially PQRA), CMS runs the EHR incentives program. Whereas, in the MU2 stage ( as per ONC), it is also required by the participants to demonstrate that they have achieved interoperability within ACO, while implementing HIE, this requires C-CDA. This stage must demonstrate that coordinated clinical processes have been achieved.

Also, clinical decision support system (CDSS) has been established addressing at least 5 critical or clinical priority areas.  Unfortunately this particular capability does not seems to be addressed adequately by the ACOs; who only pursue to demonstrate quality measures have been achieved which necessarily does not mean clinical efficacy have been addressed. 

It seems an important architectural problem has been glossed over by the policy designers, who proposed quality measures model with the motivation for capturing the metrics that eventually demonstrate “pay for performance”; and somehow assumed that the proposed metrics based on QRDA also demonstrate that the clinical efficacies have been achieved. This leads into systemic entropic discontinuity, where the efforts at macro states that represents healthcare management leading into healthcare efficiency  is not necessarily a cumulative realization for the efforts at the micro states which represents gaining clinical efficacy. This entropic discountuinity between the macro state and the micro states is measured by Boltzmann Constant.

Link to more discussion on micro states and macro states within a complex system. Basically discusses for a given complex system, and for all the efforts towards the input; the entropy arrested and created loss, so the output is a actually created incurring loss. This means the systemic efficiency incurred losses and did not realize all the benefits arising out of the clinical efficacy. This is a model problem which inaccurately represents the “phenomenon of interest”.

https://books.google.com/books?id=dAhQBAAAQBAJ&pg=PT295&lpg=PT295&dq=boltzmann+constant+macro+state&source=bl&ots=ubpGEUymWc&sig=cQ4Nz9f6OA0ryDGEupOHDUAyiRc&hl=en&sa=X&ved=0CCwQ6AEwA2oVChMI0qeqv4G4yAIVCzo-Ch07WAkU#v=onepage&q=boltzmann%20constant%20macro%20state&f=false

To achieve Clinical Decision Support System capability which rather plays a very important role in enhancing clinical efficacy, developing data mining driven Evidence Based Medicine capability is imperative. This capability does not seem as being achieved because most HIE / ACO is being developed around QRDA; although discussed in the ONC Connecting Health and Care for the Nation: A Shared Nationwide Interoperability Roadmap version 1.0 (Roadmap) [PDF – 3.7 MB]; unless data mining related algorithmic challenges are addressed which means standards beyond mere capture of the required data fields, interoperability efforts will be in vain.

Role of EBM in achieving CDSS discussed on following sites

CMS Site

https://www.healthit.gov/providers-professionals/achieve-meaningful-use/core-measures/clinical-decision-support-rule

NIH Site

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC130063/

As such it must be noted clinical errors is one among the highest risk becoming the No 3 Killer in the US.

http://www.healthcareitnews.com/news/deaths-by-medical-mistakes-hit-records

From above link

“””It’s a chilling reality – one often overlooked in annual mortality statistics: Preventable medical errors persist as the No. 3 killer in the U.S. – third only to heart disease and cancer – claiming the lives of some 400,000 people each year. At a Senate hearing Thursday, patient safety officials put their best ideas forward on how to solve the crisis, with IT often at the center of discussions. “””

P.S:-

Bioingine (www.bioingine.com); a Cognitive Computing Platform transforms the patient information (millions of records) created by the HIE into Ecosystem Knowledge Landscape that is inherently evidence based, allowing for study of the Tacit Knowledge, as discovered from the millions of patient records (large data sets) by mining and knowledge inference in an automated way. This is achieved employing AI, Machine Learning and such techniques. Thereby, creating Clinical Decision Support System.