complex system

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.””

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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.

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Implicate Order as Ontology For Complex System – Creating Generative Transformation

For a messy complex system - undergoing generative transformation - Implicate Order provides the direction.

For a messy complex system – undergoing generative transformation – Implicate Order provides the direction.

Implicate Order as an Ontology v1.1

 

When Cartesian Breaks Down - Implicate Order Reins

When Cartesian Breaks Down – Implicate Order Reins

Generative Transformation :- System is the Method

Simple View – Cognition

Multi-Lateral View – Cognition

System is the Method:-

Application :- Accountable Care Organization (ACO) – Complex Adaptive / Generative System – CAS Modeling

ACO objective is to achieve Systemic Healthcare Effectiveness through High Quality Rendering at Least Possible Cost, by co-ordinated efforts, while the engaging to achieve a share in  the savings. This is completely a different Systemic behavior, such as seen in swarming of birds.

Cognition – result of social observation
Where each social unit employs relevant symbols to capture the knowledge and each of these knowledge could be a different level of abstraction. Enterprise or System Architecture as a architecture is sum of several architecture abstractions (various social observations), where architecture is considered as set of decisions. Architecture is established to navigate the system complexities. System architecture is described by sets of abstractions and system occupies different orders to mitigate complexities owing to entropy in a very complex system (messy).

The above challenge of observation (discernment) by different stakeholders can be brought into an assimilation, realized from the integrative influence, where different disciplines intermingle and create an ecosystem influence. Such findings are being studied to understand the rich diversity that emerged during cambrian explosion. Example the the geological influence on the sedimentation and its impact on the living organisms developing functional capabilities deriving from certain calcite properties as in bones, teeth etc

Combining discussion around Context and Integrative Social Inquiry

A. Observer (unconditioned and defeating imposition) merely observing system and asking only one question “why” – in context

(incorporating Bohm – Science, Order and Creativity – significance of social abilities in languaging, sensing, cognition, assimilation etc playing a role in formation of situational experience / knowledge).

B. Within system complexity becoming more complex – higher order of complexity. System “constraints” are experienced.

Change which is a constant by itself, has ‘conflict’ traveling along side constantly. This is a paradoxical phenomenon. Conflict is because of the inherent ‘constraint’ that is pushing the system to seek transformation. The constraint emerges due to the conflict in the resources, for which all living organisms are fighting for its own sustenance. Seeking to resolve the conflict, in order to overcome the constraint, the system transforms and it undergoes entropy occupying different increasing orders, as it mitigates levels of complexities in a complex system.

C. Observer (set of observers) record the “order” of the system as a result of integrative influences. Observers have to acquire ways to achieve equilibrium in the new order of complexity, by overcoming system constraints.

D. There are two contending ways the society can respond to achieve harmony within complex system.

References:
From – De’Arcy Wentworth Thomson (DWT) – Growth and Form
http://archive.org/stream/ongrowthform00thom#page/n7/mode/2up

and Form and Transformation – Gerry Webster and Brian Goodwin; this book makes a case for generative development in biology

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.