Clinical Tacit Knowledge – Represents Clinical Pathways
Interoperability Health ecosystem includes
- Clinical Integration Network (CIN)
- Patient-Centered Medical Home (PCMH) and
- Accountable Care Organization (ACO)
To achieve efficacy in Clinical Care, it means a design strategy around Clinical Care Redesign. The outcomes is Tacit Knowledge (Clinical Pathways) driven improvement in the overarching architecture involving PCMH, CI, and ACO
The article below by Sarah O’Hara discusses the Care Transformation as a consequence of improvement in PCMH, CI, and ACO.
- PCMH focuses on care improvement for primary care services
- CI focuses on care improvement for physician practices across specialty types
- ACO focuses on care improvement for an entire patient population, across the continuum
IoT coupled with Cognitive Computing has a huge potential in driving the Clinical Care Improvement efforts.
Knowledge Management :- Discharge Planning
Bioingine.com; Cognitive Computing Platform
Addressing Clinical Care Improvement driven by algorithms that extract Tacit Knowledge from the Health ecosystem involving PCMH, CIN and ACO / HIE.
We extend Q-UEL, our Quantum – 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. We describe a new QUEL compatible toolkit including data analytics application (DiracMiner) that also delivers more standard biostatistical results, DiracBuilder that uses its output to build Hyperbolic Dirac Nets (HDN) for decision support, and HDNcoherer that ensures that probabilities are mutually consistent. Use is exemplified by participating in a real word health-screening project, and also by deployment in a industrial platform called the BioIngine, a cognitive computing platform for health management.
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.