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Cybernetic Applications and Health-Care Julien Libbrecht. Motivations. Increasing needs and costs of health-care and health care systems in the future Increasing difficulties on behalf of health-care workers in their relation with patients: complexification and differentiation
Motivations • Increasing needs and costs of health-care and health care • systems in the future • Increasing difficulties on behalf of health-care workers in • their relation with patients: complexification and differentiation • of relations, stress, burn-out, early exit,… • Increasing demands in performance of the workers, the institutions • and the system on macro-scale
Why cybernetics? • Art and science of regulation and control • Health-care is regulation of health-condition of patients • and regulation of systems • Applicability of cybernetics in a broad range of domains: • economy, sociology, biology: many activities in almost • every branch of science and life. • Cybernetics: art of managing relationships (also by • quantification)
Tree different elementsCommon terms • Two players: patient and care provider (in the most • simple form) • Tree elements as basic system: • Patient (D), care provider (R) and outcome (E) • One desired outcome: patient’s health or health- • stabilisation (idealised expectation)
Tree elementsCybernetic terms (Ashby) Patient: disturbance or variety inducer Amount of different problems Care provider: regulator Amount of different responses Outcome: amount of different results
Conceiving variety • Each element has variety of a certain amount • The amount of variety depends of both the element and the • observer of the element (we must be aware of the trap of the • detached observer: he does not exist) • One’s variety is determined, it can be measured • Measuring variety is the beginning of knowing it • Knowing it is the beginning of handling it
Conceiving variety • Variety reflects a state of a element at a certain moment • The element will always be composed of parts or components • that can change in function of time • This element will be called a vector • The components of the vector can have different values • Change of the vector always means change of values
Variety and constraint • Variety and constraint are intimately interlinked • Variety without constraint is not conceivable • Constraint sets a limit on variety and enables us to handle • the world • Constraint makes things predictable • Constraints are the rules for every transformation
Transformation • Transformation is always the action of an operator on • an operand with transform as result • Transformation can be closed or open • In theory, every transformation on an unlimited number • of operands can result in an unlimited number of • transforms • In practice, a transformation operates under certain rules • or constraints, e.g. the transformation of the embryo into • a foetus will occur under certain constraints
Transformation • A transformation can be single valued or multiple valued: • Single: when each operand will be transformed in one • transform • Multiple: when each operand will be transformed in many • transforms • A transformation can be single valued and one-one, when each • operand corresponds with a different transform • In all other cases they will be of the many-one type • Another type of transformation (the most frequent one) is the • identical transformation
Variety and health-care • Every patient is a vector with different possible values • Every patient can have an infinite number of values • Every care provider is a vector which can take a limited number • of values • The variety of the outcome is the different possible values • the outcome can take. • The table T is the system (unit, hospital,…) which provides the • outcome.
Confronting varietyVo Vd/Vr R T D E
The Law of Requisite Variety • Only increasing variety of R can force down the variety of • outcomes • To force down the variety of outcomes, the variety in • R must increase • Vo >or = Vd / Vr • Only variety can destroy variety • If we want the outcomes to take one value, the variety of R must • be at last equal to that of D.
Variety of patients and care providers • How to control the patient’s variety? • The variety of patient needs is in most cases larger than • the variety of care providers. • The classic way of confronting variety of patient needs is to • enlarge the variety of the care providers so that • Vd = Vr or • Vo = 0
The art of Regulation • Care providers are in se regulators who transform patient • needs in one outcome: stabilisation, health,… • The aim of cybernetic approaches consists in investigating • the ways in which this regulation can be reached in the most • effective way • The most effective way to do this begins with accepting • the law of requisite variety and to investigate the ways of • organising care taking into account the law.
What’s regulation? • Regulation is the way the regulator follows to transform • the input of D into a certain range of desired outcomes. • The only way for the regulator to do this is to fully take into • account the variety of D. • Without respect of the law, the regulator fails in his mission: • suffering and decline of the whole system are the result.
Regulating health-problems • Three questions – three answers : • Confronting patient’s problems = defining the set of problems • When the patient’s problems are known, define the target: result • How can we transform the problems in the target: desired • Outcome?
Defining the problems (D) Vd = 64 (possible states) = log2 = 5.17
Defining problems (D) • Different aspects as components of one problem or vector • Each component can have two possible values (0-1) • Variety of the D-vector = 64 different states • Describing patient’s problems = result of an interaction process • Defining patient’s problems = first important step in delimiting • variety on the input of the system T (blocking).
Defining outcome1 = pos. Outcome (target) Vo = or > D / R
Defining Outcome (E) • Limiting outcome = enlarging R • If outcome is limited to 1 value (1), Vr must be = Vd. • Health-care is idealistic because it claims a limited set of • outcomes • Health care providers must be precise in formulating outcome • and results.
Possible solutionsIncrease Vr • Vr = Vd • Certain components of R becomes multi-functional: increasing • the competence of certain components of R. • Increase the competence of R . • R ‘s possibilities aren’t illimited • Paradoxical with spcialisation
Decreasing D • Division of D in different components • Block Vd = selection in variety of components of D • Patient information: what can the patient expect? • Sequentialisation of the transformation: stochastic problem • transformation of components • Transform certain components of D in components of R • Implication of patients in the treatment. • Clustering of different components
Increasing Vo • Non-limiting the possible outcomes: compound target • Non-defining the target • Defining the possibile outcomes in function of Vr
5 4 3 1 2 Beer S. Brain of the firm, Whiley, 1981
5 systems or functions System 1: operational level-activity-regulation-registration System 2: metasystem subsuming all system one – coordination by information System 3: information transmitter – coordination by regulation algodonometer – stabilisator/inhibitor System 4: big switch – modeling System 5: decision-making.
Division of workFrom opposition to collaboration Preliminary condition: patient and care-provider must be considered as one unity. Switch from applicatif to process-care (2th cybernetics). Autonomic activities System 1: patient – CP System 2: CP System 3: CP Management activities System 4: patient System 5: patient - CP
Managing variety • System 1: patient too assumes a regulation function. • Enhance patients capabilities: • Increase Vr • System 5: Define the target and objectives, evaluate and plan. • Increase Vo • Decrease Vd
Cross-matching LRV and Managing HC-model • Objective: manage care trought the interaction between • Patient and CP in one system • Increase Vr: • patient becomes a regulator too in collaboration with CP • regulation becomes auto-regulation • transformation becomes auto-transformation
Cross-matching LRV and Managing HC-model • Decrease Vd: • Defining variety of D through negociation (interaction) • Defining limits on variety • Increase Vo: • Defining objectives as possible outcomes • Defining targets in a step by step method