Cybernetic Management Model

The language of cybernetics is dominated by a few key terms and it is important to understand and differentiate between these terms.

Terminology – Feedback, Feedforward

There are three known monitoring and controlling strategies as e.g. suggested by Rasmussen, to ensure safety (Rasmussen J. , 1991) and which are used in cybernetic:

 Feedback

 Feedforward

 Combination of Feedforward and Feedback

 Feedback

This is a reactive method to control the implementation of measures set by the achieved feedback reaction (reactive control measure), i.e. it responds to actual values and is directing reactive (plan/actual comparison). It is used i.g. for performance review and plan-achievement.

 Feedforward

This is a proactive method, which is used to control measures being imple-mented proactive to information disclosure (proactive control measure).

This means it is responded proactive to information disclosure (plan/to comparison). It is used i.g. for intermediate-data-control or planning-progress-control

Terminology – Closed Loop, Open Loop

In the cybernetic the terms “closed loop” and “open loop” are used which can be linked to the PDCA-Cycle (Figure 38).

 Closed Loop

Closed Loop Management is a method which is based on the regulation of methods/models and includes the Plan-Do-Check-Act activities in terms of the PDCA-cycle (regulation model, cybernetic regulation model). It can be differentiated between a closed-single-loop regulation model with a correc-tive adjustment and a closed-double-loop regulation model with a correccorrec-tive and adaptive adjustment.

 Open Loop

Open Loop Management is a method which is based on the control of methods/models and includes the Plan-Do-Act-activities in terms of the PDCA-cycle (control model, cybernetic control model). In this model there is no check activity according to the PDCA-cycle induced and the model is state-dependent. Further, it can be differentiated between an open-single-loop control model whereat control inputs are corrective adjustments back into the act-activities activities, and an open-double-loop control model whereat control inputs are corrective adjustments back into act-activities and adaptive adjustments back into plan-activities.

The different control process variants are shown in Figure 37.

Figure 37: Control System Design – Control Process Variants (Schwaiger, 2011)

Management Process Modelling

The management processes can be modelled as PDCA diagrams (Figure 1):

The PDCA components correspond to Activities (rounded rectangles) in the Business and Management Domains (Swim Lanes). The meanings of the activi-ties are characterized by stereotypes (<<Guillemets>>-brackets) (Schwaiger, 2012).

Own illustration Source: (Schwaiger, 2012)

Figure 38: Management Process - Modelling as PDCA diagram

The cybernetic model for Closed Loop Management and the Open Loop Man-agement is shown in below Figure 39. According to Schwaiger (Schwaiger, 2012), the Closed Loop Management includes closed loop control rules which specify the control inputs that are related to the different deviations, and the Open Loop Management includes open loop control rules which specify the con-trol inputs that are related to the different realisations of the state variable.

Figure 39: Closed versus Open Loop Management (Schwaiger, 2012)

From the terminology point of view, following table gives a summarised overview relation between the used terms in PDCA and cybernetics, including the German meaning:

PDCA Cycle Cybernetic Management

Plan (Planung) -> Objective (Normgröße) Do (Ausführung) -> Performance (Prüfgröße) Check (Kontrolle) -> Deviation (Abweichung) Act (Lenkung) -> Check input (Stellgröße) Table 24: Management Process Modelling – terminology English/German

Based on the Cybernetic Management Model a Business Management System can be seen as a system including the three sub-systems (Figure 40)

 planning system

 operating system

 control system.

Figure 40: Cybernetic Business Management System, including Sub-Systems (Schwaiger, 2011)

Activities related to <<PLAN>> belong to the planning system and marked green. Activities related to <<DO>> belong to the operating system and marked yellow. Finally activities related to <<CONTROL>> belong to the control system and marked blue.

A generic Cybernetic Management model respective framework is shown in Figure 41. The supervisory enforcement rules are included here which are im-plemented to supervise the agents in order to keep their actual behaviour in line with the proposed rules. According to Schwaiger (Schwaiger, 2011), “the super-vision activity is modelled in the management (MGT)-activity-diagram as an ex-ception handler. The exex-ception handler catches disturbances from the observed processes. This is indicated by the lightning bolt symbol and the small rectangle to which it shows. In the supervision-activity itself the information is processed and the corresponding exception handler action is selected and fed back to the region where the out-going arrow refers to. Thus the exception handler pos-sesses an embedded cybernetic structure which is not explicitly shown in its symbol.”

Figure 41: Generic Cybernetic Management Framework – Double Loop Learning and Supervision (Schwaiger, 2011)

The generic Cybernetic Management framework consists of 8 managerial activi-ties:

(1) Plan-activity (1a) Objective (1b) DO-Rules (1c) CHECK-Rules

(1d) Corrective ACT-Rules and Adaptive ACT-Rules (1e) Measure-Rules

(1f) PLAN-Rules (2) Measure-activity

(2a) Performance Measure (3) Check-activity

(3a) Deviation (4) Corrective Act-activity

(4a) Closed Loop Control Input (5) Adaptive Act-activity

(5a) Closed Loop Control Input (6) Process related Supervision-activity (7) Control related Supervision-activity (8) System related Supervision-activity

Figure 41 shows, compared to a 2nd-order Cybernetic Management framework, three additional included supervisory activities ((6), (7), (8)) which take care of the control-in-the-small. As Schwaiger pointed out, “the supervisory activities are established to ensure the actual execution of the operational DO-rules and the managerial CONTROL-rules. At the overall level they are implemented to ensure the rule compliant behaviour of the complete management system architecture”

(Schwaiger, 2011).

The supervisory control process which is represented by the three supervisory activities as shown in Figure 41 should ensure the enforcement rules in terms of the control in the small approach according to Arrow. The enforcement rules are implemented to supervise the agents in order to keep their actual behaviour in line with the proposed rules.

The humans’ work is affected by their behaviour which means human factors influence the work and as consequence they influence the risks that might ap-pear. For example concentration, tiredness, level of education and others are such human factors. In practice mostly of these human factors can be managed

by regulations, directives, law as well as standards and organisation-internal regulations, e.g. industrial safety law, work equipment regulation, technical measures for occupational safety and health, education and training plans, etc.

This means, each improvement in one of these areas will positively impact the overall process. As a consequence the patient safety can be improved too. Fur-thermore, not only risks, but also chances are given by the proposed RiDeM-H model.

6 Discussion

Based on the available data and results of the analysis, some questions have been pointed out and have been discussed:

 To which restrictions and limitation do the CIRS databases lead?

 How generalised are the results?

 Who are the stakeholders of the proposed HFdFMEA technique and RiDeM-H model?

 What are the implications for the health system, practitioners and pa-tients?

 What are the implications for the HFdFMEA and RiDeM-H?

 Validation of human factors – why has the multiple linear regression analysis been used and is there another model that could be recom-mended for validation of the model?

To which restrictions and limitation do the CIRS databases lead?

CIRS data are very sensitive data. As summarized by Streimelweger in (Streimelweger, Wac, & Seiringer, 2015): Based on the research it can be con-cluded that it is important that all fields of a CIRS database are filled with in-formative, real data. Otherwise each kind of evaluation result becomes vague and inconclusive. Furthermore it is necessary to define the required information in a way, that there is no space for giving an answer like “all”, “other” or a blank field. A concrete assignment is absolutely necessary, since this leads to more accurate results for subsequent analysis.

To be able to compare with other hospitals also over their own national borders, it is necessary to use similar contributory factors, which results in human factors.

Therefore a framework like Vincent’s framework (Vincent, Taylor-Adams, &

Stanhope, 1998), the guideline by WHO (WHO, 2005), or ISO standards could be used as guideline for the definition.

Further restrictions are also given by the person providing or reporting the data, this means, who is going to input the data into the database. It would be most accurate to follow a defined process or standard in terms of the provided data and how to report and fill-in those data.

How generalised are the results?

As summarized by Streimelweger in (Streimelweger, Wac, & Seiringer, 2015):

The derived human factors from a CIRS database are only valid for the analysed database for a given period of data available for us. A CIRS database can only provide national data. Ideally all healthcare facilities are obligated to provide information for a CIRS database. However it makes sense to perform it within an individual hospital. In this case the hospital can profit mostly because this will help hospitals to improve their individual standards for patient safety.

Who are the stakeholders of the proposed HFdFMEA technique and RiDeM-H model?

Figure 42 gives an overview on important stakeholders. Each of those of course has different expectations, but also some of them have different responsibilities with respect to their own stakeholders.

Figure 42: Risk-Dependent-Management (RiDeM) in Healthcare - Stakeholders

 Medical Experts and other Staff

Of course, medical experts, nurses and other medical staff are affected most. On the one hand they are the ones reporting adverse events and in-cidents, but on the other hand they could profit most because it is up to them to define the prevention measures. So they are one of the key-players in improving patient safety.

 Hospital – the Management

The introduction of a new method to improve patient safety would be highly appreciated because patient safety is a top priority of many hospital man-agers. RiDeM-H by using HFdFMEA would help them to identify and ana-lyse their risks and to set appropriate actions to minimize those potential risks. Furthermore it would help them to compare to other hospitals or to hospitals within their own association.

 Government

The government is responsible for the social systems which include medical care of the state and therefore it is in their interest to improve patient safety and to generate profit or at least contain costs.

 Representatives of Health Care Facilities

Representatives of health care facilities and organisations have often to fol-low guidelines and directives as provided by the government.

 Provider of CIRS database

For database providers it would be much easier to follow generalised and recognised standards as well as defined processes. Therefore an improved method with generalised recommendations in the form of a framework or a new established standard would be appreciated for such providers.

 Investors

Investors are only interested into getting back their investments and to gen-erate a high profit. Therefore any methods that would help are appreciated.

 Patient

Certainly each patient who has to stay in hospital is interested in being well taken care of. At least it is in the interest of every patient to be exemplary supplied and cared for and to receive the best possible treatment and to re-cover rapidly.

Looking at the stakeholders' interests and the benefits they get from the model, HFdFMEA could definitely help in handling risks and as consequence to improve patient safety.

What are the implications for the health system, practitioners and pa-tients?

As summarized by Streimelweger in (Streimelweger, Wac, & Seiringer, 2015):

The proposed HFdFMEA method allows improving patient safety by means of enabling better understanding and management of human factors influencing risks and the Risk Priority Number (RPN), which in turn is of importance for the patient. Investigations in the relation between risks and human factors help healthcare professionals to identify potential problems, to improve processes, to minimize risk and respectively to avoid adverse events and incidents by setting proactive and predictive measures. An open communication regarding the re-porting shall be encouraged. Anonymity should help the practitioners to ensure that they have no fear of negative consequences when reporting an event.

The HFdFMEA could be implemented by hospitals using a CIRS database through which the human-factor-based RPN (RPNE_i,HF_j) could be evaluated per event and depending on the assigned human factors it would be possible to take appropriate measures to minimize risks. On the other hand it also offers the possibility to handle chances.

What are the implications for the HFdFMEA and RiDeM-H?

The overall objective should be to consider the use of the proposed HFdFMEA in daily life. Therefore HFdFMEA needs to be designed and implemented in a way that allows a widely use in practice. For example it could be integrated into a general ISO standard or framework for healthcare highlighting the significance of evaluating and managing the human factors. As mentioned in previous chap-ter, humans’ behaviour will affect the humans’ work and this means humans have an impact on possible risks, but also on possible chances. Minimizing risks could and should lead to an improved patient safety. Further, some risks might lead to new chances. Therefore it is necessary to realize those positive risks as chances and work out strategies. Next to standards and frameworks there are appropriate laws, regulations and directives which are compulsory in their appli-cation and implementation. Often these are seen as a nuisance, as well as time-consuming and cost-time-consuming. But much more this should be seen as the base for opportunities to improve patient safety either by minimizing risk or using the chances. HFdFMEA and RiDeM in healthcare would support the medical experts and medical staff in handling risks. So it is up to them to be part of a working system and to improve patient safety.

Validation of human factors – why has multiple linear regression analysis been used and is there another model that could be recommended for val-idation of the model?

One target of my research was to investigate whether the risk factors of the tra-ditional RPN are dependent or independent and how they correlate to the hu-man factors. That resulted to the question, how to get access to the required information to be able to validate the HFdFMEA model.

 Surveys and interviews based methods

At a very early stage of the research work I wasn’t able to get access to any kind of real data. Therefore I started interviews with medical ex-perts, hospitals and representatives of Health Care Facilities to gather relevant information. Therefore the Delphi method, a well-known and wide established questionnaire model, was used. Due to the high sen-sitivity of the data, nobody was able nor willing to provide data.

 Stochastics & statistics, empirical & probabilistic methods

In a second step I moved to stochastics and statistics and took a look to empirical methods as well as probabilistic methods. Models such as the portfolio model or the Markov model were envisaged, but the prob-lem remained that there were no real data available that could be used.

 Regression analysis

After the HFdFMEA model was developed and after I got access to the two CIRS database as used for the case studies, it was decided to use multiple linear regression analysis. Regression analysis seeks to find

the relationship between one or more independent variables and a de-pendent variable. Even if the used contributory factors converted into human factors by weighting, the contributory factors are still qualitative data (text encoded into 1 or 0) and not quantitative data.

As the results of the regression analysis shows, all six defined assumptions have been fulfilled, but R, R² and the adjusted R² are very low. Therefore I de-cided to use another method for the validation of the HFdFMEA model (section 7.2, paragraph “Validation of the proposed HFdFMEA model”).

7 Conclusion and Perspective