Computer modelling for mediation and negotiation

In my years as a consultant working in hospitals I used what I had learnt at the gynaecology department. Each time I entered another part of the hospital, I knew little about every health care process at the start. That was a great advantage. I knew from previous experience that data are hard to find and that I would have to ask doctors and nurses to get a clue of how things really worked. For them to do be willing to do tell me, I had to be a recognized and accepted outsider. And being young helps too. To the people in the hospitals it was natural that I did not know a lot about their work. And so they told me everything.

I started to see that using computer models is effective, not so much as a projection of how things should work, as an ideal situation, but as a tool to show what was going on. A common view on the logistical system makes people involved solve problems by themselves. I will give an example.

In a hospital  the eye doctors and board of directors were not on speaking terms on the facilities available to the eye outpatient clinic. The eye doctors and the adjacent dental surgery clinic both argued that they had too little space and demanded more of it. The hospital board argued that the hospital as a whole already had too much space for what they could afford. The hospital board added that maybe the priority for the doctors should be the reduction of the number of waiting patients. The board seemed to view it as a zero sum game: if the eye doctors needed more space, the dental surgeons would have to give it to them. They had to basically figure it out themselves. The result was they were all stuck and no progress was made on the matter. The views of the stakeholders were different, contradictive and yet all true to some extent.

The simulation model built showed the patient processes as they were. This was all based on factual input provided by the doctors, nurses and assistants supported by planning and production data. At this point we are talking logistics and systems engineering. But instead of calculating the ideal situation the model ‘fact checked’ the perceived problems. We, of course, first had to know what the perceived problems were and what people thought were the causes. Were the waiting patients really there? How come? Is space the real bottleneck?

The model that we made highlighted all the concerns and assumptions that people had. The making of the model already made people aware of what was happening. By cross checking stories told and verifying these with data, people started to adjust their perception of reality or they introduced other factors that were relevant. The process of making the model together with those who are concerned was, again, very effective.

It turned out that lack of space was indeed one of causes of the problems that the eye doctors experienced. Not to the degree they had claimed though. The study also showed that 10 % more patients were planned into the scheme than what was even possible. No wonder there were patients waiting. And the fact that the doctors worked in a room of their own, resulted in patients having to walk from one room to the other, with delays and space unavailability issues as a consequence. It also turned out that waiting is part of the eye patient process – when they get eye drips and have to wait for it to work. And elderly patients who tend to show up early for appointments, was another relevant factor.

The model presented a balanced and objective part of reality for all parties. It brought all stakeholders back to the negotiation table and they were able to solve the space issue themselves. Every stakeholder had cards in their pockets, that they did not want to play before, but now they did. Whether they negotiated the perfect solution according to the model, was not important anymore. They had found an effective solution for now. It was a boost for the relationships and mutual trust increased.  This was a more fruitful ground for perhaps more optimization in the future.

Having had these experiences on a relatively small scale of one or a number of outpatient departments, the next step was to apply this method on a hospital wide scale. This is where my PhD questions were born as this turned out to be a lot harder.

 

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