Solving the issue of poor performance in a large service organisation

If a Hospital is to optimise/improve the performance of their emergency waiting times, we have two approaches to solving the problem:

Option A – Monitor the patient numbers, bed numbers and availability of resources as well as waiting and treatment times.

Option B – Speak to the hospital staff – Doctors, Ambulence crew, Nurses etc. to gather their views on what are the areas that need improvement.

Buchanan and Huczynski (2010) challenge us to ask “why?” and tell us how difficult it is to have a single correct answer; I’d like to pose the same thinking pattern to this situation and not choose a single correct method for solving this solution.

As discussed by Buchanan and Huczynski (2010) throughout the first chapter, we should not expect an organisation to be bound by the rules of natural sciences; looking at just the numbers as proposed by the first group would give us good measurements and accurate figures on how things should be working – where the problems lie – but, if we are running a hospital inundated by overworked, underpaid emergency room doctors, nurses and orderlies then simply adding more beds and more underpaid employees will not necessarily solve our problem.

I am inclined to think that option B (speaking to the employees) would provide an answer to option A but it is not safe to assume based on the opinions of others only so I do believe that the numbers must also be produced. An article by Ference (2001) on Improving Organizational Performance illustrates a trademarked process used by a Casino in Las Vegas which is survey based to produce what they call a “Service-Culture Map” which focuses on “employee satisfaction, commitment, and customer responsiveness as the keys to a strong return on owner investment”. It might be considered “cold” to compare a hospital to a casino but I think that the same rules could quite well apply which would, in-turn, support option B’s approach.

The case study in the paper by Ference (2001) talks about cross-functional meetings, and I believe that this is something that would also support option B – issues may lie with miscommunication between doctors and nurses, without conducting an equal-opportunity meeting between the two parties the problem may never be solved.

To conclude it may seem like I have strayed more towards group B, while I am more inclined towards that approach, the problem could simply lie in the number of beds available or the efficacy of the booking system. Perhaps an approach of using option B to narrow down areas of concern and option A to back up where necessary the problem could be solved or improved.

References

Buchanan, A. & Huczynski, A. (2010) Organizational Behavior. 7th Edition. Upper Saddle River: Prentice Hall.

Ference, G (2001). Improving Organizational Performance [Online]. Available from: http://www.hvs.com/emails/newsletters/ference/Cornell-Q.article.pdf (Accessed: 6 February 2011).

What makes one algorithm better than another?

There are two main aspects which make an algorithm better than another. The first one I would like to discuss is the measure of how efficient an algorithm is in terms of the time the algorithm takes to complete its calculations and come to a result (an end).

Consider the examples as outlined by Brookshear (2009) of a binary search and a sequential search for a specific item in an ordered list. While the sequential search runs through a list one by one until it finds an entry, on average this search will search through at least half of the list – if the list is very large this may add up to quite a sufficient amount of time per search. A binary search divides a list into two, looks at the middle entry and calculates whether the result is in the first or last half of the resultant lists, then further breaks the respective half in half again, and repeats this process until it finds the item it is looking for. One can plainly see that the binary search method will access a record far less times on average than the sequential search.

Another aspect of measurement is the physical space the algorithm consumes when performing its calculations/operations. A very simple way of explaining this is the one of sorting a list alphabetically. The two methods are firstly the bubble sort versus secondly creating a new list in the correct order. In the bubble sort the method is to sort the list in the form of looking at it in small pieces and sorting them by shuffling around the entries until the entire list is sorted. This way we only occupy the memory/space of the original list. In the method of sorting into a new list, the memory occupied by the original list is used and another entire list is created from the same entries sorted into the correct order. The latter occupies double the amount of memory than the former.

References

Brookshear, J.G (2009) Computer Science: An Overview. 10th ed. China: Pearson Education Asia Ltd.