Changing to new Shift Patterns
Visual Rota allows the scheduling(rostering) of staff to be completed easily and quickly by the manager while at the same time Visual Rota compiles additional alecrmation to be used by the operations/financial manager to be linked into forecasting on the employment of staff and budget decisions. Visual Rota can supply the manager with advanced alecrmation enabling alecrmed decisions to be made in advance at no extra cost in time. We have a closely associated suite of programs that convert the Schedule(Roster) into the types of charts often seen at meetings.
Statistics were concerned mostly with the collection of data and its presentation in tables and charts. Now, it has evolved and its impact is felt in almost every area of human endeavour. This is because modern statistics is looked upon as encompassing a process as old as history itself, that of decision making in the face of uncertainty. Needless to say, there are uncertainties wherever we turn, -when we predict the weather, experiment with a new paint or a new product, a new business extension.
Thus, the most important feature in today's statistics is a shift of emphasis from methods merely describing to methods which serve to make generalisations, or in other words, a shift in emphasis from descriptive statistics to statistical inference. Descriptive statistics is treating data to summarise or describe some important feature without attempting to infer anything that goes beyond the data. For instance, the number of passengers carried on a train during the last year would be a descriptive statistic. the use of that statistic to predict the number of passengers carried on a train next year would be a statistical inference.
Descriptive statistics is an important branch of statistics and is widely used in all businesses large and small. In most cases, the alecrmation arises from samples or large scale observations on a small set of items. The time, cost and impossibility of doing otherwise usually limits the alecrmation gathering procedure, even though our real interest lies in the whole large set of items from which the sample was obtained, and not in the past, but the future. Since generalisations of any kind lie outside the scope of descriptive statistics, we are thus led to the use of statistical inferences in making both short- and long-range plans and in solving many problems of day-to-day operations. To mention but a few examples, the methods of statistical inference are required to estimate; the length of stay of post-op patients, the stock levels of drugs, the effective dose of an antibiotic, the ratio of beds to surgical operations.
It must be understood, of course, that when we make a statistical inference, that is, a generalisation which goes beyond the limits of our data, we must proceed with considerable caution. We must decide carefully how far we can go in generalising from a given set of data, whether such generalisations are at all reasonable or justified, whether it might be wise to wait until we have more data, and so forth. Indeed, the most important problem of statistical inference is to appraise the risks to which we are exposed by making generalisations from sample data, the probabilities of making wrong decisions or incorrect predictions, and the chances of obtaining estimates which do not lie within permissible limits of error. These various possibilities may seem somewhat frightening, but they cannot be eliminated; so long as we have to live with uncertainties, we simply must learn how to live with them intelligently.
There are tools available to help in the decision making process, computer programs being the most important. By far the largest cost in industry is staffing costs. Budgets are set for the future and expenditure examined from the past. Any program that can track staffing levels on a shift-by-shift, week-by-week, month-by-month basis and report on under or over manning of the shifts, both in the past, today, and as far into the future as you like, will serve to eliminate a major portion of any generalisation, giving instead actual factual data. The computer program eliminates the need to sample data, because it is possible to examine all the data. The program can display the data as a table or chart, or as a pattern. Changes to any pattern of working are immediately visible without the need to manually manipulate the data, it is all done automatically no matter how complicated the manipulation.
Imagine the scenario whereby a new operation becomes possible and you need to build a new extension and staff it. Visual Rota gives you a tool to staff the new extension before it's built and determine the number of new staff to be recruited, the staffing costs, the training costs, etc. before any money is actually spent. From this data and an estimate of the numbers of patients, the cost per operation is found and whether it is profitable.
A second scenario is 'how will any EC regulations on staff hours per shift affect us'. This can be tried out in advance using Visual Rota to determine any changes needed to shifts. New contracts of employment and working practises can be worked out well in advance.
And all this of course, comes from a computer program that saves a lot of time every working day and pays for itself as soon as you start to use it.
We have written a series of programs in order to use the data produced by Visual Rota to monitor our performance and how to improve our future performance.
Please note that the programs are linked together, so that a change in one value feeds through to all the calculations and charts that use that value, hence this requires almost no effort to maintain. However, the computer cannot make decisions for us. We still have to do that ourselves.
Every day the number of patients is calculated using the admission and discharge dates/todays date. The number of patient days is also calculated for a given period, usually monthly. The data goes back for many years, but we tend to only use the last 4 years of data in our analyses. This data is illustrated by charts of patients/day against a suitable time period. The time period can be; everyday, weekly, 4-weekly, monthly or quarterly. The number of patient days can be averaged over the same period of time. We use this data to show trends in variations of the number of patients. A typical trend might be to see if we have more admissions in winter or summer. As it transpires, the numbers of admissions remains equal throughout the year, and so do deaths.
The program also tells us how long each patient has been with us. This is useful in predicting the best types of patients to have.
A variation to this program is to have the patients categorised and chart each of the numbers of each type of patient. We decided not to complicate our charts to much, however when we did we found some very interesting data. Typically, our patient mix at any one time is ¾ female and ¼ male, however our admissions are 2/3 females and 1/3 male. The reason for this was apparent when plotted against length of stay. Females lasted longer. We did similar analyses for Age, Date of Birth, Illness, Marital Status and cause of death. All very interesting stuff, but this was typical descriptive statistics and mostly of little value, with a few rare exceptions.
The Management Operations Data Program
We combine the data produced by Visual Rota about the Staff, with the data produced above about the Patients. Typical charts, from about 50 that can be produced, are; Staff hours per day; Staff hours per month; Staff hours per patient per day; Staff hours for each category of staff per patient per day; and perhaps the most important, staff cost per patient.
We then look at averages, and the reasons for variations from this average. Reasons might be as diverse as; new legislation requiring expensive training programs, flu epidemics, patients getting better more quickly. We can then act on these reasons to improve our performance in the future.
For example, we observe that our staffing ratios increase because there are less patients to look after each day. Solutions could range from; increase marketing activity to increase the numbers of patients next month and at the same time give holiday leave to some staff, right through to closing the hospital down. There are many treatments in the past that aren't practised any more, like TB, tonsillectomies, smallpox and polio. However, to counter balance that, new treatments come along all the time. So, perhaps we should change to looking after new types of patients.
Our actions are usually governed by two principles.
1. If we do nothing then things will remain the same, whether the situation is good or bad is irrelevant.
2. For as long as we have waited for something external to happen, if we do nothing we can expect to wait that time again before something does happen.
The big C - Change
Whenever we have had to instigate change, the use of charts about staffing have proved invaluable in convincing the staff that something has to be done. The pictures express in a clear and unambiguous way the trend and reasons. Also, because the computer acts in an impartial manner, in the eyes of the staff, we have found it to be an excellent means of achieving their co-operation in what could be a confrontation situation.
We use real data produced by ourselves to perform our financial management tasks as efficiently as possible. The staff scheduling(rostering) data is fed directly into this type of presentation automatically without the need for anyone to do anything else. All the calculations are done by the computer and inserted directly into the graphs. The charts are used to observe when the current trend is worse than it should be. From the chart data, calculations can be done about the parameters which caused the trend to be worse and what value the parameter should have to bring the chart back to normal. For instance, a ward has 5000 nursing hours per month to look after 50 patient days. The average is 100 nursing hours per patient per month, about 3.5 nursing hours per patient per day. If the average increases to 4 nursing hours per patient per day, say because of there being less patients, then this would cause a blip in the chart and is immediately obvious. Assuming that the trend will continue and it is undesirable, the question is; how much does it cost and how to reduce the staff numbers to return to the previous average. Running the equations backwards gives a reduction of about one member of staff less on each shift, which is achieved using Visual Rota to reduce the numbers in the future. It isn't easy to reduce staffing numbers without the staff co-operating. Some ideas are given in the section on over-staffing.
Myth - We are always short of staff
This is a frequent complaint and is perpetuate from staff to staff as if it were true. Using the data and charts it is possible to debunk this myth.
The typical scenario is as follows. A member of staff phones in sick at short notice. We have staff segregated into day trained nurses, day care assistants, night trained nurses, night care assistants, head cooks, catering assistants, cleaning, maintenance. 8 categories in all. There are about 60 full and part time staff and during each day (24 hour day) something like 25-30 staff come into work. Typically 4(2 lots of 2) day trained nurses, 15(8 in the morning and 7 in the afternoon) day care assistants, 1 night trained nurse, 3 night care assistants, 1 cook and 1 assistant, 1 cleaning and 1 laundry, 1 gardener
The person phoning in might be a day trained nurse, in which case the other trained nurse has double the work to do.
The person phoning in might be a day care assistant, in which case the other care assistants have 20% more work to do.
The person phoning in might be the cook, in which case the assistant has all the work to do.
And so on. The absence of one member of staff in the 30 that should turn up is observed by those affected as increasing their work load tremendously. If this happens, say, every other day, then the absentee rate is around 1.5%, which in any other business would be a good rate, instead translates into a perceived problem of continuous short staffing. Because those affected by the absence moan, a tremendous effort is made to bring in someone else to cover the shift. The same people are phoned time and again, as they see it, and the myth is perpetuated because it affects even the staff who are at home. Usually after some 30 minutes of phoning around, the staffing situation is back to the correct levels. But it has caused huge disruptions in the meanwhile and affected everyone. In fact, the only one not affected is the person off sick.
It is essential to debunk myths like that, because it affects morale and future recruitment of staff. The charts produced by the computer programs go a long way in doing just that. We have had staff in the past who have 'felt' over worked because of being short staffed. If a chart is plotted of the hours in each category worked every day and this shows that the correct staffing levels were adhered to each day, then it is possible to convince them of their error.
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