JPH11203348A - Required volume of lunch estimating device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To exactly predict, at the time of purchase, required volume of lunch at the time of service by providing a means to predict the number of persons to change schedule based on the number of persons liable to change schedule from the moment of the purchase to the moment to serve lunch and on a condition as a factor to change the schedule. SOLUTION: Required volume of lunch is predicted based on kind of meal information and the number of patients for whom the kind of lunch is required by a basic required volume generating means 1. Under the control of a means 2 to product the number of persons to change the schedule predicted volume 22 for the number of persons to change schedule is generated from the number 20 of persons liable 10 change schedule as the number of patients with a possibility of potentially changing the schedule from the time of prediction to the time of lunch and the change influence condition 21 to influence change and selection of the schedule. And the known number 10 of patients for whom the lunch is scheduled to be required and the predicted volume 22 for the number of persons to change the schedule are given to the basic required volume generating means 1 at the time of production. Thus, the required volume of lunch at the time lunch is exactly predicted at the time of purchase.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to the field of information processing in hospital business, and more particularly to a business system in a catering department.

[0002]

BACKGROUND OF THE INVENTION Hospital catering departments provide meals for inpatients. Hospital meals are different from general food service shops, and nutritionally-considered meals according to the patient's condition must be provided at the correct time. Therefore, in large hospitals that handle many types of diseases with many patients, nutritionists and other specialists
A wide variety of menus are prepared in the morning, lunch, evening, and evening on the 65th, depending on the type of food, such as regular, soft, diabetic, and kidney meals. A hospital that cooks all menus in its own kitchen,
There are differences in the preparation of ingredients, such as raw materials, semi-cooked goods, finished food, etc., with hospitals that outsource part or all of them. Required. Because, as mentioned earlier, the nutritional menu is fixed, so it is difficult to flexibly use overbought ingredients continuously or reuse them for other menus, and use the remaining ingredients even if they can be stored frozen Few are possible. In this point, it is greatly different from a store such as a general convenience store or a restaurant mainly used for regular products.

On the other hand, accurate estimation of the required quantity is a very difficult task. This is because such large hospitals have large daily requirements, and there are no vendors who can order and deliver the next day, so it is usually necessary to place an order several days before cooking and anticipate the required amount several days later. Because it must be. For example, in a hospital example, on November 14th, an order for 30 kg of silver raw cuts to be used for menu on November 20 was placed,
It was delivered on November 19th. In this hospital, an instruction transmission system using an in-hospital LAN called an ordering system is operating, and meal plans of inpatients are also transmitted from each ward to the lunch system. In this case, the basic requirement based on the schedule for the patient on the 20th was 475 meals on the 14th, but the veteran catering room manager reduced the variable requirement 25 meals to this based on his long experience and intuition. The amount was 450 servings and 30 kg was ordered. On the 20th, 429 meals were actually cooked. In a hospital, it is not permissible to run out of food, so cook a little more in preparation for an emergency, but having too much residue creates a problem for hospital management.

As described above, in the conventional lunch system, cooking menu registration, food disassembly,
Although it has functions such as supplier disassembly and order sheet printing, the number of required meals was adjusted by the person in charge according to the system presentation information.

[0005] Further, a technique for making a prediction at a general store such as a convenience store or a restaurant is known. For example, in Japanese Patent Application Laid-Open No. 04-353970, attention is paid to factors affecting the consumption behavior of foreign customers in a general store. ,
The purchase quantity is predicted by predicting the sales quantity from the correlation with the past sales of each product. However, hospital meals are completely unrelated to outpatients, and inpatients must take a prescribed menu unless they have permission to sleep or skip a meal. The techniques shown are not applicable.

[0006]

As described above, in the prior art, it is not possible to accurately predict the required amount of meal at the time of preparation at the time of preparation. However, there is a problem in that a large amount of residual material is wasted at the time of actual feeding. In order to solve such a problem, the present invention is based on the number of people who can change the schedule potentially from the time of preparation to the time of lunch, and the change influence condition that causes the change of the schedule. It is an object of the present invention to provide a required meal amount prediction device that accurately predicts a required meal amount at the time of preparation at the time of preparation by providing a scheduled change number of people predicting unit that predicts and generates the number of changed schedule persons.

[0007]

SUMMARY OF THE INVENTION The above-mentioned problem is solved by a required meal amount predicting apparatus constructed as shown in FIG. 1 in a required meal amount predicting apparatus in a hospital catering department which supplies a hospitalized patient's meal. That is, in the configuration of FIG. 1, the basic requirement generation unit 1 that generates the required amount of food based on the type of food and the number of required patients for the type of food, Of the number of patients who can be rescheduled, which is potentially the number of patients who can potentially be rescheduled, and the change influence condition 21, which is the condition that affects the selection of the rescheduling, generates the estimated number of rescheduled persons 22 Means for predicting the number of patients who need to change meals at the time of the prediction, and
Is supplied to the basic required amount generation means 1 as the number of patients required to provide a meal, it is possible to provide a required meal amount predicting apparatus for accurately predicting the required amount of meal at the time of preparation at the time of preparation.

[0008]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of an apparatus for predicting the required amount of meal will be described with reference to FIGS. Note that the computer processing according to the present invention is executed on a main storage device of the computer by a computer program, and the provision form of the computer program includes an auxiliary storage device connected to the computer, a floppy disk,
It is provided by being stored in a recording medium such as a portable storage device such as a D-ROM or a main storage device and an auxiliary storage device of another computer connected to a network. Is loaded onto the main storage device and executed.

[0009] The information on the necessity of feeding a patient from each ward and nurse station is grasped as the number of patients of each meal type for each date. The food type is a type of meal according to the patient's condition.
It is divided into three types: digestible soft meal such as porridge, and special meal prepared as part of treatment. Regular meals are further divided into adults and children, soft meals are further divided into whole porridge, five-minute porridge, etc. Special diets are further subdivided into kidney disease, liver disease and diabetic diet. There is. The type of food required for each patient is determined by the doctor's instruction, and a change instruction is made according to the condition. In the catering department, it is necessary to know the number of patients who need each type of food. Menus are planned for each food type according to a predetermined menu cycle, so the menu is determined when the date and food type are determined. When the menu is determined, the type and unit amount of the food material for one meal are determined, and multiplying this by the required number of patients gives the required amount for each food material. As described above, the required amount of meal is obtained for each food ingredient for each food type, and finally, these are merged for each food ingredient. Therefore, in the following description of the present invention, for convenience, one food species, For example, a description will be given of a required meal amount for an adult regular meal. Therefore, when the number of patients is hereinafter referred to, it means the number of patients who need this diet.

In general, the number of patients 10 who need to be fed can be obtained by summing up the numbers transmitted from the ward to the feeding system as a feeding order. This order instructs the patient to be delivered at a certain date th in the future. Yes, this date will be referred to hereinafter as the distribution point. In addition, the current date ts referring to this order is generally earlier than th, and is referred to, for example, for charging, and is hereinafter referred to as charging time. Since the meal order can be updated every day, the number of patients 10 who need to eat can be regarded as a function that changes depending on the values at the time of preparation and at the time of distribution. In order to express this, we will use the notation of the number of patients who need to be fed (ts, th) in the future. Figure 2 shows the number of patients who need to be fed (t
s, th), the number of patients who can stay overnight (ts, th), and the number of patients who can be discharged (ts, th) have the same meaning.

FIG. 2 shows an embodiment of the required lunch amount prediction device of FIG. FIG. 3 shows the operation of the required meal amount prediction device of FIG.
This will be described below. In this example, at the preparation time point ts, by providing the known number of patients requiring a meal 10, the number of people who can change the schedule 20, and the change influence condition 21, the predicted required meal amount at the distribution time point th becomes the second required meal amount. Is shown as being obtained as quantity 31. In FIG. 3, step S1 is in the initial state, and the two switches a and a 'of the basic requirement generating means 1 are respectively linked to k and k' at the beginning. In step S2, the basic required amount generation means 1 calculates a first required meal amount 11 from the input planned number of required patients (ts, th) 10. The details will be described later with reference to FIG.

Next, in step S 3, the user inputs the number of persons 20 who can change the schedule and the change influence condition 21 as parameters for the prediction to the number-of-scheduled-persons predicting means 2. In FIG. 2, the number of patients who can stay asleep 20G and the number of patients who can leave the hospital 20T are shown as examples of the number of people 20 who can change the schedule, and the overnight effect 21G and the discharge effect 21T are given as examples of the change influence conditions 21 corresponding to each. . Other than that, the hospital may have its own change impact conditions21. In step S4, these pairs of the number of persons 20 who can change the schedule and the change influence condition 21 are received, and the overnight stay preference coefficient table 2G 'and the discharge postponement preference coefficient table which are previously incorporated as coefficient tables in the number of persons who have changed the schedule 2
Using the 2T ', the predicted change number of persons 22 is calculated. The calculated estimated number of changers 22 is the estimated number of unscheduled overnight guests 22.
G, which is calculated as 22T, the estimated number of unscheduled discharges. Details of the operation of this part will be described with reference to FIG.

Next, in step S5, the basic requirement generation means 1 prepares the switches a, a 'for the input of the predicted number of changers 22.
Is switched to h, h '. Then, in step S6, the same logic as in step S2 is used to estimate the expected number of changers.
The required change amount 23 is calculated from 22. In the example of FIG. 2, the required change amount 23 is calculated as a required overnight change amount and a required discharge change amount. In this example, the required fluctuation amount 23 is obtained by the basic requirement generation means 1 from the estimated number of planned changers 22, but another method is also possible depending on how the estimated number of planned changers 22 is obtained. In step S7, the calculation unit 1E calculates the second required meal amount 31 from the determined first required meal amount 11 and all the required variable amounts 23, and this is the required required meal amount.

Next, the operation of the basic requirement generation means 1 will be described with reference to FIG. The basic required amount generating means 1 calculates a first required meal amount 11 according to the planned number of required patients 10. First
The required meal amount 11 is the required amount when preparing the lunch material,
This is the required amount for each foodstuff cooked in the hospital. As already mentioned, the number of patients required for feeding 10 is the number of patients totaled for each food type, and when the distribution date date th and the food type are determined,
The menu is determined by referring to the menu database 1B shown in FIG. For example, the dinner on November 24 for adult meals is wakame rice, squid and potato stew. As described above, the menu conversion unit 1A obtains a corresponding menu name set from the menu database 1B from the patient food type information and the meal distribution time th. Next, the food conversion unit 1C refers to the food database 1D to determine the type of required food and the required amount from these menus. Fig. 4 (b)
Is an example of the foodstuff database 1D.For example, for an adult meal on November 24, frozen sashimi squid 80 g × N, washing taro 10
0 g × N is obtained as the first meal requirement 11. Where N
Is the number of patients given as the estimated number of patients who need to eat (ts, th).

Next, an operation example of the schedule changer number predicting means 2 will be described with reference to FIG. Known number of patients requiring meals (ts, t
h) is the number of patients (th-ts) obtained by summarizing the lunch order that instructs the distribution in the future,
Lunch orders are often changed and are confirmed the day before or the day before. Many hospitals allow changes in orders for breakfast and lunch the day before and dinner for the morning. Therefore, the given number of patients requiring meals, that is, the number of patients requiring meals (ts, th), does not usually match the true number of patients requiring meals, that is, the number of patients requiring meals (th, th), and the difference is changed. Minutes. The reasons for this schedule change include (a) schedule change due to sleepover, (b) schedule change due to postponement of discharge,
(C) Uncertainty period (th-ts) There are new hospitalizations and discharges within the day. Of these, I gave it as (c)
(th-ts) New hospitalizations and discharges during the day are usually admitted to hospitals in a way that balances hospital capacity and resource status, and has already been included in the planned number of patients requiring meals (ts, th) it is conceivable that. In other words, a lunch order is automatically generated for the future together with the hospitalization process, and is automatically required unless there is a discharge order or other change order. On the day of discharge to the hospital, or until about the day before, the order to stop lunch will not be made. Therefore, the number of patients scheduled to need lunch (ts, t
In h), the number of discharged persons in the uncertain period is counted extra, but this is just allocated to newly admitted persons during that period. For this reason, (a), (b)
Occupy a large weight in the number of schedule changers. This will be described below.

First of all, it is an unscheduled overnight stay in (a). As described above, since the hospitalized patients are ordered to have lunch as a default, unless the sleep is very planned,
It is not included in the number of patients who need to eat several days in advance (ts, th).
In this sense, unscheduled overnight stay is largely a matter of arbitrarily choosing a sleepover by a patient who can stay overnight, and factors that induce the arbitraryness include the day of the week and the weather. Of course, there are cases where students are staying out of education such as diabetic meals, but this is easily suppressed by planning. Therefore, the number of people who can change the schedule for unscheduled overnight stays
For 20, the number of patients who can stay overnight is taken as 20G, and the change impact condition
Day 21 and weather are taken as the overnight stay effect condition 21G for 21.

FIG. 5 (a) shows correlation factors and coefficients for days of week and weather and unscheduled overnight stays obtained from a past lunch performance database at a hospital. This table shows the number of outpatients who can stay abroad from the nursing system database, and estimates the approximate number of outpatients who applied for an overnight stay within 3 days using day of week factors and weather factors. It is a coefficient value. The number of patients who could stay overnight was determined based on the classification based on nursing standards. The weather is a factor in the case of heavy snowfall or typhoons where the distribution day is particularly remarkable. From this table, for example, if there are 100 sleepable patients,
It can be seen that 16 people and 8 people on weekends stay outside unexpectedly, and heavy snow or typhoons will halve this.

Similarly, FIG. 5B shows the relationship between the number of unscheduled discharges with respect to the number of patients who can be discharged, the day of the week, and weather factors. Here, the term “unscheduled discharge” refers to the number of meal orders canceled as discharge within 3 days, and does not indicate a relationship with a medical condition. As described above, there are very few cases in which a person who applies for discharge at least four days in advance is required for lunch beforehand. In the case of our hospital, there are many elderly patients in rural areas, and there is a tendency in hospitals to avoid discharge from Buddha's destruction and wait for Da'an the next day before leaving the hospital. Also, the weather was similar to that of staying out. Conversely, a decrease in unscheduled discharge means an increase in postponement, so Fig. 5 (b) shows the discharge postponement preference coefficient.
Can be used as T '. Strictly speaking, the number of patients who can be discharged should be determined by examining the judgment of the physician of each patient. Here, the same parameter as the number of patients who can stay overnight was used.

In these analyses, the parameters for the year-end, the beginning of the year, and the holidays during the Golden Week are different. In other words, in these special cases, the schedule is collected as soon as possible for the sake of treatment and nursing. Therefore, there are many pre-applications for 4 days or more, and there are few changes immediately before these. As a result, the number obtained by subtracting the number of these pre-applicants matches well.

FIGS. 5 (a) and 5 (b) show the schedule changer number predicting means 2 as a coefficient table database for performing the fluctuation prediction calculation.
Is stored in advance. The sleepover preference coefficient table 2G 'and the discharge postponement preference coefficient table 2T' in FIG. 2 are the same. The number of people who can change the schedule corresponding to this is 20, change impact conditions
The following parameters are given as 21.

First, as the number of patients who can stay overnight (ts, th), in the case of adult meals, the nurse in charge determines the nursing system based on the classification of the nursing system in the ts, and tabulates them in the nursing system. More simply, the number of patients who need to eat (ts, t
h) It can be considered that it can be used to some extent even if it is calculated based on experience values, such as a fraction of h). In addition, sleepover effect condition 21
G gives the day of week factor and the weather factor at the distribution time th.
Since the weather factor is the future, the weather factor is input based on the forecast based on the weekly weather forecast. However, since the weather factor is expected based on extreme bad weather, an input determination is made. Similarly, the number of patients who can be discharged 20T and the condition for effecting discharge 21T are also input. In this example, the same parameters as those for the number of patients 20G capable of staying asleep and the condition 21G for staying asleep were used.

From these inputs, the schedule changer number predicting means 2
Uses the coefficient tables of FIGS. 5 (a) and 5 (b) to calculate the unscheduled unscheduled guest count 22G and the unscheduled discharge count forecast 22T. Here, a prediction example of the adult meal at the dinner on November 24 will be described with reference to FIG.

In this example, as shown in column A of FIG.
Prepare necessary ingredients for adult meal on November 24 November 18
It is an example of the prediction made on the day. At this point, the number of patients on the order (ts, th) was 302, as shown in column B. As already explained, this number includes many possible sleep-outs and discharges on the safe side, so the true number of patients requiring meals should be reduced.

Row C shows the situation at this point in time, where only 12 and 6 people have already applied for staying out and discharge on the 24th.
At this time, 110 adults who are eligible to stay overnight
People. The number 20 of patients whose schedule can be changed input to this device is the number of patients who can stay asleep 20G and the number of patients who can be discharged 20T, both of which adopted the value 110 shown in column C. As the change influence condition 21, common day-of-week conditions and weather conditions were adopted for both the stay-over effect condition 21G and the discharge effect condition 21T, and the values shown in column E, transfer holiday, Monday, Buddha death, and sunny were used. These conditions are compared with the overnight stay preference coefficient table 2G 'and the discharge postponement preference table 2T' of the schedule changer number predicting means 2, that is, FIGS. 5 (a) and 5 (b). An overnight stay preference coefficient of 16% and a discharge postponement preference coefficient of 4% were obtained, and the fluctuation forecast calculation unit 2A of the schedule change number forecasting means 2 calculates the number of unexpected sleep nights (110
-12) x 0.16 = 15.68, unscheduled discharges (110-6) x 0.04 = 4.16
Get. These values were output as the predicted number of changed persons 22, and as shown in column G, the estimated number of unscheduled overnight stays 22 G and the estimated number of unscheduled discharges 22 T were 15.68 and 4.16.

In step S7, the above-mentioned predicted amount is used as the food material prediction through the basic required amount generating means 1, and finally the second required food supply amount 31 is output by the calculating unit 3. In the example of FIG. 6, the predicted number of required meals is finally 282.16, but since the actual number of required meals on the day was 277, it is evaluated to be a good match.

[0026]

As is apparent from the above description, according to the present invention, the actual required amount after a few days is predicted at the time of material arrangement,
There is a remarkable industrial effect that foodstuffs can be efficiently prepared without actually causing a shortage and generating excess inventory as much as possible.

[Brief description of the drawings]

FIG. 1 is a schematic diagram of the configuration of a required lunch amount prediction device.

FIG. 2 shows an embodiment of a required lunch amount prediction device

FIG. 3 outlines the operation of the required lunch amount prediction device.

FIG. 4 is an example of a database of a basic requirement generation means.

FIG. 5 shows an example of a database of a schedule changer number prediction means.

FIG. 6: Prediction example of November 24 dinner adult meal

[Explanation of symbols]

 1 Basic requirement generation means 2 Schedule changer number prediction means 10 Scheduled meal needing patients 11 First meal requirement 20 Schedule changeable number 21 Change influence condition 22 Planned changer forecast number 23 Variation requirement 31 Second 1A menu conversion section 1B menu conversion section 1C food conversion section 1D food database 1E calculation section 2A fluctuation prediction calculation section 2G 'overnight stay preference coefficient table 2T' discharge delay preference coefficient table

Claims (4)

[Claims] 1. An apparatus for predicting the required amount of meal for an inpatient, comprising: a basic requirement generation means for generating a required amount of meal based on the type of food and the number of required patients for the type of food; Means for predicting the number of scheduled changers based on the number of scheduled changeable persons during the meal implementation time and the change influence condition, and A required meal amount prediction device, which generates a required meal amount predicted from the predicted number of changers. The number of patients who can stay outside at the time of the prediction is set as the number of patients whose schedule can be changed, and the condition that affects the selection of patients to sleep at the time of lunch is set as a change influence condition, and the estimated number of unscheduled overnight guests is calculated. The required lunch amount prediction device according to claim 1, further comprising a schedule changer number prediction unit that generates the number. The number of patients who can be discharged at the time of the prediction is defined as the number of patients who can be scheduled to be changed, and the condition affecting the selection of patients to be discharged at the time of lunch is changed as a change affecting condition. The required lunch amount prediction device according to claim 1, further comprising a schedule changer number prediction unit that generates the number. 4. A recording medium on which a program for causing a computer to predict a required amount of meal for an inpatient is recorded, wherein the required amount of meal is generated based on the type of food and the number of required patients for the type of food. The required amount generation means and the scheduled change number prediction means for generating the planned change number prediction from the forecast change time and the meal change execution time and the change influence condition and realizing the forecast change number prediction means A computer-readable recording medium storing a program for generating a required amount of meal predicted from the number of patients scheduled to require meal known at the time and the estimated number of persons who have changed the schedule.