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latient iow analysis and the delivery of radiology service 161 -110- Table 2. Data concerning various examinations: probability of having the examination (as function of time in the day). times(mean.range).roomequipped toperform theexaminations(before í I a.m. and afier 1 a.m.) , 1 2 3 4 S 6 7 8 9 10 LI 1: 1) NlAl.' f El.,,Il;il.ll... " SLik,. SIi'1.i" Cl..t .. n Ny..lo Abd. GI BE IvP tojo Fluor., PEt; :II Pi (J) 8-10 A.M. 0 0 32 0 0 0 20 20 20 0 0 0 u f 2 (J) 10-12 A.M. 13 17 42 20 0 0 0 0 0 2 2 2 P 3 1 () 12-2 P.H. 13 17 42 20 4 0 0 0 0 1 1 I r'. (J> ¿-:30 P.M., U1 I 8l 20 0 12 0 0 0 0 0 U 0 "an Exao. Time (HIn.) 12 t16 6 10 60 10 100 45 60 40 20 YU i5 Rane Ex~.. TI~ (
162 CONGESTION POINTS ANU CONTROL.ABLE VARIL'.LES It should be recognized however, that such a complex system has several congestion points. Specifically, some of the more important ones are: Q, In front of the reception desk; Q2 In front of the dressing room; Q, Inpatients waiting for an orderly to be assisted to the department; Q4 In front of the control dcsk: Q, In front of each examination room; Q" Inpatients waiting for an orderly to be returned lo the room. In addition to the queues, there is the problem of technician flow and orderly flow. In many instances. Iechnician and orderly flows are delayed because of the congestions in the system which are beyond their personal control. Before evaluating the behavior of the system as results from changes in the input, it is necessary lo determine what parameters can be changed. The capacity of the facility is greater than the volume of patients and, therefore, it does not seem reasonable to reject patíents. Thcrc is a limil to Ihe numbor of patients in somc caUegories, but, in general, more patients can be seen than at the present level. The scheduling procedure determines the time of arrivals but the number of patients is under limited control. There is some control regarding the patient's arrival time, but in many cases this situation is limited. As noted previously, some of the examinations may require an empty stomach, and therefore must be completed early in the moming. Another problematic area is that patients will arrive two hours before or after the scheduled time, and must be accepted for treatment. In other words, there is some control on the arrival but it is often limited by extraneous circumstances. Certainly, the parameters of the system can be changed; i.e. number of technicians, the number o' orderlies and the increase rate of service at the reception desk. But most important, the decision that is made at the control desk regarding the room assignment appears to be one of the key factors for the efficiency of such systems. In this sense, one way to improve the performance of the system would be through changes in the room assignmenlt decision. This decision is not easy since it must take into consideration the rooms that have the :uipment to perform Ihe examination, as well as the lines in front of each room. At present, this decision is performed by a clerk. An example of the decision tree for the room assignment of a patient requiring a chest examination is presented in Fig. 2. It should also be pointed out that Rooms 4 and 7 are available for chest X-rays before II a.m., and Room 4 does not have the equipment to handle stretcher cases. Decision trees for other examinations are even more complicated when requiring multiple phasc examinations, and more than one room as for cases concerning upper gastrointestinal studies (GI), and barium enemas (BE). Table 2 clearly indicates the rooms that can perform the various examinations before 11 a.m. an " - II a.m. (before 11 a.m. some of the rooms are used tor special studies). MTHOWDOLOGY The department described above is a large and complex facility. The behavior of one part of the system is dependent on the output of another. Therefore, the B. LEY et al. Fig. 2. Chcsl palient lecision trec for nrim assignment. - 111 - arrivais to one service station represents the departures from another service station. Consequently, variables are interrelated and there are numerous system parameters. Since the matter is complex, it will be impossible to analytically optimize the operation of the department, and to develop compact results in closed forms. Therefore, a decision was made to utilize simulation techniques which have been proven useful in similar applications. A simulation program was then written for the patient flow in DRD. More specifically, GPSS (General Purpose Simulation System V) is combined with Fortrar IV, and the program has been implemented on an IBM 0/165. For each experiment, the following sample siz, vas taken. Four vectors were generated: P(i) = (P,, P, .... ":.; = 1, 2, 3, 4. All four vectors have a similar patient mix ¡( -me percentage of the 13 categories). For each one of t. - %k tors P(i), 10 repetitions were simulated with rando,,,._í. '-r the stochastic variables. In certain cases, a larger nu,. ' r of runs were performed in order to test convergence. T' - randomness was in the service time, method of patí..i, transportatinn (walking, wheelchair, stretcher) and oth,. ctochast's i,.ameters. Thus, cach experiment included 4 uu, x 160 aticnll/day x 10 repetitions, which resulted in 6400 patients. To observe changes over vanrious number of technicians, ;..y 4, 6, 8, 10, the sample size was 4 x 6400 = 25,600 patients. VAUDATION One of the important phases of any simulation is to verify its performance as to whether or not it replicates the actual system. In order to satisfy the requirements of thc study, statistical information w:as derivcd from DRD over a two year period, includin? Jata on performance, and corpp.-zd with the results from the simulation model. The results which include a X 2 test for goodness of fit at a level of significance, a = 0.05, c' ,níy indicates a good agreement between -tu; jRD and the simulation model. The res" ,.resented in Table 3. Tests wer .,ipleted in the simulation model to measure t' ..rtormances; under a typical daytime load of 1i · .ents, under a heavy load of patients (220 pat:. ..s) and under a light load of patients (135 patients).
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- ii - liancock, W., D. Magerlein y
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FOREWORD These last years, PAHO wit
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Workshop .- Education in Operationa
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-3. The format of the workshop was
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-5- 2. Programs for bringing togeth
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II. R.commendations -7- A series of
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-9- there are technically trained i
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-11- and selected individuals who h
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-13- 6. In order to improve the qua
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-15- cnve!opment will typically be
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-17- 16. All participants sbould pr
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A. Short Courses -19- Short courses
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-21- 4. Presentation of a case to b
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Topic Introduction to Systems Analy
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-25- 12. Elementary Statistics ior
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-27- 8. Applications of data proces
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-29- problems. The graduate should
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-31- Lblo-w. Also given is that typ
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- 33 -' -Case Mix Definition by Dia
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- 35 - Acknowledgment The authors w
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ML ICAL CAE1 Februurl 1980, Vol. XV
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X;' VIII, No. 2. Suippllce,t - 39 -
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- 41 - dMLDICAL C.ARE '.Fcbrtanj 19
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\'nKV 111,I No. 2, IIIJ·,ri - 43 -
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?Vol. XVIII. No. 2. Sttilluleareilt
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.V'oL XVIII. No. 2. Stpphlidary dia
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- 57 - MEDICAL CARE Febiuary 1980,
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Page 109 and 110: GREENLAND ET AL - 103 - the case (p
Page 111 and 112: CREENLAND iT AL - 105 - TABLE 2. Fi
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Page 115: 160 I indicates the flow of patient
Page 119 and 120: 164 c 8o Table 4. The cffect of cha
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Page 123 and 124: MEREDITH - 117 - ciety. However. ch
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Page 139 and 140: V Xl. IX, No. 3 - 133 - PERFORMANCE
Page 141 and 142: - 135 - The fourth requirement is l
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Page 151 and 152: - 145 - Thc cu rrent appointmren t-
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.Recomme;tded systenz - 161 - .;any
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- 163 - system, there is nr'. :vste
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- 165 - - A major problem with such
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- 167 - T1c Iteole of Olieraionls R
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- 169 - Larry J. Shumc- Harvey Wolf
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- 171 - '- ionoal Heolth Ploanning
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- 173 - Regional Health Planning ar
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- 175 - Regio.,oi Health Planning w
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- 177 - Regionao rfeclth Planning l
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A^CJonoal neonah ronning 21. 1'. F.
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- .,1 - Programming, Budgeting, and
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- 183 - the health care industí ,
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- 185 - was apparently no scarcity.
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ut, u a. íL ._c E 0 t42 a .. 0m u
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plex stochastic distribution of arr
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- 191 cumulated according to any or
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- 193 - Step 2 represents the heart
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ÍA en .aa
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- 197 - analysis, forecasting, and
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Budgeting Attitudes - 199 - The pla
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- 201 Health Services Research most
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a. - 203 - Health Services Research
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- 205 - DESIGN OF ALTERNATIVE PROVI
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- 207 - 1. A change in the immigrat
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- 209 - accessibility of health car
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- 211 - dimensional graph to illust
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- 213 - Primary Care Health Service
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- 215 - different health and educat
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- 217 - The tasks that were rated "
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IC1 o -a - i - w im W..i C -J M. -
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0 z 0 u _ I- 0 Ut. I,, uj CD Q U- ,
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TABLE 3 TASK ANALYSIS OF MALNUTRITI
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11 - 225 - TASK ANALYSIS OF MALNUTR
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- 227 - 12. Reisman, A., F.Staub, B
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- 229 - METHODS Parameters Affectin
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- 231 - ~HNCOCK Fig. 1. Daily censu
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- 233 - HANCOCK Table 1. Parameters
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- 235 - Fig. 3. Maximum average occ
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- 237 - HANCOCK ET AL Fig. 4. Maxim
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- 239 - AECOCL. Table 2. Eflect of
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- 241 - HANCOCK Discussion ET AL Th
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#12 Estinating the Need for Additio
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Pri c= arvicc a ia Indina. - 245 -
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. 1' - - IIINDLE Algorithmn for Det
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- 249 - HINDLE that all tlie primar
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- 251 - HINDLE 6. Computc Mhe Avail
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- 253 - HINDLA Tablek Meamm of Zfic
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HINDLE Tr AL HIALTH falvCU - 255 -
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- 257 - HIiDLZ 26. Ado, An . ad L.
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- 259 - REISMIAN, DEAN, ESOOBUE, AG
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- 261 - REISMAN, DEAN, ESOGBUE, AGG
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- 263 - 1350 REISMiAN, DEAN, ESOOBU
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- 265 - REISMAN, DEAN, ESOOBUE, AGO
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- 267 - 1354 REISMAN, IEAN, ESOGCUE
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- 269 - William J. Abernothy and Jo
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- 271 - William J. Abernathy and Jo
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- 273 - William 1. Abernothy ond Jo
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- 275 - William J. Abrnarthy and Jo
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- 277 - William J. Abernathy and Jo
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- 279 - William J. Aberncthy and Jo
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- 281 - 642 William J. Abernathy an
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Decmand in Mintu és/Day Numnlber o
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Disposal System 326 IIstlic I,',i,
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i Trays 334 Colorful pattenis anid
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tiie, gency L:ghting 347 ,i',., .:.
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- 296 - REVEI.LE we believe to be t
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- 293 R£VELLE is apparent in light
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- 295 - REVEt.EI. ET AL. poSJcd pro
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- 297 - RM.VELLE EL AL. of faciliti
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REVELLE ET AL. HALTOI SERVICUS RESE
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REVELLE ET Al.. - 301 - Fig. 4. Ano
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- 303 - REVet. ¡T AL Act of 1973.
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- 305 - REVELLE possible within ilh
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- 307 - RLVELI.E 29- Khumawala. a.
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- 309 - assuring thal the maxinmum
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-311 - the implemented strategy he
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- 313 - analytical approximalions w
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- 315 - rcquired Ior shipping and o
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- 317 - Controlling ¡he system The
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- 319 - The light pen techniques ar
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- 321 - shipnients have to be on ro
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- 323 THE CREATER NEIW' YORK BLOOD
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_,~ .- - 3. 2- - TABLE 1. PATIENT C
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TABLE 3. MEDICAL INTENSIVE CARE UNI
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Nursing Stalf Level (i of nurss/day
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Table 1. Factors ¡i denad for medi
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e forecast. This section descijbcs
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ei, paid to these standards in cond
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O Emphasis u.on the explicit enumer
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*1 1 21 1 «i 1 1 1 1 - 331, -- For
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Therc are ihreec basic prcessecs fi
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- 343 - Forecasting Hcalth Care Ser
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#20 Jaime 1B. Henrs Rtxlnc. I.. Ro:
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- 347 - I v,'N ti.K 1tHm.pi.l !, t1
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Ra.fo.renem.. aid %.hs '. - 31!9 -
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; / larman u empirical research has
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I Klarman - >3 - benefits that for
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I Klaman Ctitena for Inchmion - 355
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IClarman - 37 - The prevailing tend
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1 Klarman Intargibl Benefits - .5 -
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Kllarman - 361 - thi prospects of r
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- 363 - Thua, it did not consider w
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I Klannan - 365 - again if the new
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Klarman - 367 - At the other pole,
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- 369 - seem worth exploring. Howev
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iKlarman - 371 - in the use of serv
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Kmnnan - 373 - REFERENCES 1. Havema
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- 375 - 59. Valr y, W. S. One eonoi
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- 377 - i aSll a _ . b, CuaMu L. L,
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WAHRNER ANI) I|UTTr)N The puroses I
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WARNER AND HUTfON - 381 - observers
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.AHNF:R AND IIl'TIoN - 383 - rare I
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WARNER AND HUTTrU., TABLE 1. Trenda
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W'AHNER AND IHUTTON - 387 - treatme
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AAHNER AND HU'TON - 389 - Several d
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WARNER AND HUTTON pal orientations
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w'ARNER AND UL'TTOit'u ' . N Engl I
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I,,qr *ir 'ilu.,i XV. Seplteri¡br
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In.lumin- i',)i.eIu XV. Seph.irhb'r
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In#unrvlVolutn XV. September 1978 h
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Problemas identificados - 401 - Dur
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Programas educativos 3 - 403 - 1. S
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- 405 - maestrfa, donde se supone q
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- 407 - presentación formal de inf
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- 409 - * Curso para ingenieros ind
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- 411 - ¢ 16. Hancock, W., D. Mage
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- 413 - 2. Barrenechea, J. J. La se
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- 415 - Curwso (8 a 16 horas) para
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- 417 - 4. Necesidades de los admin
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MOISES ARTEAGA DR. DAVID GOMEZ COVA
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