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operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
operation research at medical emergency at AIIMS
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operation research at medical emergency at AIIMS

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a study of patients waiting time at aiims, new delhi done by dr harsh vardhan pandey, mba hhm symbiosis institute of health sciences

a study of patients waiting time at aiims, new delhi done by dr harsh vardhan pandey, mba hhm symbiosis institute of health sciences

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  • 1. Introduction  Accident and Emergency services are rightly considered as the “shop window” of a hospital and because of the real or perceived “emergency”; everyone demands prompt action. Hence it is of utmost importance that doctors posted in the casualty should be calm and composed, and give their best effort as quickly as possible. The objective of this department is to provide medical care to the patient as quickly as possible; thus increasing the patient’s chances of survival.  However with the escalation of the ED Waiting line strength , a need for study was perceived to get a scientific approach towards the causes of such waiting line build ups and also to assess the most suitable alternative and to formulate the most efficient one to tackle the emergency queue .
  • 2. Aim : To evaluate the waiting time of an emergency patient in a seeking emergency healthcare at a tertiary care hospital. Objectives :  To study the process flow of emergency department.  To study the various time consuming parametres which are generated during emergency healthcare services delivery.  To study the arrival rate , service rate and queue capacity and number of servers.
  • 3. Methodology :  Study Area- New Emergency Department , AIIMS  Study Period – 15th May -30th June 2012  Study Type- Descriptive (Observational), Cross Sectional  Study Population- All patients who come to seek eergency healthcare services.  Sample Size – 500 ( observation ) , 100 ( questionnaire ) and 13,200 for studying the arrival pattern.  Sampling Method- Simple Random Selection  Study Tool- 1. observation form 2. CHECKLIST  Inclusion Criteria All the Patients who seek emergency healthcare services at New Emergency.  Exclusion criteria EHS (Employee Health Scheme) Patients of AIIMS were excluded from the study and staff peronnels or their kins , and also who were referred to opd after triage without start of nursing care were excluded from the study.
  • 4.  Sample size : The sample size was calculated by the formula E = zσ/√n Where , z = area under the graph within the specified confidence interval σ is the standard deviation obtained under the pilot study. n = number of samlple to be collected E = expected margin of error. Thus , for study , it was assumed that within 95 % of confidence interval with a expected margin of error of 5 % and σ = 0.57 ( as got by the pilot study of 25 patients ) , Therefore , the sample size came out to be = 499.254 patients , rounding off the value gives a total of 500 patients . For the questionnaire , with the same method , it was calculated that the sample size came out to be 99.57 patients . With σ coming out to be 1.02 . The arrival rate was calculated by studying the arrival pattern of patients during the study period ( = 13,200 patients )
  • 5. Casuality department The hospital consists of 5 emergency departments, dedicate to various types, viz. eye, pediatrics, surgical , medicine and trauma emergency department. However a broad look on all the departments have reflected that the medicine emergency was regularly suffering from the build up queues in front of the ED Gate , so the New Emergency Department have been set up to undertake the study . The department is located on ground floor in front of the surgical emergency ( main emergency ) in the AB Wing . The department serves for the medical emergency needs of emergency patients in their acute health.
  • 6. New Emergency Department The medical emergency locared in the AB wing of Main AIIMS is serving a total of approx 7000 to 10,000 patient per month . The patients coming here are of variety types and from various demographies . The emergency serves these patient round the clock with the help of a 19 doctors including 3 CMO’s working in 3 shifts and 45 nurses divided in 4 grades and working in 3 shifts round the clock . Although the above personnels seem to be efficient in maintaining the emergency but many a times it has been seen that a long waiting line of patients seeking emergency care is build up in front of the medical emergency door .
  • 7. Lay out
  • 8. Working Procedure The working procedure of the emergency department is as follows :
  • 9. Emergency
  • 10. Casuality
  • 11. CUBICLECUBICLE DESK
  • 12. CUBICLECUBICLE DESK
  • 13. CUBICLE CUBICLE DESK
  • 14. Waiting Line Model One of the most important managerial applications of random processes is the prediction of congestion is a system, as measured by delays caused by waiting in line for a service. Patients arriving at a bank, a checkout counter in a clothing store, a theater ticket office, a fast food drive- through, a supermarket checkout, or at emergency services of a hospital etc. may perceive that they are wasting their time when they have to wait in line for service. Repeated and excessive delays may ultimately influence the patients’ perception about the brand and experience. The medical emergency of AIIMS hospital is overwhelmed with the rush of patients throughout the day , to overcome the same , the AIIMS Adminstration has deployed the maximum staff as compared to the other emergency services . a total of 19 Junior Doctors and 45 nurses divided in 3 shifts work tirelessly to tackle the patient line but still many a times waiting lines can be seen outside the medical emergency gate . The study was undertaken to find the true picture and to find the possible ways to tackle the problem .
  • 15.  The Number of Waiting Lines AIIMS New Emergency has a three waiting line of patients viz . the general waiting line , the staff waiting line and the emergency waiting line .  Number of phase AIIMS New Emergency has a 2 phase system , i.e , in the first phase the doctors are the servers whereas in the next phase the nurses are the servers .  The Number of Servers AIIMS New Emergency is a multiserver system as a total of 4 junior resident doctors ( servers ) are deployed to tackle the waiting line of the patients . Also in the second phase , 4 nurses are assigned for the job to start the new emergency arrivals. Thus for the AIIMS New Emergency Department is a multiphase , multi server and single line system with “emergency first” criteria.
  • 16. Arrival Pattern  Waiting line models that assess the performance of service systems usually assume that patients arrive according to a Poisson probability distribution, and service times are described by an exponential distribution. The Poisson distribution specifies the probability that a certain number of patients will arrive in a given time period (such as per hour). The exponential distribution describes the service times as the probability that a particular service time will be less than or equal to a given amount of time.
  • 17. The arrival pattern at main aiims new emergency department strictly follows the negative exponential distribution or poisson distribution as the the spacing between the arrivals , inter arrival time does not occur uniformly . for example , having a look over the data collected from the registration counter of the casuality department , between 9 am to 10 am on Monday 4 june , 2012 , we see that ,
  • 18. Queue Discipline It refers to the order in which the customers are processed . the AIIMS New Emergency delivers the healthcare services to the patients on first come first serve basis for the general patients and emergency first for the overall patient population . Thus in a nut shell , the queing model classification for the services delivered in the emergency department are as follows :  A: Specification of arrival process : M : negative exponential or poisson distribution  B. specification of service process : M : negative exponential or poisson distribution  C : Specification of number of servers( here doctors serving in the New Emergency Department ) = 4  D. Specification of the queue : = infinity as there is an unlimited queuing capacity.  E. Specification of patients : = except eye , paediatrics , surgical and trauma emergency , all other emergency can seek emergency healthcare services at New Emergency. Thus the New Emergency will be described by M/M/4 , and as the last two specification are unlimited , hence will be omitted .
  • 19. 5 4 6 14 13 20 18 20 21 18 17 19 14 18 14 14 11 12 10 10 11 5 5 6 0 5 10 15 20 25 5:00 AM 6:00 AM 7:00 AM 8:00 AM 9:00 AM 10:00 AM 11:00 AM 12:00 PM 1:00 PM 2:00 PM 3:00 PM 4:00 PM 5:00 PM 6:00 PM 7:00 PM 8:00 PM 9:00 PM 10:00 PM 11:00 PM 12:00 AM 1:00 AM 2:00 AM 3:00 AM 4:00 AM Tuesday
  • 20. 2 2 1 3 11 19 16 20 19 19 18 16 13 14 13 11 11 13 11 9 10 7 3 5 0 5 10 15 20 25 5:00 AM 6:00 AM 7:00 AM 8:00 AM 9:00 AM 10:00 AM 11:00 AM 12:00 PM 1:00 PM 2:00 PM 3:00 PM 4:00 PM 5:00 PM 6:00 PM 7:00 PM 8:00 PM 9:00 PM 10:00 PM 11:00 PM 12:00 AM 1:00 AM 2:00 AM 3:00 AM 4:00 AM Wednesday
  • 21. 5 5 7 11 16 16 14 16 19 18 14 12 11 10 11 14 8 11 9 9 10 6 5 6 0 5 10 15 20 25 5:00 AM 6:00 AM 7:00 AM 8:00 AM 9:00 AM 10:00 AM 11:00 AM 12:00 PM 1:00 PM 2:00 PM 3:00 PM 4:00 PM 5:00 PM 6:00 PM 7:00 PM 8:00 PM 9:00 PM 10:00 PM 11:00 PM 12:00 AM 1:00 AM 2:00 AM 3:00 AM 4:00 AM Thursday
  • 22. 5 3 4 8 14 21 18 17 20 18 16 18 18 18 16 15 11 16 14 12 8 9 8 9 0 5 10 15 20 25 5:00 AM 6:00 AM 7:00 AM 8:00 AM 9:00 AM 10:00 AM 11:00 AM 12:00 PM 1:00 PM 2:00 PM 3:00 PM 4:00 PM 5:00 PM 6:00 PM 7:00 PM 8:00 PM 9:00 PM 10:00 PM 11:00 PM 12:00 AM 1:00 AM 2:00 AM 3:00 AM 4:00 AM Friday
  • 23. 7 4 5 13 10 19 17 15 16 16 10 17 19 15 20 14 9 14 12 5 6 6 5 5 0 5 10 15 20 25 5:00 AM 6:00 AM 7:00 AM 8:00 AM 9:00 AM 10:00 AM 11:00 AM 12:00 PM 1:00 PM 2:00 PM 3:00 PM 4:00 PM 5:00 PM 6:00 PM 7:00 PM 8:00 PM 9:00 PM 10:00 PM 11:00 PM 12:00 AM 1:00 AM 2:00 AM 3:00 AM 4:00 AM Saturday
  • 24. 7 9 10 13 19 13 13 16 14 15 8 12 18 16 19 18 7 12 14 6 5 3 4 5 0 5 10 15 20 25 5:00 AM 6:00 AM 7:00 AM 8:00 AM 9:00 AM 10:00 AM 11:00 AM 12:00 PM 1:00 PM 2:00 PM 3:00 PM 4:00 PM 5:00 PM 6:00 PM 7:00 PM 8:00 PM 9:00 PM 10:00 PM 11:00 PM 12:00 AM 1:00 AM 2:00 AM 3:00 AM 4:00 AM Sunday
  • 25. Percentage utilization of the servers : 80% 69% 53% 61% 62% 60% 64% 88% 79% 73% 62% 81% 73% 66% 68% 53% 51% 47% 60% 52% 53% 28% 23% 17% 25% 31% 25% 25% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Monday Tuesday wednesday Thursday Friday Saturday Sunday 7 am to 1 pm 1 pm to 7 pm 7 pm to 1 am 1 am to 7 am
  • 26. Inference We can infer by the graph that the on Monday, the physicians were drained highest of their efficiency especially in the morning hours, however on all days, the time at which opd closes also views the highest efficiency utilization of the servers (between 1 pm to 2 pm). Also, the weekend days show a continuous stable utilization of the servers as the opds are closed on these days. According to facts, the OPD reroute 10 % of its patients daily towards the emergency admission, which can be clearly seen in the above graph as the non opd hours have got slightly lesser utilization coefficient than the opd working days.
  • 27. 8 4 1 2 2 2 3 18 7 5 2 8 5 3 1 1 1 0 1 1 1 0 0 0 0 0 0 0 0 2 4 6 8 10 12 14 16 18 20 Monday Tuesday wednesday Thursday Friday Saturday Sunday 7 am to 1 pm 1 pm to 7 pm 7 pm to 1 am 1 am to 7 am Waiting Line
  • 28. Inference Thus it can be clearly visualized about the correct picture of waiting line conditions in emergency department, also, it can be inferred by the graph that the highest waiting time is that of Monday that too just after the opd closing time that is between 1 pm to 2 pm, the other higher side can be visualized by seeing the waiting time conditions of all the weekdays between 9 am to 1 pm (as the peak hours). The weekend days however show a very minimal time spent in the queue, the most probable cause for this seems to be the closing day for opds, and also the most equalized and continuous arrival of patients without any ups and downs in the arrival pattern of the patients.
  • 29. Probability of getting an empty system : 3% 5% 11% 8% 8% 8% 7% 1% 3% 4% 8% 3% 4% 6%6% 11% 13% 15% 8% 12% 12% 32% 40% 50% 37% 29% 37% 36% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Monday Tuesday wednesday Thursday Friday Saturday Sunday 7 am to 1 pm 1 pm to 7 pm 7 pm to 1 am 1 am to 7 am
  • 30. Probability of having to wait for seeking the emergency healthcare 60% 41% 21% 29% 31% 29% 34% 75% 57% 47% 31% 61% 48% 37% 40% 21% 18% 14% 28% 19% 20% 3% 1% 1% 2% 4% 2% 2% 0% 10% 20% 30% 40% 50% 60% 70% 80% Monday Tuesday wednesday Thursday Friday Saturday Sunday 7 am to 1 pm 1 pm to 7 pm 7 pm to 1 am 1 am to 7 am
  • 31. Traffic Intensity 3.2 2.8 2.1 2.4 2.5 2.4 2.5 3.5 3.1 2.9 2.5 3.2 2.9 2.6 2.7 2.1 2.0 1.9 2.4 2.1 2.1 1.1 0.9 0.7 1.0 1.2 1.0 1.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Monday Tuesday wednesday Thursday Friday Saturday Sunday 7 am to 1 pm 1 pm to 7 pm 7 pm to 1 am 1 am to 7 am
  • 32. FEEDBACK FROM THE PATIENTS
  • 33. Age Group 23% 32% 26% 19% 0% 20% 40% 60% 80% 100% 0 - 20 20 - 40 40 - 60 > 60 Age Group Break Up Age Group Break Up
  • 34. Monthly Income Of Family 0% 20% 40% 60% 80% 100% < 10,000 10,000 - 25000 25,000 - 50,000 > 50,000 52% 28% 18% 2% monthly income of family
  • 35. Visit(s) 0% 20% 40% 60% 80% 100% new revisit 78% 22% visit
  • 36. Approach 22% 68% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% direct referred Approach
  • 37. Referre consultant / rmp 32% public body 38% private body 30% referee
  • 38. Getting the order of preference brand name, 35% cost of hospital, 23% location of hospital, 12% experienced staff, 19% higher facilities, 11%
  • 39. Unique Selling Point of the hospital free service, 21% reknowned doctors, 28% ease of documentaion, 9% quality, 18% higher facilities, 24%
  • 40. Factor Analysis The questionnaire was also composed of a 5 point likert scale asking the patients satisfaction towards the promptness of the delivery of in following aspects :  Ease of locating the emergency department  Availability of patient assistance  Ease of documentation  Availability of doctors  Prompt investigation reports  Availability of nurses to start treatment The likert scale used to assess the patient satisfaction was as follows :  Highly satisfied  Partially Satisfied  Neutral  Partially dissatisfied  Highly dissatisfied
  • 41. Sampling adequacy KMO and Bartlett's Test was showing the following results : Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .652 Bartlett's Test of Sphericity Approx. Chi-Square 81.685 df 15 Sig. .000
  • 42. Total Variance Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative % 1 2.164 36.066 36.066 2.164 36.066 36.066 1.757 29.279 29.279 2 1.208 20.132 56.197 1.208 20.132 56.197 1.407 23.457 52.736 3 .853 14.225 70.422 .853 14.225 70.422 1.061 17.686 70.422 4 .764 12.734 83.156 5 .600 9.997 93.152 6 .411 6.848 100.000
  • 43. Scree Plot
  • 44. Component Plot In Rotated Space
  • 45. Results ( Factor Analysis ) The results from the component analysis in rotated space has shown that the most time consuming factors, which the patient perceived, were the nurse availability and ED Physician availability for the treatment , following which the ease of documentation factor was being reported .The most satisfying factor among the patients , the location , medicine availability and the patient assistance at gate were, in the perception of the patients, on within acceptable time and they do not think that these processes have delayed the emergency healthcare delivery at AIIMS .
  • 46. Results :  Process time break up ( min . ) 21.43 4.47 1.01 7.48 2.13 8.68 10.95 1.65 28.40
  • 47. Break Up ( percentage ) dr delay 25% triage 5% going for registration 1%registration timing 9% post registered travel time 2% clinical examination initiation timing 10% clinical examination 13% sample withdrawl 2% nursing treatment delay 33% Percentage Break Up Of Delay
  • 48. CLUSTER ANALYSIS
  • 49. Total delay  Thus a total average delay was found to be 85.69 minutes from entry till the start of treatment by a nurse .
  • 50. Waiting time 0 10 20 30 40 50 60 70 Monday Tuesday wednesday Thursday Friday Saturday Sunday 7 am to 1 pm 1 pm to 7 pm 7 pm to 1 am 1 am to 7 am
  • 51. Recommendations On the basis of above results , the recommendations to reduce the waiting line strength in front of the emergency department were as follows ;  Assignment of a unique number of patients to the doctors as their target number on daily basis , falling which they will be liable to get terminated .  Making the 2 phase model for the emergency healthcare delivery that is In first phase doctor starts treatment and writes prescription for the emergency patient And in the second phase , the patient goes to the nursing station for the start of nursing treatment
  • 52. Recommendations (contd.)  Making this model as a single phase model by assigning 1 nurse to each doctor , the feasibility of working of this model is 100 % as there are a total of 19 nurses in the peak time and already 4 nurses are assigned specially for the emergency patient but the lack of communication between the doctor , patient and nurse is the major drawback in retardation of emergency healthcare delivery , also , the nurse only takes a total of 5 minutes (on an average of 500 patients) to start the treatment and as observed it is half the time of a doctor’s service time , so assigning one nurse to one doctor and making the 2 phase model as single phase will eventually rid off the drawback of communication failure between the trio and eventually reduce the delay in the service delivery.
  • 53. Recommendation(s)  The morning shift from 8:30 am till 1:30 am faces a lot of problem while the closing timings of OPD i.e. 1:00 PM , this may be tackled by increasing the morning shift by 30 minutes , i.e till 2:00 PM and starting the evening hours earlier by 30 minutes that is at 1:00 pm , this will eventually result in an overlap of two shifts for 1 hour , and that too in the peak timing of traffic intensity , this will result into availability of 8 servers ( double the number of normal servers ) during the highest traffic intensity hour i.e 1 pm to 2 pm.  Availability of handy and portable saturation meters , which may reduce the ABG analysis timing of certain patients , for which the ABG is done only to check their saturation pressures ( for eg . the chest patients ) , this will reduce a significant time of physician in analyzing the spo2 of the patient . also , the emergency department is starving for simple instruments such as a blood pressure moniter , the whole emergency department with average arrival of 300 patients each day has only a single blood pressure monitor to serve the diagnose the patients for their bood pressure . suggestion is given to immediate introduction of 3 more blood pressure monitors i.e 1 each for Emergency Physicians who work on the counter.
  • 54. Conclusion The queue assessment of the New Emergency Department has came up with many unforeseen factors that were identified as the most retarding factors and now as these factors have been drained out , these factors must be checked to enhance the medical emergency department services in delivering a faster delivery to the patients thus minimizing the waiting line build up in front of the emergency department.

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