# Lecture 4: PATA Case - Process Analysis and Queuing Theory **Date:** July 25 **Duration:** 1.5 hours **Instructor:** Prof. Vivek Farias **Assignment:** GRADED CASE WRITE-UP DUE AT 8:30 AM (TEAM) ---- [[2025-07-25|25-07-25-08]] [[paradox of entrepreneurship]] - product strategy in service industry (process flow diagram and process utlitization, tool: buildup diagram for predictable variability, queuing theory (unpredictable variablity ) - smoothing out process - better prediction - teaching [[style]] - case discussion, - patient 🟧checks in, wait, 🟧🟧vitals + ekg in lab, 🟧🟧🟧rn (review, writesup),🟧🟧🟧🟧 md visit (reviews, chat, write chart), 🟠 blodd work in lab, 🟠🟧check out - staggering over lunch (2.8pt/hr; rho = 1) - evaluation on task force recommendation - which unpredictable variablity is contributing the most ? - service time? if CV = 1, can contribute ⭐️bottleneck at RY -> build up -> translate build up to delay, fluid proces -> lose integrality (goal is capture first order effect) peak delay will be higher, avg delay will be lower not give process diagram ⭐️run this simulation, ppl are waiting for the room. 현상적으로 기다리는 건 병목과 다르다. what are the rooms in the first place? [[2025-07-25|25-07-25-09]] room became glorified waiting room?? rooms are not meant to wait but why [[vivek_ferias]] likes teaching annual leaked revneue per employed physician --- ## Learning Objectives - Apply capacity analysis tools to service operations - Use queuing theory to diagnose bottlenecks and wait times - Analyze sources of variability in healthcare processes - Develop operational improvement recommendations ## Case Studies - **Primary Case:** [[Assignment_PATA_Massachusetts_General_Hospital_s_Pre-Admission_Testing_Area__PATA_.pdf]] - **Supporting Case:** [[Lec4_Intermountain_Health_Care.pdf]] (SKIM) ## Key Concepts ### Process Analysis Tools 1. **Build-up Diagrams:** Visual representation of capacity vs. demand over time 2. **Queuing Theory:** Mathematical analysis of waiting lines 3. **Bottleneck Identification:** Finding constraining resources 4. **Variability Analysis:** Understanding sources and impacts of uncertainty ### Healthcare Operations Characteristics - **Service nature:** Cannot inventory "procedures" - **High variability:** Patient conditions, procedure times, arrival patterns - **Multiple resources:** Physicians, nurses, rooms, equipment - **Quality implications:** Patient safety and satisfaction - **Regulatory environment:** Compliance and standards ### Queuing Theory Fundamentals - **Little's Law:** L = λW (Items in system = Arrival rate × Wait time) - **Utilization:** ρ = λ/μ (Arrival rate / Service rate) - **Queue length:** Expected number waiting - **Wait time:** Expected delay before service begins ## Case Analysis Framework ### PATA Background - **Purpose:** Pre-admission testing for surgical patients - **Patient flow:** Registration → Nursing → Physician → Departure - **Resources:** Nurses, physicians, examination rooms - **Performance issues:** Long patient wait times, system inefficiency ### Process Flow Analysis The provided process flow diagram shows: 1. **Patient arrivals** at scheduled appointment times 2. **Registration process** 3. **Nursing assessment** (vital signs, medical history) 4. **Physician evaluation** (physical exam, clearance) 5. **Administrative completion** and departure ### Given Data Analysis - **Input rates:** Patient arrival patterns - **Processing rates:** Time requirements for each step - **Resource availability:** Number of nurses, physicians, rooms - **Performance metrics:** Current wait times and utilization ## Graded Assignment Questions ### Question 1: Bottleneck Analysis **Prompt:** Use capacity analysis tools (build-up diagrams or/and queuing) to decide if and where there is a bottleneck in the clinic. If a bottleneck does indeed exist, how long do patients wait as a result of the bottleneck? **Analysis Framework:** - Calculate capacity at each process step - Compare capacity to demand (arrival rate) - Identify the constraining resource - Apply queuing formulas to estimate wait times - Assume all appointment slots filled and on-time arrivals ### Question 2: Task Force Diagnosis Evaluation **Prompt:** Evaluate the three Task Force diagnoses: not enough time between appointments, not enough rooms, not enough physicians. Are these diagnoses valid? If so, are they primary contributors to long patient wait times? **Analysis Approach:** - Test each hypothesis against calculated bottlenecks - Determine if diagnosis addresses actual constraint - Assess magnitude of impact on wait times - Consider interaction effects between constraints ### Question 3: Variability Analysis **Prompt:** What factors contribute to variability in PATA process flow and what control, if any, does the clinic have to eliminate it? **Sources of Variability:** - **Arrival variability:** Late patients, no-shows, early arrivals - **Service time variability:** Patient complexity, physician efficiency - **Resource variability:** Staff availability, room availability - **External factors:** Emergency interruptions, equipment issues **Control Strategies:** - **Demand-side:** Appointment scheduling, patient preparation - **Supply-side:** Staffing policies, process standardization - **Buffer management:** Capacity cushions, flexible resources ### Question 4: Improvement Recommendations **Prompt:** What changes would you recommend to improve PATA? **Recommendation Categories:** 1. **Capacity changes:** Staffing levels, resource allocation 2. **Process redesign:** Flow improvements, task reallocation 3. **Scheduling optimization:** Appointment timing, patient mix 4. **Variability reduction:** Standardization, buffer management 5. **Information systems:** Real-time tracking, communication ## Quantitative Analysis Tools ### Build-up Diagram Construction 1. Plot demand over time (arrival pattern) 2. Plot available capacity over time 3. Identify periods where demand exceeds capacity 4. Calculate cumulative gap (queue buildup) ### Queuing Calculations - **M/M/1 Queue:** Single server, exponential arrivals and service - **M/M/c Queue:** Multiple servers - **Expected wait time:** W = ρ/(μ(1-ρ)) for M/M/1 - **Expected queue length:** L = λW ### Performance Metrics - **Average wait time:** Time from arrival to service start - **Cycle time:** Total time in system - **Utilization rates:** Resource busy time / Available time - **Throughput:** Patients served per time period ## Healthcare Service Operations Context ### Unique Challenges - **Variability amplification:** Uncertainty compounds through process steps - **Service quality:** Patient experience and medical outcomes - **Cost pressures:** Efficiency vs. access trade-offs - **Regulatory compliance:** Safety and quality standards ### Improvement Strategies - **Lean principles:** Waste elimination, flow optimization - **Six Sigma:** Variation reduction, quality improvement - **Capacity pooling:** Flexible resources, cross-training - **Technology:** EMR integration, real-time tracking ## 🔺 The Six Questions Framework ``` 1. Capabilities? 2. Customer? 🟢 🟣 Hospital resources Pre-surgery patients Multiple specialists Anxious, time-pressed \ / \ / \ / 5. Coordinate? ←→ 6. Compel? / \ / \ / \ 🟠 🔴 Patient safety Medical clearance Efficient throughput One-stop service ``` ### PATA Analysis 1. **🟢 Capabilities:** Nurses, physicians, exam rooms, testing equipment 2. **🟣 Customer:** Patients needing pre-surgical clearance, often elderly/anxious 3. **🟠 Goals:** Ensure surgical safety, minimize wait times, maximize throughput 4. **🔴 Offering:** Comprehensive pre-admission testing in single visit 5. **Coordinate:** Resource scheduling must match variable patient flow 6. **Compel:** Convenience of one-stop testing vs. multiple appointments ### Bottleneck Insights - **Problem:** Long waits despite adequate resources - **Root cause:** RN assessment creating queues - **Variability impact:** Service time CV contributes significantly - **Solution space:** Smooth arrivals, standardize processes, flexible staffing - Classic example of how utilization × variability = delays ## Key Takeaways - **Strategic Message:** Process analysis, particularly managing variability in arrivals and service times, is critical for identifying and alleviating bottlenecks in service operations to reduce customer wait times. - **Queuing Insights:** Small increases in utilization can cause disproportionate increases in wait times. - **Healthcare Operations:** Service operations require balancing efficiency, quality, and access in highly variable environments. ## Assignment Submission Guidelines - **Team work:** Study groups of max 5 students - **Due:** 8:30 AM on July 25 - **Length:** Less than 4 pages (excluding appendices) - **Format:** 12-point font minimum - **Calculations:** Must include clear explanations of all formulas and methods ## Preparation for Next Class - Review inventory management concepts - Begin thinking about demand uncertainty and safety stock - Read inventory control fundamentals ## Recitation Support - **Focus:** Capacity analysis techniques and PATA case preparation - **Quantitative methods:** Bottleneck identification and queuing calculations - **Office hours:** Available for team questions ## Teaching Notes - Emphasize practical application of queuing theory - Connect theoretical concepts to healthcare realities - Guide students through bottleneck identification process - Stress importance of clear quantitative reasoning in recommendations