Appendix A: Introduction to Queueing Theory

Similar documents
1.010 Uncertainty in Engineering Fall 2008

Lecture 5. 1 Online Learning. 1.1 Learning Setup (Perspective of Universe) CSCI699: Topics in Learning & Game Theory

Scheduling arrivals to queues: a model with no-shows

Section 3.1: Discrete Event Simulation

Modelling Anti-Terrorist Surveillance Systems from a Queueing Perspective

MAS187/AEF258. University of Newcastle upon Tyne

Handout 8: Introduction to Stochastic Dynamic Programming. 2 Examples of Stochastic Dynamic Programming Problems

EE266 Homework 5 Solutions

Lesson Plan for Simulation with Spreadsheets (8/31/11 & 9/7/11)

Final exam solutions

X(t) = B(t), t 0,

Handout 4: Deterministic Systems and the Shortest Path Problem

ECON 214 Elements of Statistics for Economists 2016/2017

Dynamic Admission and Service Rate Control of a Queue

Case Study: Heavy-Tailed Distribution and Reinsurance Rate-making

Chapter 5. Sampling Distributions

6.5: THE NORMAL APPROXIMATION TO THE BINOMIAL AND

ECE 340 Probabilistic Methods in Engineering M/W 3-4:15. Lecture 10: Continuous RV Families. Prof. Vince Calhoun

Chapter 9: Sampling Distributions

Do all of Part One (1 pt. each), one from Part Two (15 pts.), and four from Part Three (15 pts. each) <><><><><> PART ONE <><><><><>

Quantile Regression. By Luyang Fu, Ph. D., FCAS, State Auto Insurance Company Cheng-sheng Peter Wu, FCAS, ASA, MAAA, Deloitte Consulting

BEHAVIOUR OF PASSAGE TIME FOR A QUEUEING NETWORK MODEL WITH FEEDBACK: A SIMULATION STUDY

1 Consumption and saving under uncertainty

1 Asset Pricing: Bonds vs Stocks

To acquaint yourself with the practical applications of simulation methods.

First Order Delays. Nathaniel Osgood CMPT

2.1 Mathematical Basis: Risk-Neutral Pricing

University of Groningen. Inventory Control for Multi-location Rental Systems van der Heide, Gerlach

IEOR 3106: Introduction to Operations Research: Stochastic Models SOLUTIONS to Final Exam, Sunday, December 16, 2012

Eindhoven University of Technology BACHELOR. Price directed control of bike sharing systems. van der Schoot, Femke A.

Contents Utility theory and insurance The individual risk model Collective risk models

Markov Processes and Applications

The Stigler-Luckock model with market makers

Simulation Wrap-up, Statistics COS 323

The Two-Sample Independent Sample t Test

CREDITRISK + By: A V Vedpuriswar. October 2, 2016

UPDATED IAA EDUCATION SYLLABUS

FAILURE RATE TRENDS IN AN AGING POPULATION MONTE CARLO APPROACH

SCHEDULE CREATION AND ANALYSIS. 1 Powered by POeT Solvers Limited

Panel Size and Overbooking Decisions for Appointment-based Services under Patient No-shows

The Normal Distribution

Subject CS2A Risk Modelling and Survival Analysis Core Principles

Slides for Risk Management

Heuristics in Rostering for Call Centres

Institute of Actuaries of India Subject CT6 Statistical Methods

Discrete-Event Simulation

Assembly systems with non-exponential machines: Throughput and bottlenecks

17 MAKING COMPLEX DECISIONS

Homework Assignments

Introduction to Real-Time Systems. Note: Slides are adopted from Lui Sha and Marco Caccamo

On the Existence of Constant Accrual Rates in Clinical Trials and Direction for Future Research

Report 2 Instructions - SF2980 Risk Management

Practical example of an Economic Scenario Generator

Estimation. Focus Points 10/11/2011. Estimating p in the Binomial Distribution. Section 7.3

1 Introduction. Term Paper: The Hall and Taylor Model in Duali 1. Yumin Li 5/8/2012

Lecture 17: More on Markov Decision Processes. Reinforcement learning

Perspectives on Stochastic Modeling

1. For two independent lives now age 30 and 34, you are given:

Hand and Spreadsheet Simulations

The Optimization Process: An example of portfolio optimization

Pass-Through Pricing on Production Chains

A new Loan Stock Financial Instrument

Confidence Intervals for the Difference Between Two Means with Tolerance Probability

Stat511 Additional Materials

Reasoning with Uncertainty

Simulation Optimization: Improving decisions under uncertainty

PRE CONFERENCE WORKSHOP 3

Information aggregation for timing decision making.

A Discrete Event Simulation Model for Outpatient Appointment Scheduling

Problem 1: Random variables, common distributions and the monopoly price

M.S. in Quantitative Finance & Risk Analytics (QFRA) Fall 2017 & Spring 2018

Parallel Accommodating Conduct: Evaluating the Performance of the CPPI Index

Economic factors and solvency

Markov Chains (Part 2)

STUDY SET 1. Discrete Probability Distributions. x P(x) and x = 6.

Chapter 10 Inventory Theory

Properly Assessing Diagnostic Credit in Safety Instrumented Functions Operating in High Demand Mode

Brooks, Introductory Econometrics for Finance, 3rd Edition

The Central Limit Theorem. Sec. 8.2: The Random Variable. it s Distribution. it s Distribution

Chapter 4: Commonly Used Distributions. Statistics for Engineers and Scientists Fourth Edition William Navidi

Calibration of Ornstein-Uhlenbeck Mean Reverting Process

BROWNIAN MOTION Antonella Basso, Martina Nardon

Chapter 5 Discrete Probability Distributions. Random Variables Discrete Probability Distributions Expected Value and Variance

Financial Risk Management

University of New South Wales Semester 1, Economics 4201 and Homework #2 Due on Tuesday 3/29 (20% penalty per day late)

Weighted Earliest Deadline Scheduling and Its Analytical Solution for Admission Control in a Wireless Emergency Network

Dynamic Resource Allocation for Spot Markets in Cloud Computi

Forecast Horizons for Production Planning with Stochastic Demand

Exam in TFY4275/FY8907 CLASSICAL TRANSPORT THEORY Feb 14, 2014

Increasing Efficiency for United Way s Free Tax Campaign

Project Trainee : Abhinav Yellanki 5 th year Integrated M.Sc. Student Mathematics and Computing Indian Institute of Technology, Kharagpur

CPSC 540: Machine Learning

Characterization of the Optimum

The value of foresight

Chapter 5. Continuous Random Variables and Probability Distributions. 5.1 Continuous Random Variables

Rapid computation of prices and deltas of nth to default swaps in the Li Model

Comparison of theory and practice of revenue management with undifferentiated demand

Chapter Fourteen: Simulation

PERFORMANCE ANALYSIS OF TANDEM QUEUES WITH SMALL BUFFERS

The Erlang-R Queue. Time-Varying QED Queues with Reentrant Customers in Support of Healthcare Staffing

Transcription:

Appendix A: Introduction to Queueing Theory Queueing theory is an advanced mathematical modeling technique that can estimate waiting times. Imagine customers who wait in a checkout line at a grocery store. This waiting line is referred to as a queue. Customers who finish their shopping will wait in the queue until a checkout clerk is available to scan the items in their carts. Since customers arrive randomly and the number of items they have can vary substantially, the delays they encounter are highly variable and depend upon the number of clerks who are working and how fast they can work. A queueing model can be used to translate the arrival pattern and processing times to estimate important system performance measures, e.g., customer average waiting times and the likelihood of a random customer encountering zero delays, for any number of clerks. In our study, patient appointment calls play the role of customers in a store, patient appointment schedule serves as the queue, and providers act as store clerks who are needed to provide the required service. Thus we can use queueing models to evaluate the productivity and cost-efficiencies of different practice models by allowing the estimation of average AWTs. Queueing analysis is one of the most practical and effective tools for understanding and aiding decisionmaking in managing critical resources (Green 2006). For recent studies using queueing theory in health services research, please refer to Green (2002) and Green and Nguyen (2001). For a general technical reference, please refer to Gross and Harris (1985).

Appendix B: Analysis of the Models For model i=i, II and III, we denote its productivity under the exponential and deterministic service time distributions by N M i ( M stands for Markovian) and N D i, respectively. Dividing the total annual staffing costs of model i by its productivity, we obtain its annual costs per patient, denoted by c M i and c D i, respectively, under the two service time distributions. The cost-efficiency of Model i is then measured by these costs. Model I If the service time follows an exponential distribution, then the model becomes an M/M/1 queueing model, the simplest and most classical model in queueing theory (Gross, and Harris 1985). It follows from a standard Markov Chain analysis of such models that /. Given that, we obtain that / 1. Hence 1. If the services times are deterministic, numerical procedure to compute waiting times are available. The readers can refer to any standard queueing textbook for such methods, e.g., Gross and Harris (1985). Model II Model II is a special queueing network called Jackson Network (Gross, and Harris 1985) if service times follow exponential distributions. In this case, the NP queue and the PCP queue behave as if two independent M/M/1 queues with arrival rates and, respectively, where 1. It follows that the average waiting time of all patients can be calculated as if no supervision is required (replace by 0.5 or 1 when lower or high level of supervision is needed, respectively). Setting, we obtain, which is equivalent to the following quadratic function for :.

Solving it for, we obtain, where, and. Since the stability condition requires that 1 and 1, we have. Recall that and. We obtain 2 4 2, and thus 2 4. If service times are deterministic, model II does not have a closed-form solution but queueing network approximation algorithms are available. See, e.g., Gross and Harris (1985). Model III We model the system as a two-server queueing system where patients will be served by the NP or the PCP whoever becomes available. If a patient arrives at the system and both providers are free, the patient will be served by the PCP. Since the PCP has shorter consultation times, this setup provides better model performances. Consider the case where the service times follow exponential distributions. Let for 0,2,3,4, if there are totally patients waiting in both the NP queue and the PCP queue (including those in service) at time. Let 1,1 if there is only one patient in the system and being served by the NP at time, and let 1,2 if there is only one patient in the system and being served by the PCP at time. Thus the system state of Model III at time 0 can be described by, which evolves over time as a Continuous Time Markov Chain (CTMC) with state space Ω 0, 1,1, 1,2, 2,3,4. Let lim for Ω. The balance equations for, 0 can be written as follows.

,,,, 2. Solving the above linear equation system, we obtain,,, and for 3, where,, Using the normalization condition that Ω,. 1, one can obtain for Ω, and in particular, It follows from Little s Law that 1 1. 1 2 1. Setting, we can calculate. Recall that and, we obtain. When the service times are deterministic, it is difficult to study the model analytically and hence we resort to a discrete-event simulation study (Law, and Kelton 1991). In the study, we simulate the operation of the clinic as a chronological sequence of events, which include arrival of patients, start of consultations, and completion of consultations (i.e., departure of patients). At any single point of time, only one event can occur. The timing of events is determined by relevant probability distributions associated with those

events. For example, time between two consecutive patient arrivals is drawn from an exponential distribution parameterized by patient arrival rate, since patient arrival is assumed to follow a Poisson Process. The choice of model parameters is discussed in the main text. The sequence of events, i.e., which event to occur next, relies on the timing of all events that could happen next. For example, the event following a patient arrival could be either another patient arrival or completion of a consultation, depending on which one would occur first. The simulation program is developed using Matlab software (The MathWorks, Inc., Natick, MA). By simulating the system and collecting relevant statistics over time, we can generate estimates for the mean and standard deviation of the AWT for a given panel size. We can then identify the largest panel size (i.e., productivity) under which the mean of average AWT equals the threshold T specified in the AWT requirement, i.e., 1 day in our study. Compared to analytical solutions which typically offer a quick and exact procedure to determine the relationship between system inputs and outputs, simulation modeling and analysis usually take a much longer time (to develop and run) and the outputs are usually random variables (since they are usually based on random inputs). Therefore, it may be hard to determine whether an observation is a result of system interrelations or randomness (Banks 1999). However, simulation allows consideration of more realistic/complex systems which would otherwise have to be simplified in order to obtain analytical solutions. Readers are referred to Law and Kelton (1991) and Fishman (2001) for more detailed introduction of discrete-event simulation.

Appendix C: Numerical Results of All Tested Cases Table 1. Results when NP service rate µ 1 =12 patients/day non-markovian Model Productivity Average Cost 1 Cost Ratio 2 Markovian Model Productivity Average Cost 1 Model I - Solo-physician 2440 $66.18 100% 2381 $67.83 100% Model II - Supervision Model (no supervision) Referral rate Cost Ratio 2 Substitution ratio 0% 3064 $73.96 111.76% 3004 $75.45 111.24% 20% 5% 3025 $74.92 113.20% 2965 $76.43 112.69% 10% 2988 $75.84 114.60% 2927 $77.41 114.13% 15% 2951 $76.79 116.04% 2891 $78.39 115.58% 20% 2915 $77.74 117.47% 2855 $79.38 117.03% 25% 2880 $78.69 118.90% 2820 $80.36 118.48% Substitution ratio 0% 3509 $64.58 97.59% 3444 $65.80 97.02% 30% 5% 3434 $65.99 99.72% 3369 $67.26 99.16% 10% 3362 $67.41 101.85% 3298 $68.71 101.31% 15% 3294 $68.80 103.96% 3230 $70.17 103.45% 20% 3228 $70.20 106.08% 3164 $71.63 105.61% 25% 3164 $71.62 108.23% 3101 $73.09 107.76% Substitution ratio 0% 3856 $58.77 88.80% 3747 $60.48 89.17% 38% 5% 3807 $59.53 89.95% 3694 $61.36 90.46% 10% 3718 $60.95 92.10% 3617 $62.65 92.37% 15% 3620 $62.60 94.59% 3530 $64.20 94.66% 20% 3523 $64.33 97.20% 3440 $65.89 97.14% 25% 3430 $66.07 99.83% 3351 $67.63 99.71% Substitution ratio 0% 3681 $61.56 93.03% 3602 $62.92 92.77% 40% 5% 3678 $61.62 93.10% 3590 $63.12 93.07% 10% 3669 $61.77 93.33% 3570 $63.48 93.60% 15% 3646 $62.16 93.92% 3533 $64.14 94.57% 20% 3584 $63.23 95.54% 3473 $65.24 96.19% 25% 3495 $64.84 97.98% 3397 $66.72 98.37% Substitution ratio 0% 2938 $77.13 116.55% 2876 $78.80 116.18% 50% 5% 2938 $77.13 116.55% 2875 $78.82 116.21% 10% 2938 $77.13 116.55% 2874 $78.84 116.24% 15% 2937 $77.16 116.59% 2873 $78.87 116.29% 20% 2937 $77.16 116.59% 2872 $78.91 116.34% 25% 2937 $77.16 116.59% 2870 $78.96 116.41% Substitution ratio 0% 2439 $92.92 140.40% 2380 $95.24 140.41% 60% 5% 2439 $92.92 140.40% 2379 $95.25 140.43%

Model II Supervision Model (Low level of supervision) 10% 2439 $92.92 140.40% 2379 $95.26 140.45% 15% 2439 $92.92 140.40% 2378 $95.28 140.48% 20% 2439 $92.92 140.40% 2378 $95.30 140.51% 25% 2439 $92.92 140.40% 2377 $95.32 140.54% Referral rate substitution ratio p 0% 2986 $75.89 114.68% 2926 $77.46 114.21% 20% 5% 2948 $76.87 116.16% 2888 $78.47 115.69% 10% 2912 $77.82 117.59% 2851 $79.48 117.18% 15% 2876 $78.80 119.06% 2816 $80.48 118.66% 20% 2841 $79.77 120.53% 2781 $81.49 120.15% 25% 2807 $80.73 121.99% 2747 $82.50 121.64% Substitution ratio 0% 3420 $66.26 100.13% 3355 $67.54 99.58% 30% 5% 3347 $67.71 102.31% 3283 $69.03 101.78% 10% 3277 $69.15 104.49% 3213 $70.53 103.99% 15% 3211 $70.58 106.64% 3146 $72.03 106.20% 20% 3146 $72.03 108.85% 3082 $73.53 108.41% 25% 3084 $73.48 111.03% 3020 $75.03 110.62% Substitution ratio 0% 3818 $59.36 89.69% 3704 $61.18 90.20% 38% 5% 3731 $60.74 91.78% 3629 $62.45 92.08% 10% 3630 $62.43 94.33% 3540 $64.02 94.39% 15% 3531 $64.18 96.98% 3447 $65.74 96.92% 20% 3435 $65.97 99.69% 3356 $67.52 99.56% 25% 3344 $67.77 102.40% 3268 $69.35 102.25% Substitution ratio 0% 3679 $61.60 93.08% 3593 $63.07 92.98% 40% 5% 3672 $61.72 93.25% 3575 $63.39 93.46% 10% 3652 $62.05 93.76% 3541 $64.00 94.35% 15% 3596 $63.02 95.23% 3484 $65.05 95.90% 20% 3506 $64.64 97.67% 3407 $66.52 98.07% 25% 3411 $66.44 100.39% 3321 $68.23 100.60% Substitution ratio 0% 2938 $77.13 116.55% 2876 $78.81 116.19% 50% 5% 2938 $77.13 116.55% 2875 $78.83 116.23% 10% 2938 $77.13 116.55% 2874 $78.86 116.27% 15% 2937 $77.16 116.59% 2872 $78.90 116.32% 20% 2937 $77.16 116.59% 2871 $78.94 116.39% 25% 2937 $77.16 116.59% 2869 $79.00 116.47% Substitution ratio 0% 2439 $92.92 140.40% 2379 $95.24 140.42% 60% 5% 2439 $92.92 140.40% 2379 $95.25 140.44% 10% 2439 $92.92 140.40% 2379 $95.27 140.46% 15% 2439 $92.92 140.40% 2378 $95.29 140.49% 20% 2439 $92.92 140.40% 2378 $95.31 140.52%

Model II Supervision Model (High level of supervision) 25% 2439 $92.92 140.40% 2377 $95.33 140.56% Referral rate Substitution ratio 0% 2908 $77.93 117.75% 2848 $79.58 117.33% 20% 5% 2871 $78.93 119.27% 2811 $80.62 118.86% 10% 2835 $79.94 120.79% 2775 $81.65 120.39% 15% 2801 $80.91 122.25% 2741 $82.69 121.92% 20% 2767 $81.90 123.75% 2707 $83.73 123.45% 25% 2733 $82.92 125.29% 2673 $84.77 124.98% Substitution ratio 0% 3330 $68.05 102.83% 3267 $69.38 102.29% 30% 5% 3259 $69.54 105.07% 3196 $70.91 104.55% 10% 3191 $71.02 107.31% 3128 $72.45 106.82% 15% 3126 $72.50 109.54% 3063 $73.99 109.09% 20% 3063 $73.99 111.80% 3000 $75.53 111.36% 25% 3003 $75.46 114.03% 2940 $77.08 113.64% Substitution ratio 0% 3745 $60.51 91.44% 3640 $62.25 91.78% 38% 5% 3642 $62.22 94.02% 3550 $63.83 94.11% 10% 3539 $64.04 96.76% 3456 $65.58 96.69% 15% 3441 $65.86 99.51% 3362 $67.41 99.39% 20% 3347 $67.71 102.31% 3271 $69.28 102.15% 25% 3257 $69.58 105.14% 3184 $71.18 104.95% Substitution ratio 0% 3674 $61.68 93.20% 3580 $63.30 93.33% 40% 5% 3658 $61.95 93.61% 3549 $63.85 94.14% 10% 3607 $62.83 94.93% 3495 $64.85 95.61% 15% 3518 $64.42 97.34% 3418 $66.31 97.76% 20% 3421 $66.24 100.10% 3331 $68.04 100.32% 25% 3325 $68.16 102.99% 3241 $69.92 103.08% Substitution ratio 0% 2938 $77.13 116.55% 2875 $78.82 116.21% 50% 5% 2938 $77.13 116.55% 2874 $78.85 116.25% 10% 2937 $77.16 116.59% 2873 $78.88 116.30% 15% 2937 $77.16 116.59% 2871 $78.92 116.36% 20% 2937 $77.16 116.59% 2870 $78.97 116.44% 25% 2936 $77.19 116.63% 2867 $79.04 116.54% Substitution ratio 0% 2439 $92.92 140.40% 2379 $95.25 140.43% 60% 5% 2439 $92.92 140.40% 2379 $95.26 140.45% Model III Shared-panel model 10% 2439 $92.92 140.40% 2379 $95.28 140.47% 15% 2439 $92.92 140.40% 2378 $95.30 140.50% 20% 2439 $92.92 140.40% 2377 $95.32 140.54% 25% 2438 $92.95 140.46% 2377 $95.35 140.58% 3942 $57.49 86.87% 3881 $58.39 86.09%

Table 2. Results when NP service rate µ 1 =15 patients/day non-markovian Model Productivity Average Cost 1 Cost Ratio 2 Markovian Model Productivity Average Cost 1 Model I - Solo-physician 2440 $66.18 100% 2381 $67.83 100% Model II - Supervision Model (no supervision) Referral rate Cost Ratio 2 Substitution ratio 0% 3064 $73.96 111.76% 3004 $75.44 111.22% 20% 5% 3025 $74.92 113.20% 2966 $76.42 112.67% 10% 2988 $75.84 114.60% 2928 $77.40 114.11% 15% 2951 $76.79 116.04% 2891 $78.38 115.56% 20% 2915 $77.74 117.47% 2856 $79.36 117.01% 25% 2880 $78.69 118.90% 2821 $80.34 118.46% Substitution ratio 0% 3510 $64.56 97.56% 3448 $65.73 96.91% 30% 5% 3435 $65.97 99.69% 3373 $67.19 99.06% 10% 3363 $67.39 101.82% 3301 $68.64 101.21% 15% 3294 $68.80 103.96% 3233 $70.10 103.36% 20% 3228 $70.20 106.08% 3167 $71.57 105.51% 25% 3,165 $71.60 108.19% 3103 $73.03 107.67% Substitution ratio 0% 4099 $55.29 83.54% 4023 $56.33 83.06% 40% 5% 3966 $57.14 86.34% 3893 $58.21 85.82% 10% 3842 $58.98 89.13% 3771 $60.10 88.61% 15% 3724 $60.85 91.95% 3655 $62.01 91.42% 20% 3614 $62.71 94.75% 3545 $63.93 94.25% 25% 3509 $64.58 97.59% 3441 $65.85 97.09% Substitution ratio 0% 4251 $53.31 80.55% 4136 $54.79 80.78% 43% 5% 4143 $54.70 82.65% 4040 $56.10 82.71% 10% 4006 $56.57 85.48% 3917 $57.85 85.29% 15% 3872 $58.53 88.44% 3791 $59.77 88.13% 20% 3745 $60.51 91.44% 3669 $61.77 91.06% 25% 3626 $62.50 94.44% 3552 $63.79 94.06% Substitution ratio 0% 3687 $61.46 92.87% 3621 $62.59 92.28% 50% 5% 3686 $61.48 92.90% 3618 $62.63 92.34% 10% 3685 $61.50 92.93% 3615 $62.69 92.42% 15% 3684 $61.51 92.95% 3610 $62.78 92.55% 20% 3682 $61.55 93.00% 3602 $62.92 92.77% 25% 3677 $61.63 93.13% 3586 $63.19 93.17% Substitution ratio 0% 3064 $73.96 111.76% 3003 $75.47 111.28% 60% 5% 3064 $73.96 111.76% 3002 $75.49 111.30% 10% 3063 $73.99 111.80% 3001 $75.50 111.32% 15% 3063 $73.99 111.80% 3001 $75.52 111.35%

Model II Supervision Model (Low level of supervision) 20% 3063 $73.99 111.80% 3000 $75.55 111.39% 25% 3063 $73.99 111.80% 2998 $75.58 111.44% Referral rate substitution ratio p 0% 2986 $75.89 114.68% 2926 $77.45 114.19% 20% 5% 2948 $76.87 116.16% 2889 $78.45 115.67% 10% 2912 $77.82 117.59% 2852 $79.46 117.16% 15% 2876 $78.80 119.06% 2816 $80.47 118.64% 20% 2841 $79.77 120.53% 2781 $81.48 120.13% 25% 2807 $80.73 121.99% 2747 $82.49 121.62% Substitution ratio 0% 3421 $66.24 100.10% 3359 $67.47 99.48% 30% 5% 3348 $67.69 102.28% 3286 $68.97 101.68% 10% 3278 $69.13 104.46% 3216 $70.47 103.89% 15% 3210 $70.60 106.68% 3149 $71.97 106.10% 20% 3146 $72.03 108.85% 3085 $73.47 108.32% 25% 3084 $73.48 111.03% 3023 $74.97 110.54% Substitution ratio 0% 3996 $56.71 85.69% 3923 $57.77 85.18% 40% 5% 3866 $58.62 88.57% 3795 $59.71 88.03% 10% 3744 $60.53 91.46% 3675 $61.66 90.92% 15% 3630 $62.43 94.33% 3562 $63.63 93.81% 20% 3522 $64.34 97.23% 3454 $65.60 96.72% 25% 3420 $66.26 100.13% 3353 $67.58 99.64% Substitution ratio 0% 4184 $54.16 81.84% 4075 $55.61 82.00% 43% 5% 4046 $56.01 84.63% 3954 $57.31 84.49% 10% 3907 $58.00 87.65% 3825 $59.25 87.35% 15% 3775 $60.03 90.71% 3698 $61.28 90.34% 20% 3651 $62.07 93.79% 3577 $63.35 93.40% 25% 3535 $64.11 96.87% 3463 $65.45 96.50% Substitution ratio 0% 3687 $61.46 92.87% 3620 $62.61 92.31% 50% 5% 3686 $61.48 92.90% 3617 $62.66 92.38% 10% 3685 $61.50 92.93% 3612 $62.73 92.49% 15% 3683 $61.53 92.98% 3606 $62.85 92.67% 20% 3679 $61.60 93.08% 3593 $63.07 92.98% 25% 3670 $61.75 93.31% 3568 $63.51 93.63% Substitution ratio 0% 3064 $73.96 111.76% 3002 $75.48 111.28% 60% 5% 3063 $73.99 111.80% 3002 $75.49 111.30% 10% 3063 $73.99 111.80% 3001 $75.51 111.33% 15% 3063 $73.99 111.80% 3000 $75.53 111.37% 20% 3063 $73.99 111.80% 2999 $75.56 111.41% 25% 3062 $74.01 111.83% 2998 $75.60 111.46%

Model II Supervision Model (High level of supervision) Referral rate Substitution ratio 0% 2908 $77.93 117.75% 2848 $79.57 117.31% 20% 5% 2871 $78.93 119.27% 2812 $80.60 118.84% 10% 2836 $79.91 120.74% 2776 $81.64 120.37% 15% 2801 $80.91 122.25% 2741 $82.68 121.90% 20% 2767 $81.90 123.75% 2707 $83.71 123.43% 25% 2733 $82.92 125.29% 2674 $84.75 124.96% Substitution ratio 0% 3331 $68.03 102.80% 3270 $69.31 102.19% 30% 5% 3260 $69.52 105.04% 3199 $70.85 104.45% 10% 3192 $71.00 107.28% 3131 $72.39 106.72% 15% 3127 $72.47 109.51% 3065 $73.93 109.00% 20% 3064 $73.96 111.76% 3003 $75.47 111.28% 25% 3004 $75.44 113.99% 2942 $77.02 113.56% Substitution ratio 0% 3893 $58.21 87.96% 3822 $59.30 87.43% 40% 5% 3766 $60.18 90.93% 3697 $61.30 90.38% 10% 3647 $62.14 93.89% 3579 $63.32 93.36% 15% 3535 $64.11 96.87% 3468 $65.34 96.34% 20% 3430 $66.07 99.83% 3364 $67.38 99.34% 25% 3331 $68.03 102.80% 3265 $69.41 102.34% Substitution ratio 0% 4089 $55.42 83.74% 3993 $56.76 83.68% 43% 5% 3945 $57.44 86.80% 3861 $58.70 86.54% 10% 3807 $59.53 89.95% 3730 $60.76 89.59% 15% 3678 $61.62 93.10% 3604 $62.88 92.72% 20% 3557 $63.71 96.27% 3485 $65.03 95.88% 25% 3443 $65.82 99.46% 3372 $67.20 99.08% Substitution ratio 0% 3686 $61.48 92.90% 3618 $62.63 92.34% 50% 5% 3685 $61.50 92.93% 3615 $62.70 92.44% 10% 3684 $61.51 92.95% 3609 $62.79 92.58% 15% 3681 $61.56 93.03% 3599 $62.97 92.83% 20% 3674 $61.68 93.20% 3580 $63.30 93.33% 25% 3652 $62.05 93.76% 3538 $64.06 94.44% Substitution ratio 0% 3064 $73.96 111.76% 3002 $75.48 111.29% 60% 5% 3063 $73.99 111.80% 3002 $75.50 111.31% Model III Shared-panel model 10% 3063 $73.99 111.80% 3001 $75.52 111.34% 15% 3063 $73.99 111.80% 3000 $75.55 111.38% 20% 3063 $73.99 111.80% 2998 $75.58 111.43% 25% 3062 $74.01 111.83% 2997 $75.62 111.50% 4317 $52.49 79.32% 4256 $53.25 78.51%

Table 3. Results when NP service rate µ 1 =18 patients/day non-markovian Model Productivity Average Cost 1 Cost Ratio 2 Markovian Model Productivity Average Cost 1 Model I - Solo-physician 2440 $66.18 100% 2381 $67.83 100% Model II - Supervision Model (no supervision) Referral rate Cost Ratio 2 Substitution ratio 0% 3064 $73.96 111.76% 3004 $75.43 111.21% 20% 5% 3026 $74.89 113.16% 2966 $76.41 112.66% 10% 2988 $75.84 114.60% 2928 $77.39 114.10% 15% 2951 $76.79 116.04% 2892 $78.37 115.55% 20% 2915 $77.74 117.47% 2856 $79.35 117.00% 25% 2880 $78.69 118.90% 2821 $80.34 118.45% Substitution ratio 0% 3510 $64.56 97.56% 3449 $65.71 96.88% 30% 5% 3435 $65.97 99.69% 3374 $67.16 99.02% 10% 3363 $67.39 101.82% 3303 $68.62 101.17% 15% 3295 $68.78 103.92% 3234 $70.08 103.32% 20% 3228 $70.20 106.08% 3168 $71.54 105.48% 25% 3165 $71.60 108.19% 3104 $73.01 107.64% Substitution ratio 0% 4104 $55.22 83.44% 4038 $56.12 82.74% 40% 5% 3970 $57.08 86.25% 3905 $58.04 85.57% 10% 3844 $58.95 89.08% 3780 $59.96 88.40% 15% 3726 $60.82 91.90% 3662 $61.89 91.24% 20% 3615 $62.69 94.72% 3551 $63.82 94.09% 25% 3510 $64.56 97.56% 3446 $65.76 96.95% Substitution ratio 0% 4621 $49.04 74.10% 4510 $50.25 74.09% 47% 5% 4443 $51.01 77.07% 4353 $52.06 76.75% 10% 4265 $53.13 80.29% 4185 $54.15 79.83% 15% 4098 $55.30 83.56% 4023 $56.33 83.05% 20% 3943 $57.47 86.84% 3871 $58.55 86.32% 25% 3799 $59.65 90.14% 3728 $60.79 89.62% Substitution ratio 0% 4432 $51.13 77.26% 4355 $52.04 76.73% 50% 5% 4426 $51.20 77.37% 4336 $52.27 77.06% 10% 4398 $51.53 77.86% 4284 $52.90 78.00% 15% 4268 $53.10 80.23% 4165 $54.41 80.22% 20% 4098 $55.30 83.56% 4011 $56.50 83.30% 25% 3935 $57.59 87.02% 3856 $58.78 86.66% Substitution ratio 0% 3688 $61.45 92.85% 3626 $62.50 92.15% 60% 5% 3688 $61.45 92.85% 3625 $62.52 92.18% 10% 3688 $61.45 92.85% 3623 $62.54 92.21% 15% 3687 $61.46 92.87% 3622 $62.57 92.26%

Model II Supervision Model (Low level of supervision) 20% 3686 $61.48 92.90% 3619 $62.62 92.32% 25% 3686 $61.48 92.90% 3615 $62.69 92.42% Referral rate substitution ratio p 0% 2986 $75.89 114.68% 2926 $77.44 114.18% 20% 5% 2948 $76.87 116.16% 2889 $78.45 115.66% 10% 2912 $77.82 117.59% 2852 $79.46 117.15% 15% 2876 $78.80 119.06% 2816 $80.46 118.63% 20% 2841 $79.77 120.53% 2782 $81.47 120.12% 25% 2807 $80.73 121.99% 2747 $82.48 121.61% Substitution ratio 0% 3421 $66.24 100.10% 3360 $67.45 99.44% 30% 5% 3348 $67.69 102.28% 3287 $68.94 101.65% 10% 3278 $69.13 104.46% 3217 $70.44 103.86% 15% 3211 $70.58 106.64% 3150 $71.94 106.07% 20% 3146 $72.03 108.85% 3086 $73.45 108.29% 25% 3084 $73.48 111.03% 3024 $74.95 110.50% Substitution ratio 0% 4000 $56.66 85.61% 3935 $57.59 84.91% 40% 5% 3869 $58.57 88.51% 3805 $59.56 87.82% 10% 3746 $60.50 91.41% 3682 $61.54 90.73% 15% 3631 $62.41 94.31% 3568 $63.52 93.65% 20% 3523 $64.33 97.20% 3459 $65.51 96.58% 25% 3421 $66.24 100.10% 3358 $67.50 99.51% Substitution ratio 0% 4521 $50.13 75.74% 4423 $51.24 75.54% 47% 5% 4334 $52.29 79.01% 4252 $53.30 78.59% 10% 4158 $54.50 82.35% 4082 $55.52 81.85% 15% 3995 $56.73 85.71% 3922 $57.78 85.19% 20% 3843 $58.97 89.10% 3773 $60.07 88.57% 25% 3702 $61.22 92.50% 3633 $62.38 91.97% Substitution ratio 0% 4430 $51.16 77.30% 4347 $52.13 76.86% 50% 5% 4417 $51.31 77.53% 4315 $52.52 77.44% 10% 4337 $52.25 78.96% 4224 $53.65 79.09% 15% 4167 $54.38 82.18% 4075 $55.61 81.99% 20% 3996 $56.71 85.69% 3915 $57.89 85.35% 25% 3836 $59.08 89.27% 3760 $60.28 88.87% Substitution ratio 0% 3688 $61.45 92.85% 3625 $62.51 92.16% 60% 5% 3688 $61.45 92.85% 3624 $62.53 92.19% 10% 3687 $61.46 92.87% 3623 $62.55 92.23% 15% 3687 $61.46 92.87% 3621 $62.59 92.28% 20% 3686 $61.48 92.90% 3617 $62.65 92.36% 25% 3685 $61.50 92.93% 3612 $62.73 92.49%

Model II Supervision Model (High level of supervision) Referral rate Substitution ratio 0% 2908 $77.93 117.75% 2848 $79.56 117.30% 20% 5% 2871 $78.93 119.27% 2812 $80.60 118.83% 10% 2836 $79.91 120.74% 2776 $81.63 120.36% 15% 2801 $80.91 122.25% 2741 $82.67 121.89% 20% 2767 $81.90 123.75% 2707 $83.71 123.42% 25% 2733 $82.92 125.29% 2674 $84.75 124.95% Substitution ratio 0% 3332 $68.01 102.77% 3271 $69.29 102.15% 30% 5% 3260 $69.52 105.04% 3200 $70.82 104.42% 10% 3192 $71.00 107.28% 3132 $72.36 106.69% 15% 3127 $72.47 109.51% 3066 $73.91 108.97% 20% 3064 $73.96 111.76% 3004 $75.45 111.24% 25% 3004 $75.44 113.99% 2943 $77.00 113.52% Substitution ratio 0% 3895 $58.18 87.92% 3831 $59.15 87.21% 40% 5% 3768 $60.14 90.88% 3704 $61.18 90.20% 10% 3649 $62.10 93.84% 3585 $63.21 93.19% 15% 3537 $64.07 96.81% 3473 $65.25 96.20% 20% 3431 $66.05 99.80% 3368 $67.29 99.21% 25% 3332 $68.01 102.77% 3269 $69.33 102.22% Substitution ratio 0% 4409 $51.40 77.67% 4322 $52.43 77.31% 47% 5% 4223 $53.66 81.09% 4146 $54.67 80.60% 10% 4051 $55.94 84.53% 3978 $56.97 84.00% 15% 3891 $58.24 88.01% 3820 $59.32 87.46% 20% 3743 $60.55 91.49% 3674 $61.68 90.94% 25% 3606 $62.85 94.96% 3538 $64.06 94.44% Substitution ratio 0% 4426 $51.20 77.37% 4334 $52.29 77.09% 50% 5% 4390 $51.62 78.00% 4274 $53.02 78.17% 10% 4240 $53.45 80.76% 4142 $54.72 80.67% 15% 4062 $55.79 84.30% 3978 $56.97 84.00% 20% 3893 $58.21 87.96% 3816 $59.39 87.56% 25% 3737 $60.64 91.63% 3663 $61.87 91.22% Substitution ratio 0% 3688 $61.45 92.85% 3625 $62.52 92.17% 60% 5% 3688 $61.45 92.85% 3624 $62.54 92.20% Model III Shared-panel model 10% 3687 $61.46 92.87% 3622 $62.57 92.25% 15% 3687 $61.46 92.87% 3619 $62.61 92.31% 20% 3686 $61.48 92.90% 3616 $62.68 92.41% 25% 3684 $61.51 92.95% 3609 $62.79 92.58% 1 Average cost: average annual staffing cost per patient 2 Cost ratio: the ratio of the average costs of a model to that of Model I (solo-physician model) 4689 $48.33 73.03% 4630 $48.95 72.16%

Appendix D: Comparison of Markovian and non-markovian Models As expected, the productivity (and hence cost-efficiency) of a non-markovian model is larger than that of its Markovian counterpart with everything else being equal (see Appendix C). However, the percentage gap in the productivity (and cost-efficiency) estimated under these two service time assumptions is quite small (only 2~3% across all testing cases), indicating that system performances are not very sensitive to the variability in provider consultation times. For example, the productivity of Model II (assuming µ 1 =15, p=40%, r=0% and no PCP supervision) is estimated to be 4023 and 4099 under Markovian and non-markovian service time assumptions, respectively, and the percentage gap between them is less than 2%. In addition, the qualitative comparison results among different practice models are the same regardless of service time assumptions. For example, under both service time assumptions, Model II with µ 1 =15, p=30%, r=0% and no supervisions is more cost-efficient than Model I, and this comparative result reverses when r 10%. More importantly, the relative performance among these practice models is highly consistent across different service time assumptions. For all scenarios tested for Models II and III, the difference in cost ratios under the two service time assumptions is less than 1%, where cost ratio is defined as the ratio of the average annual cost of a model to that of Model I (our benchmark model). Considering that the two service time distributions we used represent two extremes of the variability in provider consultation times, the comparative results across Models I, II and III are likely to be insensitive to such variability. In summary, both performances within a practice model and comparative results across these models seem not sensitive to the variability in provider consultation times, implying that our results can be generalizable to different consultation time distributions.