Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources

Response to Reviewer om5H29

We appreciate the reviewer's efforts in helping us with improving the manuscript and providing an opportunity to make clarifications. We have corrected the minor typo as suggested. Please find our point-by-point response below. > 1. I don't think the proposed Q-learning framework fits this problem. In fact, the proposed Q-network cannot see the interaction of patients; changing $a_i$ does not change $Q_\\theta(s)_{j,a_j}, j\\neq i$ . Please clarify if this is not the case. We appreciate the reviewer for bringing this out. We would like to provide further clarification regarding patient interactions within our model. Patient interactions are considered in both the states and actions. In terms of states, each patient state $s_i$, is input into the transformer framework as an individual token. Consequently, changing the value of $s_i$ can impact all $Q_\\theta(s)_{j,a_j}, j\\neq i$, due to the attention mechanism within the transformer architecture. This interaction is analogous to how changing a single word in a sentence affects the output embeddings of all other words in the sentence, considering the contextual information. We also conducted experiments (DDQN-individual model in Appendix 5) where interactions were ignored, and changing the value of $s\_i$ did not impact any $Q\_\\theta(s)\_{j,a\_j}, j\\neq i$. This interaction-ignored model exhibited lower performance in terms of both survival rate and fairness. Regarding actions, the interaction lies in the constraints imposed by the available ventilators, denoted as $B$. Modifying the action $a\_i$ will not affect $Q\_\\theta(s)\_{j,a\_j}, j\\neq i$. However, it does have an influence on the greedy action, $a^* = \\arg\\max\_{a \\in\\mathcal{A}} Q(s,a)$, which is determined by identifying the extra extra ($B-|I|$)-largest indices of $ \\{ (Q\_\\theta(s)\_{i, 1} -Q\_\\theta(s)\_{i, 0}) | 1\\leq i \\leq N, I\_i=0\\}$. For instance, if we change $a\_i$ from 1 to 0, indicating that a patient's ventilator is freed up, the count of patients already on ventilation ($|I|$ ) decreases by 1, and the greedy action will be adjusted to find the extra ($B-|I-1|$) indices of $ \\{ (Q\_\\theta(s)\_{i, 1} -Q\_\\theta(s)\_{i, 0}) | 1\\leq i \\leq N, I\_i=0\\}$ to allocate ventilators. Therefore, the action $a\_j$ , whose $Q\_\\theta(s)\_{j, 1} -Q\_\\theta(s)\_{j, 0}$ ranks exactly at the $B-|I-1|$ position will change from 0 to 1. > 2. It's not clear what value this Q function is estimating. Further elaboration is required here. The Q function estimates the expected cumulative reward (Eq. (5)) when allocating ventilators under the constraint of the total available ventilators, taking into account the clinical conditions of the patients. This Q function estimates a trade-off between the survival rate of all patients and the fairness of allocation across different ethnoracial groups. To address computational challenges, we decomposed the overall $Q(s,a)$ into the sum of individual $Q_\\theta(s)_{i,a_i}$ and used an attention mechanism to consider interactions among patients. We have included a clarification of the Q function in Section 4 of the revision. > 3. I cannot see how survival rate as a measure of performance is estimated from offline data. What off-policy technique is used? Thank you for your question. We've developed a simulator using real-world data, obviating the need for off-policy evaluation techniques. The simulator allows us to directly evaluate our proposed strategy and compare its performance with existing protocols. The usage of a simulator derived from real-world clinical data also enhances the utility of our proposed model compared to off-policy techniques. Within the simulator, a patient progresses to their subsequent stage in the real clinical trajectory if a ventilator is allocated; otherwise, they experience immediate decease due to ventilator shortage. The death count comprises patient fatalities within their clinical progressions and those resulting from insufficient ventilation. The survival rate is simply calculated as 1 minus the death rate. In addition, we present results from alternative simulators in the appendix, the simulators where patients do not progress to immediate death in the absence of ventilators.

Hi thanks for the response but let's clarify the misunderstanding

Hello thanks for the response. Your example is great, and we believe we share the same understanding. It is true that TDDQN uses a parameterized Q network which doesn't capture every interaction. However, we would hope to invite you to understand the complexity of the problem. The original Bellman equation for this problem involves exponentially many actions, which would create a massive network whose input size is **$O(N \\times 2^N)$**. There is no hope to construct such a network and train it in practice. Classical DQN simply doesn't work. In fact, one cannot directly apply any RL algorithm to this problem. This is why we couldn't find any prior work using RL for this problem. In other words, **it is exponentially hard to capture all patients' interactions**. This is what motivates our work. In this paper we do not try to capture all interactions. Instead, we propose a reparametrization of Q network to circumvent the issue of exponentially many actions: $Q(s,a) \\approx \\sum_{i=1}^N Q_{\\theta}(s)\_{i,a\_i}$. **This parametrization reduces the network input dimension to $O(N)$**. In the meantime, this parametrization of Q network also leads to a policy class where greedy policies can be calculated very easily, i.e., choosing actions by ranking values of $Q_{\theta}$. In fact, our policy class has a spirit similar to the so-called "index policy" in queueing theory which is asymptotic optimal in some special cases (see eg. Whittle's index policy for a multi-class queueing system with convex holding costs). Your concerns come from the fact that we are using this particular Q parametrization to approximately solve the problem. We agree with what you pointed out. Our reduction of complexity does come at a cost: We agree that this parametrization of Q network doesn't capture all patients' interactions, and that $Q_{\theta}(s)_{i,a_i}$ is only a proxy to patient's value but may not be accurate. But please bear in mind that we are approximately solving an exponentially hard problem using a Q parametrization with linear space complexity. Please also note that all DQN algorithms use parametrized versions of Q network to save computation costs and there is always approximation error. To justify that this parametrization works, we conducted extensive experiments using the policies learnt by TDDQN. Our Table 2,3 reported performances of our policy compared to other baselines commonly used in the hospital. You can see that our policies work significantly better than all other baselines. In other words, although TDDQN only provides an approximate solution to the Bellman equation using a simplified Q network, it actually can learn a pretty good SOTA policy beating all other baselines. We really appreciate your comments and discussions and we hope that we could clarify the misunderstanding

Response to Reviewer GCVq

We thank the reviewers for their dedicated effort and valuable time spent on reviewing our manuscript, which has greatly contributed to enhancing the quality of our study. In the upcoming camera-ready version, we will incorporate a summary of the study's limitations to the main text. We will share the de-identified dataset upon publication of the paper.

Response to Authors

Thank you for the response. I look forward to inspecting the de-identified dataset. I am keeping my score where it stands currently.

Response to Reviewer dm6i

We appreciate the reviewers' efforts in helping us polish the study. Please find our itemized responses to the weaknesses, questions, and ethical concerns below. > **Weakness 1** The notations are confusing in Section 3.3 and 3.4. Based on the context, are $x_i$ and $s_i$ the same (the medical condition of patient $i$), and $P^{\text{vent}}$ and $P^{\text{on}}$ the same (the ventilation transition)? If so, please ensure that the notations are consistent. Also, I think the action $a$ is N-dimensional and the i-th coordinate $a_i\in\{0,1\}$ is the action applied on patient $i$, if this is the case, does $I_i'=a$ mean $I_i'=a_i$ in 1? Also, what is the dimension of the overall transition matrix $P$? We thank the reviewer for pointing out the notation inconsistency. We have addressed them in Section 3 of the revision. Specifically, the notation $x_i$ and $s_i$ are the same, we updated all notation of $s_i$ to $x_i$; The notation $P^\text{vent}$ and $P^\text{on}$ are different, meaning the transition of patient ventilation status and medical status, respectively; Typo of $a_i$ is corrected; The dimension of the transition matrix $P$ is $(|S|\times |A|) \times |S|$, where $|S| = Nk+N$ denotes the dimension of the state space, and $|A| = 2^N$ denotes the dimension of the action space. When considering fairness, $|S|$ expands to $Nk + N + 8$ by adding 8 dimensions to account for cumulative total and ventilated patients across four ethnoracial groups. We updated Eq. (5) for the correct formula of $P$. > **Weakness 2** Currently, the state and action are the concatenations of the medical/ventilator states and the ventilator assignments of individual patients. Will there be scalability issue when the number of patients is really large and if there is, how to handle that with the current framework? We thank the reviewer for asking this important question. Naively concatenating all patients' state vectors together does indeed pose computational challenges as the size of the input is exponentially large. To overcome this computational intractability, we therefore proposed the use of a transformer structure that treats patients' states as 'tokens' and inputs them into the model rather than simply concatenating them. By employing this parametrization, we were able to decompose the transitions of patients' states, improving scalability while maintaining interactions among patients. Our approach has already substantially reduced the input complexity from $O(N \times 2^N)$ to $O(N)$. In our implementation, we utilized only one layer of the transformer structure with an embedding dimension of 1024 and 16 attention heads, resulting in just 5.3 million trainable parameters. Our model can recommend resource allocation for over 10,000 beds with a single 32 GB Tesla V100 GPU card. As a reference, the state of Illinois has 3,535 critical care beds [1]. [1] UIC School of Public Health. (2023). Number of hospital beds in Illinois. Retrieved November 21, 2023. > **"...but did not compare with any data-driven or machine learning baselines."** Note that the action space is exponential in $N$. It means that even finding the greedy action for a given state is combinatorially hard. Further, even representing the Q function requires $O(N\times 2^N)$ space, which means that any classical method would not be implementable. Given this immense complexity, we didn't find any classical machine learning baseline that can directly apply to our problem immediately. To circumvent this complexity challenge, we constructed the decomposed Q network approach which not only reduces input size of Q but also leads to a policy class that can be easily computed. We strongly believe that it is a most straightforward and practical approximation to this combinatorially hard problem.

Cont'd Response to Reviewer dm6i

> **Weakness 3** Some details in the transition model are missing. From the description of the simulator in Section 5.2, it seems that you consider the factual data, e.g. $P(x'_i| x_i)=1$ if $x_i$ is the actual next state for patient i and $P(x'_i| x_i)=0$ otherwise? Did you consider any counterfactuals? Will the results be negatively impacted by any potential selection bias in the data if only factual data is used? Also, how are bed assignment distribution $q_i(s)$ and initial medical condition distribution $\xi$ determined? We thank the reviewer for suggesting the need for clarification. In our project, we built a simulator to model individual patient's state path and the multi-patient ventilator allocation process. The simulator is built on real-world patients data. All patients in our real-world datasets were ventilated. Our simulator reflected scenarios where some patients cannot receive ventilators due to resource shortages. We assumed, following Bhavani et al. [1], that patients not allocated a ventilator do not survive (In our sensitivity analysis, see Appendix A.4, we also conducted additional experiments, exploring the possibility that patients not ventilated may continue to survive along their own trajectory with various probabilities). Thus we believe the simulator can provide a reasonable state-transition model for a random patient. Please note that our TDDQN method can work with any simulator of this kind, and building better simulators to approximate real-world hospital operation will be long-term efforts. We totally agree that having an accurate state-transition simulation model is crucial for success of deploying ML model in practice, and in particular counterfactuals could be a big concern. In this initial study, we tried our best to collect available data and present a proof-of-concept simulator+RL approach. In the longer term, we do envision this initial efforts would lead to transformative health care resource allocation in real healthcare systems. Thus, beyond the scope of this ICLR submission, we are actively collaborating with physicians and three other academic health systems, each with multiple hospitals to expand the efforts of equitable and effective health care resource allocation. We clarified these two distributions $q_i(s)$ and $\xi$ in the revised version of our manuscript, as follows. For the distribution of bed assignments, $q_i(s)$ depends on how new patients are allocated to empty beds. It satisfies $q_i(s) = 0$ if bed $i$ is already occupied, and $\sum_{i=1}^N q_i(s) = E \sim Pois(\Lambda)$, assuming the number of incoming patients $E$ follows a Poisson distribution with parameter $\Lambda$. In our implementation, we randomly sampled $E$ patients from the training cohort without replacement and randomly assigned them to the empty beds, where the number of patients $E$ is drawn from a Poisson distribution with $\Lambda=12$, as estimated from our training data. The initial medical condition distribution $\xi$ represents the clinical condition of patients on their first day in the critical care units. [1] Bhavani et al. "Simulation of ventilator allocation in critically ill patients with COVID-19." American journal of respiratory and critical care medicine 204.10 (2021): 1224-1227. > **Weakness 4** Some details in the Method and Experiments are missing. The learning objective is missing. From the context, I am assuming that you are using Q-learning with the proposed reward and then used the greedy policy with the learned Q function, but it's not clear from what's written now in Section 4. Also, the choice of the ventilator cost $c_1$ and the $\lambda$ to trade-off the fairness reward term are missing in the main paper (found them in appendix). Is there any justification on how they are determined? I am also wondering how the results will change if $c_1$ and $\lambda$ are chosen differently. We used the Q-learning objective with the proposed reward. We incorporated an action constraint to model the limited number of ventilators, when selecting the best action via the greedy policy in Eq.(10) and Eq.(11). We have clarified these details in Section 4 of the revision. In practice, these parameters should be tuned and determined by doctors. In our experiment, we tested various candidate values of $\lambda$ to identify the best-performing one based on both effectiveness and fairness on the validation set. The $\lambda$ value has an impact on the balance between survival and fairness. In Appendix 2, we present a Pareto frontier obtained by varying the value of $\lambda$. We observe that the increase of $\lambda$ does not compromise the survival rate but enhances fairness until reaching a threshold. When $\lambda$ is set to a very large number, e.g. 1e6, the trained protocol tends towards a random policy, which means equal allocation but lowers the overall survival rate.

Cont'd Response to Reviewer dm6i

> **Question 1** Will the dataset be made public if the paper is published? So that the results can be reproduced. The dataset used seems to be proprietary and the authors did not provide detailed information on the dataset or whether it can be made public upon paper publication. We will share the de-identified dataset upon publication of the paper. > **Question 2** The experiments showed that the proposed method outperformed existing government policies. I am wondering if there is any data-driven baselines in the literature and how will your method perform compared to them? Note that the action space is exponential in $N$. It means that even finding the greedy action for a given state is combinatorially hard. Further, even representing the Q function requires $O(N\times 2^N)$ space, which means that any classical method would not be implementable. Given this immense complexity, we didn't find any classical machine learning or RL baseline that can directly apply to our problem. To circumvent this complexity challenge, we constructed the decomposed Q network approach which not only reduces input size of Q but also leads to a policy class that can be easily computed. We strongly believe that it is a most straightforward and practical approximation to this combinatorially hard problem. Further, we discovered a decision-tree heuristic developed for a similar ventilator problem by (Grand-Clement et al. [1]). Although their problem is different from ours, we managed to test this heuristic in our setting and got the following **new results** (added to Appendix A.5). In the comparison, our TDDQN clearly outperforms the tree heuristics. **Table R1.** Impact of triage protocols on survival, fairness, and allocation rates corresponding to ~ 50% scarcity. | | Performance | Fairness | Allocation Rates | | | | | |----------------------|--------------|--------------|------------------|--------------|--------------|--------------|--------------| | | Survival, % | DPR, % | Overall, % | Asian, % | Black, % | Hispanic, % | White, % | | TDDQN-fair | 85.41 ± 0.23 | 95.24 ± 1.65 | 81.90 ± 0.24 | 80.01 ± 1.79 | 79.95 ± 1.05 | 81.26 ± 1.24 | 82.80 ± 0.42 | | Grand-Clement et al. [1] | 76.16 ± 0.23 | 92.24 ± 1.97 | 76.53± 0.45 | 81.01 ± 1.74 | 75.53 ± 1.58 | 77.96 ± 1.29 | 76.20 ± 0.52 | [1] Grand-Clément, Julien, et al. "Interpretable machine learning for resource allocation with application to ventilator triage." arXiv preprint arXiv:2110.10994 (2021). > **Question 3** There are some confusions in the writing. Thank you for pointing those confusions out. We have corrected these typos in the revised manuscript.