Automated Diagnosis of Clinic Workflows

Outpatient clinics often run behind schedule due to patients who arrive late or appointments that run longer than expected. We sought to develop a generalizable method that would allow healthcare providers to diagnose problems in workflow that disrupt the schedule on any given provider clinic day. We use a constraint optimization problem to identify the least number of appointment modifications that make the rest of the schedule run on-time. We apply this method to an outpatient clinic at Vanderbilt. For patient seen in this clinic between March 27, 2017 and April 21, 2017, long cycle times tended to affect the overall schedule more than late patients. Results from this workflow diagnosis method could be used to inform interventions to help clinics run smoothly, thus decreasing patient wait times and increasing provider utilization

An Automatic and Intelligent System for Integrated Healthcare Processes Management

In this work, an automatic and intelligent system for integrated healthcare processes management is developed on a constraint based system. This project has been carried out in collaboration with a real assisted repro-duction clinic. Our goal is to improve the efficiency of the clinic by facilitating the management of the integrated healthcare system. This is very important in an environment in which the healthcare processes present complex temporal and resource constraints.Ministerio de Economía y Competitividad TIN2016-76956-C3-2-RMinisterio de Economía y Competitividad TIN2015-71938-RED

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

Operating theatre modelling: integrating social measures

Hospital resource modelling literature is primarily focussed on productivity and efficiency measures. In this paper, our focus is on the alignment of the most valuable revenue factor, the operating room (OR) with the most valuable cost factor, the staff. When aligning these economic and social decisions, respectively, into one sustainable model, simulation results justify the integration of these factors. This research shows that integrating staff decisions and OR decisions results in better solutions for both entities. A discrete event simulation approach is used as a performance test to evaluate an integrated and an iterative model. Experimental analysis show how our integrated approach can benefit the alignment of the planning of the human resources as well as the planning of the capacity of the OR based on both economic related metrics (lead time, overtime, number of patients rejected) and social related metrics (personnel preferences, aversions, roster quality)

Prospective payment system : consequences for hospital-physician interactions in the private sector

In 2004, French health authorities plan to introduce a prospective payment system for hospitals delivering acute care based on the DRG classification system. In this paper, we analyze the consequences of this switch from a retrospective to a prospective payment system on the ability of physicians and hospital managers to coordinate their activity in the production of hospital stays. Our analysis follows those of Dor and Watson (1995) and Custer et al. (1990) but is adapted to the context of the French hospital private sector. Different types of interactions are considered : non-cooperative, dominant-reactive, and cooperative. The main result of this analysis is that, in a context in which average per-patient fees are maintained, the change of payment system is potentially gainful for both partners. Although their fees are not concerned by the reform, physicians are even in a better position than hospitals tot ake advantage of the change of payment system. A minimum level of coordination is nevertheless required, i.e. either cooperative or dominant-reactive interactions. Furthermore, two elements limits the importance of these potential gains : these are only one-shot gains and hence depend on the ability to reduce the length of hospital stays. Finally, some extensions regarding competition between public and private hospitals and negotiation issues are discussed.prospective payment system; retrospective payment system; physician behabivour, for-profit hospitals

Enriching Information to Prevent Bank Runs

Sequential service in the banking sector, as modeled by Diamondand Dybvig (1983), is a barrier to full insurance and potential source offinancial fragility against which deposit insurance is infeasible (Wallace,1988). In this paper, we pursue a different perspective, viewingthe sequence of contacts as opportunities to extract informationthrough a larger message space with commitment to richer promises.As we show, if preferences satisfy a separating property then the desiredelimination of dominated strategies (Green and Lin, 2003) occurseven when shocks are correlated. In this manner the sequential servicepromotes stability.

Optimized Time Management for Declarative Workflows

Declarative process models are increasingly used since they fit better with the nature of flexible process-aware information systems and the requirements of the stakeholders involved. When managing business processes, in addition, support for representing time and reasoning about it becomes crucial. Given a declarative process model, users may choose among different ways to execute it, i.e., there exist numerous possible enactment plans, each one presenting specific values for the given objective functions (e.g., overall completion time). This paper suggests a method for generating optimized enactment plans (e.g., plans minimizing overall completion time) from declarative process models with explicit temporal constraints. The latter covers a number of well-known workflow time patterns. The generated plans can be used for different purposes like providing personal schedules to users, facilitating early detection of critical situations, or predicting execution times for process activities. The proposed approach is applied to a range of test models of varying complexity. Although the optimization of process execution is a highly constrained problem, results indicate that our approach produces a satisfactory number of suitable solutions, i.e., solutions optimal in many cases

Combination Strategies for Semantic Role Labeling

This paper introduces and analyzes a battery of inference models for the problem of semantic role labeling: one based on constraint satisfaction, and several strategies that model the inference as a meta-learning problem using discriminative classifiers. These classifiers are developed with a rich set of novel features that encode proposition and sentence-level information. To our knowledge, this is the first work that: (a) performs a thorough analysis of learning-based inference models for semantic role labeling, and (b) compares several inference strategies in this context. We evaluate the proposed inference strategies in the framework of the CoNLL-2005 shared task using only automatically-generated syntactic information. The extensive experimental evaluation and analysis indicates that all the proposed inference strategies are successful -they all outperform the current best results reported in the CoNLL-2005 evaluation exercise- but each of the proposed approaches has its advantages and disadvantages. Several important traits of a state-of-the-art SRL combination strategy emerge from this analysis: (i) individual models should be combined at the granularity of candidate arguments rather than at the granularity of complete solutions; (ii) the best combination strategy uses an inference model based in learning; and (iii) the learning-based inference benefits from max-margin classifiers and global feedback