Paul Martin

Bio: Paul Martin is an academic researcher from Cornell University. The author has contributed to research in topics: Nurse scheduling problem & Dynamic priority scheduling. The author has an hindex of 3, co-authored 4 publications receiving 186 citations.

A New Approach to Computing Optimal Schedules for the Job-Shop Scheduling Problem

Abstract: From a computational point of view, the job-shop scheduling problem is one of the most notoriously intractable NP-hard optimization problems. In spite of a great deal of substantive research, there are instances of even quite modest size for which it is beyond our current understanding to solve to optimality. We propose several new lower bounding procedures for this problem, and show how to incorporate them into a branch-and-bound procedure. Unlike almost all of the work done on this problem in the past thirty years, our enumerative procedure is not based on the disjunctive graph formulation, but is rather a time-oriented branching scheme. We show that our approach can solve most of the standard benchmark instances, and obtains the best known lower bounds on each.

A Multidisciplinary Discharge Timeout Checklist Improves Patient Education and Captures Discharge Process Errors.

Abstract: OBJECTIVE To design and implement a discharge timeout checklist, and to assess its effects on patients' understanding as well as the potential impact on preventable medical errors surrounding hospital discharges to home. METHODS Based on the structure successfully used for surgical procedures and using the Model for Improvement framework, we designed a discharge checklist to review and assess patients' understanding of discharge medications, catheters, home care plans, follow-up, symptoms, and who to call with problems after discharge. In parallel, we developed a process of integrating the checklist into the discharge process after routine discharge procedures were completed. We used the checklists to assess patients' level of understanding and need for additional education as well as changes in discharge documentation; we also noted whether good catches of significant errors in the discharge process occurred. RESULTS Over 6 months of study, 190 discharge timeouts out of 429 eligible discharges were completed. Additional education was provided in 53 of 190 discharge timeouts (27.8%), with 62% of this education being related to medications. Twenty-one (11.1%) discharge timeouts resulted in at least one change to the discharge documentation or a good catch. CONCLUSIONS A multidisciplinary discharge timeout directly involving the patient can be effective in targeting additional areas for patient education and in potentially reducing preventable adverse events.

What to expect that you're not expecting: A pilot video education intervention to improve patient self-efficacy surrounding discharge medication barriers.

Abstract: The objective of this study was to test the feasibility of video discharge education to improve self-efficacy in dealing with medication barriers around hospital discharge. We conducted a single-ar...

Thirty-Day Post-Discharge Outcomes Following COVID-19 Infection.

Abstract: The clinical course of COVID-19 includes multiple disease phases. Data describing post-hospital discharge outcomes may provide insight into disease course. Studies describing post-hospitalization outcomes of adults following COVID-19 infection are limited to electronic medical record review, which may underestimate the incidence of outcomes. To determine 30-day post-hospitalization outcomes following COVID-19 infection. Retrospective cohort study Quaternary referral hospital and community hospital in New York City. COVID-19 infected patients discharged alive from the emergency department (ED) or hospital between March 3 and May 15, 2020. Outcomes included return to an ED, re-hospitalization, and mortality within 30 days of hospital discharge. Thirty-day follow-up data were successfully collected on 94.6% of eligible patients. Among 1344 patients, 16.5% returned to an ED, 9.8% were re-hospitalized, and 2.4% died. Among patients who returned to the ED, 50.0% (108/216) went to a different hospital from the hospital of the index presentation, and 61.1% (132/216) of those who returned were re-hospitalized. In Cox models adjusted for variables selected using the lasso method, age (HR 1.01 per year [95% CI 1.00–1.02]), diabetes (1.54 [1.06–2.23]), and the need for inpatient dialysis (3.78 [2.23–6.43]) during the index presentation were independently associated with a higher re-hospitalization rate. Older age (HR 1.08 [1.05–1.11]) and Asian race (2.89 [1.27–6.61]) were significantly associated with mortality. Among patients discharged alive following their index presentation for COVID-19, risk for returning to a hospital within 30 days of discharge was substantial. These patients merit close post-discharge follow-up to optimize outcomes.

Handbook of Constraint Programming

Abstract: Constraint programming is a powerful paradigm for solving combinatorial search problems that draws on a wide range of techniques from artificial intelligence, computer science, databases, programming languages, and operations research. Constraint programming is currently applied with success to many domains, such as scheduling, planning, vehicle routing, configuration, networks, and bioinformatics. The aim of this handbook is to capture the full breadth and depth of the constraint programming field and to be encyclopedic in its scope and coverage. While there are several excellent books on constraint programming, such books necessarily focus on the main notions and techniques and cannot cover also extensions, applications, and languages. The handbook gives a reasonably complete coverage of all these lines of work, based on constraint programming, so that a reader can have a rather precise idea of the whole field and its potential. Of course each line of work is dealt with in a survey-like style, where some details may be neglected in favor of coverage. However, the extensive bibliography of each chapter will help the interested readers to find suitable sources for the missing details. Each chapter of the handbook is intended to be a self-contained survey of a topic, and is written by one or more authors who are leading researchers in the area. The intended audience of the handbook is researchers, graduate students, higher-year undergraduates and practitioners who wish to learn about the state-of-the-art in constraint programming. No prior knowledge about the field is necessary to be able to read the chapters and gather useful knowledge. Researchers from other fields should find in this handbook an effective way to learn about constraint programming and to possibly use some of the constraint programming concepts and techniques in their work, thus providing a means for a fruitful cross-fertilization among different research areas. The handbook is organized in two parts. The first part covers the basic foundations of constraint programming, including the history, the notion of constraint propagation, basic search methods, global constraints, tractability and computational complexity, and important issues in modeling a problem as a constraint problem. The second part covers constraint languages and solver, several useful extensions to the basic framework (such as interval constraints, structured domains, and distributed CSPs), and successful application areas for constraint programming. - Covers the whole field of constraint programming - Survey-style chapters - Five chapters on applications Table of Contents Foreword (Ugo Montanari) Part I : Foundations Chapter 1. Introduction (Francesca Rossi, Peter van Beek, Toby Walsh) Chapter 2. Constraint Satisfaction: An Emerging Paradigm (Eugene C. Freuder, Alan K. Mackworth) Chapter 3. Constraint Propagation (Christian Bessiere) Chapter 4. Backtracking Search Algorithms (Peter van Beek) Chapter 5. Local Search Methods (Holger H. Hoos, Edward Tsang) Chapter 6. Global Constraints (Willem-Jan van Hoeve, Irit Katriel) Chapter 7. Tractable Structures for CSPs (Rina Dechter) Chapter 8. The Complexity of Constraint Languages (David Cohen, Peter Jeavons) Chapter 9. Soft Constraints (Pedro Meseguer, Francesca Rossi, Thomas Schiex) Chapter 10. Symmetry in Constraint Programming (Ian P. Gent, Karen E. Petrie, Jean-Francois Puget) Chapter 11. Modelling (Barbara M. Smith) Part II : Extensions, Languages, and Applications Chapter 12. Constraint Logic Programming (Kim Marriott, Peter J. Stuckey, Mark Wallace) Chapter 13. Constraints in Procedural and Concurrent Languages (Thom Fruehwirth, Laurent Michel, Christian Schulte) Chapter 14. Finite Domain Constraint Programming Systems (Christian Schulte, Mats Carlsson) Chapter 15. Operations Research Methods in Constraint Programming (John Hooker) Chapter 16. Continuous and Interval Constraints(Frederic Benhamou, Laurent Granvilliers) Chapter 17. Constraints over Structured Domains (Carmen Gervet) Chapter 18. Randomness and Structure (Carla Gomes, Toby Walsh) Chapter 19. Temporal CSPs (Manolis Koubarakis) Chapter 20. Distributed Constraint Programming (Boi Faltings) Chapter 21. Uncertainty and Change (Kenneth N. Brown, Ian Miguel) Chapter 22. Constraint-Based Scheduling and Planning (Philippe Baptiste, Philippe Laborie, Claude Le Pape, Wim Nuijten) Chapter 23. Vehicle Routing (Philip Kilby, Paul Shaw) Chapter 24. Configuration (Ulrich Junker) Chapter 25. Constraint Applications in Networks (Helmut Simonis) Chapter 26. Bioinformatics and Constraints (Rolf Backofen, David Gilbert)