A multi-method scheduling framework for medical staff

Hospital planning teams are always concerned with optimizing staffing and scheduling decisions in order to improve hospital performance, patient experience, and staff satisfaction. A multi-method approach including data analytics, modeling and simulation, machine learning, and optimization is proposed to provide a framework for smart and applicable solutions for staffing and shift scheduling. Factors regarding patients, staff, and hospitals are considered in the decision. This framework is piloted using the Emergency Department(ED) of a leading university hospital in Dublin. The optimized base staffing patterns and shift schedules actively contributed to solving ED overcrowding problem and reduced the average waiting time for patients by 43% compared to the current waiting time of discharged patients. The reduction was achieved by optimizing the staffing level and then determining the shift schedule that minimized the understaffing and overstaffing of the personnel need to meet patient demand

Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

Achieving a New Standard in Primary Care for Low-Income Populations -- Case Studies of Redesign and Change Through a Learning Collaborative

Describes four case studies that focus on improving patient care delivery systems through learning collaboratives that were undertaken by New York City's nonprofit Primary Care Development Corporation

A survey of health care models that encompass multiple departments

In this survey we review quantitative health care models to illustrate the extent to which they encompass multiple hospital departments. The paper provides general overviews of the relationships that exists between major hospital departments and describes how these relationships are accounted for by researchers. We find the atomistic view of hospitals often taken by researchers is partially due to the ambiguity of patient care trajectories. To this end clinical pathways literature is reviewed to illustrate its potential for clarifying patient flows and for providing a holistic hospital perspective

Business Process Redesign in the Perioperative Process: A Case Perspective for Digital Transformation

This case study investigates business process redesign within the perioperative process as a method to achieve digital transformation. Specific perioperative sub-processes are targeted for re-design and digitalization, which yield improvement. Based on a 184-month longitudinal study of a large 1,157 registered-bed academic medical center, the observed effects are viewed through a lens of information technology (IT) impact on core capabilities and core strategy to yield a digital transformation framework that supports patient-centric improvement across perioperative sub-processes. This research identifies existing limitations, potential capabilities, and subsequent contextual understanding to minimize perioperative process complexity, target opportunity for improvement, and ultimately yield improved capabilities. Dynamic technological activities of analysis, evaluation, and synthesis applied to specific perioperative patient-centric data collected within integrated hospital information systems yield the organizational resource for process management and control. Conclusions include theoretical and practical implications as well as study limitations

Evaluating a virtual learning environment in the context of its community of practice

The evaluation of virtual learning environments (VLEs) and similar applications has, to date, largely consisted of checklists of system features, phenomenological studies or measures of specific forms of educational efficacy. Although these approaches offer some value, they are unable to capture the complex and holistic nature of a group of individuals using a common system to support the wide range of activities that make up a course or programme of study over time. This paper employs Wenger's theories of 'communities of practice' to provide a formal structure for looking at how a VLE supports a pre-existing course community. Wenger proposes a Learning Architecture Framework for a learning community of practice, which the authors have taken to provide an evaluation framework. This approach is complementary to both the holistic and complex natures of course environments, in that particular VLE affordances are less important than the activities of the course community in respect of the system. Thus, the VLE's efficacy in its context of use is the prime area of investigation rather than a reductionist analysis of its tools and components. An example of this approach in use is presented, evaluating the VLE that supports the undergraduate medical course at the University of Edinburgh. The paper provides a theoretical grounding, derives an evaluation instrument, analyses the efficacy and validity of the instrument in practice and draws conclusions as to how and where it may best be used

Reducing No-Shows and Late Cancellations in Primary Care

No-shows and late cancellations are a challenge across medical practices, resulting in costly, fragmented care. Many patients do not understand the impact that not showing or cancelling an appointment less than 48 hours prior to a visit can have. While reminding the patient of the appointment has been a known tactic to improve patient’s attendance, the most effective mode of the reminder can vary significantly across patient populations. Just as critical as reminding the patient of the appointment is to ensure they understand the purpose of the visit along with showing respect for their time and any competing priorities. This quality improvement initiative aimed to reduce the no-show rate of 21.4% and late cancellation rate of 21.1% for the MassHealth population by 5%. Learning from previous studies, a hybrid approach to meet this population’s needs included a 7-day reminder call with a Patient Engagement Coordinator (PEC) and a 2-day automated reminder. During the 7-day reminder call the PEC identified barriers to attending the appointment through concrete planning and motivational interviewing strategies. Appointments were rescheduled as needed, additional information was provided to solidify shared goals for the visit, and patient’s time/obligations were validated. The intervention resulted in positive feedback from the majority of patients and revealed concrete planning prompts to be a very effective communication form. The post-intervention data analysis revealed both the no-show and late cancellation results were reduced for the MassHealth population. Due to data and confounding variable limitations this study is recommended to be a basis for future investigation as the principal investigators enter into the next pilot phase of this model

Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

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