Quantitative thresholds based decision support approach for the home health care scheduling and routing problem

Contrasting rule and machine learning based digital self triage systems in the USA

Patient smart access and self-triage systems have been in development for decades. As of now, no LLM for processing self-reported patient data has been published by health systems. Many expert systems and computational models have been released to millions. This review is the first to summarize progress in the field including an analysis of the exact self-triage solutions available on the websites of 647 health systems in the USA.

Minimizing total weighted latency in home healthcare routing and scheduling with patient prioritization

We study a home healthcare routing and scheduling problem, where multiple healthcare service provider teams should visit a given set of patients at their homes. The problem involves assigning each patient to a team and generating the routes of the teams such that each patient is visited once. When patients are prioritized according to the severity of their condition or their service urgency, the problem minimizes the total weighted waiting time of the patients, where the weights represent the triage levels. In this form, the problem generalizes the multiple traveling repairman problem. To obtain optimal solutions for small to moderate-size instances, we propose a level-based integer programming (IP) model on a transformed input network. To solve larger instances, we develop a metaheuristic algorithm that relies on a customized saving procedure and a general variable neighborhood search algorithm. We evaluate the IP model and the metaheuristic on various small-, medium- and large-sized instances coming from the vehicle routing literature. While the IP model finds the optimal solutions to all the small- and medium-sized instances within three hours of run time, the metaheuristic algorithm achieves the optimal solutions to all instances within merely a few seconds. We also provide a case study involving Covid-19 patients in a district of Istanbul and derive insights for the planners by means of several analyses.

Using a route planner to optimize door-to-door visits for a pediatric home-hospitalization program: Feasibility study

BACKGROUND

Hospital Sant Joan de Déu (Barcelona) initiated a pediatric acute home-hospitalization program. Due to high patient turnover and the health staff's lack of planning training, daily scheduling was a time-consuming task. Home-hospitalization planning is a vehicle routing problem that can be solved with a technological solution. It was therefore decided to evaluate the efficacy and necessity of the SmartMonkey.io planner.

OBJECTIVES

To compare traditional manual route planning with a route optimizer, and to evaluate the technical feasibility of the implementation of a route planner into a homecare program.

METHODS

Eight participants (experienced homecare staff and inexperienced hospital staff) were included. Personal interviews were performed to assess their eagerness to try a technological solution to the planning problem. Objective benefits including reduced travel time (time planning, distance traveled, and time traveled) were evaluated. Paired t-test, t-test, and Pearson's correlation were used to compare manual and route planner scheduling. Participants then answered a questionnaire to assess planning difficulty and the acceptance of the route planner.

RESULTS

Homecare staff were initially reluctant to use the technology. Significant differences (P < 0.0001) in three variables were found between manual planning and the route planner. A moderate correlation between time planning and plan difficulty (r = 0.59, P < 0.0001) was found with manual planning but not with the route planner. All route planner schedules saved time and distance. No significant differences were found between expertise and planning method. It was noted that it was easy to create plans with the route planner, while difficulty with manual planning increased as more locations were added. All participants evaluated the route planning tool favorably.

CONCLUSIONS

Route-planning technology saved planning time and generated better plans than manual planning. The route planner's learning curve was fast and results were obtained in the same amount of time regardless of difficulty and expertise. SmartMonkey.io also has the potential to reduce internal and environmental costs and increase staff productivity.

Home care vehicle routing problem with chargeable overtime and strict and soft preference matching

A new scheduling problem arising in the home care context is addressed, whose novelty with respect to the literature lies in the way overtime is paid. In this problem, some clients are willing to pay a higher fee to cover the additional overtime cost, if such overtime is incurred because a caregiver works extra time with the client to preserve continuity of care. These overtime hours charged to clients unburden the company, which no longer has to balance between cost and continuity of care in a traditional way. The problem is also studied in a context that includes preferences expressed by both clients and caregivers. Strict preferences must be respected with a high priority, while soft preferences increase the satisfaction and should be preferably respected. We formalize the problem as a Mixed Integer Linear Problem and also propose a cluster-based decomposition to solve real-life instances. The problem is inspired by the real case study of a provider operating in the USA. Numerical results validate the model and confirm the capability of the decomposition approach to deal with real-life instances.