Optimizing the setting of medical interactive rehabilitation assistant platform to improve the performance of the patients: A case study

Hand therapists' usage and perception of technology and telerehabilitation: An online survey study with a qualitative component

BACKGROUND

The COVID-19 pandemic prompted a shift in healthcare delivery, necessitating the rapid adoption of technology, including telerehabilitation. This study focused on understanding the current perspectives and experiences of hand therapists in Canada and the United States regarding implementation of telerehabilitation in hand therapy, traditionally reliant on in-person interactions.

PURPOSE

The study aimed to assess the utilization, perceptions, and barriers of telerehabilitation among hand therapists, providing insights into challenges and opportunities for incorporation into practices.

STUDY DESIGN

A cross-sectional survey collected responses from hand therapists through electronic surveys distributed via the American Society of Hand Therapists (ASHT) and the Canadian Society of Hand Therapists (CSHT) mailing lists.

METHODS

The survey, approved by the Western University Health Sciences Research Ethics Board, included nine demographic questions, eight rating questions, and four open-ended questions, focusing on technology acceptance and telerehabilitation satisfaction. Participants met criteria as licensed therapists with expertise in hand rehabilitation, practicing in Canada or the United States.

RESULTS

A total of 129 participants completed the survey. Most therapists (84.3%) reported high comfort with basic technology, yet only 56.8% used technology in practice. Common modalities included internet (71.2%), telephone (61.8%), and smartphone applications (53.2%). Only 1.6% reported frequently using biometrics, and 0.0% for advanced technologies like wearables. While 75% recognized telerehabilitation's usefulness in improving patient accessibility, 91.5% believed it could not fully replace traditional methods. Concerns included poor quality in assessing orthoses fit (69.9%) and strength (53.3%). United States respondents were less likely to report current use of telerehabilitation (OR: 0.195; 95% CI [0.064-0.595]).

CONCLUSIONS

The study addressed technology-related challenges and highlights hand therapists' perceptions towards integrating telerehabilitation. Barriers related to rural areas and diverse patient conditions emphasize the importance of tailored approaches. Future directions should explore emerging technologies like virtual reality and artificial intelligence for enhanced clinical effectiveness of telerehabilitation.

Medical resource allocation planning by integrating machine learning and optimization models

Patients' waiting time is a major issue in the Canadian healthcare system. The planning for resource allocation impacts patients' waiting time in medicare settings. This research focuses on the reduction of patients' waiting time by providing better planning for radiological resource allocation and efficient workload distribution. Resource allocation planning is directly related to the number of patient-arrival and it is hard to predict such uncertain parameters in the future time frame. The number of patient-arrival also varies across different modalities and different timeframes which makes the patient-arrival prediction challenging. In this research, a new three-phase solution framework is proposed where a new multi-target machine learning technique is integrated with an optimization model. In the first phase, a novel Ensemble of Pruned Regressor Chain (EPRC) model is developed and trained offline to predict uncertain parameters, such as patients' arrival. The proposed model is then compared with two popular multi-target prediction methods to evaluate the model's accuracy. In the second phase, the trained model is deployed in the real-time environment to forecast patients' arrival, miss Turn Around Time (miss-TAT) rate, and probable workload count. The forecasted data is used in phase three where a new multi-objective optimization model is developed to determine workload allocation. The Weighted-sum method is used to get efficient solutions. The proposed model is deployed in a Canadian healthcare company and evaluated using real-time healthcare data. It is observed in terms of accuracy, the proposed EPRC model performed 10.81 % better compared to the other multi-target models considered in this study. It is also noticed that the forecasting results have a direct impact on the workload distribution, where the proposed model decreases the total workload by approximately 25 %. Besides, the result shows the efficient workload distribution provided by the proposed framework can reduce the average patients' waiting time by 8.17 %.