Analyzing Groups of Inpatients’ Healthcare Needs to Improve Service Quality and Sustainability

Abstract

The trend towards personalized healthcare has led to an increase in applying deep learning techniques to improve healthcare service quality and sustainability. With the increasing number of patients with multiple comorbidities, they need comprehensive care services, where comprehensive care is a synonym for complete patient care to respond to a patient’s physical, emotional, social, economic, and spiritual needs, and, as such, an efficient prediction system for comprehensive care suggestions could help physicians and healthcare providers in making clinical judgement. The experiment dataset contained a total of 2.9 million electrical medical records (EMRs) from 250 thousand hospitalized patients collected retrospectively from a first-tier medical center in Taiwan, where the EMRs were de-identified and anonymized and where 949 cases had received comprehensive care. Recurrent neural networks (RNNs) are designed for analyzing time-series data but are still lacking in studying predicting personalized healthcare. Furthermore, in most cases, the collected evaluation data are imbalanced with a small portion of positive cases. This study examined the impact of imbalanced data in model training and suggested an effective approach to handle such a situation. To address the above-mentioned research issue, this study analyzed the care need in the different patient groupings, proposed a personalized care suggestion system by applying RNN models, and developed an efficient model training scheme for building AI-assisted prediction models. This study observed several findings: (1) the data resampling schemes could mitigate the impact of imbalanced data on model training, and the under-sampling scheme achieved the best performance with an ACC of 99.80%, a PPV of 70.18%, an NPV of 99.87%, a recall of 82.91%, and an F1 score of 0.7602, while the model trained with the original data had a very low PPV of 6.42% and a low F1 score of 0.1116; (2) patient clustering with multi-classier could predict comprehensive care needs efficiently with an ACC of 99.87%, a PPV of 77.90%, an NPV of 99.90%, a recall of 92.19%, and an F1 score of 0.8404; (3) the proposed long short-term memory (LSTM) prediction model achieved the best overall performance with an ACC of 99.80%, a PPV of 70.18%, an NPV of 99.87%, a recall of 82.91%, and an F1 score of 0.7602.

Key Success Factors for Medical Service Quality of Early Treatment and Rehabilitation for Children in Taiwan

“…Parents have higher expectations for the two items therapists have sufficient professional knowledge and therapists have sufficient experience in rehabilitation treatment, which show that parents attach great importance to whether the therapist has sufficient professional knowledge, experience, and skills [31,35]. Tsai and co-workers [30] also found that medical staff that can provide technically proficient professional services have the highest importance weight in their study.…”

Application of artificial intelligence for resilient and sustainable healthcare system: systematic literature review and future research directions

“…Unlike the previous systematic reviews Wang and Alexander, 2021;Pascu-Gabara and Cepoi, 2021;Ahad et al, 2020;Haleem and Javaid, 2020;Angioni, and Musso 2020;Sonnessa et al, 2017), the authors use the extended antecedents, practices, and outcome themes to provide a novel research framework drawn from the systematic literature review. We also observed that past studies lack in proving future research directions using any framework (Elavarasan et al, 2021;Hsu et al, 2021;Wang and Wu, 2021;Islam et al, 2021;Haleem and Javaid, 2020;Berquedich et al, 2020). Therefore, to fill this gap, our study uses the theory, methodology and context framework for providing future research directions.…”

“…AI contributes toward making the healthcare system to be resilient and sustainable. Therefore, AI helps in managing healthcare resources optimally, uses low energy consumption, reduces carbon footprints, saves energy, reduces losses, and saves more and more lives Sood et al, 2022;Hsu et al, 2021;Yigitcanlar and Cugurullo, 2020;Jung et al, 2018).…”

“…Further, given the wide application of AI in healthcare, the extent of literature on AI technology in the context of a resilient and sustainable healthcare system is minimal and under-researched (Dasgupta et al, 2022;van der Schaar et al, 2021;Milne-Ives et al, 2020). The recent literature has either only focused on the resilient healthcare system (Abdel-Basset et al, 2021;Sitharthan and Rajesh, 2021;Wang and Alexander, 2021;Leimanis and Palkova, 2021;Pascu-Gabara and Cepoi, 2021;Bansal et al, 2020) or the sustainable healthcare system Elavarasan et al, 2021;Rahman et al, 2021a;Mishra et al, 2021a;Ahmed et al, 2021;Hsu et al, 2021;Ahad et al, 2020;Yigitcanlar and Cugurullo, 2020). As a result, a combination of resilience and sustainability in the context of AI in healthcare is not yet discussed in any single work.…”