AI in patient flow: applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient units
Introduction: Growing demand for mental health services, coupled with funding and resource limitations, creates an opportunity for novel technological solutions including artificial intelligence (AI). This study aims to identify issues in patient flow on mental health units and align them with poten...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-05-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844021010963 |
| _version_ | 1818643649190166528 |
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| author | Fatema Mustansir Dawoodbhoy Jack Delaney Paulina Cecula Jiakun Yu Iain Peacock Joseph Tan Benita Cox |
| author_facet | Fatema Mustansir Dawoodbhoy Jack Delaney Paulina Cecula Jiakun Yu Iain Peacock Joseph Tan Benita Cox |
| author_sort | Fatema Mustansir Dawoodbhoy |
| collection | DOAJ |
| description | Introduction: Growing demand for mental health services, coupled with funding and resource limitations, creates an opportunity for novel technological solutions including artificial intelligence (AI). This study aims to identify issues in patient flow on mental health units and align them with potential AI solutions, ultimately devising a model for their integration at service level. Method: Following a narrative literature review and pilot interview, 20 semi-structured interviews were conducted with AI and mental health experts. Thematic analysis was then used to analyse and synthesise gathered data and construct an enhanced model. Results: Predictive variables for length-of-stay and readmission rate are not consistent in the literature. There are, however, common themes in patient flow issues. An analysis identified several potential areas for AI-enhanced patient flow. Firstly, AI could improve patient flow by streamlining administrative tasks and optimising allocation of resources. Secondly, real-time data analytics systems could support clinician decision-making in triage, discharge, diagnosis and treatment stages. Finally, longer-term, development of solutions such as digital phenotyping could help transform mental health care to a more preventative, personalised model. Conclusions: Recommendations were formulated for NHS trusts open to adopting AI patient flow enhancements. Although AI offers many promising use-cases, greater collaborative investment and infrastructure are needed to deliver clinically validated improvements. Concerns around data-use, regulation and transparency remain, and hospitals must continue to balance guidelines with stakeholder priorities. Further research is needed to connect existing case studies and develop a framework for their evaluation. |
| first_indexed | 2024-12-17T00:02:18Z |
| format | Article |
| id | doaj.art-d95d7842052544fda90c816a0fdc465a |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-12-17T00:02:18Z |
| publishDate | 2021-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-d95d7842052544fda90c816a0fdc465a2022-12-21T22:11:02ZengElsevierHeliyon2405-84402021-05-0175e06993AI in patient flow: applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient unitsFatema Mustansir Dawoodbhoy0Jack Delaney1Paulina Cecula2Jiakun Yu3Iain Peacock4Joseph Tan5Benita Cox6Imperial College London Business School, London, UK; Imperial College School of Medicine, South Kensington Campus, London, SW7 2BU, UK; Corresponding author.Imperial College London Business School, London, UK; Imperial College School of Medicine, South Kensington Campus, London, SW7 2BU, UKImperial College London Business School, London, UK; Imperial College School of Medicine, South Kensington Campus, London, SW7 2BU, UKImperial College London Business School, London, UK; Imperial College School of Medicine, South Kensington Campus, London, SW7 2BU, UKImperial College London Business School, London, UK; Brighton and Sussex Medical School, Brighton, East Sussex, BN1 9PX, UKImperial College London Business School, London, UK; Brighton and Sussex Medical School, Brighton, East Sussex, BN1 9PX, UKImperial College London Business School, London, UKIntroduction: Growing demand for mental health services, coupled with funding and resource limitations, creates an opportunity for novel technological solutions including artificial intelligence (AI). This study aims to identify issues in patient flow on mental health units and align them with potential AI solutions, ultimately devising a model for their integration at service level. Method: Following a narrative literature review and pilot interview, 20 semi-structured interviews were conducted with AI and mental health experts. Thematic analysis was then used to analyse and synthesise gathered data and construct an enhanced model. Results: Predictive variables for length-of-stay and readmission rate are not consistent in the literature. There are, however, common themes in patient flow issues. An analysis identified several potential areas for AI-enhanced patient flow. Firstly, AI could improve patient flow by streamlining administrative tasks and optimising allocation of resources. Secondly, real-time data analytics systems could support clinician decision-making in triage, discharge, diagnosis and treatment stages. Finally, longer-term, development of solutions such as digital phenotyping could help transform mental health care to a more preventative, personalised model. Conclusions: Recommendations were formulated for NHS trusts open to adopting AI patient flow enhancements. Although AI offers many promising use-cases, greater collaborative investment and infrastructure are needed to deliver clinically validated improvements. Concerns around data-use, regulation and transparency remain, and hospitals must continue to balance guidelines with stakeholder priorities. Further research is needed to connect existing case studies and develop a framework for their evaluation.http://www.sciencedirect.com/science/article/pii/S2405844021010963Mental healthNHSAIPatient flowMachine learningNatural language processing |
| spellingShingle | Fatema Mustansir Dawoodbhoy Jack Delaney Paulina Cecula Jiakun Yu Iain Peacock Joseph Tan Benita Cox AI in patient flow: applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient units Heliyon Mental health NHS AI Patient flow Machine learning Natural language processing |
| title | AI in patient flow: applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient units |
| title_full | AI in patient flow: applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient units |
| title_fullStr | AI in patient flow: applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient units |
| title_full_unstemmed | AI in patient flow: applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient units |
| title_short | AI in patient flow: applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient units |
| title_sort | ai in patient flow applications of artificial intelligence to improve patient flow in nhs acute mental health inpatient units |
| topic | Mental health NHS AI Patient flow Machine learning Natural language processing |
| url | http://www.sciencedirect.com/science/article/pii/S2405844021010963 |
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